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[
"MIT"
] | 6.0.28 | f82ff5b9e85ef972634d3312f52bbf7653dc8459 | code | 3102 | using Aqua: Aqua
using DataFrames: DataFrame
using Dates
using PlutoStaticHTML
using Pluto:
Cell,
Markdown,
Notebook,
Pluto,
ServerSession,
SessionActions
using Test
const PS = PlutoStaticHTML
const PKGDIR = string(pkgdir(PlutoStaticHTML))::String
const NOTEBOOK_DIR = joinpath(PKGDIR, "docs", "src", "notebooks")
function pluto_notebook_content(code)
return """
### A Pluto.jl notebook ###
# v0.18.1
using Markdown
using InteractiveUtils
# ╔═╡ a6dda572-3f2c-11ec-0eeb-69e2323a92de
$(code)
# ╔═╡ Cell order:
# ╠═a6dda572-3f2c-11ec-0eeb-69e2323a92de
"""
end
function drop_cache_info(html::AbstractString)
n = PlutoStaticHTML.n_cache_lines()
sep = '\n'
lines = split(html, sep)
return join(lines[n:end], sep)
end
function drop_begin_end(html::AbstractString)
sep = '\n'
lines = split(html, sep)
return join(lines[2:end-1], sep)
end
function nb_tmppath(
nb::Notebook,
use_distributed::Bool;
in_path::String=joinpath(mktempdir(), "notebook.jl")
)
Pluto.save_notebook(nb, in_path)
session = ServerSession()
session.options.evaluation.workspace_use_distributed = use_distributed
PlutoStaticHTML._add_extra_preamble!(session)
nb = PlutoStaticHTML.run_notebook!(in_path, session)
if use_distributed
@async begin
sleep(5)
Pluto.SessionActions.shutdown(session, nb)
end
end
return (nb, in_path)
end
function notebook2html_helper(
nb::Notebook,
oopts=OutputOptions();
use_distributed::Bool=true
)
nb, tmppath = nb_tmppath(nb, use_distributed)
html = PlutoStaticHTML.notebook2html(nb, tmppath, oopts)
has_begin_end = contains(html, PlutoStaticHTML.BEGIN_IDENTIFIER)
without_begin_end = has_begin_end ? drop_begin_end(html) : html
# Remove the caching information because it's not important for most tests.
has_cache = contains(html, PlutoStaticHTML.STATE_IDENTIFIER)
without_cache = has_cache ? drop_cache_info(without_begin_end) : html
return (without_cache, nb)
end
function notebook2tex_helper(
nb::Notebook,
oopts=OutputOptions();
use_distributed::Bool=true
)
nb, tmppath = nb_tmppath(nb, use_distributed)
tex = PlutoStaticHTML.notebook2tex(nb, tmppath, oopts)
return (tex, nb)
end
function notebook2pdf_helper(
nb::Notebook,
in_path::String,
oopts=OutputOptions();
use_distributed::Bool=true
)
nb, tmppath = nb_tmppath(nb, use_distributed; in_path)
pdf_path = PlutoStaticHTML.notebook2pdf(nb, tmppath, oopts)
return (pdf_path, nb)
end
function notebook2html(
path::AbstractString;
oopts::OutputOptions=OutputOptions(),
session=ServerSession()
)
nb = PlutoStaticHTML.run_notebook!(path, session; run_async=false)
html = PlutoStaticHTML.notebook2html(nb, path, oopts)
return html
end
# Hide output when using `TestEnv.activate(); include("test/preliminaries.jl")`.
nothing
| PlutoStaticHTML | https://github.com/rikhuijzer/PlutoStaticHTML.jl.git |
|
[
"MIT"
] | 6.0.28 | f82ff5b9e85ef972634d3312f52bbf7653dc8459 | code | 562 | include("preliminaries.jl")
@testset "pdf" begin
include("pdf.jl")
end
@testset "context" begin
include("context.jl")
end
@testset "cache" begin
include("cache.jl")
end
@testset "mimeoverride" begin
include("mimeoverride.jl")
end
@testset "with_terminal" begin
include("with_terminal.jl")
end
@testset "html" begin
include("html.jl")
end
@testset "style" begin
include("style.jl")
end
@testset "build" begin
include("build.jl")
end
@testset "aqua" begin
Aqua.test_all(PlutoStaticHTML; ambiguities=false)
end
nothing
| PlutoStaticHTML | https://github.com/rikhuijzer/PlutoStaticHTML.jl.git |
|
[
"MIT"
] | 6.0.28 | f82ff5b9e85ef972634d3312f52bbf7653dc8459 | code | 2102 | before = """
<p>foo</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This is a note.</p>
</div>
<p>bar</p>
"""
# Do not accept Gumbo's full HTML body because `_convert_admonitions` is called for each cell.
after = """
<p>foo</p>
<div class="admonition is-note"><header class="admonition-header">Note</header><div class="admonition-body"><p>This is a note.</p></div></div>
<p>bar</p>
"""
@test PlutoStaticHTML._convert_admonitions(before) == rstrip(after)
nb = Notebook([
Cell("""
md\"\"\"
!!! note
This is a note.
\"\"\"
""")
])
use_distributed = false
html, _ = notebook2html_helper(nb; use_distributed)
# This tests that there has been a hit on the `_convert_admonitions` replacer.
@test contains(html, "admonition-header")
# https://github.com/rikhuijzer/PlutoStaticHTML.jl/issues/148.
before = """
<div class="markdown">
<div class="admonition info">
<p class="admonition-title">This is how the Error we expect here looks like</p>
<pre><code>DomainError with 0.0: Lorem</code></pre>
</div>
</div>
"""
after = """
<div class="markdown">
<div class="admonition is-info">
<header class="admonition-header">
This is how the Error we expect here looks like
</header>
<div class="admonition-body">
<pre>
<code>
DomainError with 0.0: Lorem
</code>
</pre>
</div>
</div>
</div>
"""
expected = replace(after, '\n' => "")
@test PlutoStaticHTML._convert_admonitions(before) == expected
nb = Notebook([
Cell("""
function pretty_error(err)
Markdown.parse(\"\"\"
!!! info "This is how the Error we expect here looks like"
```
\$(replace(sprint(showerror, err), "\n" => "\n "))
```
\"\"\")
end
"""),
Cell("""pretty_error(DomainError(0.0, "Lorem"))""")
])
html, _ = notebook2html_helper(nb; use_distributed)
@test contains(html, "admonition-header")
| PlutoStaticHTML | https://github.com/rikhuijzer/PlutoStaticHTML.jl.git |
|
[
"MIT"
] | 6.0.28 | f82ff5b9e85ef972634d3312f52bbf7653dc8459 | code | 2071 | # Body for `cell.output.body` of
# using PlutoUI
# f(x) = Base.inferencebarrier(x);
# with_terminal() do
# @code_warntype f(1)
# end
body = raw"""
<div style="display: inline; white-space: normal;">
<script type="text/javascript" id="plutouiterminal">
let txt = "MethodInstance for Main.workspace#4.f(::Int64)\n from f(x) in Main.workspace#4 at /home/rik/git/blog/posts/notebooks/inference.jl#==#9dbfb7d5-7035-4ea2-a6c0-efa00e39e90f:1\nArguments\n #self#[36m::Core.Const(Main.workspace#4.f)[39m\n x[36m::Int64[39m\nBody[91m[1m::Any[22m[39m\n[90m1 ─[39m %1 = Base.getproperty(Main.workspace#4.Base, :inferencebarrier)[36m::Core.Const(Base.inferencebarrier)[39m\n[90m│ [39m %2 = (%1)(x)[91m[1m::Any[22m[39m\n[90m└──[39m return %2\n\n"
var container = html`
<pre
class="PlutoUI_terminal"
style="
max-height: 300px;
overflow: auto;
white-space: pre;
color: white;
background-color: black;
border-radius: 3px;
margin-top: 8px;
margin-bottom: 8px;
padding: 15px;
display: block;
"
></pre>
`
try {
const { default: AnsiUp } = await import("https://cdn.jsdelivr.net/gh/JuliaPluto/[email protected]/ansi_up.js");
container.innerHTML = new AnsiUp().ansi_to_html(txt);
} catch(e) {
console.error("Failed to import/call ansiup!", e)
container.innerText = txt
}
return container
</script>
</div>
"""
txt = strip(PlutoStaticHTML._extract_txt(body))
@test startswith(txt, "MethodInstance")
@test endswith(txt, "return %2")
@test contains(txt, '\n')
patched = PlutoStaticHTML._patch_with_terminal(body)
@test contains(patched, """<pre id="plutouiterminal">""")
| PlutoStaticHTML | https://github.com/rikhuijzer/PlutoStaticHTML.jl.git |
|
[
"MIT"
] | 6.0.28 | f82ff5b9e85ef972634d3312f52bbf7653dc8459 | docs | 1838 | # PlutoStaticHTML.jl
[![Docs dev][docs-dev-img]][docs-dev-url]
[![Code Style Blue][blue-img]][blue-url]
[![Contributor's Guide on Collaborative Practices][colprac-img]][colprac-url]
A [Julia package](https://julialang.org/) to convert [Pluto notebooks](https://github.com/fonsp/Pluto.jl) to static HTML.
Unlike [PlutoSliderServer](https://github.com/JuliaPluto/PlutoSliderServer.jl), the HTML files generated using PlutoStaticHTML are very minimal, and do not require JavaScript on the user side to render.
This makes it easier to style the output using CSS.
For example, with this package it is possible to create a website full with plots generated in Julia and all code hidden.
In effect, no one would be able to tell that the website was built with Julia.
## Automated builds
Next to outputting static HTML, this package is also aimed at building multiple Pluto.jl notebooks as reliably and quickly as possible in unsupervised settings such as CI.
Therefore, this package implements:
1. Parallel evaluation of notebooks 🚀.
1. Caching of notebooks to avoid re-running code if nothing changed 🚀.
1. Throwing an error if something goes wrong.
This avoids publishing broken notebooks 🎯.
See the [documentation](https://PlutoStaticHTML.huijzer.xyz/dev/) for more information.
[ci-url]: https://github.com/JuliaData/DataFrames.jl/actions?query=workflow%3ACI+branch%3Amain
[ci-img]: https://github.com/rikhuijzer/PlutoStaticHTML.jl/workflows/CI/badge.svg
[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg
[docs-dev-url]: https://PlutoStaticHTML.huijzer.xyz/dev/
[blue-img]: https://img.shields.io/badge/code%20style-blue-4495d1.svg
[blue-url]: https://github.com/invenia/BlueStyle
[colprac-img]: https://img.shields.io/badge/ColPrac-contributor's%20guide-blueviolet
[colprac-url]: https://github.com/SciML/ColPrac
| PlutoStaticHTML | https://github.com/rikhuijzer/PlutoStaticHTML.jl.git |
|
[
"MIT"
] | 6.0.28 | f82ff5b9e85ef972634d3312f52bbf7653dc8459 | docs | 7620 | # PlutoStaticHTML
Convert Pluto notebooks to pure HTML (without Javascript).
This allows Pluto notebooks to be embedded in Documenter, Franklin and (optionally) to be styled manually via CSS.
Also, it is possible to hide code blocks making it easy to show Julia generated output without showing code.
Typically, converting Pluto notebooks to HTML is useful for things like
- tutorials (a ready-to-use template can be found at <https://rikhuijzer.github.io/JuliaTutorialsTemplate/>)
- blogs
- documentation
For a quick preview, this package is used for the tutorials at [TuringGLM.jl](https://turinglang.github.io/TuringGLM.jl/dev/tutorials/linear_regression/).
Also, I'm using this package for my own blog, for example: <https://huijzer.xyz/posts/frequentist-bayesian-coin-flipping/>.
## API overview
The most important method is `build_notebooks` ([API](@ref)).
!!! note
`build_notebooks` ensures that the original notebook will not be changed.
In general, the idea is to
1. Create a bunch of Pluto notebooks.
1. Get the name of the directory `dir` which contains your Pluto notebooks.
1. Choose one or more appropriate `output_format`s depending on how the output will be used.
The output format can be `html_output`, `documenter_output`, `franklin_output` or `pdf_output`.
1. Pass the paths to [`build_notebooks`](@ref) which, depending on `output_format`, writes HTML or Markdown outputs to files.
1. Read the output from the files and show them on a website via either your own logic or Documenter or Franklin.
Note that this is a very nice development workflow because developing in Pluto notebooks is easy and allows for quick debugging.
Also, Pluto has a lot of conversions built-in.
This package will take the converted outputs, such as plots or tables, from Pluto which ensures that what you see in Pluto is what you see in the HTML output.
As an extension of Pluto, this package provides `# hide` and `# hideall` comments like Franklin and Documenter.
A `# hideall` somewhere in a Pluto code block will hide the code (but not the output).
A `# hide` behind a line in a code block will hide the line.
Also, by default, this package hides all Markdown code blocks since readers are probably only interested in reading the output of the Markdown code block.
This and more options can be tuned via [`OutputOptions`](@ref).
See below for more specific instructions on
- [Documenter.jl](@ref)
- [Franklin.jl](@ref)
- [Parallel build](@ref)
## Documenter.jl
The `output_format=documenter_output` is used at various places which can all serve as an example:
- "docs/make.jl" in this repository.
- [TuringGLM.jl](https://github.com/TuringLang/TuringGLM.jl); for example output see the [linear regression tutorial](https://turinglang.github.io/TuringGLM.jl/dev/tutorials/linear_regression/).
- [Resample.jl](https://github.com/rikhuijzer/Resample.jl); for example output see the [SMOTE tutorial](https://rikhuijzer.github.io/Resample.jl/dev/notebooks/smote/).
- [GraphNeuralNetworks.jl](https://github.com/CarloLucibello/GraphNeuralNetworks.jl) tutorials, see for example [Hand-On Graph Neural Networks](https://carlolucibello.github.io/GraphNeuralNetworks.jl/dev/tutorials/gnn_intro_pluto/).
!!! warn
Avoid calling the conversion from inside a Documenter.jl code block.
For some reason, that is likely to freeze or hang; probably due to `stdout` being flooded with information.
Instead generate Markdown files via `docs/make.jl` and point to these files in `pages`.
## Franklin.jl
For `output_format=franklin_output` examples, see
- The template at <https://rikhuijzer.github.io/JuliaTutorialsTemplate/>.
- [My blog](https://gitlab.com/rikh/blog).
For example, a post on [random forests](https://huijzer.xyz/posts/random-forest/).
Specifically, use the following KaTeX options:
```javascript
const options = {
delimiters: [
{left: "$$", right: "$$", display: true},
{left: "\\begin{equation}", right: "\\end{equation}", display: true},
{left: "\\begin{align}", right: "\\end{align}", display: true},
{left: "\\begin{alignat}", right: "\\end{alignat}", display: true},
{left: "\\begin{gather}", right: "\\end{gather}", display: true},
{left: "\\(", right: "\\)", display: false},
{left: "\\[", right: "\\]", display: true}
]
};
document.addEventListener('DOMContentLoaded', function() {
renderMathInElement(document.body, options);
});
```
Note that `$x$` will not be interpreted as inline math by this KaTeX configuration.
This is to avoid conflicts with using the dollar symbol to represent the dollar (currency).
Instead, `PlutoStaticHTML.jl` automatically converts inline math from `$x$` to `\($x\)`.
With above KaTeX settings, `Franklin.jl` will interpret this as inline math.
By default, `Documenter.jl` will also automatically interpret this as inline math.
## Parallel build
To speed up the build, this package defines [`build_notebooks`](@ref).
This function evaluates the notebooks in parallel by default.
Also, it can use [Caching](@ref) to speed up the build even more.
To use it, pass a `dir` to write HTML files for all notebook files (the files are recognized by the ".jl" extension and that the file starts with `### A Pluto.jl notebook ###`):
```julia
julia> using PlutoStaticHTML: build_notebooks
julia> dir = joinpath("posts", "notebooks");
julia> bopts = BuildOptions(dir);
julia> build_notebooks(bopts);
[...]
```
To run only specific notebooks, specify the `files`:
```julia
julia> files = ["notebook1.jl", "notebook2.jl"];
julia> build_notebooks(bopts, files)
[...]
```
In CI, be sure to call this before using Franklin `serve` or `optimize`.
For more options, such as `append_build_context` to add Julia and packages version information, you can pass [`OutputOptions`](@ref):
```julia
julia> oopts = OutputOptions(; append_build_context=true);
julia> build_notebooks(bopts, files, oopts)
[...]
```
See [`build_notebooks`](@ref) for more information.
### Caching
Using caching can greatly speed up running times by avoiding to re-evaluate notebooks.
Caching can be enabled by passing `previous_dir` via [`BuildOptions`](@ref).
This `previous_dir` should point to a location where HTML or Markdown files are from the previous build.
Then, `build_notebooks` will, for each input file `file.jl`, check:
1. Whether `joinpath(previous_dir, "file.html")` exists
2. Whether the SHA checksum of the current `$file.jl` matches the checksum of the previous `$file.jl`.
When assuming that Pluto's built-in package manager is used to manage packages, this check ensures that the packages of the previous run match the packages of the current run.
3. Whether the Julia version of the previous run matches the Julia version of the current run.
!!! note
Caching assumes that notebooks are deterministic, that is, the notebook will produce the same output from the same input.
!!! note
The `previous_dir` provides a lot of flexibility.
For example, it is possible to point towards a Git directory with the HTML or Markdown output files from last time.
Alternatively, for `output_format=html_output` it is possible to download the web pages where the notebooks are shown and put these web pages in a directory.
This works by extracting the state from the previous run from the output.
## LaTeX equations
Pluto uses MathJax by default, so make sure to setup MathJax in Franklin or Documenter.
For Franklin, see <https://rikhuijzer.github.io/JuliaTutorialsTemplate/>.
For Documenter, see `docs/make.jl` in this repository.
## API
```@docs
build_notebooks
BuildOptions
OutputOptions
```
| PlutoStaticHTML | https://github.com/rikhuijzer/PlutoStaticHTML.jl.git |
|
[
"MIT"
] | 6.0.28 | f82ff5b9e85ef972634d3312f52bbf7653dc8459 | docs | 3268 | # Terminal output from Pluto
`PlutoUI` has a well-known function `with_terminal` to show terminal output with a black background and colored text.
For example, when having loaded `PlutoUI` via `using PlutoUI`, the following code will show the text "Some terminal output" in a mini terminal window inside `Pluto`:
```julia
with_terminal() do
println("Some terminal output")
end
```
This functionality is supported by `PlutoStaticHTML` too.
To make it work, `PlutoStaticHTML` takes the output from `Pluto`, which looks roughly as follows:
```html
<div style="display: inline; white-space: normal;">
<script type="text/javascript" id="plutouiterminal">
let txt = "Some terminal output"
...
</script>
</div>
```
and changes it to:
```html
<pre id="plutouiterminal">
Some terminal output
</pre>
```
This output is now much simpler to style to your liking.
Below, there is an example style that you can apply which will style the terminal output just like it would in `Pluto`.
In terminals, the colors are enabled via so called ANSI escape codes.
These ANSI colors can be shown correctly by adding the following Javascript to the footer of your website.
This code will loop through all the HTML elements with `id="plutouiterminal"` and apply the `ansi_to_html` function to the body of those elements:
```html
<script type="text/javascript">
async function color_ansi() {
const terminalOutputs = document.querySelectorAll("[id=plutouiterminal]");
// Avoid loading AnsiUp if there is no terminal output on the page.
if (terminalOutputs.length == 0) {
return
};
try {
const { default: AnsiUp } = await import("https://cdn.jsdelivr.net/gh/JuliaPluto/[email protected]/ansi_up.js");
const ansiUp = new AnsiUp();
// Indexed loop is needed here, the array iterator doesn't work for some reason.
for (let i = 0; i < terminalOutputs.length; ++i) {
const terminalOutput = terminalOutputs[i];
const txt = terminalOutput.innerHTML;
terminalOutput.innerHTML = ansiUp.ansi_to_html(txt);
};
} catch(e) {
console.error("Failed to import/call ansiup!", e);
};
};
color_ansi();
</script>
```
Next, the output can be made more to look like an embedded terminal by adding the following to your CSS:
```css
#plutouiterminal {
font-family: JuliaMono-Regular, SFMono-Regular, DejaVu Sans Mono, monospace;
/* Without this, the output of BenchmarkTools looks weird. */
line-height: 114%;
max-height: 300px;
overflow: auto;
white-space: pre;
color: white;
background-color: black;
border-radius: 6px;
margin-top: 8px;
margin-bottom: 8px;
padding: 15px;
display: block;
font-size: 14px;
}
```
!!! note
Note that the Javascript code above downloads the `ansi_up.js` file from a content delivery network (CDN).
This is not advised because CDNs are bad for privacy, may go offline and are bad for performance.
They are bad for performance because browsers do not cache CDN downloaded content between different domains for security reasons.
Therefore, CDN content will cause at least an extra DNS lookup in most cases.
| PlutoStaticHTML | https://github.com/rikhuijzer/PlutoStaticHTML.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 431 | module InstantiateFromURL
# Deps. `using BinaryProvider` needed to instantiate gen_ commands.
using Pkg, BinaryProvider, Suppressor
using HTTP # for activate_github_path
# TOML module
import Pkg.TOML: parsefile
using Markdown
# Code
include("activate.jl")
include("github_project.jl")
# Exports
export activate_github, activate_github_path # default
export packages_to_default_environment, github_project # IJulia-refactor
end
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 5176 | function activate_github(reponame; tag = nothing, sha = nothing, force = false, add_default_environment = false)
# Make sure that our .projects environment is kosher.
projdir = joinpath(pwd(), ".projects")
mkpath(projdir)
# For each case of inputs, end up with a concrete URL to download.
if sha != nothing
length(sha) >= 6 || throw(ArgumentError("SHA needs to be at least 6 characters."))
tag == nothing || throw(ArgumentError("You can't give both a tag and an SHA hash."))
tarprefix = sha
elseif tag != nothing # Given a version but no SHA.
tarprefix = tag
else # Default to master if nothing is given.
tarprefix = "master"
end
# Common objects for all cases.
repostr = split(reponame, "/")[2]
target = joinpath(projdir, "$repostr-$tarprefix")
# Branches of logic.
if isdir(target) && tarprefix != "master" && force == false # Static prefix that's already downloaded, no force.
Pkg.activate(target)
else
# Make a temporary directory for our use.
tmpdir = tempdir()
# Download the tarball to that directory.
tarurl = "https://github.com/$(reponame)/archive/$(tarprefix).tar.gz"
tarpath = joinpath(tmpdir, "$repostr-$tarprefix.tar.gz")
printstyled("Downloading ", bold=true, color=:light_green); println("$reponame-$tarprefix → $projdir")
try
run(gen_download_cmd(tarurl, tarpath)) # Download the tarball.
catch e
if e isa MethodError
printstyled("Package installation and activation not supported in this setup. You should add packages manually.", bold = true, color = :red)
return nothing
else
throw(e)
end
end
# Unpack the tarball to that directory.
run(gen_unpack_cmd(tarpath, tmpdir))
# Remove the tarball to avoid path conflict with the next steps.
rm(tarpath)
# Find the path of the unpacked tarball (could be a full SHA)
sourcedir = filter(object -> occursin("$repostr", object), readdir(tmpdir))[1] # There will only be one of these.
# Move to .projects
mv(joinpath(tmpdir, sourcedir), target, force = true) # Force will overwrite existing dir.
# Clean.
isdir(joinpath(tmpdir, sourcedir)) == false || rm(joinpath(tmpdir, sourcedir), recursive = true) # Important for logic.
# Instantiate and precompile.
printstyled("Instantiating ", bold=true, color=:light_green); println(target)
Pkg.activate(target)
pkg"instantiate"
pkg"precompile"
end
projpath = joinpath(target, "Project.toml")
packages = parsefile(projpath)["deps"]
if add_default_environment # seed the default environment with the new packages if true
printstyled("Adding to the default environment... ", bold=true, color=:light_green);
pkg"activate "
@show pkglist = Array{String}(collect(keys(packages)))
Pkg.add(pkglist)
Pkg.activate(target) # go back to the activated environment
end
tarprefix, target, packages
end
function copy_env(reponame, oldtag, newtag)
repostr = split(reponame, "/")[2]
projdir = joinpath(pwd(), ".projects")
olddir = joinpath(projdir, "$repostr-$oldtag")
newdir = joinpath(projdir, "$repostr-$newtag")
cp(olddir, newdir, force = true)
end
# Clone the TOML files from some git repo's _current state_ to the local dir
function activate_github_path(reponame; path = "",
tag = "master",
force = false,
activate = true)
# download step
if "Project.toml" ∈ readdir(pwd()) && force == false
@warn "There's already a Project.toml in the current directory, and force = false."
else
# url construction and download
url_project = (path == "") ? join(["https://raw.githubusercontent.com", reponame, tag, "Project.toml"], "/") : join(["https://raw.githubusercontent.com", reponame, tag, path, "Project.toml"], "/")
url_manifest = (path == "") ? join(["https://raw.githubusercontent.com", reponame, tag, "Manifest.toml"], "/") : join(["https://raw.githubusercontent.com", reponame, tag, path, "Manifest.toml"], "/")
try # project
resp_project = HTTP.get(url_project);
io = open("Project.toml", "w")
println(io, String(resp_project.body))
close(io)
catch e
@warn "There was an error retrieving the Project.toml"
throw(e)
end
try # manifest
resp_manifest = HTTP.get(url_manifest);
io = open("Manifest.toml", "w")
println(io, String(resp_manifest.body))
close(io)
catch e
# do nothing, since we aren't too fussed about the Manifest.
end
end
# activation step
need_activation = (Base.active_project() != joinpath(pwd(), "Project.toml"))
if activate && need_activation
pkg"activate ."
pkg"instantiate"
pkg"precompile"
end
end
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 5143 | # Code for the refactor in https://github.com/QuantEcon/InstantiateFromURL.jl/issues/37
# Without versions for now (so that we don't force downgrades, etc.)... can get version information easily enough, though.
function packages_to_default_environment() # no arg, since it operates on the activated environment
if (Base.active_project() == Base.load_path_expand("@v#.#"))
printstyled("No project activated.\n", color = :cyan)
return
end
ctx = Pkg.Types.Context();
packages = collect(keys(ctx.env.project.deps)); # collect: KeySet -> Array{String}
original_env = ctx.env.project_file;
default_env = Base.load_path_expand("@v#.#");
Pkg.activate(default_env)
Pkg.add(packages)
Pkg.activate(original_env)
return
end
function github_project(reponame; # e.g., "QuantEcon/quantecon-notebooks-jl"
path = "", # relative path within the repo (root by default)
version = "master",
force = false,
instantiate = false,
precompile = false)
#= summary variables for logic
- is_project_activated = are we using a non-default project
- is_project_local = are we using a project in our pwd
- does_local_project_exist = need this, because scripts/REPLs will not always activate the local project
- url... = online resource paths
=#
current_proj = Base.active_project()
is_project_activated = !(Base.active_project() == Base.load_path_expand("@v#.#"))
is_project_local = dirname(current_proj) == pwd()
does_local_project_exist = isfile(joinpath(pwd(), "Project.toml"))
url_version = version == "master" ? version : "v" * version
url_project = (path == "") ? join(["https://raw.githubusercontent.com", reponame, url_version, "Project.toml"], "/") : join(["https://raw.githubusercontent.com", reponame, url_version, path, "Project.toml"], "/")
url_manifest = (path == "") ? join(["https://raw.githubusercontent.com", reponame, url_version, "Manifest.toml"], "/") : join(["https://raw.githubusercontent.com", reponame, url_version, path, "Manifest.toml"], "/")
# unified display for all cases
function display_proj()
ctx = Pkg.Types.Context();
project_file = ctx.env.project_file;
printstyled(Markdown.parse("\e[1mActivated\e[0m $project_file"), color = :green)
end
function display_info()
ctx = Pkg.Types.Context();
project_information = parsefile(ctx.env.project_file);
project_version = haskey(project_information, "version") ? project_information["version"] : "NA"
project_name = haskey(project_information, "name") ? project_information["name"] : "NA"
# Display depending on results
project_requested = replace(split(reponame, "/")[2], ".jl" => "") # strip out ".jl" if it exists
if project_name == project_requested && project_version != version && project_version != "NA"
printstyled(Markdown.parse("\e[1mInfo\e[0m $project_name $project_version activated, $version requested"), color = :cyan)
elseif project_name != project_requested && project_name != "NA"
printstyled(Markdown.parse("\e[1mInfo\e[0m Project name $project_name activated, $project_requested requested."), color = :cyan)
else
printstyled(Markdown.parse("\e[1mInfo\e[0m Project name is $project_name, version is $project_version"), color = :cyan)
end
end
# use a local project if it exists and we don't have it set to force
if !is_project_local && does_local_project_exist && !force
Pkg.activate(pwd());
display_info()
if instantiate
Pkg.instantiate()
end
if precompile
Pkg.precompile()
end
return
end
# if we're satisfied with the project activated, just display
# this case catches most scenarios
# NOTE: Need the third check in cases where we've deleted the project file after activating
if is_project_activated && !force && isfile(Base.active_project())
display_proj()
display_info()
if instantiate
Pkg.instantiate()
end
if precompile
Pkg.precompile()
end
return
end
# at this point, need to do downloading/overwriting/etc.
if does_local_project_exist
printstyled(Markdown.parse("\e[1mInfo\e[0m Local TOML exists; removing now.\n"), color = :cyan)
rm(joinpath(pwd(), "Project.toml"), force = true) # force = true so non-existing path doesn't error
rm(joinpath(pwd(), "Manifest.toml"), force = true)
end
try @suppress Base.download(url_project, joinpath(pwd(), "Project.toml"));
catch e
@warn "Can't download Project. Make sure the URL is accurate."
throw(e)
end
# try/catch on Manifest because it isn't always required
try @suppress Base.download(url_manifest, joinpath(pwd(), "Manifest.toml"));
catch e
end
Pkg.activate(pwd())
Pkg.instantiate()
if precompile
pkg"precompile"
end
display_info()
return # return nothing
end
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 1380 | reponame = "arnavs/InstantiationTest"
tag1 = "master"
tag2 = "v0.1.0"
sha = "181f673" # 7 chars
pdir = pwd()
# Clean environment.
isdir(".projects") == false || rm(".projects", recursive = true)
prefix, path = activate_github(reponame)
@test prefix == "master"
@test path == joinpath(pdir, ".projects", "InstantiationTest-master")
@test Base.active_project() == joinpath(path, "Project.toml")
@warn "Shouldn't take too long on new precompilations, etc."
prefix, path = activate_github(reponame, tag = tag1)
@test prefix == "master"
@test path == joinpath(pdir, ".projects", "InstantiationTest-master")
@test Base.active_project() == joinpath(path, "Project.toml")
prefix, path = activate_github(reponame, tag = tag2)
@test prefix == "v0.1.0"
@test path == joinpath(pdir, ".projects", "InstantiationTest-v0.1.0")
@test Base.active_project() == joinpath(path, "Project.toml")
InstantiateFromURL.copy_env(reponame, "v0.1.0", "v0.1.0-edit")
prefix, path = activate_github(reponame, tag = "v0.1.0-edit")
@test prefix == "v0.1.0-edit"
@test path == joinpath(pdir, ".projects", "InstantiationTest-v0.1.0-edit")
@test Base.active_project() == joinpath(path, "Project.toml")
prefix, path = activate_github(reponame, sha = sha)
@test prefix == sha
@test path == joinpath(pdir, ".projects", "InstantiationTest-$sha")
@test Base.active_project() == joinpath(path, "Project.toml")
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 1657 | spacename = "Name With Spaces, Commas; and other bad characters"
reponame = "arnavs/InstantiationTest"
tag1 = "master"
tag2 = "v0.1.0"
sha = "181f673" # 7 chars
pdir = pwd()
mkpath(joinpath(pdir, spacename))
cd(spacename) # Run all of our tests in this new directory.
newpdir = joinpath(pdir, spacename)
# Clean environment.
isdir(".projects") == false || rm(".projects", recursive = true)
prefix, path = activate_github(reponame)
@test prefix == "master"
@test path == joinpath(newpdir, ".projects", "InstantiationTest-master")
@test Base.active_project() == joinpath(path, "Project.toml")
@warn "Shouldn't take too long on new precompilations, etc."
prefix, path = activate_github(reponame, tag = tag1)
@test prefix == "master"
@test path == joinpath(newpdir, ".projects", "InstantiationTest-master")
@test Base.active_project() == joinpath(path, "Project.toml")
prefix, path = activate_github(reponame, tag = tag2)
@test prefix == "v0.1.0"
@test path == joinpath(newpdir, ".projects", "InstantiationTest-v0.1.0")
@test Base.active_project() == joinpath(path, "Project.toml")
InstantiateFromURL.copy_env(reponame, "v0.1.0", "v0.1.0-edit")
prefix, path = activate_github(reponame, tag = "v0.1.0-edit")
@test prefix == "v0.1.0-edit"
@test path == joinpath(newpdir, ".projects", "InstantiationTest-v0.1.0-edit")
@test Base.active_project() == joinpath(path, "Project.toml")
prefix, path = activate_github(reponame, sha = sha)
@test prefix == sha
@test path == joinpath(newpdir, ".projects", "InstantiationTest-$sha")
@test Base.active_project() == joinpath(path, "Project.toml")
cd(pdir)
rm(joinpath(pdir, spacename), force = true, recursive = true)
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 693 | reponame = "QuantEcon/SimpleDifferentialOperators.jl"
path = "docs/notebooks"
# vanilla call
activate_github_path(reponame, path = path, activate = false)
@test "Project.toml" ∈ readdir()
@test "Manifest.toml" ∈ readdir()
# vanilla call repeated (should return 0)
@test_logs (:warn, "There's already a Project.toml in the current directory, and force = false.") activate_github_path(reponame, path = path, force = false, activate = false)
# clean
rm("Project.toml")
rm("Manifest.toml")
# error handling
@test_throws Exception activate_github_path(reponame * "flerg", path, force = false, activate = false)
@test_throws Exception activate_github_path(reponame, path = "src") # no TOML here
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 100 | github_project("QuantEcon/quantecon-notebooks-julia", precompile = true) # should see precompiling
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 420 | # Standard deps.
using InstantiateFromURL, Test, Pkg
# TOML deps.
import Pkg.TOML: parsefile
@testset "Main Tests" begin include("maintest.jl") end
@testset "TOML Tests" begin include("tomltest.jl") end
@testset "Directory Names Tests" begin include("namestest.jl") end # same as maintest, but in a different directory
@testset "Test activate_github_path" begin include("pathtest.jl") end
include("precompiletest.jl") | InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | code | 303 | x, y, z = activate_github("arnavs/InstantiationTest")
@test z == Dict{AbstractString,Any}("Expectations"=>"2fe49d83-0758-5602-8f54-1f90ad0d522b","Distances"=>"b4f34e82-e78d-54a5-968a-f98e89d6e8f7","Suppressor"=>"fd094767-a336-5f1f-9728-57cf17d0bbfb","StatsFuns"=>"4c63d2b9-4356-54db-8cca-17b64c39e42c")
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"BSD-3-Clause"
] | 0.6.0 | 24073857e0f6f80eea90e9748ee39c5eff630ea0 | docs | 4237 | # InstantiateFromURL
[](https://travis-ci.com/QuantEcon/InstantiateFromURL.jl)
A way to bind dependency information to Julia assets without the need to pass around TOML files.
The use case is that your notebooks would have something like the following in their first cell:
```
using InstantiateFromURL
github_project("QuantEcon/QuantEconLecturePackages")
```
Based on [Valentin Churavy](https://github.com/vchuravy)'s idea in https://github.com/JuliaLang/IJulia.jl/issues/673#issuecomment-425306944
## Overview/Method Signature
[**Note**] To account for changes in `IJulia` (where notebooks will now look recursively up the tree for TOML, and use either what they find or the default `v1.x` environment), we've introduced the new function/entrypoint below. The functions we had previously provided are still around, in the subsequent **deprecated** section.
The signature is:
```
function github_project(reponame; # e.g., "QuantEcon/quantecon-notebooks-jl"
path = "", # relative path within the repo (root by default)
version = "master",
force = false,
instantiate = false)
```
Where:
* `reponame` and `path` identify the TOML on the internet. Reponame is something like `"QuantEcon/QuantEconLecturePackages"` The path refers to the path to the TOML within the source repo, and is `""` by default (i.e., referring to top-level.)
* `version` refers to a specific version of the TOML, corresponding to a **github tag** of the `reponame` repo.
* `force` decides whether or not we're comfortable using whatever project-specific IJulia finds (if there is any.) Essentially, **the `force` parameter decides whether to use a soft or hard match.**
* `instantiate` decides whether or not to instantiate the project upon activation.
The logic here is:
* If a **local project** is activated (i.e., if there is TOML up the source tree), use that unless `force = true`, and print intelligent information about it (e.g., if we asked for version `v0.2.1`, and version `v0.2.0` is found, it will still use `v0.2.0`, and alert you to the difference.)
* ...But, if `force = true`, then regardless of what is activated, the precise set of `Project.toml, Manifest.toml` will be pulled down to the notebook's directory from the specified internet location.
## Utilities
We also defined:
* `packages_to_default_environment()`, which will take the current environment's packages, and `Pkg.add()` them to your `v1.x` environment. Useful for "seeding" or setting up new Julia installs, or making sure that deps for one project are available for other projects, etc.
## Deprecated Methods
For `activate_github`, the signature is:
```
function activate_github(reponame; # like "QuantEcon/QuantEconLecturePackages"
tag = nothing, # could be "master" or a git tag
sha = nothing, # could point to a specific commit
force = false, # will overwrite the local version of this TOML if true
add_default_environment = false) # will add these projects to the default (e.g., v1.1) TOML if true
```
Example calls include:
* `activate_github("QuantEcon/QuantEconLecturePackages")`
* `activate_github("QuantEcon/QuantEconLecturePackages", tag = "master")`
* `activate_github("QuantEcon/QuantEconLecturePackages", tag = "v0.9.5")`
* `activate_github("QuantEcon/QuantEconLecturePackages", sha = "0c2985")`
For `activate_github_path`, the signature is:
```
function activate_github_path(reponame; # something like "QuantEcon/SimpleDifferentialOperators.jl"
path = "", # like "docs/examples", or where in the (master) source tree the TOML is
tag = "master",
force = false,
activate = true) # decide whether to `activate; instantiate; precompile` or not
```
**Note:** Because switching project files currently leads to repeat precompilations in Julia, `activate` will only bind if the current Project.toml (i.e., `Base.active_project()`) is in a different location than the pwd (i.e., `joinpath(pwd(), "Project.toml")`.)
| InstantiateFromURL | https://github.com/QuantEcon/InstantiateFromURL.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 638 | using StatGeochem
using Documenter
DocMeta.setdocmeta!(StatGeochem, :DocTestSetup, :(using StatGeochem); recursive=true)
makedocs(;
modules=[StatGeochem],
authors="C. Brenhin Keller",
repo="https://github.com/brenhinkeller/StatGeochem.jl/blob/{commit}{path}#{line}",
sitename="StatGeochem.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://brenhinkeller.github.io/StatGeochem.jl",
assets=String[],
),
pages=[
"Home" => "index.md",
],
)
deploydocs(;
repo="github.com/brenhinkeller/StatGeochem.jl",
devbranch = "main",
)
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 14877 |
## --- Load (and install if neccesary) the StatGeochem package which has the resampling functions we'll want
using StatGeochem
using Plots
## --- Input dataset
# We'll store the data in a data structure called a "dictionary"
test = Dict()
# Make fields called "Latitude" and "Longitude" with locations
test["Latitude"] = [-26.18, -21.55, 36.6, 54.4, 36.59, 32.5, 49.85, 49.58, 50.2, 42.4725, 48.47, 25.7725, 12.1851, 17.9, -18.1378, 16.1, 67.11, 48.25, 24.42, 23.2, 40.78, -15.38, 21.94, 61.2296, 49.0, 57.03, 63.6, 62.0, 69.47, 54.8189, 22.39, -17.9317, 48.12, 46.47, 49.0, 13.5, 13.5, 33.0, 20.536, 41.5783, 46.5045, 44.5633, 37.7, 43.7506, 37.2202, 58.07, 34.75, 38.25, 40.42, 43.96, 45.38, 37.78, 35.62, 65.25, 46.23, 53.68, 46.38, 45.11, 42.25, 60.25,46.2, 43.31, 39.81, 35.36, 34.88, 34.75, 62.76, 46.45, 54.75, 44.12, 33.61, 38.0, 36.62, 41.11, 47.95, 37.55, 36.5, 36.75, 48.1732, 22.045, 38.45, 61.4597, 36.0, 66.0, 37.5, 23.544, 69.45, 70.0, 6.25, 52.4, 4.58, -32.0, 64.45, 63.89, 63.95, 64.45, 57.03, 8.11, 7.35, 5.14]
test["Longitude"] = [27.84, 119.9, -118.3, -67.0, 27.17, 103.0, 7.88, 7.15, -60.6, 11.9731, -81.4, 100.32, -84.1862, -65.75, -69.1483, 76.6, 28.85, -78.25, 33.43, 35.1, 14.05, 167.83, 100.92, -131.514, -85.5, 120.72, 36.3, -94.5, 30.6, -100.804, -3.76, -177.567, -77.77, 89.58, -78.0, 78.0, 78.0, 102.0, -104.7, -121.658, -114.802, -114.282, -119.3, -114.641, -119.35, -135.63, -118.75, -91.41, -75.45, -123.66, -109.77, -107.66, -115.16, -147.0, -122.25,-166.4, -122.06, -109.9, -72.12, -152.25, -122.19, -117.31, -120.0, -112.7, -118.88, -118.86, -150.93, -122.03, -131.99, -121.75, -113.09, -72.87, -116.5, -106.25, -91.95, -90.7, -106.12, -106.25, 99.8333, -160.223, 27.3, -139.599, -78.9, -30.0, 140.5, 141.495, 86.22, -52.0, 10.5, -92.75, 9.66, 147.0, 29.05, 28.95, 29.08, 29.05, 120.72, 38.37, 38.42, 123.67]
# Fill in some other fields with age [Ma], major element [wt. %], and trace elements [ppm] data
# Notice there are some NaNs, but this is OK. This is a small sampling of real data from EarthChem
test["Age"] = [3210.0, 3300.0, 85.0, 2100.0, 0.0, 218.0, 275.0, 275.0, 1500.0, 0.254, 2650.0, 44.25, 19.5, 105.8, 1.305, 2600.0, 2415.0, 2100.0, 90.0, 975.0, 32.7685, 0.1, 249.0, 401.5, 2750.0, 3250.0, 2750.0, 2700.0, 2050.0, 1900.0, 188.8, 19.6, 1400.0, 407.0, 2720.0, 2520.0, 2520.0, 748.0, 2.0, 0.149, 46.0, 49.0, 123.0, 46.0, 114.0, 385.5, 33.4, 1400.0, 175.0, 33.4, 2700.0, 33.4, 2100.0, 395.5, 0.005, 33.4, 19.5, 2700.0, 395.5, 175.0, 0.005, 14.55, 33.4, 12.8, 14.55, 156.5, 44.4, 19.5, 429.5, 0.905, 33.4, 397.0, 14.55, NaN, 2750.0, 1400.0, 1400.0, 2100.0, 12.8, 0.5, 218.3, 226.3, 173.55, 44.25, 22.0, 12.8, 158.25, 34.05, 5.0e-6, 2750.0, 44.85, 47.5, 2750.0,2750.0, 2750.0, 2750.0, 3250.0, 1.31, 1.31, 44.85]
test["SiO2"] = [73.94, 68.07, 67.53, 58.4, 58.8485, 72.53, 72.31, 76.65, 64.37, 50.82, 59.7, 50.87, 55.55, 51.71, 66.01, 50.8, 50.51, 72.11, 58.1, 52.3, 56.69, 50.78, 55.05, 53.2, 58.9, 50.3, 77.9, 57.2, 61.03, 50.68, 54.48, 56.83, 50.34, 50.33, 69.84, 69.57, 73.2, 68.2, 50.26, 52.1, 53.06, 54.01, 69.3, 72.42, 61.47, 50.6, 51.1, 51.6, 52.2, 54.6, 55.3, 57.1, 57.2, 59.9, 60.7, 60.9, 61.25, 61.7, 62.1, 62.5, 63.1, 63.8, 64.9, 66.9, 67.5, 67.8, 68.0, 68.6, 68.7, 68.9, 69.7, 71.3, 73.1, 73.2, 74.3, 75.8, 76.0, 76.9, 47.53, 45.81, 46.21, 48.99, 46.9, 43.86, 48.73, 48.56, 48.3, 45.6, 49.25, 45.74, 47.29, 44.93, 41.7, 44.9, 47.8, 48.2, 49.6, 48.1, 46.35, 49.47]
test["TiO2"] = [0.28, 0.32, 0.517, 0.72, 1.10718, 0.29, 0.18, 0.07, 0.59, 0.79, 1.31, 0.53, 0.58, 0.7, 0.657, 0.98, 0.93, 0.66, 0.46, 1.3, 0.39, 1.19, 1.19, 0.29, 0.29, 0.83, 0.235, 0.78, 1.29, 0.9, 1.4, 1.2, 0.46, 2.17, 0.34, 0.42,0.2, 0.3, 1.6, 0.93, 1.88, 0.83, 0.48, 0.219, 0.89, 1.65, 1.8, 0.79, 1.16, 2.2, 0.7, 0.84, 1.63, 0.53, 0.7, 0.42, 1.07, 5.00543e-5, 1.6, 0.57, 0.67, 0.25, 0.14, 0.31, 0.34, 0.54, 0.54, 0.54, 0.41, 0.36, 0.37, 0.26, 0.2, 0.25, 0.04, 0.15, 0.18, 0.08, 2.23, 1.31, 3.13, 0.64, 0.55, 3.72, 0.6, 0.77, 1.33, 1.35, 2.93, 0.6, 2.71, 2.2, 0.59, 0.24, 0.48, 0.58, 0.94, 2.37, 2.55, 1.22]
test["Al2O3"] = [14.19, 16.55, 15.61, 18.5, 17.3459, 14.15, 14.23, 12.6, 14.64, 19.29, 10.42, 9.89, 15.32, 17.14, 16.33, 12.37, 15.94, 14.83, 16.99, 16.41, 18.83, 15.57, 16.85, 13.1, 18.7, 8.48, 12.4, 14.7, 9.2, 15.41, 14.02, 14.16, 18.04, 16.84, 15.11, 15.49, 13.75, 16.83, 13.26, 18.8, 15.82, 10.08, 14.9, 13.27, 16.29, 14.2, 17.1, 13.0, 14.1, 12.3, 14.2, 16.0, 14.2, 15.1, 17.8, 17.4, 15.64, 17.9, 15.4, 15.7, 17.8, 11.8, 18.2, 15.0, 14.7, 15.7, 16.2, 13.8, 13.8, 14.7, 15.2, 15.1, 13.1, 13.3, 13.5, 11.1, 12.1, 13.1, 14.8, 15.13, 14.54, 15.48, 17.8, 9.32, 14.67, 16.29, 16.42, 13.5, 17.88, 11.58, 15.57, 13.53, 4.0, 6.82, 11.3, 14.0, 15.4, 15.17, 14.88, 18.13]
test["FeOT"] = [2.12355, 2.69043, 3.36529, 6.67659, 6.40665, 1.64665, 1.43683, 0.82, 4.58, 6.87, 6.25366, 7.08, 6.45455, 8.22, 3.63, 11.19, 10.71, 0.57, 8.77, 8.37855, 3.42, NaN, 11.2868, 8.1, 7.56, 12.5, 2.37, 7.74, 15.394, 11.14, 11.31, 9.77, 10.66, 10.55, 2.7, 3.03, 1.61, 2.36, 7.16506, 7.66, 8.82, 8.7, 3.18979, 1.55, NaN, 11.0677, 4.02967, 12.6873, 10.2578, 13.2096, 7.90033, 6.80257, 10.1679, 4.32809, 4.82298, 4.79599, 6.10971, 5.37187, 4.55973, 6.10971, 4.56204, 2.13255, 1.06178, 2.3755, 3.32968, 3.17894, 3.62087, 4.41807, 3.00537, 2.3575, 1.74563,2.59693, 1.1498, 2.77977, 0.584877, 2.45648, 1.51168, 1.36091, 9.84955, 12.28, 12.07, 7.5, 9.21966, 14.1458,NaN, 8.98, 12.02, 10.89, 10.03, 10.84, 11.29, 11.75, 11.4, 9.71, 10.5, 9.28, 13.0, 11.27, 11.56, 6.46]
test["MgO"] = [0.66, 0.62, 1.44, 3.48, 2.71457, 0.58, 0.71, 0.55, 1.99, 4.32, 1.15, 16.22, 5.75, 5.53, 1.35, 9.97, 6.64, 0.22, 0.44, 4.62, 0.39, 6.35, 3.68, 11.01, 3.4, 13.3, 0.34, 6.1, 1.83, 7.99, 5.36, 2.09, 10.91, 5.62, 1.74, 0.88, 0.16, 1.42, 9.79, 7.16, 7.3, 10.63, 1.1, 0.31, 3.29, 0.98, 2.4, 10.3, 7.51, 2.4, 5.55, 3.11, 3.25, 1.11, 2.32, 2.1, 3.3, 3.12, 1.7, 2.77, 2.2, 0.18, NaN, 1.2, 0.5, 1.6, 1.3, 0.59, 1.11, 0.53, 0.32, 0.55, 0.16, 0.6,0.19, NaN, 0.05, 0.19, 8.05, 11.09, 7.13, 8.82, 9.9, 8.86, 12.71, 8.76, 6.83, 9.57, 3.66, 4.36, 7.13, 11.44,21.5, 22.0, 15.3, 5.45, 7.3, 8.26, 7.57, 7.42]
test["CaO"] = [0.58, 2.64, 3.65, 3.41, 6.51955, 2.1, 0.34, 0.35, 4.78, 8.6, 6.84, 6.11, 8.68, 7.33, 3.57, 9.88, 9.63, 1.97, 2.97, 7.45, 1.94, 10.56, 0.92, 8.12, 4.3, 13.4, 0.9, 10.36, 2.49, 10.52, 9.74, 8.68, 6.46, 6.56, 3.28, 3.45, 1.21, 2.66, 7.57, 9.23, 7.37, 9.78, 2.8, 3.38, 6.07, 6.95, 10.3, 0.43, 11.0, 5.9, 7.51, 4.2, 6.05, 3.29, 5.57, 4.79, 5.64, 1.73, 4.4, 6.25, 5.45, 1.21, 0.49, 2.9, 2.1, 3.3, 3.7, 2.3, 1.71, 1.83, 1.54, 2.2, 2.0, 2.3, 0.83, 0.03, 0.6, 0.95, 6.59, 10.66, 5.61, 7.69, 11.0, 12.29, 10.2, 11.46, 11.02, 11.1, 9.21, 10.47, 9.23, 10.16, 8.63, 7.57, 9.3, 9.7, 11.5, 9.78, 10.94, 12.45]
test["Na2O"] = [4.15, 4.89, 3.77, 3.8, 4.15415, 3.22, 2.94, 1.59, 4.31, 1.96, 3.81, 1.46, 2.63, 4.24, 4.17, 1.98, 3.44, 5.67, 6.34, 4.31, 5.69, 3.03, 4.92, 3.3, 2.8, 0.6, 3.36, 1.5, 1.52, 2.95, 2.4, 2.87, 0.05, 4.25, 5.24, 4.48, 3.42, 5.07, 2.98, 3.42, 3.49, 2.57, 3.8, 4.5, 3.23, 3.38, 3.7, NaN, 2.21, 3.4, 3.24, 3.83, 2.49, 2.71, 4.32, 3.85, 4.32, 4.35, 3.0, 2.75, 4.5, 3.92, 3.75, 3.5, 3.7, 3.2, 3.9, 4.2, 3.51, 4.45, 4.9, 3.6, 3.4, 4.0, 3.48, 1.34, 3.1, 3.8, 4.65, 2.52, 3.64, 2.88, 2.02, 1.15, 1.88, 2.14, 2.17, 1.58, 3.79, 2.35, 3.28, 3.46, 0.02, 0.12, 1.42, 2.55, 1.81, 2.98, 2.6, 3.16]
test["K2O"] = [4.02, 3.1, 3.63, 4.19, 1.81769, 4.48, 5.36, 5.02, 1.1, 6.06, 0.47, 4.95, 1.5, 0.4, 3.82, 0.26, 0.69, 1.55, 3.48, 1.02, 7.33, 1.23, 0.81, 0.76, 3.1, 0.22, 2.04, 0.44, 2.76, 0.13, 0.91, 0.71, 1.82, 1.57, 1.0, 1.25, 5.23, 1.92, 4.66, 0.561, 1.67, 2.91, 3.3, 4.2, 2.6, 2.23, 1.9, 3.21, 0.61, 0.65, 1.39, 3.85, 2.45, 10.3, 1.23, 2.0, 1.79, 4.98, 3.3, 1.59, 1.27, 2.75, 8.98, 2.59, 3.8, 3.2, 2.7, 2.81, 4.58, 2.77, 4.9, 3.1, 4.5, 1.3, 5.27,7.54, 5.1, 2.7, 4.26, 0.38, 0.7, 1.63, 0.24, 0.7, 0.22, 1.09, 0.23, 0.07, 1.86, 0.45, 1.06, 1.48, 0.53, 0.02, 0.05, 0.19, 0.13, 0.97, 0.49, 0.16]
test["La"] = [15.78, 46.0, 28.8, 35.6, 27.2, 30.18, 50.0, 49.0, 31.1, 91.5, 9.87, 21.4, 9.17, 8.4, 28.6113, 4.34, 8.0, 45.52, 80.7, NaN, 108.0, 15.2, 7.67, 0.8, 36.3, NaN, 46.7, 21.0, 34.2, 2.1, 14.22, 8.81, 1.34, 25.8, 9.0, NaN, 85.1, 7.0, 48.1, 8.76, 36.0, 28.6, 30.0, 44.0, 24.1, 44.7, 50.0, 12.0, 10.7, 50.0, NaN, 43.0, 75.0, 91.0, 13.0, 17.5, 27.0, NaN, 122.0, 8.45, 9.0, 73.0, 53.5, 27.5, 50.0, 50.0, 93.0, 32.0, 28.9, 23.0, 83.0, 13.0, 100.0, 22.6, NaN, 20.2, 200.0, NaN, 67.3, 8.16, 0.0, 2.16, 4.8, 17.707, 3.06, 31.5, 5.54, 2.95, 61.05, 2.18, 31.35, 50.0, 10.0, 20.0, 0.0, 20.0, 3.54, 25.7, 18.39, 2.85]
test["Yb"] = [NaN, 2.4, NaN, 0.72, 3.1, 0.69, NaN, NaN, 3.28, 2.61, 6.19, 1.46, 1.48, 1.64, 1.47864, 2.2, NaN, 2.71, 5.72, NaN, 4.7, 2.84, 2.81, 1.3, 1.76, NaN, 5.66, 1.71, 5.65, 2.03, 2.48, 5.5, 1.78, 4.1, NaN, NaN, 0.94, 0.36, 1.61, 1.3, 1.65, 1.86, 2.5, 2.49, 2.6, 4.1, 5.0, 2.7, 2.2, 7.0, NaN, 2.6, 5.0, 3.16, 2.0, 1.48, 2.0, 1.5, 5.8,2.74, 1.0, 9.36, 3.0, 1.12, 1.0, 1.5, 2.2, 4.0, 1.64, 3.0, 3.83, 0.77, 3.0, 4.81, NaN, 8.34, 15.0, NaN, 1.36, 1.59, NaN, 1.37, 2.3, 1.435, 1.76, 2.17, 2.65, 1.96, 2.53, 1.31, 1.88, 1.98, NaN, NaN, NaN, NaN, 2.1, 2.18,2.15, 2.26]
# We're also going to want a list called "elements" with names of all the fields we want to resample
elements = ["Latitude","Longitude","Age","SiO2","TiO2","Al2O3","FeOT","MgO","CaO","Na2O","K2O","La","Yb"]
# Now let's add uncertainties, starting with age uncertainty
test["Age_sigma"] = [10.0, 10.0, 10.0, 400.0, 10.0, 10.0, 24.0, 24.0, 10.0, 0.027, 150.0, 21.25, 3.5, 6.2, 1.295, 10.0, 35.0, 400.0, 10.0, 125.0, 32.7315, 10.0, 10.0, 42.5, 250.0, 250.0, 250.0, 10.0, 450.0, 10.0, 12.8, 14.3, 300.0, 9.0, 10.0, 10.0, 10.0, 7.0, 1.0, 0.26, 4.0, 1.0, 23.0, 3.7, 2.0, 31.5, 31.6, 300.0, 31.0, 31.6, 150.0, 31.6, 400.0, 147.5, 0.005, 31.6, 14.2, 150.0, 147.5, 31.0, 0.005, 9.25, 31.6, 11.0, 9.25, 91.5, 20.6, 14.2, 13.5, 0.895,31.6, 146.0, 9.25, 10.0, 250.0, 300.0, 300.0, 400.0, 10.2, 0.5, 16.7, 24.7, 28.05, 21.25, 10.0, 10.2, 92.75,31.45, 10.0, 250.0, 10.95, 2.5, 250.0, 250.0, 250.0, 250.0, 250.0, 1.295, 1.295, 10.95]
# For this dataset, lat and lon are good to 0.01 degrees (absolute)
test["Latitude_sigma"] = 0.01 * ones(size(test["Latitude"]))
test["Longitude_sigma"] = 0.01 * ones(size(test["Longitude"]))
# We'll use a 1% relative (1-sigma) default analytical uncertainty for the rest of the elements
for i=4:length(elements)
test[elements[i]*"_sigma"] = test[elements[i]] * 0.01
end
## --- Resample
# Compute proximity coefficients (inverse weights)
k = invweight(test["Latitude"], test["Longitude"], test["Age"])
# # Alternatively, we could weight only by location or only by age (though won't make much difference with this dataset)
# k = invweight_location(test["Latitude"], test["Longitude"])
# k = invweight_age(test["Age"])
# Probability of keeping a given data point when sampling
p = 1.0 ./ ((k .* nanmedian(5.0 ./ k)) .+ 1.0) # Keep rougly one-fith of the data in each resampling
# Resample a few hundred times (all elements!)
nresamplings = 200
mctest = bsresample(test, nresamplings*length(test["SiO2"]), elements, p)
## --- Approach 1: use the bulk-resampled dataset we just created
# Calculate mean MgO for 8 bins between 40% SiO2 and 80% SiO2 from resampled dataset
# (c = bin centers, m = means, e = 1-sigma S.E.M)
(c,m,e) = binmeans(mctest["SiO2"],mctest["MgO"],40,80,8; resamplingratio=nresamplings)
# Plot results
plot(c,m,yerror=2*e,label="",xlabel="SiO2", ylabel="MgO",xlims=(40,80),framestyle=:box)
## --- Approach 2: resample the binned means for one element at a time (Can resample many times)
# Calculate binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(test["SiO2"],test["MgO"],40,80,8, p=p, x_sigma=test["SiO2_sigma"], nresamplings=10000)
# Plot results
plot(c,m,yerror=(el,eu),label="",xlabel="SiO2", ylabel="MgO",xlims=(40,80), framestyle=:box)
## --- Download and unzip Keller and Schoene (2012) dataset
if ~isfile("ign.h5") # Unless it already exists
download("https://storage.googleapis.com/statgeochem/ign.h5.gz","./ign.h5.gz")
run(`gunzip -f ign.h5.gz`) # Unzip file
end
# Read HDF5 file
using HDF5
ign = h5read("ign.h5","vars")
## --- Compute proximity coefficients (inverse weights)
# # Compute inverse weights
# k = invweight(ign["Latitude"] .|> Float32, ign["Longitude"] .|> Float32, ign["Age"] .|> Float32)
# Since this is somewhat computatually intensive, let's load a precomputed version instead
k = ign["k"]
# Probability of keeping a given data point when sampling
p = 1.0 ./ ((k .* nanmedian(5.0 ./ k)) .+ 1.0) # Keep rougly one-fith of the data in each resampling
p[vec(ign["Elevation"].<-100)] .= 0 # Consider only continental crust
# Age uncertainty
ign["Age_sigma"] = (ign["Age_Max"]-ign["Age_Min"])/2;
t = (ign["Age_sigma"] .< 50) .| isnan.(ign["Age_sigma"]) # Find points with < 50 Ma absolute uncertainty
ign["Age_sigma"][t] .= 50 # Set 50 Ma minimum age uncertainty (1-sigma)
# Location uncertainty
ign["Latitude_sigma"] = ign["Loc_Prec"]
ign["Longitude_sigma"] = ign["Loc_Prec"]
## --- Try resampling a single variable to reproduce the MgO trend from K&S 2012
xmin = 0
xmax = 3900
nbins = 39
elem = "SiO2"
unit = "wt. %"
# Look only at samples in the basaltic silica range
# (note that if uncertainty in SiO2 were more significant, we should be resampling this too)
t = 43 .< ign["SiO2"] .< 51 # Mafic
# Calculate binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign["Age"][t],ign[elem][t],xmin,xmax,nbins, p=p[t], x_sigma=ign["Age_sigma"][t])
# Plot results
plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkred,mscolor=:darkred,label="")
plot!(xlabel="Age (Ma)", ylabel="$elem ($unit)",xlims=(0,4000),framestyle=:box,grid=:off,xflip=true) # Format plot
## --- Same as above, but for Na2O
xmin = 0
xmax = 3900
nbins = 39
elem = "Na2O"
unit = "wt. %"
# Look only at samples in the basaltic silica range
# (note that if uncertainty in SiO2 were more significant, we should be resampling this too)
t = 43 .< ign["SiO2"] .< 51 # Mafic
# Calculate binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign["Age"][t],ign[elem][t],xmin,xmax,nbins, p=p[t], x_sigma=ign["Age_sigma"][t])
# Plot results
plot(c,m,yerror=(el,eu),seriestype=:scatter,markerstrokecolor=:auto,label="")
plot!(xlabel="Age (Ma)", ylabel="$elem ($unit)",xlims=(0,4000),framestyle=:box,grid=:off,xflip=true) # Format plot
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 8012 | ## --- Load the StatGeochem package which has the resampling functions we'll want
using StatGeochem
using Plots
## --- Download and unzip Keller and Schoene (2012) dataset
if ~isfile("ign.h5") # Unless it already exists
download("https://storage.googleapis.com/statgeochem/ign.h5.gz","./ign.h5.gz")
download("https://storage.googleapis.com/statgeochem/err2srel.csv","./err2srel.csv")
run(`gunzip -f ign.h5.gz`) # Unzip file
end
# Read HDF5 file
using HDF5
ign = h5read("ign.h5","vars")
## --- Compute proximity coefficients (inverse weights)
# # Compute inverse weights
# k = invweight(ign["Latitude"] .|> Float32, ign["Longitude"] .|> Float32, ign["Age"] .|> Float32)
# Since this is somewhat computatually intensive, let's load a precomputed version instead
k = ign["k"]
# Probability of keeping a given data point when sampling
p = 1.0 ./ ((k .* nanmedian(5.0 ./ k)) .+ 1.0) # Keep rougly one-fith of the data in each resampling
p[vec(ign["Elevation"].<-100)] .= 0 # Consider only continental crust
# Set absolute uncertainties for each element where possible, using errors defined inerr2srel.csv
err2srel = importdataset("err2srel.csv", ',', importas=:Dict)
for e in ign["elements"]
# If there's an err2srel for this variable, create a "_sigma" if possible
if haskey(err2srel, e) && !haskey(ign, e*"_sigma")
ign[e*"_sigma"] = ign[e] .* (err2srel[e] / 2);
end
end
# Special cases: age uncertainty
ign["Age_sigma"] = (ign["Age_Max"]-ign["Age_Min"])/2;
t = (ign["Age_sigma"] .< 50) .| isnan.(ign["Age_sigma"]) # Find points with < 50 Ma absolute uncertainty
ign["Age_sigma"][t] .= 50 # Set 50 Ma minimum age uncertainty (1-sigma)
# Special cases: location uncertainty
ign["Latitude_sigma"] = ign["Loc_Prec"]
ign["Longitude_sigma"] = ign["Loc_Prec"]
## --- Single element differentiation example
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 8
elem = "K2O"
h = plot(xlabel=xelem, ylabel="$(elem)",xlims=(xmin,xmax),framestyle=:box,grid=:off,fg_color_legend=:white) # Format plot
rt = [0,1,2,3,4] # Time range (Ga)
colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign[xelem][t],ign[elem][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], y_sigma=ign[elem*"_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),color=colors[i],mscolor=colors[i],seriestype=:scatter,label="$(rt[i])-$(rt[i+1]) Ga")
plot!(h, c,m,style=:dot,color=colors[i],mscolor=colors[i],label="")
end
display(h)
## --- Ratio differentiation example
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 8
num = "Rb" # Numerator
denom = "Sr" # Denominator
h = plot(xlabel=xelem, ylabel="$(num) / $(denom)",xlims=(xmin,xmax),framestyle=:box,grid=:off,legend=:topleft,fg_color_legend=:white) # Format plot
rt = [0,1,2,3,4]
colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_ratios(nanbinmedian!, ign[xelem][t],ign[num][t],ign[denom][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], num_sigma=ign[num*"_sigma"][t], denom_sigma=ign[denom*"_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),color=colors[i],mscolor=colors[i],seriestype=:scatter,label="$(rt[i])-$(rt[i+1]) Ga")
plot!(h, c,m,style=:dot,color=colors[i],mscolor=colors[i],label="")
end
display(h)
## --- High-level functions for calculating combined averages over a set or silica ranges
function constprop(binbsrfunction::Function, dataset::Dict, elem, xmin, xmax, nbins, p; xelem="Age", norm_by="SiO2", norm_bins=[43,55,65,78], nresamplings=1000)
c = zeros(nbins)
m = zeros(nbins)
el = zeros(nbins)
eu = zeros(nbins)
for i=1:length(norm_bins)-1
# Find the samples we're looking for
t = (norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (dataset[elem] .> 0)
# See what proportion of the modern crust falls in this norm_bin
prop = sum((norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (p .> 0)) / sum((norm_bins[1] .< dataset[norm_by] .< norm_bins[end]) .& (p .> 0))
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c[:],m1,el1,eu1) = binbsrfunction(dataset[xelem][t], dataset[elem][t], xmin, xmax, nbins, x_sigma=dataset["$(xelem)_sigma"][t], nresamplings=nresamplings, p=p[t])
m .+= prop.*m1
el .+= prop.*el1
eu .+= prop.*eu1
end
el ./= sqrt(length(norm_bins)-1) # Standard error
eu ./= sqrt(length(norm_bins)-1) # Standard error
return c, m, el, eu
end
function constprop(binbsrfunction::Function, dataset::Dict, num, denom, xmin, xmax, nbins, p; xelem="Age", norm_by="SiO2", norm_bins=[43,55,65,78], nresamplings=1000)
c = zeros(nbins)
m = zeros(nbins)
el = zeros(nbins)
eu = zeros(nbins)
for i=1:length(norm_bins)-1
# Find the samples we're looking for
t = (norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (dataset[num].>0) .& (dataset[denom].>0)
# See what proportion of the modern crust falls in this norm_bin
prop = sum((norm_bins[i] .< dataset[norm_by] .< norm_bins[i+1]) .& (p .> 0)) / sum((norm_bins[1] .< dataset[norm_by] .< norm_bins[end]) .& (p .> 0))
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c[:],m1,el1,eu1) = binbsrfunction(dataset[xelem][t],dataset[num][t],dataset[denom][t],xmin,xmax,nbins,x_sigma=dataset["$(xelem)_sigma"][t],num_sigma=dataset["$(num)_sigma"][t],denom_sigma=dataset["$(denom)_sigma"][t],nresamplings=nresamplings,p=p[t])
m .+= prop.*m1
el .+= prop.*el1
eu .+= prop.*eu1
end
el ./= sqrt(length(norm_bins)-1) # Standard error
eu ./= sqrt(length(norm_bins)-1) # Standard error
return c, m, el, eu
end
## --- Single element constant-silica reference model
tmin = 0
tmax = 3900
nbins = 39
plotmin = 0
plotmax = 4000
elem = "MgO"
(c, m, el, eu) = constprop(bin_bsr_means, ign, elem, tmin, tmax, nbins, p)
# Plot results
h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,mscolor=:darkblue,label="")
plot!(h, c,m,style=:dot,color=:darkblue,mscolor=:darkblue,label="")
plot!(h, xlabel="Age (Ma)", ylabel="$(elem)",xlims=(plotmin,plotmax),framestyle=:box,grid=:off,xflip=true) # Format plot
savefig(h,"Constant Silica $(elem).pdf")
display(h)
## --- Ratio constant-silica reference model
tmin = 0
tmax = 4000
nbins = 8
plotmin = 0
plotmax = 4000
num = "Rb"
denom = "Sr"
(c, m, el, eu) = constprop(bin_bsr_ratio_medians, ign, num, denom, tmin, tmax, nbins, p)
# Plot results
h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,mscolor=:darkblue,label="")
plot!(h, c,m,style=:dot,color=:darkblue,mscolor=:darkblue,label="")
plot!(h, xlabel="Age (Ma)", ylabel="$(num) / $(denom)",xlims=(plotmin,plotmax),framestyle=:box,grid=:off,xflip=true) # Format plot
savefig(h,"Constant Silica $(num)_$(denom).pdf")
display(h)
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 2849 | ## --- Load required packages
using StatGeochem
using Plots
## --- # # # # # # # # # # # pMelts equil. batch melting # # # # # # # # # # # #
# # Download precompiled executable from caltech
alphameltsversion = "linux_alphamelts_1-9"
download("https://magmasource.caltech.edu/alphamelts/zipfiles/$alphameltsversion.zip","./$alphameltsversion.zip")
run(`unzip -o $alphameltsversion.zip`);
run(`mv $alphameltsversion/alphamelts_linux64 $alphameltsversion/alphamelts`);
# Note: melts_configure requires a working alphamelts installation.
# If you don't have alphamelts, you'll need to download, install, and
# configure it. The absolute path to the alphamelts perl script must be input here:
meltspath = abspath("$alphameltsversion/run_alphamelts.command")
# meltspath = "/usr/local/bin/run_alphamelts.command"
# The absolute or relative path to a direcory where you want to run MELTS -
# A number of configuration and output files will be written here.
scratchdir = "scratch/"
# Conditions
P_range = (20000,20000)
T_range = (1700,800)
# Starting composition
elements = ["SiO2", "TiO2","Al2O3","Fe2O3","Cr2O3", "FeO", "MnO", "MgO", "NiO", "CoO", "CaO", "Na2O", "K2O", "P2O5", "H2O",]
composition=[44.8030, 0.1991, 4.4305, 0.9778, 0.3823, 7.1350, 0.1344, 37.6345, 0.2489, 0.0129, 3.5345, 0.3584, 0.0289, 0.0209, 0.15,] #mcdbse (McDonough Pyrolite)
# Run simulation
melts_configure(meltspath, scratchdir, composition, elements, T_range, P_range,
batchstring="1\nsc.melts\n10\n1\n3\n1\nliquid\n1\n1.0\n0\n10\n0\n4\n0\n",
dT=-10, dP=0, index=1, version="pMELTS",mode="isobaric",fo2path="FMQ")
# Read results
melt_comp = melts_query_liquid(scratchdir, index=1)
solid_comp = melts_query_solid(scratchdir, index=1)
modes = melts_query_modes(scratchdir, index=1)
## --- Plot melt composition
h = plot(xlabel="Percent melt",ylabel="Abudance (wt. %) in melt")
for e in ["SiO2","Al2O3","CaO","MgO","FeO","Na2O","K2O"]
plot!(h,melt_comp["mass"],melt_comp[e],label=e)
end
plot!(h,xlims=(0,100),framestyle=:box,fg_color_legend=:white,legend=:right)
display(h)
## --- Plot solid composition
h = plot(xlabel="Percent melt",ylabel="Abudance (wt. %) in solid")
for e in ["SiO2","Al2O3","CaO","MgO","FeO","Na2O","K2O"]
plot!(h,100 .- solid_comp["mass"],solid_comp[e],label=e)
end
plot!(h,xlims=(0,100),framestyle=:box,fg_color_legend=:white,legend=:right)
display(h)
## --- Plot phase modes
h = plot(xlabel="Temperature (C)",ylabel="Abudance (wt. %)")
for m in modes["elements"][4:end]
plot!(h,modes["Temperature"],modes[m],label=m)
end
plot!(h,ylims=(0,100),framestyle=:box,fg_color_legend=:white,legend=:topleft)
display(h)
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 16542 | ################################################################################
# A few PerpleX calculation examples using the Julia-Perplex interface
## --- Import some useful packages
using StatGeochem
using Plots
## --- Configure
# Absolute paths to perplex resources
perplexdir = joinpath(resourcepath,"perplex-stable")
scratchdir = "./scratch/" # Location of directory to store output files
# Attempt to install perplex, if not already extant
if !isfile(joinpath(perplexdir,"vertex"))
# Make sure resourcepath exists
run(`mkdir -p $resourcepath`)
# Try to compile PerpleX from source; if that fails, try to download linux binaries
try
# Check if there is a fortran compiler
run(`gfortran -v`)
# Download Perplex v6.8.7 -- known to work with interface used here
file = download("https://storage.googleapis.com/statgeochem/perplex-6.8.7-source.zip", joinpath(resourcepath,"perplex-stable.zip"))
# # For a more updated perplex version, you might also try
# file = download("https://petrol.natur.cuni.cz/~ondro/perplex-sources-stable.zip", joinpath(resourcepath,"perplex-stable.zip"))
run(`unzip -u $file -d $resourcepath`) # Extract
system("cd $perplexdir; make") # Compile
catch
@warn "Failed to compile from source, trying precompiled linux binaries instead"
run(`mkdir -p $perplexdir`)
file = download("https://petrol.natur.cuni.cz/~ondro/Perple_X_6.8.7_Linux_64_gfortran.tar.gz","perplex-6.8.7-linux.tar.gz")
run(`tar -xzf $file -C $perplexdir`)
end
end
## --- # # # # # # # # # # # # # Initial composition # # # # # # # # # # # # # #
## McDonough Pyrolite
#elements = [ "SIO2", "TIO2", "AL2O3", "FEO", "MNO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
#composition = [45.1242, 0.2005, 4.4623, 8.0723, 0.1354, 37.9043, 3.5598, 0.3610, 0.0291, 0.1511, 0.0440,]
# # Kelemen (2014) primitive continental basalt. H2O and CO2 are guesses
# elements = [ "SIO2", "TIO2", "AL2O3", "FEO", "MNO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
# composition = [50.0956, 0.9564, 15.3224, 8.5103, 0.1659, 9.2520, 9.6912, 2.5472, 0.8588, 2.0000, 0.6000,]
# # Kelemen (2014) primitive continental basalt excluding Mn. H2O and CO2 are guesses
# elements = [ "SIO2", "TIO2", "AL2O3", "FEO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
# composition = [50.0956, 0.9564, 15.3224, 8.5103, 9.2520, 9.6912, 2.5472, 0.8588, 2.0000, 0.6000,]
# Kelemen (2014) primitive continental basalt excluding Mn and Ti since most melt models can"t handle them..
elements = [ "SIO2", "AL2O3", "FEO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
composition = [50.0956, 15.3224, 8.5103, 9.2520, 9.6912, 2.5472, 0.8588, 2.0000, 0.6000,]
# # Average Archean basalt (EarthChem data)
# elements = [ "SIO2", "TIO2", "AL2O3", "FEO", "MNO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
# composition = [49.2054, 0.8401, 12.0551, 11.4018, 0.2198, 12.3997, 9.3113, 1.6549, 0.4630, 1.8935, 0.5555,]
# # A random granite
# elements = [ "SIO2", "TIO2", "AL2O3", "FEO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
# composition = [ 69.16, 0.42, 14.96, 2.9064, 1.52, 1.81, 3.7, 4.95, 1.8386, 0.4407,]
## --- # # # # # # # # # # # Some solution model options # # # # # # # # # # # #
# Emphasis on phases from Green (2016) -- developed for metabasites, includes what is probably the best (and most expensive) amphibole model. Use with hp11ver.dat
G_solution_phases = "Augite(G)\nOpx(JH)\ncAmph(G)\noAmph(DP)\nO(JH)\nSp(JH)\nGrt(JH)\nfeldspar_B\nMica(W)\nBio(TCC)\nChl(W)\nCtd(W)\nCrd(W)\nSa(WP)\nSt(W)\nIlm(WPH)\nAtg(PN)\nT\nB\nF\nDo(HP)\nScap\nChum\nNeph(FB)\n"
G_excludes ="ged\nfanth\ngl\nilm\nilm_nol\n"
# Emphasis on phases from White (2014) -- developed for metapelites. Use with hp11ver.dat (though can apparenty run with hp02ver.dat without crashing)
W_solution_phases = "Omph(HP)\nOpx(W)\ncAmph(DP)\noAmph(DP)\nO(JH)\nSp(JH)\nGt(W)\nfeldspar_B\nMica(W)\nBi(W)\nChl(W)\nCtd(W)\nCrd(W)\nSa(WP)\nSt(W) \nIlm(WPH)\nAtg(PN)\nT\nB\nF\nDo(HP)\nScap\nChum\nPu(M)\n"
W_excludes = "andr\nts\nparg\ngl\nged\nfanth\n"
# Emphasis on phases from Jennings and Holland (2015) -- developed for mantle melting. Use with hp11ver.dat
JH_solution_phases = "Cpx(JH)\nOpx(JH)\ncAmph(DP)\noAmph(DP)\nO(JH)\nSp(JH)\nGrt(JH)\nfeldspar_B\nMica(W)\nBio(TCC)\nChl(W)\nCtd(W)\nCrd(W)\nSa(WP)\nSt(W)\nIlm(WPH)\nAtg(PN)\nT\nB\nF\nDo(HP)\nScap\nChum\nNeph(FB)\n"
JH_excludes = "ts\nparg\ngl\nged\nfanth\n"
# Emphasis on phases from Holland and Powell -- all phases can be used with hp02ver.dat.
HP_solution_phases = "Omph(HP)\nOpx(HP)\nGlTrTsPg\nAnth\nO(HP)\nSp(HP)\nGt(HP)\nfeldspar_B\nMica(CF)\nBio(TCC)\nChl(HP)\nCtd(HP)\nSapp(HP)\nSt(HP)\nIlHm(A)\nDo(HP)\nT\nB\nF\n"
HP_excludes = ""
## --- # # # # # # # # # # # # # Isobaric example # # # # # # # # # # # # # # # #
# Input parameters
P = 1000 # Pressure, bar
T_range = (0+273.15, 1500+273.15) # Temperature range, Kelvin
# # Configure (run build and vertex)
# melt_model = "melt(G)"
# @time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range,
# dataset="hpha11ver.dat",
# npoints=100,
# excludes=G_excludes,
# solution_phases=melt_model*"\n"*G_solution_phases
# )
melt_model = "melt(HP)"
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range,
dataset="hp02ver.dat",
npoints=100,
excludes=HP_excludes,
solution_phases=melt_model*"\n"*HP_solution_phases
)
## --- Query all properties at a single temperature -- results returned as text
T = 850+273.15
data_isobaric = perplex_query_point(perplexdir, scratchdir, T) |> print
## --- Query the full isobar -- results returned as dict
bulk = perplex_query_system(perplexdir, scratchdir) # Get system data for all temperatures. Set include_fluid = "n" to get solid+melt only
modes = perplex_query_modes(perplexdir, scratchdir) # || phase modes
melt = perplex_query_phase(perplexdir, scratchdir, melt_model) # || melt data
# Melt wt.% seems to be slightly inaccurate; use values from modes instead
melt["wt_pct"] = modes[melt_model]
# Create dictionary to hold solid composition and fill it using what we know from system and melt
solid = Dict()
solid["wt_pct"] = 100 .- melt["wt_pct"]
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
solid[e] = (bulk[e] - (melt[e] .* melt["wt_pct"]/100)) ./ (solid["wt_pct"]/100)
end
renormalize!(solid,["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"],total=100)
## --- Plot melt composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in melt", title="$melt_model + G_solution_phases, $P bar")
i = 0
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], melt[e], label=e, color=lines[global i += 1])
plot!(h, melt["wt_pct"], bulk[e], label="", color=lines[i], linestyle=:dot)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"MeltComposition.pdf")
display(h)
## --- Plot melt composition as a function of melt SiO2
h = plot(xlabel="Magma SIO2 (wt.%)", ylabel="Wt. % in melt", title="$melt_model + G_solution_phases, $P bar")
i = 1
for e in ["AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h,melt["SIO2"], melt[e], label=e, color = lines[global i +=1])
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"MeltCompositionvsSiO2.pdf")
display(h)
## --- Plot solid composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in solid", title="$melt_model + G_solution_phases, $P bar")
i = 0
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], solid[e], label=e, color=lines[global i +=1])
end
plot!(h,fg_color_legend=:white, framestyle=:box, legend=:topleft)
savefig(h,"SolidComposition.pdf")
display(h)
## --- Plot modes of all phases as a function of temperature
h = plot(xlabel="T (C)", ylabel="Weight percent", title="$melt_model + G_solution_phases, $P bar")
for m in modes["elements"][3:end]
plot!(h, modes["T(K)"] .- 273.15, modes[m], label=m)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"PhaseModes.pdf")
display(h)
## --- Plot modes of all phases as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Weight percent", title="$melt_model + G_solution_phases, $P bar")
for m in modes["elements"][3:end]
plot!(h, modes[melt_model], modes[m], label=m)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"PhaseModesvsF.pdf")
display(h)
## --- Plot seismic properties
# Query seismic properties along the whole profile
seismic = perplex_query_seismic(perplexdir, scratchdir)
seismic["vp/vs"][seismic["vp/vs"] .> 100] .= NaN # Exclude cases where vs drops to zero
h = plot(xlabel="Pressure", ylabel="Property")
plot!(h,seismic["T(K)"],seismic["vp,km/s"], label="vp,km/s")
plot!(h,seismic["T(K)"],seismic["vp/vs"], label="vp/vs")
plot!(h,seismic["T(K)"],seismic["rho,kg/m3"]/1000, label="rho, g/cc")
savefig(h,"GeothermSeismicProperties.pdf")
display(h)
## --- # # # # # # # # # # # Geothermal gradient example # # # # # # # # # # # #
# Input parameters
P_range = (280, 28000) # Pressure range to explore, bar (roughly 1-100 km depth)
T_surf = 273.15 # Temperature of surface (K)
geotherm = 0.01 # Geothermal gradient of 0.1 K/bar == about 28.4 K/km
melt_model = ""
# Configure (run build and vertex)
@time perplex_configure_geotherm(perplexdir, scratchdir, composition, elements,
P_range, T_surf, geotherm, dataset="hp02ver.dat", excludes=HP_excludes,
solution_phases=HP_solution_phases, npoints=200, index=2)
# # Alternative configuration, using hpha02ver.dat
# @time perplex_configure_geotherm(perplexdir, scratchdir, composition, elements,
# P_range, T_surf, geotherm, dataset="hpha02ver.dat", excludes="qGL\n"*HP_excludes,
# solution_phases=HP_solution_phases, npoints=200, index=2)
# # Alternative configuration, using hpha02ver.dat and new phases for metapelites
# @time perplex_configure_geotherm(perplexdir, scratchdir, composition, elements,
# P_range, T_surf, geotherm, dataset="hpha02ver.dat", excludes="qGL\n"*W_excludes,
# solution_phases=W_solution_phases, npoints=200, index=2)
## --- Plot modes of all phases as a function of temperature
modes = perplex_query_modes(perplexdir, scratchdir, index=2) # || phase modes
h = plot(xlabel="T (C)", ylabel="Weight percent")
for m in modes["elements"][3:end]
plot!(h, modes["T(K)"] .- 273.15, modes[m], label=m)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"GeothermPhaseModes.pdf")
display(h)
## --- Plot seismic properties
# Query seismic properties along the whole profile
seismic = perplex_query_seismic(perplexdir, scratchdir, index=2)
seismic["vp/vs"][seismic["vp/vs"] .> 100] .= NaN # Exclude cases where vs drops to zero
h = plot(xlabel="Pressure", ylabel="Property")
plot!(h,seismic["P(bar)"],seismic["vp,km/s"], label="vp,km/s")
plot!(h,seismic["P(bar)"],seismic["vp/vs"], label="vp/vs")
plot!(h,seismic["P(bar)"],seismic["rho,kg/m3"]/1000, label="rho, g/cc")
plot!(h,seismic["P(bar)"],seismic["T(K)"]/1000, label="T(K)/1000")
savefig(h,"GeothermSeismicProperties.pdf")
display(h)
## --- Query all properties at a single pressure
P = 10000
data_geotherm = perplex_query_point(perplexdir, scratchdir, P, index=2) |> print
## --- Compare seismic properties for several different geotherms, as a function of Pressure
# Input parameters
P_range = (280, 14000) # Pressure range to explore, bar (roughly 1-50 km depth)
T_surf = 273.15 # Temperature of surface (K)
dataset="hp02ver.dat"
yelem = "vp,km/s"
# yelem = "rho,kg/m3"
h = plot(xlabel="Pressure (bar)", ylabel=yelem)
for i=1:5
geotherm = i/50
# Configure (run build and vertex)
@time perplex_configure_geotherm(perplexdir, scratchdir, composition, elements,
P_range, T_surf, geotherm, dataset=dataset, excludes=HP_excludes,
solution_phases=HP_solution_phases, npoints=200, index=10+i)
# Query perplex results
seismic = perplex_query_seismic(perplexdir, scratchdir, index=10+i)
# Plot results
plot!(h,seismic["P(bar)"],seismic[yelem], label="$geotherm K/bar")
display(h)
end
## --- Compare seismic properties for several different geotherms, as a function of temperature
# Input parameters
P_range = (280, 16000) # Pressure range to explore, bar (roughly 1-60 km depth)
T_surf = 273.15 # Temperature of surface (K)
dataset="hp02ver.dat"
yelem = "vp,km/s"
# yelem = "rho,kg/m3"
h = plot(xlabel="Temperature (K)", ylabel=yelem)
for i=1:5
geotherm = i/50
# Configure (run build and vertex)
@time perplex_configure_geotherm(perplexdir, scratchdir, composition, elements,
P_range, T_surf, geotherm, dataset=dataset, excludes=HP_excludes,
solution_phases=HP_solution_phases, npoints=200, index=10+i)
# Query perplex results
seismic = perplex_query_seismic(perplexdir, scratchdir, index=10+i)
# Plot results
plot!(h,seismic["T(K)"],seismic[yelem], label="$geotherm K/bar")
display(h)
end
## --- Build a pseudosection
# Input parameters
mingeotherm = 0.009 # Geothermal gradient, K/bar. For reference, 0.1 K/bar ≈ 28.4 K/km
max_lith_T = 1300 # Maximum temperature of the TBL lithosphere (no point calculating beyond that)
P_range = (280, (max_lith_T-273.15)/mingeotherm) # Pressure range to explore, bar (roughly 1-100 km depth)
T_range = (273.15, max_lith_T) # Temperature range to explore, K
melt_model = ""
# Configure (run build and vertex)
@time perplex_configure_pseudosection(perplexdir, scratchdir, composition,
elements, P_range, T_range, dataset="hpha02ver.dat", excludes=HP_excludes,
solution_phases=melt_model*HP_solution_phases, index=3, xnodes=200, ynodes=200)
## --- Query modes from a pseudosection
geotherm = 0.015 # Geothermal gradient, K/bar. For reference, 0.1 K/bar ≈ 28.4 K/km
@time modes = perplex_query_modes(perplexdir, scratchdir, [280, (max_lith_T-273.15)/geotherm], T_range, index=3, npoints=200)
h = plot(xlabel="T (C)", ylabel="Weight percent")
for m in modes["elements"][3:end]
plot!(h, modes["T(K)"] .- 273.15, modes[m], label=m)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"GeothermPhaseModesPseudosection.pdf")
display(h)
## --- Query seismic properties from a pseudosection
geotherm = 0.15 # Geothermal gradient, K/bar. For reference, 0.1 K/bar ≈ 28.4 K/km
@time seismic = perplex_query_seismic(perplexdir, scratchdir, (280, (max_lith_T-273.15)/geotherm), T_range, index=3, npoints=200)
h = plot(xlabel="Pressure", ylabel="Property")
plot!(h,seismic["P(bar)"],seismic["vp,km/s"], label="vp,km/s")
plot!(h,seismic["P(bar)"],seismic["vp/vs"], label="vp/vs")
plot!(h,seismic["P(bar)"],seismic["rho,kg/m3"]/1000, label="rho, g/cc")
plot!(h,seismic["P(bar)"],seismic["T(K)"]/1000, label="T(K)/1000")
# savefig(h,"GeothermSeismicProperties.pdf")
# display(h)
## ---
geotherm = 0.15 # Geothermal gradient, K/bar. For reference, 0.1 K/bar ≈ 28.4 K/km
@time bulk = perplex_query_system(perplexdir, scratchdir, (280, (max_lith_T-273.15)/geotherm), T_range, index=3, npoints=200, include_fluid="n")
h = plot(xlabel="Pressure", ylabel="Property")
plot!(h,bulk["P(bar)"],bulk["vp,km/s"], label="vp,km/s")
plot!(h,bulk["P(bar)"],bulk["vp/vs"], label="vp/vs")
plot!(h,bulk["P(bar)"],bulk["rho,kg/m3"]/1000, label="rho, g/cc")
plot!(h,bulk["P(bar)"],bulk["T(K)"]/1000, label="T(K)/1000")
# savefig(h,"GeothermSeismicProperties.pdf")
# display(h)
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 19678 | ################################################################################
# Use the Julia-PerpleX interface to run the same isobaric Perplex calculation
# with different solution models to compare results
## --- Import some useful packages
using StatGeochem
using Plots
## --- Configure
# Absolute paths to perplex resources
perplexdir = joinpath(resourcepath,"perplex-stable")
scratchdir = "./scratch/" # Location of directory to store output files
# Attempt to install perplex, if not already extant
if !isfile(joinpath(perplexdir,"vertex"))
# Make sure resourcepath exists
run(`mkdir -p $resourcepath`)
# Download Perplex v6.8.7 -- known to work with interface used here
file = download("https://storage.googleapis.com/statgeochem/perplex-stable-6.8.7.zip", joinpath(resourcepath,"perplex-stable.zip"))
# # For a more updated perplex version, also try
# file = download("https://petrol.natur.cuni.cz/~ondro/perplex-sources-stable.zip", joinpath(resourcepath,"perplex-stable.zip"))
run(`unzip -u $file -d $resourcepath`) # Extract
system("cd $perplexdir; make") # Compile
end
## --- # # # # # # # # # # # # # Initial composition # # # # # # # # # # # # # #
## McDonough Pyrolite
#elements = [ "SIO2", "TIO2", "AL2O3", "FEO", "MNO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
#composition = [45.1242, 0.2005, 4.4623, 8.0723, 0.1354, 37.9043, 3.5598, 0.3610, 0.0291, 0.1511, 0.0440,]
## Kelemen (2014) primitive continental basalt. H2O and CO2 are guesses
#elements = [ "SIO2", "TIO2", "AL2O3", "FEO", "MNO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
#composition = [50.0956, 0.9564, 15.3224, 8.5103, 0.1659, 9.2520, 9.6912, 2.5472, 0.8588, 2.0000, 0.6000,]
# Kelemen (2014) primitive continental basalt excluding Mn and Ti since most melt models can"t handle them..
elements = [ "SIO2", "AL2O3", "FEO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
composition = [50.0956, 15.3224, 8.5103, 9.2520, 9.6912, 2.5472, 0.8588, 2.0000, 0.6000,]
## Average Archean basalt (EarthChem data)
#elements = [ "SIO2", "TIO2", "AL2O3", "FEO", "MNO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
#composition = [49.2054, 0.8401, 12.0551, 11.4018, 0.2198, 12.3997, 9.3113, 1.6549, 0.4630, 1.8935, 0.5555,]
## --- # # # # # # # # # # # Some solution model options # # # # # # # # # # # #
# Emphasis on phases from Green (2016) -- developed for metabasites, includes what is probably the best (and most expensive) amphibole model. Use with hp11ver.dat
G_solution_phases = "Augite(G)\nOpx(JH)\ncAmph(G)\noAmph(DP)\nO(JH)\nSp(JH)\nGrt(JH)\nfeldspar_B\nMica(W)\nBio(TCC)\nChl(W)\nCtd(W)\nCrd(W)\nSa(WP)\nSt(W)\nIlm(WPH)\nAtg(PN)\nT\nB\nF\nDo(HP)\nScap\nChum\nNeph(FB)\n"
G_excludes ="ged\nfanth\ngl\n"
# Emphasis on phases from White (2014) -- developed for metapelites. Use with hp11ver.dat
W_solution_phases = "Omph(HP)\nOpx(W)\ncAmph(DP)\noAmph(DP)\nO(JH)\nSp(JH)\nGt(W)\nfeldspar_B\nMica(W)\nBi(W)\nChl(W)\nCtd(W)\nCrd(W)\nSa(WP)\nSt(W) \nIlm(WPH)\nAtg(PN)\nT\nB\nF\nDo(HP)\nScap\nChum\nPu(M)\n"
W_excludes = "andr\nts\nparg\ngl\nged\nfanth\n"
# Emphasis on phases from Jennings and Holland (2015) -- developed for mantle melting. Use with hp11ver.dat
JH_solution_phases = "Cpx(JH)\nOpx(JH)\ncAmph(DP)\noAmph(DP)\nO(JH)\nSp(JH)\nGrt(JH)\nfeldspar_B\nMica(W)\nBio(TCC)\nChl(W)\nCtd(W)\nCrd(W)\nSa(WP)\nSt(W)\nIlm(WPH)\nAtg(PN)\nT\nB\nF\nDo(HP)\nScap\nChum\nNeph(FB)\n"
JH_excludes = "ts\nparg\ngl\nged\nfanth\n"
# Emphasis on phases from Holland and Powell -- all phases can be used with hp02ver.dat.
HP_solution_phases = "Omph(HP)\nOpx(HP)\nGlTrTsPg\nAnth\nO(HP)\nSp(HP)\nGt(HP)\nfeldspar_B\nMica(CF)\nBio(TCC)\nChl(HP)\nCtd(HP)\nSapp(HP)\nSt(HP)\nIlHm(A)\nDo(HP)\nT\nB\nF\n"
HP_excludes = "";
## --- Prepare for plotting
h0 = plot(xlabel="T (C)", ylabel="Melt percent")
## --- # # # # # # # # # # melt(G) + G_solution_phases # # # # # # # # # # # # #
# Input parameters
P = 10000 # bar
T_range = (500+273.15, 1500+273.15)
idx = 1
print("\nmelt(G) + G_solution_phases\n")
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range, dataset="hp11ver.dat", solution_phases="melt(G)\n"*G_solution_phases, excludes=G_excludes, index=idx)
# Query the full isobar -- results returned as elementified dictionary
T_range_inc = (floor(Int,first(T_range))+1, ceil(Int,last(T_range))-1)
npoints = last(T_range_inc) - first(T_range_inc) + 1
bulk = perplex_query_system(perplexdir, scratchdir, index=idx) # Get system data for all temperatures. Set include_fluid = "n" to get solid+melt only
modes = perplex_query_modes(perplexdir, scratchdir, index=idx) # || phase modes
melt = perplex_query_phase(perplexdir, scratchdir, "melt(G)", index=idx) # || melt data
# Create dictionary to hold solid composition and fill it using what we know from system and melt
solid = Dict()
solid["wt_pct"] = 100 .- melt["wt_pct"]
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
solid[e] = (bulk[e] .- (melt[e] .* melt["wt_pct"]/100)) ./ (solid["wt_pct"]/100)
end
# Add results to melt % vs temperature figure
plot!(h0, melt["T(K)"] .- 273.15, melt["wt_pct"], label="melt(G) + G")
# Plot melt composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in melt", title="melt(G) + G_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], melt[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"Perplex_MeltTest_G.pdf")
# Plot solid composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in solid", title="melt(G) + G_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], solid[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box, legend=:topleft)
savefig(h,"Perplex_SolidTest_G.pdf")
## --- # # # # # # # # # # melt(G) + W_solution_phases # # # # # # # # # # # # #
# Input parameters
P = 10000 # bar
T_range = (500+273.15, 1500+273.15)
idx = 2
print("\nmelt(G) + W_solution_phases\n")
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range, dataset="hp11ver.dat", solution_phases="melt(G)\n"*W_solution_phases, excludes=W_excludes, index=idx)
# Query the full isobar -- results returned as elementified dictionary
T_range_inc = (floor(Int,first(T_range))+1, ceil(Int,last(T_range))-1)
npoints = last(T_range_inc) - first(T_range_inc) + 1
bulk = perplex_query_system(perplexdir, scratchdir, index=idx) # Get system data for all temperatures. Set include_fluid = "n" to get solid+melt only
modes = perplex_query_system(perplexdir, scratchdir, index=idx) # || phase modes
melt = perplex_query_phase(perplexdir, scratchdir, "melt(G)", index=idx) # || melt data
# Create dictionary to hold solid composition and fill it using what we know from system and melt
solid = Dict()
solid["wt_pct"] = 100 .- melt["wt_pct"]
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
solid[e] = (bulk[e] - (melt[e] .* melt["wt_pct"]/100)) ./ (solid["wt_pct"]/100)
end
# Add results to melt % vs temperature figure
plot!(h0, melt["T(K)"] .- 273.15, melt["wt_pct"], label="melt(G) + W")
# Plot melt composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in melt", title="melt(G) + W_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], melt[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"Perplex_MeltTest_G_W.pdf")
# Plot solid composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in solid", title="melt(G) + W_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], solid[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box, legend=:topleft)
savefig(h,"Perplex_SolidTest_G_W.pdf")
## --- # # # # # # # # # # melt(G) +JH_solution_phases # # # # # # # # # # # # #
# Input parameters
P = 10000 # bar
T_range = (500+273.15, 1500+273.15)
idx = 3
print("\nmelt(G) + JH_solution_phases\n")
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range, dataset="hp11ver.dat", solution_phases="melt(G)\n"*JH_solution_phases, excludes=JH_excludes, index=idx)
# Query the full isobar -- results returned as elementified dictionary
T_range_inc = (floor(Int,first(T_range))+1, ceil(Int,last(T_range))-1)
npoints = last(T_range_inc) - first(T_range_inc) + 1
bulk = perplex_query_system(perplexdir, scratchdir, index=idx) # Get system data for all temperatures. Set include_fluid = "n" to get solid+melt only
modes = perplex_query_modes(perplexdir, scratchdir, index=idx) # || phase modes
melt = perplex_query_phase(perplexdir, scratchdir, "melt(G)", index=idx) # || melt data
# Create dictionary to hold solid composition and fill it using what we know from system and melt
solid = Dict()
solid["wt_pct"] = 100 .- melt["wt_pct"]
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
solid[e] = (bulk[e] - (melt[e] .* melt["wt_pct"]/100)) ./ (solid["wt_pct"]/100)
end
# Add results to melt % vs temperature figure
plot!(h0, melt["T(K)"] .- 273.15, melt["wt_pct"], label="melt(G) + JH")
# Plot melt composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in melt", title="melt(G) + JH_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], melt[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"Perplex_MeltTest_G_JH.pdf")
# Plot solid composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in solid", title="melt(G) + JH_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], solid[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box, legend=:topleft)
savefig(h,"Perplex_SolidTest_G_JH.pdf")
## --- # # # # # # # # # # pMELTS(G) +HP_solution_phases # # # # # # # # # # # #
# Input parameters
P = 10000 # bar
T_range = (500+273.15, 1500+273.15)
idx = 4
print("\npMELTS(G) + JH_solution_phases\n")
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range, dataset="hp02ver.dat", solution_phases="pMELTS(G)\n"*JH_solution_phases, excludes=JH_excludes, index=idx)
# Query the full isobar -- results returned as elementified dictionary
T_range_inc = (floor(Int,first(T_range))+1, ceil(Int,last(T_range))-1)
npoints = last(T_range_inc) - first(T_range_inc) + 1
bulk = perplex_query_system(perplexdir, scratchdir, index=idx) # Get system data for all temperatures. Set include_fluid = "n" to get solid+melt only
modes = perplex_query_modes(perplexdir, scratchdir, index=idx) # || phase modes
melt = perplex_query_phase(perplexdir, scratchdir, "pMELTS(G)", index=idx) # || melt data
# Create dictionary to hold solid composition and fill it using what we know from system and melt
solid = Dict()
solid["wt_pct"] = 100 .- melt["wt_pct"]
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
solid[e] = (bulk[e] - (melt[e] .* melt["wt_pct"]/100)) ./ (solid["wt_pct"]/100)
end
# Add results to melt % vs temperature figure
plot!(h0, melt["T(K)"] .- 273.15, melt["wt_pct"], label="pMELTS(G) + JH")
# Plot melt composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in melt", title="pMELTS(G) + JH_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], melt[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"Perplex_MeltTest_pMELTS_JH.pdf")
# Plot solid composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in solid", title="pMELTS(G) + JH_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], solid[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box, legend=:topleft)
savefig(h,"Perplex_SolidTest_pMELTS_JH.pdf")
## --- # # # # # # # # # # # melt(W) + W_solution_phases # # # # # # # # # # # #
# Input parameters
P = 10000 # bar
T_range = (500+273.15, 1500+273.15)
idx = 5
print("\nmelt(W) + W_solution_phases\n")
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range, dataset="hp11ver.dat", solution_phases="melt(W)\n"*W_solution_phases, excludes=W_excludes, index=idx)
# Query the full isobar -- results returned as elementified dictionary
T_range_inc = (floor(Int,first(T_range))+1, ceil(Int,last(T_range))-1)
npoints = last(T_range_inc) - first(T_range_inc) + 1
bulk = perplex_query_system(perplexdir, scratchdir, index=idx) # Get system data for all temperatures. Set include_fluid = "n" to get solid+melt only
modes = perplex_query_modes(perplexdir, scratchdir, index=idx) # || phase modes
melt = perplex_query_phase(perplexdir, scratchdir, "melt(W)", index=idx) # || melt data
# Create dictionary to hold solid composition and fill it using what we know from system and melt
solid = Dict()
solid["wt_pct"] = 100 .- melt["wt_pct"]
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
solid[e] = (bulk[e] - (melt[e] .* melt["wt_pct"]/100)) ./ (solid["wt_pct"]/100)
end
# Add results to melt % vs temperature figure
plot!(h0, melt["T(K)"] .- 273.15, melt["wt_pct"], label="melt(W) + W")
# Plot melt composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in melt", title="melt(W) + W_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], melt[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"Perplex_MeltTest_W.pdf")
# Plot solid composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in solid", title="melt(W) + W_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], solid[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box, legend=:topleft)
savefig(h,"Perplex_SolidTest_W.pdf")
## --- # # # # # # # # # # melt(W) + G_solution_phases # # # # # # # # # # # # #
# Input parameters
P = 10000 # bar
T_range = (500+273.15, 1500+273.15)
idx = 6
print("\nmelt(W) + G_solution_phases\n")
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range, dataset="hp11ver.dat", solution_phases="melt(W)\n"*G_solution_phases, excludes=G_excludes, index=idx)
# Query the full isobar -- results returned as elementified dictionary
T_range_inc = (floor(Int,first(T_range))+1, ceil(Int,last(T_range))-1)
npoints = last(T_range_inc) - first(T_range_inc) + 1
bulk = perplex_query_system(perplexdir, scratchdir, index=idx) # Get system data for all temperatures. Set include_fluid = "n" to get solid+melt only
modes = perplex_query_modes(perplexdir, scratchdir, index=idx) # || phase modes
melt = perplex_query_phase(perplexdir, scratchdir, "melt(W)", index=idx) # || melt data
# Create dictionary to hold solid composition and fill it using what we know from system and melt
solid = Dict()
solid["wt_pct"] = 100 .- melt["wt_pct"]
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
solid[e] = (bulk[e] - (melt[e] .* melt["wt_pct"]/100)) ./ (solid["wt_pct"]/100)
end
# Add results to melt % vs temperature figure
plot!(h0, melt["T(K)"] .- 273.15, melt["wt_pct"], label="melt(W) + G")
# Plot melt composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in melt", title="melt(W) + G_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], melt[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"Perplex_MeltTest_W_G.pdf")
# Plot solid composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in solid", title="melt(W) + G_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], solid[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box, legend=:topleft)
savefig(h,"Perplex_SolidTest_W_G.pdf")
## --- # # # # # # # # # # # melt(W) +JH_solution_phases # # # # # # # # # # # #
# Input parameters
P = 10000 # bar
T_range = (500+273.15, 1500+273.15)
idx = 7
print("\nmelt(W) + JH_solution_phases\n")
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range, dataset="hp11ver.dat", solution_phases="melt(W)\n"*JH_solution_phases, excludes=JH_excludes, index=idx)
# Query the full isobar -- results returned as elementified dictionary
T_range_inc = (floor(Int,first(T_range))+1, ceil(Int,last(T_range))-1)
npoints = last(T_range_inc) - first(T_range_inc) + 1
bulk = perplex_query_system(perplexdir, scratchdir, index=idx) # Get system data for all temperatures. Set include_fluid = "n" to get solid+melt only
modes = perplex_query_modes(perplexdir, scratchdir, index=idx) # || phase modes
melt = perplex_query_phase(perplexdir, scratchdir, "melt(W)", index=idx) # || melt data
# Create dictionary to hold solid composition and fill it using what we know from system and melt
solid = Dict()
solid["wt_pct"] = 100 .- melt["wt_pct"]
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
solid[e] = (bulk[e] - (melt[e] .* melt["wt_pct"]/100)) ./ (solid["wt_pct"]/100)
end
# Add results to melt % vs temperature figure
plot!(h0, melt["T(K)"] .- 273.15, melt["wt_pct"], label="melt(W) + JH")
# Plot melt composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in melt", title="melt(W) + JH_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], melt[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box)
savefig(h,"Perplex_MeltTest_W_JH.pdf")
# Plot solid composition as a function of melt percent
h = plot(xlabel="Percent melt", ylabel="Wt. % in solid", title="melt(W) + JH_solution_phases, $P bar")
for e in ["SIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O"]
plot!(h, melt["wt_pct"], solid[e], label=e)
end
plot!(h,fg_color_legend=:white, framestyle=:box, legend=:topleft)
savefig(h,"Perplex_SolidTest_W_JH.pdf")
## --- Format melt comparison
plot!(h0,fg_color_legend=:white,legend=:topleft)
display(h0)
savefig(h0,"Perplex_T-F_comparison.pdf")
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 9828 | ## --- Load (and install if neccesary) the StatGeochem package which has the resampling functions we'll want
using StatGeochem
using Plots
## --- Download and unzip Keller and Schoene (2012) dataset
if ~isfile("ign.h5") # Unless it already exists
download("https://storage.googleapis.com/statgeochem/ign.h5.gz","./ign.h5.gz")
download("https://storage.googleapis.com/statgeochem/err2srel.csv","./err2srel.csv")
run(`gunzip -f ign.h5.gz`) # Unzip file
end
# Read HDF5 file
using HDF5
ign = h5read("ign.h5","vars")
## --- Compute proximity coefficients (inverse weights)
# # Compute inverse weights
# k = invweight(ign["Latitude"] .|> Float32, ign["Longitude"] .|> Float32, ign["Age"] .|> Float32)
# Since this is pretty computatually intensive, let's load a precomputed version instead
k = ign["k"]
# Probability of keeping a given data point when sampling
p = 1.0 ./ ((k .* nanmedian(5.0 ./ k)) .+ 1.0) # Keep roughly one-fith of the data in each resampling
# Set absolute uncertainties for each element where possible, using errors defined inerr2srel.csv
err2srel = importdataset("err2srel.csv", ',', importas=:Dict)
for e in ign["elements"]
# If there's an err2srel for this variable, create a "_sigma" if possible
if haskey(err2srel, e) && !haskey(ign, e*"_sigma")
ign[e*"_sigma"] = ign[e] .* (err2srel[e] / 2);
end
end
# Special cases: age uncertainty
ign["Age_sigma"] = (ign["Age_Max"]-ign["Age_Min"])/2;
t = (ign["Age_sigma"] .< 50) .| isnan.(ign["Age_sigma"]) # Find points with < 50 Ma absolute uncertainty
ign["Age_sigma"][t] .= 50 # Set 50 Ma minimum age uncertainty (1-sigma)
# Special cases: location uncertainty
ign["Latitude_sigma"] = ign["Loc_Prec"]
ign["Longitude_sigma"] = ign["Loc_Prec"]
## --- Resample a single variable
xmin = 0 # Minimum Age
xmax = 3900 # Maximum Age
nbins = 39
elem = "K2O" # Element to plot
# Look only at samples from a specific silica range
t = 43 .< ign["SiO2"] .< 51 # Mafic
# t = 51 .< ign["SiO2"] .< 62 # Intermediate
# t = 62 .< ign["SiO2"] .< 74 # Felsic
# t = 40 .< ign["SiO2"] .< 80 # All normal igneous
# t = trues(size(ign[elem])) # Everything
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign["Age"][t],ign[elem][t],xmin,xmax,nbins, p=p[t], x_sigma=ign["Age_sigma"][t])
# Plot results
plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:darkblue,label="")
plot!(xlabel="Age (Ma)", ylabel="$elem (wt. %)",xlims=(xmin,xmax),framestyle=:box,grid=:off,xflip=true) # Format plot
## --- Multiple silica ranges together
xmin = 0 # Minimum Age
xmax = 800 # Maximum Age
nbins = 40
elem = "Al2O3" # Element to plot
rsi = [43,51,62,74,80] # Ranges of silica to plot together
t = trues(size(ign[elem]))
h = plot(xlabel="Age (Ma)", ylabel="$elem (wt. %)",xlims=(xmin,xmax),framestyle=:box,grid=:off,xflip=true) # Format plot
for i=1:length(rsi)-1
t .= rsi[i] .< ign["SiO2"] .< rsi[i+1]
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign["Age"][t],ign[elem][t],xmin,xmax,nbins, p=p[t], x_sigma=ign["Age_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),seriestype=:scatter,color=lines[i],markerstrokecolor=lines[i],label="$(rsi[i])-$(rsi[i+1]) % SiO2")
end
savefig("$(elem)_$(xmin)-$(xmax) Ma.pdf")
display(h)
## --- Resample a ratio
tmin = 0 # Minimum age
tmax = 3900 # Maximum age
nbins = 39
num = "Sm" # Numerator
denom = "Nd" # Denominator
# Look only at samples from a specific silica range
t = 43 .< ign["SiO2"] .< 51 # Mafic
# t = 51 .< ign["SiO2"] .< 62 # Intermediate
# t = 62 .< ign["SiO2"] .< 74 # Felsic
# Exclude outliers
t = t .& inpctile(ign[num], 99) .& inpctile(ign[denom], 99)
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_ratios(ign["Age"][t],ign[num][t],ign[denom][t],tmin,tmax,nbins, p=p[t],
x_sigma=ign["Age_sigma"][t])
# Plot results
h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkred,markerstrokecolor=:darkred,label="")
plot!(h, xlabel="Age (Ma)", ylabel="$(num) / $(denom)",xlims=(tmin,tmax),framestyle=:box,grid=:off,xflip=true) # Format plot
display(h)
# savefig(h,"$(num)$(denom)_$(tmax)-$(tmin)Ma.pdf")
## --- Single element differentiation example
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 8
elem = "K2O"
h = plot(xlabel=xelem, ylabel="$(elem)",xlims=(xmin,xmax),framestyle=:box,grid=:off,fg_color_legend=:white) # Format plot
rt = [0,1,2,3,4] # Time range (Ga)
colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign[xelem][t],ign[elem][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], y_sigma=ign[elem*"_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),color=colors[i],mscolor=colors[i],seriestype=:scatter,label="$(rt[i])-$(rt[i+1]) Ga")
plot!(h, c,m,style=:dot,color=colors[i],mscolor=colors[i],label="")
end
# savefig(h,"$(xelem)_$(num)$(denom).pdf")
display(h)
## --- Ratio differentiation example
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 8
num = "Sm" # Numerator
denom = "Nd" # Denominator
h = plot(xlabel=xelem, ylabel="$(num) / $(denom)",xlims=(xmin,xmax),framestyle=:box,grid=:off,legend=:topleft,fg_color_legend=:white) # Format plot
rt = [0,1,2,3,4]
colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_ratios(ign[xelem][t],ign[num][t],ign[denom][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], num_sigma=ign[num*"_sigma"][t], denom_sigma=ign[denom*"_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),color=colors[i], mscolor=colors[i], seriestype=:scatter,label="$(rt[i])-$(rt[i+1]) Ga")
plot!(h, c,m,style=:dot,color=colors[i],label="")
end
display(h)
## --- Ratio differentiation
xelem = "SiO2"
xmin = 40 # Minimum age
xmax = 80 # Maximum age
nbins = 20
num = "Sc" # Numerator
denom = "Yb" # Denominator
# Exclude outliers
t = inpctile(ign[num], 99) .& inpctile(ign[denom], 99)
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_ratios(ign[xelem][t],ign[num][t],ign[denom][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem][t]*0.01, num_sigma=ign[num][t]*0.05, denom_sigma=ign[denom][t]*0.05)
# Plot results
h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:darkblue,label="")
plot!(h, xlabel=xelem, ylabel="$(num) / $(denom)",xlims=(xmin,xmax),framestyle=:box,grid=:off) # Format plot
display(h)
## --- Export differentiation trends
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 10
rt = [0,1,2,3,4] # Time range (Ga)
data = Dict()
for elem in ("Al2O3", "MgO", "Na2O", "Fe2O3T", "K2O", "CaO")
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
(c,m,e) = bin_bsr(ign[xelem][t],ign[elem][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], y_sigma=ign[elem*"_sigma"][t])
data[xelem] = c
data[elem*"_$(rt[i])-$(rt[i+1])Ga"] = m
data[elem*"_$(rt[i])-$(rt[i+1])Ga_sigma"] = e
end
end
exportdataset(data,"MajorDifferentiation.csv",',')
## --- Export averages over time
xmin = 0 # Minimum Age
xmax = 3900 # Maximum Age
nbins = 39
# Look only at samples from a specific silica range
t = 43 .< ign["SiO2"] .< 51; name="Basaltic" # Mafic
# t = 51 .< ign["SiO2"] .< 62; name="Intermediate" # Intermediate
# t = 62 .< ign["SiO2"] .< 74; name="Granitic" # Felsic
# t = 40 .< ign["SiO2"] .< 80; name="All" # All normal igneous
major = ("SiO2", "TiO2", "Al2O3", "FeOT", "MgO", "CaO", "MnO", "Na2O", "K2O", "P2O5")
trace = ("Li", "Rb", "Cs", "Sr", "Ba", "Sc", "Y",
"La", "Ce", "Pr", "Nd", "Sm", "Eu", "Gd", "Tb", "Dy", "Ho", "Er", "Yb", "Lu",
"Zr", "Hf", "V", "Nb", "Ta", "Cr", "Mo", "W", "Co", "Ni"
)
data = Dict{String,Array{Union{Float64, String}}}()
data["elements"] = ["Age (Ma)"]
for elem in (major ∪ trace)
# Resample, returning binned means and uncertainties
(c,m,e) = bin_bsr(ign["Age"][t], ign[elem][t], xmin, xmax, nbins;
p = p[t],
x_sigma = ign["Age_sigma"][t],
y_sigma = ign[elem*"_sigma"][t]
)
data["Age (Ma)"] = c
data[elem] = m
push!(data["elements"], elem)
data[elem*"_sigma"] = e
push!(data["elements"], elem*"_sigma")
end
exportdataset(data, name*"Averages.csv",',')
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 4300 | module StatGeochemPlotsExt
using StatGeochem, Plots
StatGeochem.mapplot(args...; kwargs...) = mapplot!(plot(), args...; kwargs...)
function StatGeochem.mapplot!(h, args...; file="world2048.jpg", seriestype=:scatter, kwargs...)
if !isfile(file)
file = joinpath(StatGeochem.moduleresourcepath, "maps", file)
end
img = reverse!(StatGeochem.load(file), dims=1)
x = range(-180, 180, length=size(img,2))
y = range(-90, 90, length=size(img,1))
plot!(h, x, y, img,
framestyle=:box,
yflip=false,
xlabel="Longitude",
ylabel="Latitude",
xlims=(-180, 180),
ylims=(-90, 90),
size=(800,400),
)
plot!(h, args...; seriestype, kwargs...)
return h
end
export mapplot, mapplot!
taylormclennan = (
La = 0.367,Ce = 0.957,Pr = 0.137,Nd = 0.711,Sm = 0.231,Eu = 0.087,Gd = 0.306,
Tb = 0.058,Dy = 0.381,Ho = 0.085,Er = 0.249,Tm = 0.036,Yb = 0.248,Lu = 0.038,
)
"""
Construct a `chondrite` normalized multi-element diagram (spider diagram) from the
rare earth elements in `data`.
Use `spidergram` to create a new plot object, and `spidergram!` to add to an existing
one:
```julia
spidergram(data; [chondrite], kwargs...) # Create a new spider diagram
spidergram!(plotobj, data; [chondrite], kwargs...) # Add to the plot `plotobj`
```
If no chondrite values are specified, `data` will be normalized to the values reported
by Taylor and McLennan (1985).
Values in `data` and `chondrite` may be passed as a dictonary, named tuple, or an
array. All arrays should be in element order:
La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu
Dictonaries and NamedTuples should be organized by element:
NamedTuple with 14 elements:
La = Float64 0.367
Ce = Float64 0.957
Pr = Float64 0.137
⋮ = ⋮
Or
Dict{String, Float64} with 14 entries:
"La" => 20.78
"Ce" => 35.61
"Pr" => 2.344
⋮ => ⋮
If `data` is not passed as an array, `chondrite` must be a named tuple in element
order.
"""
function StatGeochem.spidergram(data; chondrite=taylormclennan, markershape=:circle, kwargs...)
h = Plots.plot(
ylabel="Chondrite Normalized",
fg_color_legend=:white,
framestyle=:box,
grid=false,
yaxis=:log10,
ylims=(10^0, 10^3),
yticks=(10.0.^(0:3), ("1", "10", "100", "1000")),
xticks=(1:15, ["La","Ce","Pr","Nd","","Sm","Eu","Gd","Tb","Dy","Ho","Er","Tm",
"Yb","Lu"]),
yminorticks=log.(1:10),
)
spidergram!(h, data; chondrite=chondrite, markershape=markershape, kwargs...)
end
function StatGeochem.spidergram!(h, data::Dict; chondrite::NamedTuple=taylormclennan,
markershape=:circle, kwargs...
)
REEindex = NamedTuple{keys(chondrite)}(i for i in collect([1:4; 6:15]))
Key = keytype(data)
x = collect(values(REEindex))
y = [(haskey(data, Key(k)) ? data[Key(k)]/chondrite[Symbol(k)] : NaN) for k in keys(chondrite)]
_spidergram!(h, x, y; markershape=markershape, kwargs...)
end
function StatGeochem.spidergram!(h, data::NamedTuple; chondrite::NamedTuple=taylormclennan,
markershape=:circle, kwargs...
)
REEindex = NamedTuple{keys(chondrite)}(i for i in collect([1:4; 6:15]))
x = [REEindex[Symbol(k)] for k in keys(data)]
y = [data[k]/chondrite[k] for k in keys(data)]
_spidergram!(h, x, y; markershape=markershape, kwargs...)
end
StatGeochem.spidergram!(h, data::AbstractArray; chondrite=taylormclennan,
markershape=:circle, kwargs...) =
_spidergram!(h, collect([1:4; 6:15]), data ./ collect(values(chondrite));
markershape=markershape, kwargs...
)
function _spidergram!(h, x::AbstractArray, y::AbstractArray; kwargs...,)
Plots.plot!(h, x[.!isnan.(y)], y[.!isnan.(y)]; kwargs...)
return h
end
export spidergram, spidergram!
end
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 1674 | __precompile__()
module StatGeochem
using Reexport
@reexport using NaNStatistics
@reexport using StatGeochemBase
# Vectorization and parallelization tools
using LoopVectorization: @turbo
using Polyester: @batch
# General requirements
using DelimitedFiles, Random, Downloads
using ProgressMeter: @showprogress, Progress, update!, next!
const Collection{T} = Union{DenseArray{<:T}, AbstractRange{<:T}, NTuple{N,T}} where N
include("utilities/System.jl")
include("utilities/Resampling.jl")
include("utilities/Changepoint.jl")
include("resources/Chemistry.jl")
include("utilities/Geochronology.jl")
include("utilities/Geochemistry.jl")
include("utilities/GIS.jl")
include("utilities/Etc.jl")
# Resources
using LazyArtifacts
resourcepath = joinpath(homedir(),"resources")
moduleresourcepath = joinpath(Base.source_dir(),"resources")
export resourcepath, moduleresourcepath
using FileIO: load
using HDF5: h5read
using Colors: Color, RGBX, RGB, N0f8
include("resources/tc1/tc1.jl")
include("resources/Crust1.jl")
include("resources/Litho1.jl")
include("resources/Geology.jl")
include("resources/Geography.jl")
include("resources/Seafloorage.jl")
include("resources/PartitionCoefficients/PartitionCoefficients.jl")
# Custom pretty printing for some types
include("utilities/Display.jl")
# Functions for which methods will be added in package extensions
function mapplot end
function mapplot! end
function spidergram end
function spidergram! end
export mapplot, mapplot!, spidergram, spidergram!
end # module
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 6735 | ## -- Common elemental masses in g/mol (AMU/atom)
const molarmass = Dict{String,Float64}("Pd" => 106.421,"Fl" => 289.0,"Nb" => 92.906372,"C" => 12.011,"P" => 30.9737619985,"Ag" => 107.86822,"Gd" => 157.253,"Si" => 28.085,"Ru" => 101.072,"At" => 210.0,"Uus" => 294.0,"Sb" => 121.7601,"Cs" => 132.905451966,"Cn" => 285.0,"Uut" => 285.0,"Be" => 9.01218315,"Ac" => 227.0,"Cf" => 251.0,"Bh" => 270.0,"Sr" => 87.621,"Ga" => 69.7231,"Ta" => 180.947882,"Te" => 127.603,"Np" => 237.0,"Lr" => 262.0,"Pu" => 244.0,"U" => 238.028913,"Kr" => 83.7982,"Y" => 88.905842,"Sg" => 271.0,"Ca" => 40.0784,"Au" => 196.9665695,"K" => 39.09831,"Rn" => 222.0,"Ra" => 226.0,"Ce" => 140.1161,"Uuo" => 294.0,"V" => 50.94151,"Fr" => 223.0,"Mo" => 95.951,"Pr" => 140.907662,"Th" => 232.03774,"Br" => 79.904,"Zn" => 65.382,"He" => 4.0026022,"Sc" => 44.9559085,"H" => 1.008,"Al" => 26.98153857,"S" => 32.06,"Uup" => 289.0,"Ar" => 39.9481,"Ge" => 72.6308,"Er" => 167.2593,"Fe" => 55.8452,"Mg" => 24.305,"F" => 18.9984031636,"La" => 138.905477,"Rf" => 267.0,"W" => 183.841,"Li" => 6.94,"Dy" => 162.5001,"O" => 15.999,"B" => 10.81,"Bi" => 208.980401,"Mn" => 54.9380443,"Re" => 186.2071,"Db" => 270.0,"Hf" => 178.492,"Cm" => 247.0,"Cl" => 35.45,"In" => 114.8181,"Ds" => 281.0,"Rb" => 85.46783,"Po" => 209.0,"Lv" => 293.0,"Am" => 243.0,"Pa" => 231.035882,"Se" => 78.9718,"Ba" => 137.3277,"Nd" => 144.2423,"Pm" => 145.0,"Rh" => 102.905502,"Ti" => 47.8671,"Tb" => 158.925352,"Hs" => 277.0,"Zr" => 91.2242,"Sm" => 150.362,"Cr" => 51.99616,"Cu" => 63.5463,"Fm" => 257.0,"Tc" => 97.0,"Tl" => 204.38,"Ne" => 20.17976,"Hg" => 200.5923,"Mt" => 276.0,"N" => 14.007,"Es" => 252.0,"Yb" => 173.0455,"Lu" => 174.96681,"Eu" => 151.9641,"Na" => 22.989769282,"No" => 259.0,"Os" => 190.233,"Ni" => 58.69344,"Ho" => 164.930332,"Co" => 58.9331944,"Md" => 258.0,"Ir" => 192.2173,"Pt" => 195.0849,"Tm" => 168.934222,"As" => 74.9215956,"Sn" => 118.7107,"Xe" => 131.2936,"I" => 126.904473,"Cd" => 112.4144,"Pb" => 207.21,"Rg" => 282.0,"Bk" => 247.0)
export molarmass
const molarmasspercation = Dict{String,Float64}("MgO" => 40.304,"SO2" => 64.058,"TiO2" => 79.8651,"CaO" => 56.077400000000004,"P2O5" => 70.9712619985,"NiO" => 74.69244,"CoO" => 74.9321944,"SiO2" => 60.083,"Al2O3" => 50.980038570000005,"CO2" => 44.009,"Cr2O3" => 75.99466000000001,"FeO" => 71.8442,"K2O" => 47.097809999999996,"Fe2O3" => 79.8437,"H2O" => 9.0075,"MnO" => 70.9370443,"Na2O" => 30.989269282000002)
export molarmasspercation
"""
julia```
ionicradius::NamedTuple
```
A named tuple containing the ionic radii, in picometers, for the naturally ocurring
elements in their geologically common redox states. Where multiple redox states may
be expected for a single element in nature, they are suffixed to the atomic symbol,
e.g. `Fe2` vs `Fe3`.
Radii are those reported as "crystal" ionic radii by Shannon [1], as tabulated at
https://en.wikipedia.org/wiki/Ionic_radius#Tables
[1] R. D. Shannon (1976). "Revised effective ionic radii and systematic studies of interatomic
distances in halides and chalcogenides". Acta Crystallogr A. 32 (5): 751-767.
doi:10.1107/S0567739476001551
"""
# Picometers, from https://en.wikipedia.org/wiki/Ionic_radius#cite_note-Shannon-6
const ionicradius = (;
H = -4.,
Li = 90.,
Be = 59.,
B = 41.,
C = 30.,
N = 27.,
O = 126.,
F = 119.,
Na = 116.,
Mg = 86.,
Al = 67.5,
Si = 54.,
P = 52.,
S = 170.,
Cl = 167.,
K = 152.,
Ca = 114.,
Sc = 88.5,
Ti = 74.5,
V3 = 78.,
V5 = 68.,
Cr3 = 75.5,
Cr6 = 58.,
Mn2 = 81.,
Mn4 = 67.,
Fe2 = 75.,
Fe3 = 69.,
Co = 79.,
Ni = 83.,
Cu1 = 91.,
Cu2 = 87.,
Zn = 88.,
Ga = 76.,
Ge = 67.,
As = 60.,
Se = 184.,
Br = 182.,
Rb = 166.,
Sr = 132.,
Y = 104.,
Zr = 86.,
Nb = 78.,
Mo4 = 79.,
Mo6 = 73.,
Ru = 82.,
Rh = 80.5,
Pd = 100.,
Ag = 129.,
Cd = 109.,
In = 94.,
Sn = 83.,
Sb = 90.,
Te = 207.,
I = 206.,
Cs = 167.,
Ba = 149.,
La = 117.2,
Ce3 = 115.,
Ce4 = 101.,
Pr = 113.,
Nd = 112.3,
Sm = 109.8,
Eu2 = 131,
Eu3 = 108.7,
Gd = 107.8,
Tb = 106.3,
Dy = 105.2,
Ho = 104.1,
Er = 103.,
Tm = 102.,
Yb = 100.8,
Lu = 100.1,
Hf = 85.,
Ta = 78.,
W = 74.,
Re4 = 77.,
Re7 = 67.,
Os4 = 77.,
Os8 = 53.,
Ir = 76.5,
Pt = 76.5,
Au = 151.,
Hg = 116.,
Tl = 164.,
Pb = 133.,
Bi = 117.,
Th = 108.,
U4 = 103.,
U6 = 87.,
)
export ionicradius
"""
julia```
ioniccharge::NamedTuple
```
A named tuple containing the ionic charges corresponding to the ionic radii in `ionicradius`.
"""
const ioniccharge = (;
H = +1,
Li = +1,
Be = +2,
B = +3,
C = +4,
N = +5,
O = -2,
F = -1,
Na = +1,
Mg = +2,
Al = +3,
Si = +4,
P = +5,
S = -2,
Cl = -1,
K = +1,
Ca = +2,
Sc = +3,
Ti = +4,
V3 = +3,
V5 = +5,
Cr3 = +3,
Cr6 = +6,
Mn2 = +2,
Mn4 = +4,
Fe2 = +2,
Fe3 = +3,
Co = +2,
Ni = +2,
Cu1 = +1,
Cu2 = +2,
Zn = +2,
Ga = +3,
Ge = +4,
As = +5,
Se = -2,
Br = -1,
Rb = +1,
Sr = +2,
Y = +3,
Zr = +4,
Nb = +5,
Mo4 = +4,
Mo6 = +6,
Ru = +3,
Rh = +3,
Pd = +2,
Ag = +1,
Cd = +2,
In = +3,
Sn = +4,
Sb = +3,
Te = -2,
I = -1,
Cs = +1,
Ba = +2,
La = +3,
Ce3 = +3,
Ce4 = +4,
Pr = +3,
Nd = +3,
Sm = +3,
Eu2 = +2,
Eu3 = +3,
Gd = +3,
Tb = +3,
Dy = +3,
Ho = +3,
Er = +3,
Tm = +3,
Yb = +3,
Lu = +3,
Hf = +4,
Ta = +5,
W = +6,
Re4 = +4,
Re7 = +7,
Os4 = +4,
Os8 = +8,
Ir = +4,
Pt = +4,
Au = +1,
Hg = +2,
Tl = +1,
Pb = +2,
Bi = +3,
Th = +4,
U4 = +4,
U6 = +6,
)
export ioniccharge
## ---
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 12870 | ## --- CRUST 1.0
function crust1layer(layer::Number)
if isinteger(layer) && (1 <= layer <= 9)
Int(layer)
else
@error """crust1 layer $layer not found.
Available layers include:
1 | :water
2 | :ice
3 | :upper_sediments
4 | :middle_sediments
5 | :lower_sediments
6 | :upper_crust
7 | :middle_crust
8 | :lower_crust
9 | :mantle
"""
end
end
function crust1layer(layer::Symbol)
if layer===:water
1
elseif layer===:ice
2
elseif layer===:upper_sediments
3
elseif layer===:middle_sediments
4
elseif layer===:lower_sediments
5
elseif layer===:upper_crust
6
elseif layer===:middle_crust
7
elseif layer===:lower_crust
8
elseif layer===:mantle
9
else
@error """crust1 layer $layer not found.
Available layers include:
1 | :water
2 | :ice
3 | :upper_sediments
4 | :middle_sediments
5 | :lower_sediments
6 | :upper_crust
7 | :middle_crust
8 | :lower_crust
9 | :mantle
"""
end
end
"""
```julia
find_crust1_layer(lat,lon,layer)
```
Return all point data (Vp, Vs, Rho, layer thickness) for a given `lat`itude,
`lon`gitude, and crustal `layer`.
Accepts `lat` and `lon` both as `Numbers` and as `AbstractArray`s, but given
the overhead of opening and reading the crust1 files, you should generally
aim to provide large arrays with as many values in a single query as possible.
Available `layer`s:
```
1 | :water
2 | :ice
3 | :upper_sediments
4 | :middle_sediments
5 | :lower_sediments
6 | :upper_crust
7 | :middle_crust
8 | :lower_crust
9 | :mantle
```
Results are returned in form `(Vp, Vs, Rho, thickness)`
## Examples
```julia
julia> vp, vs, rho, thickness = find_crust1_layer([43.702245], [-72.0929], 8)
([7.0], [3.99], [2950.0], [7.699999999999999])
```
"""
function find_crust1_layer(lat,lon,layer)
# Get Vp, Vs, Rho, and thickness for a given lat, lon, and crustal layer.
@assert eachindex(lat) == eachindex(lon)
layerindex = crust1layer(layer)
nlayers=9
nlon=360
nlat=180
# Allocate data arrays
vp = Array{Float64,3}(undef,nlayers,nlat,nlon)
vs = Array{Float64,3}(undef,nlayers,nlat,nlon)
rho = Array{Float64,3}(undef,nlayers,nlat,nlon)
bnd = Array{Float64,3}(undef,nlayers,nlat,nlon)
# Open data files
vpfile = open(artifact"crust1/crust1.vp", "r")
vsfile = open(artifact"crust1/crust1.vs", "r")
rhofile = open(artifact"crust1/crust1.rho", "r")
bndfile = open(artifact"crust1/crust1.bnds", "r")
# Read data files into array
for j=1:nlat
for i=1:nlon
vp[:,j,i] = delim_string_parse(readline(vpfile), ' ', Float64, merge=true)
vs[:,j,i] = delim_string_parse(readline(vsfile), ' ', Float64, merge=true)
rho[:,j,i] = delim_string_parse(readline(rhofile), ' ', Float64, merge=true) * 1000 # convert to kg/m3
bnd[:,j,i] = delim_string_parse(readline(bndfile), ' ', Float64, merge=true)
end
end
# Close data files
close(vpfile)
close(vsfile)
close(rhofile)
close(bndfile)
# Allocate output arrays
vpout = Array{Float64}(undef,size(lat))
vsout = Array{Float64}(undef,size(lat))
rhoout = Array{Float64}(undef,size(lat))
thkout = Array{Float64}(undef,size(lat))
# Fill output arrays
@inbounds for j ∈ eachindex(lat)
# Avoid edge cases at lat = -90.0, lon = 180.0
lonⱼ = mod(lon[j] + 180, 360) - 180
latⱼ = lat[j]
if -90 < latⱼ < 90 && -180 < lonⱼ < 180
# Convert lat and lon to index
ilat = 91 - ceil(Int,latⱼ)
ilon = 181 + floor(Int,lonⱼ)
vpout[j] = vp[layerindex,ilat,ilon]
vsout[j] = vs[layerindex,ilat,ilon]
rhoout[j] = rho[layerindex,ilat,ilon]
thkout[j] = bnd[layerindex,ilat,ilon] - bnd[layerindex+1,ilat,ilon]
else
vpout[j] = NaN
vsout[j] = NaN
rhoout[j] = NaN
thkout[j] = NaN
end
end
# The end
return (vpout, vsout, rhoout, thkout)
end
export find_crust1_layer
"""
```julia
find_crust1_seismic(lat,lon,layer)
```
Return all seismic data (Vp, Vs, Rho) for a given `lat`itude, `lon`gitude,
and crustal `layer`.
Accepts `lat` and `lon` both as `Numbers` and as `AbstractArray`s, but given
the overhead of opening and reading the crust1 files, you should generally
aim to provide large arrays with as many values in a single query as possible.
Available `layer`s:
```
1 | :water
2 | :ice
3 | :upper_sediments
4 | :middle_sediments
5 | :lower_sediments
6 | :upper_crust
7 | :middle_crust
8 | :lower_crust
9 | :mantle
```
Results are returned in form `(Vp, Vs, Rho, thickness)`
## Examples
```julia
julia> vp, vs, rho = find_crust1_seismic([43.702245], [-72.0929], 8)
([7.0], [3.99], [2950.0])
julia> vp, vs, rho = find_crust1_seismic([43.702245], [-72.0929], :lower_crust)
([7.0], [3.99], [2950.0])
```
"""
function find_crust1_seismic(lat,lon,layer)
# Vp, Vs, and Rho for a given lat, lon, and crustal layer.
@assert eachindex(lat) == eachindex(lon)
layerindex = crust1layer(layer)
nlayers=9
nlon=360
nlat=180
# Allocate data arrays
vp = Array{Float64,3}(undef,nlayers,nlat,nlon)
vs = Array{Float64,3}(undef,nlayers,nlat,nlon)
rho = Array{Float64,3}(undef,nlayers,nlat,nlon)
# Open data files
vpfile = open(artifact"crust1/crust1.vp", "r")
vsfile = open(artifact"crust1/crust1.vs", "r")
rhofile = open(artifact"crust1/crust1.rho", "r")
# Read data files into array
for j=1:nlat
for i=1:nlon
vp[:,j,i] = delim_string_parse(readline(vpfile), ' ', Float64, merge=true)
vs[:,j,i] = delim_string_parse(readline(vsfile), ' ', Float64, merge=true)
rho[:,j,i] = delim_string_parse(readline(rhofile), ' ', Float64, merge=true) * 1000 # convert to kg/m3
end
end
# Close data files
close(vpfile)
close(vsfile)
close(rhofile)
# Allocate output arrays
vpout = Array{Float64}(undef,size(lat))
vsout = Array{Float64}(undef,size(lat))
rhoout = Array{Float64}(undef,size(lat))
# Fill output arrays
@inbounds for j ∈ eachindex(lat)
# Avoid edge cases at lat = -90.0, lon = 180.0
lonⱼ = mod(lon[j] + 180, 360) - 180
latⱼ = lat[j]
if -90 < latⱼ < 90 && -180 < lonⱼ < 180
# Convert lat and lon to index
ilat = 91 - ceil(Int,latⱼ)
ilon = 181 + floor(Int,lonⱼ)
vpout[j] = vp[layerindex,ilat,ilon]
vsout[j] = vs[layerindex,ilat,ilon]
rhoout[j] = rho[layerindex,ilat,ilon]
else
vpout[j] = NaN
vsout[j] = NaN
rhoout[j] = NaN
end
end
# The end
return (vpout, vsout, rhoout)
end
export find_crust1_seismic
"""
```julia
find_crust1_thickness(lat,lon,layer)
```
Return layer thickness for a crust 1.0 `layer` at a given `lat`itude and
`lon`gitude.
Accepts `lat` and `lon` both as `Numbers` and as `AbstractArray`s, but given
the overhead of opening and reading the crust1 files, you should generally
aim to provide large arrays with as many values in a single query as possible.
Available `layer`s:
```
1 | :water
2 | :ice
3 | :upper_sediments
4 | :middle_sediments
5 | :lower_sediments
6 | :upper_crust
7 | :middle_crust
8 | :lower_crust
9 | :mantle
```
Results are returned in form `(Vp, Vs, Rho, thickness)`
## Examples
```julia
julia> find_crust1_thickness([43.702245], [-72.0929], 8)
1-element Vector{Float64}:
7.699999999999999
julia> find_crust1_thickness([43.702245], [-72.0929], :lower_crust)
1-element Vector{Float64}:
7.699999999999999
```
"""
function find_crust1_thickness(lat,lon,layer)
# Layer thickness for a given lat, lon, and crustal layer.
@assert eachindex(lat) == eachindex(lon)
layerindex = crust1layer(layer)
nlayers=9
nlon=360
nlat=180
# Allocate data arrays
bnd = Array{Float64,3}(undef,nlayers,nlat,nlon)
# Open data files
bndfile = open(artifact"crust1/crust1.bnds", "r")
# Read data files into array
for j=1:nlat
for i=1:nlon
bnd[:,j,i] = delim_string_parse(readline(bndfile), ' ', Float64, merge=true)
end
end
# Close data files
close(bndfile)
# Allocate output arrays
thkout = Array{Float64}(undef,size(lat))
# Fill output arrays
@inbounds for j ∈ eachindex(lat)
# Avoid edge cases at lat = -90.0, lon = 180.0
lonⱼ = mod(lon[j] + 180, 360) - 180
latⱼ = lat[j]
if -90 < latⱼ < 90 && -180 < lonⱼ < 180
# Convert lat and lon to index
ilat = 91 - ceil(Int,latⱼ)
ilon = 181 + floor(Int,lonⱼ)
thkout[j] = bnd[layerindex,ilat,ilon]-bnd[layerindex+1,ilat,ilon]
else
thkout[j] = NaN
end
end
# The end
return thkout
end
export find_crust1_thickness
"""
```julia
find_crust1_base(lat,lon,layer)
```
Return elevation (relative to sea level) of the layer base for a crust 1.0
`layer` at a given `lat`itude and `lon`gitude.
Accepts `lat` and `lon` both as `Numbers` and as `AbstractArray`s, but given
the overhead of opening and reading the crust1 files, you should generally
aim to provide large arrays with as many values in a single query as possible.
Available `layer`s:
```
1 | :water
2 | :ice
3 | :upper_sediments
4 | :middle_sediments
5 | :lower_sediments
6 | :upper_crust
7 | :middle_crust
8 | :lower_crust
9 | :mantle
```
Results are returned in form `(Vp, Vs, Rho, thickness)`
## Examples
```julia
julia> find_crust1_base([43.702245], [-72.0929], 8)
1-element Vector{Float64}:
-36.26
julia> find_crust1_base([43.702245], [-72.0929], :lower_crust)
1-element Vector{Float64}:
-36.26
```
"""
function find_crust1_base(lat,lon,layer)
# Depth to layer base for a given lat, lon, and crustal layer.
@assert eachindex(lat) == eachindex(lon)
layerindex = crust1layer(layer)
nlayers=9
nlon=360
nlat=180
# Allocate data arrays
bnd = Array{Float64,3}(undef,nlayers,nlat,nlon)
# Open data files
bndfile = open(artifact"crust1/crust1.bnds", "r")
# Read data files into array
for j=1:nlat
for i=1:nlon
bnd[:,j,i] = delim_string_parse(readline(bndfile), ' ', Float64, merge=true)
end
end
# Close data files
close(bndfile)
# Allocate output arrays
baseout = Array{Float64}(undef,size(lat))
# Fill output arrays
@inbounds for j ∈ eachindex(lat)
# Avoid edge cases at lat = -90.0, lon = 180.0
lonⱼ = mod(lon[j] + 180, 360) - 180
latⱼ = lat[j]
if -90 < latⱼ < 90 && -180 < lonⱼ < 180
# Convert lat and lon to index
ilat = 91 - ceil(Int,latⱼ)
ilon = 181 + floor(Int,lonⱼ)
baseout[j] = bnd[layerindex+1,ilat,ilon]
else
baseout[j] = NaN
end
end
# The end
return baseout
end
export find_crust1_base
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 14857 | ## --- Land
"""
```julia
find_land(lat,lon)
```
Find whether or not a given set of `lat`itude, `lon`gitude points on the globe
is above sea level, based on the `etopo` bedrock elevation dataset
## Examples
```julia
julia> find_land(43.702245, -72.0929)
0-dimensional Array{Bool, 0}:
1
```
"""
function find_land(lat, lon)
# Interpret user input
@assert eachindex(lat) == eachindex(lon)
filepath = artifact"land/land.h5"
land = h5read(filepath, "vars/land")
# Scale factor (cells per degree) = 30 = arc minutes in an arc degree
sf = 30
maxrow = 180 * sf
maxcol = 360 * sf
# Create and fill output vector
result = zeros(Bool, size(lat))
for i ∈ eachindex(lat)
if (-90 <= lat[i] <= 90) && (-180 <= lon[i] < 180)
# Convert latitude and longitude into indicies of the elevation map array
row = 1 + trunc(Int,(90+lat[i])*sf)
row == (maxrow+1) && (row = maxrow) # Edge case
col = 1 + trunc(Int,(180+lon[i])*sf)
col == (maxcol+1) && (col = maxcol) # Edge case
# Find result by indexing
result[i] = land[row,col]
end
end
return result
end
export find_land
## --- Geolcont
continentcolors = parse.(Color, ["#333399","#0066CC","#06A9C1","#66CC66","#FFCC33","#FFFF00","#FFFFFF"])
export continentcolors
continents = ["Africa","Eurasia","North America","South America","Australia","Antarctica","NA"]
export continents
"""
```julia
find_geolcont(lat,lon)
```
Find which geographic continent a sample originates from.
Continents:
```
1: "Africa"
2: "Eurasia"
3: "North America"
4: "South America"
5: "Australia"
6: "Antarctica"
7: "NA"
```
See also: `continents`, `continentcolors`.
## Examples
```julia
julia> find_geolcont(43.702245, -72.0929)
0-dimensional Array{Int64, 0}:
3
julia> continents[find_geolcont(43.702245, -72.0929)]
0-dimensional Array{String, 0}:
"North America"
```
"""
function find_geolcont(lat,lon)
@assert eachindex(lat) == eachindex(lon)
# Construct file path
filepath = artifact"geolcont/geolcontwshelf.png"
img = load(filepath)
ind = fill(7,size(img))
for i=1:6
ind[img .== continentcolors[i]] .= i
end
# Create and fill output vector
contindex = Array{Int}(undef,size(lat))
for i ∈ eachindex(lat)
if (-90 <= lat[i] <= 90) && (-180 <= lon[i] <= 180)
# Convert latitude and longitude into indicies of the elevation map array
# Note that STRTM15 plus has N+1 columns where N = 360*sf
row = 1 + trunc(Int,(90-lat[i])*512/180)
col = 1 + trunc(Int,(180+lon[i])*512/180)
# Find result by indexing
contindex[i] = ind[row,col]
else
# Result is unknown if either input is NaN or out of bounds
contindex[i] = 7
end
end
return contindex
end
export find_geolcont
## --- geolprov
"""
```julia
find_geolprov(lat,lon)
```
Find which tectonic setting a sample originates from, based on a modified version
of the USGS map of tectonic provinces of the world
(c.f. https://commons.wikimedia.org/wiki/File:World_geologic_provinces.jpg)
Settings:
```
10: Accreted Arc
11: Island Arc
12: Continental Arc
13: Collisional orogen
20: Extensional
21: Rift
22: Plume
31: Shield
32: Platform
33: Basin
00: No data
```
Settings returned are most representative modern setting at a given location
and may not represent the tectonic setting where rocks (especially older/Precambrian
rocks) originally formed.
## Examples
```julia
julia> find_geolprov(43.702245, -72.0929)
0-dimensional Array{Int64, 0}:
10
julia> lat = rand(4)*180 .- 90
4-element Vector{Float64}:
-28.352224011759773
14.521710123066882
43.301961981794335
79.26368353708557
julia> lon = rand(4)*360 .- 180
4-element Vector{Float64}:
5.024149409750521
161.04362679392233
123.21726489255786
-54.34797401313695
julia> find_geolprov(lat, lon)
4-element Vector{Int64}:
0
0
32
0
```
"""
function find_geolprov(lat, lon)
@assert eachindex(lat) == eachindex(lon)
filepath = artifact"geolprov/geolprov.h5"
geolprov = h5read(filepath, "geolprov")
result = zeros(Int, size(lat))
for i ∈ eachindex(lat)
if -180 < lon[i] <= 180 && -90 <= lat[i] < 90
x = ceil(Int, (lon[i]+180) * 2161/360)
y = ceil(Int, (90-lat[i]) * 1801/180)
result[i] = geolprov[y,x]
end
end
return result
end
export find_geolprov
## --- ETOPO1 (1 arc minute topography)
"""
```julia
get_etopo([varname])
```
Read ETOPO1 (1 arc minute topography) file from HDF5 storage, downloading
from cloud if necessary.
Available `varname`s (variable names) include:
```
"elevation"
"y_lat_cntr"
"x_lon_cntr"
"cellsize"
"scalefactor"
"reference"
```
Units are meters of elevation and decimal degrees of latitude and longitude.
Reference:
Amante, C. and B.W. Eakins, 2009. ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24.
National Geophysical Data Center, NOAA. doi:10.7289/V5C8276M.
http://www.ngdc.noaa.gov/mgg/global/global.html
See also: `find_etopoelev`.
## Examples
```julia
julia> get_etopo()
Dict{String, Any} with 6 entries:
"cellsize" => 0.0166667
"scalefactor" => 60
"x_lon_cntr" => [-179.992, -179.975, -179.958, -179.942, -179.925, -1…
"reference" => "Amante, C. and B.W. Eakins, 2009. ETOPO1 1 Arc-Minut…
"y_lat_cntr" => [-89.9917, -89.975, -89.9583, -89.9417, -89.925, -89.…
"elevation" => [-58.0 -58.0 … -58.0 -58.0; -61.0 -61.0 … -61.0 -61.0…
julia> get_etopo("elevation")
10800×21600 Matrix{Float64}:
-58.0 -58.0 -58.0 … -58.0 -58.0 -58.0
-61.0 -61.0 -61.0 -61.0 -61.0 -61.0
-62.0 -63.0 -63.0 -63.0 -63.0 -62.0
-61.0 -62.0 -62.0 -62.0 -62.0 -61.0
⋮ ⋱
-4226.0 -4226.0 -4227.0 -4227.0 -4227.0 -4227.0
-4228.0 -4228.0 -4229.0 -4229.0 -4229.0 -4229.0
-4229.0 -4229.0 -4229.0 -4229.0 -4229.0 -4229.0
```
"""
function get_etopo(varname="")
# Available variable names: "elevation", "y_lat_cntr", "x_lon_cntr",
# "cellsize", "scalefactor", and "reference". Units are meters of
# elevation and decimal degrees of latitude and longitude
# Construct file path
filedir = joinpath(resourcepath,"etopo")
filepath = joinpath(filedir,"etopo1.h5")
# Download HDF5 file from Google Cloud if necessary
if ~isfile(filepath)
@info "Downloading etopo1.h5 from google cloud storage to $filedir"
run(`mkdir -p $filedir`)
Downloads.download("https://storage.googleapis.com/statgeochem/etopo1.references.txt", joinpath(filedir,"etopo1.references.txt"))
Downloads.download("https://storage.googleapis.com/statgeochem/etopo1.h5", filepath)
end
# Read and return the file
return h5read(filepath, "vars/"*varname)
end
export get_etopo
"""
```julia
find_etopoelev([etopo], lat, lon, [T=Float64])
```
Find the elevation of points at position (`lat`, `lon`) on the surface of the
Earth, using the ETOPO1 one-arc-degree elevation model.
Units are meters of elevation and decimal degrees of latitude and longitude.
Reference:
Amante, C. and B.W. Eakins, 2009. ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24.
National Geophysical Data Center, NOAA. doi:10.7289/V5C8276M.
http://www.ngdc.noaa.gov/mgg/global/global.html
See also: `get_etopo`.
## Examples
```julia
julia> etopo = get_etopo("elevation")
10800×21600 Matrix{Float64}:
-58.0 -58.0 -58.0 … -58.0 -58.0 -58.0
-61.0 -61.0 -61.0 -61.0 -61.0 -61.0
-62.0 -63.0 -63.0 -63.0 -63.0 -62.0
-61.0 -62.0 -62.0 -62.0 -62.0 -61.0
⋮ ⋱
-4226.0 -4226.0 -4227.0 -4227.0 -4227.0 -4227.0
-4228.0 -4228.0 -4229.0 -4229.0 -4229.0 -4229.0
-4229.0 -4229.0 -4229.0 -4229.0 -4229.0 -4229.0
julia> find_etopoelev(etopo, 43.702245, -72.0929)
0-dimensional Array{Float64, 0}:
294.0
```
"""
find_etopoelev(lat,lon) = find_etopoelev(get_etopo(),lat,lon)
find_etopoelev(etopo::Dict, lat, lon) = find_etopoelev(etopo["elevation"], lat, lon)
function find_etopoelev(etopo::AbstractArray, lat, lon, T=Float64)
# Interpret user input
@assert eachindex(lat) == eachindex(lon)
# Scale factor (cells per degree) = 60 = arc minutes in an arc degree
sf = 60
maxrow = 180 * sf
maxcol = 360 * sf
# Create and fill output vector
result = Array{T}(undef,size(lat))
for i ∈ eachindex(lat)
if (-90 <= lat[i] <= 90) && (-180 <= lon[i] <= 180)
# Convert latitude and longitude into indicies of the elevation map array
row = 1 + trunc(Int,(90+lat[i])*sf)
row == (maxrow+1) && (row = maxrow) # Edge case
col = 1 + trunc(Int,(180+lon[i])*sf)
col == (maxcol+1) && (col = maxcol) # Edge case
# Find result by indexing
result[i] = etopo[row,col]
else
# Result is NaN if either input is NaN or out of bounds
result[i] = NaN
end
end
return result
end
export find_etopoelev
## --- SRTM15_PLUS (15 arc second topography)
"""
```julia
get_srtm15plus([varname])
```
Read SRTM15plus file from HDF5 storage (15 arc second topography from the
Shuttle Radar Topography Mission), downloading from cloud if necessary.
Available `varname`s (variable names) include:
```
"elevation"
"y_lat_cntr"
"x_lon_cntr"
"cellsize"
"scalefactor"
"nanval"
"reference"
```
Units are meters of elevation and decimal degrees of latitude and longitude.
Reference: https://doi.org/10.5069/G92R3PT9
See also: `find_srtm15plus`.
## Examples
```julia
julia> get_srtm15plus()
Dict{String, Any} with 7 entries:
"cellsize" => 0.00416667
"scalefactor" => 240
"x_lon_cntr" => [-180.0, -179.996, -179.992, -179.988, -179.983,…
"reference" => "http://topex.ucsd.edu/WWW_html/srtm30_plus.html"
"y_lat_cntr" => [-90.0, -89.9958, -89.9917, -89.9875, -89.9833, …
"nanval" => -32768
"elevation" => Int16[-32768 -32768 … -32768 -32768; 3124 3124 ……
julia> get_srtm15plus("elevation")
43201×86401 Matrix{Int16}:
-32768 -32768 -32768 -32768 … -32768 -32768 -32768
3124 3124 3124 3124 3113 3113 3124
3123 3123 3123 3122 3111 3111 3123
3121 3121 3121 3121 3110 3110 3121
⋮ ⋱ ⋮
-4225 -4224 -4224 -4224 -4224 -4225 -4225
-4223 -4222 -4222 -4223 -4223 -4223 -4223
-4223 -4223 -4223 -4223 -4223 -4223 -4223
-4230 -4230 -4230 -4230 … -4230 -4230 -4230
```
"""
function get_srtm15plus(varname="")
# Available variable names: "elevation", "y_lat_cntr", "x_lon_cntr",
# "nanval", "cellsize", "scalefactor", and "reference". Units are
# meters of elevation and decimal degrees of latitude and longitude
# Construct file path
filedir = joinpath(resourcepath,"srtm15plus")
filepath = joinpath(filedir,"srtm15plus.h5")
# Download HDF5 file from Google Cloud if necessary
if ~isfile(filepath)
@info "Downloading srtm15plus.h5 from google cloud storage to $filedir"
run(`mkdir -p $filedir`)
Downloads.download("https://storage.googleapis.com/statgeochem/srtm15plus.references.txt", joinpath(filedir,"srtm15plus.references.txt"))
Downloads.download("https://storage.googleapis.com/statgeochem/srtm15plus.h5", filepath)
end
# Read and return the file
return h5read(filepath,"vars/"*varname)
end
export get_srtm15plus
"""
```julia
find_srtm15plus([srtm], lat, lon, [T=Float64])
```
Find the elevation of points at position (`lat`, `lon`) on the surface of the
Earth, using the SRTM15plus 15-arc-second elevation model.
Units are meters of elevation and decimal degrees of latitude and longitude.
Reference: https://doi.org/10.5069/G92R3PT9
See also: `get_srtm15plus`.
## Examples
```julia
julia> srtm = get_srtm15plus("elevation")
43201×86401 Matrix{Int16}:
-32768 -32768 -32768 -32768 … -32768 -32768 -32768
3124 3124 3124 3124 3113 3113 3124
3123 3123 3123 3122 3111 3111 3123
3121 3121 3121 3121 3110 3110 3121
⋮ ⋱ ⋮
-4225 -4224 -4224 -4224 -4224 -4225 -4225
-4223 -4222 -4222 -4223 -4223 -4223 -4223
-4223 -4223 -4223 -4223 -4223 -4223 -4223
-4230 -4230 -4230 -4230 … -4230 -4230 -4230
julia> find_srtm15plus(srtm, 43.702245, -72.0929)
0-dimensional Array{Float64, 0}:
252.0
```
"""
find_srtm15plus(lat,lon) = find_srtm15plus(get_srtm15plus(),lat,lon)
find_srtm15plus(srtm::Dict, lat, lon) = find_srtm15plus(srtm["elevation"], lat, lon)
function find_srtm15plus(srtm::AbstractArray, lat, lon, T=Float64)
# Interpret user input
length(lat) != length(lon) && error("lat and lon must be of equal length")
# Scale factor (cells per degree) = 60 * 4 = 240
# (15 arc seconds goes into 1 arc degree 240 times)
sf = 240
# Create and fill output vector
out = Array{T}(undef,size(lat))
for i ∈ eachindex(lat)
if isnan(lat[i]) || isnan(lon[i]) || lat[i]>90 || lat[i]<-90 || lon[i]>180 || lon[i]<-180
# Result is NaN if either input is NaN or out of bounds
out[i] = NaN
else
# Convert latitude and longitude into indicies of the elevation map array
# Note that STRTM15 plus has N+1 columns where N = 360*sf
row = 1 + round(Int,(90+lat[i])*sf)
col = 1 + round(Int,(180+lon[i])*sf)
# Find result by indexing
res = srtm[row,col]
if res == -32768
out[i] = NaN
else
out[i] = res
end
end
end
return out
end
export find_srtm15plus
## --- End of File | StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 693 |
## --- Geologic timescale
gts = importdataset(joinpath(moduleresourcepath,"timescale","gts_intervals.tsv"), '\t', importas=:Tuple)
e = (:Age_min, :Age_min_sigma, :Age_max, :Age_max_sigma)
const timescale = NamedTuple{e}(((Dict{String,Float64}() for _ in e)...,))
for i in eachindex(gts.Name)
timescale.Age_max[gts.Name[i]] = gts.Age_max[i]
timescale.Age_max_sigma[gts.Name[i]] = gts.Age_max_sigma[i]
timescale.Age_min[gts.Name[i]] = gts.Age_min[i]
timescale.Age_min_sigma[gts.Name[i]] = gts.Age_min_sigma[i]
end
export timescale
## --- Rock type classification
const rock_type_key = importdataset(joinpath(moduleresourcepath,"rock_type_key.tsv"), '\t', importas=:Tuple)
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 3536 | litho1layer(layer::Number) = Int(layer)
function litho1layer(layer::Symbol)
if layer===:ice
1
elseif layer===:water
2
elseif layer===:upper_sediments
3
elseif layer===:middle_sediments
4
elseif layer===:lower_sediments
5
elseif layer===:upper_crust
6
elseif layer===:middle_crust
7
elseif layer===:lower_crust
8
elseif layer===:lithosphere || layer===:sclm || layer===:lid
9
elseif layer===:asthenosphere
10
else
@error """litho1 layer $layer not found.
Available layers include:
1 | :ice
2 | :water
3 | :upper_sediments
4 | :middle_sediments
5 | :lower_sediments
6 | :upper_crust
7 | :middle_crust
8 | :lower_crust
9 | :sclm (or :lithosphere)
10 | :asthenosphere
"""
end
end
"""
```julia
find_litho1_property(lat, lon, layer::Symbol, property::Symbol)
```
Return values for a LITHO1.0 `property` of a given `layer` at one or more given
`lat`itudes and `lon`gitudes, to the nearest 0.5-arc-degree grid point.
Accepts `lat` and `lon` both as `Numbers` and as `AbstractArray`s, but given
the overhead of opening and reading the LITHO1.0 files, you should generally
aim to provide large arrays with as many values in a single query as possible.
Available properties include:
```
:vp | p-wave velocity [m/s]
:vs | s-wave velocity [m/s]
:rho | density [kg/m^3]
:bottom | depth to bottom of the layer [km] (above sea level = negative)
:thickness | layer thickness [km]
```
while avialble `layer`s are:
```
1 | :ice
2 | :water
3 | :upper_sediments
4 | :middle_sediments
5 | :lower_sediments
6 | :upper_crust
7 | :middle_crust
8 | :lower_crust
9 | :sclm (or :lithosphere)
10 | :asthenosphere
```
## Examples
```julia
julia> find_litho1_property([43.702245, 44], [-72.0929, -73], :upper_crust, :vp)
2-element Vector{Float64}:
6219.99
6253.16
```
"""
function find_litho1_property(lat, lon, layer, property::Symbol)
@assert eachindex(lat)==eachindex(lon)
layerindex = litho1layer(layer)::Int
@assert 0 < layerindex < 11
litho1path = artifact"litho1-gridded/litho1-gridded.h5"
result = fill(NaN, size(lat))
if property===:vp
data = h5read(litho1path, "vp")::Array{Float64,3}
elseif property===:vs
data = h5read(litho1path, "vs")::Array{Float64,3}
elseif property===:rho || property===:density
data = h5read(litho1path, "rho")::Array{Float64,3}
elseif property===:base || property===:bottom
data = h5read(litho1path, "bottom")::Array{Float64,3}
elseif property===:thickness
data = h5read(litho1path, "thickness")::Array{Float64,3}
else
@error """litho1 property `$property` not found. Available options include:
:vp, :vs, :rho, :bottom, :thickness
"""
return result
end
# For reference:
# lats = -90:0.5:90
# lons = -179.5:0.5:180
scale = 2
@inbounds for i in eachindex(lat)
r = scale*(lat[i]+90) + 1
c = scale*(lon[i]+180)
if 1 <= r <= size(data,1) && 0 <= c <= size(data,2)
row, col = trunc(Int, r), trunc(Int, c)
col < 1 && (col = size(data,2))
result[i] = data[row, col, layerindex]
end
end
return result
end
export find_litho1_property
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 5215 | # --- Müller et al. seafloor age and spreading rate
"""
```julia
get_seafloorage(varname="")
```
Read seafloor age file from HDF5 storage, downloading from cloud if necessary.
Available `varname`s (variable names) include:
```
"seafloorage"
"seafloorage_sigma",
"seafloorrate"
"information"
"reference"
```
Units are millions of years for age and mm/yr for rate.
Reference:
Müller, R. D., M. Sdrolias, C. Gaina, and W. R. Roest (2008). Age, spreading
rates, and spreading asymmetry of the world's ocean crust, Geochem. Geophys. Geosyst., 9,
Q04006, doi:10.1029/2007GC001743. ftp://ftp.es.usyd.edu.au/pub/agegrid/2008/Grids/
See also: `find_seafloorage`.
## Examples
```julia
julia> get_seafloorage()
Dict{String, Any} with 5 entries:
"seafloorrate" => [6.83 6.79 … 6.91 6.87; 6.82 6.78 … 6.91 6.…
"reference" => "Muller, R. D., M. Sdrolias, C. Gaina, and …
"seafloorage" => [8.02 8.13 … 7.8 7.91; 8.01 8.12 … 7.81 7.9…
"information" => "Mercator projection, from 80.738 to -80.73…
"seafloorage_sigma" => [2.15 2.19 … 2.08 2.11; 2.15 2.19 … 2.08 2.…
julia> get_seafloorage("seafloorage")
8640×10800 Matrix{Float64}:
8.02 8.13 8.24 8.36 … 7.6 7.7 7.8 7.91
8.01 8.12 8.23 8.34 7.61 7.71 7.81 7.91
8.01 8.11 8.22 8.32 7.62 7.72 7.81 7.91
8.01 8.11 8.2 8.3 7.64 7.73 7.82 7.91
⋮ ⋱
NaN NaN NaN NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN NaN NaN NaN
```
"""
function get_seafloorage(varname="")
# Available variable names: "seafloorage", "seafloorage_sigma",
# "seafloorrate", "information", and "reference".
# Construct file path
filedir = joinpath(resourcepath,"seafloorage")
filepath = joinpath(filedir,"seafloorage.h5")
# Download HDF5 file from Google Cloud if necessary
if ~isfile(filepath)
@info "Downloading seafloorage.h5 from google cloud storage to $filedir"
run(`mkdir -p $filedir`)
Downloads.download("https://storage.googleapis.com/statgeochem/seafloorage.references.txt", joinpath(filedir,"seafloorage.references.txt"))
Downloads.download("https://storage.googleapis.com/statgeochem/seafloorage.h5", filepath)
end
# Read and return the file
return h5read(filepath,"vars/"*varname)
end
export get_seafloorage
"""
```julia
find_seafloorage([sfdata], lat, lon)
```
Find the age of the seafloor at positions (`lat`, `lon`) on the ocean crust,
using the Müller et al. (2008) dataset.
Units are millions of years for age and mm/yr for rate.
Reference:
Müller, R. D., M. Sdrolias, C. Gaina, and W. R. Roest (2008). Age, spreading
rates, and spreading asymmetry of the world's ocean crust, Geochem. Geophys. Geosyst., 9,
Q04006, doi:10.1029/2007GC001743. ftp://ftp.es.usyd.edu.au/pub/agegrid/2008/Grids/
See also: `get_seafloorage`.
## Examples
```julia
julia> sfdata = get_seafloorage("seafloorage")
8640×10800 Matrix{Float64}:
8.02 8.13 8.24 8.36 … 7.6 7.7 7.8 7.91
8.01 8.12 8.23 8.34 7.61 7.71 7.81 7.91
8.01 8.11 8.22 8.32 7.62 7.72 7.81 7.91
8.01 8.11 8.2 8.3 7.64 7.73 7.82 7.91
⋮ ⋱
NaN NaN NaN NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN NaN NaN NaN
NaN NaN NaN NaN NaN NaN NaN NaN
julia> find_seafloorage(sfdata, 43.702245, -40)
0-dimensional Array{Float64, 0}:
75.95
```
"""
find_seafloorage(lat, lon) = find_seafloorage(get_seafloorage("seafloorage"), lat, lon)
find_seafloorage(sfdata::Dict, lat, lon) = find_seafloorage(sfdata["seafloorage"], lat, lon)
function find_seafloorage(sfdata::AbstractArray, lat, lon)
# Interpret user input
eachindex(lat) == eachindex(lon) || error("`lat` and `lon` must be the same size")
# Make and fill output array
result = fill(NaN, size(lat))
for i ∈ eachindex(lat)
# Find the column numbers (using mod to convert lon from -180:180 to 0:360
x = floor(Int, mod(lon[i], 360) * 10800/360) + 1
# find the y rows, converting from lat to Mercator (lat -80.738:80.738)
y = 4320 - floor(Int, 8640 * asinh(tan(lat[i]*pi/180)) / asinh(tan(80.738*pi/180)) / 2 ) + 1
# If there is out data for row(i), col(i)
if (1 <= x <= 10800) && (1 <= y <= 8640)
# Then fill in the output data (Age, Age_Min, Age_Max)
result[i] = sfdata[y, x]
end
end
return result
end
export find_seafloorage
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 1246813 | """
```julia
claiborne_zircon_kd(elem::String, T::Number)
```
Returns the temperature-dependent zircon/melt partition coefficients of
Claiborne et al. (2017) for the given element `elem` at temperature `T`
degrees C, using equations of the form `a * exp(b/TK)` where `TK = T + 273.15`
In addition to the elements for which Claiborne provides equations, we also
extrapolate these partition coefficients to include `"La"` and `"Pr"` using a
lattce strain model, following the equations of Blundy and Wood (2003).
Reference:
Claiborne, L. L., Miller, C. F., Gualda, G. A., Carley, T. L., Covey, A. K.,
Wooden, J. L., & Fleming, M. A. (2018). Zircon as magma monitor: Robust,
temperature‐dependent partition coefficients from glass and zircon surface and
rim measurements from natural systems. in Microstructural geochronology:
Planetary records down to atom scale, 1-33.
https://doi.org/10.1002/9781119227250.ch1
## Examples
```julia
julia> claiborne_zircon_kd("Yb", 600)
1016.9198328977473
```
"""
function claiborne_zircon_kd(elem::String, T::Number)
# Convert temperature to Kelvin
T += 273.15
# Calculate partition coefficient
if elem=="Hf"
0.0965*exp(10206/T)
elseif elem=="Th"
0.0126*exp(6696/T)
elseif elem=="U"
0.0465*exp(7167/T)
elseif elem=="Y"
0.0036*exp(9806/T)
elseif elem=="Nb"
0.0003*exp(7241/T)
elseif elem=="La"
# Extended by lattice strain model, see extend_claiborne.jl
1.3798739589052778e-6 * exp(2589.726053162119/T)
elseif elem=="Pr"
# Extended by lattice strain model, see extend_claiborne.jl
5.133827751234487e-5 * exp(5290.150996818675/T)
elseif elem=="Nd"
0.0001*exp(5867/T)
elseif elem=="Sm"
0.0009*exp(6636/T)
elseif elem=="Tb"
0.0021*exp(9160/T)
elseif elem=="Eu"
0.0032*exp(6026/T)
elseif elem=="Dy"
0.0041*exp(9090/T)
elseif elem=="Gd"
0.0005*exp(9436/T)
elseif elem=="Ho"
0.0038*exp(9948/T)
elseif elem=="Er"
0.0052*exp(10088/T)
elseif elem=="Yb"
0.0044*exp(10784/T)
elseif elem=="Tm"
0.0086*exp(9990/T)
elseif elem=="Lu"
0.0032*exp(11358/T)
else
NaN
end
end
export claiborne_zircon_kd
germ_kd = Dict{String, Union{Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}, Vector{Float64}, Vector{String}}}("Olivine" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => 0.6983679307289166, "Pd" => [-1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567, -1.9462254351276567], "Nb" => [-2.8213818962849513, -2.7536925899071996, -2.6899736591649552, -2.646033292849799, -2.5979719820708627, -2.5611546494742026, -2.526684176968295, -2.4943753115718694, -2.4562111963459023, -2.4351771530522988, -2.3959816448300724, -2.3706810404814416, -2.3354032026113574, -2.2960101962919137, -2.2514460357812527, -2.2136961008380425, -2.1661681075521346, -2.132956774617309, -2.0911423220314593, -2.0494841158127834, -2.0080900200474856, -1.9894699776173301, -1.9366205935726397, -1.9151062019283098, -1.8650710629469938, -1.8279898296937824, -1.7705173719404421, -1.7415239378641576, -1.6992709358170945, -1.6712508644445707, -1.6280115049175714, -1.5967093422083407, -1.5627959810892962, -1.533002858583591, -1.5130365527624852, -1.5185052394119032, -1.5082549821130373, -1.49785673615257, -1.486633272380175, -1.4767997762168064, -1.4260312129338726], "P" => [-1.4751250542022158, -1.4776538211562393, -1.4755407734515542, -1.4672433390806308, -1.459475963353387, -1.4584477048304731, -1.4509880813268319, -1.4448094057820118, -1.4438079532063088, -1.4388341754497336, -1.435691702124203, -1.4343791159071897, -1.4306214677429043, -1.4312011389516668, -1.4303096768937615, -1.4275347559266318, -1.4216543940134576, -1.4166365763538131, -1.4094658082253413, -1.4048592351351876, -1.3993162648338606, -1.3929239316427988, -1.3888688964883535, -1.3824837545064959, -1.3754926431624224, -1.366574563338045, -1.3655236376356739, -1.3633541805096363, -1.3582576438936034, -1.3559264375686733, -1.3557756283040816, -1.347371635427632, -1.3402066065820262, -1.340234311926065, -1.3279463331064911, -1.315502651454478, -1.321951315655071, -1.320528943093673, -1.304819594237794, -1.3001344733639129, -1.298987888219444], "Si" => [0.03191626304302531, 0.018373672541005515, 0.008888475503393455, 0.00183284978274364, -0.002698522407835879, -0.004426612239525758, -0.009605742495882759, 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-2.9300604567993815], "Ge_sigma" => 0.549438469246615, "Ta_sigma" => 0.90511374682273, "As_sigma" => 0.9668817068556799, "Gd_sigma" => 0.9739006084581442, "Cd" => [-1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, -1.4604093769761874, 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[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "P" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Si" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ag" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892, 3.413028611837892], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Be_sigma" => NaN, "Sb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => NaN, "Cs" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U_sigma" => 0.677980011047734, "Ce_sigma" => 0.6060546701745756, "Dy_sigma" => 0.6892306573645867, "Be" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pt_sigma" => NaN, "Ta" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ga" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "W_sigma" => NaN, "Cs_sigma" => NaN, "Y" => [2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804, 2.592801856956804], "Sm_sigma" => 0.47566097124264567, "Lu_sigma" => 1.1481932566992847, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435, 3.956231554357435], "Eu_sigma" => 0.5084859166701661, "Sn_sigma" => NaN, "Ca" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu_sigma" => NaN, "K" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977, 3.7763686662175977], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Yb_sigma" => 1.0575974501732253, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => 0.8190537611280542, "Pr" => [3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484, 3.6965281197449484], "Pm_sigma" => NaN, "Th" => [3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003, 3.793121764888003], "Y_sigma" => 0.3611620029318388, "Zn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pr_sigma" => 0.435674810421887, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => NaN, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => NaN, "Al" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => 0.8928774110386941, "Er" => [2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164, 2.9958125938214164], "Ge" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb_sigma" => NaN, "Zr_sigma" => NaN, "Mg" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386, 3.7457262983669386], "Ru_sigma" => NaN, "La_sigma" => 0.5880585935168291, "W" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au_sigma" => NaN, "Hf_sigma" => NaN, "Li" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Dy" => [3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724, 3.2229150977412724], "Ti_sigma" => NaN, "Th_sigma" => 0.6010687638825071, "V_sigma" => NaN, "B" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd_sigma" => 0.4288836124684533, "Re" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sb_sigma" => NaN, "F_sigma" => NaN, "Hf" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb_sigma" => 0.6335317862095986, "Ca_sigma" => NaN, "Cl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr_sigma" => NaN, "Ag_sigma" => NaN, "Mg_sigma" => NaN, "Rh_sigma" => NaN, "Mo_sigma" => NaN, "Ba" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd" => [3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166, 3.6707153762364166], "Pm" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rh" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi_sigma" => NaN, "Ti" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb" => [3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803, 3.290539037041803], "Tm_sigma" => 0.9724420711700568, "Zr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sm" => [3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704, 3.5177927140097704], "Ba_sigma" => NaN, "Cr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Al_sigma" => NaN, "Tl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => NaN, "Yb" => [2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473, 2.8252902875193473], "Lu" => [2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586, 2.734614423659586], "Eu" => [3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794, 3.067470654251794], "Na" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Os" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Na_sigma" => NaN, "Ni" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb_sigma" => NaN, "Ho" => [3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443, 3.075444715998443], "Co" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pt" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "K_sigma" => NaN, "Tm" => [2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172, 2.912392064284172], "As" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Xe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ge_sigma" => NaN, "Ta_sigma" => NaN, "As_sigma" => NaN, "Gd_sigma" => 0.5413108620976865, "Cd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN]), "Nepheline" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => NaN, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [-1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775], "P" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Si" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ag" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [-1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214, -1.8785401090769214], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Be_sigma" => NaN, "Sb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => NaN, "Cs" => [-0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599, -0.384049948343599], "U_sigma" => NaN, "Ce_sigma" => NaN, "Dy_sigma" => NaN, "Be" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr" => [-0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394, -0.619788758288394], "Pt_sigma" => NaN, "Ta" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ga" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "W_sigma" => NaN, "Cs_sigma" => NaN, "Y" => [-2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0], "Sm_sigma" => NaN, "Lu_sigma" => NaN, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [-1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751, -1.9208187539523751], "Eu_sigma" => NaN, "Sn_sigma" => NaN, "Ca" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu_sigma" => NaN, "K" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [-1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Yb_sigma" => NaN, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => NaN, "Pr" => [-1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505, -1.9261171386652505], "Pm_sigma" => NaN, "Th" => [-1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762, -1.853871964321762], "Y_sigma" => NaN, "Zn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pr_sigma" => NaN, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => NaN, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => NaN, "Al" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => NaN, "Er" => [-1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769, -1.851158417230769], "Ge" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb_sigma" => NaN, "Zr_sigma" => NaN, "Mg" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [-1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225, -1.9714892183842225], "Ru_sigma" => NaN, "La_sigma" => NaN, "W" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au_sigma" => NaN, "Hf_sigma" => NaN, "Li" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Dy" => [-1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324, -1.8633934233756324], "Ti_sigma" => NaN, "Th_sigma" => NaN, "V_sigma" => NaN, "B" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd_sigma" => NaN, "Re" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sb_sigma" => NaN, "F_sigma" => NaN, "Hf" => [-2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562], "Tb_sigma" => NaN, "Ca_sigma" => NaN, "Cl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [-0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126, -0.3565473235138126], "Sr_sigma" => NaN, "Ag_sigma" => NaN, "Mg_sigma" => NaN, "Rh_sigma" => NaN, "Mo_sigma" => NaN, "Ba" => [-1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752, -1.0457574905606752], "Nd" => [-1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539, -1.9182176333363539], "Pm" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rh" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi_sigma" => NaN, "Ti" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb" => [-1.8681107849032792, -1.8681107849032792, -1.8681107849032792, -1.8681107849032792, 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[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => NaN, "Yb" => [-1.8445392086670742, -1.8445392086670742, -1.8445392086670742, 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=> [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb_sigma" => NaN, "Ho" => [-1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929, -1.854923249855929], "Co" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pt" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "K_sigma" => NaN, "Tm" => [-1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, -1.8476990736066679, 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NaN, NaN, NaN, NaN, NaN], "Ge_sigma" => NaN, "Ta_sigma" => NaN, "As_sigma" => NaN, "Gd_sigma" => NaN, "Cd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb" => [-0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, -0.826813731587726, 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"Orthopyroxene", "Phlogopite", "Rutile", "Sphene", "Spinel", "Whitlockite", "Xenotime", "Zircon"], "Rutile" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => NaN, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [1.318795546765203, 1.326041665829185, 1.3270537016248722, 1.3239431785433842, 1.328624948015323, 1.3290299022502192, 1.3332460631694283, 1.3392506828460888, 1.349172564244462, 1.363523055519777, 1.3747902266295435, 1.3941074468126544, 1.4116709524103896, 1.4270062281019462, 1.4460081435289074, 1.469632491959768, 1.4948172815270753, 1.5190572567994403, 1.544288654685767, 1.5641379637890498, 1.5864109514258482, 1.604576725566597, 1.6339650546007323, 1.6565738722878356, 1.6821408155662279, 1.710508076381061, 1.7368424756898033, 1.7511521627248308, 1.7685368432929809, 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-1.1597477246233656, -1.135508986392543, -1.12073034945034, -1.096383609683599, -1.0839166009493568, -1.0740003751658989, -1.0701381341917229, -1.0689720400105613, -1.0813251418141878, -1.0887140816955663, -1.102587519526231, -1.1068468051861304, -1.1147898838059254, -1.1193077590053442, -1.1230438178052442, -1.1379307075260867], "Pt_sigma" => NaN, "Ta" => [1.3421890614845136, 1.3442005652828326, 1.3421139362243169, 1.3475787359025173, 1.346546277709137, 1.3529791235697364, 1.3605688339583655, 1.3686658204585693, 1.3743262143723527, 1.3866804151602004, 1.3968750861387838, 1.4068870189770428, 1.426529058096746, 1.4467220163808454, 1.4655724541623185, 1.4834735176637253, 1.5052399774616954, 1.5204855213791153, 1.5346962603291059, 1.5480431159878176, 1.5606227568099131, 1.570261501237257, 1.5795940644988025, 1.5876469038943228, 1.589915837412634, 1.59614878147082, 1.5984046606764573, 1.5982041850663364, 1.5969998505384524, 1.6025428856201256, 1.6023943226636292, 1.603459864927149, 1.6058124601112969, 1.6061122696857872, 1.6060434498044458, 1.6108415509410554, 1.6175355428582878, 1.6186625496474984, 1.6224310805763986, 1.623953883009483, 1.6300632510187751], "Ga" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "W_sigma" => NaN, "Cs_sigma" => NaN, "Y" => [-0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912, -0.9239366344050912], "Sm_sigma" => 0.9527769474794857, "Lu_sigma" => 0.34528506170571366, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [-0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316, -0.059593203859604316], "Eu_sigma" => 0.4366948204145265, "Sn_sigma" => NaN, "Ca" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu_sigma" => NaN, "K" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [-1.971509134108053, -1.9413863212201727, -1.914060017691085, -1.8771336041646605, -1.8645671537583908, -1.8359688527258002, -1.8118008842808537, -1.7864850237095786, -1.7659814723239873, -1.742369858427276, -1.7096920419385622, -1.6768493081042408, -1.644445618328128, -1.6153501694017232, -1.552293486006985, -1.5215459343284734, -1.4649426601025497, -1.409271164644384, -1.3427385751948642, -1.3072010065680473, -1.2407370299791907, -1.1981056730332666, -1.1518460828708148, -1.120638653496862, -1.082146517019288, -1.0551573872808653, -1.0287638453573484, -1.0132451300675274, -0.9827757956306576, -0.9539940803205624, -0.9389703146464824, -0.9330688972476752, -0.9172171193013754, -0.9237429258398374, -0.9531253912845512, -0.9549117834679572, -0.9614028483079822, -0.9835335468495489, -1.0124103023222124, -1.019914777034426, -1.083116179687335], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [1.2852506976528824, 1.3140278626288762, 1.337678291851288, 1.356216444819382, 1.3678301378721769, 1.3733443240704573, 1.3808070769054142, 1.3770871327371108, 1.3881080084267583, 1.38708194407626, 1.3815756533721744, 1.3681845808230335, 1.3669796822015798, 1.350709481817883, 1.3427395387467929, 1.3365570384809442, 1.346283686954646, 1.349796648658174, 1.3611437617069195, 1.3765721630968235, 1.395051167554705, 1.4104797018939268, 1.4336897764575618, 1.444829768278476, 1.4614407287801183, 1.475531437024887, 1.4851552370316319, 1.4908468916460924, 1.5070980118991637, 1.514116761204423, 1.5163678962751275, 1.5153353510059588, 1.5150174452630536, 1.5128258517840887, 1.5046968430725387, 1.506819582372319, 1.5096533732690534, 1.4940107553809978, 1.4787064713485025, 1.4676701357338, 1.4449364438678365], "Yb_sigma" => 0.33143719736816096, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => 0.4191225961746201, "Pr" => [-2.035027097460608, -1.9806475909084362, -1.9546475999852804, -1.9329724017864878, -1.9209608535159053, -1.9075334414906195, -1.913532955690693, -1.8959973932334073, -1.8849614285799652, -1.869338946107418, -1.8581480336732723, -1.838642418078331, -1.836012373458535, -1.8201682808335418, -1.813786566193911, -1.7970529159363997, -1.7742855994916649, -1.7468736191842489, -1.7170457106108552, -1.6819905678064542, -1.647889052492652, -1.6122557380060518, -1.5761338906396958, -1.5427834051803018, -1.5152334962119274, -1.4751928796436946, -1.4458966545703016, -1.4082715366838046, -1.3878781543750518, -1.36605950765529, -1.3563392277915816, -1.3564223670695974, -1.3684495649673332, -1.358428219930177, -1.3441494128622984, -1.357341033037491, -1.3567704272024508, -1.3603911379955198, -1.3768509625198255, -1.4159500358169683, -1.4578169222809112], "Pm_sigma" => NaN, "Th" => [-1.6525356336272197, -1.6235105049112188, -1.5663594920546262, -1.5269270350922008, -1.496738768338633, -1.481166310605023, -1.4576874734855818, -1.451666120450362, -1.4365089595949498, -1.4507845934018906, -1.466618199936056, -1.4782956398620015, -1.5022015346596955, -1.521815368089601, -1.5227627573991482, -1.5208101002455878, -1.522719965449135, -1.5093532575601303, -1.493457839882103, -1.4780326496154341, -1.452422598792062, -1.4245658119711235, -1.3734109651962114, -1.3521499698360804, -1.3351684386629161, -1.301298044165338, -1.2831567965990025, -1.2673152228538884, -1.2216557906337666, -1.1915380652427845, -1.1869680298987004, -1.1754144297396663, -1.1946135733508343, -1.2429732929552562, -1.25798700151681, -1.2676497598556318, -1.2843814679244496, -1.3069339291989028, -1.331855764087681, -1.3514818288606207, -1.3968208062625584], "Y_sigma" => 0.2761248774710973, "Zn" => [0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312, 0.7385606273598312], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pr_sigma" => 1.4004603837974252, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => 0.0, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => NaN, "Al" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => 0.3712511371237863, "Er" => [-2.228755843396815, -2.225719348956145, -2.2234062194402933, -2.225614245021308, -2.2240798571513505, -2.2229781654718566, -2.2150448371192843, -2.207866677359511, -2.1941332229702892, -2.1809847711886508, -2.166055852837862, -2.151547329252273, -2.137312027061819, -2.12019008242349, -2.102499007642324, -2.081202831069113, -2.053401694871198, -2.023305304900954, -1.9918690171533244, -1.9594877036786627, -1.9278337548376379, -1.9016973890271702, -1.8765808340488452, -1.8533682606124529, -1.834046600800424, -1.8132974530792574, -1.7978225286756941, -1.78059416682008, -1.7710084074119357, -1.7570674460176157, -1.7553218406188076, -1.7523691764902123, -1.7497864847716098, -1.753724956710362, -1.7613932394083787, -1.7584273715899388, -1.7611117790247355, -1.767623839393537, -1.7671966741951943, -1.7627660786940795, -1.775960040638027], "Ge" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb_sigma" => NaN, "Zr_sigma" => 0.08921823313696692, "Mg" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [-1.9702415185890367, -1.9074281494274357, -1.8685287839829932, -1.8450023590447262, -1.7929198809633362, -1.7840681127158733, -1.775821981805064, -1.7464731558305089, -1.7388737678842925, -1.7532616531914023, -1.73672655385547, -1.7313574009481005, -1.739095137069675, -1.7389176038470182, -1.7131377299266757, -1.7047812445491286, -1.703003435729898, -1.6891846623001479, -1.6567609337808586, -1.6259587111605307, -1.591481454913139, -1.535164262116585, -1.5050750140559894, -1.4709007830137903, -1.448299213699331, -1.4131881756440183, -1.3827886671938894, -1.332108693375042, -1.3062503173083952, -1.293400982893416, -1.2669665263112033, -1.2634133725867016, -1.2691549386454282, -1.2698857861268622, -1.264109315966669, -1.3036911490648948, -1.3346416747852432, -1.3764083751042964, -1.424263086899599, -1.419019629221755, -1.4449232664134875], "Ru_sigma" => NaN, "La_sigma" => 1.8679459434250234, "W" => [0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968, 0.4301690032854968], "Au_sigma" => NaN, "Hf_sigma" => 0.02850906489054337, "Li" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Dy" => [-2.1917571200355366, -2.182175394059771, -2.1707073529677974, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [-0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, -0.46852108295774486, 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-2.1441651299901925, -2.122989627846979, -2.1014423564779667, -2.078404012394749, -2.0523133963419085, -2.0228558403162897, -1.995371156796769, -1.9668744104520197, -1.9326617224773084, -1.9036269329894158, -1.8772612084046953, -1.8525436313723713, -1.8279131571701126, -1.8074248481512847, -1.7923479987833335, -1.7746956937828493, -1.7585414660395406, -1.7440877045512198, -1.7348288746928413, -1.724564202712699, -1.7196088672136818, -1.7186113960885328, -1.7200742244937526, -1.7211636874612872, -1.7191830378095654, -1.7171849169981095, -1.711473878018854, -1.717448885915572, -1.7177053560117599, -1.7356200961868755], "Co" => [-1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189, -1.5975896606394189], "Pt" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "K_sigma" => NaN, "Tm" => [-2.2475246472142194, -2.247311063363288, -2.2446054710578034, -2.2429855948320854, -2.241597597633364, -2.235165481736845, -2.2298307744471155, -2.2239797216038846, -2.2207794201809965, -2.209550581615479, -2.2018622373304293, -2.1885402398338676, -2.169477580421759, -2.143560099264786, -2.1229089666186653, -2.0964903511160498, -2.069305930921821, -2.0422655047901026, -2.015098332876554, -1.9847741334200544, -1.9567638998346992, -1.9292332281380822, -1.9052014403972997, -1.8846046057021077, -1.8664893183058244, -1.8496581845763254, -1.8362161504115917, -1.8213589574891675, -1.8081695003358282, -1.7988808737295625, -1.792016452289292, -1.7879133461101029, -1.7926380403942128, -1.7940024967173474, -1.7957339771426077, -1.7980099954589412, -1.7956329962411992, -1.7926357089407055, -1.7978577742649593, -1.804601942641224, -1.811476947359416], "As" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Xe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ge_sigma" => NaN, "Ta_sigma" => 0.3614456378740879, "As_sigma" => NaN, "Gd_sigma" => 0.7828422963972494, "Cd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb" => [-1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537, -1.812479279163537]), "Melilite" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => NaN, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "P" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Si" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ag" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [-0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725, -0.26122671658811725], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Be_sigma" => NaN, "Sb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => NaN, "Cs" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U_sigma" => NaN, "Ce_sigma" => NaN, "Dy_sigma" => NaN, "Be" => [-0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313, -0.003105593090647313], "Sr" => [-0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875, -0.9793036574208875], "Pt_sigma" => NaN, "Ta" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ga" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "W_sigma" => NaN, "Cs_sigma" => NaN, "Y" => [-0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184, -0.35971081481596184], "Sm_sigma" => 0.1077688068818574, "Lu_sigma" => NaN, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Eu_sigma" => 0.2784480647474514, "Sn_sigma" => NaN, "Ca" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu_sigma" => NaN, "K" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [-0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624, -0.9958702925043624], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Yb_sigma" => 0.16550005731915624, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => NaN, "Pr" => [-0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445, -0.2034266430423445], "Pm_sigma" => NaN, "Th" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Y_sigma" => NaN, "Zn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [-1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824, -1.4789308051053824], "Pr_sigma" => NaN, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => NaN, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => NaN, "Al" => [-0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541, -0.5307401374117541], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => NaN, "Er" => [-0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256, -0.49478974485567256], "Ge" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb_sigma" => NaN, "Zr_sigma" => NaN, "Mg" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [-0.7591310398555121, -0.7604257038071528, -0.7646384239104237, -0.7651273788293143, -0.7651387555203834, -0.765770658758235, -0.764598382806957, -0.7615787674054211, -0.7611070402053015, -0.7585473892304111, -0.7572978794764148, -0.7558746086305015, -0.756606653874132, -0.7547253348794467, -0.754811413116586, -0.7516793730110368, -0.7458661042788259, -0.7389428895002476, -0.7335931901541283, -0.7275262616230188, -0.7206664593625116, -0.7181818825506965, -0.7120022479814074, -0.7071812038426923, -0.7011562668601518, -0.6954009493259623, -0.6891995157330222, -0.6874284965098811, -0.6821245958942548, -0.6785114013955705, -0.6732043485153507, -0.6685553418552721, -0.665477210555161, -0.6645690369621583, -0.6602628163864062, -0.6628927874368142, -0.6614330034191637, -0.6558871091992313, -0.6558795873557265, -0.6573770636460038, -0.6562694040844194], "Ru_sigma" => NaN, "La_sigma" => 0.35018613482121685, "W" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au_sigma" => NaN, "Hf_sigma" => NaN, "Li" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Dy" => [-0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844, -0.35835642999410844], "Ti_sigma" => NaN, "Th_sigma" => NaN, "V_sigma" => NaN, "B" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd_sigma" => NaN, "Re" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sb_sigma" => NaN, "F_sigma" => NaN, "Hf" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb_sigma" => NaN, "Ca_sigma" => NaN, "Cl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr_sigma" => NaN, "Ag_sigma" => NaN, "Mg_sigma" => NaN, "Rh_sigma" => NaN, "Mo_sigma" => NaN, "Ba" => [-1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128, -1.3187587626244128], "Nd" => [-0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303, -0.19626337254302303], "Pm" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rh" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi_sigma" => NaN, "Ti" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb" => [-0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864, -0.3214722330377864], "Tm_sigma" => 0.4491978277897027, "Zr" => [-0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126, -0.4625917796774126], "Sm" => [-0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305, -0.29591855253193305], "Ba_sigma" => NaN, "Cr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Al_sigma" => NaN, "Tl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => 0.438784126690263, "Yb" => [-0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, -0.4891488194956156, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "K_sigma" => NaN, "Tm" => [-0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722, -0.8661405237832722], "As" => [NaN, NaN, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN]), "Phlogopite" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => NaN, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [-1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, -1.055762471587501, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U_sigma" => 0.300993542588303, "Ce_sigma" => 0.5119465866378986, "Dy_sigma" => 0.29756767189466665, "Be" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr" => [0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 0.07368355389689324, 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1.793457475836852], "Sm_sigma" => 0.3058657533830539, "Lu_sigma" => 0.5762548971134096, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [1.2327927484828416, 1.2228775604346043, 1.2082437054043453, 1.2022430759526461, 1.2003406000958263, 1.1879201519564786, 1.1865848204525347, 1.1829409366329566, 1.1691395963654172, 1.1607916834691923, 1.1540126572469631, 1.1465601612150853, 1.1441423489575213, 1.1410763442966179, 1.1312015658525967, 1.120300451324953, 1.111399706580632, 1.1050106610378088, 1.100492212275135, 1.0971257404891903, 1.095294974352586, 1.0886474908988937, 1.0809985317142925, 1.0718890917680926, 1.0651968605963196, 1.0542070095914484, 1.0469847051698746, 1.0413448465843451, 1.0341593832091964, 1.0283850546914417, 1.0274715033933537, 1.0253872718599613, 1.0236191695476757, 1.0220867858539997, 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NaN, NaN, NaN, NaN, NaN, NaN], "Yb_sigma" => 0.516945432167898, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => 0.3652827300192123, "Pr" => [2.455111308440163, 2.4696906840817214, 2.477317583106314, 2.4832387131936144, 2.496125318442963, 2.498665500580939, 2.504190080186804, 2.5156493396104764, 2.5235512815417347, 2.5307846679916177, 2.5373690172732855, 2.547954033944346, 2.5555849266183306, 2.5707274760399303, 2.586140722535334, 2.6003341637148023, 2.6149599639139423, 2.632851994100319, 2.6441092043047356, 2.660506132469885, 2.675684769405879, 2.6906082727581517, 2.703299338470813, 2.7194841260837266, 2.7286699287491056, 2.7432203110720925, 2.756209163250811, 2.7666304146087684, 2.7747600989746974, 2.785251308260661, 2.7971749837271918, 2.8099897148728363, 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"Y_sigma" => 0.3787562137592503, "Zn" => [0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099, 0.960273611310099], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082, 1.7441074977914082], "Pr_sigma" => 0.4479451296781434, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => 0.18377137946765407, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => 0.482188073796932, "Al" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => 0.41052735824776426, "Er" => [2.0439717766660617, 2.0379514451334155, 2.028948774926324, 2.0214327878688243, 2.021453436544233, 2.0126759131621723, 2.004439001672672, 1.9991145562026997, 1.9868070660572712, 1.9789120786139736, 1.962756187809544, 1.9522560631657286, 1.9492472725537326, 1.9392903378674011, 1.9300195110058436, 1.9220866303927107, 1.9105414135516314, 1.8916223345637528, 1.8792525115423389, 1.8644099797972113, 1.8490130333169048, 1.8400282579455356, 1.830669115929473, 1.8192157942304237, 1.8072492146285133, 1.7979498195776054, 1.7869345838399364, 1.7727173635367406, 1.7640701976759248, 1.754742333789133, 1.748520409093601, 1.7352483468653048, 1.727566769070838, 1.7200565780136552, 1.713107516618567, 1.6987147443698245, 1.6920595930532203, 1.683982399881322, 1.674745021124703, 1.6616556539646592, 1.6555391838714215], "Ge" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 1.2944158627971036, 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[2.44883110585546, 2.4575486189914355, 2.4711640796210403, 2.4744437618761674, 2.485024284217339, 2.4973825169443065, 2.510783024788453, 2.5231377787805744, 2.537006130255542, 2.5485189322695456, 2.555239879142134, 2.566624555313245, 2.5860557740143166, 2.609107687704632, 2.6275914012993646, 2.650296650465988, 2.6759432190045107, 2.6935967132362135, 2.7088433095489712, 2.7270828221680095, 2.7449983625040955, 2.760701365840357, 2.7806123231190694, 2.8008208109483843, 2.818958068530796, 2.8391885752957244, 2.8569218679598953, 2.871191638217686, 2.8845092561384, 2.8985335366955014, 2.910422904421413, 2.9263924277988695, 2.940561495223462, 2.9524802216292225, 2.9673199212631505, 2.9826151283278493, 2.997662470265476, 3.0081551370112525, 3.0220457470729696, 3.0317021824999433, 3.039636052008032], "Ru_sigma" => NaN, "La_sigma" => 0.576679514032326, "W" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au_sigma" => NaN, "Hf_sigma" => 0.5706433789123915, "Li" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Dy" => [2.1916856991705522, 2.1925511641376194, 2.189034811692246, 2.1874793890755533, 2.18117978456559, 2.1767268157074824, 2.173003215607316, 2.1714627987683683, 2.1679062207249333, 2.165550136013926, 2.162812196439713, 2.1594594252982056, 2.156668825559649, 2.1529409286352124, 2.147216279391112, 2.14285649725584, 2.136887520438646, 2.133159914148936, 2.1280572390146513, 2.1240395955424565, 2.1179397600487992, 2.114057684926037, 2.1092572141578536, 2.1045256266033183, 2.0997331610820766, 2.094950861824871, 2.0907967234203424, 2.0874308795997414, 2.08273154810916, 2.079310335442304, 2.073889342483405, 2.069795796103346, 2.062542686226702, 2.059946643201377, 2.0544638648238336, 2.053384444458897, 2.047714218037504, 2.0444844779265496, 2.0379503002733244, 2.0356395395006883, 2.029929477090991], "Ti_sigma" => NaN, "Th_sigma" => 0.4055492907184116, "V_sigma" => NaN, "B" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 1.186340266468827, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [-1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075, -1.1294242005741075], "Sr_sigma" => 0.2568055575732152, "Ag_sigma" => NaN, "Mg_sigma" => NaN, "Rh_sigma" => NaN, "Mo_sigma" => NaN, "Ba" => [-0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134, -0.7107518349878134], "Nd" => [2.4703440699356904, 2.4629533751887553, 2.4649599462649094, 2.4717713612657684, 2.476273415422035, 2.482269790290418, 2.4962237491761616, 2.505682805474971, 2.510301122232634, 2.5211128606639335, 2.5314558152226847, 2.538009234734551, 2.548179720152156, 2.5621259060639514, 2.577658206998189, 2.5873329028449636, 2.6014060516497324, 2.61430581233329, 2.6257732567984493, 2.6347084840880117, 2.6476595601795174, 2.6582718218056582, 2.6684998551086174, 2.6770134737838713, 2.6907842704178875, 2.703380056403054, 2.711630011001053, 2.7252153038987412, 2.7374219192585394, 2.7450973372661833, 2.7520141088198384, 2.7654945298854536, 2.773641674308559, 2.781680808285151, 2.787241817491087, 2.793722230117232, 2.79691380921364, 2.8010864610454935, 2.8061127525627914, 2.812723171690238, 2.8193177931325586], "Pm" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rh" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi_sigma" => NaN, "Ti" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb" => [2.2150291135986655, 2.2256806082281972, 2.2277183394263567, 2.2309948809202167, 2.238577561476962, 2.2361231506977095, 2.2333629833853648, 2.234152945530714, 2.233093697717775, 2.2277634792009215, 2.2232483920972417, 2.2190634029292458, 2.215794121948707, 2.2113429649910152, 2.2099325550956297, 2.206020740229853, 2.205318511866795, 2.204945358084583, 2.2024643905942245, 2.1994421956143966, 2.200774026531576, 2.1987888215283613, 2.192864318557014, 2.1885869919493297, 2.185894877223373, 2.1797586352455425, 2.177505968598322, 2.1743486513218615, 2.173531648583995, 2.172316580433365, 2.171505332801416, 2.168133232670241, 2.166935598561965, 2.1660230054870486, 2.1639632944492133, 2.1588284471442964, 2.1549353593013203, 2.15537678303625, 2.1507022215547957, 2.1445292998565955, 2.14523904627242], "Tm_sigma" => 0.461437484349667, "Zr" => [0.24502364593540576, 0.23619006223247438, 0.20816852107137668, 0.19800627603646448, 0.18843004710787636, 0.1670011988763472, 0.1461982895923302, 0.1272463490998124, 0.10550151548517084, 0.07895140311385074, 0.07248451553462949, 0.045350469797411405, 0.030074812838367864, 0.017247306379676634, -0.0030822175649792938, -0.02757061074207758, -0.04643024676119376, -0.06408273152164688, -0.09074362948272229, -0.11624137392641319, -0.14449953696927173, -0.16352920338408955, -0.19177354036214495, -0.21679548153615188, -0.23967042984559922, -0.26381477289236976, -0.288925363326704, -0.3068289057851708, -0.3293176909948901, -0.35483815241515576, -0.37171392133717956, -0.39222172528343296, -0.4183066124768775, -0.4403038853556291, -0.4566399037490747, -0.47218398452843074, -0.48574376391849483, -0.4977018442928574, -0.49860688083988874, -0.5080726175869691, -0.5197086356330036], "Sm" => [2.371594646054084, 2.3742326228904864, 2.3792333257917986, 2.38488213817129, 2.389365870718167, 2.389929211380502, 2.3945440044855, 2.3976716771625526, 2.3994893159857136, 2.4062787384746587, 2.411953693800826, 2.4164085923988043, 2.4222214500570627, 2.426529708140674, 2.4293926299884756, 2.4359197400731833, 2.441467436730384, 2.447789312626444, 2.45452846906016, 2.462860043080421, 2.468795201573123, 2.4728763197993096, 2.478316859682466, 2.481035222106085, 2.4826110925888214, 2.4844373730061706, 2.4893913537580596, 2.4918647370658995, 2.496216535726786, 2.5020048044431746, 2.506230356841083, 2.5117893394502837, 2.5164296214606816, 2.5205936138043796, 2.522675756593441, 2.5268881823427813, 2.5283986519249444, 2.530951601850558, 2.534443451706345, 2.5383625574775124, 2.540902113131519], "Ba_sigma" => NaN, "Cr" => [2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635, 2.5796015617774635], "Cu" => [0.7634279935629372, 0.7634279935629372, 0.7634279935629372, 0.7634279935629372, 0.7634279935629372, 0.7634279935629372, 0.7634279935629372, 0.7634279935629372, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb_sigma" => NaN, "Zr_sigma" => NaN, "Mg" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [-1.567585319024758, -1.567585319024758, -1.567585319024758, -1.567585319024758, -1.567585319024758, -1.567585319024758, -1.567585319024758, -1.567585319024758, -1.567585319024758, -1.567585319024758, -1.567585319024758, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb_sigma" => NaN, "Ca_sigma" => NaN, "Cl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [-1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, -1.0314085410674878, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sm" => [-0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801, -0.514286001954801], "Ba_sigma" => NaN, "Cr" 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"Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => NaN, "Yb" => [0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 0.42479317681561213, 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=> 0.42896916807838537, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => 0.45629650190364673, "Al" => [-0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383, -0.9318141382538383], "In_sigma" => NaN, "Si_sigma" => 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1.0000069782971364, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [-1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, -1.7581879101362345, 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-0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596, -0.7916797463308596], "Pb" => [-1.2071937483108168, -1.192331351584937, -1.184014272485567, -1.1663826581825911, -1.1434748539667277, -1.1142652809323517, -1.0801704034094946, -1.0236387471573856, -0.9615840286748281, -0.8910299054033969, -0.8094933155225408, -0.7252246704666391, -0.6472466027780823, -0.5822088511440099, -0.5062818615625395, -0.45390623282041204, -0.4009291095812392, -0.35261021046240487, -0.3090336576920902, -0.2868001643624337, -0.25630422980415485, -0.24697166311429553, -0.24858300494799843, -0.2547911718681562, -0.26061386529971264, -0.27917928106013223, -0.2957219046972969, -0.3181216012833157, -0.34529723824298314, -0.3729326829976062, -0.3960637938905317, -0.4200094264542648, -0.44196486816565217, -0.45517530271635703, -0.47186106424874075, -0.48351406110663736, -0.4942445060315726, -0.5010820006795396, -0.5047444546936949, -0.5037183917376385, -0.5025287398484802]), "note" => ["kd for Albite is nanmean of AlkaliFeldspar, Orthoclase, and Anorthite"], "Xenotime" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => NaN, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "P" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Si" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ag" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128, 3.634957359885128], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Be_sigma" => NaN, "Sb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => NaN, "Cs" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U_sigma" => 0.3582726756856637, "Ce_sigma" => 0.33330816389498397, "Dy_sigma" => 0.016488468792807874, "Be" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pt_sigma" => NaN, "Ta" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ga" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "W_sigma" => NaN, "Cs_sigma" => NaN, "Y" => [3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957, 3.9632316165546957], "Sm_sigma" => 0.1777003815140682, "Lu_sigma" => 0.29316187796823445, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416, 4.4333414083447416], "Eu_sigma" => 0.15113586772036428, "Sn_sigma" => NaN, "Ca" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu_sigma" => NaN, "K" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322, 1.7133783916370322], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Yb_sigma" => 0.23348451501231662, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => 0.07346575404883254, "Pr" => [2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046, 2.4354867317384046], "Pm_sigma" => NaN, "Th" => [2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106, 2.714217325262106], "Y_sigma" => 0.12671878954972698, "Zn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pr_sigma" => 0.23914483297907088, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => NaN, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => NaN, "Al" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => 0.12354840193123647, "Er" => [4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049, 4.191030405594049], "Ge" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb_sigma" => NaN, "Zr_sigma" => NaN, "Mg" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877, 1.1747365990065877], "Ru_sigma" => NaN, "La_sigma" => 0.1898154330154243, "W" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au_sigma" => NaN, "Hf_sigma" => NaN, "Li" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Dy" => [3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477, 3.964054393106477], "Ti_sigma" => NaN, "Th_sigma" => 0.6051441212398219, "V_sigma" => NaN, "B" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd_sigma" => 0.2353656463148461, "Re" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sb_sigma" => NaN, "F_sigma" => NaN, "Hf" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb_sigma" => 0.056183941509285976, "Ca_sigma" => NaN, "Cl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr_sigma" => NaN, "Ag_sigma" => NaN, "Mg_sigma" => NaN, "Rh_sigma" => NaN, "Mo_sigma" => NaN, "Ba" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd" => [2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023, 2.645875747110023], "Pm" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rh" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi_sigma" => NaN, "Ti" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb" => [3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325, 3.86600493352325], "Tm_sigma" => 0.17691733929285758, "Zr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sm" => [3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516, 3.3557977232396516], "Ba_sigma" => NaN, "Cr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Al_sigma" => NaN, "Tl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => NaN, "Yb" => [4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034, 4.288641560935034], "Lu" => [4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145, 4.3220165183806145], "Eu" => [3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352, 3.097437532890352], "Na" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Os" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Na_sigma" => NaN, "Ni" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb_sigma" => NaN, "Ho" => [4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107, 4.126340050585107], "Co" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pt" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "K_sigma" => NaN, "Tm" => [4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768, 4.245055216191768], "As" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Xe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ge_sigma" => NaN, "Ta_sigma" => NaN, "As_sigma" => NaN, "Gd_sigma" => 0.12457135392666065, "Cd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN]), "Sphene" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => NaN, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [0.5545406874089481, 0.561710557741396, 0.564773190508495, 0.5678723278616895, 0.5696415570547602, 0.5673697746753525, 0.571458615076818, 0.5741950661570273, 0.5827008078335902, 0.5873555969068268, 0.593462023205647, 0.598757716677038, 0.6057198177523541, 0.6103592007910648, 0.6159075347865152, 0.6222350985053032, 0.628681071623825, 0.634310812168304, 0.6406355766366927, 0.648468172503559, 0.6557269194632184, 0.6619430762285443, 0.6683897881435844, 0.6735178846885365, 0.6781669405314421, 0.6838050122432069, 0.6886199715407557, 0.6938757826154656, 0.6983642509269475, 0.7040528637602329, 0.7071911756274226, 0.7106640824974587, 0.7139686907301496, 0.7181886389380635, 0.7189380688738412, 0.7227498534366565, 0.725937773819789, 0.7281317637454688, 0.7296979525485409, 0.7307563201595362, 0.7330127023590576], "P" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Si" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ag" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [1.4762735154685953, 1.427960750392427, 1.3659468883450796, 1.3795181036830686, 1.3351211392161964, 1.31370489223043, 1.2783592819025202, 1.2809954280970801, 1.2223865499726385, 1.2160569778213068, 1.195594488306511, 1.1862578260833765, 1.159356983435906, 1.1594057787890668, 1.1483683915117402, 1.1494078505191256, 1.140038250397419, 1.143537396988511, 1.1384764542445742, 1.1382094507889553, 1.1344496663046728, 1.15278840890956, 1.1721872601119196, 1.214698135056136, 1.292546333420906, 1.3956728062453252, 1.543562199421701, 1.7257046870927548, 1.9311645465532035, 2.1355342535755577, 2.352428825816423, 2.5377471137287353, 2.691914404720112, 2.830469005431612, 2.9261384430051636, 2.994881028830743, 3.0414229411363243, 3.0750980224303217, 3.097015907036745, 3.116546060308246, 3.1313140568596722], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Be_sigma" => 0.2754660848484892, "Sb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => 0.3333777196295222, "Cs" => [-2.619602087593735, -2.6208034457436353, -2.618955735921995, -2.6229006286976326, -2.6287034950001438, -2.6280218913451665, -2.632133488768249, -2.636127468963059, -2.6383140321302276, -2.638966504477907, -2.6445222549026517, -2.645352556277779, -2.648419039200402, -2.6503993215761503, -2.65293598410594, -2.655039757648653, -2.6580085661091006, -2.660693721339667, -2.6629814540750782, -2.6651118715621736, -2.6664216037711332, -2.667993519272292, -2.6688644869326157, -2.670391122662296, -2.671671700650406, -2.6734779938997884, -2.674772863204792, -2.6761449327346245, -2.6774379804412973, -2.6784362102507555, -2.678046817218581, -2.6791516203888603, -2.6786893897378254, -2.6787462671412117, -2.6787302777764017, -2.678667480171388, -2.6779618913341086, -2.680047231051657, -2.6794560781243324, -2.679963467394186, -2.681691693760173], "U_sigma" => 1.0091077907433228, "Ce_sigma" => 1.0239782859939492, "Dy_sigma" => 1.190547429405426, "Be" => [-2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, -2.8409595817601123, 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1.5954637924782642, 1.6014221304143266, 1.6070679365896752, 1.6059229195629425, 1.612659516915216], "Ga" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "W_sigma" => NaN, "Cs_sigma" => 0.09412953794609052, "Y" => [0.8587324844387525, 0.8641648668747567, 0.8777642372211548, 0.8874034140333638, 0.9023363911647854, 0.9227155846666769, 0.9334352571672417, 0.943456191199095, 0.958428814670532, 0.9705180530604858, 0.9808628687694703, 0.9925934514517385, 1.0064914460557388, 1.0167306287986086, 1.0325953112202542, 1.0536741087138697, 1.0806187212061356, 1.1118811539138502, 1.1421776953338105, 1.1830904440136558, 1.2227856639535382, 1.275674936732393, 1.339151355047908, 1.4128783723907055, 1.4912710046848356, 1.5811625023966147, 1.67935874318887, 1.7762743625853654, 1.896185830784879, 2.0107518903561377, 2.124050033805042, 2.23642572077894, 2.3510841214619607, 2.4383328361237204, 2.5369741346048027, 2.61903747681163, 2.6951742547709254, 2.754172129377429, 2.8112584499769646, 2.8396420009529266, 2.8848508915938442], "Sm_sigma" => 1.140331153344517, "Lu_sigma" => 1.2118954048237645, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [-0.5825081409293522, -0.5844184350227672, -0.5976922759705153, -0.5869736704510804, -0.6023752585157647, -0.5996033158744757, -0.5896723539673572, -0.5860280763878349, -0.5945176568343307, -0.5920966823192272, -0.5934022664319903, -0.5978871360893461, -0.5930558971923607, -0.5920590538200527, -0.5870767755278057, -0.5838516473844649, -0.5755920022211921, -0.5656877951767232, -0.5497440144789845, -0.5336152866356869, -0.5114236933428635, -0.4807577670215384, -0.4478786510051496, -0.40451167241521463, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [0.9133124150567713, 0.907105984497758, 0.9192541640831103, 0.9151030366787808, 0.9011620315876963, 0.8992642060184428, 0.9011308317401848, 0.894632145167433, 0.9101160072612826, 0.9139321953487017, 0.9176452176112029, 0.920863608715071, 0.9278046806320843, 0.9184334931136211, 0.9223822254192825, 0.9261011150299607, 0.9394595477284912, 0.9544857508828408, 0.9826191725481895, 1.0089507734753815, 1.0385484145632204, 1.0714417452989917, 1.1068119591957846, 1.149197686119635, 1.2006640520679228, 1.2639866553668218, 1.3362701575783138, 1.4262726356156012, 1.5154602991045547, 1.6081023875456808, 1.717426847636516, 1.824120089977281, 1.9255842595608648, 2.030657443738859, 2.1342533251418283, 2.216588770444058, 2.2852746030700057, 2.3490209739634675, 2.405470183929509, 2.4452471385610024, 2.4928858715889213], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396, 0.8163436183345396], "Yb_sigma" => 1.2099494165505826, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => 1.2009843484473597, "Pr" => [1.1655868333607984, 1.1674691338264798, 1.1519193188333696, 1.1501530387094892, 1.1457926231892916, 1.1579334072092893, 1.1717004872271677, 1.1595691727841657, 1.1372813983588042, 1.1265598351826223, 1.1117191448158774, 1.0979444373231824, 1.0934343963979098, 1.0943366827816579, 1.089578294353419, 1.0976079429078527, 1.0960965334902029, 1.0970817884535808, 1.0964313429000359, 1.0983354765495732, 1.0973667931899609, 1.1139894870908993, 1.133781104297199, 1.1727718189392744, 1.2416614766378726, 1.3322052834419484, 1.4535554734727283, 1.6255043427107747, 1.8119054911025079, 2.015085840881569, 2.2040725664707637, 2.3748101064271263, 2.517660786736783, 2.6385696588677177, 2.724474526848591, 2.7954191112878592, 2.8444093319703208, 2.8754435361061232, 2.896382254684812, 2.907157008617297, 2.9157091292788357], "Pm_sigma" => NaN, "Th" => [-0.3339597997214247, -0.3304004160931002, -0.340779759177421, -0.3376371471978698, -0.3293546922675971, -0.3144168335359962, -0.29748281905078305, -0.27973102089146545, -0.264816588410601, -0.24493587109023465, -0.2253165246456904, -0.20918229198984342, -0.18392881829156218, -0.15897973103587296, -0.13901368623141322, -0.11615886622071211, -0.0835734056493735, -0.04710806921656769, -0.00102664241614564, 0.0581918970586842, 0.1334518775784888, 0.22124697067754964, 0.32148018725864863, 0.4343878761322698, 0.5643614095995066, 0.6790920023127907, 0.8022352102500914, 0.9291765993374023, 1.0345703399265054, 1.1313242759757662, 1.2173467635752853, 1.2889774644522292, 1.3369504844129014, 1.381169549488552, 1.4090094955728096, 1.43516808041533, 1.4518000169702052, 1.4670047284445509, 1.4748782616967446, 1.482727567278488, 1.4852642401541551], "Y_sigma" => 1.1928961292961524, "Zn" => [0.5910646070264992, 0.5910646070264992, 0.5910646070264992, 0.5910646070264992, 0.5910646070264992, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd_sigma" => 1.0697853631226277, "Re" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sb_sigma" => NaN, "F_sigma" => NaN, "Hf" => [0.5223949387066479, 0.5294191512188643, 0.5336272600351809, 0.5363283811651877, 0.5370710914010767, 0.5408566298711347, 0.5460569607674055, 0.5525615079250842, 0.5570257766804468, 0.5642791388732353, 0.5699791964940922, 0.5751302602985626, 0.5805245906630571, 0.5857363388941472, 0.5921810210163925, 0.5992928794548532, 0.6051565165763562, 0.6106200157819421, 0.616496521502901, 0.6210140086301564, 0.625134858733193, 0.6311173652589231, 0.6352565264024982, 0.6399016234743413, 0.6451087693340779, 0.6493667623918676, 0.6516072798696575, 0.6560814604486722, 0.6604067549313123, 0.6627423131934528, 0.6682811704722228, 0.6724942843079207, 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0.39094212969236963, 0.394144469125553, 0.39758786499989857, 0.4016795805778224, 0.4041475041180099, 0.40638547859681656, 0.4090897026631538, 0.41199722923010107, 0.41567273786117925, 0.42014482722851404, 0.4244686284434093, 0.4284517113159936, 0.4328189569498765, 0.4373537707804879, 0.4403565620136364, 0.44436188019252926, 0.4495147856871886, 0.452690765437991, 0.4561691695542646, 0.46040574465850126, 0.4642184661393534, 0.4662059432141636, 0.4707728469021623, 0.4728840003015107, 0.4754598434909802, 0.4786684410502701, 0.48226969000898273, 0.48509847217112834, 0.48790203440321156, 0.4915001818248365, 0.49493284710967156, 0.4971658361525908, 0.4978361165304229, 0.49994631168486575, 0.503536167003026], "Sm" => [1.4151500110393598, 1.3990459127493664, 1.367162339048097, 1.3466485658838043, 1.3317453883067032, 1.3058574352431902, 1.290021176241298, 1.2803993271713623, 1.2649503956601444, 1.2468272924690071, 1.231535521841601, 1.2228072904960603, 1.211524291552702, 1.2059731095653985, 1.1975324239185319, 1.1874205709529226, 1.181552455906384, 1.1751377392813587, 1.1691246117952434, 1.1712981266459102, 1.1830919078584616, 1.1935748544716864, 1.2183868682176442, 1.2584224324583688, 1.3224686604261802, 1.4164999637024835, 1.5520683133417308, 1.7278586931102906, 1.9363960151831388, 2.149538134977726, 2.36412341619187, 2.5510767543520187, 2.707626712143944, 2.8335759453719604, 2.925179447291818, 2.9938485734635774, 3.0420897939476936, 3.0796967945233167, 3.101886247641991, 3.1188810034120156, 3.123729810079209], "Ba_sigma" => 1.3015810891112376, "Cr" => [1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193, 1.3222192947339193], "Cu" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Al_sigma" => NaN, "Tl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => 0.5581753214388885, "Yb" => [1.0032492362423104, 0.9863243156232171, 0.9621869469981206, 0.9399768812495771, 0.9312024961642998, 0.8920643794354235, 0.8508352173451409, 0.8030654178181668, 0.7602481911603056, 0.7150113344226171, 0.6832333744282428, 0.6538505965154491, 0.6331209998596906, 0.6140900534365302, 0.6015276617531702, 0.5943986677210684, 0.5846891180156099, 0.5766746406300394, 0.5735603191643524, 0.5684899268846834, 0.57346114704146, 0.58809375130099, 0.6123453496125734, 0.6548264680028033, 0.7384136098272934, 0.8443733229146652, 0.994621842514932, 1.192738958277014, 1.4245737874761104, 1.6579866936171097, 1.8842830511312976, 2.094317123007008, 2.2559949677560627, 2.3874601395610457, 2.491643142545828, 2.5695291849044404, 2.6199648542055827, 2.664981711244723, 2.6948258014350275, 2.7118155220763853, 2.7225546858430945], "Lu" => [0.9169703927031786, 0.8950058712501847, 0.8460593602774266, 0.802428869894639, 0.7516923408161291, 0.7181262342752366, 0.6765663532350528, 0.6474295003110764, 0.6134469018570635, 0.5855867084885966, 0.56479193815128, 0.5426196933849833, 0.5262661350571095, 0.5078605393583123, 0.501014893897598, 0.4798743246858365, 0.4813923054121779, 0.4707683713363997, 0.46494191906108884, 0.4585942286354948, 0.46792785157555694, 0.4680532637377613, 0.49730054921947037, 0.5471552876894569, 0.6154876420486033, 0.7261261088844289, 0.8798823298357447, 1.065413308857857, 1.2963618725788206, 1.5513097880365598, 1.7839204515406106, 1.9882368619213555, 2.1643110889466852, 2.2976470088065586, 2.391998775432592, 2.474236067983961, 2.5279171712448267, 2.56582619063931, 2.594932829992103, 2.61550571231252, 2.6252086028347037], "Eu" => [-0.3201396226680595, -0.2343223929581372, -0.16509107724140804, -0.050262544469449326, 0.03969891672119063, 0.15940548240151978, 0.25606915521151963, 0.36088807742604917, 0.4520532076340798, 0.5331468764097259, 0.6039984632131593, 0.6630805329369432, 0.7130699500024529, 0.741488609257408, 0.7702085635643476, 0.790236720025081, 0.7964347792632275, 0.7945807048707703, 0.8100828840634872, 0.8207253856535769, 0.8213173591321659, 0.8321947153577568, 0.8610809150499455, 0.9020963025706219, 0.9712872196085085, 1.0953469017456203, 1.2545763050841001, 1.4499501985994858, 1.6669094544266416, 1.8897413628122888, 2.096579289266147, 2.297636722220138, 2.448854847153859, 2.567353985476731, 2.66116354803528, 2.7325551699010915, 2.773390534762396, 2.8057933360441525, 2.8262032869400007, 2.8439335186477974, 2.846068837434366], "Na" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Os" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Na_sigma" => NaN, "Ni" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb_sigma" => 1.2604498065744543, "Ho" => [1.2537868004275885, 1.219226543619382, 1.1740853269242133, 1.112109178394195, 1.0911909098640338, 1.0647234964814352, 1.0577737155518485, 1.0111289860207815, 0.9916418056231211, 0.9687170444858504, 0.9539167048359055, 0.9340213672825249, 0.9392544691111191, 0.9223882859452729, 0.9088073812668656, 0.8945133401273353, 0.8773114383837032, 0.8521116252963391, 0.8429053801989456, 0.8399045074355446, 0.8420917504560599, 0.8545814108330407, 0.8862899693156177, 0.9332231201123934, 1.0032612321918186, 1.1115015778696482, 1.2618356828807413, 1.4503540987933219, 1.6789062504699612, 1.917334944174929, 2.1452522500685056, 2.341998571742256, 2.51476812183072, 2.6387978907998146, 2.726546408155101, 2.788886588114587, 2.8430493985705887, 2.8759195747778112, 2.901482072630063, 2.918624847702412, 2.93983464121687], "Co" => [0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004, 0.6232492903979004], "Pt" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "K_sigma" => NaN, "Tm" => [1.0564105673106976, 1.0612576670506326, 1.021322089639721, 0.9797689727255048, 0.9472973844384711, 0.9090068418887278, 0.857829846765677, 0.8395285367234184, 0.8240635835108227, 0.8062061500141474, 0.7816219610851295, 0.7630561594736285, 0.7368174602439016, 0.7164504786558705, 0.6947156122508095, 0.6871697001878532, 0.6745532019443947, 0.6668385285244637, 0.6549504722382117, 0.6576583280245519, 0.665132215243193, 0.6783815176251184, 0.7094094854536271, 0.7589434410282514, 0.8364312031206049, 0.9315772276744939, 1.083685959240878, 1.266891034058221, 1.5000726136568139, 1.7404197267169803, 1.9754082713380179, 2.184458808450053, 2.360191636514764, 2.489526997220931, 2.586957932947773, 2.663344024641644, 2.718301349011104, 2.752936155765202, 2.7763788290232236, 2.7968936608118367, 2.8073950146534283], "As" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Xe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ge_sigma" => NaN, "Ta_sigma" => 0.5013987152362616, "As_sigma" => NaN, "Gd_sigma" => 1.1651237644131838, "Cd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb" => [-1.003077103961532, -0.9949567941008034, -0.9790617407025366, -0.9739164709330994, -0.9590207480975341, -0.9427852086252132, -0.9251315537363073, -0.9062722717062065, -0.8953307554235138, -0.8834833724524476, -0.8614872575020722, -0.841711068460859, -0.8257633781193664, -0.8028870047136619, -0.7763174995402495, -0.758051845262449, -0.7325619457805198, -0.7097615524800936, -0.6827892236621187, -0.6600375646216243, -0.633721747234209, -0.6100438793436431, -0.5791501671394756, -0.5497644107087566, -0.5195205681354061, -0.48780366360696464, -0.45933596950434713, -0.4363389706298781, -0.413535716792421, -0.389224510845614, -0.369417087067262, -0.349286189424879, -0.3332114301370682, -0.3178806577039429, -0.30851733822963595, -0.3022063171930349, -0.2954593176380497, -0.2868098375613261, -0.2844854590977915, -0.28461414386608064, -0.2800908716859528]), "Whitlockite" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => NaN, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "P" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Si" => [-2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255, -2.033864612050255], "Ag" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133, 0.46345044988133], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Be_sigma" => NaN, "Sb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => NaN, "Cs" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U_sigma" => 0.6592839866212752, "Ce_sigma" => NaN, "Dy_sigma" => NaN, "Be" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pt_sigma" => NaN, "Ta" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ga" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "W_sigma" => NaN, "Cs_sigma" => NaN, "Y" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sm_sigma" => NaN, "Lu_sigma" => NaN, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [-0.7686278443847518, -0.7715404679609238, -0.7726285795989121, -0.7757176308663679, -0.7733184323413036, -0.7693775230582524, -0.7723898451517445, -0.7711459917681358, -0.769298764583748, -0.769418268305541, -0.7659304350815656, -0.7558344115295694, -0.7516789679949761, -0.7481705943973986, -0.7382328660927485, -0.7314235237093758, -0.726316688909163, -0.7179516815593077, -0.7067432656506548, -0.7020069590349685, -0.6901997935530568, -0.6753573454630273, -0.6577442485312335, -0.6401694143849883, -0.6133051990953845, -0.595425505369353, -0.5683794238356787, -0.5483824028326848, -0.5233155465028703, -0.4981487793147562, -0.4724007977599791, -0.4477968290843204, -0.4213296854769776, -0.40293526632517096, -0.39029222546275444, -0.3722833679114382, -0.3626621571544993, -0.3518583974368666, -0.3311229324304821, -0.3196793459247816, -0.31086335817953653], "Eu_sigma" => NaN, "Sn_sigma" => NaN, "Ca" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu_sigma" => NaN, "K" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833, 0.6711728427150833], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Yb_sigma" => NaN, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => NaN, "Pr" => [0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072, 0.6481423915367072], "Pm_sigma" => NaN, "Th" => [0.11817829078080455, 0.11833692582255327, 0.11848367754721198, 0.1186584571943671, 0.11872998124055138, 0.11884638906157163, 0.11886978610230316, 0.11872302176891922, 0.11864396525484751, 0.11851307099559796, 0.1184751979722131, 0.11831437900181242, 0.11828403450142458, 0.11807733118547545, 0.11779097395625093, 0.11744050868934099, 0.11702166569083972, 0.11660837741536023, 0.11610979517045894, 0.11564164604357828, 0.11500669896965918, 0.11434598740459823, 0.11347857766860567, 0.11260339666885272, 0.11184895433061702, 0.11088278221432336, 0.1101364444058136, 0.10922548817530617, 0.10826969860589426, 0.1070818661117671, 0.10606184575913195, 0.1047753088704793, 0.10360284094919467, 0.10273178399917696, 0.10177899044370443, 0.10098220066827043, 0.10051560744098076, 0.10026163936907345, 0.09958201585610262, 0.09915376097286034, 0.0985684264433408], "Y_sigma" => NaN, "Zn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pr_sigma" => NaN, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => NaN, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => NaN, "Al" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => NaN, "Er" => [0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194, 0.15003302737634194], "Ge" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb_sigma" => NaN, "Zr_sigma" => NaN, "Mg" => [-0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016, -0.26790431467950016], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952, 0.6439683506493952], "Ru_sigma" => NaN, "La_sigma" => NaN, "W" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au_sigma" => NaN, "Hf_sigma" => NaN, "Li" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Dy" => [0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336, 0.32324059823005336], "Ti_sigma" => NaN, "Th_sigma" => 0.10770926892274886, "V_sigma" => NaN, "B" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd_sigma" => NaN, "Re" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sb_sigma" => NaN, "F_sigma" => NaN, "Hf" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb_sigma" => NaN, "Ca_sigma" => NaN, "Cl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr_sigma" => NaN, "Ag_sigma" => NaN, "Mg_sigma" => NaN, "Rh_sigma" => NaN, "Mo_sigma" => NaN, "Ba" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd" => [0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402, 0.6348081328140402], "Pm" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rh" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi_sigma" => NaN, "Ti" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Tb" => [0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613, 0.37376793563954613], "Tm_sigma" => NaN, "Zr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sm" => [0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083, 0.537348548615083], "Ba_sigma" => NaN, "Cr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Al_sigma" => NaN, "Tl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => NaN, "Yb" => [0.01747231672714933, 0.01747231672714933, 0.01747231672714933, 0.01747231672714933, 0.01747231672714933, 0.01747231672714933, 0.01747231672714933, 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0.0853994182525748, 0.0853994182525748], "As" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Xe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ge_sigma" => NaN, "Ta_sigma" => NaN, "As_sigma" => NaN, "Gd_sigma" => NaN, "Cd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN]), "Orthoclase" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => 0.18394801551277337, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [-2.0613451571456225, -1.9797184247322126, -1.963498083582221, -1.9169335784790555, -1.8635120521686426, -1.856711870242726, -1.8468626019451213, -1.7922758582359737, -1.777550778962718, -1.7463093605555986, -1.6726394711200572, -1.6248614422956509, -1.5747679542799446, -1.5178179485013887, -1.4768558026989114, -1.440206492898983, -1.3955970090363352, -1.3449455299292579, -1.302016159680403, -1.2615863873592086, -1.2201493693973258, -1.1720478931326996, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [-1.4902166439568731, -1.4999303639091033, -1.5032272881292594, -1.4963956575792932, -1.4820127778228807, -1.4607687223404775, -1.4359795747498403, -1.4443006329838903, -1.4453662304223247, -1.4368597269035548, -1.4258638504675656, -1.4205983090633272, -1.398560665039975, -1.38549502170722, -1.3801915248986805, -1.3752728406289847, -1.371270418662753, -1.3674120428003418, -1.3626402462000564, -1.3580474159518563, -1.3577569823080207, -1.354152251530723, -1.3534601811173304, -1.3483467449858675, -1.340221168608623, -1.3369187612111555, -1.3292466164009815, -1.3233765849532508, -1.322588871005254, -1.3210448253324996, -1.318505043205055, -1.3139309203241123, -1.3154036937477096, -1.3140131811270075, -1.3126719186011153, -1.3095112120711883, -1.3181719379205175, -1.314866232791123, -1.3144476396803435, -1.3148543939164947, -1.3152714076151595], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Be_sigma" => NaN, "Sb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => 1.1065431849372158, "Cs" => [-1.1998725979818663, -1.1805727727382993, -1.173268090729109, -1.1948113977161863, -1.2081011901881948, -1.1898190626796423, -1.1956235438196698, -1.1847429285282336, -1.1573964304367632, -1.1321745895470599, -1.1350910309577908, -1.1189360263402126, -1.1258354577686545, -1.1093919021175564, -1.1189100496824906, -1.1134018095101081, -1.1123803061211552, -1.0927137932084567, -1.1022565937580349, -1.0904941967587134, -1.078599966622292, -1.0728772399623956, -1.068082042293241, -1.0601512890702505, -1.0533141067568619, -1.0557787095467985, -1.060952074667475, -1.0707966677258651, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => NaN, "Cs" => [-1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633], "U_sigma" => NaN, "Ce_sigma" => NaN, "Dy_sigma" => NaN, "Be" => [-2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562, -2.0969100130080562], "Sr" => [-1.8860566476931633, -1.8860566476931633, -1.8860566476931633, -1.8860566476931633, 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NaN, NaN, NaN], "Au" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu_sigma" => NaN, "K" => [-0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775, -0.958607314841775], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [-0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692, -0.4034377008227692], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Yb_sigma" => NaN, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => NaN, "Pr" => [-0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, -0.28557170926212705, 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0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908, 0.8996702747267908], "Y_sigma" => NaN, "Zn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pr_sigma" => NaN, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => NaN, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => NaN, "Al" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => NaN, "Er" => [0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 0.8727223260219354, 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[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [-1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918, -1.037058157492918], "Ru_sigma" => NaN, "La_sigma" => NaN, "W" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au_sigma" => NaN, "Hf_sigma" => NaN, "Li" => [-2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752, -2.0457574905606752], "Dy" => [0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617, 0.6988083543438617], "Ti_sigma" => NaN, "Th_sigma" => NaN, "V_sigma" => NaN, "B" => [-1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694, -1.744727494896694], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd_sigma" => NaN, "Re" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sb_sigma" => NaN, "F_sigma" => NaN, "Hf" => [2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238, 2.146128035678238], "Tb_sigma" => NaN, "Ca_sigma" => NaN, "Cl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [-1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775, -1.958607314841775], "Sr_sigma" => NaN, "Ag_sigma" => NaN, "Mg_sigma" => NaN, "Rh_sigma" => NaN, "Mo_sigma" => NaN, "Ba" => [-2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0, -2.0], "Nd" => [-0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222, -0.15705152440240222], "Pm" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rh" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi_sigma" => NaN, "Ti" => [0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 0.02460901133509081, 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0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082, 0.6290746711861082], "Tm_sigma" => NaN, "Zr" => [2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531, 2.296665190261531], "Sm" => [0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829, 0.2882662415713829], "Ba_sigma" => NaN, "Cr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Al_sigma" => NaN, "Tl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => NaN, "Yb" => [0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 0.9604072883915626, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ag" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [1.1006308686996666, 1.0997091337243912, 1.0765119025504242, 1.0651556174818955, 1.0720023817449855, 1.067513536821475, 1.0648593950392562, 1.0585825296399995, 1.0632955039456249, 1.0559877905160173, 1.0591699924703715, 1.0437956814667284, 1.0310773160443993, 1.0227648266639378, 1.0194898463968174, 1.014597245079165, 1.0113600869227866, 1.0106908020582037, 1.003707095136755, 0.994538435817119, 0.9810766055905841, 0.9715278361466356, 0.9579730572396503, 0.9417274632506629, 0.9331962300329735, 0.9258721013789527, 0.9127999030716765, 0.9039650919401584, 0.8981470977438248, 0.8863753921276929, 0.8739035255545129, 0.8621987067683629, 0.850981929664827, 0.8427509966298212, 0.831344284100101, 0.822450348149932, 0.8126706126730544, 0.8030090923615874, 0.7872119820771274, 0.7749662431035955, 0.7584219540273605], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Be_sigma" => NaN, "Sb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb_sigma" => 0.6596816274998616, "Cs" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U_sigma" => 0.5140056015742133, "Ce_sigma" => 0.6600375427276298, "Dy_sigma" => 0.4710785676959089, "Be" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pt_sigma" => NaN, "Ta" => [0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179, 0.5717274686646179], "Ga" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "W_sigma" => NaN, "Cs_sigma" => NaN, "Y" => [1.9221029800967986, 1.919127507864537, 1.9041873442360597, 1.8870648230467766, 1.8834518028211102, 1.885103470887592, 1.8767479435462822, 1.8817682041643768, 1.8834409707609694, 1.8880740047102844, 1.881416335902289, 1.8809825352283585, 1.8675442926239292, 1.8623408433659527, 1.8576464263483257, 1.8539442417659358, 1.854217346037126, 1.8544036239454795, 1.853316621731363, 1.8512044934172427, 1.8509181455660457, 1.8505325946108118, 1.848339789786062, 1.8438417511095873, 1.8349205614091375, 1.8292477894401267, 1.8218789715308317, 1.8142945004105029, 1.8106675206674212, 1.8062338580109547, 1.8028835651062782, 1.7971131193635443, 1.796379666992673, 1.793082267368106, 1.7933624238093928, 1.790805169701975, 1.785707805841382, 1.7840821331442036, 1.7800136567948857, 1.777097763758218, 1.7721622880158567], "Sm_sigma" => 0.6069079139580453, "Lu_sigma" => 0.4353827093860849, "Kr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "U" => [1.967625291646163, 1.9799038815064698, 1.9884895844096322, 1.9886335974643108, 1.989076896091737, 2.000847114051035, 2.009727493586966, 1.999639334245454, 1.982771393762061, 2.0040552639333464, 2.0022269023883137, 1.9976484252173325, 2.004397223484309, 2.0066398088321145, 1.9855055728901312, 1.9781374932672071, 1.9680908760715112, 1.9622251562154187, 1.9570399223703996, 1.9550233540770625, 1.9571660393374832, 1.9564236947331828, 1.9504963192634734, 1.949073558839453, 1.9465803111975164, 1.9414752417095509, 1.9372408265087675, 1.9358785995311083, 1.9338309882262092, 1.9279885123283949, 1.9217086773932965, 1.913906890276414, 1.9108421210301703, 1.9064773660284777, 1.9065921662930534, 1.9028987364081158, 1.9024222600618295, 1.9012076813067784, 1.8981489455385305, 1.8964535808778475, 1.8964406885904974], "Eu_sigma" => 0.4851309447896303, "Sn_sigma" => NaN, "Ca" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cu_sigma" => NaN, "K" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Kr_sigma" => NaN, "Cl_sigma" => NaN, "Ce" => [0.4002935718364337, 0.3708881232468827, 0.32206503713149354, 0.32999882692320076, 0.30557707161526393, 0.2622921064590657, 0.26161050216827514, 0.2655756663779959, 0.23436098806327083, 0.2173539498602227, 0.191824535295073, 0.15953610406725302, 0.12641901008878065, 0.10450221017352115, 0.08399223927944517, 0.07591858174544847, 0.05814888686785107, 0.03993828754110063, 0.021247485660850003, 0.015191502432990656, -0.0008619084110567801, -0.009876360470032748, -0.023844991482328996, -0.03681047957052399, -0.054981932632234, -0.0689467926683075, -0.08935046130368118, -0.0956439307142268, -0.10780169572965251, -0.12176586102915415, -0.137467330472208, -0.145987813691122, -0.16440078797265759, -0.17578966651326, -0.1933828414164907, -0.2028588804211598, -0.22090156375019165, -0.22875437567660994, -0.2444006952972053, -0.2517615175390402, -0.26691756304084246], "Ne_sigma" => NaN, "Os_sigma" => NaN, "Xe_sigma" => NaN, "Fe_sigma" => NaN, "V" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Yb_sigma" => 0.42826512847978926, "He_sigma" => NaN, "Co_sigma" => NaN, "Mo" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ho_sigma" => 0.4416651717304589, "Pr" => [-0.10296251825201733, -0.0853929304833407, -0.08588593740121833, -0.09904940040806309, -0.10718848119495882, -0.17004087556341535, -0.20905557254024665, -0.2432988503992997, -0.3230959277159322, -0.3561076560313258, -0.3588529972242773, -0.39457026736353656, -0.41747596286578226, -0.4468650891672042, -0.4903346497927024, -0.5565250702909407, -0.5963172465935886, -0.634966408824239, -0.6679250982913175, -0.7036649906667433, -0.727666766458575, -0.7619691931165958, -0.7969735721184964, -0.84133423047469, -0.8704611388757767, -0.9051445129854322, -0.9398578073119445, -0.9711591353168859, -0.9939977991973457, -1.0201346703005272, -1.0457757041149183, -1.0651830660161978, -1.0901506103394853, -1.1068234794752156, -1.129921731363244, -1.148021305409372, -1.1682859322541173, -1.181451466745008, -1.2017492722812528, -1.2203401602504607, -1.2349996917358574], "Pm_sigma" => NaN, "Th" => [1.2006026282080875, 1.2058616707764886, 1.2043875394837815, 1.2090967563866317, 1.2150018396534399, 1.2113646849429736, 1.1980475367316346, 1.1862637392769608, 1.1754151911649713, 1.1680932219079037, 1.1681810334145766, 1.1636867328697877, 1.1689994703725937, 1.1657725268483423, 1.1615368584791008, 1.15557399870338, 1.1533092506236984, 1.1557770541994266, 1.1534064255248784, 1.1513795211277256, 1.1518156440531921, 1.1472593550506258, 1.1421000233700882, 1.1446326685252108, 1.1409606141273583, 1.134683708726404, 1.135804061265719, 1.1319385521034506, 1.1275351526901916, 1.1219763383492654, 1.1230682426121372, 1.1198355704218426, 1.1140160660193943, 1.1130054500646542, 1.115253832776429, 1.109895343232032, 1.1044696273396633, 1.1064833798816347, 1.1005943798717495, 1.094155639858376, 1.0900938067184434], "Y_sigma" => 0.4686164227445187, "Zn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ni_sigma" => NaN, "He" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sc" => [1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165, 1.8334040186563165], "Pr_sigma" => 0.8066069390705665, "B_sigma" => NaN, "Li_sigma" => NaN, "Tl_sigma" => NaN, "Mn_sigma" => NaN, "Cd_sigma" => NaN, "P_sigma" => NaN, "Zn_sigma" => NaN, "Al" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In_sigma" => NaN, "Si_sigma" => NaN, "Ar" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Er_sigma" => 0.42538590860707204, "Er" => [2.02530312715655, 2.025166316220645, 2.027305350357183, 2.034623904039982, 2.0141925640160476, 2.005717589258137, 2.0064776276437577, 2.0117596767341825, 2.000564582583306, 2.0116205007902117, 2.012985660061662, 2.027932827727857, 2.0325746557584825, 2.0384770074111827, 2.039982785767159, 2.045064195759585, 2.045432889238003, 2.0526596126266465, 2.0531861235015216, 2.0566648228370408, 2.0483901431041844, 2.042639894333241, 2.035918924469684, 2.033914442777116, 2.0311437009553965, 2.0345460974434695, 2.032352640195457, 2.0316363343672843, 2.028116667519411, 2.022713943849642, 2.0160527410141555, 2.0117551452956977, 2.0048595187333036, 1.9985510969234352, 1.993938721775074, 1.9895211198121703, 1.9823396360006316, 1.9792024539491007, 1.9762884237860077, 1.9711858922388599, 1.9634085847784235], "Ge" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Cr_sigma" => NaN, "Fe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb_sigma" => NaN, "Zr_sigma" => NaN, "Mg" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "F" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "La" => [-0.901271305607572, -0.9246056663149533, -1.0123691772270764, -1.0297859933458484, -1.1724937842260388, -1.2513486575926298, -1.2808276260996507, -1.309552476957329, -1.354479156257439, -1.3595473101916533, -1.410487886381192, -1.4830529621205857, -1.5318796927781295, -1.6249452744037187, -1.6925009797277508, -1.7519504049099928, -1.7793297760574451, -1.8381546398150204, -1.9005375967553904, -1.959742592660878, -2.0267699126735086, -2.0958290173412313, -2.166941374649509, -2.2015559046069155, -2.250480660552166, -2.290607786040931, -2.3495395908327765, -2.3784168367116334, -2.4284693921162996, -2.4650375893707035, -2.504928937517275, -2.5339977616012415, -2.571058840645687, -2.602710466902641, -2.6332685090889374, -2.65862627175003, -2.685876505402198, -2.7163169047477664, -2.7433688111254244, -2.7664992097906613, -2.795603910972815], "Ru_sigma" => NaN, "La_sigma" => 1.1741373038798755, "W" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Au_sigma" => NaN, "Hf_sigma" => 0.40907616323801765, "Li" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Dy" => [1.563123684237502, 1.565544997185886, 1.563183348369927, 1.5599402977136216, 1.5619438785218838, 1.5806661274435154, 1.5617255696867887, 1.5518626660531147, 1.5503198174605137, 1.5458292107326816, 1.5464419039545938, 1.5573142481116176, 1.5709923964436232, 1.5771140771580383, 1.5776565350207765, 1.5755712240198998, 1.572015389039459, 1.5713622325531609, 1.571382262141472, 1.5669557962013656, 1.564916661813715, 1.566281376650504, 1.5596652133726676, 1.5549641336226105, 1.555108895341264, 1.550314432744362, 1.5461269969034763, 1.5398052326855354, 1.527716559006098, 1.5199418253271224, 1.5122139985207665, 1.5033210642357426, 1.4983674762278272, 1.4921465100039615, 1.4849326292991578, 1.4783008232380543, 1.4701242415558182, 1.455103959027658, 1.4474772603935115, 1.4376251937045532, 1.4276692377822398], "Ti_sigma" => 0.2165923602142866, "Th_sigma" => 0.45773272261054454, "V_sigma" => NaN, "B" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Mn" => [0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844, 0.17539527371322844], "Nd_sigma" => 0.7481419519082676, "Re" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sb_sigma" => NaN, "F_sigma" => NaN, "Hf" => [2.9974414824669346, 2.9972827911219353, 2.9974544936647596, 3.00392001050072, 3.0091023031063635, 3.012893929356688, 3.0110675536842297, 3.0165483576693415, 3.0236371613157664, 3.0292490896274002, 3.0414475012293685, 3.0480989733507338, 3.0524328578580926, 3.0535168703075435, 3.0577458962924773, 3.0647141955657067, 3.0804487551493778, 3.096812550559326, 3.111525060285996, 3.1199447980698136, 3.127624973984179, 3.1339356991192124, 3.1420696513289, 3.1440354646166777, 3.1523199254907963, 3.1583228479600325, 3.163614091546314, 3.1659124674493566, 3.1720478302112634, 3.1755127154085074, 3.1778366811067573, 3.18002342464891, 3.186218806725823, 3.191597864964508, 3.193797266532152, 3.195371939221182, 3.199850016792679, 3.197063592040513, 3.195382340967928, 3.199225292978252, 3.2049896115110035], "Tb_sigma" => 0.47714254700205877, "Ca_sigma" => NaN, "Cl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "In" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sr_sigma" => NaN, "Ag_sigma" => NaN, "Mg_sigma" => NaN, "Rh_sigma" => NaN, "Mo_sigma" => NaN, "Ba" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nd" => [0.18376771552654972, 0.15927167833409947, 0.09525960798929652, 0.09054078982964699, 0.07793189924974173, 0.04092538139453736, 0.011659463017772686, -0.01490229200847637, -0.04947626982661442, -0.0990420659502731, -0.14548603554068065, -0.19284605445195, -0.2328324927698998, -0.27498284704589104, -0.3085588315359427, -0.34502534400236096, -0.3742220570657914, -0.4020465776272124, -0.43901961958311086, -0.47450672305744973, -0.5098140275254814, -0.5438452214320023, -0.5753985976825561, -0.6017913927264247, -0.6218968351901627, -0.6468832547423738, -0.6726963254945723, -0.7012869668193653, -0.7243381882264316, -0.7483729429024651, -0.7706222275369486, -0.7864093328667111, -0.8077163765980379, -0.8268190957807797, -0.8501115579119307, -0.8653250615756489, -0.8896357043477076, -0.9024395602545857, -0.9172052037824454, -0.9302421334299432, -0.9465846630142266], "Pm" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rh" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Bi_sigma" => NaN, "Ti" => [-2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363, -2.045217008584363], "Tb" => [1.376670954138383, 1.3637587071085737, 1.4066841108469101, 1.3870427288387943, 1.3933280168647884, 1.3908288039522048, 1.3995673185301645, 1.3901625950911287, 1.3818476096569818, 1.3835728470383637, 1.3761682877380896, 1.369353665084911, 1.358850801836643, 1.3551902858221565, 1.3540336243409494, 1.3466406982704482, 1.3408585125401908, 1.3437982318710882, 1.3379264701035258, 1.336403839598023, 1.3384828739448429, 1.3378693820711236, 1.3312966882097754, 1.3305289051572753, 1.3222542795219232, 1.3139437538932137, 1.3096436859350462, 1.3059974617750199, 1.2973454776246351, 1.2920831436667863, 1.2878186988925997, 1.2791771041802886, 1.269173740988282, 1.2625606822412205, 1.2521636965534675, 1.2419118906823159, 1.2334793049532145, 1.2244044212711294, 1.2137681666475209, 1.2052651462408241, 1.1971081542180821], "Tm_sigma" => 0.4603559767634602, "Zr" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sm" => [0.7892618515351859, 0.790897933662704, 0.7783933495536092, 0.7832533840954347, 0.7612700328469084, 0.7284133941338999, 0.7176902592636393, 0.7008900530298982, 0.6862969464948611, 0.6684331317144132, 0.6695525620622265, 0.6513784727276238, 0.6424423286751076, 0.6184374108142795, 0.6060172085836734, 0.5934166763975215, 0.58469604011231, 0.5687452219865389, 0.5650252939574727, 0.5415511457629578, 0.5174549823936335, 0.5025083141769782, 0.4941349902366116, 0.4870263506703595, 0.4848984801318755, 0.4776724061093673, 0.46758719265000775, 0.45410806481815597, 0.4427026355314559, 0.4308633990742789, 0.4203773543837224, 0.4041817485531017, 0.391178640308624, 0.3764324413527586, 0.3576401078397293, 0.3439679221135583, 0.3348557240551124, 0.32188315277811713, 0.30590653266511647, 0.29600434023509614, 0.277275418995652], "Ba_sigma" => NaN, "Cr" => [2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674, 2.245260154681674], "Cu" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Al_sigma" => NaN, "Tl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => NaN, "Yb" => [2.2636807243185353, 2.2655490991786675, 2.2860723959810247, 2.3122725480957773, 2.321570016183162, 2.3357483941221333, 2.3425237346731103, 2.338268318321346, 2.3404395588392597, 2.34058350948796, 2.3427771918174245, 2.345715674009461, 2.3592946375932, 2.36880103088252, 2.3760430448091103, 2.376236411449628, 2.38545730266846, 2.3840376700739507, 2.38409363657724, 2.386689986710236, 2.3951044280498555, 2.397439531776729, 2.398702888639905, 2.39772207686025, 2.397468413805389, 2.3921049375254406, 2.3911754397662315, 2.390575123792958, 2.388573729140791, 2.38486728053788, 2.3818661134930417, 2.3744852720492626, 2.372617431089218, 2.3698484929062924, 2.365012633539865, 2.3621555333884405, 2.3580148959579836, 2.351877198557362, 2.3452231703811726, 2.343700272945446, 2.339243715603531], "Lu" => [2.3026958720223343, 2.322052169935707, 2.3378195740888095, 2.345661798851817, 2.3456473572984775, 2.3597635609658814, 2.3723633123587393, 2.3814352219036077, 2.393318040293831, 2.399318697955778, 2.422688997934408, 2.4353812138713558, 2.447488977435052, 2.4589122304296214, 2.4729002414050507, 2.4751199638868275, 2.4834144847328368, 2.4869195591103543, 2.493407887755695, 2.49654346280228, 2.4965952189439595, 2.4990608772554053, 2.5089988147643796, 2.5098220514409477, 2.513027046872447, 2.5208539044350324, 2.522970811620781, 2.5185614255987523, 2.519632915647378, 2.5182244681431385, 2.5149772306420317, 2.5135239814382464, 2.5095534595370017, 2.506917380214592, 2.506992694763458, 2.5063477300673727, 2.5045575446191353, 2.502518427573054, 2.500606149476818, 2.4928190504091208, 2.482843386512017], "Eu" => [0.42435247253186714, 0.4283748222762724, 0.40374374310286315, 0.3931317389208743, 0.3752359739879285, 0.3610042337858782, 0.34821062649455287, 0.3294307944050972, 0.3138654361023164, 0.30526507315278856, 0.3041757240394133, 0.2903318563683136, 0.2874961975130406, 0.2803232111366716, 0.2676326572554267, 0.2560144494666511, 0.24959368254019348, 0.24456014509312304, 0.23898407846192465, 0.2375436003145063, 0.2327873751049064, 0.22824104980116164, 0.2287628586565236, 0.2276422111881241, 0.2200833294899211, 0.21433206889982642, 0.20911952647641316, 0.20585659834279163, 0.19651072134423314, 0.19162156754756263, 0.1869192614612718, 0.17764190593404952, 0.170859363576028, 0.1652211934281961, 0.1618437265700597, 0.1570401707214934, 0.15833091748163847, 0.14693716710255475, 0.14431581831491694, 0.13822674018129327, 0.1300399797407369], "Na" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Os" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Na_sigma" => NaN, "Ni" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb_sigma" => NaN, "Ho" => [1.9783028958839617, 1.9630783993913865, 2.0038550201453407, 1.979992392892994, 1.9616697226001132, 1.9609907201326555, 1.9428394936265048, 1.9533450363756004, 1.9437882363076149, 1.9430686466474376, 1.9374586498680944, 1.9204408906561383, 1.9148885761232133, 1.9212339014749251, 1.9182422537047845, 1.9067535121317398, 1.9142506495023972, 1.9084867879216372, 1.9016633412161033, 1.894409468078016, 1.89132863339209, 1.8861841157676493, 1.8848658024756535, 1.885890362106689, 1.8882642110244505, 1.8851333845045548, 1.8813448361359246, 1.872446694665718, 1.8627546276657108, 1.8521793645541906, 1.8441172573520936, 1.8377801199167427, 1.8343148446168234, 1.8302882719562754, 1.8273541429415479, 1.8240182715378352, 1.8165123969606505, 1.809307270895033, 1.8018166779431104, 1.7933513778142511, 1.7857594016229033], "Co" => [1.157985172728459, 1.157985172728459, 1.157985172728459, 1.157985172728459, 1.157985172728459, 1.157985172728459, 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2.1424494246924337, 2.1384712169098963, 2.1728965851172473, 2.1753081844785123, 2.1995036611933307, 2.208419279786558, 2.2212386947380587, 2.228114662874084, 2.2435547453067946, 2.240108231777522, 2.235522850406685, 2.2321473819080615, 2.229037202227005, 2.23024157506083, 2.232279002788033, 2.2364819368319258, 2.2403824478113545, 2.2406580767244884, 2.2380367678114017, 2.2367740537485026, 2.2389653575158883, 2.2397149940580343, 2.2424106708797917, 2.240292056128848, 2.2427197597970734, 2.237404512841314, 2.2297748386244924, 2.224381421642534, 2.221521120151803, 2.2109675280902166, 2.2041519643127687, 2.2009705979338037, 2.1925040952341157, 2.1836422596850276, 2.177407512104185, 2.171900892286714, 2.165984059129724], "As" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Xe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ge_sigma" => NaN, "Ta_sigma" => 0.5713503413966559, "As_sigma" => NaN, "Gd_sigma" => 0.5301672746735275, "Cd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb" => [0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001, 0.8750612633917001]), "Apatite" => Dict{String, Union{Float64, Vector{Float64}, Vector{String}}}("Ga_sigma" => NaN, "Pd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Nb" => [-2.732926661273226, -2.7358250178744803, -2.741110765840801, -2.7453201608781126, -2.7488724974476257, -2.751746895369104, -2.7557048769641086, 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NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ar_sigma" => NaN, "Gd" => [1.0376576717082853, 1.0556552774173402, 1.0731265482797758, 1.082692005647792, 1.0986204891023619, 1.1186841675227857, 1.133060345938195, 1.148488673321005, 1.1669994474609044, 1.1742674410917333, 1.1795276218964619, 1.1868457494504052, 1.1985905106890546, 1.1987411151937633, 1.2101568324096783, 1.2238694565557104, 1.2392293414086337, 1.2427609732963252, 1.2587464421789825, 1.2679382278268683, 1.2849526625080618, 1.2966389000860348, 1.310224917738631, 1.3284348702067863, 1.3458584335875718, 1.3514538307743682, 1.3652073100133668, 1.3881332335106527, 1.3967186821630573, 1.4006766949019793, 1.4161018905559242, 1.4237692088224194, 1.4228872545075069, 1.4403137063144014, 1.46501516096712, 1.4699041370910944, 1.4808921297430682, 1.4919927408615434, 1.4875495192667298, 1.4879110370647075, 1.4955286737870348], "Ru" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 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0.8423391326985882, "Zr" => [-1.2208615830140348, -1.2107533449470036, -1.2064750710557837, -1.191356832578274, -1.1885229684917282, -1.1779063751637864, -1.1652212264816262, -1.1522732039734007, -1.1451414793108894, -1.1311792393167164, -1.1280278662652365, -1.1258228838106397, -1.1147506789535906, -1.1117581291502108, -1.1091375366293754, -1.1016908720284075, -1.0842367621723024, -1.07694744192722, -1.0667313083351462, -1.0633293685961942, -1.0465320612151157, -1.0480856027866798, -1.045706880723093, -1.0353032734509235, -1.0198843299382656, -1.0151325398565016, -1.0087004552402137, -0.9981868984262343, -0.9928595208726927, -0.9907312601020616, -0.987611323873487, -0.9775026216959218, -0.9690569982538044, -0.9712213667053097, -0.9625682099563055, -0.9469643063823743, -0.9375733538715942, -0.9223029767353766, -0.905220324756013, -0.8952374929990434, -0.9041310167484209], "Sm" => [1.0841486154160058, 1.1042869860215527, 1.1194253643201328, 1.1360304055480095, 1.1431909240776257, 1.1525417314413615, 1.1713032534149752, 1.1788694457929327, 1.1927000173432316, 1.2035680084533305, 1.2176588746987156, 1.219282114011967, 1.2315386510999193, 1.2359584487196422, 1.245354547528307, 1.250717002516718, 1.2611797255447081, 1.2693899442694414, 1.2845046779009675, 1.2991707917650932, 1.3111266104033314, 1.3230094074552843, 1.3328561785491104, 1.340815586004415, 1.3574713757344554, 1.3655757390798429, 1.3753319536089375, 1.3959296823907141, 1.407471265015103, 1.4097381874821748, 1.4225734296341854, 1.4380988453263006, 1.444756492817881, 1.4540390981551345, 1.4724063673374084, 1.4906983393082283, 1.5040365911693896, 1.513566281933361, 1.5242957908023531, 1.541685168721875, 1.5570869209144498], "Ba_sigma" => 0.42850015333818847, "Cr" => [0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249, 0.9542425094393249], "Cu" => [-0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808, -0.5528419686577808], "Al_sigma" => NaN, "Tl" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "elements" => ["Ag", "Al", "Ar", "As", "Au", "B", "Ba", "Be", "Bi", "Ca", "Cd", "Ce", "Cl", "Co", "Cr", "Cs", "Cu", "Dy", "Er", "Eu", "F", "Fe", "Ga", "Gd", "Ge", "He", "Hf", "Ho", "In", "K", "Kr", "La", "Li", "Lu", "Mg", "Mn", "Mo", "Na", "Nb", "Nd", "Ne", "Ni", "Os", "P", "Pb", "Pd", "Pm", "Pr", "Pt", "Rb", "Re", "Rh", "Ru", "Sb", "Sc", "Si", "Sm", "Sn", "Sr", "Ta", "Tb", "Th", "Ti", "Tl", "Tm", "U", "V", "W", "Xe", "Y", "Yb", "Zn", "Zr"], "Ne" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pd_sigma" => NaN, "Re_sigma" => NaN, "Sc_sigma" => 0.15585364801251983, "Yb" => [0.6130040535181531, 0.6230059798307069, 0.6306122873280476, 0.6468201241271943, 0.6686044922744384, 0.6886919344751854, 0.6959544178493355, 0.7170154437937734, 0.730396989331953, 0.7363339922970974, 0.7457466198363795, 0.7694025111246404, 0.775664858190803, 0.7767012247874628, 0.7916429267468055, 0.7991754628498913, 0.8127319099561424, 0.8387548162747708, 0.865550067256091, 0.8709934214165519, 0.8819640872909614, 0.891102528024485, 0.8900023157152946, 0.8926397457447555, 0.9207896643121904, 0.9437003445783635, 0.9540848412689901, 0.9628825246188301, 0.9850188627029819, 0.9863757475747783, 0.9904722627982238, 1.007135886783678, 1.0353809632224367, 1.0583584878598689, 1.0776850783171201, 1.0891249413861774, 1.106414261677348, 1.1194638496689568, 1.1255944685854566, 1.1397737343942125, 1.1636599746373466], "Lu" => [0.5342012623334972, 0.554497931610912, 0.5724357684984288, 0.5837348532261342, 0.5948030781830755, 0.6017224271202802, 0.6059132347350695, 0.6246078474620494, 0.6357664606209995, 0.6459714813341292, 0.6667127249599275, 0.679178813723874, 0.684924377599282, 0.6986200044881014, 0.709067430547627, 0.7209499759166386, 0.7307336185238281, 0.7406993477897252, 0.7530989359021796, 0.7735466142725427, 0.7833923115137953, 0.8058188921047129, 0.8196860679586784, 0.8356839940393755, 0.8462043159101484, 0.8559368965556952, 0.8630707358788638, 0.8805331733605812, 0.885766783745766, 0.9044841148058177, 0.9104047178420692, 0.919878952302488, 0.9316928546919937, 0.9592211272799834, 0.9664193106377703, 0.9839319006297552, 1.0082046733034753, 1.0279688291933335, 1.0389917947091556, 1.060434889953089, 1.0970984781956106], "Eu" => [0.735513808281332, 0.7454885831094821, 0.7636138033444276, 0.7694217877108769, 0.7765193208695133, 0.7782868482556099, 0.7886797253498705, 0.8013246467972286, 0.8159817893527294, 0.8240868303330153, 0.8455884424237256, 0.8616944371341797, 0.870345427873425, 0.8796828052355253, 0.8914526565034858, 0.8962596971336766, 0.8993037886929526, 0.9094503508845034, 0.9145895586243701, 0.9221712830450649, 0.9259096760303925, 0.9380855585595067, 0.939329782935998, 0.9503393890498798, 0.9566153592189286, 0.9622714251747871, 0.9631238569844686, 0.970551740674732, 0.9812608181091308, 0.9930639969927851, 1.0043173336379165, 1.015680442712144, 1.0322011630829044, 1.0346739542590047, 1.0467330385437796, 1.0541737152308006, 1.0593464832241246, 1.058010321680179, 1.0682595551894645, 1.0705873381282813, 1.0777394249197516], "Na" => [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0], "Os" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Na_sigma" => NaN, "Ni" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Rb_sigma" => 0.739505529790944, "Ho" => [0.8147476178758294, 0.8415895163930153, 0.8504865027309254, 0.8663682965529658, 0.8785885623822713, 0.8881917000437491, 0.9003379248596761, 0.9262789448774934, 0.9325113762636141, 0.9456649117396431, 0.964946256245232, 0.9676239572903234, 0.9794393047747079, 0.9889489930329839, 0.9988521682585453, 1.0093488482764672, 1.0231585691894103, 1.0346179974632435, 1.0608512240405799, 1.0729906432465135, 1.0715010755537366, 1.078175485896283, 1.0882517720962956, 1.0848960933487097, 1.0927818145522084, 1.1196076824243508, 1.1363037037183559, 1.1508689522199993, 1.171289970577339, 1.1919189111597666, 1.1960720631414665, 1.2205523844176744, 1.228843760942939, 1.2410370210527994, 1.250588841062352, 1.267567544830378, 1.27432627852236, 1.2929025434474481, 1.3021270535753142, 1.33014393779967, 1.3523603259504549], "Co" => [-1.4295253355860025, -1.4048787877894, -1.3841111067644942, -1.361514711465519, -1.3248454650180508, -1.293981773760099, -1.25762805301342, -1.2142590012823546, -1.1613130754217396, -1.1070345396822927, -1.0515026686437157, -0.9901563258712663, -0.9331434385374346, -0.8770895415547314, -0.8180945399766069, -0.7612983129730524, -0.7102630300949581, -0.6618384019899481, -0.6196586394556678, -0.5873607652078449, -0.5650646831459515, -0.5521924937270196, -0.5519253861221398, -0.553563217281726, -0.5595006837588505, -0.564317121045184, -0.5763178538972827, -0.5850201152160205, -0.5983226101367553, -0.6148368286643145, -0.6316491017393324, -0.6448487360745851, -0.6491141611127678, -0.6580273695799959, -0.6613100930420743, -0.6653051531799897, -0.6637406675873236, -0.6707374158589784, -0.6608509292443078, -0.6698144917624538, -0.6771638110281861], "Pt" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "K_sigma" => 0.13650460106928072, "Tm" => [0.702259289211199, 0.7117421555649243, 0.709992732923363, 0.7100352345712451, 0.7235669205718357, 0.7428705885835887, 0.7678362328461716, 0.7883479606458532, 0.8050252365084616, 0.8151782057820294, 0.822290779994544, 0.8192382132945132, 0.8265395235076862, 0.8346663465986877, 0.8534112872856863, 0.8650625506178405, 0.873847396107292, 0.8932410084231409, 0.9076476017133444, 0.9187690434470045, 0.9399900964391662, 0.9589714883623381, 0.9635827663422916, 0.9818847698838117, 0.9974271152781377, 1.0131658841573168, 1.0284305496152528, 1.0539791432136993, 1.0609146225457045, 1.0686860538293737, 1.066670506906653, 1.0730660476663842, 1.0767697740492927, 1.0923419029878163, 1.1116137301791214, 1.1344946144132029, 1.1543868164325086, 1.1682234394390305, 1.1734640231981577, 1.198302589576773, 1.2246629311915098], "As" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Sn" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Xe" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Ge_sigma" => NaN, "Ta_sigma" => 1.2505134228050216, "As_sigma" => NaN, "Gd_sigma" => 0.7938849289914328, "Cd" => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], "Pb" => [-0.4671935272239688, -0.4573576818380285, -0.4499105063453611, -0.4443516765206707, -0.43659442775650636, -0.438197835411475, -0.4332027910956682, -0.426594934952413, -0.41728228484679714, -0.4114570899542551, -0.40962871195251427, -0.4059386099629168, -0.4091828278729961, -0.4078107863453115, -0.40738092926762903, -0.401376007469091, -0.3975957941221201, -0.3940224553062201, -0.39320635820960326, -0.39209120800972685, -0.3882541640574412, -0.3899532546528656, -0.3911459742452506, -0.3906579336290577, -0.39264225687764065, -0.39397874639125735, -0.401401757152259, -0.4018510997210429, -0.4085347100799935, -0.4147169364887582, -0.4147758949814092, -0.4092951148196519, -0.4119013557557643, -0.41795261802596323, -0.4162610636475467, -0.42302802574357035, -0.4316649178335365, -0.4345828479477705, -0.44639256430146634, -0.4564807447528021, -0.4753534038166786]))
export germ_kd
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 1838 | using StatGeochem, LsqFit, Plots
ree3 = ["Pr","Pr","Nd","Sm","Gd","Tb","Dy","Ho","Er","Tm","Yb","Lu",] # 3+ rare earth elements
r = [0.106,0.101,0.100,0.096,0.094,0.092,0.091,0.089,0.088,0.087,0.086,0.085,] # Ionic Radii
rp = 0.085:0.001:0.107 # Radius range to plot over
# Equation we're fitting to (from Blundy and Wood, 1994):
# lnD0 + a * (r0/2*(x-r0)^2 + 1/3*(x-r0)^3)
# where a = 4π E Na / RT
@. blundy_wood(x,param) = param[1] + param[2] * (param[3]/2*(x-param[3])^2 + 1/3*(x-param[3])^3)
h = plot(framestyle=:box, xlabel="Ionic radius", ylabel="log10 kD")
T = 500:10:1000
kd_La = zeros(length(T))
kd_Pr = zeros(length(T))
rD = r[3:end]
kD = zeros(length(rD))
for j in eachindex(T)
for i in 1:10
kD[i] = log10(claiborne_zircon_kd(ree3[2+i], T[j]))
end
plot!(h, rD, kD, seriestype=:scatter, label="")
# Fit to Blundy and Wood curve
param = [maximum(kD), -10000, 0.095] # initial guess
fobj = curve_fit(blundy_wood, rD, kD, param) # Fit
plot!(h, r, blundy_wood(r,fobj.param), label="") # Plot
kd_La[j] = 10.0^blundy_wood(r[1],fobj.param)
kd_Pr[j] = 10.0^blundy_wood(r[2],fobj.param)
end
display(h)
@. f(T, param) = param[1]*exp(param[2]/(T+273.15))
param = [nanmean(kd_La), 5000] # Initial guess
## --- La vs temp
h1 = plot(T, kd_La, xlabel="Temperature (C)", ylabel="La kD", seriestype=:scatter, framestyle=:box, label="")
fobj = curve_fit(f, T, kd_La, param)
plot!(h1, T, f(T, fobj.param), label="")
display(h1)
@info "La zrn/melt kd = $(fobj.param[1]) * exp($(fobj.param[2])/T)"
## -- Fit kd Pr vs temp
h1 = plot(T, kd_Pr, xlabel="Temperature (C)", ylabel="Pr kD", seriestype=:scatter, framestyle=:box, label="")
fobj = curve_fit(f, T, kd_Pr, param)
plot!(h1, T, f(T, fobj.param), label="")
display(h1)
@info "Pr zrn/melt kd = $(fobj.param[1]) * exp($(fobj.param[2])/T)"
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 8081 | using StatGeochem
using Plots
using LsqFit: curve_fit
## --- Load partition coefficients from GERM into a dict
# path = joinpath(moduleresourcepath,"PartitionCoefficients")
path = joinpath("src/resources/","PartitionCoefficients")
# Obtain the names of downloaded mineral files
filenames = readdir(path)
# Load every `.tsv` file into a struct
pr = Dict{String,Any}()
pr["minerals"] = String[]
for n in filenames
m = match(r"(.*).tsv", n)
if ~ isnothing(m)
name = m[1]
push!(pr["minerals"], name)
pr[name] = importdataset(joinpath(path, name*".tsv"), '\t', mindefinedcolumns=2, importas=:Dict)
end
end
# Approximate content of various rock types, for when actual melt SiO2 not reported
str = ["basalt","andesit","dacit","rhyolit","hawaiite","alkali basalt","basanit","benmoreite","camptonite","eclogit","garnet pyroxenit","kimberlit","komatiit","lampro","latite","leucitit","lherzolit","mugearite","pantellerite","trachyt","peridotit","phonolit","picrit","syen","morb","tholeiit","tonalit","granodiorit","granit","shoshonit","carbonatit","aplite","monzonit","synthetic"]
val = [49,57.5,67.5,73,48.5,47,44,57,42,49,49,40,45,46.5,62.5,47,42,52,73,61,42,59,45.5,61,49,50,65,67.5,72,55,2,73,67.4,60]
err = [5,6.5,5.5,5,3.5,4,3,4,3,5,5,4,5,11.5,11.5,5,5,5,5,9,5,5,5.5,9,5,4,8,7.5,5,5,5,5,5,20]
# Determine the unique reference / rock type pairs
samples = Dict{String,Vector{Tuple{String,String,Float64,Float64}}}()
allsamples = Tuple{String,String,Float64,Float64}[]
allelements = String[]
for m in pr["minerals"]
if ~haskey(pr[m],"SiO2")
pr[m]["SiO2"] = fill(NaN, size(pr[m]["Reference"]))
end
if ~haskey(pr[m],"SiO2_sigma")
pr[m]["SiO2_sigma"] = fill(NaN, size(pr[m]["Reference"]))
end
# Estimate SiO2 from rock type
needssi = isnan.(pr[m]["SiO2"])
for i ∈ eachindex(str)
t = containsi.(pr[m]["Rock Type"], str[i]) .& needssi
# Average together, such that "basaltic andesite" is halfway between "basalt" and "andesite"
pr[m]["SiO2"][t] = nanmean([pr[m]["SiO2"][t] val[i]*ones(count(t))], dim=2)
pr[m]["SiO2_sigma"][t] = nanmean([pr[m]["SiO2_sigma"][t] err[i]*ones(count(t))], dim=2)
end
# Save unique samples
println(m)
flush(stdout)
samples[m] = collect(zip(string.(pr[m]["Reference"]), string.(pr[m]["Rock Type"]), pr[m]["SiO2"], pr[m]["SiO2_sigma"]))
append!(allsamples, unique(samples[m]))
# Save unique elements
append!(allelements, unique(pr[m]["Elem"]))
end
allsamples = sort(unique(allsamples))
allelements = sort(unique(allelements))
# Second dict: sort the data by sample ID
pd = Dict{String, Union{Vector, Dict{String, Union{Vector{Float64}, Vector{String}}}}}()
pd["samples"] = allsamples
pd["Reference"] = allsamples .|> x -> x[1]
pd["Rock Type"] = allsamples .|> x -> x[2]
pd["SiO2"] = allsamples .|> x -> x[3]
pd["SiO2_sigma"] = allsamples .|> x -> x[4]
pd["minerals"] = pr["minerals"]
for m in pd["minerals"]
pd[m] = Dict{String, Union{Vector{Float64}, Vector{String}}}()
pd[m]["elements"] = allelements
for e in allelements
pd[m][e] = fill(NaN, length(allsamples))
pd[m][e*"_sigma"] = fill(NaN, length(allsamples))
t = pr[m]["Elem"] .== e
kd = map(x->(x>0 ? log10(x) : NaN), pr[m]["Value"][t])
kd_sigma = log10.(pr[m]["Value"][t] .+ pr[m]["SD"][t]) .- kd
kdl = map(x->(x>0 ? log10(x) : NaN), pr[m]["Low"][t])
kdh = map(x->(x>0 ? log10(x) : NaN), pr[m]["High"][t])
if any(t)
rows = findmatches(samples[m][t], allsamples)
pd[m][e][rows] = nanmean([kd nanmean([kdl kdh], dim=2)], dim=2)
pd[m][e*"_sigma"][rows] = nanmean([kd_sigma (kdh .- kdl)/4], dim=2)
end
end
end
## --- Fit REEs as a function of activation energy and bulk modulus
ree3 = ["La","Pr","Nd","Sm","Gd","Tb","Dy","Ho","Er","Tm","Yb","Lu",] # 3+ rare earth elements
r = [0.106,0.101,0.100,0.096,0.094,0.092,0.091,0.089,0.088,0.087,0.086,0.085,] # Atomic Radii
rp = 0.085:0.001:0.107 # Radius range to plot over
# Equation we're fitting to (from Blundy and Wood, 1994):
# lnD0 + a * (r0/2*(x-r0)^2 + 1/3*(x-r0)^3)
# where a = 4π E Na / RT
@. blundy_wood(x,param) = param[1] + param[2] * (param[3]/2*(x-param[3])^2 + 1/3*(x-param[3])^3)
# for mineral={'Apatite','Amphibole','Clinopyroxene','Orthopyroxene','Garnet','Sphene','Allanite','Baddeleyite'}
for m in pd["minerals"]
h = plot(title=m, legend=:bottomleft)
for i ∈ eachindex(pd["samples"])
kD = fill(NaN,length(ree3))
for j ∈ eachindex(ree3)
kD[j] = pd[m][ree3[j]][i]
end
if count(.~isnan.(kD)) > 1
# plot!(h, r, kD, label="$i", seriestype=:scatter, color=lines[mod(i,length(lines))+1], msalpha=0)
plot!(h, r, kD, label="", seriestype=:scatter, color=lines[mod(i,length(lines))+1], msalpha=0)
end
if (count(.~isnan.(kD)) > 3) && (nanrange(r[.~isnan.(kD)]) > 0.013)
# Fit to Blundy and Wood curve
t = .~( isnan.(kD) .| isinf.(kD) )
param = [maximum(kD[t]), -10000, 0.095] # initial guess
f = curve_fit(blundy_wood, r[t], kD[t], param) # Fit
plot!(h, r, blundy_wood(r,f.param), label="", color=lines[mod(i,length(lines))+1]) # Plot
# Replace stored partition coefficients with new fits
for j ∈ eachindex(ree3)
pd[m][ree3[j]][i] = blundy_wood(r[j],f.param)
end
end
end
savefig(h, "Calc_$(m)_REE.pdf")
end
# Interpolate Eu partition coefficients where missing, assuming
# 60% Eu as Eu2+ (c.f. Ba, Sr, Ca) and 40% as Eu3+ (c.f. Sm, Gd)
# for m in = ["Albite","Anorthite","Orthoclase","Apatite"]
for m in pd["minerals"]
for i ∈ eachindex(pd["samples"])
if isnan(pd[m]["Eu"][i])
pd[m]["Eu"][i] = log10(0.6*10^nanmean([pd[m]["Ba"][i], pd[m]["Sr"][i]]) + 0.4*10^nanmean([pd[m]["Sm"][i], pd[m]["Gd"][i]]))
end
end
end
for m in ("Monazite", "Xenotime", "Allanite")
for i ∈ eachindex(pd["samples"])
if isnan(pd[m]["Eu"][i])
pd[m]["Eu"][i] = log10(0.6*0 + 0.4*10^nanmean([pd[m]["Sm"][i], pd[m]["Gd"][i]]))
end
end
end
## --- Convert data to average as a function of SiO2
# Convert from row-based to Si-based Dict
kd = Dict{String, Union{Vector{String}, Vector{Float64}, Dict{String,Union{Float64, Vector{String}, Vector{Float64}}}}}()
kd["minerals"] = pd["minerals"]
for m in kd["minerals"]
kd[m] = Dict{String,Union{Float64, Vector{String}, Vector{Float64}}}()
kd[m]["elements"] = allelements
for e in allelements
t = .!isnan.(pd[m][e])
if (count(t) > 2) && (nanrange(pd["SiO2"][t]) > 8)
kd[m][e] = mcfit(pd["SiO2"], pd["SiO2_sigma"], pd[m][e], pd[m][e*"_sigma"], 40, 80, 41, binwidth=5)[2]
else
kd[m][e] = ones(41) * nanmean(pd[m][e])
end
kd[m][e*"_sigma"] = nanstd(pd[m][e])
end
end
kd["SiO2"] = collect(40:80.)
# Set Albite partiton coefficients
for e in kd["Albite"]["elements"]
kd["Albite"][e] = nanmean([kd["Albite"][e] kd["Orthoclase"][e] kd["Anorthite"][e]], dim=2)
end
kd["note"] = ["kd for Albite is nanmean of AlkaliFeldspar, Orthoclase, and Anorthite",]
## --- Save results
# using MAT
# matwrite(joinpath(path,"partitioncoeffs.mat"),p)
f = open(joinpath(path,"PartitionCoefficients.jl"), "a")
print(f, "\ngerm_kd = $kd\nexport germ_kd\n")
close(f)
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 5358 | ## --- Query TC1 properties
const tc1_550 = readdlm(joinpath(moduleresourcepath,"tc1","tc1_550.csv"), ',', Float64)
const tc1_1300 = readdlm(joinpath(moduleresourcepath,"tc1","tc1_1300.csv"), ',', Float64)
const tc1_age = readdlm(joinpath(moduleresourcepath,"tc1","tc1_age.csv"), ',', Int)
"""
`find_tc1_crust(lat::Number,lon::Number)`
Find the depth to the 550C isotherm for the 1x1 arc degree grid cell
containing `lat` and `lon`
`find_tc1_crust(lat::AbstractArray,lon::AbstractArray)`
For each pair of latitudes and longitudes given by lat and lon, find the
depth to the 550C isotherm for the 1x1 arc degree grid cell containing
`lat[i]` and `lon[i]`
Data is sourced from the global 1x1° "TC1" thermal model of Artemieva 2006,
doi: 10.1016/j.tecto.2005.11.022
"""
function find_tc1_crust(lat::Number,lon::Number)
if !isnan(lat) && !isnan(lon)
i = round(Int, 91-lat)
j = round(Int, lon+181)
crust=tc1_550[i,j]
else
crust = NaN
end
return crust
end
function find_tc1_crust(lat::AbstractArray,lon::AbstractArray)
# Check input dimensions match
eachindex(lat) == eachindex(lon) || @error "lat and lon must be equal size"
# Query the tc1_550 array for our lat and lon
crust = fill(NaN, size(lat))
for n=1:length(lat)
if !isnan(lat[n]) && !isnan(lon[n])
i = round(Int, 91-lat[n])
j = round(Int, lon[n]+181)
crust[n]=tc1_550[i,j]
end
end
return crust
end
export find_tc1_crust
"""
`find_tc1_lith(lat::Number,lon::Number)`
Find the depth to the 1300C isotherm for the 1x1 arc degree grid cell
containing `lat` and `lon`
`find_tc1_lith(lat::AbstractArray,lon::AbstractArray)`
For each pair of latitudes and longitudes given by lat and lon, find the
depth to the 1300C isotherm for the 1x1 arc degree grid cell containing
`lat[i]` and `lon[i]`
Data is sourced from the global 1x1° "TC1" thermal model of Artemieva 2006,
doi: 10.1016/j.tecto.2005.11.022
"""
function find_tc1_lith(lat::Number, lon::Number)
if (-90 <= lat <= 90) && (-180 <= lon <= 180)
i = round(Int, 91-lat)
j = round(Int, lon+181)
return tc1_1300[i,j]
else
return NaN
end
end
function find_tc1_lith(lat::AbstractArray, lon::AbstractArray)
# Check input dimensions match
eachindex(lat) == eachindex(lon) || @error "lat and lon must be equal size"
# Query the tc1_1300 array for our lat and lon
lith = fill(NaN, size(lat))
for n in eachindex(lat)
if (-90 <= lat[n] <= 90) && (-180 <= lon[n] <= 180)
i = round(Int, 91-lat[n])
j = round(Int, lon[n]+181)
lith[n] = tc1_1300[i,j]
end
end
return lith
end
export find_tc1_lith
"""
`find_tc1_age(lat::Number,lon::Number)`
Return a tuple `(age, age_min, age_max)` containing the nominal, upper, and
lower tc1 age bounds for the 1x1 arc degree grid cell containing `lat`
and `lon`
`find_tc1_age(lat::AbstractArray,lon::AbstractArray)`
Return a tuple `(age, age_min, age_max)` where `age`, `age_min`, and
`age_max` are arrays containing the nominal, upper and lower tc1 age bounds
for each location pair `lat[i]`, `lon[i]`
Data is sourced from the global 1x1° "TC1" thermal model of Artemieva 2006,
doi: 10.1016/j.tecto.2005.11.022
"""
function find_tc1_age(lat::Number,lon::Number)
ages=[ NaN NaN NaN
25 0 50
150 50 250
395 250 540
695 540 850
975 850 1100
1400 1100 1700
2100 1700 2500
2750 2500 3000
3250 3000 3500]
if (-90 <= lat <= 90) && (-180 <= lon <= 180)
i = round(Int, 91-lat)
j = round(Int, lon+181)
return ntuple(k->ages[tc1_age[i,j],k], 3)
else
return (NaN, NaN, NaN,)
end
end
function find_tc1_age(lat::AbstractArray,lon::AbstractArray)
# Check input dimensions match
eachindex(lat) == eachindex(lon) || @error "lat and lon must be equal size"
ages=[ NaN NaN NaN
25 0 50
150 50 250
395 250 540
695 540 850
975 850 1100
1400 1100 1700
2100 1700 2500
2750 2500 3000
3250 3000 3500]
# Query the tc1_1300 array for our lat and lon
age = fill(NaN, size(lat))
minage = fill(NaN, size(lat))
maxage = fill(NaN, size(lat))
for n in eachindex(lat)
if (-90 <= lat[n] <= 90) && (-180 <= lon[n] <= 180)
i = round(Int, 91-lat[n])
j = round(Int, lon[n]+181)
ageindex = tc1_age[i,j]
age[n] = ages[ageindex,1]
minage[n] = ages[ageindex,2]
maxage[n] = ages[ageindex,3]
end
end
return (age, minage, maxage)
end
export find_tc1_age
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 10036 | function update_changepoint_model!(m, σ, d, boundaries, np)
@inbounds for i=1:np+1
r = boundaries[i]:boundaries[i+1]
for col = 1:size(d,2)
m[r,col] .= nanmean(view(d,r,col))
σ[r,col] .= nanstd(view(d,r,col))
end
end
end
function update_changepoint_mu!(m, d, boundaries, np)
@inbounds for i=1:np+1
r = boundaries[i]:boundaries[i+1]
for col = 1:size(d,2)
m[r,col] .= nanmean(view(d,r,col))
end
end
end
# # Not currently used
# function update_changepoint_sigma!(σ, d, boundaries, np)
# @inbounds for i=1:np+1
# r = boundaries[i]:boundaries[i+1]
# for col = 1:size(d,2)
# σ[r,col] .= nanstd(view(d,r,col))
# end
# end
# end
"""
```julia
changepoint(data, [sigma], nsteps; np, npmin, npmax)
```
Given an ordered array of `data` points, optionally with uncertainties `sigma`,
use a Markov chain Monte Carlo approach based on that of Gallagher et al., 2010
(10.1016/j.epsl.2011.09.015) to estimate the position (by index) and optionally
number of changepoints that best explain the `data`. Will return the results for
`nsteps` steps of the Markov chain.
Optional keyword arguments:
np
Specify an exact integer number of changepoints.
npmin
nmpax
Specify the maximum and minimum possible integer number of changepoints. If `np`
is not specified, the number of changepoints will allowed to vary freely between
these bounds, as in the "transdimensional" approach of Gallagher et al.
### Examples
```julia
julia> A = [randn(100).-2; randn(100).+2];
julia> dist = changepoint(A, 10000; np=1);
julia> dist[9000:end] # after burnin
1001-element Vector{Int64}:
101
101
101
101
⋮
101
101
101
julia> dist = changepoint(A, ones(size(A)), 10000; np=1)
10000×1 Matrix{Int64}:
61
61
61
61
⋮
101
101
101
```
"""
function changepoint(data::Collection, nsteps::Integer; np::Integer=0, npmin::Integer=0, npmax::Integer=min(size(data,1) ÷ 2, 11))
MOVE = 0.70
BIRTH = 0.15
DEATH = 0.15
DEBUG = false
FORMATTED = true
T = float(eltype(data))
nrows = size(data,1)
ncolumns = size(data,2)
m = similar(data, T)
σ = similar(data, T) #Array{T}(undef, ncolumns)
σₚ = similar(data, T) #Array{T}(undef, ncolumns)
# Number of possible changepoint locations
K = nrows-1
# Parse provided options
if 0 < np <= K
# If np is specified, use that
npmin = npmax = np
else
# Otherwise, ensure all provided values are plausible and go with that
npmax > K && (npmax = K)
npmin < 0 && (npmin = 0)
np = min(max(npmin, 2), npmax)
end
# Allocate output array of changepoints
result = fill(0, nsteps, npmax)
# Create and fill initial boundary point array
boundaries = Array{Int}(undef, K+2)
boundaries[1] = 1
boundaries[np+2] = nrows
boundaries[2:np+1] .= rand(2:nrows-1, np)
boundariesₚ = similar(boundaries)
boundary_sigma = nrows/np
np = count_unique!(view(boundaries,1:np+2)) - 2
# Calculate initial proposal and log likelihood
update_changepoint_model!(m, σ, data, boundaries, np)
ll = normpdf_ll(m, σ, data)
# The actual loop
@inbounds for i = 1:nsteps
# Randomly choose a type of modification to the model
r = rand()
u = rand()
# Update the model with the chosen modification
if r < MOVE && np>0
# Move a changepoint
copyto!(boundariesₚ,1,boundaries,1,np+2)
# Pick which changepoint to move
pick = rand(2:np+1)
# Move the changepoint
boundary_adj = randn()*boundary_sigma
boundary_prop = boundariesₚ[pick] + round(Int, boundary_adj)
# Treat ends of array as periodic boundary conditions
boundariesₚ[pick] = mod(boundary_prop - 1, nrows-1) + 1
# Check if this has caused any redundancies
npₚ = count_unique!(view(boundariesₚ,1:np+2)) - 2
# Update the model
update_changepoint_mu!(m, data, boundariesₚ, npₚ)
# Calculate log likelihood for proposal
if (1 < boundary_prop < nrows)
llₚ = normpdf_ll(m, σ, data)
else
llₚ = -Inf
end
DEBUG && println("Move: llₚ-ll = $llₚ - $ll")
if log(u) < llₚ-ll
DEBUG && println("Accepted!")
ll = llₚ
boundary_sigma = abs(boundary_adj)*2.9
# println("sigma: $boundary_sigma")
copyto!(boundaries,1,boundariesₚ,1,np+2)
# for n=1:np
# print("$(boundariesₚ[n+1]),")
# end
# FORMATTED && print("\n")
end
elseif r < MOVE+BIRTH
# Add a changepoint
if np < npmax
copyto!(boundariesₚ,1,boundaries,1,np+2)
# Propose a new changepoint
boundariesₚ[np+3] = rand(2:nrows-1)
npₚ = count_unique!(view(boundariesₚ,1:np+3)) - 2
# Update the model
# update_changepoint_model!(m, σ, data, boundariesₚ, npₚ)
update_changepoint_mu!(m, data, boundariesₚ, npₚ)
# Calculate log likelihood for proposal
lqz = sum(1 ./ (2*σ.*σ))
llₚ = normpdf_ll(m, σ, data)
DEBUG && println("Birth: -lqz+llₚ-ll = $(-lqz) + $llₚ - $ll")
if log(u) < llₚ-lqz-ll
DEBUG && println("Accepted!")
ll = llₚ
np = npₚ
copyto!(boundaries,1,boundariesₚ,1,np+2)
# for n=1:np
# print("$(boundariesₚ[n+1]),")
# end
# FORMATTED && print("\n")
end
end
elseif r < MOVE+BIRTH+DEATH
# Delete a changepoint
if np > npmin
copyto!(boundariesₚ,1,boundaries,1,np+2)
# Pick which changepoint to delete
pick = rand(2:np+1)
boundariesₚ[pick]=boundariesₚ[pick+1]
npₚ = count_unique!(view(boundariesₚ,1:np+2)) - 2
# Update the model
# update_changepoint_model!(m, σ, data, boundariesₚ, npₚ)
update_changepoint_mu!(m, data, boundariesₚ, npₚ)
# Calculate log likelihood for proposal
llₚ = normpdf_ll(m, σ, data)
lqz = sum(1 ./ (2*σ.*σ))
DEBUG && println("Death: lqz+llₚ-ll = $lqz + $llₚ - $ll")
if log(u) < llₚ+lqz-ll
DEBUG && println("Accepted!")
ll = llₚ
np = npₚ
copyto!(boundaries,1,boundariesₚ,1,np+2)
# for n=1:np
# print("$(boundariesₚ[n+1]),")
# end
# FORMATTED && print("\n")
end
end
end
# Record results
result[i, 1:np] .= boundaries[2:(np+1)]
end
return result
end
function changepoint(data::Collection, sigma::Collection, nsteps::Integer; np::Integer=0, npmin::Integer=0, npmax::Integer=min(size(data,1) ÷ 2, 11))
MOVE = 0.70
BIRTH = 0.15
DEATH = 0.15
DEBUG = false
FORMATTED = true
T = float(eltype(data))
nrows = size(data,1)
ncolumns = size(data,2)
m = similar(data, T)
# Number of possible changepoint locations
K = nrows-1
# Parse provided options
if 0 < np <= K
# If np is specified, use that
npmin = npmax = np
else
# Otherwise, ensure all provided values are plausible and go with that
npmax > K && (npmax = K)
npmin < 0 && (npmin = 0)
np = min(max(npmin, 2), npmax)
end
# Allocate output array of changepoints
result = fill(0, nsteps, npmax)
# Create and fill initial boundary point array
boundaries = Array{Int}(undef, K+2)
boundaries[1] = 1
boundaries[np+2] = nrows
boundaries[2:np+1] .= rand(2:nrows-1, np)
boundariesₚ = similar(boundaries)
boundary_sigma = nrows/np
np = count_unique!(view(boundaries,1:np+2)) - 2
# Calculate initial proposal and log likelihood
update_changepoint_mu!(m, data, boundaries, np)
ll = normpdf_ll(m, sigma, data)
# The actual loop
@inbounds for i = 1:nsteps
# Randomly choose a type of modification to the model
r = rand()
u = rand()
# Update the model with the chosen modification
if r < MOVE && np>0
# Move a changepoint
copyto!(boundariesₚ,1,boundaries,1,np+2)
# Pick which changepoint to move
pick = rand(2:np+1)
# Move the changepoint
boundary_adj = randn()*boundary_sigma
boundary_prop = boundariesₚ[pick] + round(Int, boundary_adj)
# Treat ends of array as periodic boundary conditions
boundariesₚ[pick] = mod(boundary_prop - 1, nrows-1) + 1
# Check if this has caused any redundancies
npₚ = count_unique!(view(boundariesₚ,1:np+2)) - 2
# Update the model
update_changepoint_mu!(m, data, boundariesₚ, npₚ)
# Calculate log likelihood for proposal
if (1 < boundary_prop < nrows)
llₚ = normpdf_ll(m, sigma, data)
else
llₚ = -Inf
end
DEBUG && println("Move: llₚ-ll = $llₚ - $ll")
if log(u) < llₚ-ll
DEBUG && println("Accepted!")
ll = llₚ
boundary_sigma = abs(boundary_adj)*2.9
# println("sigma: $boundary_sigma")
copyto!(boundaries,1,boundariesₚ,1,np+2)
# for n=1:np
# print("$(boundariesₚ[n+1]),")
# end
# FORMATTED && print("\n")
end
elseif r < MOVE+BIRTH
# Add a changepoint
if np < npmax
copyto!(boundariesₚ,1,boundaries,1,np+2)
# Propose a new changepoint
boundariesₚ[np+3] = rand(2:nrows-1)
npₚ = count_unique!(view(boundariesₚ,1:np+3)) - 2
# Update the model
update_changepoint_mu!(m, data, boundariesₚ, npₚ)
# Calculate log likelihood for proposal
lqz = sum(1 ./ (2 .* sigma .* sigma))
llₚ = normpdf_ll(m, sigma, data)
DEBUG && println("Birth: -lqz+llₚ-ll = $(-lqz) + $llₚ - $ll")
if log(u) < llₚ-lqz-ll
DEBUG && println("Accepted!")
ll = llₚ
np = npₚ
copyto!(boundaries,1,boundariesₚ,1,np+2)
# for n=1:np
# print("$(boundariesₚ[n+1]),")
# end
# FORMATTED && print("\n")
end
end
elseif r < MOVE+BIRTH+DEATH
# Delete a changepoint
if np > npmin
copyto!(boundariesₚ,1,boundaries,1,np+2)
# Pick which changepoint to delete
pick = rand(2:np+1)
boundariesₚ[pick]=boundariesₚ[pick+1]
npₚ = count_unique!(view(boundariesₚ,1:np+2)) - 2
# Update the model
update_changepoint_mu!(m, data, boundariesₚ, npₚ)
# Calculate log likelihood for proposal
llₚ = normpdf_ll(m, sigma, data)
lqz = sum(1 ./ (2 .* sigma .* sigma))
DEBUG && println("Death: lqz+llₚ-ll = $lqz + $llₚ - $ll")
if log(u) < llₚ+lqz-ll
DEBUG && println("Accepted!")
ll = llₚ
np = npₚ
copyto!(boundaries,1,boundariesₚ,1,np+2)
# for n=1:np
# print("$(boundariesₚ[n+1]),")
# end
# FORMATTED && print("\n")
end
end
end
# Record results
result[i, 1:np] .= boundaries[2:(np+1)]
end
return result
end
export changepoint
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 1501 |
## -- Import and extend Base.display for various custom types
import Base.display
# Custom pretty printing for named tuples as datasets
# TODO: possibly avoid blatant type piracy 🏴☠️ 🏴☠️ 🏴☠️
function display(x::NamedTuple)
i = 1
println("NamedTuple with $(length(keys(x))) elements:")
l = max(length.(string.(keys(x)))...)
for s in keys(x)
t = typeof(x[s])
sp = " "^(l-length(string(s)))
print(" $s$sp = $t")
if t<:Number
print("\t$(x[s])")
elseif t<:AbstractRange
print("\t$(x[s])")
elseif t<:AbstractArray
print(size(x[s]))
if length(x[s]) < 2
print("\t[$(x[s])]")
else
print("\t[$(first(x[s])) ... $(last(x[s]))]")
end
elseif t<:NTuple
if length(x[s]) < 2
print("\t[$(x[s])]")
else
print("\t[$(first(x[s])) ... $(last(x[s]))]")
end
elseif t<:AbstractString
if length(x[s]) < 50
print("\t\"$(x[s])\"")
else
print("\t\"$(x[s][firstindex(x[s])+(1:50)])...")
end
end
print("\n")
i += 1
if i > 222
print(".\n.\n.\n")
break
end
end
end
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 5441 | ## --- Digitize image data
"""
```julia
digitize_plotmarkers(img, marker_color, xlims, ylims; atol=0.16)
```
Calculate approximate `x` (horizontal) and `y` (vertical) positions and
position uncertainties for distinct colored markers in an image.
### Examples
```julia
img = load("xyscatter.png") # using FileIO, ImageIO
C = eltype(img)
(x,dx,y,dy) = digitize_plotmarkers(img, C(0,0.604,0.976,1), [0,10], [0,10])
```
"""
function digitize_plotmarkers(img, marker_color, xlims, ylims; atol=0.16)
# Test for approximate equality in color to marker
t = isapprox.(marker_color,img,atol=atol)
# Figure out our image dimensions
xrows = size(t,2)
yrows = size(t,1)
# Allocate index arrays
imin = Array{Float64}(undef,round(Int,xrows/2))
imax = Array{Float64}(undef,round(Int,xrows/2))
jmin = Array{Float64}(undef,round(Int,xrows/2))
jmax = Array{Float64}(undef,round(Int,xrows/2))
# Fill index arrays
found = false
markernumber = 0
for j ∈ 1:xrows
tⱼ = view(t,:,j)
if any(tⱼ)
list = findall(tⱼ)
if ~found
markernumber += 1
imin[markernumber] = minimum(list)
imax[markernumber] = maximum(list)
jmin[markernumber] = j
jmax[markernumber] = j
else
imin[markernumber] = min(imin[markernumber],minimum(list))
imax[markernumber] = max(imax[markernumber],maximum(list))
jmax[markernumber] = j
end
found = true
else
found = false
end
end
# Return only the filled indices
imin = imin[1:markernumber]
imax = imax[1:markernumber]
jmin = jmin[1:markernumber]
jmax = jmax[1:markernumber]
# Calculate x and y positions from indices
Δy = last(ylims) - first(ylims)
y = last(ylims) .- (imin+imax)/2 * Δy / yrows
dy = (imax-imin)/2 * Δy / yrows
Δx = last(xlims) - first(xlims)
x = (jmin+jmax)/2 * Δx / xrows .+ first(xlims)
dx = (jmax-jmin)/2 * Δx / xrows
return x, dx, y, dy
end
export digitize_plotmarkers
"""
```julia
digitize_plotline(img, line_color, xlims, ylims; atol=0.16)
```
Calculate approximate `x` (horizontal) and `y` (vertical) positions for
a colored line in an image
### Examples
```julia
img = load("xysin.png") # using FileIO, ImageIO
C = eltype(img)
(x,y) = digitize_plotline(img, C(0,0.604,0.976,1), [0,2pi], [-1.1,1.1])
```
"""
function digitize_plotline(img, line_color, xlims, ylims; atol=0.16)
# Test for approximate equality in color to marker
t = isapprox.(line_color,img,atol=atol)
# Figure out our image dimensions
xrows = size(t,2)
yrows = size(t,1)
# Calculate x for each column
x = cntr(range(first(xlims), last(xlims), length=xrows+1))
# y as a function of i-position in image
# (note: images are typically flipped)
Δy = last(ylims) - first(ylims)
yᵢ(i) = last(ylims) - i * Δy / yrows
# Calculate y, defaulting to NaN if no matches
y = fill(NaN, xrows)
for j = 1:xrows
tⱼ = view(t,:,j)
y[j] = yᵢ(findmeanindex(tⱼ))
end
return x, y
end
export digitize_plotline
function findmeanindex(x)
@assert isa(firstindex(x), Int)
μ = 0
n = 0
@inbounds for i ∈ eachindex(x)
if x[i] == true
μ += i
n += 1
end
end
return μ/n
end
## --- Retain deprecated functions with matlab-like syntax, to avoid breakages in user scripts that may depend on them
if ~ @isdefined linspace
"""
```julia
linspace(l::Number,u::Number,n::Number)
```
Returns a linearly spaced array with `n` points between the starting
bound `l` and ending bound `u`
"""
function linspace(l::Number,u::Number,n::Number)
return range(l,stop=u,length=n)
end
export linspace
end
if ~ @isdefined contains
"""
```julia
contains(haystack, needle)
```
Converts both `haystack` and `needle` to strings (if not already strings)
and checks whether `string(haystack)` contains `string(needle)`.
"""
contains(haystack::AbstractString, needle::Union{AbstractString,Regex,AbstractChar}) = occursin(needle, haystack)
contains(haystack, needle) = occursin(string(needle), string(haystack))
export contains
end
if ~ @isdefined containsi
"""
```julia
containsi(haystack, needle)
```
Converts both `haystack` and `needle` to strings and checks whether
`string(haystack)` contains `string(needle)`, ignoring case.
"""
containsi(haystack::AbstractString, needle::Union{AbstractString,AbstractChar}) = occursin(lowercase(needle), lowercase(haystack))
containsi(haystack, needle) = occursin(lowercase(string(needle)), lowercase(string(haystack)))
export containsi
end
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 16466 | ## --- Read ESRI Arc/Info ASCII grid files
function importAAIGrid(fname, T=Float64; undefval=NaN)
# Open the file
fid = open(fname)
metadata = Dict{String,Number}()
metadata["ncols"] = parse(Int64, first(match(r" *(.*?)$", readline(fid))))
metadata["nrows"] = parse(Int64, first(match(r" *(.*?)$", readline(fid))))
metadata["xll_corner"] = parse(Float64, first(match(r" *(.*?)$", readline(fid))))
metadata["yll_corner"] = parse(Float64, first(match(r" *(.*?)$", readline(fid))))
metadata["cellsize"] = parse(Float64, first(match(r" *(.*?)$", readline(fid))))
metadata["nodata"] = parse(Float64, first(match(r" *(.*?)$", readline(fid))))
nrows = metadata["nrows"]
ncols = metadata["ncols"]
data = Array{T}(undef,ncols,nrows)
for i = 1:nrows
l = readline(fid)
delim_string_parse!(data, l, ' ', T, offset=(i-1)*ncols, undefval=undefval)
end
# Close the file
close(fid)
return (data', metadata)
end
export importAAIGrid
## --- Calculate slope from a DEM
function maxslope(matrix, x_lon_cntr, y_lat_cntr, cellsize, T=UInt16; minmatval=-12000, km_per_lat=111.1)
# Returns slope in units/kilometer given a latitude-longitude grid of z-values
# Allocate output array
slope = Array{T}(undef,size(matrix))
# Fill in the center first
distNS = 2 * cellsize * km_per_lat
for i = 2:(size(matrix,1)-1)
# Distance between grid cell centers
km_per_lon = cos(y_lat_cntr[i] * pi/180) * km_per_lat
distEW = 2 * cellsize * km_per_lon
distDiag = sqrt(distNS^2 + distEW^2)
for j = 2:(size(matrix,2)-1)
# Gradients, in matrix units per km
if matrix[i,j] < minmatval
slope[i,j] = 0
else
if (matrix[i+1,j] < minmatval) || (matrix[i-1,j] < minmatval)
NS = 0
else
NS = abs(matrix[i+1,j] - matrix[i-1,j]) / distNS
end
if (matrix[i,j+1] < minmatval) || (matrix[i,j-1]<minmatval)
EW = 0
else
EW = abs(matrix[i,j+1] - matrix[i,j-1]) / distEW
end
if (matrix[i+1,j-1] < minmatval) || (matrix[i-1,j+1] < minmatval)
NESW = 0
else
NESW = abs(matrix[i+1,j-1] - matrix[i-1,j+1]) / distDiag
end
if (matrix[i+1,j+1] < minmatval) || (matrix[i-1,j-1] < minmatval)
NWSE = 0
else
NWSE = abs(matrix[i+1,j+1] - matrix[i-1,j-1]) / distDiag
end
# Record the steepest slope
slope[i,j] = nearest(T, max(NS,EW,NESW,NWSE))
end
end
# Fill in edges too
distEW = cellsize * km_per_lon
distDiag = sqrt((distNS/2)^2 + distEW^2)
# Left edge
if (matrix[i+1,1] < minmatval) || (matrix[i-1,1] < minmatval)
NS = 0
else
NS = abs(matrix[i+1,1] - matrix[i-1,1]) / distNS
end
if (matrix[i,2] < minmatval) || (matrix[i,1] < minmatval)
EW = 0
else
EW = abs(matrix[i,2] - matrix[i,1]) / distEW
end
if (matrix[i+1,1] < minmatval) || (matrix[i-1,2] < minmatval)
NESW = 0
else
NESW = abs(matrix[i+1,1] - matrix[i-1,2]) / distDiag
end
if (matrix[i+1,2] < minmatval) || (matrix[i-1,1] < minmatval)
NWSE = 0
else
NWSE = abs(matrix[i+1,2] - matrix[i-1,1]) / distDiag
end
slope[i,1] = nearest(T, max(NS,EW,NESW,NWSE))
# Right edge
if (matrix[i+1,end] < minmatval) || (matrix[i-1,end] < minmatval)
NS = 0
else
NS = abs(matrix[i+1,end] - matrix[i-1,end]) / distNS
end
if matrix[i,end]<minmatval || matrix[i,end-1]<minmatval
EW = 0
else
EW = abs(matrix[i,end] - matrix[i,end-1]) / distEW
end
if (matrix[i+1,end-1] < minmatval) || (matrix[i-1,end] < minmatval)
NEWS = 0
else
NESW = abs(matrix[i+1,end-1] - matrix[i-1,end]) / distDiag
end
if (matrix[i+1,end] < minmatval) || (matrix[i-1,end-1] < minmatval)
NWSE = 0
else
NWSE = abs(matrix[i+1,end] - matrix[i-1,end-1]) / distDiag
end
slope[i,end] = nearest(T, max(NS,EW,NESW,NWSE))
end
# Fill in the top and bottom row
distNS = cellsize * km_per_lat
# Top row
km_per_lon = cos(y_lat_cntr[1]*pi/180) * km_per_lat
distEW = 2*cellsize*km_per_lon
distDiag = sqrt(distNS^2+(distEW/2)^2)
for j = 2:(size(matrix,2)-1)
# Gradients, in meters per km
if (matrix[2,j] < minmatval) || (matrix[1,j] < minmatval)
NS = 0
else
NS = abs(matrix[2,j] - matrix[1,j]) / distNS
end
if (matrix[1,j+1] < minmatval) || (matrix[1,j-1] < minmatval)
EW = 0
else
EW = abs(matrix[1,j+1] - matrix[1,j-1]) / distEW
end
if (matrix[2,j-1] < minmatval) || (matrix[1,j] < minmatval)
NESW = 0
else
NESW = abs(matrix[2,j-1] - matrix[1,j]) / distDiag
end
if (matrix[2,j+1] < minmatval) || (matrix[1,j] < minmatval)
NWSE = 0
else
NWSE = abs(matrix[2,j+1] - matrix[1,j]) / distDiag
end
slope[1,j] = nearest(T, max(NS,EW,NESW,NWSE))
end
slope[1,1] = 0
slope[1,end] = 0
# Bottom row
km_per_lon = cos(y_lat_cntr[end] * pi/180) * km_per_lat
distEW = 2 * cellsize * km_per_lon
distDiag = sqrt(distNS^2 + (distEW/2)^2)
for j = 2:(size(matrix,2)-1)
# Gradients, in meters per Km
if (matrix[end-1,j] < minmatval) || (matrix[end,j] < minmatval)
NS = 0
else
NS = abs(matrix[end-1,j] - matrix[end,j]) / distNS
end
if (matrix[end,j+1] < minmatval) || (matrix[end,j-1] < minmatval)
EW = 0
else
EW = abs(matrix[end,j+1] - matrix[end,j-1]) / distEW
end
if (matrix[end-1,j-1] < minmatval) || (matrix[end,j] < minmatval)
NESW = 0
else
NESW = abs(matrix[end-1,j-1] - matrix[end,j]) / distDiag
end
if (matrix[end-1,j+1] < minmatval) || (matrix[end,j] < minmatval)
NWSE = 0
else
NWSE = abs(matrix[end-1,j+1] - matrix[end,j]) / distDiag
end
slope[end,j] = nearest(T, max(NS,EW,NESW,NWSE))
end
slope[end,1] = 0
slope[end,end] = 0
return slope
end
export maxslope
function aveslope(matrix, x_lon_cntr, y_lat_cntr, cellsize, T=UInt16; minmatval=-12000, maxmatval=9000, km_per_lat=111.1)
# Returns slope in units/kilometer given a latitude-longitude grid of z-values
# Allocate intermediate and output arrays
distance = Array{Float64}(undef,8)
local_slopes = Array{Float64}(undef,8)
slope = Array{T}(undef,size(matrix))
# Index offsets to cycle through:
# [N,NE,E,SE,S,SW,W,NW]
ioffset = [-1,-1,0,1,1,1,0,-1]
joffset = [0,1,1,1,0,-1,-1,-1]
#
# i.e. Layout:
# 8 1 2
# 7 x 3
# 6 5 4
# Distance between grid cell centers
# N, S
distance[[1,5]] .= cellsize * km_per_lat
# Fill in the center first
for i = 2:(size(matrix,1)-1)
# Distance between grid cell centers
km_per_lon = cos(y_lat_cntr[i]*pi/180) * km_per_lat
distance[[3,7]] .= cellsize*km_per_lon; #E, W
distance[[2,4,6,8]] .= sqrt(distance[1]^2+distance[3]^2) # Diagonals
# Center
for j = 2:(size(matrix,2)-1)
# Gradients, in matrix z-units per km
here = matrix[i,j]
if (here < minmatval) || (here > maxmatval)
slope[i,j] = 0
else
for k = 1:8
there = matrix[i+ioffset[k], j+joffset[k]]
if (there < minmatval) || (there > maxmatval)
local_slopes[k] = 0
else
local_slopes[k] = abs(there-here) / distance[k]
end
end
# Record the average slope
slope[i,j] = nearest(T, nanmean(local_slopes))
end
end
# Left edge
here = matrix[i,1]
if (here < minmatval) || (here > maxmatval)
slope[i,1] = 0
else
for k = 1:5
there = matrix[i+ioffset[k], 1+joffset[k]]
if (there < minmatval) || (there > maxmatval)
local_slopes[k] = 0
else
local_slopes[k] = abs(there-here) / distance[k]
end
end
slope[i,1] = nearest(T, nanmean(local_slopes[1:5]))
end
# Right edge
here = matrix[i,end]
if (here < minmatval) || (here > maxmatval)
slope[i,end] = 0
else
for k = [5,6,7,8,1]
there = matrix[i+ioffset[k], end+joffset[k]]
if (there < minmatval) || (there > maxmatval)
local_slopes[k] = 0
else
local_slopes[k] = abs(there-here) / distance[k]
end
end
slope[i,end] = nearest(T, nanmean(view(local_slopes, [5,6,7,8,1])))
end
end
# Top row
km_per_lon = cos(y_lat_cntr[1] * pi/180) * km_per_lat
distance[[3,7]] .= cellsize * km_per_lon #E, W
distance[[2,4,6,8]] .= sqrt(distance[1]^2 + distance[3]^2) # Diagonals
for j = 2:(size(matrix,2)-1)
# Gradients, in matrix units per km
here = matrix[1,j]
if (here < minmatval) || (here > maxmatval)
slope[1,j] = 0
else
for k=3:7
there = matrix[1+ioffset[k], j+joffset[k]]
if (there < minmatval) || (there > maxmatval)
local_slopes[k] = 0
else
local_slopes[k] = abs(there-here) / distance[k]
end
end
slope[1,j] = nearest(T, nanmean(view(local_slopes, 3:7)))
end
end
slope[1,1] = 0
slope[1,end] = 0
# Bottom row
km_per_lon = cos(y_lat_cntr[end] *pi/180) * km_per_lat
distance[[3,7]] .= cellsize * km_per_lon #E, W
distance[[2,4,6,8]] .= sqrt(distance[1]^2+distance[3]^2) # Diagonals
for j = 2:(size(matrix,2)-1)
# Gradients, in matrix units per km
here = matrix[end,j]
if (here < minmatval) || (here > maxmatval)
slope[end,j] = 0
else
for k = [7,8,1,2,3]
there = matrix[end+ioffset[k], j+joffset[k]]
if (there < minmatval) || (there > maxmatval)
local_slopes[k] = 0
else
local_slopes[k] = abs(there-here) / distance[k]
end
end
slope[end,j] = nearest(T, nanmean(view(local_slopes, [7,8,1,2,3])))
end
end
slope[end,1] = 0
slope[end,end] = 0
return slope
end
export aveslope
## --- Generate random latitude and longitude pairs uniformly distributed across the globe
CONST_180_PI = 180/pi
function randlatlon(n::Integer; land=false)
if land
c = 0
while c < n
lats, lons = 90 .- CONST_180_PI*acos.(2*rand(5n) .- 1), rand(5n)*360 .- 180
notland = .!find_land(lats, lons)
@inbounds for i in eachindex(notland)
c += !notland[i]
if c > n
notland[i] = true
end
end
c < n && (c=0)
deleteat!(lats, notland)
deleteat!(lons, notland)
end
else
lats, lons = 90 .- CONST_180_PI*acos.(2*rand(n) .- 1), rand(n)*360 .- 180
end
return lats, lons
end
function randlatlon()
90 - CONST_180_PI*acos(2*rand() - 1), rand()*360 - 180
end
export randlatlon
## --- Calculate distance uncertainty in arc degrees
"""
```julia
haversine(lat₁, lon₁, lat₂, lon₂)
```
Calculate the arc degree distance between two decimal degree points (lat₁, lon₁) and
(lat₂, lon₂).
"""
function haversine(lat₁, lon₁, lat₂, lon₂)
lat₁ᵣ, lon₁ᵣ, lat₂ᵣ, lon₂ᵣ = (lat₁, lon₁, lat₂, lon₂) .* (pi/180)
dist = acos(sin(lat₁ᵣ) * sin(lat₂ᵣ) + cos(lat₁ᵣ) * cos(lat₂ᵣ) * cos(lon₁ᵣ - lon₂ᵣ))
return dist * 180/pi
end
export haversine
## --- Calculate maximum arc-degree distance between a series of points
"""
```julia
dist_uncert(lats, lons)
```
Find the decimal degree center and associated uncertainty (in arc degrees) from
lists `lats` and `lons` of decimal degree coordinates.
### Examples
```julia
(lat_ctr, lon_ctr, uncertainty) = dist_uncert(lats, lons)
```
"""
function dist_uncert(lats, lons)
@assert eachindex(lats) == eachindex(lons)
latc, lonc = centroid(lats, lons)
maxdist = zero(float(eltype(lats)))
for i in eachindex(lats)
for j in 1+firstindex(lats):lastindex(lats)
# If a point is compared to itself, distance is 0; comparison is susceptible to roundoff error
if i != j
dist = haversine(lats[i], lons[i], lats[j], lons[j])
dist > maxdist && (maxdist = dist)
end
end
end
return latc, lonc, maxdist/2
end
export dist_uncert
## --- Other lat and lon conversions
"""
```julia
centroid(lats, lons)
```
Return the centroid of a set of latitudes and longitudes on a sphere.
"""
function centroid(lats::AbstractArray{T1}, lons::AbstractArray{T2}) where {T1,T2}
T = float(promote_type(T1, T2))
x, y, z = similar(lats, T), similar(lats, T), similar(lats, T)
@inbounds for i in eachindex(lats, lons)
φ = deg2rad(90 - lats[i])
θ = deg2rad(lons[i])
x[i], y[i], z[i] = cartesian(one(T), φ, θ)
end
x₀ = nanmean(x)
y₀ = nanmean(y)
z₀ = nanmean(z)
ρ, φ, θ = spherical(x₀, y₀, z₀)
latc = 90 - rad2deg(φ)
lonc = rad2deg(θ)
return latc, lonc
end
export centroid
"""
```julia
x, y, z = cartesian(ρ, φ, θ)
```
Convert from spherical coordinates (`ρ`,`φ`,`θ`) to cartesian coordinates (`x`,`y`,`z`).
"""
function cartesian(ρ::Number, φ::Number, θ::Number)
x = ρ * sin(φ) * cos(θ)
y = ρ * sin(φ) * sin(θ)
z = ρ * cos(φ)
return x, y, z
end
export cartesian
"""
```julia
ρ, θ, φ = cartesian(x, y, z)
```
Convert from cartesian coordinates (`x`,`y`,`z`) to spherical coordinates (`ρ`,`φ`,`θ`).
"""
function spherical(x::Number, y::Number, z::Number)
ρ = sqrt(x^2 + y^2 + z^2)
φ = acos(z/ρ)
θ = atan(y,x)
return ρ, φ, θ
end
export spherical
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 126099 | ## --- Calculate Eu*
"""
```julia
eustar(Nd::Number, Sm::Number, Gd::Number, Tb::Number)
```
Calculate expected europium concentration, Eu*, based on abundance of
adjacent rare earths.
Full four-element log-linear interpolation, assuming 3+ ionic radii and the
chondritic abundances of Sun and McDonough 1989 (doi: 10.1144/gsl.sp.1989.042.01.19)
"""
function eustar(Nd::Number, Sm::Number, Gd::Number, Tb::Number)
# Ionic radii, in pm [Tb, Gd, Sm, Nd]
r = [106.3, 107.8, 109.8, 112.3] # or x = [1, 2, 4, 6]
# Normalize to chondrite
y = log.([Tb/0.0374, Gd/0.2055, Sm/0.1530, Nd/0.4670])
notnan = .!isnan.(y)
# Make sure we're interpolating and not extrapolating
if any(view(notnan, 1:2)) && any(view(notnan, 3:4))
# Fit a straight line through the chondrite-normalized values
x = r[notnan]
(a,b) = hcat(fill!(similar(x), 1), x) \ y[notnan]
# De-dormalize output for Eu, interpolating at r = 108.7 pm or x = 3
eu_interp = 0.0580*exp(a + b*108.7)
else
eu_interp = NaN
end
return eu_interp
end
"""
```julia
eustar(Sm::Number, Gd::Number)
```
Calculate expected europium concentration, Eu*, based on abundance of
adjacent rare earths.
Simple geometric mean interpolation from Sm and Gd alone, assuming the chondritic
abundances of Sun and McDonough 1989 (doi: 10.1144/gsl.sp.1989.042.01.19), that is
Eu* = `0.0580*sqrt(Sm/0.1530 * Gd/0.2055)`
"""
function eustar(Sm::Number, Gd::Number)
# Geometric mean in regular space is equal to the arithmetic mean in log space. Fancy that!
return 0.0580*sqrt(Sm/0.1530 * Gd/0.2055)
end
export eustar
## --- CIPW norm
"""
```julia
cipw_norm(SiO2, TiO2, Al2O3, Fe2O3, FeO, MnO, MgO, CaO, Na2O, K2O, P2O5)
```
Returns
```
quartz, orthoclase, plagioclase, corundum, nepheline, diopside, orthopyroxene, olivine, magnetite, ilmenite, apatite
```
"""
function cipw_norm(SiO2, TiO2, Al2O3, Fe2O3, FeO, MnO, MgO, CaO, Na2O, K2O, P2O5)
SiO2 /= 60.0843
TiO2 /= 79.8988
Al2O3 /= 101.9613
Fe2O3 /= 159.6922
FeO /= 71.8464
MnO /= 70.9374
MgO /= 40.3044
CaO /= 56.0794
Na2O /= 61.9789
K2O /= 94.1960
P2O5 /= 141.9445
FeO = nanadd(FeO, MnO)
CaO -= 3.333333333333333 * P2O5
apatite = 0.6666666666666666 * P2O5
# P2O5 = 0
FeO -= TiO2
ilmenite = TiO2
FeO -= Fe2O3
magnetite = Fe2O3
# Fe2O3 = 0
Al2O3 -= K2O
orthoclase = K2O
# K2O = 0
Al2O3 -= Na2O
albite = Na2O
if CaO > Al2O3
CaO -= Al2O3
anorthite = Al2O3
Al2O3 = 0
else
Al2O3 -= CaO
anorthite = CaO
CaO = 0
end
if Al2O3 > 0
corundum = Al2O3
Al2O3 = 0
else
corundum = 0
end
Mg′ = MgO / (MgO + FeO)
FMO = FeO + MgO
FMO_weight = (Mg′*40.3044)+((1-Mg′)*71.8464)
if CaO > 0
FMO -= CaO
diopside = CaO
else
diopside = 0
end
orthopyroxene = FMO
pSi1 = 6orthoclase + 6albite + 2anorthite + 2diopside + orthopyroxene
if pSi1 < SiO2
quartz = SiO2 - pSi1
nepheline = 0
olivine = 0
else
quartz = 0
pSi2 = 6orthoclase + 6albite + 2anorthite + 2diopside
pSi3 = SiO2 - pSi2
if FMO > 2pSi3
orthopyroxene = 0
olivine = FMO
FMO = 0
pSi4 = 6orthoclase + 2anorthite + 2diopside + 0.5olivine
pSi5 = SiO2 - pSi4
Albite = (pSi5-(2*Na2O))/4
nepheline = Na2O-Albite
else
nepheline = 0
orthopyroxene = 2pSi3 - FMO
olivine = FMO - pSi3
end
end
orthoclase *= 2
nepheline *= 2
albite *= 2
An′ = anorthite/(anorthite+albite)
plag_weight = (An′*278.2093)+((1-An′)*262.2230)
plagioclase = albite+anorthite
quartz *= 60.0843
orthoclase *= 278.3315
plagioclase *= plag_weight
corundum *= 101.9613
nepheline *= 142.0544
diopside *= (172.248 + FMO_weight)
orthopyroxene *= (60.0843 + FMO_weight)
olivine *= (60.0843 + 2FMO_weight)
magnetite *= 231.5386
ilmenite *= 151.7452
apatite *= 504.3152
return (quartz=quartz, orthoclase=orthoclase, plagioclase=plagioclase,
corundum=corundum, nepheline=nepheline, diopside=diopside,
orthopyroxene=orthopyroxene, olivine=olivine, magnetite=magnetite,
ilmenite=ilmenite, apatite=apatite)
end
# export cipw_norm
## --- Fe oxide conversions
"""
```julia
feoconversion(FeO::Number=NaN, Fe2O3::Number=NaN, FeOT::Number=NaN, Fe2O3T::Number=NaN)
```
Compiles data from FeO, Fe2O3, FeOT, and Fe2O3T into a single FeOT value.
"""
function feoconversion(FeO::Number=NaN, Fe2O3::Number=NaN, FeOT::Number=NaN, Fe2O3T::Number=NaN)
# To convert from Fe2O3 wt % to FeO wt %, multiply by
conversionfactor = (55.845+15.999) / (55.845+1.5*15.999)
# If FeOT or Fe2O3T already exists, use that
if isnan(FeOT)
if isnan(Fe2O3T)
if isnan(Fe2O3)
FeOT = FeO
elseif isnan(FeO)
FeOT = Fe2O3*conversionfactor
else
FeOT = Fe2O3*conversionfactor + FeO
end
else
FeOT=Fe2O3T*conversionfactor
end
end
return FeOT
end
export feoconversion
## --- Oxide conversions
function fillifnan!(dest::AbstractArray, source::AbstractArray)
@inbounds for i in eachindex(dest, source)
if isnan(dest[i]) && !isnan(source[i])
dest[i] = source[i]
end
end
return dest
end
function fillifnan!(dest::AbstractArray, source::AbstractArray, factor::Number)
@inbounds for i in eachindex(dest, source)
if isnan(dest[i]) && !isnan(source[i])
dest[i] = source[i] * factor
end
end
return dest
end
function nannegative!(a::AbstractArray)
@inbounds for i in eachindex(a)
if a[i] < 0
a[i] = NaN
end
end
return a
end
"""
```julia
converted_dataset = oxideconversion(dataset::Union{Dict,NamedTuple}; unitratio::Number=10000)
```
As `oxideconversion!`, but returning a copy rather than modifying in-place
"""
oxideconversion(ds::Union{Dict,NamedTuple}; kwargs...) = oxideconversion!(deepcopy(ds); kwargs...)
export oxideconversion
"""
```julia
oxideconversion!(dataset::Dict; unitratio::Number=10000)
```
Convert major elements (Ti, Al, etc.) into corresponding oxides (TiO2, Al2O3, ...) in place if extant.
If metals are expected as PPM, set unitratio=10000 (default); if metals are as wt%,
set unitratio = 1
See also `oxideconversion`, c.f. `metalconversion!`
"""
function oxideconversion!(dataset::NamedTuple; unitratio::Number=10000)
# List of elements to convert
source = (:Si, :Ti, :Al, :Fe, :Fe, :Mg, :Ca, :Mn, :Li, :Na, :K, :P, :Cr, :Ni, :Co, :S, :H)
dest = (:SiO2, :TiO2, :Al2O3, :FeOT, :Fe2O3T, :MgO, :CaO, :MnO, :Li2O, :Na2O, :K2O, :P2O5, :Cr2O3, :NiO, :CoO, :SO3, :H2O)
conversionfactor = (2.13932704290547,1.66847584248889,1.88944149488507,1.28648836426407,1.42973254639611,1.65825961736268,1.39919258253823,1.29121895771597,2.1526657060518732,1.34795912485574,1.20459963614796,2.29133490474735,1.46154369861159,1.27258582901258,1.27147688434143,2.4970991890205863,8.93601190476191)
@assert eachindex(source) == eachindex(dest) == eachindex(conversionfactor)
# If source field exists, fill in destination from source
for i ∈ eachindex(source)
if haskey(dataset, source[i])
if haskey(dataset, dest[i]) # If destination field doesn't exist, make it.
oxide, metal = dataset[dest[i]], dataset[source[i]]
fillifnan!(oxide, metal, conversionfactor[i]/unitratio)
end
end
end
return dataset
end
oxideconversion!(ds::Dict; kwargs...) = (oxideconversion!(TupleDataset(ds); kwargs...); ds)
export oxideconversion!
"""
```julia
converted_dataset = metalconversion(dataset::Union{Dict,NamedTuple}; unitratio::Number=10000)
```
As `metalconversion!`, but returning a copy rather than modifying in-place
"""
metalconversion(ds::Union{Dict,NamedTuple}; kwargs...) = metalconversion!(copy(ds); kwargs...)
export metalconversion
"""
```julia
dataset = metalconversion!(dataset::Union{Dict,NamedTuple}; unitratio::Number=10000)
```
Convert minor element oxides (MnO, Cr2O3, NiO, ...) into corresponding metals (Mn, Cr, Ni, ...) in place if extant.
If metals are expected as parts per million (ppm), set unitratio=10000 (default); if metals are as wt%, set unitratio = 1
See also `metalconversion`, c.f. `oxideconversion!`
"""
function metalconversion!(dataset::NamedTuple; unitratio::Number=10000)
# List of elements to convert
dest = (:Mn, :P, :Cr, :Ni, :Co, :Sr, :Ba, :Li, :S,)
source = (:MnO, :P2O5, :Cr2O3, :NiO, :CoO, :SrO, :BaO, :Li2O, :SO3)
conversionfactor = (0.7744619872751028, 0.4364268173666496, 0.6842080746199798, 0.785801615263874, 0.786486968277016, 0.8455993051534453, 0.8956541815613328, 0.46454031259412965, 0.4004646689233921)
# If source field exists, fill in destination from source
for i ∈ eachindex(source)
if haskey(dataset, source[i])
if haskey(dataset, dest[i]) # If destination field doesn't exist, make it.
metal, oxide = dataset[dest[i]], dataset[source[i]]
fillifnan!(metal, oxide, conversionfactor[i]*unitratio)
end
end
end
return dataset
end
metalconversion!(ds::Dict; kwargs...) = (metalconversion!(TupleDataset(ds); kwargs...); ds)
export metalconversion!
"""
```julia
carbonateconversion!(dataset::NamedTuple)
```
Convert carbonates (CaCO3, MgCO3) into corresponding metal oxides and CO2 if extant, in place,
as well as synchonizing TIC, TOC, TC, C and CO2. All are assumed to be reported in the same units,
(likely wt. %) except for C, which is assumed to be equivalent to unitratio * TC,
"""
function carbonateconversion!(ds::NamedTuple; unitratio=10000)
# Calculate CO2 if both CaCO3 and MgCO3 are reported
if haskey(ds, :CaCO3) && haskey(ds, :MgCO3) && haskey(ds, :CO2)
fillifnan!(ds.CO2, ds.CaCO3*0.43971009048182363 .+ ds.MgCO3*0.5219717006867268)
end
# Populate oxides and CO2 from carbonates and TIC
source = (:CaCO3, :CaCO3, :MgCO3, :MgCO3, :TIC,)
dest = (:CaO, :CO2, :MgO, :CO2, :CO2)
conversionfactor = (0.5602899095181764, 0.43971009048182363, 0.4780282993132732, 0.5219717006867268, 3.664057946882025)
for i in eachindex(source)
if haskey(ds, source[i])
if haskey(ds, dest[i])
d, s = ds[dest[i]], ds[source[i]]
fillifnan!(d, s, conversionfactor[i])
end
end
end
# Fill TC from C and TIC from CO2
if haskey(ds,:TC) && haskey(ds, :C)
fillifnan!(ds.TC, ds.C, 1e-4)
end
if haskey(ds,:TIC) && haskey(ds, :CO2)
fillifnan!(ds.TIC, ds.CO2, 0.27292144788565975)
end
# Synchronise TOC, TIC, TC
if haskey(ds, :TC) && haskey(ds, :TOC) && haskey(ds, :TIC)
fillifnan!(ds.TC, ds.TOC + ds.TIC)
fillifnan!(ds.TOC, ds.TC - ds.TIC)
nannegative!(ds.TOC)
fillifnan!(ds.TIC, ds.TC - ds.TOC)
nannegative!(ds.TIC)
if haskey(ds, :CO2)
# If we have new TIC values, fill CO2 again
fillifnan!(ds.CO2, ds.TIC, 3.664057946882025)
end
end
# Fill C from any available source
if haskey(ds,:TC) && haskey(ds, :C)
fillifnan!(ds.C, ds.TC, 1e4)
end
if haskey(ds,:TOC) && haskey(ds,:TIC) && haskey(ds, :C)
fillifnan!(ds.C, ds.TOC + ds.TIC, 1e4)
end
if haskey(ds,:TOC) && haskey(ds,:CO2) && haskey(ds, :C)
fillifnan!(ds.C, ds.TOC + ds.CO2/3.664057946882025, 1e4)
end
if haskey(ds,:TOC) && haskey(ds, :C)
fillifnan!(ds.C, ds.TOC, 1e4)
end
if haskey(ds,:TIC) && haskey(ds, :C)
fillifnan!(ds.C, ds.TIC, 1e4)
end
if haskey(ds,:CO2) && haskey(ds, :C)
fillifnan!(ds.C, ds.CO2, 1e4/3.664057946882025)
end
return ds
end
carbonateconversion!(ds::Dict) = (carbonateconversion!(TupleDataset(ds)); ds)
export carbonateconversion!
## --- Chemical Index of Alteration
# Chemical Index of Alteration as defined by Nesbitt and Young, 1982
# Note that CaO should be only igneous CaO excluding any Ca from calcite or apatite
function CIA(Al2O3::Number, CaO::Number, Na2O::Number, K2O::Number)
A = Al2O3 / 101.96007714
C = CaO / 56.0774
N = Na2O / 61.978538564
K = K2O / 94.19562
return A / (A + C + N + K) * 100
end
export CIA
# "Weathering Index of Parker" as defined by Parker, 1970
function WIP(Na2O::Number, MgO::Number, K2O::Number, CaO::Number)
Na = Na2O / 30.9895
Mg = MgO / 40.3044
K = K2O / 47.0980
Ca = CaO / 56.0774
# Denominator for each element is a measure of Nicholls' bond strengths
return (Na/0.35 + Mg/0.9 + K/0.25 + Ca/0.7) * 100
end
export WIP
## --- MELTS interface
"""
```julia
melts_configure(meltspath::String, scratchdir::String, composition::Collection{Number},
\telements::Collection{String},
\tT_range=(1400, 600),
\tP_range=(10000,10000);)
```
Configure and run a MELTS simulation using alphaMELTS.
Optional keyword arguments and defaults include:
batchstring::String = "1\nsc.melts\n10\n1\n3\n1\nliquid\n1\n1.0\n0\n10\n0\n4\n0\n"
A string defining the sequence of options that would be entered to produce
the desired calculation if running alphaMELTS at the command line. The
default string specifies a batch calculation starting at the liquidus.
dT = -10
The temperature step, in degrees, between each step of the MELTS calculation
dP = 0
The pressure step, in bar, between each step of the MELTS calculation
index = 1
An optional variable used to specify a unique suffix for the run directory name
version::String = "pMELTS"
A string specifying the desired version of MELTS. Options include `MELTS` and `pMELTS`.
mode::String = "isobaric"
A string specifying the desired calculation mode for MELTS. Options include
`isothermal`, `isobaric`, `isentropic`, `isenthalpic`, `isochoric`,
`geothermal` and `PTPath`.
fo2path::String = "FMQ"
A string specifying the oxygen fugacity buffer to follow, e.g., `FMQ` or `NNO+1`.
Available buffers include `IW`,`COH`,`FMQ`,`NNO`,`HM`, and `None`
fractionatesolids::Bool = false
Fractionate all solids? default is `false`
suppress::Collection{String} = String[]
Supress individual phases (specify as strings in array, i.e. `["leucite"]`)
verbose::Bool = true
Print verbose MELTS output to terminal (else, write it to `melts.log`)
"""
function melts_configure(meltspath::String, scratchdir::String, composition::Collection{Number},
elements::Collection{String}, T_range::Collection{Number}=(1400, 600), P_range::Collection{Number}=(10000,10000);
batchstring::String="1\nsc.melts\n10\n1\n3\n1\nliquid\n1\n1.0\n0\n10\n0\n4\n0\n",
dT=-10, dP=0, index=1, version="pMELTS",mode="isobaric",fo2path="FMQ",
fractionatesolids::Bool=false, suppress::Collection{String}=String[], verbose::Bool=true)
############################ Default Settings ###############################
##MELTS or pMELTS
#version = "pMELTS"
##Mode (isothermal, isobaric, isentropic, isenthalpic, isochoric, geothermal or PTPath)
#mode = "isobaric"
## Set fO2 constraint, i.e. "IW","COH","FMQ","NNO","HM","None" as a string
#fo2path = "FMQ"
## Fractionate all solids? ("!" for no, "" for yes)
#fractionatesolids = "!"
# Mass retained during fractionation
massin = 0.001
# Ouptut temperatures in celcius? ("!" for no, "" for yes)
celciusoutput = ""
# Save all output? ("!" for no, "" for yes)
saveall = "!"
# Fractionate all water? ("!" for no, "" for yes)
fractionatewater = "!"
# Fractionate individual phases (specify as strings in cell array, i.e. {"olivine","spinel"})
fractionate = String[]
# Coninuous (fractional) melting? ("!" for no, "" for yes)
continuous = "!"
# Threshold above which melt is extracted (if fractionation is turned on)
minf = 0.005
# Do trace element calculations
dotrace = "!"
# Treat water as a trace element
dotraceh2o = "!"
# Initial trace compositionT
tsc = Float64[]
# Initial trace elements
telements = String[]
# Default global constraints
Pmax = 90000
Pmin = 2
Tmax = 3000
Tmin = 450
# Simulation number (for folder, etc)
########################## end Default Settings ############################
# Guess if intention is for calculation to end at Tf or Pf as a min or max
if last(T_range)<first(T_range)
Tmin=last(T_range)
end
if last(T_range)>first(T_range)
Tmax=last(T_range)
end
if last(P_range)<first(P_range)
Pmin=last(P_range)
end
if last(P_range)>first(P_range)
Pmax=last(P_range)
end
if fractionatesolids
fractionatesolids = ""
else
fractionatesolids = "!"
end
# Normalize starting composition
composition = composition./sum(composition)*100
# output prefixectory name
prefix = joinpath(scratchdir, "out$(index)/")
# Ensure directory exists and is empty
system("rm -rf $prefix; mkdir -p $prefix")
# Make .melts file containing the starting composition you want to run simulations on
fp = open(prefix*"sc.melts", "w")
for i ∈ eachindex(elements)
write(fp,"Initial Composition: $(elements[i]) $(trunc(composition[i],digits=4))\n")
end
for i ∈ eachindex(telements)
write(fp, "Initial Trace: $(telements[i]) $(trunc(tsc[i],digits=4))\n")
end
write(fp, "Initial Temperature: $(trunc(first(T_range),digits=2))\nInitial Pressure: $(trunc(first(P_range),digits=2))\nlog fo2 Path: $fo2path\n")
for i ∈ eachindex(fractionate)
write(fp,"Fractionate: $(fractionate[i])\n")
end
for i ∈ eachindex(suppress)
write(fp,"Suppress: $(suppress[i])\n")
end
close(fp)
# Make melts_env file to specify type of MELTS calculation
fp = open(prefix*"/melts_env.txt", "w")
write(fp, "! *************************************\n! Julia-generated environment file\n! *************************************\n\n" *
"! this variable chooses MELTS or pMELTS; for low-pressure use MELTS\n" *
"ALPHAMELTS_VERSION $version\n\n" *
"! do not use this unless fO2 anomalies at the solidus are a problem\n" *
"!ALPHAMELTS_ALTERNATIVE_FO2 true\n\n" *
"! use this if you want to buffer fO2 for isentropic, isenthalpic or isochoric mode\n! e.g. if you are doing isenthalpic AFC\n" *
"!ALPHAMELTS_IMPOSE_FO2 true\n\n" *
"! use if you want assimilation and fractional crystallization (AFC)\n" *
"!ALPHAMELTS_ASSIMILATE true\n\n" *
"! isothermal, isobaric, isentropic, isenthalpic, isochoric, geothermal or PTPath\n" *
"ALPHAMELTS_MODE $mode\n" *
"!ALPHAMELTS_PTPATH_FILE ptpath.txt\n\n" *
"! need to set DELTAP for polybaric paths; DELTAT for isobaric paths\nALPHAMELTS_DELTAP $(trunc(dP,digits=1))\n" *
"ALPHAMELTS_DELTAT $(trunc(dT,digits=1))\n" *
"ALPHAMELTS_MAXP $(trunc(Pmax,digits=1))\n" *
"ALPHAMELTS_MINP $(trunc(Pmin,digits=1))\n" *
"ALPHAMELTS_MAXT $(trunc(Tmax,digits=1))\n" *
"ALPHAMELTS_MINT $(trunc(Tmin,digits=1))\n\n" *
"! this one turns on fractional crystallization for all solids\n! use Fractionate: in the melts file instead for selective fractionation\n" *
"$(fractionatesolids)ALPHAMELTS_FRACTIONATE_SOLIDS true\n" *
"$(fractionatesolids)ALPHAMELTS_MASSIN $massin\n\n" *
"! free water is unlikely but can be extracted\n" *
"$(fractionatewater)ALPHAMELTS_FRACTIONATE_WATER true\n" *
"$(fractionatewater)ALPHAMELTS_MINW 0.005\n\n" *
"! the next one gives an output file that is always updated, even for single calculations\n" *
"$(saveall)ALPHAMELTS_SAVE_ALL true\n" *
"!ALPHAMELTS_SKIP_FAILURE true\n\n" *
"! this option converts the output temperature to celcius, like the input\n" *
"$(celciusoutput)ALPHAMELTS_CELSIUS_OUTPUT true\n\n" *
"! the next two turn on and off fractional melting\n" *
"$(continuous)ALPHAMELTS_CONTINUOUS_MELTING true\n" *
"$(continuous)ALPHAMELTS_MINF $minf\n" *
"$(continuous)ALPHAMELTS_INTEGRATE_FILE integrate.txt\n\n" *
"! the next two options refer to the trace element engine\n" *
"$(dotrace)ALPHAMELTS_DO_TRACE true\n" *
"$(dotraceh2o)ALPHAMELTS_DO_TRACE_H2O true\n")
close(fp)
# Make a batch file to run the above .melts file starting from the liquidus
fp = open(prefix*"/batch.txt", "w")
write(fp,batchstring)
close(fp)
# Run the command
# Edit the following line(s to make sure you have a correct path to the "run_alphamelts.command" perl script
if verbose
system("cd " * prefix * "; " * meltspath * " -f melts_env.txt -b batch.txt")
else
system("cd " * prefix * "; " * meltspath * " -f melts_env.txt -b batch.txt &>./melts.log")
end
return 0
end
export melts_configure
"""
```julia
melts_query(scratchdir::String; index=1)
```
Read all phase proportions from `Phase_main_tbl.txt` in specified MELTS run directory
Returns an elementified dictionary
"""
function melts_query(scratchdir::String; index=1, importas=:Dict)
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
if importas==:Dict
melts = Dict{String, Union{Vector{String}, Dict}}()
else
melts = Dict{String, Union{Vector{String}, NamedTuple}}()
end
if isfile(prefix*"/Phase_main_tbl.txt")
data = readdlm(prefix*"/Phase_main_tbl.txt", ' ', skipblanks=false)
pos = findall(all(isempty.(data), dims=2) |> vec)
melts["minerals"] = Array{String}(undef, length(pos)-1)
for i=1:(length(pos)-1)
name = data[pos[i]+1,1]
melts[name] = elementify(data[pos[i]+2:pos[i+1]-1,:], skipnameless=true, importas=importas)
melts["minerals"][i] = name
end
end
return melts
end
export melts_query
"""
```julia
melts_query_modes(scratchdir::String; index=1)
```
Read modal phase proportions from `Phase_mass_tbl.txt` in specified MELTS run
Returns an elementified dictionary
"""
function melts_query_modes(scratchdir::String; index=1, importas=:Dict)
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Read results and return them if possible
if isfile(prefix*"/Phase_mass_tbl.txt")
# Read data as an Array{Any}
data = readdlm(prefix*"Phase_mass_tbl.txt", ' ', skipstart=1)
# Convert to a dictionary
data = elementify(data, standardize=true, skipnameless=true, importas=importas)
else
# Return empty dictionary if file doesn't exist
data = importas==:Dict ? Dict() : ()
end
return data
end
export melts_query_modes
"""
```julia
melts_clean_modes(scratchdir::String; index=1)
```
Read and parse / clean-up modal phase proportions from specified MELTS run directory
Returns an elementified dictionary
"""
function melts_clean_modes(scratchdir::String; index=1)
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Read results and return them if possible
if isfile(prefix*"/Phase_mass_tbl.txt")
# Read data as an Array{Any}
data = readdlm(prefix*"Phase_mass_tbl.txt", ' ', skipstart=1)
# Convert to a dictionary
data = elementify(data, standardize=true, skipnameless=true, importas=:Dict)
# Start by transferring over all the non-redundant elements
modes = typeof(data)()
for e in data["elements"]
m = replace(e, r"_.*" => s"")
if haskey(modes, m)
modes[m] .+= data[e]
else
modes[m] = copy(data[e])
end
end
# Add the sum of all solids
modes["solids"] = zeros(size(data["Temperature"]))
for e in data["elements"][4:end]
if !contains(e, "water") && !contains(e, "liquid")
modes["solids"] .+= data[e]
end
end
# Get full mineral compositions, add feldspar and oxides
melts = melts_query(scratchdir, index=index)
if containsi(melts["minerals"],"feldspar")
modes["anorthite"] = zeros(size(modes["Temperature"]))
modes["albite"] = zeros(size(modes["Temperature"]))
modes["orthoclase"] = zeros(size(modes["Temperature"]))
end
An_Ca = (238.12507+40.0784) / (15.999+40.0784)
Ab_Na = (239.22853+22.98977*2) / (15.999+22.98977*2)
Or_K = (239.22853+39.09831*2) / (15.999+39.09831*2)
if containsi(melts["minerals"],"rhm_oxide")
modes["ilmenite"] = zeros(size(modes["Temperature"]))
modes["magnetite"] = zeros(size(modes["Temperature"]))
modes["hematite"] = zeros(size(modes["Temperature"]))
end
for m in melts["minerals"]
if containsi(m,"feldspar")
t = vec(findclosest(melts[m]["Temperature"],modes["Temperature"]))
AnAbOr = [melts[m]["CaO"]*An_Ca melts[m]["Na2O"]*Ab_Na melts[m]["K2O"]*Or_K] |> x -> x ./ sum(x, dims=2)
modes["anorthite"][t] .+= AnAbOr[:,1] .* melts[m]["mass"]
modes["albite"][t] .+= AnAbOr[:,2] .* melts[m]["mass"]
modes["orthoclase"][t] .+= AnAbOr[:,3] .* melts[m]["mass"]
elseif containsi(m,"rhm_oxide")
t = vec(findclosest(melts[m]["Temperature"],modes["Temperature"]))
Ilmenite = Vector{Float64}(undef, length(t))
Magnetite = Vector{Float64}(undef, length(t))
if haskey(melts[m],"MnO")
Ilmenite .= (melts[m]["TiO2"] + melts[m]["MnO"]+(melts[m]["TiO2"]*(71.8444/79.8768) - melts[m]["MnO"]*(71.8444/70.9374))) / 100
Magnetite .= (melts[m]["FeO"] - (melts[m]["TiO2"])*71.8444/79.8768) * (1+159.6882/71.8444)/100
else
Ilmenite .= (melts[m]["TiO2"] + melts[m]["TiO2"]*71.8444/79.8768) / 100
Magnetite .= (melts[m]["FeO"] - melts[m]["TiO2"]*71.8444/79.8768) * (1+159.6882/71.8444)/100
end
Magnetite[Magnetite.<0] .= 0
Hematite = (melts[m]["Fe2O3"] - Magnetite*100*159.6882/231.5326)/100
modes["ilmenite"][t] .+= melts[m]["mass"] .* Ilmenite
modes["magnetite"][t] .+= melts[m]["mass"] .* Magnetite
modes["hematite"][t] .+= melts[m]["mass"] .* Hematite
end
end
minerals = sort(collect(keys(modes)))
modes["elements"] = ["Pressure","Temperature","mass","solids","liquid"] ∪ minerals[.!containsi.(minerals, "feldspar") .& .!containsi.(minerals, "rhm")]
else
# Return empty dictionary if file doesn't exist
modes = Dict()
end
return modes
end
export melts_clean_modes
"""
```julia
melts_query_liquid(scratchdir::String; index=1)
```
Read liquid composition from `Liquid_comp_tbl.txt` in specified MELTS run directory
Returns an elementified dictionary
"""
function melts_query_liquid(scratchdir::String; index=1, importas=:Dict)
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Read results and return them if possible
if isfile(prefix*"/Liquid_comp_tbl.txt")
# Read data as an Array{Any}
data = readdlm(prefix*"Liquid_comp_tbl.txt", ' ', skipstart=1)
# Convert to a dictionary
data = elementify(data, standardize=true, skipnameless=true, importas=importas)
else
# Return empty dictionary if file doesn't exist
data = importas==:Dict ? Dict() : ()
end
return data
end
export melts_query_liquid
"""
```julia
melts_query_solid(scratchdir::String; index=1)
```
Read solid composition from `Solid_comp_tbl.txt` in specified MELTS run directory
Returns an elementified dictionary
"""
function melts_query_solid(scratchdir::String; index=1, importas=:Dict)
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Read results and return them if possible
if isfile(prefix*"/Solid_comp_tbl.txt")
# Read data as an Array{Any}
data = readdlm(prefix*"Solid_comp_tbl.txt", ' ', skipstart=1)
# Convert to a dictionary
data = elementify(data, standardize=true, skipnameless=true, importas=importas)
else
# Return empty dictionary if file doesn't exist
data = importas==:Dict ? Dict() : ()
end
return data
end
export melts_query_solid
"""
```julia
melts_query_system(scratchdir::String; index=1, importas=:Dict)
```
Read system thermodynamic and composition data from `System_main_tbl.txt` in
specified MELTS run directory. Returns an elementified dictionary or tuple.
"""
function melts_query_system(scratchdir::String; index=1, importas=:Dict)
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Read results and return them if possible
if isfile(prefix*"/System_main_tbl.txt")
# Read data as an Array{Any}
data = readdlm(prefix*"System_main_tbl.txt", ' ', skipstart=1)
# Convert to a dictionary
data = elementify(data, standardize=true, skipnameless=true, importas=importas)
else
# Return empty dictionary if file doesn't exist
data = importas==:Dict ? Dict() : ()
end
return data
end
export melts_query_system
## -- Perplex interface: 1. Configuration
"""
```julia
perplex_configure_geotherm(perplexdir::String, scratchdir::String, composition::Collection{Number},
\telements::String=["SIO2","TIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O","H2O"],
\tP_range=(280,28000), T_surf::Number=273.15, geotherm::Number=0.1;
\tdataset::String="hp02ver.dat",
\tindex::Integer=1,
\tnpoints::Integer=100,
\tsolution_phases::String="O(HP)\\nOpx(HP)\\nOmph(GHP)\\nGt(HP)\\noAmph(DP)\\ncAmph(DP)\\nT\\nB\\nChl(HP)\\nBio(TCC)\\nMica(CF)\\nCtd(HP)\\nIlHm(A)\\nSp(HP)\\nSapp(HP)\\nSt(HP)\\nfeldspar_B\\nDo(HP)\\nF\\n",
\texcludes::String="ts\\nparg\\ngl\\nged\\nfanth\\ng\\n",
\tmode_basis::String="vol", #["vol", "wt", "mol"]
\tcomposition_basis::String="wt", #["vol", "wt", "mol"]
\tfluid_eos::Integer=5)
```
Set up a PerpleX calculation for a single bulk composition along a specified
geothermal gradient and pressure (depth) range. P specified in bar and T_surf
in Kelvin, with geothermal gradient in units of Kelvin/bar
"""
function perplex_configure_geotherm(perplexdir::String, scratchdir::String, composition::Collection{Number},
elements::Collection{String}=["SIO2","TIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O","H2O"],
P_range::NTuple{2,Number}=(280,28000), T_surf::Number=273.15, geotherm::Number=0.1;
dataset::String="hp02ver.dat",
index::Integer=1,
npoints::Integer=100,
solution_phases::String="O(HP)\nOpx(HP)\nOmph(GHP)\nGt(HP)\noAmph(DP)\ncAmph(DP)\nT\nB\nChl(HP)\nBio(TCC)\nMica(CF)\nCtd(HP)\nIlHm(A)\nSp(HP)\nSapp(HP)\nSt(HP)\nfeldspar_B\nDo(HP)\nF\n",
excludes::String="ts\nparg\ngl\nged\nfanth\ng\n",
mode_basis::String="vol",
composition_basis::String="wt",
fluid_eos::Integer=5
)
build = joinpath(perplexdir, "build")# path to PerpleX build
vertex = joinpath(perplexdir, "vertex")# path to PerpleX vertex
#Configure working directory
prefix = joinpath(scratchdir, "out$(index)/")
system("rm -rf $prefix; mkdir -p $prefix")
# Place required data files
system("cp $(joinpath(perplexdir,dataset)) $prefix")
system("cp $(joinpath(perplexdir,"perplex_option.dat")) $prefix")
system("cp $(joinpath(perplexdir,"solution_model.dat")) $prefix")
# Edit perplex_option.dat to specify number of nodes at which to solve
system("sed -e \"s/1d_path .*|/1d_path $npoints $npoints |/\" -i.backup $(prefix)perplex_option.dat")
# Edit perplex_option.dat to output all seismic properties
#println("editing perplex options ")
system("sed -e \"s/seismic_output .*|/seismic_output all |/\" -i.backup $(prefix)perplex_option.dat")
# Specify whether we want volume or weight percentages
system("sed -e \"s/proportions .*|/proportions $mode_basis |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/composition_system .*|/composition_system $composition_basis |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/composition_phase .*|/composition_phase $composition_basis |/\" -i.backup $(prefix)perplex_option.dat")
# Create build batch file.
fp = open(prefix*"build.bat", "w")
# Name, components, and basic options. P-T conditions.
# default fluid_eos = 5: Holland and Powell (1998) "CORK" fluid equation of state
elementstring = join(elements .* "\n")
write(fp,"$index\n$dataset\nperplex_option.dat\nn\n3\nn\nn\nn\n$elementstring\n$fluid_eos\nn\ny\n2\n1\n$T_surf\n$geotherm\n$(first(P_range))\n$(last(P_range))\ny\n") # v6.8.7
# write(fp,"$index\n$dataset\nperplex_option.dat\nn\nn\nn\nn\n$elementstring\n5\n3\nn\ny\n2\n1\n$T_surf\n$geotherm\n$(first(P_range))\n$(last(P_range))\ny\n") # v6.8.1
# Whole-rock composition
for i ∈ eachindex(composition)
write(fp,"$(composition[i]) ")
end
# Solution model
if length(excludes) > 0
write(fp,"\nn\ny\nn\n$excludes\ny\nsolution_model.dat\n$solution_phases\nGeothermal")
else
write(fp,"\nn\nn\ny\nsolution_model.dat\n$(solution_phases)\nGeothermal")
end
close(fp)
# build PerpleX problem definition
system("cd $prefix; $build < build.bat > build.log")
println("Built problem definition")
# Run PerpleX vertex calculations
result = system("cd $prefix; echo $index | $vertex > vertex.log")
return result
end
export perplex_configure_geotherm
"""
```julia
perplex_configure_isobar(perplexdir::String, scratchdir::String, composition::Collection{Number},
\telements::String=["SIO2","TIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O","H2O"]
\tP::Number=10000, T_range::NTuple{2,Number}=(500+273.15, 1500+273.15);
\tdataset::String="hp11ver.dat",
\tindex::Integer=1,
\tnpoints::Integer=100,
\tsolution_phases::String="O(HP)\\nOpx(HP)\\nOmph(GHP)\\nGt(HP)\\noAmph(DP)\\ncAmph(DP)\\nT\\nB\\nChl(HP)\\nBio(TCC)\\nMica(CF)\\nCtd(HP)\\nIlHm(A)\\nSp(HP)\\nSapp(HP)\\nSt(HP)\\nfeldspar_B\\nDo(HP)\\nF\\n",
\texcludes::String="ts\\nparg\\ngl\\nged\\nfanth\\ng\\n",
\tmode_basis::String="wt", #["vol", "wt", "mol"]
\tcomposition_basis::String="wt", #["vol", "wt", "mol"]
\tnonlinear_subdivision::Bool=false,
\tfluid_eos::Integer=5)
```
Set up a PerpleX calculation for a single bulk composition along a specified
isobaric temperature gradient. P specified in bar and T_range in Kelvin
"""
function perplex_configure_isobar(perplexdir::String, scratchdir::String, composition::Collection{Number},
elements::Collection{String}=("SIO2","TIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O","H2O"),
P::Number=10000, T_range::NTuple{2,Number}=(500+273.15, 1500+273.15);
dataset::String="hp11ver.dat",
index::Integer=1,
npoints::Integer=100,
solution_phases::String="O(HP)\nOpx(HP)\nOmph(GHP)\nGt(HP)\noAmph(DP)\ncAmph(DP)\nT\nB\nChl(HP)\nBio(TCC)\nMica(CF)\nCtd(HP)\nIlHm(A)\nSp(HP)\nSapp(HP)\nSt(HP)\nfeldspar_B\nDo(HP)\nF\n",
excludes::String="ts\nparg\ngl\nged\nfanth\ng\n",
mode_basis::String="wt",
composition_basis::String="wt",
nonlinear_subdivision::Bool=false,
fluid_eos::Integer=5
)
build = joinpath(perplexdir, "build")# path to PerpleX build
vertex = joinpath(perplexdir, "vertex")# path to PerpleX vertex
#Configure working directory
prefix = joinpath(scratchdir, "out$(index)/")
system("rm -rf $prefix; mkdir -p $prefix")
# Place required data files
system("cp $(joinpath(perplexdir,dataset)) $prefix")
system("cp $(joinpath(perplexdir,"perplex_option.dat")) $prefix")
system("cp $(joinpath(perplexdir,"solution_model.dat")) $prefix")
# Edit perplex_option.dat to specify number of nodes at which to solve
system("sed -e \"s/1d_path .*|/1d_path $npoints $npoints |/\" -i.backup $(prefix)perplex_option.dat")
# Specify whether we want volume or weight percentages
system("sed -e \"s/proportions .*|/proportions $mode_basis |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/composition_system .*|/composition_system $composition_basis |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/composition_phase .*|/composition_phase $composition_basis |/\" -i.backup $(prefix)perplex_option.dat")
# Turn on nonlinear subdivision and change resolution
if nonlinear_subdivision
system("sed -e \"s/non_linear_switch .*|/non_linear_switch T |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s:initial_resolution .*|:initial_resolution 1/2 1/4 |:\" -i.backup $(prefix)perplex_option.dat")
end
# Create build batch file
# Options based on Perplex v6.8.7
fp = open(prefix*"build.bat", "w")
# Name, components, and basic options. P-T conditions.
# default fluid_eos = 5: Holland and Powell (1998) "CORK" fluid equation of state
elementstring = join(elements .* "\n")
write(fp,"$index\n$dataset\nperplex_option.dat\nn\n3\nn\nn\nn\n$elementstring\n$fluid_eos\nn\nn\n2\n$(first(T_range))\n$(last(T_range))\n$P\ny\n") # v6.8.7
# write(fp,"$index\n$dataset\nperplex_option.dat\nn\nn\nn\nn\n$elementstring\n$fluid_eos\n3\nn\nn\n2\n$(first(T_range))\n$(last(T_range))\n$P\ny\n") # v6.8.1
# Whole-rock composition
for i ∈ eachindex(composition)
write(fp,"$(composition[i]) ")
end
# Solution model
write(fp,"\nn\ny\nn\n$excludes\ny\nsolution_model.dat\n$solution_phases\nIsobaric")
close(fp)
# build PerpleX problem definition
system("cd $prefix; $build < build.bat > build.log")
# Run PerpleX vertex calculations
result = system("cd $prefix; printf \"$index\ny\ny\ny\ny\ny\ny\ny\ny\n\" | $vertex > vertex.log")
return result
end
export perplex_configure_isobar
"""
```julia
perplex_configure_path(perplexdir::String, scratchdir::String, composition::Collection{Number}, PTdir::String="", PTfilename::String="",
\telements::String=("SIO2","TIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O","H2O"),
\tT_range::NTuple{2,Number}=(500+273.15, 1050+273.15);
\tdataset::String="hp11ver.dat",
\tindex::Integer=1,
\tsolution_phases::String="O(HP)\\nOpx(HP)\\nOmph(GHP)\\nGt(HP)\\noAmph(DP)\\ncAmph(DP)\\nT\\nB\\nChl(HP)\\nBio(TCC)\\nMica(CF)\\nCtd(HP)\\nIlHm(A)\\nSp(HP)\\nSapp(HP)\\nSt(HP)\\nfeldspar_B\\nDo(HP)\\nF\\n",
\texcludes::String="ts\\nparg\\ngl\\nged\\nfanth\\ng\\n",
\tmode_basis::String="wt", #["vol", "wt", "mol"]
\tcomposition_basis::String="wt", #["vol", "wt", "mol"]
\tnonlinear_subdivision::Bool=false,
\tfluid_eos::Integer=5)
```
Set up a PerpleX calculation for a single bulk composition along a specified
pressure–temperature path with T as the independent variable.
P specified in bar and T_range in Kelvin
"""
function perplex_configure_path(perplexdir::String, scratchdir::String, composition::Collection{Number}, PTdir::String="", PTfilename = "",
elements::Collection{String}=("SIO2","TIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O","H2O"),
T_range::NTuple{2,Number}=(500+273.15, 1050+273.15);
dataset::String="hp11ver.dat",
index::Integer=1,
solution_phases::String="O(HP)\\nOpx(HP)\\nOmph(GHP)\\nGt(HP)\\noAmph(DP)\\ncAmph(DP)\\nT\\nB\\nChl(HP)\\nBio(TCC)\\nMica(CF)\\nCtd(HP)\\nIlHm(A)\\nSp(HP)\\nSapp(HP)\\nSt(HP)\\nfeldspar_B\\nDo(HP)\\nF\\n",
excludes::String="ts\\nparg\\ngl\\nged\\nfanth\\ng\\n",
mode_basis::String="wt", #["vol", "wt", "mol"]
composition_basis::String="wt", #["vol", "wt", "mol"]
nonlinear_subdivision::Bool=false,
fluid_eos::Integer=5,
)
build = joinpath(perplexdir, "build")# path to PerpleX build
vertex = joinpath(perplexdir, "vertex")# path to PerpleX vertex
# Configure working directory
prefix = joinpath(scratchdir, "out$(index)/")
system("rm -rf $prefix; mkdir -p $prefix")
# Place required data files
system("cp $(joinpath(perplexdir, dataset)) $prefix")
system("cp $(joinpath(perplexdir,"perplex_option.dat")) $prefix")
system("cp $(joinpath(perplexdir,"solution_model.dat")) $prefix")
# Specify whether we want volume or weight percentages
system("sed -e \"s/proportions .*|/proportions $mode_basis |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/composition_system .*|/composition_system $composition_basis |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/composition_phase .*|/composition_phase $composition_basis |/\" -i.backup $(prefix)perplex_option.dat")
# Turn on nonlinear subdivision and change resolution
if nonlinear_subdivision
system("sed -e \"s/non_linear_switch .*|/non_linear_switch T |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s:initial_resolution .*|:initial_resolution 1/2 1/4 |:\" -i.backup $(prefix)perplex_option.dat")
end
# Create default P–T.dat path if one is not provided
if PTdir == ""
# Input parameters
P_range = (2000, 6000, 10000, 14000, 18000) #bar
T_range = (550+273.15, 1050+273.15) #K
T_int = 10 #Interval for T
T = T_range[1]:T_int:T_range[2]
P = zeros(length(T))
for i in 1:length(T)
if i == length(T)
P[i] = P_range[end]
else
P[i] = P_range[floor(Int64, (i/length(T)) * length(P_range) + 1)]
end
end
PTfile = joinpath(prefix, "P–T.dat")
# PTdir = "P–T.dat"
# Save P–T path as .dat file
# Apparently you need to have it as T and then P despite what Perplex tells you
open(PTfile, "w") do file
for i in zip(T, P)
write(file, "$(i[1])\t$(i[2])\n")
end
end
system("cp $(PTfile) $perplexdir")
system("cp $(PTfile) $prefix")
PTfilename = "P–T.dat"
else
system("cp $(PTdir) $prefix")
end
# Create build batch file
# Options based on Perplex v6.8.7
fp = open(prefix*"build.bat", "w")
# Name, components, and basic options. P-T conditions.
# default fluid_eos = 5: Holland and Powell (1998) "CORK" fluid equation of state
elementstring = join(elements .* "\n")
# write(fp,"$index\n$dataset\nperplex_option.dat\nn\n3\nn\nn\nn\n$elementstring\n$fluid_eos\ny\n$PTdir\n2\n$(first(T_range))\n$(last(T_range))\ny\n")
write(fp,"$index\n$dataset\nperplex_option.dat\nn\n3\nn\nn\nn\n$elementstring\n$fluid_eos\ny\n$PTfilename\n2\ny\n") #6.8.7
# Whole-rock composition
for i ∈ eachindex(composition)
write(fp,"$(composition[i]) ")
end
# Solution model
write(fp,"\nn\ny\nn\n$excludes\ny\nsolution_model.dat\n$solution_phases\nP-T Path")
close(fp)
# build PerpleX problem definition
system("cd $prefix; $build < build.bat > build.log")
# Run PerpleX vertex calculations
result = system("cd $prefix; printf \"$index\ny\ny\ny\ny\ny\ny\ny\ny\ny\ny\ny\n0\" | $vertex > vertex.log")
return result
end
export perplex_configure_path
"""
```julia
perplex_configure_pseudosection(perplexdir::String, scratchdir::String, composition::Collection{Number},
\telements::Collection{String}=("SIO2","TIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O","H2O"),
\tP::NTuple{2,Number}=(280, 28000), T::NTuple{2,Number}=(273.15, 1500+273.15);
\tdataset::String="hp11ver.dat",
\tindex::Integer=1,
\txnodes::Integer=42,
\tynodes::Integer=42,
\tsolution_phases::String="O(HP)\\nOpx(HP)\\nOmph(GHP)\\nGt(HP)\\noAmph(DP)\\ncAmph(DP)\\nT\\nB\\nChl(HP)\\nBio(TCC)\\nMica(CF)\\nCtd(HP)\\nIlHm(A)\\nSp(HP)\\nSapp(HP)\\nSt(HP)\\nfeldspar_B\\nDo(HP)\\nF\\n",
\texcludes::String="ts\\nparg\\ngl\\nged\\nfanth\\ng\\n",
\tmode_basis::String="vol", #["vol", "wt", "mol"]
\tcomposition_basis::String="wt", #["wt", "mol"]
\tfluid_eos::Number=5)
```
Set up a PerpleX calculation for a single bulk composition across an entire
2d P-T space. P specified in bar and T in Kelvin.
"""
function perplex_configure_pseudosection(perplexdir::String, scratchdir::String, composition::Collection{Number},
elements::Collection{String}=("SIO2","TIO2","AL2O3","FEO","MGO","CAO","NA2O","K2O","H2O"),
P::NTuple{2,Number}=(280, 28000), T::NTuple{2,Number}=(273.15, 1500+273.15);
dataset::String="hp11ver.dat",
index::Integer=1,
xnodes::Integer=42,
ynodes::Integer=42,
solution_phases::String="O(HP)\nOpx(HP)\nOmph(GHP)\nGt(HP)\noAmph(DP)\ncAmph(DP)\nT\nB\nChl(HP)\nBio(TCC)\nMica(CF)\nCtd(HP)\nIlHm(A)\nSp(HP)\nSapp(HP)\nSt(HP)\nfeldspar_B\nDo(HP)\nF\n",
excludes::String="ts\nparg\ngl\nged\nfanth\ng\n",
mode_basis::String="vol",
composition_basis::String="wt",
fluid_eos::Number=5
)
build = joinpath(perplexdir, "build")# path to PerpleX build
vertex = joinpath(perplexdir, "vertex")# path to PerpleX vertex
#Configure working directory
prefix = joinpath(scratchdir, "out$(index)/")
system("rm -rf $prefix; mkdir -p $prefix")
# Place required data files
system("cp $(joinpath(perplexdir,dataset)) $prefix")
system("cp $(joinpath(perplexdir,"perplex_option.dat")) $prefix")
system("cp $(joinpath(perplexdir,"solution_model.dat")) $prefix")
# Edit data files to specify number of nodes at which to solve
system("sed -e \"s/x_nodes .*|/x_nodes $xnodes $xnodes |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/y_nodes .*|/y_nodes $ynodes $ynodes |/\" -i.backup $(prefix)perplex_option.dat")
# Specify whether we want volume or weight percentages
system("sed -e \"s/proportions .*|/proportions $mode_basis |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/composition_system .*|/composition_system $composition_basis |/\" -i.backup $(prefix)perplex_option.dat")
system("sed -e \"s/composition_phase .*|/composition_phase $composition_basis |/\" -i.backup $(prefix)perplex_option.dat")
# Create build batch file
# Options based on Perplex v6.8.7
fp = open(prefix*"build.bat", "w")
# Name, components, and basic options. P-T conditions.
# default fluid_eos = 5: Holland and Powell (1998) "CORK" fluid equation of state
elementstring = join(elements .* "\n")
write(fp,"$index\n$dataset\nperplex_option.dat\nn\n2\nn\nn\nn\n$elementstring\n$fluid_eos\nn\n2\n$(first(T))\n$(last(T))\n$(first(P))\n$(last(P))\ny\n") # v6.8.7
# Whole-rock composition
for i ∈ eachindex(composition)
write(fp,"$(composition[i]) ")
end
# Solution model
write(fp,"\nn\ny\nn\n$excludes\ny\nsolution_model.dat\n$solution_phases\nPseudosection")
close(fp)
# build PerpleX problem definition
system("cd $prefix; $build < build.bat > build.log")
# Run PerpleX vertex calculations
result = system("cd $prefix; echo $index | $vertex > vertex.log")
return result
end
export perplex_configure_pseudosection
## -- Perplex interface: 2. 0d queries
"""
```julia
perplex_query_point(perplexdir::String, scratchdir::String, indvar::Number; index::Integer=1)
```
Query perplex results at a single temperature on an isobar or single pressure
on a geotherm. Results are returned as a string.
"""
function perplex_query_point(perplexdir::String, scratchdir::String, indvar::Number; index::Integer=1)
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Sanitize T inputs to avoid PerpleX escape sequence
if indvar == 999
indvar = 999.001
end
# Create werami batch file
# Options based on Perplex v6.7.2
fp = open(prefix*"werami.bat", "w")
write(fp,"$index\n1\n$indvar\n999\n0\n")
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.txt")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Read results and return them if possible
data = ""
try
# Read entire output file as a string
fp = open("$(prefix)$(index)_1.txt", "r")
data = read(fp, String)
close(fp)
catch
# Return empty string if file doesn't exist
@warn "$(prefix)$(index)_1.txt could not be parsed, perplex may not have run"
end
return data
end
"""
```julia
perplex_query_point(perplexdir::String, scratchdir::String, P::Number, T::Number; index::Integer=1)
```
Query perplex results at a single P,T point in a pseudosection.
Results are returned as a string.
"""
function perplex_query_point(perplexdir::String, scratchdir::String, P::Number, T::Number; index::Integer=1)
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Sanitize T inputs to avoid PerpleX escape sequence
if P == 99
P = 99.001
end
if T == 99
T = 99.001
end
# Create werami batch file
# Options based on Perplex v6.7.2
fp = open(prefix*"werami.bat", "w")
write(fp,"$index\n1\n$T\n$P\n99\n99\n0\n")
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.txt")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Read results and return them if possible
data = ""
try
# Read entire output file as a string
fp = open("$(prefix)$(index)_1.txt", "r")
data = read(fp, String)
close(fp)
catch
# Return empty string if file doesn't exist
@warn "$(prefix)$(index)_1.txt could not be parsed, perplex may not have run"
end
return data
end
export perplex_query_point
## --- Perplex interface: 3. 1d queries
# # We'll need this for when perplex messes up
# molarmass = Dict("SIO2"=>60.083, "TIO2"=>79.8651, "AL2O3"=>101.96007714, "FE2O3"=>159.6874, "FEO"=>71.8442, "MGO"=>40.304, "CAO"=>56.0774, "MNO"=>70.9370443, "NA2O"=>61.978538564, "K2O"=>94.19562, "H2O"=>18.015, "CO2"=>44.009, "P2O5"=>141.942523997)
"""
```julia
perplex_query_seismic(perplexdir::String, scratchdir::String;
\tdof::Integer=1, index::Integer=1, include_fluid="n")
```
Query perplex seismic results along a previously configured 1-d path (dof=1,
isobar or geotherm) or 2-d grid / pseudosection (dof=2).
Results are returned as a dictionary.
"""
function perplex_query_seismic(perplexdir::String, scratchdir::String;
dof::Integer=1, index::Integer=1, include_fluid::String="n", importas=:Dict)
# Query a pre-defined path (isobar or geotherm)
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
if dof == 1
# v6.7.8 1d path
write(fp,"$index\n3\n2\nn\n$include_fluid\n13\nn\n$include_fluid\n15\nn\n$include_fluid\n0\n0\n")
elseif dof == 2
# v6.7.8 2d grid
write(fp,"$index\n2\n2\nn\n$include_fluid\n13\nn\n$include_fluid\n15\nn\n$include_fluid\n0\nn\n1\n0\n")
else
error("Expecting dof = 1 (path) or 2 (grid/pseudosection) degrees of freedom")
end
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.tab")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
data = nothing
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
# Convert to a dictionary
data = elementify(data, importas=importas)
catch
# Return empty dictionary if file doesn't exist
data = importas==:Dict ? Dict() : ()
end
return data
end
"""
```julia
perplex_query_seismic(perplexdir::String, scratchdir::String, P::NTuple{2,Number}, T::NTuple{2,Number};
\tindex::Integer=1, npoints::Integer=200, include_fluid="n")
```
Query perplex seismic results along a specified P-T path using a pre-computed
pseudosection. Results are returned as a dictionary.
"""
function perplex_query_seismic(perplexdir::String, scratchdir::String, P::NTuple{2,Number}, T::NTuple{2,Number};
index::Integer=1, npoints::Integer=200, include_fluid="n", importas=:Dict)
# Query a new path from a pseudosection
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
# v6.7.8 pseudosection
write(fp,"$index\n3\nn\n$(first(T))\n$(first(P))\n$(last(T))\n$(last(P))\n$npoints\n2\nn
$include_fluid\n13\nn\n$include_fluid\n15\nn\n$include_fluid\n0\n0\n")
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.tab")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
data = nothing
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
# Convert to a dictionary
data = elementify(data, importas=importas)
catch
# Return empty dictionary if file doesn't exist
data = importas==:Dict ? Dict() : ()
end
return data
end
function perplex_query_seismic(perplexdir::String, scratchdir::String, P::AbstractArray, T::AbstractArray;
index::Integer=1, include_fluid="n", importas=:Dict)
# Query a new path from a pseudosection
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Write TP data to file
fp = open(prefix*"TP.tsv", "w")
for i in eachindex(T,P)
write(fp,"$(T[i])\t$(P[i])\n")
end
close(fp)
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
# v6.7.8 pseudosection
write(fp,"$index\n4\n2\nTP.tsv\n1\n2\nn\n$include_fluid
13\nn\n$include_fluid\n15\nn\n$include_fluid\n0\n0\n")
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.tab")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
data = nothing
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
# Convert to a dictionary
data = elementify(data, importas=importas)
catch
# Return empty dictionary if file doesn't exist
data = importas==:Dict ? Dict() : ()
end
return data
end
export perplex_query_seismic
"""
```julia
perplex_query_phase(perplexdir::String, scratchdir::String, phase::String;
\tdof::Integer=1, index::Integer=1, include_fluid="y", clean_units::Bool=true)
```
Query all perplex-calculated properties for a specified phase (e.g. "Melt(G)")
along a previously configured 1-d path (dof=1, isobar, geotherm, or P–T path) or 2-d
grid / pseudosection (dof=2). Results are returned as a dictionary.
"""
function perplex_query_phase(perplexdir::String, scratchdir::String, phase::String;
dof::Integer=1, index::Integer=1, include_fluid="y", clean_units::Bool=true, importas=:Dict)
# Query a pre-defined path (isobar or geotherm)
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
if dof == 1
# v6.7.8, 1d path
write(fp,"$index\n3\n36\n2\n$phase\n$include_fluid\n5\n0\n")
# If a named phase (e.g. feldspar) has multiple immiscible phases, average them (5)
elseif dof == 2
# v6.7.8, 2d grid
write(fp,"$index\n2\n36\n2\n$phase\n$include_fluid\nn\n1\n0\n") # v6.7.8
else
error("Expecting dof = 1 (path) or 2 (grid/pseudosection) degrees of freedom")
end
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.tab*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
elements = data[1,:]
# Renormalize weight percentages
t = contains.(elements,"wt%")
total_weight = nansum(Float64.(data[2:end,t]),dim=2)
# Check if perplex is messing up and outputting mole proportions
if nanmean(total_weight) < 50
@warn "Perplex seems to be reporting mole fractions instead of weight percentages"
# Attempt to change back to weight percentages
# for col = findall(t)
# data[2:end,col] .*= molarmass[replace(elements[col], ",wt%" => "")]
# end
# total_weight = nansum(Float64.(data[2:end,t]),dim=2)
end
data[2:end,t] .*= 100 ./ total_weight
# Clean up element names
if clean_units
elements = elements .|> x -> replace(x, ",%" => "_pct") # substutue _pct for ,% in column names
elements = elements .|> x -> replace(x, ",wt%" => "") # Remove units on major oxides
end
# Convert to a dictionary
result = elementify(data,elements, skipstart=1,importas=importas)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.tab could not be parsed, perplex may not have run"
end
return result
end
"""
```julia
perplex_query_phase(perplexdir::String, scratchdir::String, phase::String, P::NTuple{2,Number}, T::NTuple{2,Number};
\tindex::Integer=1, npoints::Integer=200, include_fluid="y", clean_units::Bool=true)
```
Query all perplex-calculated properties for a specified phase (e.g. "Melt(G)")
along a specified P-T path using a pre-computed pseudosection. Results are
returned as a dictionary.
"""
function perplex_query_phase(perplexdir::String, scratchdir::String, phase::String, P::NTuple{2,Number}, T::NTuple{2,Number};
index::Integer=1, npoints::Integer=200, include_fluid="y", clean_units::Bool=true, importas=:Dict)
# Query a new path from a pseudosection
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
# v6.7.8 pseudosection
# If a named phase (e.g. feldspar) has multiple immiscible phases, average them (5)
write(fp,"$index\n3\nn\n$(first(T))\n$(first(P))\n$(last(T))\n$(last(P))\n$npoints\n36\n2\n$phase\n$include_fluid\n5\n0\n")
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.tab*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
elements = data[1,:]
# Renormalize weight percentages
t = contains.(elements,"wt%")
total_weight = nansum(Float64.(data[2:end,t]),dim=2)
# Check if perplex is messing up and outputting mole proportions
if nanmean(total_weight) < 50
@warn "Perplex seems to be reporting mole fractions instead of weight percentages"
# , attempting to correct
# for col = findall(t)
# data[2:end,col] .*= molarmass[replace(elements[col], ",wt%" => "")]
# end
# total_weight = nansum(Float64.(data[2:end,t]),dim=2)
end
data[2:end,t] .*= 100 ./ total_weight
# Clean up element names
if clean_units
elements = elements .|> x -> replace(x, ",%" => "_pct") # substutue _pct for ,% in column names
elements = elements .|> x -> replace(x, ",wt%" => "") # Remove units on major oxides
end
# Convert to a dictionary
result = elementify(data,elements, skipstart=1,importas=importas)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.tab could not be parsed, perplex may not have run"
end
return result
end
function perplex_query_phase(perplexdir::String, scratchdir::String, phase::String, P::AbstractArray, T::AbstractArray;
index::Integer=1, include_fluid="y", clean_units::Bool=true, importas=:Dict)
# Query a new path from a pseudosection
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Write TP data to file
fp = open(prefix*"TP.tsv", "w")
for i in eachindex(T,P)
write(fp,"$(T[i])\t$(P[i])\n")
end
close(fp)
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
# v6.7.8 pseudosection
# If a named phase (e.g. feldspar) has multiple immiscible phases, average them (5)
write(fp,"$index\n4\n2\nTP.tsv\n1\n36\n2\n$phase\n$include_fluid\n5\n0\n")
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.tab*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
elements = data[1,:]
# Renormalize weight percentages
t = contains.(elements,"wt%")
total_weight = nansum(Float64.(data[2:end,t]),dim=2)
# Check if perplex is messing up and outputting mole proportions
if nanmean(total_weight) < 50
@warn "Perplex seems to be reporting mole fractions instead of weight percentages"
# , attempting to correct
# for col = findall(t)
# data[2:end,col] .*= molarmass[replace(elements[col], ",wt%" => "")]
# end
# total_weight = nansum(Float64.(data[2:end,t]),dim=2)
end
data[2:end,t] .*= 100 ./ total_weight
# Clean up element names
if clean_units
elements = elements .|> x -> replace(x, ",%" => "_pct") # substutue _pct for ,% in column names
elements = elements .|> x -> replace(x, ",wt%" => "") # Remove units on major oxides
end
# Convert to a dictionary
result = elementify(data,elements, skipstart=1,importas=importas)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.tab could not be parsed, perplex may not have run"
end
return result
end
export perplex_query_phase
"""
```julia
perplex_query_modes(perplexdir::String, scratchdir::String;
\tdof::Integer=1, index::Integer=1, include_fluid="y")
```
Query modal mineralogy (mass proportions) along a previously configured 1-d
path (dof=1, isobar, geotherm, or P–T path) or 2-d grid / pseudosection (dof=2).
Results are returned as a dictionary.
Currently returns wt%
"""
function perplex_query_modes(perplexdir::String, scratchdir::String;
dof::Integer=1, index::Integer=1, include_fluid="y", importas=:Dict)
# Query a pre-defined path (isobar or geotherm)
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
if dof == 1 # v6.7.8 1d path
write(fp,"$index\n3\n38\n3\nn\n37\n0\n0\n")
elseif dof == 2
# v6.7.8 2d grid
write(fp,"$index\n2\n25\nn\n$include_fluid\nn\n1\n0\n")
else
error("Expecting dof = 1 (path) or 2 (grid/pseudosection) degrees of freedom")
end
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.phm*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.phm")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.phm")
# Replace "Missing data" with just "Missing"
file_content = read("$(prefix)$(index)_1.phm", String)
modified_content = replace(file_content, "Missing data" => replace("Missing data", "Missing data" => "Missing"))
write("$(prefix)$(index)_1.phm", modified_content)
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
if dof == 1
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.phm", skipstart=8)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.phm could not be parsed, perplex may not have run"
end
# Convert to a dictionary.
table = elementify(data, importas=importas)
# Create results dictionary
phase_names = unique(table["Name"])
if haskey(table, "node#") #PT path
nodes = unique(table["node#"])
# Create result dictionary
result = Dict{String, Vector{Float64}}(i => zeros(length(nodes)) for i in phase_names)
result["P(bar)"] = zeros(length(nodes))
# Loop through table
for n in nodes
# Index table
n_idx = table["node#"] .== n
# Index phase name and weight(kg)
name = table["Name"][n_idx]
kg = table["phase,kg"][n_idx]
# Calculate wt% and add to results dictionary
for i in zip(name, kg)
result[i[1]][floor(Int64, n)] = (i[2]/nansum(kg)) * 100
end
result["P(bar)"][floor(Int64, n)] = table["P(bar)"][n_idx][1]
end
result["T(K)"] = unique(table["T(K)"])
result["node"] = nodes
else # isobar or geotherm
t_steps = unique(table["T(K)"])
result = Dict{String, Vector{Float64}}(i => zeros(length(t_steps)) for i in phase_names)
id = 1
# Loop through table
for t in t_steps
# Index table
t_idx = table["T(K)"] .== t
# Index phase name and weight(kg)
name = table["Name"][t_idx]
kg = table["phase,kg"][t_idx]
# Calculate wt% and add to results dictionary
for i in zip(name, kg)
result[i[1]][id] = (i[2]/nansum(kg)) * 100
end
id+=1
end
result["T(K)"] = t_steps
end
else
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
# Convert to a dictionary.
# Perplex sometimes returns duplicates of a single solution model, sum them.
result = elementify(data, sumduplicates=true, importas=importas)
end
return result
end
"""
```julia
perplex_query_modes(perplexdir::String, scratchdir::String, P::NTuple{2,Number}, T::NTuple{2,Number};
\tindex::Integer=1, npoints::Integer=200, include_fluid="y")
```
Query modal mineralogy (mass proportions) along a specified P-T path using a
pre-computed pseudosection. Results are returned as a dictionary.
"""
function perplex_query_modes(perplexdir::String, scratchdir::String, P::NTuple{2,Number}, T::NTuple{2,Number};
index::Integer=1, npoints::Integer=200, include_fluid="y", importas=:Dict)
# Query a new path from a pseudosection
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
# v6.7.8 pseudosection
write(fp,"$index\n3\nn\n$(first(T))\n$(first(P))\n$(last(T))\n$(last(P))\n$npoints\n25\nn\n$include_fluid\n0\n")
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.tab*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
# Convert to a dictionary
result = elementify(data, sumduplicates=true, importas=importas)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.tab could not be parsed, perplex may not have run"
end
return result
end
function perplex_query_modes(perplexdir::String, scratchdir::String, P::AbstractArray, T::AbstractArray;
index::Integer=1, include_fluid="y", importas=:Dict)
# Query a new path from a pseudosection
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Write TP data to file
fp = open(prefix*"TP.tsv", "w")
for i in eachindex(T,P)
write(fp,"$(T[i])\t$(P[i])\n")
end
close(fp)
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
# v6.7.8 pseudosection
write(fp,"$index\n4\n2\nTP.tsv\n1\n25\nn\n$include_fluid\n0\n")
close(fp)
# Make sure there isn"t already an output
system("rm -f $(prefix)$(index)_1.tab*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
# Convert to a dictionary
result = elementify(data, sumduplicates=true, importas=importas)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.tab could not be parsed, perplex may not have run"
end
return result
end
export perplex_query_modes
"""
```julia
perplex_query_system(perplexdir::String, scratchdir::String;
\tindex::Integer=1, include_fluid="y", clean_units::Bool=true)
```?
Query all perplex-calculated properties for the system (with or without fluid)
along a previously configured 1-d path (dof=1, isobar or geotherm) or 2-d
grid / pseudosection (dof=2). Results are returned as a dictionary.
Set include_fluid="n" to return solid+melt only.
"""
function perplex_query_system(perplexdir::String, scratchdir::String;
index::Integer=1, include_fluid="y", clean_units::Bool=true, dof::Integer=1, importas=:Dict)
# Query a pre-defined path (isobar or geotherm)
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
if dof == 1
# v6.7.8, 1d path
write(fp,"$index\n3\n36\n1\n$include_fluid\n0\n")
elseif dof == 2
# v6.7.8, 2d grid
write(fp,"$index\n2\n36\n1\n$include_fluid\nn\n1\n0\n")
else
error("Expecting dof = 1 (path) or 2 (grid/pseudosection) degrees of freedom")
end
close(fp)
# Make sure there isn't already an output
system("rm -f $(prefix)$(index)_1.tab*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
elements = data[1,:]
# Renormalize weight percentages
t = contains.(elements,"wt%")
total_weight = nansum(Float64.(data[2:end,t]),dim=2)
data[2:end,t] .*= 100 ./ total_weight
# Clean up element names
if clean_units
elements = elements .|> x -> replace(x, ",%" => "_pct") # substutue _pct for ,% in column names
elements = elements .|> x -> replace(x, ",wt%" => "") # Remove units on major oxides
end
# Convert to a dictionary
result = elementify(data,elements, skipstart=1,importas=importas)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.tab could not be parsed, perplex may not have run"
end
return result
end
"""
```julia
function perplex_query_system(perplexdir::String, scratchdir::String, P::NTuple{2,Number}, T::NTuple{2,Number};
\tindex::Integer=1, npoints::Integer=200, include_fluid="y",clean_units::Bool=true)
```
Query all perplex-calculated properties for the system (with or without fluid)
along a specified P-T path using a pre-computed pseudosection. Results are
returned as a dictionary. Set include_fluid="n" to return solid+melt only.
"""
function perplex_query_system(perplexdir::String, scratchdir::String, P::NTuple{2,Number}, T::NTuple{2,Number};
index::Integer=1, npoints::Integer=200, include_fluid="y", clean_units::Bool=true, importas=:Dict)
# Query a new path from a pseudosection
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
# v6.7.8 pseudosection
write(fp,"$index\n3\nn\n$(first(T))\n$(first(P))\n$(last(T))\n$(last(P))\n$npoints\n36\n1\n$include_fluid\n0\n")
close(fp)
# Make sure there isn't already an output
system("rm -f $(prefix)$(index)_1.tab*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
elements = data[1,:]
# Renormalize weight percentages
t = contains.(elements,"wt%")
total_weight = nansum(Float64.(data[2:end,t]),dim=2)
data[2:end,t] .*= 100 ./ total_weight
# Clean up element names
if clean_units
elements = elements .|> x -> replace(x, ",%" => "_pct") # substutue _pct for ,% in column names
elements = elements .|> x -> replace(x, ",wt%" => "") # Remove units on major oxides
end
# Convert to a dictionary
result = elementify(data,elements, skipstart=1,importas=importas)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.tab could not be parsed, perplex may not have run"
end
return result
end
function perplex_query_system(perplexdir::String, scratchdir::String, P::AbstractArray, T::AbstractArray;
index::Integer=1, include_fluid="y", clean_units::Bool=true, importas=:Dict)
# Query a new path from a pseudosection
werami = joinpath(perplexdir, "werami")# path to PerpleX werami
prefix = joinpath(scratchdir, "out$(index)/") # path to data files
# Write TP data to file
fp = open(prefix*"TP.tsv", "w")
for i in eachindex(T,P)
write(fp,"$(T[i])\t$(P[i])\n")
end
close(fp)
# Create werami batch file
fp = open(prefix*"werami.bat", "w")
# v6.7.8 pseudosection
write(fp,"$index\n4\n2\nTP.tsv\n1\n36\n1\n$include_fluid\n0\n")
close(fp)
# Make sure there isn't already an output
system("rm -f $(prefix)$(index)_1.tab*")
# Extract Perplex results with werami
system("cd $prefix; $werami < werami.bat > werami.log")
# Ignore initial and trailing whitespace
system("sed -e \"s/^ *//\" -e \"s/ *\$//\" -i.backup $(prefix)$(index)_1.tab")
# Merge delimiters
system("sed -e \"s/ */ /g\" -i.backup $(prefix)$(index)_1.tab")
# Read results and return them if possible
result = importas==:Dict ? Dict() : ()
try
# Read data as an Array{Any}
data = readdlm("$(prefix)$(index)_1.tab", ' ', skipstart=8)
elements = data[1,:]
# Renormalize weight percentages
t = contains.(elements,"wt%")
total_weight = nansum(Float64.(data[2:end,t]),dim=2)
data[2:end,t] .*= 100 ./ total_weight
# Clean up element names
if clean_units
elements = elements .|> x -> replace(x, ",%" => "_pct") # substutue _pct for ,% in column names
elements = elements .|> x -> replace(x, ",wt%" => "") # Remove units on major oxides
end
# Convert to a dictionary
result = elementify(data,elements, skipstart=1,importas=importas)
catch
# Return empty dictionary if file doesn't exist
@warn "$(prefix)$(index)_1.tab could not be parsed, perplex may not have run"
end
return result
end
export perplex_query_system
# Translate between perplex names and germ names
function germ_perplex_name_matches(germ_name, perplex_name)
# Feldspar
if germ_name == "Albite"
any(perplex_name .== ["ab", "abh"])
elseif germ_name == "Anorthite"
perplex_name == "an"
elseif germ_name == "Orthoclase"
any(perplex_name .== ["mic", "Kf", "San", "San(TH)"])
# Amphibole
elseif germ_name == "Amphibole"
any(lowercase(perplex_name) .== ["gl", "fgl", "rieb", "anth", "fanth", "cumm", "grun", "tr", "ftr", "ged", "parg", "ts"]) ||
any(contains.(perplex_name, ["Amph", "GlTrTs", "Act(", "Anth"]))
# Mica
elseif germ_name == "Biotite"
any(perplex_name .== ["ann"]) ||
any(contains.(perplex_name, ["Bi(", "Bio("]))
elseif germ_name == "Phlogopite"
any(lowercase(perplex_name) .== ["naph", "phl"])
# Pyroxene
elseif germ_name == "Clinopyroxene"
any(lowercase(perplex_name) .== ["di", "hed", "acm", "jd"]) ||
any(contains.(perplex_name, ["Augite", "Cpx", "Omph"]))
elseif germ_name == "Orthopyroxene"
any(lowercase(perplex_name) .== ["en", "fs"]) ||
contains(perplex_name, "Opx")
# Cordierite
elseif germ_name == "Cordierite"
any(lowercase(perplex_name) .== ["crd", "fcrd", "hcrd", "mncrd"]) ||
contains(perplex_name, "Crd")
# Garnet
elseif germ_name == "Garnet"
any(lowercase(perplex_name) .== ["py", "spss", "alm", "andr", "gr"]) ||
any(contains.(perplex_name, ["Grt", "Gt(", "Maj"]))
# Oxides
elseif germ_name == "Ilmenite"
perplex_name == "ilm" || any(contains.(perplex_name, ["Ilm", "IlHm", "IlGk"]))
elseif germ_name == "Magnetite"
perplex_name == "mt"
elseif germ_name == "Rutile"
perplex_name == "ru"
# Feldspathoids
elseif germ_name == "Leucite"
perplex_name == "lc"
elseif germ_name == "Nepheline"
perplex_name == "ne" || contains(perplex_name, "Neph")
# Olivine
elseif germ_name == "Olivine"
any(lowercase(perplex_name) .== ["fo", "fa"]) ||
any(contains.(perplex_name, ["O(", "Ol("]))
# Spinel
elseif germ_name == "Spinel"
any(lowercase(perplex_name) .== ["sp", "usp"]) ||
contains(perplex_name, "Sp(")
# Accessories
elseif germ_name == "Sphene"
perplex_name == "sph"
elseif germ_name == "Zircon"
perplex_name == "zrc"
elseif germ_name == "Baddeleyite"
perplex_name == "bdy"
else
false
end
end
export germ_perplex_name_matches
function perplex_phase_is_fluid(phase_name)
any(phase_name .== ["F", "WADDAH", "H2O"]) ||
any(contains.(phase_name, ["Aq_", "F(", "Fluid"]))
end
export perplex_phase_is_fluid
function perplex_phase_is_melt(phase_name)
any(phase_name .== ["h2oL", "abL", "anL", "diL", "enL", "faL", "kspL", "qL", "silL"]) ||
any(contains.(phase_name, ["liq", "melt", "LIQ", "MELTS"]))
end
export perplex_phase_is_melt
function perplex_phase_is_solid(phase_name)
!perplex_phase_is_fluid(phase_name) && !perplex_phase_is_melt(phase_name) &&
!any(contains.(phase_name, ["P(", "T(", "Pressure", "Temperature", "elements", "minerals", "CO2", "Missing", "system"]))
end
export perplex_phase_is_solid
function perplex_expand_name(name)
abbreviations = ("ak", "alm", "and", "andr", "chum", "cz", "crd", "ep", "fa", "fctd", "fcrd", "fep", "fosm", "fst", "fo", "geh", "gr", "hcrd", "tpz", "ky", "larn", "law", "merw", "mctd", "mst", "mnctd", "mncrd", "mnst", "mont", "osm1", "osm2", "phA", "pump", "py", "rnk", "sill", "spss", "sph", "spu", "teph", "ty", "vsv", "zrc", "zo", "acm", "cats", "di", "en", "fs", "hed", "jd", "mgts", "pswo", "pxmn", "rhod", "wo", "anth", "cumm", "fanth", "fgl", "ftr", "ged", "gl", "grun", "parg", "rieb", "tr", "ts", "deer", "fcar", "fspr", "mcar", "spr4", "spr7", "ann", "cel", "east", "fcel", "ma", "mnbi", "mu", "naph", "pa", "phl", "afchl", "ames", "clin", "daph", "fsud", "mnchl", "sud", "atg", "chr", "fta", "kao", "pre", "prl", "ta", "tats", "ab", "anl", "an", "coe", "crst", "heu", "abh", "kals", "lmt", "lc", "me", "mic", "ne", "q", "san", "stlb", "stv", "trd", "wrk", "bdy", "cor", "geik", "hem", "herc", "ilm","oilm","lime", "mft", "mt", "mang", "bunsn", "per", "pnt", "ru", "sp", "usp", "br", "dsp", "gth", "ank", "arag", "cc", "dol", "mag", "rhc", "sid", "diam", "gph", "iron", "Ni", "CO2", "CO", "H2", "CH4", "O2", "H2O", "abL", "anL", "diL", "enL", "faL", "fliq", "foL", "h2oL", "hliq", "kspL", "mliq", "qL", "silL", "H+", "Cl-", "OH-", "Na+", "K+", "Ca++", "Mg++", "Fe++", "Al+++", "CO3", "AlOH3", "AlOH4-", "KOH", "HCL", "KCL", "NaCl", "CaCl2", "CaCl+", "MgCl2", "MgCl", "FeCl2", "aqSi",)
full_names = ("akermanite", "almandine", "andalusite", "andradite", "clinohumite", "clinozoisite", "cordierite", "epidote(ordered)", "fayalite", "Fe-chloritoid", "Fe-cordierite", "Fe-epidote", "Fe-osumilite", "Fe-staurolite", "forsterite", "gehlenite", "grossular", "hydrous cordierite", "hydroxy-topaz", "kyanite", "larnite-bredigite", "lawsonite", "merwinite", "Mg-chloritoid", "Mg-staurolite", "Mn-chloritoid", "Mn-cordierite", "Mn-staurolite", "monticellite", "osumilite(1)", "osumilite(2)", "phase A", "pumpellyite", "pyrope", "rankinite", "sillimanite", "spessartine", "sphene", "spurrite", "tephroite", "tilleyite", "vesuvianite", "zircon", "zoisite", "acmite", "Ca-tschermaks pyroxene", "Diopside", "enstatite", "ferrosilite", "hedenbergite", "jadeite", "mg-tschermak", "pseudowollastonite", "pyroxmangite", "rhodonite", "wollastonite", "anthophyllite", "cummingtonite", "Fe-anthophyllite", "Fe-glaucophane", "ferroactinolite", "gedrite(Na-free)", "glaucophane", "grunerite", "pargasite", "riebeckite", "tremolite", "tschermakite", "deerite", "fe-carpholite", "fe-sapphirine(793)", "mg-carpholite", "sapphirine(442)", "sapphirine(793)", "annite", "celadonite", "eastonite", "Fe-celadonite", "margarite", "Mn-biotite", "muscovite", "Na-phlogopite", "paragonite", "phlogopite", "Al-free chlorite", "amesite(14Ang)", "clinochlore(ordered)", "daphnite", "Fe-sudoite", "Mn-chlorite", "Sudoite", "antigorite", "chrysotile", "Fe-talc", "Kaolinite", "prehnite", "pyrophyllite", "talc", "tschermak-talc", "albite", "analcite", "anorthite", "coesite", "cristobalite", "heulandite", "highalbite", "kalsilite", "laumontite", "leucite", "meionite", "microcline", "nepheline", "quartz", "sanidine", "stilbite", "stishovite", "tridymite", "wairakite", "baddeleyite", "corundum", "geikielite", "hematite", "hercynite", "ilmenite", "ilmenite(ordered)","lime", "magnesioferrite", "magnetite", "manganosite", "nickel oxide", "periclase", "pyrophanite", "rutile", "spinel", "ulvospinel", "brucite", "diaspore", "goethite", "ankerite", "aragonite", "calcite", "dolomite", "magnesite", "rhodochrosite", "siderite", "diamond", "graphite", "iron", "nickel", "carbon dioxide", "carbon monoxide", "hydrogen", "methane", "oxygen", "water fluid", "albite liquid", "anorthite liquid", "diopside liquid", "enstatite liquid", "fayalite liquid", "Fe-liquid (in KFMASH)", "Forsterite liquid", "H2O liquid", "H2O liquid (in KFMASH)", "K-feldspar liquid", "Mg liquid (in KFMASH)", "Silica liquid", "Sillimanite liquid", "H+(aq)", "Cl(aq)", "OH(aq)", "Na+(aq)", "K+(aq)", "Ca2+(aq)", "Mg2+(aq)", "Fe2+(aq)", "Al3+(aq)", "CO3--(aq)", "Al(OH)3(aq)", "Al(OH)4----(aq)", "KOH(aq)", "HCl(aq)", "KCl(aq)", "NaCl(aq)", "CaCl(aq)", "CaCl+(aq)", "MgCl2(aq)", "MgCl+(aq)", "FeCl(aq)", "Aqueous silica",)
t = name .== abbreviations
if any(t)
full_names[findfirst(t)]
else
name
end
end
export perplex_expand_name
function perplex_abbreviate_name(name)
abbreviations = ("ak", "alm", "and", "andr", "chum", "cz", "crd", "ep", "fa", "fctd", "fcrd", "fep", "fosm", "fst", "fo", "geh", "gr", "hcrd", "tpz", "ky", "larn", "law", "merw", "mctd", "mst", "mnctd", "mncrd", "mnst", "mont", "osm1", "osm2", "phA", "pump", "py", "rnk", "sill", "spss", "sph", "spu", "teph", "ty", "vsv", "zrc", "zo", "acm", "cats", "di", "en", "fs", "hed", "jd", "mgts", "pswo", "pxmn", "rhod", "wo", "anth", "cumm", "fanth", "fgl", "ftr", "ged", "gl", "grun", "parg", "rieb", "tr", "ts", "deer", "fcar", "fspr", "mcar", "spr4", "spr7", "ann", "cel", "east", "fcel", "ma", "mnbi", "mu", "naph", "pa", "phl", "afchl", "ames", "clin", "daph", "fsud", "mnchl", "sud", "atg", "chr", "fta", "kao", "pre", "prl", "ta", "tats", "ab", "anl", "an", "coe", "crst", "heu", "abh", "kals", "lmt", "lc", "me", "mic", "ne", "q", "san", "stlb", "stv", "trd", "wrk", "bdy", "cor", "geik", "hem", "herc", "ilm", "oilm", "lime", "mft", "mt", "mang", "bunsn", "per", "pnt", "ru", "sp", "usp", "br", "dsp", "gth", "ank", "arag", "cc", "dol", "mag", "rhc", "sid", "diam", "gph", "iron", "Ni", "CO2", "CO", "H2", "CH4", "O2", "H2O", "abL", "anL", "diL", "enL", "faL", "fliq", "foL", "h2oL", "hliq", "kspL", "mliq", "qL", "silL", "H+", "Cl-", "OH-", "Na+", "K+", "Ca++", "Mg++", "Fe++", "Al+++", "CO3", "AlOH3", "AlOH4-", "KOH", "HCL", "KCL", "NaCl", "CaCl2", "CaCl+", "MgCl2", "MgCl", "FeCl2", "aqSi",)
full_names = ("akermanite", "almandine", "andalusite", "andradite", "clinohumite", "clinozoisite", "cordierite", "epidote(ordered)", "fayalite", "Fe-chloritoid", "Fe-cordierite", "Fe-epidote", "Fe-osumilite", "Fe-staurolite", "forsterite", "gehlenite", "grossular", "hydrous cordierite", "hydroxy-topaz", "kyanite", "larnite-bredigite", "lawsonite", "merwinite", "Mg-chloritoid", "Mg-staurolite", "Mn-chloritoid", "Mn-cordierite", "Mn-staurolite", "monticellite", "osumilite(1)", "osumilite(2)", "phase A", "pumpellyite", "pyrope", "rankinite", "sillimanite", "spessartine", "sphene", "spurrite", "tephroite", "tilleyite", "vesuvianite", "zircon", "zoisite", "acmite", "Ca-tschermaks pyroxene", "Diopside", "enstatite", "ferrosilite", "hedenbergite", "jadeite", "mg-tschermak", "pseudowollastonite", "pyroxmangite", "rhodonite", "wollastonite", "anthophyllite", "cummingtonite", "Fe-anthophyllite", "Fe-glaucophane", "ferroactinolite", "gedrite(Na-free)", "glaucophane", "grunerite", "pargasite", "riebeckite", "tremolite", "tschermakite", "deerite", "fe-carpholite", "fe-sapphirine(793)", "mg-carpholite", "sapphirine(442)", "sapphirine(793)", "annite", "celadonite", "eastonite", "Fe-celadonite", "margarite", "Mn-biotite", "muscovite", "Na-phlogopite", "paragonite", "phlogopite", "Al-free chlorite", "amesite(14Ang)", "clinochlore(ordered)", "daphnite", "Fe-sudoite", "Mn-chlorite", "Sudoite", "antigorite", "chrysotile", "Fe-talc", "Kaolinite", "prehnite", "pyrophyllite", "talc", "tschermak-talc", "albite", "analcite", "anorthite", "coesite", "cristobalite", "heulandite", "highalbite", "kalsilite", "laumontite", "leucite", "meionite", "microcline", "nepheline", "quartz", "sanidine", "stilbite", "stishovite", "tridymite", "wairakite", "baddeleyite", "corundum", "geikielite", "hematite", "hercynite", "ilmenite", "ilmenite(ordered)", "lime", "magnesioferrite", "magnetite", "manganosite", "nickel oxide", "periclase", "pyrophanite", "rutile", "spinel", "ulvospinel", "brucite", "diaspore", "goethite", "ankerite", "aragonite", "calcite", "dolomite", "magnesite", "rhodochrosite", "siderite", "diamond", "graphite", "iron", "nickel", "carbon dioxide", "carbon monoxide", "hydrogen", "methane", "oxygen", "water fluid", "albite liquid", "anorthite liquid", "diopside liquid", "enstatite liquid", "fayalite liquid", "Fe-liquid (in KFMASH)", "Forsterite liquid", "H2O liquid", "H2O liquid (in KFMASH)", "K-feldspar liquid", "Mg liquid (in KFMASH)", "Silica liquid", "Sillimanite liquid", "H+(aq)", "Cl(aq)", "OH(aq)", "Na+(aq)", "K+(aq)", "Ca2+(aq)", "Mg2+(aq)", "Fe2+(aq)", "Al3+(aq)", "CO3--(aq)", "Al(OH)3(aq)", "Al(OH)4----(aq)", "KOH(aq)", "HCl(aq)", "KCl(aq)", "NaCl(aq)", "CaCl(aq)", "CaCl+(aq)", "MgCl2(aq)", "MgCl+(aq)", "FeCl(aq)", "Aqueous silica",)
t = name .== full_names
if any(t)
abbreviations[findfirst(t)]
else
name
end
end
export perplex_abbreviate_name
function perplex_common_name(name)
abbreviations = ("ak", "alm", "and", "andr", "chum", "cz", "crd", "ep", "fa", "fctd", "fcrd", "fep", "fosm", "fst", "fo", "geh", "gr", "hcrd", "tpz", "ky", "larn", "law", "merw", "mctd", "mst", "mnctd", "mncrd", "mnst", "mont", "osm1", "osm2", "phA", "pump", "py", "rnk", "sill", "spss", "sph", "spu", "teph", "ty", "vsv", "zrc", "zo", "acm", "cats", "di", "en", "fs", "hed", "jd", "mgts", "pswo", "pxmn", "rhod", "wo", "anth", "cumm", "fanth", "fgl", "ftr", "ged", "gl", "grun", "parg", "rieb", "tr", "ts", "deer", "fcar", "fspr", "mcar", "spr4", "spr7", "ann", "cel", "east", "fcel", "ma", "mnbi", "mu", "naph", "pa", "phl", "afchl", "ames", "clin", "daph", "fsud", "mnchl", "sud", "atg", "chr", "fta", "kao", "pre", "prl", "ta", "tats", "ab", "anl", "an", "coe", "crst", "heu", "abh", "kals", "lmt", "lc", "me", "mic", "ne", "q", "san", "stlb", "stv", "trd", "wrk", "bdy", "cor", "geik", "hem", "herc", "ilm", "oilm", "lime", "mft", "mt", "mang", "bunsn", "per", "pnt", "ru", "sp", "usp", "br", "dsp", "gth", "ank", "arag", "cc", "dol", "mag", "rhc", "sid", "diam", "gph", "iron", "Ni", "CO2", "CO", "H2", "CH4", "O2", "H2O", "abL", "anL", "diL", "enL", "faL", "fliq", "foL", "h2oL", "hliq", "kspL", "mliq", "qL", "silL", "H+", "Cl-", "OH-", "Na+", "K+", "Ca++", "Mg++", "Fe++", "Al+++", "CO3", "AlOH3", "AlOH4-", "KOH", "HCL", "KCL", "NaCl", "CaCl2", "CaCl+", "MgCl2", "MgCl", "FeCl2", "aqSi", "Aqfl(HGP)", "Cpx(HGP)", "Augite(G)", "Cpx(JH)", "Cpx(l)", "Cpx(h)", "Cpx(stx)", "Cpx(stx7)", "Omph(HP)", "Cpx(HP)", "Cpx(m)", "Cpx(stx8)", "Cps(HGP)", "Omph(GHP)", "cAmph(G)", "Cumm", "Gl", "Tr", "GlTrTsPg", "Amph(DHP)", "Amph(DPW)", "Ca-Amph(D)", "Na-Amph(D)", "Act(M)", "GlTrTsMr", "cAmph(DP)", "melt(HGPH)", "melt(G)", "melt(W)", "melt(HP)", "melt(HGP)", "pMELTS(G)", "mMELTS(G)", "LIQ(NK)", "LIQ(EF)", "Chl(W)", "Chl(HP)", "Chl(LWV)", "O(HGP)","O(JH)", "O(SG)", "O(HP)", "O(HPK)", "O(stx)", "O(stx7)", "Ol(m)", "O(stx8)", "Sp(HGP)", "Sp(JH)", "GaHcSp", "Sp(JR)", "Sp(GS)", "Sp(HP)", "Sp(stx)", "CrSp", "Sp(stx7)", "Sp(WPC)", "Sp(stx8)", "Pl(JH)", "Pl(h)", "Pl(stx8)", "Kf", "San", "San(TH)", "Gt(HGP)", "Grt(JH)", "Gt(W)", "CrGt", "Gt(MPF)", "Gt(B)", "Gt(GCT)", "Gt(HP)", "Gt(EWHP)", "Gt(WPH)", "Gt(stx)", "Gt(stx8)", "Gt(WPPH)", "ZrGt(KP)", "Maj", "Opx(HGP)", "Opx(JH)", "Opx(W)", "Opx(HP)", "CrOpx(HP)", "Opx(stx)", "Opx(stx8)", "Mica(W)", "Pheng(HP)", "MaPa", "Mica(CF)", "Mica(CHA1)", "Mica(CHA)", "Mica+(CHA)", "Mica(M)", "Mica(SGH)", "Ctd(W)", "Ctd(HP)", "Ctd(SGH)", "St(W)", "St(HP)", "Bi(HGP)", "Bi(W)", "Bio(TCC)", "Bio(WPH)", "Bio(HP)", "Crd(W)", "hCrd", "Sa(WP)", "Sapp(HP)", "Sapp(KWP)", "Sapp(TP)", "Osm(HP)", "F", "F(salt)", "COH-Fluid", "Aq_solven0", "WADDAH", "T", "Scap", "Carp", "Carp(M)", "Carp(SGH)", "Sud(Livi)", "Sud", "Sud(M)", "Anth", "o-Amph", "oAmph(DP)", "feldspar", "feldspar_B", "Pl(I1,HP)", "Fsp(C1)", "Do(HP)", "M(HP)", "Do(AE)", "Cc(AE)", "oCcM(HP)", "Carb(M)", "oCcM(EF)", "dis(EF)", "IlHm(A)", "IlGkPy", "Ilm(WPH)", "Ilm(WPH0)", "Neph(FB)", "Chum", "Atg(PN)", "B", "Pu(M)", "Stlp(M)", "Wus",)
common_names = ("akermanite", "almandine", "andalusite", "andradite", "clinohumite", "clinozoisite", "cordierite", "epidote", "fayalite", "Fe-chloritoid", "Fe-cordierite", "Fe-epidote", "Fe-osumilite", "Fe-staurolite", "forsterite", "gehlenite", "grossular", "hydrous cordierite", "hydroxy-topaz", "kyanite", "larnite", "lawsonite", "merwinite", "Mg-chloritoid", "Mg-staurolite", "Mn-chloritoid", "Mn-cordierite", "Mn-staurolite", "monticellite", "osumilite(1)", "osumilite(2)", "phase A", "pumpellyite", "pyrope", "rankinite", "sillimanite", "spessartine", "sphene", "spurrite", "tephroite", "tilleyite", "vesuvianite", "zircon", "zoisite", "acmite", "Ca-tschermakite", "diopside", "enstatite", "ferrosilite", "hedenbergite", "jadeite", "Mg-tschermakite", "pseudowollastonite", "pyroxmangite", "rhodonite", "wollastonite", "anthophyllite", "cummingtonite", "Fe-anthophyllite", "Fe-glaucophane", "ferroactinolite", "gedrite", "glaucophane", "grunerite", "pargasite", "riebeckite", "tremolite", "tschermakite", "deerite", "Fe-carpholite", "Fe-sapphirine(793)", "Mg-carpholite", "sapphirine(442)", "sapphirine(793)", "annite", "celadonite", "eastonite", "Fe-celadonite", "margarite", "Mn-biotite", "muscovite", "Na-phlogopite", "paragonite", "phlogopite", "Al-free chlorite", "amesite", "clinochlore", "daphnite", "Fe-sudoite", "Mn-chlorite", "sudoite", "antigorite", "chrysotile", "Fe-talc", "kaolinite", "prehnite", "pyrophyllite", "talc", "tschermak-talc", "albite", "analcite", "anorthite", "coesite", "cristobalite", "heulandite", "highalbite", "kalsilite", "laumontite", "leucite", "meionite", "microcline", "nepheline", "quartz", "sanidine", "stilbite", "stishovite", "tridymite", "wairakite", "baddeleyite", "corundum", "geikielite", "hematite", "hercynite", "ilmenite", "ilmenite(ordered)", "lime", "magnesioferrite", "magnetite", "manganosite", "nickel oxide", "periclase", "pyrophanite", "rutile", "spinel", "ulvospinel", "brucite", "diaspore", "goethite", "ankerite", "aragonite", "calcite", "dolomite", "magnesite", "rhodochrosite", "siderite", "diamond", "graphite", "iron", "nickel", "carbon dioxide", "carbon monoxide", "hydrogen", "methane", "oxygen", "water fluid", "albite liquid", "anorthite liquid", "diopside liquid", "enstatite liquid", "fayalite liquid", "Fe-liquid (in KFMASH)", "forsterite liquid", "H2O liquid", "H2O liquid (in KFMASH)", "K-feldspar liquid", "Mg liquid (in KFMASH)", "Silica liquid", "Sillimanite liquid", "H+(aq)", "Cl(aq)", "OH(aq)", "Na+(aq)", "K+(aq)", "Ca2+(aq)", "Mg2+(aq)", "Fe2+(aq)", "Al3+(aq)", "CO3--(aq)", "Al(OH)3(aq)", "Al(OH)4----(aq)", "KOH(aq)", "HCl(aq)", "KCl(aq)", "NaCl(aq)", "CaCl(aq)", "CaCl+(aq)", "MgCl2(aq)", "MgCl+(aq)", "FeCl(aq)", "Aqueous silica", "Aqueous fluid", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "melt", "melt", "melt", "melt", "melt", "melt", "melt", "melt", "melt", "chlorite", "chlorite", "chlorite", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "plagioclase", "plagioclase", "plagioclase", "k-feldspar", "k-feldspar", "k-feldspar", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "chloritoid", "chloritoid", "chloritoid", "staurolite", "staurolite", "biotite", "biotite", "biotite", "biotite", "biotite", "cordierite", "cordierite", "sapphirine", "sapphirine", "sapphirine", "sapphirine", "osumilite", "fluid", "fluid", "fluid", "fluid", "fluid", "talc", "scapolite", "carpholite", "carpholite", "carpholite", "sudoite", "sudoite", "sudoite", "orthoamphibole", "orthoamphibole", "orthoamphibole", "ternary feldspar", "ternary feldspar", "ternary feldspar", "ternary feldspar", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "ilmenite", "ilmenite", "ilmenite", "ilmenite", "nepheline", "clinohumite", "serpentine", "brucite", "pumpellyite", "stilpnomelane", "wüstite",)
t = name .== abbreviations
if any(t)
common_names[findfirst(t)]
elseif contains(name,"anorthite")
"anorthite"
elseif contains(name,"albite")
"albite"
elseif contains(name,"orthoclase")
"orthoclase"
else
name
end
end
export perplex_common_name
## -- Zircon saturation calculations
"""
```julia
M = Boehnke_tzircM(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5)
```
Calculate zircon saturation M-value based on major element concentrations
Following the zircon saturation calibration of Boehnke, Watson, et al., 2013
(doi: 10.1016/j.chemgeo.2013.05.028)
"""
function Boehnke_tzircM(SiO2::Number, TiO2::Number, Al2O3::Number, FeOT::Number, MnO::Number, MgO::Number, CaO::Number, Na2O::Number, K2O::Number, P2O5::Number)
#Cations
Na = Na2O/30.9895
K = K2O/47.0827
Ca = CaO/56.0774
Al = Al2O3/50.9806
Si = SiO2/60.0843
Ti = TiO2/79.865
Fe = FeOT/71.8444
Mg = MgO/24.3050
Mn = MnO/70.9374
P = P2O5/70.9723
# Normalize cation fractions
normconst = nansum((Na, K, Ca, Al, Si, Ti, Fe, Mg, Mn, P))
K, Na, Ca, Al, Si = (K, Na, Ca, Al, Si) ./ normconst
M = (Na + K + 2*Ca)/(Al * Si)
return M
end
function Boehnke_tzircM(SiO2::AbstractArray, TiO2::AbstractArray, Al2O3::AbstractArray, FeOT::AbstractArray, MnO::AbstractArray, MgO::AbstractArray, CaO::AbstractArray, Na2O::AbstractArray, K2O::AbstractArray, P2O5::AbstractArray)
#Cations
Na = Na2O/30.9895
K = K2O/47.0827
Ca = CaO/56.0774
Al = Al2O3/50.9806
Si = SiO2/60.0843
Ti = TiO2/79.865
Fe = FeOT/71.8444
Mg = MgO/24.3050
Mn = MnO/70.9374
P = P2O5/70.9723
# Normalize cation fractions
normconst = nansum([Na K Ca Al Si Ti Fe Mg Mn P], dim=2)
K .= K ./ normconst
Na .= Na ./ normconst
Ca .= Ca ./ normconst
Al .= Al ./ normconst
Si .= Si ./ normconst
M = (Na + K + 2*Ca)./(Al .* Si)
return M
end
export Boehnke_tzircM
tzircM = Boehnke_tzircM
export tzircM
"""
```julia
ZrSat = Boehnke_tzircZr(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5, T)
```
Calculate zircon saturation Zr concentration for a given temperature (in C)
Following the zircon saturation calibration of Boehnke, Watson, et al., 2013
(doi: 10.1016/j.chemgeo.2013.05.028)
"""
function Boehnke_tzircZr(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5, T)
M = Boehnke_tzircM(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5)
# Boehnke, Watson, et al., 2013
ZrSat = @. max(496000. /(exp(10108. /(T+273.15) -0.32 -1.16*M)), 0)
return ZrSat
end
export Boehnke_tzircZr
tzircZr = Boehnke_tzircZr
export tzircZr
"""
```julia
T = Boehnke_tzirc(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5, Zr)
```
Calculate zircon saturation temperature in degrees Celsius
Following the zircon saturation calibration of Boehnke, Watson, et al., 2013
(doi: 10.1016/j.chemgeo.2013.05.028)
"""
function Boehnke_tzirc(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5, Zr)
M = Boehnke_tzircM(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5)
# Boehnke, Watson, et al., 2013
TC = @. 10108. / (0.32 + 1.16*M + log(496000. / Zr)) - 273.15
return TC
end
export Boehnke_tzirc
tzirc = Boehnke_tzirc
export tzirc
## --- Sphene saturation calculations
function Ayers_tspheneC(SiO2::Number, TiO2::Number, Al2O3::Number, FeOT::Number, MnO::Number, MgO::Number, CaO::Number, Na2O::Number, K2O::Number, P2O5::Number)
#Cations
Na = Na2O/30.9895
K = K2O/47.0827
Ca = CaO/56.0774
Al = Al2O3/50.9806
Si = SiO2/60.0843
Ti = TiO2/79.865
Fe = FeOT/71.8444
Mg = MgO/24.3050
Mn = MnO/70.9374
P = P2O5/70.9723
# Normalize cation fractions
normconst = nansum((Na, K, Ca, Al, Si, Ti, Fe, Mg, Mn, P))
K, Na, Ca, Al, Si = (K, Na, Ca, Al, Si) ./ normconst
eCa = Ca - Al/2 + Na/2 + K/2
return (10 * eCa) / (Al * Si)
end
"""
```julia
TiO2Sat = Ayers_tspheneTiO2(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5, T)
```
Calculate sphene saturation TiO2 concentration (in wt. %) for a given temperature
(in C) following the sphene saturation calibration of Ayers et al., 2018
(doi: 10.1130/abs/2018AM-320568)
"""
function Ayers_tspheneTiO2(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5, TC)
C = Ayers_tspheneC(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5)
TiO2 = max(0.79*C - 7993/(TC+273.15) + 7.88, 0)
return TiO2
end
export Ayers_tspheneTiO2
"""
```julia
TC = Ayers_tsphene(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5)
```
Calculate sphene saturation temperature in degrees Celsius
Following the sphene saturation calibration of Ayers et al., 2018
(doi: 10.1130/abs/2018AM-320568)
"""
function Ayers_tsphene(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5)
C = Ayers_tspheneC(SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5)
TC = 7993/(0.79*C - TiO2 + 7.88) - 273.15
return TC
end
export Ayers_tsphene
## --- Rutile saturation calculations
function FM(SiO2::Number, TiO2::Number, Al2O3::Number, FeOT::Number, MgO::Number, CaO::Number, Na2O::Number, K2O::Number, P2O5::Number)
#Cations
Na = Na2O/30.9895
K = K2O/47.0827
Ca = CaO/56.0774
Al = Al2O3/50.9806
Si = SiO2/60.0843
Ti = TiO2/79.865
Fe = FeOT/71.8444
Mg = MgO/24.3050
P = P2O5/70.9723
# Normalize cation fractions
normconst = nansum((Na, K, Ca, Al, Si, Ti, Fe, Mg, P))
Na, K, Ca, Mg, Fe, Al, Si = (Na, K, Ca, Mg, Fe, Al, Si) ./ normconst
return (Na + K + 2(Ca + Mg + Fe)) / (Al * Si)
end
"""
```julia
TC = Hayden_trutile(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, P2O5)
```
Calculate rutile saturation temperature in degrees Celcius
following the rutile saturation model of Hayden and Watson, 2007
(doi: 10.1016/j.epsl.2007.04.020)
"""
function Hayden_trutile(SiO2::Number, TiO2::Number, Al2O3::Number, FeOT::Number, MgO::Number, CaO::Number, Na2O::Number, K2O::Number, P2O5::Number)
TK = 5305.0 / (7.95 - log10(TiO2 * 10000 * 47.867/(47.867+15.999*2)) + 0.124*FM(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, P2O5))
TC = TK - 273.15
end
"""
```julia
TC = Hayden_trutile(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, P2O5, TC)
```
Calculate the TiO2 concentration in weight percent required for rutile
saturation at temperature `TC` degrees Celcius, following the rutile
saturation model of Hayden and Watson, 2007
(doi: 10.1016/j.epsl.2007.04.020)
"""
function Hayden_trutileTiO2(SiO2::Number, TiO2::Number, Al2O3::Number, FeOT::Number, MgO::Number, CaO::Number, Na2O::Number, K2O::Number, P2O5::Number, TC::Number)
TK = TC + 273.15
return exp10(7.95 - 5315.0/TK + 0.124*FM(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, P2O5)) * (47.867+15.999*2)/47.867 * 1e-5
end
## --- Monazite and xenotime saturation calculations
"""
```julia
LREEmolwt(La, Ce, Pr, Nd, Sm, Gd)
```
Returns the average molecular weight of the LREE considered in the
REEt value from the monazite saturation model of Montel 1993
(doi: 10.1016/0009-2541(93)90250-M)
"""
function LREEmolwt(La, Ce, Pr, Nd, Sm, Gd)
# All as PPM
nansum((138.905477La, 140.1161Ce, 140.907662Pr, 144.2423Nd, 150.362Sm, 157.253Gd)) / nansum((La, Ce, Pr, Nd, Sm, Gd))
end
"""
```julia
LREEt(La, Ce, Pr, Nd, Sm, Gd)
```
Returns the sum of the LREE concentrations divided by their respective molar masses.
If REE are input in parts per million by weight (ppmw), the result is in units of moles per megagram.
This is equivalent to the REEt value from the monazite saturation model of Montel 1993
(doi: 10.1016/0009-2541(93)90250-M)
"""
function LREEt(La::T, Ce::T, Pr::T, Nd::T, Sm::T, Gd::T) where T <: Number
# All as PPM
nansum((La/138.905477, Ce/140.1161, Pr/140.907662, Nd/144.2423, Sm/150.362, Gd/157.253))
end
function Montel_tmonaziteD(SiO2::T, TiO2::T, Al2O3::T, FeOT::T, MgO::T, CaO::T, Na2O::T, K2O::T, Li2O::T, H2O::T) where T <: Number
#Cations
H = H2O/9.0075
Li = Li2O/14.9395
Na = Na2O/30.9895
K = K2O/47.0827
Ca = CaO/56.0774
Al = Al2O3/50.9806
Si = SiO2/60.0843
Ti = TiO2/79.865
Fe = FeOT/71.8444
Mg = MgO/24.3050
# Anions
# O = 0.5H + 0.5Li + 0.5Na + 0.5K + Ca + Mg + Fe + 1.5Al + 2Si + 2Ti
# Calculate cation fractions
normconst = nansum((H, Li, Na, K, Ca, Al, Si, Ti, Fe, Mg))
Li, Na, K, Ca, Al, Si = (Li, Na, K, Ca, Al, Si) ./ normconst
# Note that the paper incorrectly describes this equation as being
# written in terms of atomic percent ("at.%"), but in fact it appears
# to require molar cation fractions, just as does the analagous M-value
# equation found in zircon saturation papers
D = (Na + K + Li + 2Ca) / (Al * (Al + Si))
return D
end
"""
```julia
REEt = Montel_tmonaziteREE(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, Li2O, H2O, T)
```
Calculate monazite saturation REEt value (in [ppm/mol.wt.]) for a given
temperature (in C) following the monazite saturation model of Montel 1993
(doi: 10.1016/0009-2541(93)90250-M), where:
D = (Na + K + Li + 2Ca) / Al * 1/(Al + Si)) # all as molar cation fractions (not at. %!)
ln(REEt) = 9.50 + 2.34D + 0.3879√H2O - 13318/T # H2O as wt.%
REEt = Σ REEᵢ(ppm) / at. weight (g/mol)
"""
function Montel_tmonaziteREE(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, Li2O, H2O, T)
D = Montel_tmonaziteD(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, Li2O, H2O) # input as wt. %
REEt = exp(9.50 + 2.34*D + 0.3879*sqrt(H2O) - 13318/(T+272.15))
return REEt
end
export Montel_tmonaziteREE
"""
```julia
TC = Montel_tmonazite(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, Li2O, H2O, La, Ce, Pr, Nd, Sm, Gd)
```
Calculate monazite saturation temperature in degrees Celcius
following the monazite saturation model of Montel 1993
(doi: 10.1016/0009-2541(93)90250-M)
"""
function Montel_tmonazite(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, Li2O, H2O, La, Ce, Pr, Nd, Sm, Gd)
D = Montel_tmonaziteD(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, Li2O, H2O) # input as wt. %
REEt = LREEt(La, Ce, Pr, Nd, Sm, Gd) # input in PPM
TC = 13318/(9.50 + 2.34*D + 0.3879*sqrt(H2O) - log(REEt)) - 272.15
return TC
end
export Montel_tmonazite
"""
```julia
LREEt = Rusiecka_tmonaziteREE(P_ppm, TC)
```
Calculate the LREEt (mol/Megagram) value required for monazite saturation at a
temperature of `TC` degrees celcius and `P` ppmw phosphorous present,
following the solubility model of Rusiecka & Baker, 2019
(doi: 10.2138/am-2019-6931)
"""
function Rusiecka_tmonaziteREE(P_ppm, TC)
#[LREE][P] (mol^2/100g^2) = 10^(2.3055 - 1.029e4/T)
Kₛₚ = 10^(2.3055 - 1.029e4/(TC + 273.15))
LREE_μmol_g = Kₛₚ/(P_ppm/10000/30.97)*10000
return LREE_μmol_g
end
"""
```julia
LREEt = Rusiecka_txenotimeY(P_ppm, TC)
```
Calculate the Y (ppmw) concentration required for xenotime saturation at a
temperature of `TC` degrees celcius and `P` ppmw phosphorous present,
following the solubility model of Rusiecka & Baker, 2019
(doi: 10.2138/am-2019-6931)
"""
function Rusiecka_txenotimeY(P_ppm, TC)
# [Y][P] (mol^2/100g^2) = 10^(3.6932 - 1.1469e4/T)
Kₛₚ = 10^(3.6932 - 1.1469e4/(TC + 273.15))
Y_ppm = Kₛₚ/(P_ppm/10000/30.97)*10000*88.905
return Y_ppm
end
## --- Apatite saturation calculations
"""
```julia
P2O5 = Harrison_tapatiteP2O5(SiO2, Al2O3, CaO, Na2O, K2O, T)
```
Calculate `P2O5` concentration (in wt.%) required for apatite saturation at a
given `T` (in C) following the apatite saturation model of Harrison and Watson
1984 (doi: 10.1016/0016-7037(84)90403-4) with the correction of Bea et al. 1992
(doi: 10.1016/0024-4937(92)90033-U) where applicable
"""
function Harrison_tapatiteP2O5(SiO2::T, Al2O3::T, CaO::T, Na2O::T, K2O::T, TC::T) where T <: Number
TK = TC + 273.16
ASI = (Al2O3/50.9806)/(CaO/56.0774 + Na2O/30.9895 + K2O/47.0827)
P2O5sat = 52.5525567/exp( (8400 + 2.64e4(SiO2/100 - 0.5))/TK - (3.1 + 12.4(SiO2/100 - 0.5)) )
return max(P2O5sat, P2O5sat * (ASI-1) * 6429/TK)
end
function Harrison_tapatiteP2O5(SiO2::T, TC::T) where T <: Number
TK = TC + 273.16
P2O5sat = 52.5525567/exp( (8400 + 2.64e4(SiO2/100 - 0.5))/TK - (3.1 + 12.4(SiO2/100 - 0.5)) )
return P2O5sat
end
export Harrison_tapatiteP2O5
"""
As `Harrison_tapatiteP2O5`, but returns saturation phosphorus concentration in PPM P
"""
Harrison_tapatiteP(x...) = Harrison_tapatiteP2O5(x...) * 10_000 / 2.2913349
export Harrison_tapatiteP
"""
```julia
TC = Harrison_tapatite(SiO2, P2O5)
```
Calculate apatite saturation temperature in degrees Celcius
following the apatite saturation model of Harrison and Watson 1984
(doi: 10.1016/0016-7037(84)90403-4)
"""
function Harrison_tapatite(SiO2::T, P2O5::T) where T <: Number
TK = (8400 + 2.64e4(SiO2/100 - 0.5)) / (log(52.5525567/P2O5) + (3.1 + 12.4(SiO2/100 - 0.5)))
return TK - 273.16
end
export Harrison_tapatite
"""
```julia
P2O5 = Tollari_tapatiteP2O5(SiO2, CaO, T)
```
Calculate `P2O5` concentration (in wt.%) required for apatite saturation at a
given `T` (in C) following the apatite saturation model of Tollari et al. 2006
(doi: 10.1016/j.gca.2005.11.024)
"""
function Tollari_tapatiteP2O5(SiO2::T, CaO::T, TC::T) where T <: Number
# Using conversions from Tollari et al.
SiO2ₘ = 1.11 * SiO2
CaOₘ = 1.18 * CaO
TK = TC+273.15
P2O5satₘ = exp(TK * (-0.8579/(139.0 - SiO2ₘ) + 0.0165) - 10/3*log(CaOₘ))
return P2O5satₘ / 0.47
end
"""
```julia
TC = Tollari_tapatite(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, P2O5)
```
Calculate apatite saturation temperature in degrees Celcius
following the apatite saturation model of Tollari et al. 2006
(doi: 10.1016/j.gca.2005.11.024)
"""
function Tollari_tapatite(SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, P2O5)
# Cations
Na2 = Na2O/61.97854
K2 = K2O/94.19562
Ca = CaO/56.0774
Al2 = Al2O3/101.960077
Si = SiO2/60.0843
Ti = TiO2/79.865
Fe = FeOT/71.8444
Mg = MgO/24.3050
P2 = P2O5/141.94252
# Normalize to mole percent oxides
normconst = nansum((Na2, K2, Ca, Al2, Si, Ti, Fe, Mg, P2))
CaOₘ = Ca / normconst * 100
SiO2ₘ = Si / normconst * 100
P2O5ₘ = P2 / normconst * 100
TC = (log(P2O5ₘ) + 10/3*log(CaOₘ)) / (-0.8579/(139.0 - SiO2ₘ) + 0.0165) - 273.15
return TC
end
## --- Ti-in-zircon thermometry
"""
```julia
Ti = Ferry_Ti_in_zircon(TC::Number, aSiO2::Number, aTiO2::Number)
```
Parts per million by weight of titanium in zircon at temperature `TC` degrees
Celsius given `aSiO2` silica activity and `aTiO2` titanium activity, following
the equations of Ferry and Watson, 2007.
(doi: 10.1007/s00410-007-0201-0)
"""
function Ferry_Ti_in_zircon(TC::Number, aSiO2::Number, aTiO2::Number)
exp10(5.711 - 4800.0/(TC+273.15) - log10(aSiO2) +log10(aTiO2))
end
"""
```julia
TC = Ferry_Ti_in_zirconT(TC::Number, aSiO2::Number, aTiO2::Number)
```
Calculate titanium-in-zircon temperature in degrees Celcius `TC`
given `Ti` parts per million by weight of titanium in zircon,
`aSiO2` silica activity and `aTiO2` titanium activity, following
the equations of Ferry and Watson, 2007.
(doi: 10.1007/s00410-007-0201-0)
"""
function Ferry_Ti_in_zirconT(Ti::Number, aSiO2::Number, aTiO2::Number)
1 / ((5.711) - log10(aSiO2) + log10(aTiO2) - log10(Ti)) * (4800.0) .- 273.15
end
"""
```julia
Ti = Crisp_Ti_in_zircon(TC::Number, Pbar::Number, aSiO2::Number, aTiO2::Number)
```
Parts per million by weight of titanium in zircon at temperature `TC` degrees
Celsius and pressure `Pbar` bar given `aSiO2` silica activity and `aTiO2`
titanium activity, following the equations of Crisp et al., 2023.
(doi: 10.1016/j.gca.2023.04.031)
"""
function Crisp_Ti_in_zircon(TC::Number, Pbar::Number, aSiO2::Number, aTiO2::Number)
T = TC+273.15
P = Pbar*1e-4
f = 1.0/(1.0+10.0^(0.775P - 3.3713))
exp10(5.84 - 4800.0/T - 0.12*P - 0.0056*P^3 - log10(aSiO2)*f +log10(aTiO2)) / f
end
"""
```julia
Zr = Ferry_Zr_in_rutile(TC::Number, aSiO2::Number)
```
Parts per million by weight of zirconium in rutile at temperature `TC`
degrees Celsius given `aSiO2` silica activity, following the
equations of Ferry and Watson, 2007.
(doi: 10.1007/s00410-007-0201-0)
"""
function Ferry_Zr_in_rutile(TC::Number, aSiO2::Number)
exp10(7.420 - 4530.0/(TC+273.15) - log10(aSiO2))
end
# calculate the temperature of rutile saturation in degrees Celsius
"""
```julia
TC = Ferry_Zr_in_rutileT(Zr::Number, aSiO2::Number)
```
Calculate zirconium-in-rutile temperature in degrees Celcius
given `Zr` parts per million by weight of zirconium in rutile and
`aSiO2` silica activity, following the equations of Ferry and Watson, 2007.
(doi: 10.1007/s00410-007-0201-0)
"""
function Ferry_Zr_in_rutileT(Zr::Number, aSiO2::Number)
1 / ((7.420) - log10(aSiO2) - log10(Zr)) * (4530.0) .- 273.15
end
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 1694 | ## --- Hafnium isotopes
# Calculate the initial Hf ratio and epsilon Hf at time t Ma
function eHf(Hf176_Hf177, Lu176_Hf177, t; eHfOnly::Bool=true)
# Lutetium decay constant (Soderlund et al., 2004
lambda = 1.867E-11
# Present-day CHUR composition (Bouvier et al., 2008)
CHUR_Hf176_Hf177 = 0.282785
CHUR_Lu176_Hf177 = 0.0336
# Calculate initial Hf ratio at time t
Hf176_Hf177_t = Hf176_Hf177 .- Lu176_Hf177.*(exp.(t .* 10^6*lambda) .- 1)
# Calculate CHUR Hf ratio at time t
CHUR_Hf176_Hf177_t = CHUR_Hf176_Hf177 .- CHUR_Lu176_Hf177.*(exp.(t .* 10^6*lambda) .- 1)
# Calculate corresponding epsilon Hf
eHf=(Hf176_Hf177_t ./ CHUR_Hf176_Hf177_t .- 1) .* 10^4
if eHfOnly
return eHf
else
return (eHf, Hf176_Hf177_t)
end
end
export eHf
function bin_bsr_eHf(x,Hf176_Hf177,Lu176_Hf177,age,min,max,nbins,x_sigma,Hf176_Hf177_sigma,Lu176_Hf177_sigma,age_sigma,nresamples)
data = hcat(x,Hf176_Hf177,Lu176_Hf177,age)
sigma = hcat(x_sigma,Hf176_Hf177_sigma,Lu176_Hf177_sigma,age_sigma)
means = Array{Float64}(undef,nbins,nresamples)
c = Array{Float64}(undef,nbins)
for i=1:nresamples
dbs = bsresample(data,sigma,length(age))
eHf_resampled = eHf(dbs[:,2], dbs[:,3], dbs[:,4])
(c,m,s) = binmeans(dbs[:,1], eHf_resampled, min, max, nbins)
means[:,i] = m
end
m = nanmean(means,dim=2)
el = m - nanpctile(means,2.5,dim=2)
eu = nanpctile(means,97.5,dim=2) - m
return (c, m, el, eu)
end
export bin_bsr_eHf
## ---
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 43145 | ## --- Bootstrap resampling
# Kernel functions for bsr!
@inline function uniform(rng::AbstractRNG, mu, halfwidth)
mu + halfwidth * (2*rand(rng)-1)
end
export uniform
@inline function triangular(rng::AbstractRNG, mu, halfwidth)
mu + halfwidth * (rand(rng) - rand(rng))
end
export triangular
@inline function gaussian(rng::AbstractRNG, mu, sigma)
mu + sigma * randn(rng)
end
export gaussian
"""
```julia
bsr!([f::Function=gaussian], resampled::Array, index::Vector{Int}, data, sigma, p;
\trng::AbstractRNG=MersenneTwister()
)
```
Fill `resampled` with data boostrap resampled from a (sample-per-row / element-per-column)
dataset `data` with uncertainties `sigma` and resampling probabilities `p`, optionally using
random numbers generated by `f` where `f` is a function of the form `f(rng, data[i], sigma[i])`
"""
function bsr!(f::Function, resampled::Array, index::Vector{Int}, data::AbstractVecOrMat, sigma::Number, p::Number; rng::AbstractRNG=MersenneTwister())
# Prepare
ndata = size(data,1)
nrows = size(resampled,1)
ncolumns = size(resampled,2)
# Resample
n = 0
nrows_accepted = 0
while n < nrows
# Compare acceptance probability p against Unif(0,1)
@inbounds for i=1:ndata
if rand(rng) < p
nrows_accepted += 1
index[nrows_accepted] = i
if nrows_accepted == nrows
break
end
end
end
nrows_new = min(nrows_accepted - n, nrows - n)
# Columns go in outer loop because of column major indexing
for j=1:ncolumns
# Optimized inner loop
@inbounds for i = 1:nrows_new
row = index[n+i]
resampled[n+i,j] = f(rng, data[row,j], sigma)
end
end
# Keep track of current filled rows
n += nrows_new
end
return resampled
end
function bsr!(f::Function, resampled::Array, index::Vector{Int}, data::AbstractVecOrMat, sigma::Number, p::AbstractVector; rng::AbstractRNG=MersenneTwister())
# Prepare
ndata = size(data,1)
nrows = size(resampled,1)
ncolumns = size(resampled,2)
# Resample
n = 0
nrows_accepted = 0
while n < nrows
# Compare acceptance probability p against Unif(0,1)
@inbounds for i=1:ndata
if rand(rng) < p[i]
nrows_accepted += 1
index[nrows_accepted] = i
if nrows_accepted == nrows
break
end
end
end
nrows_new = min(nrows_accepted - n, nrows - n)
# Columns go in outer loop because of column major indexing
for j=1:ncolumns
# Optimized inner loop
@inbounds for i = 1:nrows_new
row = index[n+i]
resampled[n+i,j] = f(rng, data[row,j], sigma)
end
end
# Keep track of current filled rows
n += nrows_new
end
return resampled
end
function bsr!(f::Function, resampled::Array, index::Vector{Int}, data::AbstractMatrix, sigma::AbstractVector, p::Number; rng::AbstractRNG=MersenneTwister())
# Prepare
ndata = size(data,1)
nrows = size(resampled,1)
ncolumns = size(resampled,2)
# Resample
n = 0
nrows_accepted = 0
while n < nrows
# Compare acceptance probability p against Unif(0,1)
@inbounds for i=1:ndata
if rand(rng) < p
nrows_accepted += 1
index[nrows_accepted] = i
if nrows_accepted == nrows
break
end
end
end
nrows_new = min(nrows_accepted - n, nrows - n)
# Columns go in outer loop because of column major indexing
for j=1:ncolumns
# Optimized inner loop
@inbounds for i = 1:nrows_new
row = index[n+i]
resampled[n+i,j] = f(rng, data[row,j], sigma[j])
end
end
# Keep track of current filled rows
n += nrows_new
end
return resampled
end
function bsr!(f::Function, resampled::Array, index::Vector{Int}, data::AbstractMatrix, sigma::AbstractVector, p::AbstractVector; rng::AbstractRNG=MersenneTwister())
# Prepare
ndata = size(data,1)
nrows = size(resampled,1)
ncolumns = size(resampled,2)
# Resample
n = 0
nrows_accepted = 0
while n < nrows
# Compare acceptance probability p against Unif(0,1)
@inbounds for i=1:ndata
if rand(rng) < p[i]
nrows_accepted += 1
index[nrows_accepted] = i
if nrows_accepted == nrows
break
end
end
end
nrows_new = min(nrows_accepted - n, nrows - n)
# Columns go in outer loop because of column major indexing
for j=1:ncolumns
# Optimized inner loop
@inbounds for i = 1:nrows_new
row = index[n+i]
resampled[n+i,j] = f(rng, data[row,j], sigma[j])
end
end
# Keep track of current filled rows
n += nrows_new
end
return resampled
end
function bsr!(f::Function, resampled::Array, index::Vector{Int}, data::AbstractArray, sigma::AbstractArray, p::Number; rng::AbstractRNG=MersenneTwister())
# Prepare
ndata = size(data,1)
nrows = size(resampled,1)
ncolumns = size(resampled,2)
# Resample
n = 0
nrows_accepted = 0
while n < nrows
# Compare acceptance probability p against Unif(0,1)
@inbounds for i=1:ndata
if rand(rng) < p
nrows_accepted += 1
index[nrows_accepted] = i
if nrows_accepted == nrows
break
end
end
end
nrows_new = min(nrows_accepted - n, nrows - n)
# Columns go in outer loop because of column major indexing
for j=1:ncolumns
# Optimized inner loop
@inbounds for i = 1:nrows_new
row = index[n+i]
resampled[n+i,j] = f(rng, data[row,j], sigma[row,j])
end
end
# Keep track of current filled rows
n += nrows_new
end
return resampled
end
function bsr!(f::Function, resampled::Array, index::Vector{Int}, data::AbstractArray, sigma::AbstractArray, p::AbstractVector; rng::AbstractRNG=MersenneTwister())
# Prepare
ndata = size(data,1)
nrows = size(resampled,1)
ncolumns = size(resampled,2)
# Resample
n = 0
nrows_accepted = 0
while n < nrows
# Compare acceptance probability p against Unif(0,1)
@inbounds for i=1:ndata
if rand(rng) < p[i]
nrows_accepted += 1
index[nrows_accepted] = i
if nrows_accepted == nrows
break
end
end
end
nrows_new = min(nrows_accepted - n, nrows - n)
# Columns go in outer loop because of column major indexing
for j=1:ncolumns
# Optimized inner loop
@inbounds for i = 1:nrows_new
row = index[n+i]
resampled[n+i,j] = f(rng, data[row,j], sigma[row,j])
end
end
# Keep track of current filled rows
n += nrows_new
end
return resampled
end
# default method if f not specified
bsr!(resampled::Array, index::Vector{Int}, data::AbstractArray, sigma, p; rng=MersenneTwister()) = bsr!(gaussian, resampled, index, data, sigma, p, rng=rng)
export bsr!
"""
```julia
resampled = bsresample(data::AbstractArray, sigma, nrows, [p];
\t kernel = gaussian,
\t rng = MersenneTwister(),
\t return_index = false
)
```
Bootstrap resample a (sample-per-row / element-per-column) array of `data`
with uncertainties `sigma` and resampling probabilities `p`
"""
function bsresample(data::AbstractArray, sigma, nrows::Integer, p=min(0.2,nrows/size(data,1));
kernel = gaussian,
rng = MersenneTwister(),
return_index = false,
)
index = Array{Int}(undef, nrows)
resampled = Array{float(eltype(data))}(undef, nrows, size(data,2))
bsr!(kernel, resampled, index, data, sigma, p, rng=rng)
return return_index ? (resampled, index) : resampled
end
"""
```julia
resampled = bsresample(dataset::Union{Dict,NamedTuple}, nrows, [elements], [p];
\t kernel = gaussian,
\t rng = MersenneTwister()
)
```
Bootstrap resample a dictionary-based `dataset` with uncertainties stored either
in `dataset["err"]` or `dataset["[variable]_sigma"]`
"""
function bsresample(dataset::Dict, nrows::Integer, elements=dataset["elements"], p=min(0.2,nrows/length(dataset[first(elements)]));
kernel = gaussian,
rng = MersenneTwister(),
sigma = :auto,
)
# 2d array of nominal values
data = unelementify(dataset, elements, floatout=true)
# 2d array of absolute 1-sigma uncertainties
if sigma === :auto
if haskey(dataset, "err") && isa(dataset["err"], Dict)
sigma = unelementify(dataset["err"], elements, floatout=true)
else
sigma = unelementify(dataset, elements.*"_sigma", floatout=true)
end
end
# Resample
sdata = bsresample(data, sigma, nrows, p, kernel=kernel, rng=rng)
return elementify(sdata, elements, skipstart=0, importas=:Dict)
end
function bsresample(dataset::NamedTuple, nrows, elements, p=min(0.2,nrows/length(dataset[first(elements)]));
kernel = gaussian,
rng = MersenneTwister(),
sigma = :auto,
)
# 2d array of nominal values
data = unelementify(dataset, elements, floatout=true)
# 2d array of absolute 1-sigma uncertainties
if sigma === :auto
elements_sigma = (String(e)*"_sigma" for e in elements)
sigma = unelementify(dataset, elements_sigma, floatout=true)
end
# Resample
sdata = bsresample(data, sigma, nrows, p, kernel=kernel, rng=rng)
return elementify(sdata, elements, skipstart=0, importas=:Tuple)
end
export bsresample
function randsample!(resampled::DenseArray, data::Collection, nrows::Integer, p::Number, rng::AbstractRNG=MersenneTwister(), buffer::Vector{Int}=Array{Int}(undef,size(data,1)))
# Prepare
ndata = size(data,1)
ncolumns = size(resampled,2)
# Resample
n = 0
while n < nrows
# Compare acceptance probability p against Unif(0,1)
nrows_accepted = 0
@inbounds for i=1:ndata
if rand(rng) < p
nrows_accepted += 1
buffer[nrows_accepted] = i
end
end
nrows_new = min(nrows_accepted, nrows - n)
# Columns go in outer loop because of column major indexing
@inbounds @simd for j=1:ncolumns
# Optimized inner loop
for i = 1:nrows_new
resampled[n+i,j] = data[buffer[i],j]
end
end
# Keep track of current filled rows
n += nrows_new
end
return resampled
end
function randsample!(resampled::DenseArray, data::Collection, nrows::Integer, p::AbstractVector, rng::AbstractRNG=MersenneTwister(), buffer::Vector{Int}=Array{Int}(undef,size(data,1)))
# Prepare
ndata = size(data,1)
ncolumns = size(resampled,2)
# Resample
n = 0
while n < nrows
# Compare acceptance probability p against Unif(0,1)
nrows_accepted = 0
@inbounds for i=1:ndata
if rand(rng) < p[i]
nrows_accepted += 1
buffer[nrows_accepted] = i
end
end
nrows_new = min(nrows_accepted, nrows - n)
# Columns go in outer loop because of column major indexing
@inbounds @simd for j=1:ncolumns
# Optimized inner loop
for i = 1:nrows_new
resampled[n+i,j] = data[buffer[i],j]
end
end
# Keep track of current filled rows
n += nrows_new
end
return resampled
end
"""
```julia
randsample(data, nrows, [p])
```
Bootstrap resample (without uncertainty) a `data` array to length `nrows`.
Optionally provide weights `p` either as a vector (one-weight-per-sample) or scalar.
"""
function randsample(data::Collection, nrows::Integer, p=min(0.2,nrows/size(data,1));
rng::AbstractRNG=MersenneTwister(),
buffer::Vector{Int}=Array{Int}(undef,size(data,1))
)
resampled = Array{eltype(data)}(undef,nrows,size(data,2))
return randsample!(resampled, data, nrows, p, rng, buffer)
end
# Second method for randsample that takes a dictionary as input
"""
```julia
randsample(dataset::Dict, nrows, [elements], [p])
```
Bootstrap resample (without uncertainty) a `dataset` dict to length `nrows`.
Optionally provide weights `p` either as a vector (one-weight-per-sample) or scalar.
"""
function randsample(dataset::Dict, nrows::Integer, elements=dataset["elements"], p=min(0.2,nrows/length(dataset[first(elements)])))
data = unelementify(dataset, elements, floatout=true)
sdata = randsample(data, nrows, p)
return elementify(sdata, elements, skipstart=0, importas=:Dict)
end
export randsample
## --- Bin a dataset by a given independent variable
"""
```julia
(bincenters, N) = bincounts(x::AbstractArray, xmin::Number, xmax::Number, nbins::Integer;
\trelbinwidth::Number=1
)
```
Tally the number of samples that fall into each of `nbins` equally spaced `x`
bins between `xmin` and `xmax`, aligned with bin edges as
`xmin:(xmax-xmin)/nbins:xmax`
A `relbinwidth` of `1` represents normal space-filling bins, while a larger value implies bin overlap.
See also `histcounts` for a more efficient implementation without variable bin width.
"""
bincounts(x::Collection, edges::AbstractRange; kwargs...) = bincounts(x,minimum(edges),maximum(edges),length(edges); kwargs...)
function bincounts(x::Collection, xmin::Number, xmax::Number, nbins::Integer;
relbinwidth::Number=1
)
# Tally the number of samples (either resampled or corrected/original) that fall into each bin
binwidth = (xmax-xmin)/nbins
hw = binwidth*relbinwidth/2
bincenters = (xmin+binwidth/2):binwidth:(xmax-binwidth/2)
# Add up the results
N = fill(0, nbins)
@inbounds for n ∈ eachindex(bincenters)
l, u = bincenters[n]-hw, bincenters[n]+hw
for i ∈ eachindex(x)
N[n] += l < x[i] <= u
end
end
return bincenters, N
end
export bincounts
"""
```julia
binmeans(x, y, xmin:step:xmax, [weight]; resamplingratio=1, relbinwidth=1)
binmeans(x, y, xmin, xmax, nbins, [weight]; resamplingratio=1, relbinwidth=1)
```
The means (ignoring NaNs) of `y` values binned by `x`, into each of `nbins`
equally spaced `x` bins between `xmin` and `xmax`, returning bincenters,
means, and standard errors of the mean.
A `relbinwidth` of `1` represents normal space-filling bins, while a larger value implies bin overlap.
To more efficiently calculate binned means without variable bin widths (or suncertainties),
see instead `nanbinmean`/`nanbinmean!`.
### Examples
```julia
(c,m,e) = binmeans(x, y, 0, 4000, 40)
```
"""
binmeans(x::Collection, y::Collection, edges::AbstractRange, args...; kwargs...) = binmeans(x,y,minimum(edges),maximum(edges),length(edges), args...; kwargs...)
function binmeans(x::Collection, y::Collection, xmin::Number, xmax::Number, nbins::Integer;
resamplingratio::Number=1,
relbinwidth::Number=1
)
binwidth = (xmax-xmin)/nbins
hw = binwidth*relbinwidth/2
bincenters = (xmin+binwidth/2):binwidth:(xmax-binwidth/2)
T = Base.promote_op(/, eltype(y), Int)
means = Array{T}(undef,nbins)
errors = Array{T}(undef,nbins)
t = falses(size(y))
for i = 1:nbins
t .= (bincenters[i]-hw) .< x .<= (bincenters[i]+hw)
yₜ = view(y,t)
means[i] = nanmean(yₜ)
errors[i] = nanstd(yₜ, mean=means[i]) * sqrt(resamplingratio / count(t))
end
return bincenters, means, errors
end
function binmeans(x::Collection, y::Collection, min::Number, max::Number, nbins::Integer, weight::Collection;
resamplingratio::Number=1,
relbinwidth::Number=1
)
binwidth = (max-min)/nbins
hw = binwidth*relbinwidth/2
bincenters = (min+binwidth/2):binwidth:(max-binwidth/2)
T = Base.promote_op(/, eltype(y), Int)
means = Array{T}(undef,nbins)
errors = Array{T}(undef,nbins)
t = falses(size(y))
for i = 1:nbins
t .= (bincenters[i]-hw) .< x .<= (bincenters[i]+hw)
yₜ, w = view(y,t), view(weight,t)
means[i] = nanmean(yₜ, w)
errors[i] = nanstd(yₜ, w) * sqrt(resamplingratio / count(t))
end
return bincenters, means, errors
end
export binmeans
"""
```julia
(c,m,e) = binmedians(x::AbstractArray, y::AbstractArray, min::Number, max::Number, nbins::Integer;
\tresamplingratio::Number=1
\trelbinwidth::Number=1
)
```
The medians (ignoring NaNs) of `y` values binned by `x`, into each of `nbins`
equally spaced `x` bins between `xmin` and `xmax`, returning bincenters, medians,
and equivalent standard errors of the mean (1.4828 * median abolute deviation).
A `relbinwidth` of `1` represents normal space-filling bins, while a larger value implies bin overlap.
To more efficiently calculate binned medians without variable bin widths (or suncertainties),
see instead `nanbinmedian`/`nanbinmedian!`.
### Examples
```julia
(c,m,e) = binmedians(x, y, 0, 4000, 40)
```
"""
binmedians(x::Collection, y::Collection, edges::AbstractRange; kwargs...) = binmedians(x,y,minimum(edges),maximum(edges),length(edges); kwargs...)
function binmedians(x::Collection, y::Collection, min::Number, max::Number, nbins::Integer;
resamplingratio::Number=1,
relbinwidth::Number=1
)
binwidth = (max-min)/nbins
hw = binwidth*relbinwidth/2
bincenters = (min+binwidth/2):binwidth:(max-binwidth/2)
T = Base.promote_op(/, eltype(y), Int)
medians = Array{T}(undef,nbins)
errors = Array{T}(undef,nbins)
t = falses(size(y))
for i = 1:nbins
t .= ((bincenters[i]-hw) .< x .<= (bincenters[i]+hw))
yₜ = y[t]
medians[i] = nanmedian!(yₜ)
errors[i] = 1.4826 * nanmad!(yₜ) * sqrt(resamplingratio / countnotnans(yₜ))
end
return bincenters, medians, errors
end
export binmedians
## --- Bin bootstrap resampled data
"""
```julia
bin_bsr([f!::Function=nanbinmean!], x::Vector, y::VecOrMat, xmin, xmax, nbins, [w];
\tx_sigma = zeros(size(x)),
\ty_sigma = zeros(size(y)),
\tnresamplings = 1000,
\tsem = :sigma,
\tp = 0.2
)
```
Returns the bincenters `c`, means or medians `m`, and uncertainties of the
mean or median for a variable `y` binned by independent variable `x` into
`nbins` equal bins between `xmin` and `xmax`, after `nresamplings` boostrap
resamplings with acceptance probability `p`.
If a 2-d array (matrix) of `y` values is provided, each column will be treated
as a separate variable, means and uncertainties will be returned column-wise.
Optional keyword arguments and defaults:
x_sigma = zeros(size(x))
A vector representing the uncertainty (standard deviation) of each x value
y_sigma = zeros(size(y))
A vector representing the uncertainty (standard deviation) of each y value
nresamplings = 1000
The number of resamplings to conduct
sem = :sigma
Format of the uncertainty estimate of the distribution of the mean.
If `:sigma` is chosen, a tuple of three vectors `(c, m, e)` will be returned,
where `e` is the standard error of the mean.
If `:CI` or `:pctile` is chosen, a tuple of four vectors `(c, m, el, eu)`
will be returned, where `el` and `eu` are the lower and upper bounds of the 95%
confidence interval.
p = 0.2
Resampling probabilities, either as a scalar or a vector of the same length as `x`
### Examples:
```julia
(c,m,e) = bin_bsr(nanbinmedian!, x, y, 0, 4000, 40, x_sigma=0.05x, p=probability, sem=:sigma)
```
```julia
(c,m,el,eu) = bin_bsr(nanbinmean!, x, y, 0, 4000, 40, x_sigma=0.05x, p=probability, sem=:pctile)
```
"""
function bin_bsr(f!::Function, x::AbstractVector, y::AbstractVector, xmin, xmax, nbins::Integer;
x_sigma = zeros(size(x)),
y_sigma = zeros(size(y)),
nresamplings = 1000,
sem = :credibleinterval,
p = 0.2
)
data = hcat(x, y)
sigma = hcat(x_sigma, y_sigma)
binwidth = (xmax-xmin)/nbins
nrows = size(data,1)
ncols = size(data,2)
# Preallocate
dbs = Array{Float64}(undef, nrows, ncols)
index = Array{Int}(undef, nrows) # Must be preallocated even if we don't want it later
means = Array{Float64}(undef, nbins, nresamplings)
rng = MersenneTwister()
N = Array{Int}(undef, nbins)
# Resample
for i=1:nresamplings
bsr!(dbs, index, data, sigma, p, rng=rng) # Boostrap Resampling
f!(view(means,:,i), N, view(dbs,:,1), view(dbs,:,2), xmin, xmax, nbins)
end
# Return summary of results
c = (xmin+binwidth/2):binwidth:(xmax-binwidth/2) # Bin centers
m = nanmean(means,dim=2) # Mean-of-means
if sem === :sigma
# Standard deviation of means (sem)
e = nanstd(means,dim=2)
return c, m, e
elseif sem === :credibleinterval || sem === :CI || sem === :pctile
# Lower bound of central 95% CI of means
el = m .- nanpctile!(means,2.5,dim=2)
# Upper bound of central 95% CI of means
eu = nanpctile!(means,97.5,dim=2) .- m
return c, m, el, eu
else
return c, means
end
end
function bin_bsr(f!::Function, x::AbstractVector, y::AbstractMatrix, xmin, xmax, nbins::Integer;
x_sigma = zeros(size(x)),
y_sigma = zeros(size(y)),
nresamplings = 1000,
sem = :credibleinterval,
p = 0.2
)
data = hcat(x, y)
sigma = hcat(x_sigma, y_sigma)
dtype = float(eltype(data))
binwidth = (xmax-xmin)/nbins
nrows = size(data,1)
ncols = size(data,2)
# Preallocate
dbs = Array{dtype}(undef, nrows, ncols)
means = Array{dtype}(undef, nbins, nresamplings, size(y,2))
index = Array{Int}(undef, nrows) # Must be preallocated even if we don't want it later
rng = MersenneTwister()
N = Array{Int}(undef, nbins, size(y,2))
# Resample
for i=1:nresamplings
bsr!(dbs, index, data, sigma, p, rng=rng) # Boostrap Resampling
f!(view(means,:,i,:), N, view(dbs,:,1), view(dbs,:,2:1+size(y,2)), xmin, xmax, nbins)
end
# Return summary of results
c = (xmin+binwidth/2):binwidth:(xmax-binwidth/2) # Bin centers
if sem === :sigma
m = Array{dtype}(undef, nbins, size(y,2))
e = Array{dtype}(undef, nbins, size(y,2))
for j = 1:size(y,2)
m[:,j] .= nanmean(view(means,:,:,j),dim=2) # Mean-of-means
e[:,j] .= nanstd(view(means,:,:,j),dim=2) # Standard deviation of means (sem)
end
return c, m, e
elseif sem === :credibleinterval || sem === :CI || sem === :pctile
m = Array{dtype}(undef, nbins, size(y,2))
el = Array{dtype}(undef, nbins, size(y,2))
eu = Array{dtype}(undef, nbins, size(y,2))
for j = 1:size(y,2)
m[:,j] .= nanmean(view(means,:,:,j),dim=2) # Mean-of-means
el[:,j] .= m[:,j] .- nanpctile!(view(means,:,:,j), 2.5, dim=2)
eu[:,j] .= nanpctile!(view(means,:,:,j), 97.5, dim=2) .- m[:,j]
end
return c, m, el, eu
else
return c, means
end
end
function bin_bsr(f!::Function, x::AbstractVector, y::AbstractVector, xmin, xmax, nbins::Integer, w::AbstractVector;
x_sigma = zeros(size(x)),
y_sigma = zeros(size(x)),
nresamplings = 1000,
sem = :credibleinterval,
p = 0.2
)
data = hcat(x, y, w)
sigma = hcat(x_sigma, y_sigma, zeros(size(w)));
binwidth = (xmax-xmin)/nbins
nrows = size(data,1)
ncols = size(data,2)
# Preallocate
dbs = Array{Float64}(undef, nrows, ncols)
means = Array{Float64}(undef, nbins, nresamplings)
index = Array{Int}(undef, nrows) # Must be preallocated even if we don't want it later
rng = MersenneTwister()
N = Array{Int}(undef, nbins)
# Resample
for i=1:nresamplings
bsr!(dbs, index, data, sigma, p, rng=rng) # Boostrap Resampling
f!(view(means,:,i), N, view(dbs,:,1), view(dbs,:,2), view(dbs,:,3), xmin, xmax, nbins)
end
# Return summary of results
c = (xmin+binwidth/2):binwidth:(xmax-binwidth/2) # Bin centers
m = nanmean(means,dim=2) # Mean-of-means
if sem === :sigma
# Standard deviation of means (sem)
e = nanstd(means,dim=2)
return c, m, e
elseif sem === :credibleinterval || sem === :CI || sem === :pctile
# Lower bound of central 95% CI of means
el = m .- nanpctile!(means,2.5,dim=2)
# Upper bound of central 95% CI of means
eu = nanpctile!(means,97.5,dim=2) .- m
return c, m, el, eu
else
return c, means
end
end
bin_bsr(x::AbstractVector, y::AbstractVecOrMat, args...; sem=:sigma, kwargs...) = bin_bsr(nanbinmean!, x, y, args...; sem=sem, kwargs...)
export bin_bsr
bin_bsr_means(args...; kwargs...) = bin_bsr(nanbinmean!, args...; kwargs...)
export bin_bsr_means
bin_bsr_medians(args...; kwargs...) = bin_bsr(nanbinmedian!, args...; kwargs...)
export bin_bsr_medians
"""
```julia
(c, m, el, eu) = bin_bsr_ratios([f!::Function=nanbinmean!], x::Vector, num::Vector, denom::Vector, xmin, xmax, nbins, [w];
\tx_sigma = zeros(size(x)),
\tnum_sigma = zeros(size(num)),
\tdenom_sigma = zeros(size(denom)),
\tnresamplings = 1000,
\tp::Union{Number,Vector} = 0.2
)
```
Returns the bincenters `c`, means `m`, as well as upper (`el`) and lower (`eu`) 95% CIs of the mean
for a ratio `num`/`den` binned by `x` into `nbins` equal bins between `xmin` and `xmax`,
after `nresamplings` boostrap resamplings with acceptance probability `p`.
"""
function bin_bsr_ratios(f!::Function, x::AbstractVector, num::AbstractVector, denom::AbstractVector, xmin, xmax, nbins::Integer;
x_sigma::AbstractVector=zeros(size(x)),
num_sigma::AbstractVector=zeros(size(num)),
denom_sigma::AbstractVector=zeros(size(denom)),
nresamplings=1000,
p::Union{Number,AbstractVector}=0.2
)
data = hcat(x, num, denom)
sigma = hcat(x_sigma, num_sigma, denom_sigma)
binwidth = (xmax-xmin)/nbins
nrows = size(data,1)
ncols = size(data,2)
# Preallocate
dbs = Array{Float64}(undef, nrows, ncols)
index = Array{Int}(undef, nrows) # Must be preallocated even if we don't want it later
means = Array{Float64}(undef, nbins, nresamplings)
fractions = Array{Float64}(undef, nrows)
fraction_means = Array{Float64}(undef, nbins)
rng = MersenneTwister()
N = Array{Int}(undef, nbins) # Array of bin counts -- Not used but preallocated for speed
# Resample
for i=1:nresamplings
bsr!(dbs, index, data, sigma, p, rng=rng) # Boostrap Resampling
@views @turbo @. fractions = dbs[:,2] / (dbs[:,2] + dbs[:,3])
f!(fraction_means, N, view(dbs,:,1), fractions, xmin, xmax, nbins)
@. means[:,i] = fraction_means / (1 - fraction_means)
end
c = (xmin+binwidth/2):binwidth:(xmax-binwidth/2) # Bin centers
m = nanmean(means,dim=2) # Mean-of-means
el = m .- nanpctile!(means,2.5,dim=2) # Lower bound of central 95% CI
eu = nanpctile!(means,97.5,dim=2) .- m # Upper bound of central 95% CI
return c, m, el, eu
end
function bin_bsr_ratios(f!::Function, x::AbstractVector, num::AbstractVector, denom::AbstractVector, xmin, xmax, nbins::Integer, w::AbstractVector;
x_sigma::AbstractVector=zeros(size(x)),
num_sigma::AbstractVector=zeros(size(num)),
denom_sigma::AbstractVector=zeros(size(denom)),
nresamplings=1000,
p::Union{Number,AbstractVector}=0.2
)
data = hcat(x, num, denom, w)
sigma = hcat(x_sigma, num_sigma, denom_sigma, zeros(size(w)))
binwidth = (xmax-xmin)/nbins
nrows = size(data,1)
ncols = size(data,2)
# Preallocate
dbs = Array{Float64}(undef, nrows, ncols)
index = Array{Int}(undef, nrows) # Must be preallocated even if we don't want it later
means = Array{Float64}(undef, nbins, nresamplings)
fractions = Array{Float64}(undef, nrows)
fraction_means = Array{Float64}(undef, nbins)
rng = MersenneTwister()
W = Array{Float64}(undef, nbins) # Array of bin weights -- Not used but preallocated for speed
# Resample
for i=1:nresamplings
bsr!(dbs, index, data, sigma, p, rng=rng) # Boostrap Resampling
@views @turbo @. fractions = dbs[:,2] / (dbs[:,2] + dbs[:,3])
f!(fraction_means, W, view(dbs,:,1), fractions, view(dbs,:,4), xmin, xmax, nbins)
@. means[:,i] = fraction_means / (1 - fraction_means)
end
c = (xmin+binwidth/2):binwidth:(xmax-binwidth/2) # Bin centers
m = nanmean(means,dim=2) # Mean-of-means
el = m .- nanpctile!(means,2.5,dim=2) # Lower bound of central 95% CI
eu = nanpctile!(means,97.5,dim=2) .- m # Upper bound of central 95% CI
return c, m, el, eu
end
bin_bsr_ratios(x::AbstractVector, args...; kwargs...) = bin_bsr_ratios(nanbinmean!, x, args...; kwargs...)
export bin_bsr_ratios
"""
```julia
(c, m, el, eu) = bin_bsr_ratio_medians(x::Vector, num::Vector, denom::Vector, xmin, xmax, nbins, [w];
\tx_sigma = zeros(size(x)),
\tnum_sigma = zeros(size(num)),
\tdenom_sigma = zeros(size(denom)),
\tnresamplings = 1000,
\tp::Union{Number,Vector} = 0.2
)
```
Equivalent to `bin_bsr_ratios(nanbinmedian!, ...)`
"""
bin_bsr_ratio_medians(args...; kwargs...) = bin_bsr_ratios(nanbinmedian!,args...; kwargs...)
export bin_bsr_ratio_medians
## --- Quick Monte Carlo binning/interpolation functions
function mcfit(x::AbstractVector, σx::AbstractVector, y::AbstractVector, σy::AbstractVector,
xmin::Number, xmax::Number, nbins::Integer=10;
binwidth::Number=(xmax-xmin)/(nbins-1),
minrows::Number=100000
)
# Run a simplified Monte Carlo fit with nbins of witdth binwidth between xmin and xmax
# Remove missing data
hasdata = .!(isnan.(x) .| isnan.(y))
x′ = x[hasdata]
y′ = y[hasdata]
σx′ = σx[hasdata]
σy′ = σy[hasdata]
# Fill in variances where not provided explicitly
σx′[isnan.(σx′)] .= nanstd(x′)
σy′[isnan.(σy′)] .= nanstd(y′)
# Increase x uncertainty if x sampling is sparse
xsorted = [xmin; sort(x′); xmax]
minerr = maximum(xsorted[2:end] - xsorted[1:end-1]) / 2
σx′[σx′ .< minerr] .= minerr
# Bin centers
c = xmin:(xmax-xmin)/(nbins-1):xmax
halfwidth = binwidth / 2
# Run the Monte Carlo
N = fill(0, nbins)
m = fill(zero(float(eltype(y′))), nbins)
xresampled = similar(x′, float(eltype(x′)))
yresampled = similar(y′, float(eltype(y′)))
@inbounds for n = 1:ceil(Int, minrows/length(x′))
randn!(xresampled)
xresampled .= x′ .+ σx′ .* xresampled
randn!(yresampled)
yresampled .= y′ .+ σy′ .* yresampled
for j = 1:nbins
l = (c[j] - halfwidth)
u = (c[j] + halfwidth)
for i ∈ eachindex(xresampled)
if l < xresampled[i] <= u
m[j] += yresampled[i]
N[j] += 1
end
end
end
end
m ./= N
return c, m
end
export mcfit
## --- Downsample an image / array
function downsample(A::Matrix, factor::Integer, jfactor::Integer=factor)
rows = size(A,1) ÷ factor
cols = size(A,2) ÷ jfactor
result = similar(A, rows,cols)
@turbo for i=1:rows
for j=1:cols
iₛ = i*factor
jₛ = j*factor
result[i,j] = A[iₛ, jₛ]
end
end
return result
end
function downsample(A::AbstractMatrix, factor::Integer, jfactor::Integer=factor)
rows = size(A,1) ÷ factor
cols = size(A,2) ÷ jfactor
result = similar(A, rows,cols)
@inbounds for i=1:rows
for j=1:cols
result[i,j] = A[i*factor, j*factor]
end
end
return result
end
downsample(A::AbstractArray, factor::Integer) = A[factor:factor:end]
export downsample
## --- Spatiotemporal sample weighting
const PI_180 = pi/180
"""
```julia
k = invweight(lat::AbstractArray, lon::AbstractArray, age::AbstractArray;
\tlp::Number=2,
\tspatialscale=1.8,
\tagescale=38.0
)
```
Find the inverse weights `k` (proportional to spatiotemporal sample density) for
a set of geological samples with specified latitude (`lat`), logitude (`lon`),
and `age` (of crystallization, deposition, etc.).
The default `spatialscale` and `agescale` are taken from Keller and Schoene 2012.
However, alternative scalings can be supplied. If an array is supplied for either
`spatialscale`, `agescale`, or both, a 3-d matrix of `k` values will be returned,
with dimensions length(`spatialscale`)*length(`agescale`)*nrows.
"""
function invweight(lat::AbstractArray, lon::AbstractArray, age::AbstractArray;
lp::Number=2, spatialscale=1.8, agescale=38.0)
# Convert lat and lon to radians
latᵣ = materialize(vec(lat*PI_180))
lonᵣ = materialize(vec(lon*PI_180))
spatialscaleᵣ = materialize(spatialscale*PI_180)
ageᵥ = materialize(age)
if any(isnan, latᵣ) || any(isnan, lonᵣ) || any(isnan, ageᵥ)
k = fill(Inf, length(latᵣ))
t = @turbo @. !(isnan(latᵣ) | isnan(lonᵣ) | isnan(ageᵥ))
k[t] .= invweight_nonans(latᵣ[t], lonᵣ[t], ageᵥ[t], lp, spatialscaleᵣ, materialize(agescale))
return k
else
return invweight_nonans(latᵣ, lonᵣ, ageᵥ, lp, spatialscaleᵣ, materialize(agescale))
end
end
function invweight_nonans(latᵣ::AbstractArray, lonᵣ::AbstractArray, age::AbstractArray, lp::Number, spatialscaleᵣ::Number, agescale::Number)
# Precalculate some sines and cosines
latsin = @turbo sin.(latᵣ)
latcos = @turbo cos.(latᵣ)
# Allocate and fill ks
N = length(latᵣ)
k = Array{Float64}(undef, N)
p = Progress(N÷10, desc="Calculating weights: ")
@inbounds @batch for i ∈ 1:N
# Calculate weight
kᵢ = 0.0
@turbo for j ∈ 1:N
Δdᵣ = acos(min( latsin[i] * latsin[j] + latcos[i] * latcos[j] * cos(lonᵣ[i] - lonᵣ[j]), 1.0 ))
Δa = abs(age[i] - age[j])
kᵢⱼ = 1.0 / ((Δdᵣ/spatialscaleᵣ)^lp + 1.0) + 1.0 / ((Δa/agescale)^lp + 1.0)
kᵢ += kᵢⱼ
end
k[i] = kᵢ
(i % 10 == 0) && next!(p)
end
return k
end
function invweight_nonans(latᵣ::AbstractArray, lonᵣ::AbstractArray, age::AbstractArray, lp::Number, spatialscaleᵣ, agescale)
# Precalculate some sines and cosines
latsin = @turbo sin.(latᵣ)
latcos = @turbo cos.(latᵣ)
# Allocate and fill ks
N = length(latᵣ)
spatialdistᵣ = similar(latᵣ)
k = Array{Float64}(undef, length(spatialscaleᵣ), length(agescale), N)
p = Progress(N÷10, desc="Calculating weights: ")
@inbounds for i ∈ 1:N
# Calculate weight
@turbo @. spatialdistᵣ = acos(min( latsin[i] * latsin + latcos[i] * latcos * cos(lonᵣ[i] - lonᵣ), 1.0 ))
@batch for g ∈ eachindex(spatialscaleᵣ)
for h ∈ eachindex(agescale)
kᵢ = 0.0
@inbounds for j ∈ 1:N
kᵢⱼ = 1.0 / ((spatialdistᵣ[j]/spatialscaleᵣ[g])^lp + 1.0) + 1.0 / ((abs(age[i] - age[j])/agescale[h])^lp + 1.0)
kᵢ += kᵢⱼ
end
k[g,h,i] = kᵢ
end
end
(i % 10 == 0) && next!(p)
end
return k
end
export invweight
"""
```julia
k = invweight_location(lat::AbstractArray, lon::AbstractArray;
\tlp::Number=2,
\tspatialscale::Number=1.8
)
```
Find the inverse weights `k` (proportional to spatial sample density) for
a set of geological samples with specified latitude (`lat`), and logitude (`lon`).
"""
function invweight_location(lat::AbstractArray, lon::AbstractArray;
lp::Number=2, spatialscale::Number=1.8)
# Convert lat and lon to radians
latᵣ = materialize(vec(lat*PI_180))
lonᵣ = materialize(vec(lon*PI_180))
spatialscaleᵣ = materialize(spatialscale*PI_180)
if any(isnan, latᵣ) || any(isnan, lonᵣ)
k = fill(Inf, length(latᵣ))
t = @turbo @. !(isnan(latᵣ) | isnan(lonᵣ))
k[t] .= invweight_location_nonans(latᵣ[t], lonᵣ[t], lp, spatialscaleᵣ)
return k
else
return invweight_location_nonans(latᵣ, lonᵣ, lp, spatialscaleᵣ)
end
end
# Inner function that can't handle NaNs
function invweight_location_nonans(latᵣ::AbstractArray, lonᵣ::AbstractArray, lp::Number, spatialscaleᵣ::Number)
# Precalculate some sines and cosines
latsin = @turbo sin.(latᵣ)
latcos = @turbo cos.(latᵣ)
# Allocate and fill ks
N = length(latᵣ)
k = Array{Float64}(undef,N)
p = Progress(N÷10, desc="Calculating weights: ")
@inbounds @batch for i ∈ 1:N
# Otherwise, calculate weight
kᵢ = 0.0
@turbo for j ∈ 1:N
lc = latsin[i] * latsin[j] + latcos[i] * latcos[j] * cos(lonᵣ[i] - lonᵣ[j])
Δdᵣ = acos(min(lc , 1.0))
kᵢⱼ = 1.0 / ( (Δdᵣ/spatialscaleᵣ)^lp + 1.0)
kᵢ += kᵢⱼ
end
k[i] = kᵢ
(i % 10 == 0) && next!(p)
end
return k
end
export invweight_location
"""
```julia
k = invweight(nums::AbstractArray, scale::Number; lp=2)
```
Find the inverse weights for a single array `nums` for a given `scale`, and
exponent `lp` (default lp = 2).
Returns an array k where k[i] is the "inverse weight" for element i of the
input array.
"""
function invweight(nums::AbstractArray, scale::Number; lp=2)
numsₘ = materialize(nums)
if any(isnan, numsₘ)
k = fill(Inf, length(numsₘ))
t = @turbo @. !isnan(numsₘ)
k[t] .= invweight_nonans(numsₘ[t], scale, lp,)
return k
else
return invweight_nonans(numsₘ, scale, lp,)
end
end
function invweight_nonans(nums::AbstractArray, scale::Number, lp::Number)
N = length(nums)
k = Array{Float64}(undef, N)
p = Progress(N÷10, desc="Calculating weights: ")
@inbounds @batch for i ∈ 1:N
if isnan(nums[i])
# If there is missing data, set k=inf for weight=0
k[i] = Inf
else
# Otherwise, calculate weight
kᵢ = 0.0
@turbo for j ∈ 1:N
kᵢⱼ = 1.0 / ( (abs(nums[i] - nums[j])/scale)^lp + 1.0)
kᵢ += kᵢⱼ
end
k[i] = kᵢ
end
(i % 10 == 0) && next!(p)
end
return k
end
export invweight
"""
```julia
k = invweight_age(age::AbstractArray; lp::Number=2, agescale::Number=38.0)
```
Find the inverse weights `k` (proportional to temporal sample density) for
a set of geological samples with specified `age` (of crystallization, deposition, etc.).
"""
function invweight_age(age::AbstractArray; lp::Number=2, agescale::Number=38.0)
return invweight(age, agescale, lp=lp)
end
export invweight_age
## --- End of file
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 1344 | ## --- Direct access to real libc system() function
"""
```julia
system(cmdstr::AbstractString)
```
Direct access to the command line through C's `system` function -- without
stripping/sanitizing special characters, in contrast to Julia's safer
`run()` function. This allows pipelining, etc. in shell commands. Returns
0 on success.
### Examples
```julia
julia> system("pwd")
/Users/foo/code/StatGeochem.jl
0
```
"""
function system(cmdstr::AbstractString)
return ccall((:system,), Int, (Cstring,), cmdstr)
end
export system
## --- Some utilities for manipulating data types
"""
```julia
materialize(x)
```
Convert an array-like object to an materialized, actual allocated `Array`,
and leaving other types unchanged.
Unlike `collect`, will merely pass through an already-allocated `Array`
without change, rather than allocating new memory and making a copy.
### Examples
```julia
julia> StatGeochem.materialize(1:100)
100-element Vector{Int64}:
1
2
3
⋮
99
100
julia> StatGeochem.materialize(5)
5
```
"""
materialize(x) = x
materialize(x::Array) = x
materialize(x::Number) = x
materialize(x::Collection) = collect(x)
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 695 | using StatGeochem
using Test, Statistics, StatsBase, Downloads
@testset "All tests" begin
# Utilities
@testset "Resampling" begin include("testResampling.jl") end
@testset "Changepoint" begin include("testChangepoint.jl") end
@testset "Geochemistry" begin include("testGeochemistry.jl") end
using FileIO
@testset "Other Utilities" begin include("testUtilities.jl") end
# Resources
@testset "Crust 1.0" begin include("testCrust1.jl") end
@testset "Litho 1.0" begin include("testLitho1.jl") end
@testset "Other Resources" begin include("testResources.jl") end
using Plots
@testset "Package Extensions" begin include("testExtensions.jl") end
end
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 578 | ## -- Changepoint.jl tests
A = [randn(100).-2; randn(100).+2]
nsteps = 10000
burnin = 4000
dist = changepoint(A, nsteps; np=1)
@test isapprox(nanmean(dist[burnin:end]), 101, atol=4)
dist = changepoint(A, nsteps; npmin=1, npmax=5)[burnin:end,:]
@test isapprox(nanmean(dist[dist.>0]), 101, atol=4)
dist = changepoint(A, ones(200), nsteps; np=1)
@test isapprox(nanmean(dist[burnin:end]), 101, atol=4)
dist = changepoint(A, ones(200), nsteps; npmin=1, npmax=5)[burnin:end,:]
@test isapprox(nanmean(dist[dist.>0]), 101, atol=4)
## --
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 2126 | ## --- Test Crust 1.0
lats = [43.70, 39.2508, NaN]
lons = [-72.29, -106.2925, NaN]
## -- Test seismic data
# Test single lat-lon-depth point from crust1
@test [x[1] for x in find_crust1_seismic(50,50,8)] ≈ [7.1, 4.05, 3000.0]
@test [x[1] for x in find_crust1_seismic(50,NaN,8)] ≈ [NaN, NaN, NaN] nans=true
# Test fetching lat-lon array from crust1
@test all(isapprox.(find_crust1_seismic(lats,lons,6), ([6.3, 6.0, NaN], [3.63, 3.5, NaN], [2790.0, 2720.0, NaN]), nans=true))
@test all(isapprox.(find_crust1_seismic(lats,lons,7), ([6.6, 6.6, NaN], [3.80, 3.8, NaN], [2860.0, 2860.0, NaN]), nans=true))
@test all(isapprox.(find_crust1_seismic(lats,lons,8), ([7.0, 7.2, NaN], [3.99, 4.1, NaN], [2950.0, 3030.0, NaN]), nans=true))
@test all(isapprox.(find_crust1_seismic(lats,lons,:upper_crust), ([6.3, 6.0, NaN], [3.63, 3.5, NaN], [2790.0, 2720.0, NaN]), nans=true))
@test all(isapprox.(find_crust1_seismic(lats,lons,:middle_crust), ([6.6, 6.6, NaN], [3.80, 3.8, NaN], [2860.0, 2860.0, NaN]), nans=true))
@test all(isapprox.(find_crust1_seismic(lats,lons,:lower_crust), ([7.0, 7.2, NaN], [3.99, 4.1, NaN], [2950.0, 3030.0, NaN]), nans=true))
## -- Test layer thickness data
@test find_crust1_thickness(lats,lons,6) ≈ [15.4, 17.73, NaN] nans=true
@test find_crust1_thickness(lats,lons,7) ≈ [13.57, 16.21, NaN] nans=true
@test find_crust1_thickness(lats,lons,8) ≈ [7.70, 16.72, NaN] nans=true
## -- Test cumulative thickness data
@test find_crust1_base(lats,lons,6) ≈ [-14.99, -14.91, NaN] nans=true
@test find_crust1_base(lats,lons,7) ≈ [-28.56, -31.12, NaN] nans=true
@test find_crust1_base(lats,lons,8) ≈ [-36.26, -47.84, NaN] nans=true
## -- Test complete data
@test all(isapprox.(find_crust1_layer(lats,lons,6), ([6.3, 6.0, NaN], [3.63, 3.5, NaN], [2790., 2720., NaN], [15.4, 17.73, NaN]), nans=true))
@test all(isapprox.(find_crust1_layer(lats,lons,7), ([6.6, 6.6, NaN], [3.8, 3.8, NaN], [2860., 2860., NaN], [13.57, 16.21, NaN]), nans=true))
@test all(isapprox.(find_crust1_layer(lats,lons,8), ([7.0, 7.2, NaN], [3.99, 4.1, NaN], [2950., 3030., NaN], [7.70, 16.72, NaN]), nans=true))
## ---
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 290 | lat = [43.7022,-26.2041,-19.5723,-34.9285,46.4908]
lon = [-72.2896,28.0473,65.7550,138.6007,9.8355]
h = mapplot(lon, lat)
@test isa(h, Plots.Plot)
REE = [20.78,35.61,2.344,15.66,2.618,0.5873,2.077,0.3249,1.622,0.3132,0.92,0.1099,1.543,0.1509]
h = spidergram(REE)
@test isa(h, Plots.Plot)
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 40364 | ## --- General conversions
# Europium anomalies
@test eustar(6.5433, 5.9037) ≈ 2.0329978601003864
@test eustar(34.7773, 6.5433, 5.9037, 0.8904) ≈ 2.0825737578695205
# Iron oxide conversions
@test feoconversion(3.5, NaN, NaN, NaN) == 3.5
@test feoconversion(3.5, NaN, 7.5, NaN) == 7.5
@test feoconversion(3.5, NaN, 7.5, 10) == 7.5
@test feoconversion(3.5, NaN, NaN, 10) == 8.998102538090137
@test feoconversion(3.5, 4.4, NaN, NaN) ≈ 7.45916511675966
@test feoconversion(NaN, 4.4, NaN, NaN) ≈ 3.9591651167596607
@test isnan(feoconversion(NaN, NaN, NaN, NaN))
# Other oxide conversion
D = ["Fe" "Mg" "Ca" "P" "FeOT" "MgO" "CaO" "P2O5"; 10000 10000 10000 10000 NaN NaN NaN NaN; 10000 10000 10000 10000 NaN NaN NaN NaN]
M = elementify(D, importas=:Dict)
O = oxideconversion(M)
@test all(O["FeOT"] .≈ (molarmass["Fe"]+molarmass["O"])/molarmass["Fe"])
@test all(O["MgO"] .≈ (molarmass["Mg"]+molarmass["O"])/molarmass["Mg"])
@test all(O["CaO"] .≈ (molarmass["Ca"]+molarmass["O"])/molarmass["Ca"])
@test all(O["P2O5"] .≈ (molarmass["P"]+2.5*molarmass["O"])/molarmass["P"])
DT = deepcopy(TupleDataset(M))
oxideconversion!(DT)
@test all(DT.FeOT .≈ (molarmass["Fe"]+molarmass["O"])/molarmass["Fe"])
@test all(DT.MgO .≈ (molarmass["Mg"]+molarmass["O"])/molarmass["Mg"])
@test all(DT.CaO .≈ (molarmass["Ca"]+molarmass["O"])/molarmass["Ca"])
@test all(DT.P2O5 .≈ (molarmass["P"]+2.5*molarmass["O"])/molarmass["P"])
D = ["NiO" "CoO" "BaO" "SO3" "Ni" "Co" "Ba" "S"; 1 1 1 1 NaN NaN NaN NaN; 1 1 1 1 NaN NaN NaN NaN]
O = elementify(D, importas=:Dict)
M = metalconversion(O)
@test all(M["Ni"] .≈ 10000molarmass["Ni"]/(molarmass["Ni"]+molarmass["O"]))
@test all(M["Co"] .≈ 10000molarmass["Co"]/(molarmass["Co"]+molarmass["O"]))
@test all(M["Ba"] .≈ 10000molarmass["Ba"]/(molarmass["Ba"]+molarmass["O"]))
@test all(M["S"] .≈ 10000molarmass["S"]/(molarmass["S"]+3molarmass["O"]))
# Carbonate conversions
D = ["CaCO3" "MgCO3" "CaO" "MgO" "CO2" "TOC" "TIC" "TC" "C"; 1 1 NaN NaN NaN NaN NaN NaN 10000; 1 1 NaN NaN NaN NaN NaN NaN 10000]
C = elementify(D, importas=:Dict)
carbonateconversion!(C)
@test all(C["MgO"] .≈ (molarmass["Mg"]+molarmass["O"])/(molarmass["Mg"]+molarmass["C"]+3molarmass["O"]))
@test all(C["CaO"] .≈ (molarmass["Ca"]+molarmass["O"])/(molarmass["Ca"]+molarmass["C"]+3molarmass["O"]))
@test all(C["CO2"] .≈ (molarmass["C"]+2molarmass["O"])/(molarmass["Ca"]+molarmass["C"]+3molarmass["O"]) + (molarmass["C"]+2molarmass["O"])/(molarmass["Mg"]+molarmass["C"]+3molarmass["O"]))
@test all(C["TC"] .≈ 1)
@test all(C["TIC"] .≈ 0.9616817911685506*molarmass["C"]/(molarmass["C"] + 2molarmass["O"]))
@test all(C["TOC"] .≈ 1 - 0.9616817911685506*molarmass["C"]/(molarmass["C"] + 2molarmass["O"]))
D = ["CaCO3" "MgCO3" "CaO" "MgO" "CO2" "TOC" "TIC" "TC" "C"; 1 1 NaN NaN NaN NaN NaN NaN NaN; 1 1 NaN NaN NaN NaN NaN NaN NaN]
C = elementify(D, importas=:Dict)
carbonateconversion!(C)
@test all(C["MgO"] .≈ (molarmass["Mg"]+molarmass["O"])/(molarmass["Mg"]+molarmass["C"]+3molarmass["O"]))
@test all(C["CaO"] .≈ (molarmass["Ca"]+molarmass["O"])/(molarmass["Ca"]+molarmass["C"]+3molarmass["O"]))
@test all(C["CO2"] .≈ (molarmass["C"]+2molarmass["O"])/(molarmass["Ca"]+molarmass["C"]+3molarmass["O"]) + (molarmass["C"]+2molarmass["O"])/(molarmass["Mg"]+molarmass["C"]+3molarmass["O"]))
@test all(C["TIC"] .≈ 0.9616817911685506*molarmass["C"]/(molarmass["C"] + 2molarmass["O"]))
@test all(C["C"] .≈ 1e4*0.9616817911685506*molarmass["C"]/(molarmass["C"] + 2molarmass["O"]))
# Weathering indices
@test CIA(14.8577, 4.5611, 3.29641, 2.3992) ≈ 47.66582778067264
@test WIP(3.2964, 4.5611, 2.3992, 5.9121) ≈ 78.40320264846837
## -- Norms
n = StatGeochem.cipw_norm(57.05, 0.44, 14.57, 8.02, 0, 0.18, 6.79, 10.55, 1.26, 0.49, 0.06)
@test n.quartz ≈ 22.64883050985674
@test n.orthoclase ≈ 2.895716060129942
@test n.plagioclase ≈ 43.31401932989578
@test n.corundum ≈ 0.0
@test n.nepheline ≈ 0.0
@test n.diopside ≈ 13.731414049010224
@test n.orthopyroxene ≈ 3.94200752101842
@test n.olivine ≈ 0.0
@test n.magnetite ≈ 11.62824215584731
@test n.ilmenite ≈ 0.8356557044661498
@test n.apatite ≈ 0.1421161651208747
## -- Perplex name abbreviations
abbreviations = ("ak", "alm", "and", "andr", "chum", "cz", "crd", "ep", "fa", "fctd", "fcrd", "fep", "fosm", "fst", "fo", "geh", "gr", "hcrd", "tpz", "ky", "larn", "law", "merw", "mctd", "mst", "mnctd", "mncrd", "mnst", "mont", "osm1", "osm2", "phA", "pump", "py", "rnk", "sill", "spss", "sph", "spu", "teph", "ty", "vsv", "zrc", "zo", "acm", "cats", "di", "en", "fs", "hed", "jd", "mgts", "pswo", "pxmn", "rhod", "wo", "anth", "cumm", "fanth", "fgl", "ftr", "ged", "gl", "grun", "parg", "rieb", "tr", "ts", "deer", "fcar", "fspr", "mcar", "spr4", "spr7", "ann", "cel", "east", "fcel", "ma", "mnbi", "mu", "naph", "pa", "phl", "afchl", "ames", "clin", "daph", "fsud", "mnchl", "sud", "atg", "chr", "fta", "kao", "pre", "prl", "ta", "tats", "ab", "anl", "an", "coe", "crst", "heu", "abh", "kals", "lmt", "lc", "me", "mic", "ne", "q", "san", "stlb", "stv", "trd", "wrk", "bdy", "cor", "geik", "hem", "herc", "ilm", "oilm", "lime", "mft", "mt", "mang", "bunsn", "per", "pnt", "ru", "sp", "usp", "br", "dsp", "gth", "ank", "arag", "cc", "dol", "mag", "rhc", "sid", "diam", "gph", "iron", "Ni", "CO2", "CO", "H2", "CH4", "O2", "H2O", "abL", "anL", "diL", "enL", "faL", "fliq", "foL", "h2oL", "hliq", "kspL", "mliq", "qL", "silL", "H+", "Cl-", "OH-", "Na+", "K+", "Ca++", "Mg++", "Fe++", "Al+++", "CO3", "AlOH3", "AlOH4-", "KOH", "HCL", "KCL", "NaCl", "CaCl2", "CaCl+", "MgCl2", "MgCl", "FeCl2", "aqSi", "Augite(G)", "Cpx(JH)", "Cpx(l)", "Cpx(h)", "Cpx(stx)", "Cpx(stx7)", "Omph(HP)", "Cpx(HP)", "Cpx(m)", "Cpx(stx8)", "Omph(GHP)", "cAmph(G)", "Cumm", "Gl", "Tr", "GlTrTsPg", "Amph(DHP)", "Amph(DPW)", "Ca-Amph(D)", "Na-Amph(D)", "Act(M)", "GlTrTsMr", "cAmph(DP)", "melt(G)", "melt(W)", "melt(HP)", "melt(HGP)", "pMELTS(G)", "mMELTS(G)", "LIQ(NK)", "LIQ(EF)", "Chl(W)", "Chl(HP)", "Chl(LWV)", "O(JH)", "O(SG)", "O(HP)", "O(HPK)", "O(stx)", "O(stx7)", "Ol(m)", "O(stx8)", "Sp(JH)", "GaHcSp", "Sp(JR)", "Sp(GS)", "Sp(HP)", "Sp(stx)", "CrSp", "Sp(stx7)", "Sp(WPC)", "Sp(stx8)", "Pl(JH)", "Pl(h)", "Pl(stx8)", "Kf", "San", "San(TH)", "Grt(JH)", "Gt(W)", "CrGt", "Gt(MPF)", "Gt(B)", "Gt(GCT)", "Gt(HP)", "Gt(EWHP)", "Gt(WPH)", "Gt(stx)", "Gt(stx8)", "Gt(WPPH)", "ZrGt(KP)", "Maj", "Opx(JH)", "Opx(W)", "Opx(HP)", "CrOpx(HP)", "Opx(stx)", "Opx(stx8)", "Mica(W)", "Pheng(HP)", "MaPa", "Mica(CF)", "Mica(CHA1)", "Mica(CHA)", "Mica+(CHA)", "Mica(M)", "Mica(SGH)", "Ctd(W)", "Ctd(HP)", "Ctd(SGH)", "St(W)", "St(HP)", "Bi(W)", "Bio(TCC)", "Bio(WPH)", "Bio(HP)", "Crd(W)", "hCrd", "Sa(WP)", "Sapp(HP)", "Sapp(KWP)", "Sapp(TP)", "Osm(HP)", "F", "F(salt)", "COH-Fluid", "Aq_solven0", "WADDAH", "T", "Scap", "Carp", "Carp(M)", "Carp(SGH)", "Sud(Livi)", "Sud", "Sud(M)", "Anth", "o-Amph", "oAmph(DP)", "feldspar", "feldspar_B", "Pl(I1,HP)", "Fsp(C1)", "Do(HP)", "M(HP)", "Do(AE)", "Cc(AE)", "oCcM(HP)", "Carb(M)", "oCcM(EF)", "dis(EF)", "IlHm(A)", "IlGkPy", "Ilm(WPH)", "Ilm(WPH0)", "Neph(FB)", "Chum", "Atg(PN)", "B", "Pu(M)", "Stlp(M)", "Wus",)
common_names = ("akermanite", "almandine", "andalusite", "andradite", "clinohumite", "clinozoisite", "cordierite", "epidote", "fayalite", "Fe-chloritoid", "Fe-cordierite", "Fe-epidote", "Fe-osumilite", "Fe-staurolite", "forsterite", "gehlenite", "grossular", "hydrous cordierite", "hydroxy-topaz", "kyanite", "larnite", "lawsonite", "merwinite", "Mg-chloritoid", "Mg-staurolite", "Mn-chloritoid", "Mn-cordierite", "Mn-staurolite", "monticellite", "osumilite(1)", "osumilite(2)", "phase A", "pumpellyite", "pyrope", "rankinite", "sillimanite", "spessartine", "sphene", "spurrite", "tephroite", "tilleyite", "vesuvianite", "zircon", "zoisite", "acmite", "Ca-tschermakite", "diopside", "enstatite", "ferrosilite", "hedenbergite", "jadeite", "Mg-tschermakite", "pseudowollastonite", "pyroxmangite", "rhodonite", "wollastonite", "anthophyllite", "cummingtonite", "Fe-anthophyllite", "Fe-glaucophane", "ferroactinolite", "gedrite", "glaucophane", "grunerite", "pargasite", "riebeckite", "tremolite", "tschermakite", "deerite", "Fe-carpholite", "Fe-sapphirine(793)", "Mg-carpholite", "sapphirine(442)", "sapphirine(793)", "annite", "celadonite", "eastonite", "Fe-celadonite", "margarite", "Mn-biotite", "muscovite", "Na-phlogopite", "paragonite", "phlogopite", "Al-free chlorite", "amesite", "clinochlore", "daphnite", "Fe-sudoite", "Mn-chlorite", "sudoite", "antigorite", "chrysotile", "Fe-talc", "kaolinite", "prehnite", "pyrophyllite", "talc", "tschermak-talc", "albite", "analcite", "anorthite", "coesite", "cristobalite", "heulandite", "highalbite", "kalsilite", "laumontite", "leucite", "meionite", "microcline", "nepheline", "quartz", "sanidine", "stilbite", "stishovite", "tridymite", "wairakite", "baddeleyite", "corundum", "geikielite", "hematite", "hercynite", "ilmenite", "ilmenite(ordered)", "lime", "magnesioferrite", "magnetite", "manganosite", "nickel oxide", "periclase", "pyrophanite", "rutile", "spinel", "ulvospinel", "brucite", "diaspore", "goethite", "ankerite", "aragonite", "calcite", "dolomite", "magnesite", "rhodochrosite", "siderite", "diamond", "graphite", "iron", "nickel", "carbon dioxide", "carbon monoxide", "hydrogen", "methane", "oxygen", "water fluid", "albite liquid", "anorthite liquid", "diopside liquid", "enstatite liquid", "fayalite liquid", "Fe-liquid (in KFMASH)", "forsterite liquid", "H2O liquid", "H2O liquid (in KFMASH)", "K-feldspar liquid", "Mg liquid (in KFMASH)", "Silica liquid", "Sillimanite liquid", "H+(aq)", "Cl(aq)", "OH(aq)", "Na+(aq)", "K+(aq)", "Ca2+(aq)", "Mg2+(aq)", "Fe2+(aq)", "Al3+(aq)", "CO3--(aq)", "Al(OH)3(aq)", "Al(OH)4----(aq)", "KOH(aq)", "HCl(aq)", "KCl(aq)", "NaCl(aq)", "CaCl(aq)", "CaCl+(aq)", "MgCl2(aq)", "MgCl+(aq)", "FeCl(aq)", "Aqueous silica", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinopyroxene", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "clinoamphibole", "melt", "melt", "melt", "melt", "melt", "melt", "melt", "melt", "chlorite", "chlorite", "chlorite", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "olivine", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "spinel", "plagioclase", "plagioclase", "plagioclase", "k-feldspar", "k-feldspar", "k-feldspar", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "garnet", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "orthopyroxene", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "white mica", "chloritoid", "chloritoid", "chloritoid", "staurolite", "staurolite", "biotite", "biotite", "biotite", "biotite", "cordierite", "cordierite", "sapphirine", "sapphirine", "sapphirine", "sapphirine", "osumilite", "fluid", "fluid", "fluid", "fluid", "fluid", "talc", "scapolite", "carpholite", "carpholite", "carpholite", "sudoite", "sudoite", "sudoite", "orthoamphibole", "orthoamphibole", "orthoamphibole", "ternary feldspar", "ternary feldspar", "ternary feldspar", "ternary feldspar", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "calcite", "ilmenite", "ilmenite", "ilmenite", "ilmenite", "nepheline", "clinohumite", "serpentine", "brucite", "pumpellyite", "stilpnomelane", "wüstite",)
@test perplex_common_name.(abbreviations) == common_names
abbreviations = ("ak", "alm", "and", "andr", "chum", "cz", "crd", "ep", "fa", "fctd", "fcrd", "fep", "fosm", "fst", "fo", "geh", "gr", "hcrd", "tpz", "ky", "larn", "law", "merw", "mctd", "mst", "mnctd", "mncrd", "mnst", "mont", "osm1", "osm2", "phA", "pump", "py", "rnk", "sill", "spss", "sph", "spu", "teph", "ty", "vsv", "zrc", "zo", "acm", "cats", "di", "en", "fs", "hed", "jd", "mgts", "pswo", "pxmn", "rhod", "wo", "anth", "cumm", "fanth", "fgl", "ftr", "ged", "gl", "grun", "parg", "rieb", "tr", "ts", "deer", "fcar", "fspr", "mcar", "spr4", "spr7", "ann", "cel", "east", "fcel", "ma", "mnbi", "mu", "naph", "pa", "phl", "afchl", "ames", "clin", "daph", "fsud", "mnchl", "sud", "atg", "chr", "fta", "kao", "pre", "prl", "ta", "tats", "ab", "anl", "an", "coe", "crst", "heu", "abh", "kals", "lmt", "lc", "me", "mic", "ne", "q", "san", "stlb", "stv", "trd", "wrk", "bdy", "cor", "geik", "hem", "herc", "ilm","oilm","lime", "mft", "mt", "mang", "bunsn", "per", "pnt", "ru", "sp", "usp", "br", "dsp", "gth", "ank", "arag", "cc", "dol", "mag", "rhc", "sid", "diam", "gph", "iron", "Ni", "CO2", "CO", "H2", "CH4", "O2", "H2O", "abL", "anL", "diL", "enL", "faL", "fliq", "foL", "h2oL", "hliq", "kspL", "mliq", "qL", "silL", "H+", "Cl-", "OH-", "Na+", "K+", "Ca++", "Mg++", "Fe++", "Al+++", "CO3", "AlOH3", "AlOH4-", "KOH", "HCL", "KCL", "NaCl", "CaCl2", "CaCl+", "MgCl2", "MgCl", "FeCl2", "aqSi",)
full_names = ("akermanite", "almandine", "andalusite", "andradite", "clinohumite", "clinozoisite", "cordierite", "epidote(ordered)", "fayalite", "Fe-chloritoid", "Fe-cordierite", "Fe-epidote", "Fe-osumilite", "Fe-staurolite", "forsterite", "gehlenite", "grossular", "hydrous cordierite", "hydroxy-topaz", "kyanite", "larnite-bredigite", "lawsonite", "merwinite", "Mg-chloritoid", "Mg-staurolite", "Mn-chloritoid", "Mn-cordierite", "Mn-staurolite", "monticellite", "osumilite(1)", "osumilite(2)", "phase A", "pumpellyite", "pyrope", "rankinite", "sillimanite", "spessartine", "sphene", "spurrite", "tephroite", "tilleyite", "vesuvianite", "zircon", "zoisite", "acmite", "Ca-tschermaks pyroxene", "Diopside", "enstatite", "ferrosilite", "hedenbergite", "jadeite", "mg-tschermak", "pseudowollastonite", "pyroxmangite", "rhodonite", "wollastonite", "anthophyllite", "cummingtonite", "Fe-anthophyllite", "Fe-glaucophane", "ferroactinolite", "gedrite(Na-free)", "glaucophane", "grunerite", "pargasite", "riebeckite", "tremolite", "tschermakite", "deerite", "fe-carpholite", "fe-sapphirine(793)", "mg-carpholite", "sapphirine(442)", "sapphirine(793)", "annite", "celadonite", "eastonite", "Fe-celadonite", "margarite", "Mn-biotite", "muscovite", "Na-phlogopite", "paragonite", "phlogopite", "Al-free chlorite", "amesite(14Ang)", "clinochlore(ordered)", "daphnite", "Fe-sudoite", "Mn-chlorite", "Sudoite", "antigorite", "chrysotile", "Fe-talc", "Kaolinite", "prehnite", "pyrophyllite", "talc", "tschermak-talc", "albite", "analcite", "anorthite", "coesite", "cristobalite", "heulandite", "highalbite", "kalsilite", "laumontite", "leucite", "meionite", "microcline", "nepheline", "quartz", "sanidine", "stilbite", "stishovite", "tridymite", "wairakite", "baddeleyite", "corundum", "geikielite", "hematite", "hercynite", "ilmenite", "ilmenite(ordered)","lime", "magnesioferrite", "magnetite", "manganosite", "nickel oxide", "periclase", "pyrophanite", "rutile", "spinel", "ulvospinel", "brucite", "diaspore", "goethite", "ankerite", "aragonite", "calcite", "dolomite", "magnesite", "rhodochrosite", "siderite", "diamond", "graphite", "iron", "nickel", "carbon dioxide", "carbon monoxide", "hydrogen", "methane", "oxygen", "water fluid", "albite liquid", "anorthite liquid", "diopside liquid", "enstatite liquid", "fayalite liquid", "Fe-liquid (in KFMASH)", "Forsterite liquid", "H2O liquid", "H2O liquid (in KFMASH)", "K-feldspar liquid", "Mg liquid (in KFMASH)", "Silica liquid", "Sillimanite liquid", "H+(aq)", "Cl(aq)", "OH(aq)", "Na+(aq)", "K+(aq)", "Ca2+(aq)", "Mg2+(aq)", "Fe2+(aq)", "Al3+(aq)", "CO3--(aq)", "Al(OH)3(aq)", "Al(OH)4----(aq)", "KOH(aq)", "HCl(aq)", "KCl(aq)", "NaCl(aq)", "CaCl(aq)", "CaCl+(aq)", "MgCl2(aq)", "MgCl+(aq)", "FeCl(aq)", "Aqueous silica",)
@test perplex_expand_name.(abbreviations) == full_names
@test perplex_abbreviate_name.(full_names) == abbreviations
@test perplex_phase_is_solid.(("melt(HGP)", "q", "diL", "andr", "T(K)")) == (false, true, false, true, false)
@test findall(germ_perplex_name_matches.(germ_kd["minerals"], germ_kd["minerals"])) == [3, 12, 18]
## --- Saturation models
# SiO2, TiO2, Al2O3, FeOT, MnO, MgO, CaO, Na2O, K2O, P2O5
majors = [58.509, 1.022, 14.858, 4.371, 0.141, 4.561, 5.912, 3.296, 2.399, 0.279]
@test tzirc(majors..., 100) ≈ 603.4774053095614
@test tzircZr(majors..., 800) ≈ 826.1071302971219
@test mean(tzircM((repeat([m],2) for m in majors)...,)) ≈ 2.328787411099651
@test StatGeochem.Ayers_tsphene(majors...) ≈ 637.7486728299519
@test StatGeochem.Ayers_tspheneTiO2(majors..., 800) ≈ 2.3486842447760026
@test mean(StatGeochem.Ayers_tspheneC.((repeat([m],2) for m in majors)...,)) ≈ 2.4263934899817188
# SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, Li2O, H2O
montel_elems = [58.509, 1.022, 14.858, 4.371, 4.561, 5.912, 3.296, 2.399, 0.01, 4.0]
@test StatGeochem.Montel_tmonaziteREE(montel_elems..., 750.0) ≈ 11.884450325741755
@test StatGeochem.Montel_tmonazite(montel_elems..., 100,100,100,0,0,0) ≈ 631.2376817530326
@test StatGeochem.Rusiecka_tmonaziteREE(200, 750) ≈ 0.27430570654873154
@test StatGeochem.Rusiecka_txenotimeY(200, 750) ≈ 41.9312030248943
@test StatGeochem.Harrison_tapatiteP2O5(58.509, 14.858, 5.912, 3.296, 2.399, 750.) ≈ 0.10142278764336987
@test StatGeochem.Harrison_tapatiteP(58.509, 14.858, 5.912, 3.296, 2.399, 750.) ≈ 442.6362451135793
@test StatGeochem.Harrison_tapatiteP2O5(58.509, 750.) ≈ 0.10142278764336987
@test StatGeochem.Harrison_tapatite(58.509, 0.1) ≈ 748.6127179814277
# SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O, P2O5
majors = [58.509, 1.022, 14.858, 4.371, 4.561, 5.912, 3.296, 2.399, 0.279]
@test StatGeochem.Tollari_tapatite(majors...) ≈ 528.5868109033141
@test StatGeochem.Tollari_tapatiteP2O5(58.509,5.912,750.) ≈ 0.5011681927262436
@test StatGeochem.Hayden_trutile(majors...) ≈ 822.7645622408794
@test StatGeochem.Hayden_trutileTiO2(majors...,750.) ≈ 0.045228791859556305
## -- Test thermometers
@test StatGeochem.Ferry_Zr_in_rutile(750,1) ≈ 982.8714076786658
@test StatGeochem.Ferry_Zr_in_rutileT(982.8714076786658,1) ≈ 750.0
@test StatGeochem.Ferry_Ti_in_zircon(750,1,1) ≈ 10.46178465494583
@test StatGeochem.Ferry_Ti_in_zirconT(10.46178465494583, 1, 1) ≈ 750.0
@test StatGeochem.Crisp_Ti_in_zircon(750,0,1,1) ≈ 14.08608953046849
@test StatGeochem.Crisp_Ti_in_zircon(750,1000,1,1) ≈ 13.703165806686624
@test StatGeochem.Crisp_Ti_in_zircon(750,10000,1,1) ≈ 10.570721163513461
@test StatGeochem.Crisp_Ti_in_zircon(750,20000,1,1) ≈ 7.4184040803703954
## -- Test melts
if Sys.islinux() || Sys.isapple()
# Which version of Melts to use
if Sys.isapple()
alphameltsversion = "macosx_alphamelts_1-9"
alphameltsexec = "alphamelts_macosx64"
else
alphameltsversion = "linux_alphamelts_1-9"
alphameltsexec = "alphamelts_linux64"
end
# Construct file path
meltsdir = joinpath(resourcepath, alphameltsversion)
filepath = joinpath(resourcepath, alphameltsversion*".zip")
# Download precompiled executable
if !isfile(filepath)
@info "Downloading alphamelts to $meltsdir"
run(`mkdir -p $meltsdir`)
Downloads.download("https://storage.googleapis.com/statgeochem/$alphameltsversion.zip", filepath)
run(`unzip -o $filepath -d $resourcepath`)
run(`mv $meltsdir/$alphameltsexec $meltsdir/alphamelts`)
end
meltspath = joinpath(meltsdir, "run_alphamelts.command")
scratchdir = "./"
## --- # # # # # # # # # # # pMelts equil. batch melting # # # # # # # # # # # #
# Conditions
P_range = [20000,20000]
T_range = [1700,800]
# Starting composition
elements = ["SiO2", "TiO2","Al2O3","Fe2O3","Cr2O3", "FeO", "MnO", "MgO", "NiO", "CoO", "CaO", "Na2O", "K2O", "P2O5", "H2O",]
composition=[44.8030, 0.1991, 4.4305, 0.9778, 0.3823, 7.1350, 0.1344, 37.6345, 0.2489, 0.0129, 3.5345, 0.3584, 0.0289, 0.0209, 0.15,] #mcdbse (McDonough Pyrolite)
# Run simulation
melts_configure(meltspath, scratchdir, composition, elements, T_range, P_range,
batchstring="1\nsc.melts\n10\n1\n3\n1\nliquid\n1\n1.0\n0\n10\n0\n4\n0\n",
dT=-10, dP=0, index=1, version="pMELTS",mode="isobaric",fo2path="FMQ")
# Read results
melt_comp = melts_query_liquid(scratchdir, index=1, importas=:Tuple)
solid_comp = melts_query_solid(scratchdir, index=1, importas=:Tuple)
system = melts_query_system(scratchdir, index=1, importas=:Tuple)
modes = melts_query_modes(scratchdir, index=1, importas=:Tuple)
clean_modes = melts_clean_modes(scratchdir, index=1)
bulk = melts_query(scratchdir, index=1)
@test isa(melt_comp, NamedTuple)
@test isa(solid_comp, NamedTuple)
@test isa(system, NamedTuple)
@test isa(modes, NamedTuple)
@test isa(clean_modes, Dict)
@test isa(bulk, Dict)
@test all(melt_comp.Pressure .== 20000.0)
print("melt_comp.Temperature: ")
println(melt_comp.Temperature)
@test melt_comp.Temperature ≈ 1624.06:-10.0:914.06 # 624.07:-10:804.07
print("melt_comp.SiO2: ")
println(melt_comp.SiO2)
@test isapprox(melt_comp.SiO2, [44.7993, 45.1225, 45.4621, 45.9048, 46.3452, 46.7906, 45.6756, 45.2691, 44.9006, 44.5674, 44.2672, 43.9979, 43.7574, 43.5432, 43.3529, 43.184, 42.9984, 42.8732, 42.8037, 42.7829, 42.8033, 42.8579, 42.9405, 43.0453, 43.1669, 43.308, 43.5206, 43.7353, 43.9483, 44.1558, 44.3546, 44.5417, 44.7144, 44.8707, 45.0086, 45.1269, 45.2246, 45.3007, 45.3549, 45.3868, 45.3922, 45.1936, 44.9836, 44.76, 44.3911, 44.2064, 44.0531, 43.8942, 43.7199, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], nans=true)
# @test isapprox(melt_comp.SiO2, [44.7991, 45.1222, 45.4618, 45.9044, 46.3448, 46.7903, 45.6759, 45.2694, 44.9009, 44.5677, 44.2675, 43.9981, 43.7575, 43.5433, 43.353, 43.1841, 42.9985, 42.8732, 42.8038, 42.7829, 42.8033, 42.8579, 42.9404, 43.0452, 43.1668, 43.3079, 43.5205, 43.7351, 43.9481, 44.1556, 44.3545, 44.5415, 44.7143, 44.8705, 45.0085, 45.1269, 45.2245, 45.3007, 45.3549, 45.3868, 45.3963, 45.3833, 45.3478, 45.1948, 44.9076, 44.5701, 44.3443, 44.1384, 43.9297, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN], nans=true)
@test all(solid_comp.Pressure .== 20000.0)
print("solid_comp.Temperature: ")
println(solid_comp.Temperature)
@test solid_comp.Temperature ≈ 1624.06:-10.0:914.06 # 1624.07:-10:804.07
print("solid_comp.SiO2: ")
println(solid_comp.SiO2)
@test isapprox(solid_comp.SiO2, [41.4817, 41.5019, 41.4897, 41.2256, 41.0971, 41.0055, 43.7186, 44.3496, 44.73, 44.9695, 45.1228, 45.2202, 45.28, 45.3137, 45.3295, 45.3326, 45.2928, 45.2409, 45.1861, 45.1333, 45.0848, 45.0416, 45.0037, 44.9709, 44.9427, 44.9181, 44.8932, 44.8727, 44.8558, 44.842, 44.8308, 44.8219, 44.8148, 44.8093, 44.8052, 44.8023, 44.8004, 44.7993, 44.799, 44.7993, 44.8002, 44.8057, 44.8108, 44.8155, 44.8207, 44.8183, 44.8174, 44.8169, 44.8166, 44.8167, 44.8169, 44.8171, 44.8173, 44.8175, 44.8177, 44.8179, 44.8181, 44.8182, 44.8184, 44.8186, 44.8187, 44.8188, 44.8189, 44.819, 44.8191, 44.8192, 44.8193, 44.8193, 44.8194, 44.8195, 44.8195, 44.8196], nans=true)
# @test isapprox(solid_comp.SiO2, [NaN, 41.5019, 41.4897, 41.2257, 41.0972, 41.0056, 43.718, 44.3492, 44.7297, 44.9694, 45.1228, 45.2202, 45.2799, 45.3137, 45.3295, 45.3326, 45.2928, 45.2409, 45.1861, 45.1333, 45.0848, 45.0416, 45.0037, 44.9709, 44.9427, 44.9182, 44.8933, 44.8727, 44.8558, 44.842, 44.8309, 44.8219, 44.8148, 44.8093, 44.8052, 44.8023, 44.8004, 44.7993, 44.799, 44.7993, 44.8001, 44.8014, 44.803, 44.8069, 44.813, 44.817, 44.8171, 44.8169, 44.8167, 44.8167, 44.8169, 44.8171, 44.8173, 44.8175, 44.8177, 44.8179, 44.8181, 44.8182, 44.8184, 44.8185, 44.8187, 44.8188, 44.8189, 44.819, 44.8191, 44.8192, 44.8193, 44.8193, 44.8194, 44.8195, 44.8195, 44.8196, 44.8196, 44.8196, 44.8196, 44.8196, 44.8196, 44.8196, 44.8195, 44.8196, 44.8197, 44.8198, 44.8199], nans=true)
@test all(system.Pressure .== 20000.0)
@test system.Temperature ≈ 1624.06:-10.0:914.06 # 1624.07:-10:804.07
print("system.aH2O: ")
println(system.aH2O)
@test isapprox(system.aH2O, [0.00119355, 0.00143907, 0.00171302, 0.0020253, 0.00235407, 0.00269726, 0.00378774, 0.00472133, 0.00577799, 0.00696703, 0.00829895, 0.00978529, 0.0114387, 0.0132728, 0.0153034, 0.0175484, 0.0224613, 0.0285535, 0.0358671, 0.044436, 0.0542559, 0.0652938, 0.0774963, 0.0907963, 0.105118, 0.120441, 0.137091, 0.15441, 0.172315, 0.190729, 0.209584, 0.228825, 0.248403, 0.26828, 0.288424, 0.308812, 0.329427, 0.350254, 0.371286, 0.392517, 0.413978, 0.437322, 0.461004, 0.484995, 0.517313, 0.578275, 0.631048, 0.677779, 0.720523, 0.74231, 0.75994, 0.778542, 0.798166, 0.818869, 0.840709, 0.863755, 0.888076, 0.913751, 0.940863, 0.969506, 0.999777, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], nans=true)
@test all(modes.Pressure .== 20000.0)
print("modes.Temperature: ")
println(modes.Temperature)
@test modes.Temperature ≈ 1624.06:-10.0:914.06 # 1624.07:-10:804.07
print("modes.liquid_0: ")
println(modes.liquid_0)
@test isapprox(modes.liquid_0, [99.953083, 91.06567, 83.3387, 76.413373, 70.595706, 65.639854, 55.500696, 49.591648, 44.674084, 40.504401, 36.914672, 33.785529, 31.029693, 28.581541, 26.390266, 24.415352, 21.129398, 18.287815, 15.879336, 13.851438, 12.147987, 10.715663, 9.507479, 8.483825, 7.612121, 6.862318, 6.193587, 5.622014, 5.130007, 4.70375, 4.332245, 4.006636, 3.719721, 3.4656, 3.239405, 3.037098, 2.855318, 2.691253, 2.542541, 2.407194, 2.282555, 2.128077, 1.991449, 1.869639, 1.357812, 0.473618, 0.196578, 0.07415, 0.009783, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], nans=true)
# @test isapprox(modes.liquid_0, [99.95749, 91.072189, 83.344261, 76.418306, 70.599891, 65.643435, 55.505785, 49.595839, 44.677608, 40.507414, 36.917284, 33.787818, 31.031719, 28.583347, 26.391889, 24.41682, 21.131799, 18.28986, 15.881062, 13.852888, 12.149205, 10.716689, 9.508346, 8.484562, 7.61275, 6.862884, 6.194069, 5.622428, 5.130364, 4.70406, 4.332516, 4.006874, 3.719932, 3.465787, 3.239572, 3.037248, 2.855453, 2.691375, 2.542652, 2.407295, 2.283625, 2.170214, 2.065851, 1.950841, 1.825706, 0.68822, 0.268833, 0.106921, 0.027371, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], nans=true)
print("modes.olivine_0: ")
println(modes.olivine_0)
@test isapprox(modes.olivine_0, [0.004405, 8.88875, 16.612778, 23.40101, 29.115521, 33.983114, 36.014211, 38.366376, 40.308451, 41.935036, 43.312139, 44.487298, 45.495751, 46.364327, 47.113994, 47.761526, 48.589381, 49.290863, 49.881257, 50.378767, 50.799069, 51.155338, 51.45843, 51.717196, 51.938821, 52.150943, 52.510619, 52.826884, 53.106277, 53.354154, 53.574959, 53.772418, 53.949683, 54.109432, 54.253952, 54.385199, 54.50485, 54.614346, 54.714922, 54.807643, 54.892635, 54.939162, 54.987413, 55.036388, 54.729708, 53.998944, 53.756472, 53.649289, 53.596897, 53.618702, 53.654148, 53.687873, 53.719962, 53.750492, 53.779533, 53.807145, 53.833385, 53.858301, 53.881939, 53.904336, 53.925529, 53.98708, 54.051137, 54.11778, 54.187431, 54.26058, 54.337802, 54.419774, 54.507309, 54.601389, 54.703211, 54.814258], nans=true)
# @test isapprox(modes.olivine_0, [0.0, 8.882234, 16.607219, 23.396165, 29.111411, 33.979596, 36.01218, 38.364714, 40.307067, 41.93387, 43.311147, 44.486449, 45.49502, 46.363698, 47.11345, 47.761057, 48.588784, 49.290361, 49.880834, 50.37841, 50.798768, 51.155082, 51.458212, 51.717009, 51.938661, 52.150643, 52.510356, 52.826652, 53.106072, 53.353971, 53.574796, 53.772272, 53.949552, 54.109313, 54.253844, 54.385101, 54.504761, 54.614264, 54.714847, 54.807573, 54.893359, 54.972993, 55.047157, 55.101579, 55.138126, 54.216269, 53.84507, 53.698077, 53.628726, 53.623437, 53.658848, 53.692537, 53.72459, 53.755083, 53.784087, 53.811662, 53.837863, 53.862741, 53.886339, 53.908697, 53.930719, 53.992584, 54.056671, 54.123351, 54.193047, 54.266249, 54.343534, 54.425581, 54.513207, 54.607395, 54.709348, 54.820555, 54.942876, 55.078676, 55.230995, 55.403811, 55.602415, 55.833981, 56.108425, 56.206913, 56.210501, 56.213232, 56.215135], nans=true)
# Test `clean_modes` vs `modes`
@test clean_modes["Temperature"] == modes.Temperature
@test clean_modes["liquid"] == modes.liquid_0
@test clean_modes["olivine"] == modes.olivine_0
@test clean_modes["apatite"] == modes.apatite
end
## --- Test PerpleX
if Sys.isunix()
# Choose perpleX version
perplexversion = "perplex-6.8.7"
# Construct file path
perplexdir = joinpath(resourcepath, perplexversion)
scratchdir = "./"
if Sys.islinux()
# Download precompiled executable
if !isfile(joinpath(perplexdir,"vertex"))
@info "Downloading PerpleX to $perplexdir"
run(`mkdir -p $perplexdir`)
file = Downloads.download("https://storage.googleapis.com/statgeochem/$perplexversion-linux.tar.gz",joinpath(resourcepath,"$perplexversion-linux.tar.gz"))
run(`tar -xzf $file -C $perplexdir`)
end
else
# Compile from source
if !isfile(joinpath(perplexdir,"vertex"))
# Check if there is a fortran compiler
run(`gfortran -v`)
# Download Perplex v6.8.7 -- known to work with interface used here
file = Downloads.download("https://storage.googleapis.com/statgeochem/$perplexversion.zip", joinpath(resourcepath,"$perplexversion.zip"))
# # For a more updated perplex version, you might also try
# file = download("https://petrol.natur.cuni.cz/~ondro/perplex-sources-stable.zip", joinpath(resourcepath,"perplex-stable.zip"))
run(`unzip -u $file -d $resourcepath`) # Extract
system("cd $perplexdir; make") # Compile
end
end
# Kelemen (2014) primitive continental basalt excluding Mn and Ti since most melt models can"t handle them..
elements = [ "SIO2", "AL2O3", "FEO", "MGO", "CAO", "NA2O", "K2O", "H2O", "CO2",]
composition = [50.0956, 15.3224, 8.5103, 9.2520, 9.6912, 2.5472, 0.8588, 2.0000, 0.6000,]
# Emphasis on phases from Holland and Powell -- all phases can be used with hp02ver.dat.
HP_solution_phases = "Omph(HP)\nOpx(HP)\nGlTrTsPg\nAnth\nO(HP)\nSp(HP)\nGt(HP)\nfeldspar_B\nMica(CF)\nBio(TCC)\nChl(HP)\nCtd(HP)\nSapp(HP)\nSt(HP)\nIlHm(A)\nDo(HP)\nT\nB\nF\n"
HP_excludes = ""
## --- # # # # # # # # # # # # # Isobaric example # # # # # # # # # # # # # # # #
# Input parameters
P = 1000 # Pressure, bar
T_range = (0+273.15, 1500+273.15) # Temperature range, Kelvin
# Configure (run build and vertex)
melt_model = "melt(HP)"
@time perplex_configure_isobar(perplexdir, scratchdir, composition, elements, P, T_range,
dataset="hp02ver.dat",
npoints=100,
excludes=HP_excludes,
solution_phases=melt_model*"\n"*HP_solution_phases
)
## --- Query all properties at a single temperature -- results returned as text
T = 850+273.15
data_isobaric = perplex_query_point(perplexdir, scratchdir, T)
@test isa(data_isobaric, String)
## --- Query the full isobar -- results returned as dict
bulk = perplex_query_system(perplexdir, scratchdir, importas=:Tuple)
@test isa(bulk, NamedTuple)
@test haskey(bulk, :SIO2)
if haskey(bulk, :SIO2)
print("bulk.SIO2: ")
println(bulk.SIO2)
@test haskey(bulk, :SIO2) && all(isapprox.(bulk.SIO2, 50.66433039859823, atol=0.1))
end
melt = perplex_query_phase(perplexdir, scratchdir, melt_model, importas=:Tuple)
@test isa(melt, NamedTuple)
@test haskey(melt, :SIO2)
if haskey(melt, :SIO2)
print("melt.SIO2: ")
println(melt.SIO2)
@test !isempty(melt.SIO2) && !any(x->x<45, melt.SIO2) && !any(x->x>75, melt.SIO2)
# @test isapprox(melt.SIO2, [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 66.78282537747364, 66.82016525351406, 66.82117995364602, 66.80298329925418, 66.73744938571255, 66.62063664135016, 66.50000133000002, 66.22747284673613, 66.00795379443232, 65.7150802854759, 65.19438696112262, 64.29384856492115, 63.325731662865834, 62.36298129110562, 61.43282457312982, 60.47865161707871, 59.48121189624237, 58.55474098831869, 57.61577695368922, 56.7251829824451, 55.91185527051578, 55.093105509310554, 54.32996519595824, 53.61523753066627, 52.9413, 52.39319476068052, 52.11132084452833, 51.85100518510051, 51.61326903203858, 51.416005141600515, 51.37846165415398, 51.34318973136206, 51.3116, 51.27793076675845, 51.24849487515052, 51.223459021232784, 51.1924153577246, 51.16727441636279, 51.384574307712846, 50.940294905970504, 50.23598995280201, 50.23410000000001], nans=true)
end
modes = perplex_query_modes(perplexdir, scratchdir, importas=:Dict)
@test isa(modes, Dict)
@test haskey(modes, "Omph(HP)")
if haskey(modes, "Omph(HP)")
print("modes[\"Omph(HP)\"]: ")
println(modes["Omph(HP)"])
# @test isapprox(modes["Omph(HP)"],[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.5736, 7.33183, 13.3273, 13.874, 13.8044, 13.7504, 13.6605, 13.6055, 13.2465, 12.8556, 12.8012, 12.909, 12.8774, 12.8621, 12.8379, 12.8239, 12.8205, 12.839, 12.8654, 12.8914, 12.9423, 13.0084, 13.1195, 13.2487, 13.391, 13.5401, 13.7082, 13.9396, 14.1879, 14.4729, 14.754, 15.0912, 15.5081, 15.9689, 16.4671, 17.0194, 17.5064, 17.1991, 16.9685, 16.6926, 16.4602, 16.1634, 15.921, 15.659, 15.4497, 15.2485, 15.0301, 14.8809, 14.6926, 15.0711, 9.19562, NaN, NaN], nans=true)
end
## --- # # # # # # # # # # # Geothermal gradient example # # # # # # # # # # # #
# Input parameters
P_range = (280, 28000) # Pressure range to explore, bar (roughly 1-100 km depth)
T_surf = 273.15 # Temperature of surface (K)
geotherm = 0.01 # Geothermal gradient of 0.1 K/bar == about 28.4 K/km
melt_model = ""
# Configure (run build and vertex)
@time perplex_configure_geotherm(perplexdir, scratchdir, composition, elements, P_range, T_surf, geotherm;
dataset="hp02ver.dat",
excludes=HP_excludes,
solution_phases=HP_solution_phases,
npoints=200,
index=2
)
seismic = perplex_query_seismic(perplexdir, scratchdir, index=2)
@test isa(seismic, Dict)
@test haskey(seismic, "T(K)")
@test isa(seismic["T(K)"], Vector{Float64})
print("seismic[\"T(K)\"]: ")
println(seismic["T(K)"])
## --- # # # # # # # # # # # P–T path example # # # # # # # # # # # #
# Input parameters
T_range = (550+273.15, 1050+273.15) #K
PTdir = ""
PTfilename = ""
@time perplex_configure_path(perplexdir, scratchdir, composition, PTdir, PTfilename, elements, T_range,
dataset = "hp11ver.dat", index=1, solution_phases=HP_solution_phases, excludes=HP_excludes)
modes = perplex_query_modes(perplexdir, scratchdir, index=1)
@test isa(modes, Dict)
@test haskey(modes,"node")
@test haskey(modes, "Omph(HP)")
if haskey(modes, "Omph(HP)")
print("modes[\"Omph(HP)\"]: ")
println(modes["Omph(HP)"])
# @test isapprox(modes["Omph(HP)"],[NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.5736, 7.33183, 13.3273, 13.874, 13.8044, 13.7504, 13.6605, 13.6055, 13.2465, 12.8556, 12.8012, 12.909, 12.8774, 12.8621, 12.8379, 12.8239, 12.8205, 12.839, 12.8654, 12.8914, 12.9423, 13.0084, 13.1195, 13.2487, 13.391, 13.5401, 13.7082, 13.9396, 14.1879, 14.4729, 14.754, 15.0912, 15.5081, 15.9689, 16.4671, 17.0194, 17.5064, 17.1991, 16.9685, 16.6926, 16.4602, 16.1634, 15.921, 15.659, 15.4497, 15.2485, 15.0301, 14.8809, 14.6926, 15.0711, 9.19562, NaN, NaN], nans=true)
end
## --- # # # # # # # # # # # Pseudosection example # # # # # # # # # # # # #
P_range = (1000, 5000) # Pressure range to explore, bar (roughly 1-100 km depth)
T_range = (400+273.15, 600+273.15) # Temperature range to explore, K
melt_model = ""
solution_phases = "Opx(HP)\nO(HP)\nF\n"
excludes = ""
# Configure (run build and vertex)
@time perplex_configure_pseudosection(perplexdir, scratchdir, composition,
elements, P_range, T_range, dataset="hp02ver.dat", excludes=excludes,
solution_phases=melt_model*solution_phases, index=1, xnodes=50, ynodes=50)
# Query modes on diagonal line across P-T space
modes = perplex_query_modes(perplexdir, scratchdir, P_range, T_range, index=1, npoints=200)
@test isa(modes, Dict) && !isempty(modes)
@test haskey(modes,"T(K)") && all(extrema(modes["T(K)"]) .≈ T_range)
phase = perplex_query_phase(perplexdir, scratchdir, "Opx(HP)", P_range, T_range, index=1, npoints=200)
@test isa(phase, Dict) && !isempty(phase)
@test haskey(phase,"T(K)") && all(extrema(phase["T(K)"]) .≈ T_range)
system = perplex_query_system(perplexdir, scratchdir, P_range, T_range, index=1, npoints=200)
@test isa(system, Dict) && !isempty(system)
@test haskey(system,"T(K)") && all(extrema(system["T(K)"]) .≈ T_range)
# Query seismic properties on diagonal line across P-T space
seismic = perplex_query_seismic(perplexdir, scratchdir, P_range, T_range, index=1, npoints=200)
@test isa(seismic, Dict) && !isempty(seismic)
@test haskey(seismic,"T(K)") && !isempty(seismic["T(K)"]) && all(extrema(seismic["T(K)"]) .≈ T_range)
@test haskey(seismic, "rho,kg/m3") && !isempty(seismic["rho,kg/m3"]) && !any(x->x<2700, seismic["rho,kg/m3"]) && !any(x->x>3200, seismic["rho,kg/m3"])
# Query properties on a manually-specified diagonal P-T line
P = range(first(P_range), last(P_range), length=16)
T = range(first(T_range), last(T_range), length=16)
modes = perplex_query_modes(perplexdir, scratchdir, P, T, index=1)
@test isa(modes, Dict) && !isempty(modes)
@test haskey(modes,"T(K)") && all(isapprox.(modes["T(K)"], T, atol=0.1))
phase = perplex_query_phase(perplexdir, scratchdir, "Opx(HP)", P, T, index=1)
@test isa(phase, Dict) && !isempty(phase)
@test haskey(phase,"T(K)") && all(isapprox.(phase["T(K)"], T, atol=0.1))
system = perplex_query_system(perplexdir, scratchdir, P, T, index=1)
@test isa(system, Dict) && !isempty(system)
@test haskey(system,"T(K)") && all(isapprox.(system["T(K)"], T, atol=0.1))
@test haskey(system, "rho,kg/m3") && !any(x->x<2700, system["rho,kg/m3"]) && !any(x->x>3200, system["rho,kg/m3"])
seismic = perplex_query_seismic(perplexdir, scratchdir, P, T, index=1)
@test isa(seismic, Dict) && !isempty(seismic)
@test haskey(seismic,"T(K)")
if haskey(seismic,"T(K)")
@test isapprox(seismic["T(K)"], T, atol=0.01)
print("T (K):\t")
println(seismic["T(K)"])
end
@test haskey(seismic, "rho,kg/m3")
if haskey(seismic, "rho,kg/m3")
@test !any(x->x<2700, seismic["rho,kg/m3"]) && !any(x->x>3200, seismic["rho,kg/m3"])
print("rho,kg/m3:\t")
println(seismic["rho,kg/m3"])
end
end
## --- End of File
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 4458 | @test find_litho1_property(40:45, fill(-100,6), :ice, :thickness) ≈ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
@test find_litho1_property(40:45, fill(-100,6), :water, :thickness) ≈ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
@test find_litho1_property(40:45, fill(-100,6), :upper_sediments, :vp) ≈ [2500.0, 2500.0, 2500.0, 2500.0, 2500.0, 2500.0]
@test find_litho1_property(40:45, fill(-100,6), :upper_sediments, :vs) ≈ [1070.0, 1070.0, 1070.0, 1070.0, 1070.0, 1070.0]
@test find_litho1_property(40:45, fill(-100,6), :upper_sediments, :rho) ≈ [2110.0, 2110.0, 2110.0, 2110.0, 2110.0, 2110.0]
@test find_litho1_property(40:45, fill(-100,6), :upper_sediments, :bottom) ≈ [-0.25, -0.352, -0.301, -0.208, -0.063, 0.045]
@test find_litho1_property(40:45, fill(-100,6), :upper_sediments, :thickness) ≈ [0.5, 0.5, 0.5, 0.5, 0.5, 0.608]
@test find_litho1_property(40:45, fill(-100,6), :middle_sediments, :vp) ≈ [4000.0, 4000.0, 4000.0, 4000.0, 4000.0, 4000.0]
@test find_litho1_property(40:45, fill(-100,6), :middle_sediments, :vs) ≈ [2130.0, 2130.0, 2130.0, 2130.0, 2130.0, 2130.0]
@test find_litho1_property(40:45, fill(-100,6), :middle_sediments, :rho) ≈ [2370.0, 2370.0, 2370.0, 2370.0, 2370.0, 2370.0]
@test find_litho1_property(40:45, fill(-100,6), :middle_sediments, :bottom) ≈ [0.662, 0.382, 0.453, 0.297, 0.235, 0.37]
@test find_litho1_property(40:45, fill(-100,6), :middle_sediments, :thickness) ≈ [0.912, 0.734, 0.754, 0.505, 0.298, 0.325]
@test find_litho1_property(40:45, fill(-100,6), :lower_sediments, :thickness) ≈ [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
@test find_litho1_property(40:45, fill(-100,6), :upper_crust, :vp) ≈ [6252.38, 6225.32, 6094.56, 6233.38, 6180.0, 6219.24]
@test find_litho1_property(40:45, fill(-100,6), :upper_crust, :vs) ≈ [3626.89, 3610.68, 3534.85, 3615.36, 3584.4, 3607.79]
@test find_litho1_property(40:45, fill(-100,6), :upper_crust, :rho) ≈ [2791.52, 2780.64, 2722.24, 2784.24, 2760.4, 2776.46]
@test find_litho1_property(40:45, fill(-100,6), :upper_crust, :bottom) ≈ [16.877, 16.043, 14.313, 16.055, 15.497, 15.499]
@test find_litho1_property(40:45, fill(-100,6), :upper_crust, :thickness) ≈ [16.215, 15.661, 13.86, 15.758, 15.262, 15.129]
@test find_litho1_property(40:45, fill(-100,6), :middle_crust, :vp) ≈ [6641.63, 6640.34, 6500.87, 6648.94, 6592.0, 6600.29]
@test find_litho1_property(40:45, fill(-100,6), :middle_crust, :vs) ≈ [3821.52, 3818.19, 3738.0, 3823.14, 3790.4, 3798.31]
@test find_litho1_property(40:45, fill(-100,6), :middle_crust, :rho) ≈ [2888.84, 2884.4, 2823.81, 2888.13, 2863.4, 2871.72]
@test find_litho1_property(40:45, fill(-100,6), :middle_crust, :bottom) ≈ [34.838, 33.669, 29.909, 33.783, 32.659, 32.24]
@test find_litho1_property(40:45, fill(-100,6), :middle_crust, :thickness) ≈ [17.961, 17.626, 15.596, 17.728, 17.162, 16.741]
@test find_litho1_property(40:45, fill(-100,6), :lower_crust, :vp) ≈ [7147.59, 7159.11, 7008.75, 7168.39, 7107.0, 7100.2]
@test find_litho1_property(40:45, fill(-100,6), :lower_crust, :vs) ≈ [4074.5, 4077.58, 3991.94, 4082.86, 4047.9, 4048.27]
@test find_litho1_property(40:45, fill(-100,6), :lower_crust, :rho) ≈ [3028.95, 3029.65, 2966.02, 3033.58, 3007.6, 3009.81]
@test find_litho1_property(40:45, fill(-100,6), :lower_crust, :bottom) ≈ [51.119, 49.339, 43.779, 49.55, 47.917, 47.426]
@test find_litho1_property(40:45, fill(-100,6), :lower_crust, :thickness) ≈ [16.281, 15.67, 13.87, 15.767, 15.258, 15.186]
@test find_litho1_property(40:45, fill(-100,6), :sclm, :vp) ≈ [8171.22, 8173.6, 8194.44, 8242.08, 8251.74, 8220.29]
@test find_litho1_property(40:45, fill(-100,6), :sclm, :vs) ≈ [4655.96, 4657.32, 4669.19, 4696.34, 4701.85, 4683.93]
@test find_litho1_property(40:45, fill(-100,6), :sclm, :rho) ≈ [3300.0, 3300.0, 3300.0, 3300.0, 3300.0, 3300.0]
@test find_litho1_property(40:45, fill(-100,6), :sclm, :bottom) ≈ [182.204, 234.025, 241.833, 246.228, 243.389, 251.825]
@test find_litho1_property(40:45, fill(-100,6), :sclm, :thickness) ≈ [131.085, 184.686, 198.054, 196.678, 195.472, 204.399]
@test find_litho1_property(40:45, fill(-100,6), :asthenosphere, :vp) ≈ [8007.79, 8010.13, 8030.55, 8077.24, 8086.7, 8055.88]
@test find_litho1_property(40:45, fill(-100,6), :asthenosphere, :vs) ≈ [4352.06, 4353.33, 4364.43, 4389.8, 4394.94, 4378.2]
@test find_litho1_property(40:45, fill(-100,6), :asthenosphere, :rho) ≈ [3300.0, 3300.0, 3300.0, 3300.0, 3300.0, 3300.0]
@test find_litho1_property(40:45, fill(-100,6), 10, :vp) ≈ [8007.79, 8010.13, 8030.55, 8077.24, 8086.7, 8055.88]
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 11090 | ## --- Resampling.jl
index = Array{Int64}(undef,1000)
resampled = Array{Int64}(undef,1000)
@test bsr!(resampled,index,1:10,0,0.5) |> unique ⊆ 1:10
@test bsr!(resampled,index,1:10,0,fill(0.5,1000)) |> unique ⊆ 1:10
@test bsr!(resampled,index,1:10,fill(0,10),0.5) |> unique ⊆ 1:10
@test bsr!(resampled,index,1:10,fill(0,10),fill(0.5,1000)) |> unique ⊆ 1:10
index = Array{Int64}(undef,1000)
resampled = Array{Int64}(undef,1000,10)
data = repeat(1:10,1,10)
@test vec(bsr!(resampled,index,data,zeros(10),0.5)) |> unique ⊆ 1:10
@test vec(bsr!(resampled,index,data,zeros(10),fill(0.5,10))) |> unique ⊆ 1:10
resampled = Array{Float64}(undef,1000)
# Gaussian
bsr!(resampled, index, ones(10), 0.5, 0.5)
@test isapprox(mean(resampled), 1, atol=0.3)
bsr!(resampled, index, ones(10), 0.5, fill(0.5,1000))
@test isapprox(mean(resampled), 1, atol=0.3)
bsr!(resampled, index, ones(10), fill(0.5,10), 0.5)
@test isapprox(mean(resampled), 1, atol=0.3)
bsr!(resampled, index, ones(10), fill(0.5,10), fill(0.5,1000))
@test isapprox(mean(resampled), 1, atol=0.3)
# Other distributions
bsr!(uniform, resampled, index, ones(10), 0.5, 0.5)
@test isapprox(mean(resampled), 1, atol=0.3)
bsr!(triangular, resampled, index, ones(10), 0.5, fill(0.5,1000))
@test isapprox(mean(resampled), 1, atol=0.3)
bsr!(triangular, resampled, index, ones(10), fill(0.5,10), 0.5)
@test isapprox(mean(resampled), 1, atol=0.3)
bsr!(uniform, resampled, index, ones(10), fill(0.5,10), fill(0.5,1000))
@test isapprox(mean(resampled), 1, atol=0.3)
@test bsresample(1:10,fill(0.5,10),1000,0.5)::Array{Float64} |> length == 1000
@test bsresample(1:10,fill(0.5,10),1000,fill(0.5,10))::Array{Float64} |> length == 1000
d = Dict{String,Vector{Float64}}()
d["x"] = 1:10; d["y"] = 2:2:20
d["x_sigma"] = d["y_sigma"] = fill(0.5,10)
dbs = bsresample(d, 1000, ["x","y"], 0.5)
@test isapprox(mean(dbs["x"]), 5.5, atol=0.5)
@test isapprox(std(dbs["x"]), 3.03, atol=0.5)
@test isapprox(mean(dbs["y"]), 11, atol=1)
@test isapprox(std(dbs["y"]), 6.06, atol=1)
dt = TupleDataset(d)
@test isa(dt, NamedTuple)
dbs = bsresample(dt, 1000, (:x, :y), 0.5)
@test isapprox(mean(dbs[:x]), 5.5, atol=0.5)
@test isapprox(std(dbs[:x]), 3.03, atol=0.5)
@test isapprox(mean(dbs[:y]), 11, atol=1)
@test isapprox(std(dbs[:y]), 6.06, atol=1)
@test bincounts(1:100, 0.0:11.11111111111111:100.0) == (5:10:95, fill(10,10))
@test bincounts(1:100, 0, 100, 10) == (5:10:95, fill(10,10))
c,N = bincounts(1:100, 0, 100, 10; relbinwidth=3)
@test c == 5:10:95
@test N == [20, 30, 30, 30, 30, 30, 30, 30, 30, 20]
@test binmeans(1:100, 1:100, 0.0:11.11111111111111:100.0) == (5:10:95, 5.5:10:95.5, fill(0.9574271077563381,10))
@test binmeans(1:100, 1:100, 0, 100, 10) == (5:10:95, 5.5:10:95.5, fill(0.9574271077563381,10))
c,m,e = binmeans(1:100, 1:100, 0, 100, 10; relbinwidth=3)
@test c == 5:10:95
@test m == [10.5, 15.5, 25.5, 35.5, 45.5, 55.5, 65.5, 75.5, 85.5, 90.5]
@test e ≈ [1.3228756555322954, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.3228756555322954]
@test binmeans(1:100, 1:100, 0, 100, 10, ones(100)) == (5:10:95, 5.5:10:95.5, fill(0.9574271077563381,10))
c,m,e = binmeans(1:100, 1:100, 0, 100, 10, ones(100); relbinwidth=3)
@test c == 5:10:95
@test m == [10.5, 15.5, 25.5, 35.5, 45.5, 55.5, 65.5, 75.5, 85.5, 90.5]
@test e ≈ [1.3228756555322954, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.607275126832159, 1.3228756555322954]
@test binmedians(1:100,1:100,0.0:11.11111111111111:100.0) == (5:10:95, 5.5:10:95.5, fill(1.1720982147414096,10))
@test binmedians(1:100,1:100,0,100,10) == (5:10:95, 5.5:10:95.5, fill(1.1720982147414096,10))
c,m,e = binmedians(1:100, 1:100, 0, 100, 10; relbinwidth=3)
@test c == 5:10:95
@test m == [10.5, 15.5, 25.5, 35.5, 45.5, 55.5, 65.5, 75.5, 85.5, 90.5]
@test e ≈ [1.6575971917205938, 2.030133659392898, 2.030133659392898, 2.030133659392898, 2.030133659392898, 2.030133659392898, 2.030133659392898, 2.030133659392898, 2.030133659392898, 1.6575971917205938]
@test randsample(1:10., 1000, rand(1000))::Array{Float64} |> length == 1000
@test randsample(1:10, 1000)::Array{Int64} |> length == 1000
@test unique(randsample(1:10, 1000)) ⊆ 1:10
dr = randsample(d, 1000, ["x","y"], 0.5)
@test isapprox(mean(dr["x"]), 5.5, atol=0.5)
@test isapprox(std(dr["x"]), 3.03, atol=0.5)
@test unique(dr["x"]) ⊆ 1:10
@test isapprox(mean(dr["y"]), 11, atol=1)
@test isapprox(std(dr["y"]), 6.06, atol=1)
@test unique(dr["y"]) ⊆ 2:2:20
## --- Invweight
@test invweight(0:10,0:10,0:10) ≈ [13.092772378121769, 13.759663290331229, 14.079390874013654, 14.244556812410089, 14.327747696132253, 14.354508911206949, 14.331218676773712, 14.251150311763046, 14.088257739618454, 13.76917452212827, 13.101581593462868]
@test invweight_location(0:10,0:10) ≈ [2.3478642777118957, 2.950680392065609, 3.2200556525889006, 3.348975353894235, 3.4103053158720016, 3.4297605864726126, 3.413776296513463, 3.3555688532471923, 3.2289225181937002, 2.9601916238626513, 2.3566734930529947]
@test invweight_age(0:10) ≈ [10.744908100409873, 10.808982898265619, 10.859335221424754, 10.895581458515855, 10.91744238026025, 10.924748324734335, 10.91744238026025, 10.895581458515855, 10.859335221424754, 10.808982898265619, 10.744908100409873]
@test invweight(3:7, 3:7, 3:7, spatialscale=1:3, agescale=20:10:40) ≈ [6.455459194232295 6.4954174826726305 6.50970747669241; 7.221911822727031 7.261870111167367 7.276160105187145; 7.829876642142075 7.86983493058241 7.8841249246021885;;; 6.795239175454396 6.815587062014095 6.822796065519029; 7.814257428828995 7.8346053153886945 7.841814318893627; 8.464965591963413 8.485313478523112 8.492522482028043;;; 6.866607765314396 6.880327963335503 6.88516052627444; 7.9787064574491104 7.992426655470217 7.9972592184091535; 8.67354089449909 8.687261092520199 8.692093655459136;;; 6.796516961665698 6.816864848225396 6.82407385173033; 7.81538067459392 7.835728561153617 7.84293756465855; 8.46552963870305 8.485877525262747 8.49308652876768;;; 6.456852342594404 6.496810631034739 6.511100625054519; 7.223828686779423 7.263786975219759 7.278076969239537; 7.831650416268091 7.871608704708425 7.885898698728204]
# Test NaN-ful cases
lat, lon, age = [0:10..., NaN], [0:10..., NaN], [0:10..., NaN]
@test invweight(lat, lon, age) ≈ [13.092772378121769, 13.759663290331229, 14.079390874013654, 14.244556812410089, 14.327747696132253, 14.354508911206949, 14.331218676773712, 14.251150311763046, 14.088257739618454, 13.76917452212827, 13.101581593462868, Inf]
@test invweight_location(lat, lon) ≈ [2.3478642777118957, 2.950680392065609, 3.2200556525889006, 3.348975353894235, 3.4103053158720016, 3.4297605864726126, 3.413776296513463, 3.3555688532471923, 3.2289225181937002, 2.9601916238626513, 2.3566734930529947, Inf]
@test invweight_age(age) ≈ [10.744908100409873, 10.808982898265619, 10.859335221424754, 10.895581458515855, 10.91744238026025, 10.924748324734335, 10.91744238026025, 10.895581458515855, 10.859335221424754, 10.808982898265619, 10.744908100409873, Inf]
## --- bin_bsr
x = 0:100; y = 0:100
xmin = 0; xmax = 100; nbins = 5
(c,m,e) = bin_bsr(x, y, xmin, xmax, nbins, x_sigma=ones(101))
@test c == 10.0:20.0:90.0
@test isapprox(m, [10.04, 29.94, 49.94, 69.92, 89.83], atol=0.5)
@test isapprox(e, [1.17, 1.21, 1.23, 1.26, 1.28], atol=0.5)
# Upper and lower CIs
(c,m,el,eu) = bin_bsr(nanbinmean!, x, y, xmin, xmax, nbins, x_sigma=ones(101))
@test c == 10.0:20.0:90.0
@test isapprox(m, [10.04, 29.94, 49.94, 69.92, 89.83], atol=0.5)
@test isapprox(el, [2.29, 2.38, 2.41, 2.49, 2.51], atol=1.0)
@test isapprox(eu, [2.3, 2.37, 2.42, 2.51, 2.51], atol=1.0)
# Medians, upper and lower CIs
(c,m,el,eu) = bin_bsr(nanbinmedian!, x, y, xmin, xmax, nbins, x_sigma=ones(101))
@test c == 10.0:20.0:90.0
@test isapprox(m, [10.01, 29.91, 49.9, 69.88, 89.79], atol=1)
@test isapprox(el, [4.01, 3.91, 3.9, 3.88, 3.79], atol=2)
@test isapprox(eu, [3.99, 4.09, 4.1, 4.12, 4.21], atol=2)
# with weights
w = ones(101)
(c,m,e) = bin_bsr(x, y, xmin, xmax, nbins, w, x_sigma=ones(101))
@test c == 10.0:20.0:90.0
@test isapprox(m, [10.04, 29.94, 49.94, 69.92, 89.83], atol=0.5)
@test isapprox(e, [1.17, 1.21, 1.23, 1.26, 1.28], atol=0.5)
# with 2-D array (matrix) of y data
y = repeat(0:100, 1, 4)
y_sigma = ones(101,4)
(c,m,e) = bin_bsr(x, y, xmin, xmax, nbins, x_sigma=ones(101), y_sigma=y_sigma)
@test c == 10.0:20.0:90.0
@test isapprox(m, repeat([10.04, 29.94, 49.94, 69.92, 89.83], 1, 4), atol=0.5)
@test isapprox(e, repeat([1.17, 1.21, 1.23, 1.26, 1.28], 1, 4), atol=0.5)
(c,m,el,eu) = bin_bsr(x, y, xmin, xmax, nbins, x_sigma=ones(101), y_sigma=y_sigma, sem=:CI)
@test c == 10.0:20.0:90.0
@test isapprox(m, repeat([10.04, 29.94, 49.94, 69.92, 89.83], 1, 4), atol=0.5)
@test isapprox(el, repeat([2.29, 2.38, 2.41, 2.49, 2.51], 1, 4), atol=1.0)
@test isapprox(eu, repeat([2.3, 2.37, 2.42, 2.51, 2.51], 1, 4), atol=1.0)
## -- bin_bsr_ratios
x = 0:100; num = 0:100; denom=reverse(num)
xmin = 0; xmax = 100; nbins = 5
(c,m,el,eu) = bin_bsr_ratios(x, num, denom, xmin, xmax, nbins, x_sigma=ones(101))
@test c == 10.0:20.0:90.0
@test isapprox(m, [0.11, 0.43, 1.0, 2.33, 8.99], rtol=0.1)
@test isapprox(el, [0.03, 0.05, 0.09, 0.26, 2.11], rtol=0.4)
@test isapprox(eu, [0.03, 0.05, 0.1, 0.29, 3.03], rtol=0.4)
# With weights
(c,m,el,eu) = bin_bsr_ratios(x, num, denom, xmin, xmax, nbins, ones(101), x_sigma=ones(101))
@test c == 10.0:20.0:90.0
@test isapprox(m, [0.11, 0.43, 1.0, 2.33, 8.99], rtol=0.1)
@test isapprox(el, [0.03, 0.05, 0.09, 0.26, 2.11], rtol=0.4)
@test isapprox(eu, [0.03, 0.05, 0.1, 0.29, 3.03], rtol=0.4)
# Medians
(c,m,el,eu) = bin_bsr_ratio_medians(x, num, denom, xmin, xmax, nbins, x_sigma=ones(101))
@test c == 10.0:20.0:90.0
@test isapprox(m, [0.11, 0.43, 1.0, 2.34, 9.25], rtol=0.1)
@test isapprox(el, [0.05, 0.08, 0.15, 0.4, 3.1], rtol=0.5)
@test isapprox(eu, [0.05, 0.09, 0.17, 0.51, 6.42], rtol=0.5)
## --- Monte Carlo interpolation/fitting
(c,m) = mcfit(0:11, ones(12), 0:11, ones(12), 1, 10, 10)
@test c == 1:10
@test isapprox(m, [1.15, 2.02, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 8.98, 9.85], atol = 0.25)
## --- Downsampling
@test downsample(1:100, 10) == 10:10:100
A = reshape(1:100,10,10)
@test downsample(A, 2) == [12 32 52 72 92; 14 34 54 74 94; 16 36 56 76 96; 18 38 58 78 98; 20 40 60 80 100]
@test downsample(collect(A), 2) == [12 32 52 72 92; 14 34 54 74 94; 16 36 56 76 96; 18 38 58 78 98; 20 40 60 80 100]
## ---
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 2122 | ## --- tc1/tc1.jl
@test find_tc1_crust(33,-100) == 40.0
@test find_tc1_crust([33,-30,70],[-100,20,-130]) == [40.0, 38.0, 46.0]
@test find_tc1_lith(33,-100) == 132.0
@test find_tc1_lith([33,-30,70],[-100,20,-130]) == [132.0, 123.0, 167.0]
@test find_tc1_age(33,-100) == (1400.0, 1100.0, 1700.0)
@test find_tc1_age([33,-30,70],[-100,20,-130]) ==
([1400.0, 1400.0, 2100.0], [1100.0, 1100.0, 1700.0], [1700.0, 1700.0, 2500.0])
## --- PartitionCoefficients/PartitionCoefficients.jl
@test Float16.(claiborne_zircon_kd.(["Hf","Th","U","Y","Nb","Nd","Sm","Tb","Eu","Dy","Gd","Ho","Er","Yb","Tm","Lu"], 800)) ==
Float16[1.303e3, 6.46, 36.97, 33.47, 0.2556, 0.02368, 0.4363, 10.695, 0.8784, 19.56, 3.293, 40.34, 62.88, 101.75, 94.94, 126.4]
@test claiborne_zircon_kd("Pr", 800) ≈ 0.00710099886045172
@test claiborne_zircon_kd("La", 800) ≈ 1.5412711665791843e-5
## -- Geography.jl
lat = [43.7022,-26.2041,-19.5723,-34.9285,46.4908]
lon = [-72.2896,28.0473,65.7550,138.6007,9.8355]
@test find_geolcont(lat, lon) == [3, 1, 7, 5, 2]
@test find_geolcont(43.702245, -72.0929) == fill(3)
@test find_geolprov(lat, lon) == [10, 31, 0, 10, 10]
@test find_geolprov(43.702245, -72.0929) == fill(10)
@test find_land(lat, lon) == Bool[1, 1, 0, 1, 1]
@test find_land(43.702245, -72.0929) == fill(true)
# Elevation datasets
A = (1:200)*(1:200)'
@test find_etopoelev(A, -90:-89, -180:-179) == [1,3721]
@test find_srtm15plus(A, -90:0.1:-89.5, -180:0.1:-179.5) == [1, 625, 2401, 5329, 9409, 14641]
@test find_seafloorage(A, 80.738:-0.1:80, 0:0.1:0.7) == [1, 80, 266, 570, 975, 1472, 2090, 2667]
## --- Chemistry.jl
@test molarmass["Si"] ≈ 28.085
@test molarmasspercation["TiO2"] ≈ 79.8651
@test ionicradius.La ≈ 117.2
@test ioniccharge.La == +3
## --- Geology.jl
@test timescale isa NamedTuple
@test timescale.Age_min["holocene"] === 0.
@test timescale.Age_min["archean"] === 2500.
@test timescale.Age_max["archean"] === 4000.
@test timescale.Age_min["hadean"] === 4000.
@test StatGeochem.rock_type_key isa NamedTuple
## ---
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | code | 4074 | ## --- Geochronology.jl
@test eHf(0.2818792, 0.001009289, 1424.878) ≈ -0.9639408985107067
(c, m, el, eu) = bin_bsr_eHf(1:4000, fill(0.2818792, 4000), fill(0.001009289,4000), 1:4000, 0, 4000, 4, fill(10,4000), fill(0.001,4000), fill(1e-6,4000), fill(10,4000), 500)
@test c == 500:1000:3500
isapprox(m, [-21.316, 0.724, 23.187, 45.97], atol=5)
isapprox(el, [2.254, 2.216, 2.28, 2.319], atol=0.5)
isapprox(eu, [2.245, 2.216, 2.273, 2.348], atol=0.5)
## --- GIS.jl
# Calculate slope on a sphere
A = [8 7 10 -1 -1; 5 2 2 -1 -1; 8 5 3 1 6; 8 10 3 9 5; -1 8 4 10 4]
@test aveslope(A*1000, 1:4, 1:4, 1, Int64, minmatval=0, km_per_lat=111.1) ==
[0 20 0 0 0; 19 23 8 0 0; 18 15 11 22 15; 4 0 15 35 16; 0 14 15 0 0]
@test aveslope(A*1000, 1:4, 1:4, 1, Float16, minmatval=0, km_per_lat=111.1) ==
Float16[0.0 19.72 0.0 0.0 0.0; 18.75 22.72 8.29 0.0 0.0; 18.45 15.375 11.2 22.45 14.63; 3.824 0.0 15.06 35.4 15.914; 0.0 13.59 15.39 0.0 0.0]
@test maxslope(A*1000, 1:4, 1:4, 1, minmatval=0, km_per_lat=111.1) ==
[0 45 72 0 0; 27 16 32 0 6; 38 36 22 14 45; 18 23 22 41 36; 0 32 38 45 0]
@test maxslope(A*1000, 1:4, 1:4, 1, Float16, minmatval=0, km_per_lat=111.1) ==
Float16[0.0 45.0 72.0 0.0 0.0; 27.02 15.914 31.5 0.0 6.367; 38.22 36.0 22.3 13.52 45.06; 18.05 22.56 22.3 40.5 36.1; 0.0 31.86 38.22 44.6 0.0]
# Test ESRI arc/info ASCII grid import function
gridfiletext = """
ncols 4
nrows 6
xllcorner 0.0
yllcorner 0.0
cellsize 50.0
NODATA_value -9999
-9999 -9999 5 2
-9999 20 100 36
3 8 35 10
32 42 50 6
88 75 27 9
13 5 1 -9999
"""
f = open("grid.asc","w")
print(f, gridfiletext)
close(f)
(data, metadata) = importAAIGrid("grid.asc", Int64, undefval=-999)
@test eltype(data) == Int64
@test data == [-9999 -9999 5 2; -9999 20 100 36; 3 8 35 10; 32 42 50 6; 88 75 27 9; 13 5 1 -9999]
@test metadata["nodata"] == -9999
rm("grid.asc")
# Random lat-lon generation
@test isa(randlatlon(), Tuple{Float64,Float64})
lat,lon = randlatlon(100)
@test maximum(lat) <= 90
@test minimum(lat) >= -90
@test maximum(lon) <= 180
@test minimum(lon) >= -180
lat,lon = randlatlon(100,land=true)
@test maximum(lat) <= 90
@test minimum(lat) >= -90
@test maximum(lon) <= 180
@test minimum(lon) >= -180
@test all(find_land(lat, lon))
@test length(lat) == length(lon) == 100
# Calculate arc-degree distance
@test isapprox(haversine(1, 0, 0, 0), 1)
@test isapprox(haversine(0, 1, 0, 0), 1)
@test isapprox(haversine(0, 0, 1, 0), 1)
@test isapprox(haversine(0, 0, 0, 1), 1)
@test isapprox(haversine(0, 0, 0, 0), 0)
@test isapprox(haversine(90, 2, 0, 0), 90)
@test isapprox(haversine(0, 0, 90, 2), 90)
# Centroid of a set of lats and lons on a sphere
lats, lons = [-1, 1, 0, 0.], [0, 0, -1, 1.]
@test all(centroid(lats, lons) .≈ (0.0, 0.0))
lats, lons = [43.69852352,43.69852385,43.69944918,43.69945593,], [-116.0948702,-116.0936334,-116.0936182,-116.0948765,];
@test all(centroid(lats, lons) .≈ (43.698988121696146, -116.0942495750004))
# Maximum arc-degree distance between a list of points on a sphere
lats, lons = [-1, 1, 0, 0.], [0, 0, -1, 1.]
@test all(dist_uncert(lats, lons) .≈ (0.0, 0.0, 1.0))
lats, lons = [0, 0, 0, 0], [0, 30, 23, 90]
@test all(dist_uncert(lats, lons) .≈ (0.0, 34.15788270532762, 45.0))
xyz = 2rand(3) .- 1
@test xyz ≈ [cartesian(spherical(xyz...,)...,)...,]
## --- Etc.jl
# Test digitize_plotmarkers
img = load("assets/xyscatter.png")
C = eltype(img)
(x,dx,y,dy) = digitize_plotmarkers(img, C(0,0.604,0.976,1), (0,10), (0,10))
@test isapprox(x, 1:10, atol = 0.1)
@test isapprox(y, 1:10, atol = 0.1)
# Test digitize_plotline
img = load("assets/xysin.png")
C = eltype(img)
(x,y) = digitize_plotline(img, C(0,0.604,0.976,1), (0,2pi), (-1.1,1.1))
@test isapprox(sin.(x), y, atol=0.1)
## ---
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | docs | 3849 | # StatGeochem.jl
[](https://doi.org/10.17605/OSF.IO/TJHMW)
[![Docs][docs-dev-img]][docs-dev-url]
[![CI][ci-img]][ci-url]
[![CI (Julia nightly)][ci-nightly-img]][ci-nightly-url]
[![Coverage][codecov-img]][codecov-url]
_Computational tools for statistical geochemistry and petrology_
In addition to functions exported by StatGeochem directly, StatGeochem also reexports (and depends upon internally) both [StatGeochemBase.jl](https://github.com/brenhinkeller/StatGeochemBase.jl) and [NaNStatistics.jl](https://github.com/brenhinkeller/NaNStatistics.jl)
## Installation
StatGeochem.jl is written in the [Julia programming language](https://julialang.org/), and is registered on the General registry. To install, enter the Julia package manager (type `]` in the REPL) and type:
```
pkg> add StatGeochem
```
If you are trying to use a script written prior to ~2021, you may want to use the oldest registered version of the package, which you can install with (e.g.)
```Julia
julia> add [email protected]
```
## Usage
This package can be used in the [Julia](https://julialang.org) REPL, in scripts or functions in Julia `.jl` files, in the [Juno/Atom IDE](http://junolab.org/), or in a Jupyter notebook. There aren't examples yet for most of the code in this repository, but for a quick demonstration, try the interactive Jupyter notebooks (it may take a few minutes for these to launch)
* [](https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples/BootstrapResamplingDemo.ipynb) Weighted bootstrap resampling: [BootstrapResamplingDemo.ipynb](https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples/BootstrapResamplingDemo.ipynb)
* [](https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples/ConstantSilicaReferenceModel.ipynb) Constant-silica reference model: [ConstantSilicaReferenceModel.ipynb](https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples/ConstantSilicaReferenceModel.ipynb)
* [](https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples/MeltsExamples.ipynb) Julia-alphaMELTS interface demo: [MeltsExamples.ipynb](https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples%2FMeltsExamples.ipynb)
* [](https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples/PerplexExamples.ipynb) Julia-Perple_X interface demo: [PerplexExamples.ipynb](https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples%2FPerplexExamples.ipynb)
The above links run notebooks from the [examples/](examples/) folder on a [JupyterHub](https://github.com/jupyterhub/jupyterhub) server hosted by the [Binder](https://mybinder.org) project. If you make changes to the online notebook, you can save them with `File` > `Download as` > `Notebook (.ipynb)` To run a downloaded notebook locally, use [IJulia](https://github.com/JuliaLang/IJulia.jl)
```Julia
julia> using IJulia
julia> notebook()
```
[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg
[docs-dev-url]: https://brenhinkeller.github.io/StatGeochem.jl/dev/
[ci-img]: https://github.com/brenhinkeller/StatGeochem.jl/workflows/CI/badge.svg
[ci-url]: https://github.com/brenhinkeller/StatGeochem.jl/actions/workflows/CI.yml
[ci-nightly-img]:https://github.com/brenhinkeller/StatGeochem.jl/workflows/CI%20(Julia%20nightly)/badge.svg
[ci-nightly-url]:https://github.com/brenhinkeller/StatGeochem.jl/actions/workflows/CI-julia-nightly.yml
[codecov-img]: http://codecov.io/github/brenhinkeller/StatGeochem.jl/coverage.svg?branch=main
[codecov-url]: http://app.codecov.io/github/brenhinkeller/StatGeochem.jl?branch=main
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | docs | 211 | ```@meta
CurrentModule = StatGeochem
```
# StatGeochem
Documentation for the [StatGeochem.jl](https://github.com/brenhinkeller/StatGeochem.jl) package.
```@index
```
```@autodocs
Modules = [StatGeochem]
```
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | docs | 204 | The unprojected maps/images in this folder are downscalings of https://en.wikipedia.org/wiki/File:Blue_Marble_2002.png, which was produced from NASA imagery (primarily MODIS) and is in the public domain.
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.7.10 | 4a908923b30176cfb8ce4bf1041187b339ff47be | docs | 233 | Reference:
Artemieva, I. M. Global 1 x 1 thermal model TC1 for the continental lithosphere: implications for lithosphere secular evolution. Tectonophysics 416, 245–277 (2006).
Available at: http://www.lithosphere.info/TC1-2006.html
| StatGeochem | https://github.com/brenhinkeller/StatGeochem.jl.git |
|
[
"MIT"
] | 0.2.0 | 9432c80cf46350d8a37b39baa119352170a6e769 | code | 8083 | module Jupyter2Pluto
using JSON
using UUIDs
const _notebook_header = "### A Pluto.jl notebook ###"
const _cell_id_delimiter = "# ╔═╡ "
const _order_delimiter = "# ╠═"
const _order_delimiter_folded = "# ╟─"
const _cell_suffix = "\n\n"
export pluto2jupyter, jupyter2pluto
abstract type JupyterCell end
struct JupyterCodeCell <: JupyterCell
execution_count::Union{Int, Nothing}
content::Vector{String}
end
struct JupyterMarkdownCell <: JupyterCell
content::String
end
abstract type PlutoCell end
struct PlutoCodeCell <: PlutoCell
cell_id::UUID
content::String
end
struct PlutoBlockCodeCell <: PlutoCell
cell_id::UUID
content::String
end
struct PlutoMarkdownCell <: PlutoCell
cell_id::UUID
content::String
end
function Base.show(io::IO, cell::PlutoCodeCell)
gl = """
$(_cell_id_delimiter)$(cell.cell_id)
$(cell.content)$(_cell_suffix)"""
print(io, gl)
end
function Base.show(io::IO, cell::PlutoBlockCodeCell)
gl = """
$(_cell_id_delimiter)$(cell.cell_id)
begin\n$(cell.content)\nend$(_cell_suffix)"""
print(io, gl)
end
function Base.show(io::IO, cell::PlutoMarkdownCell)
content = replace(cell.content, "\"" => "\\\"")
r = """
$(_cell_id_delimiter)$(cell.cell_id)
md\"\"\"
$(content)\"\"\"$(_cell_suffix)"""
print(io, r)
end
function Base.Dict(cell::JupyterMarkdownCell)
source = String[]
content_list = split(cell.content, "\n")
for (i, content) in enumerate(content_list)
i == length(cell.content) && (push!(source, content); continue)
push!(source, content*"\n")
end
Dict(
"cell_type" => "markdown",
"metadata"=>Dict(),
"source"=> source
)
end
function Base.Dict(cell::JupyterCodeCell)
source = String[]
for (i, content) in enumerate(cell.content)
(isempty(content) || i == length(cell.content)) && (push!(source, content); continue)
push!(source, content*"\n")
end
Dict(
"cell_type" => "code",
"execution_count" => cell.execution_count,
"metadata"=>Dict(),
"outputs"=>[],
"source"=> source
)
end
function cell_id()
uuid4()
end
function generate_code(pcells::Vector{PlutoCell})
pheader = _notebook_header*"\n\n"
codestring = "$pheader$(join(pcells))$(order_code(pcells))"
return codestring
end
function order_code(pcells::Vector{PlutoCell})
orderstrprefix = "$(_cell_id_delimiter)Cell order:\n"
orderstrprefix *join(order_code.(pcells))
end
function order_code(pcell::PlutoMarkdownCell)
return _order_delimiter_folded*""*string(pcell.cell_id)*"\n"
end
function order_code(pcell::Union{PlutoCodeCell, PlutoBlockCodeCell})
return _order_delimiter*""*string(pcell.cell_id)*"\n"
end
function generate_plutocells(codecell::JupyterCodeCell)
pcells = PlutoCell[]
for content in codecell.content
id = cell_id()
push!(pcells, PlutoCodeCell(id, content))
end
return pcells
end
function PlutoCell(codecell::JupyterCodeCell)
id = cell_id()
if has_multiple_expressions(codecell)
return PlutoBlockCodeCell(id, join(" ".*codecell.content))
end
return PlutoCodeCell(id, join(codecell.content))
end
function PlutoCell(codecell::JupyterMarkdownCell)
id = cell_id()
return PlutoMarkdownCell(id, codecell.content)
end
function has_multiple_expressions(codecell::JupyterCodeCell)
cellcontent = join(codecell.content, "\n")
replmodechars = r"\W*[\?|\]|;]"
startswith(cellcontent, replmodechars) && return false
expressions = try
codewithbegin = "begin "*cellcontent*" end"
Meta.parse(codewithbegin).args
catch ex
@warn "Failed to parse expression $(codewithbegin)"
return false
end
filter!(x -> !(x isa LineNumberNode), expressions)
length(expressions) > 1
end
function jupyter2pluto(jupyter_file)
jupyter_cells = try
content = JSON.parsefile(jupyter_file)
content["cells"]
catch ex
error("Jupyter notebook parse error")
end
pluto_cells = PlutoCell[]
for cell in jupyter_cells
!haskey(cell, "cell_type") || !haskey(cell,"source") && continue
if cell["cell_type"] == "markdown" && !isempty(cell["source"])
jmark_cell = JupyterMarkdownCell(join(cell["source"]))
pc = PlutoCell(jmark_cell)
push!(pluto_cells, pc)
end
if cell["cell_type"] == "code" && !isempty(cell["source"])
jcode_cell = JupyterCodeCell(cell["execution_count"],cell["source"])
pc = PlutoCell(jcode_cell)
push!(pluto_cells, pc)
end
end
output = generate_code(pluto_cells)
fileName = getFileName(jupyter_file)
dest = "$(fileName).jl"
open(dest, "w") do f
write(f, output)
end
dest
end
function parse_pluto_cell(rawcell::String)
cellist = string.(split(rawcell, '\n'))
body = join(cellist[2:end], '\n')
multiline_mdr = r"md\"\"\"(.*)\"\"\""s
line_mdr = r"md\"(.*)\""s
matches = if (mat = match(multiline_mdr, body)) != nothing
mat
else
match(line_mdr, body)
end
if matches != nothing
PlutoMarkdownCell(UUID(cellist[1]), matches.captures[1])
else
PlutoCodeCell(UUID(cellist[1]), body)
end
end
function parse_pluto_end(rawcell::String)
main_split= string.(split(rawcell, _order_delimiter))
orderids = String[]
for order in main_split
splits = string.(split(order, _order_delimiter_folded))
for split in splits
!isempty(split) && push!(orderids, strip(split))
end
end
orderids[2:end]
end
function order(pcells::Vector{PlutoCell}, orderids::Vector{String})
sorted_pcells = PlutoCell[]
for orderid in orderids
for pcell in pcells
if occursin(string(pcell.cell_id), orderid)
push!(sorted_pcells, pcell)
end
end
end
return sorted_pcells
end
let
global JupyterCell
global parse_pluto_load
execution_count = 1
function parse_pluto_load(raw::AbstractString)
jcell = JupyterCodeCell(execution_count, split(raw, "\n"))
execution_count += 1
jcell
end
function JupyterCell(pcell::PlutoCodeCell)
jcell = JupyterCodeCell(execution_count,split(pcell.content, "\n"))
execution_count += 1
jcell
end
end
function JupyterCell(pcell::PlutoMarkdownCell)
content = pcell.content
JupyterMarkdownCell(pcell.content)
end
function pluto2jupyter(file)
# parser: pluto notebook has orderidlist, map(order_id => codesnippets) ::Plutocells
plutoraw = readchomp(file)
plutocelllist = string.(split(plutoraw, _cell_id_delimiter))
jupyterloadcell = parse_pluto_load(plutocelllist[1])
i=2
pcells = PlutoCell[]
while(i <= length(plutocelllist)-1)
pcell = parse_pluto_cell(plutocelllist[i])
push!(pcells, pcell)
i+=1
end
plutoend = parse_pluto_end(plutocelllist[end])
ordered_pcells = order(pcells, plutoend)
jcells = map( pcell -> JupyterCell(pcell), ordered_pcells)
all_main_cells = Dict.(jcells)
pushfirst!(all_main_cells, Dict(jupyterloadcell))
d_cells = Dict()
d_cells["cells"] = all_main_cells
d_cells["metadata"] = Dict(
"kernelspec"=> Dict(
"display_name"=> "Julia $(VERSION)",
"language"=> "julia",
"name"=> "julia-$(VERSION.major).$(VERSION.minor)"
),
"language_info"=> Dict(
"file_extension"=> ".jl",
"mimetype"=> "application/julia",
"name"=> "julia",
"version"=> string(VERSION)
)
)
d_cells["nbformat"]= 4
d_cells["nbformat_minor"]= 2
fileName = getFileName(file)
dest = fileName*".ipynb"
open(dest, "w") do f
JSON.print(f, d_cells , 4)
end
dest
end
function getFileName(str::AbstractString)
parts = split(str, ".")
if length(parts) == 1
return str
else
return join(parts[1:end-1], ".")
end
end
end # module
| Jupyter2Pluto | https://github.com/vdayanand/Jupyter2Pluto.jl.git |
|
[
"MIT"
] | 0.2.0 | 9432c80cf46350d8a37b39baa119352170a6e769 | code | 14190 | ### A Pluto.jl notebook ###
# v0.11.4
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
el
end
end
# ╔═╡ e5e0a0da-d45c-11ea-1042-e9b5d0654d4f
md"Fix the value of `c` below to make it `c = a * b`"
# ╔═╡ 4dff4b5e-d461-11ea-29c8-d548fdb5f08b
md"Edit the equation below to calculate the number of pizzas to order using the variables above for **people**, **avg**, and **slices**:"
# ╔═╡ f907e46a-d471-11ea-07e5-f30e2aab3d08
md"""The diameter of a pizza is often stated on a menu so let's define a **formula** to calculate the area of a pizza given the diameter **d**.
We do this by writing a formula like this: `area(d) = pi * (d/2)^2`
Let's write that below:
"""
# ╔═╡ d9575e9c-d472-11ea-1eda-2d335d039f28
md"""Now we have a function called **area** that we can pass any diameter and it will return the area of a pizza (or circle), let's try that with the pizza from before with `area(2*r)` to get the area of the pizza:
"""
# ╔═╡ edb95b14-d473-11ea-3a5a-77382d31f941
md"""## Finding the best pizza deal
Let's see if a larger pizza is a better value by calculating the price per area. There are 4 sizes: small, medium, large, extra large with the following prices:
Size | Diameter (inches) | Price ($)
:------- | :---------------: | --------:
small | 9 | 13.10
medium | 13 | 20.95
large | 15 | 24.90
XL | 17 | 30.95
### 1. How many small pizzas is the same as one XL pizza?
Edit the expression below:
"""
# ╔═╡ 5b07b8fe-d475-11ea-01aa-6b88d6ed8a05
md"""### 2. Calculate the cost per area of each pizza:
"""
# ╔═╡ a42e4eb0-d474-11ea-316a-3d864451bc01
md"Which size of pizza is the best deal? Write your answer below and assign it to the variable **best_value**."
# ╔═╡ cb419286-d4ff-11ea-1d7f-af5c8574b775
md"""### 3. Is this a good deal?
San Marinos has a special **\"Buy two medium pizzas and save \$5\"**. Is this a better deal than buying a extra-large pizza?
Calculate the total cost of two medium pizzas deal (saving \$5):"""
# ╔═╡ 0d76d97c-d500-11ea-2433-e96c6fc43b05
md"Calculate the total area of two medium pizzas:"
# ╔═╡ 20a1e9cc-d500-11ea-3d9b-279c71bc20f1
md"Now calculate cost per area by taking the total cost of two medium pizzas and divide by the total area:"
# ╔═╡ 57f024ae-d500-11ea-1cc4-ed28348fdf93
md"""Is it a better deal to get two medium pizzas for \$5 off or to just buy an extra-large?"""
# ╔═╡ 180c8fdc-d503-11ea-04ca-bf2c07fd1c17
md"""### 4. Advanced Problem
A new worker at a pizza shop was getting paid for cutting pizza into pieces. The pieces of pizza could be any size. Calculate the maximum number of pieces the worker could make with two cuts of the pizza."""
# ╔═╡ 92b4a012-d503-11ea-15a2-1f3a446d3284
md"Now what about 3 cuts across the pizza? What is the maximum number of pieces that can be made with **3 cuts**?"
# ╔═╡ 2eb9a560-d507-11ea-3b8b-9d06678fe131
md"Now, how many pieces can be made with **4 cuts**?"
# ╔═╡ d1e3dec0-d507-11ea-1213-d37a9325ee2f
md"Are you starting to see a pattern? Can you figure out a formula for how many pieces of pizza can be made with \"n\" cuts? Make a table and fill in the number of pieces for a number of cuts and see if you can find the pattern:
Cuts | Pieces
:--- | ------:
0 | 1
1 | 2
2 | 4
3 |
4 |
"
# ╔═╡ 97bfd13c-dcc2-11ea-0067-ad8c2c6517fc
md"To get an extra hint, figure out how many slices we can get from **5 cuts**:"
# ╔═╡ e0cb2822-dcc2-11ea-2c85-5748bfe526dc
md"Have you found the pattern? Write down the formula below:"
# ╔═╡ 03249876-d508-11ea-16bb-fd5afed37a1f
md"""##### Let's test your formula!"""
# ╔═╡ 14158eb0-d45c-11ea-088f-330e45412320
a = 2
# ╔═╡ 2ed4bb92-d45c-11ea-0b31-2d8e32ce7b44
b = 6
# ╔═╡ 03664f5c-d45c-11ea-21b6-91cd647a07aa
md"# Mathematics in Julia 🍕
This is an introduction to programming. Let's get started!
## Let's make a calculator!
First let's do some simple math with setting **a = $a**, **b = $b** and **c = a * b**. What will **c** equal?
Type in the cells (with the coloured background) below and press **`Shift-Enter`** or the click the right-arrow button (▶️) to the right to execute the cell after changing the values."
# ╔═╡ 30f0f882-d45c-11ea-2adc-7d84ecf8a7a6
c = 10
# ╔═╡ 262b312a-d460-11ea-26c5-df30459effc5
people = 10
# ╔═╡ 2ea7f162-d460-11ea-0e8e-25340e2e64da
avg = 2.5
# ╔═╡ 3da812c6-d460-11ea-0170-79fbb6a4347c
slices = 8
# ╔═╡ a38cb92e-d45e-11ea-2959-05be909befb2
md"""### Now you have a calculator!
You did multiplication above. Here's how you do other mathematical operations:
Operation | Type This
:------------ | :-------------:
add | +
subtract | -
multiply | *
divide | /
power | ^
### Pizza Slices
Let's try this out on a problem. Let's say you want to order pizzas for $people people (**people = $people**) and each person wants $avg slices on average (**avg = 2.5**). A pizza has $slices slices per pizza (**slices = $slices**). How many pizzas should you order (**pizzas = ?**)? So we have the following
Meaning | Variable
:------ | :--------:
Number of people | people
Average number of slices each person eats | avg
Number of slices on a piece of pizza | slices
"""
# ╔═╡ 444e2fa4-d460-11ea-12aa-57e0576c2d66
pizzas = 1
# ╔═╡ 3c12f2b4-d471-11ea-2d37-539f061f7cf2
r = 6
# ╔═╡ d9c31dfa-d470-11ea-23b2-838975b71f7c
md"""## Writing your own math functions
The area of a pizza is $$A = \pi r^2$$. Lets try calculating the area of a pizza that has a radius of $r inches (**r = $6**). Type **pi** to get the value of $$\pi$$ and **r^2** to get the radius squared.
"""
# ╔═╡ 50f0f6d6-d471-11ea-304e-8f72e7ef9d7e
A = r^2
# ╔═╡ cb36a9ee-d472-11ea-1835-bf7963137e18
area(d) = pi * (d / 2)^2
# ╔═╡ 04b010c0-d473-11ea-1767-136c7e26e122
A2 = area(r)
# ╔═╡ 637c26fa-d475-11ea-2c5b-2b0f4775b119
smalls_in_xl = 1
# ╔═╡ 3823d09e-d474-11ea-194e-59b5805f303b
small = 13.10 / area(9)
# ╔═╡ 76c11174-d474-11ea-29c5-81856d47cf74
medium = 20.95 / area(13)
# ╔═╡ 8b12d200-d474-11ea-3035-01eccf39f917
large = 24.90 / area(15)
# ╔═╡ 962e6b86-d474-11ea-11a6-a1d11e33ae42
xl = 30.95 / area(17)
# ╔═╡ 16ec3f32-d4ff-11ea-20e2-5bc6dd5db083
best_value = small
# ╔═╡ f147b6cc-d4ff-11ea-05ad-6f5b441e5d1b
two_medium_cost = 20.95 * 1 - 0
# ╔═╡ 19eb2a82-d500-11ea-3782-596adc689382
two_medium_area = 1 * area(13)
# ╔═╡ 70e85498-d500-11ea-35af-474574f5c011
two_medium_deal = 1
# ╔═╡ 6494e270-d503-11ea-38a7-df96e7f0a241
cuts2 = 1
# ╔═╡ a05aae8e-d506-11ea-190f-57e9ce53b8b9
cuts3 = 1
# ╔═╡ 5a8ede88-d507-11ea-30d9-c99a67243781
cuts4 = 1
# ╔═╡ bae0cb62-dcc2-11ea-0667-512e1c407d40
cuts5 = 1
# ╔═╡ f5f89724-d507-11ea-0a93-6d904f36bbe4
function pieces(n)
return n
end
# ╔═╡ bd9f3d24-d509-11ea-165d-3d465a0b4542
md"""Move the slider to change the number of cuts:
$(@bind n html"<input type=range max=50>")"""
# ╔═╡ b8644fb0-daa6-11ea-1e94-9bf46e7b0fad
hint(text) = Markdown.MD(Markdown.Admonition("hint", "Hint", [text]));
# ╔═╡ 8700d986-d475-11ea-0d0e-790448cf92ba
let
ans = (pi * (17 / 2)^2) / (pi * (9 / 2)^2)
if smalls_in_xl == 1
hint(md"""The diameter of the XL pizza is 17 inches while the diameter of the small pizza is 9 inches. Use the **area()** function from before to find the area of each and divide them.""")
elseif smalls_in_xl < ans - 4 * eps(ans)
md"""Keep trying, your answer is too low."""
elseif smalls_in_xl > ans + 4 * eps(ans)
md"""Keep trying, your answer is too high."""
else
md"""**Great!** You got it right. Let's move on."""
end
end
# ╔═╡ 2814a1d4-dcc0-11ea-3d42-f52765e478fe
hint(md"For each extra cut, start out with the solution for the previous number. When you add one extra cut, how many new slices do you get?")
# ╔═╡ 48647ab2-daa5-11ea-0494-ef87be7cbf7c
hint(md"A new cut will create the maximum number of _new slices_ if it intersects all previous cuts.")
# ╔═╡ 8cada086-daa5-11ea-220c-0f660938b604
if cuts5 == 5 + 4 + 3 + 2 + 1 + 1
hint(md"To get the maximum number of pieces with 5 cuts it will be $$5 + 4 + 3 + 2 + 1$$, plus 1 extra for the original pizza with 0 cuts. To find the formula of a sequence of numbers group them like so: $$5 + (4 + 1) + (3 + 2) = 3 * 5$$.")
else
md""
end
# ╔═╡ 4119d19e-dcbc-11ea-3ec8-271e88e1afca
almost(text) = Markdown.MD(Markdown.Admonition("warning", "Almost there!", [text]));
# ╔═╡ 921bba30-dcbc-11ea-13c3-87554722da8a
keep_working(text=md"The answer is not quite right.") = Markdown.MD(Markdown.Admonition("danger", "Keep working on it!", [text]));
# ╔═╡ 5a6d1a8e-dcbc-11ea-272a-6f769c8d309c
correct(text=md"Great! You got the right answer! Let's move on to the next section.") = Markdown.MD(Markdown.Admonition("correct", "Got it!", [text]));
# ╔═╡ 33b1975c-d45c-11ea-035f-ab76e46a31ed
if c == a * b
correct(md"""**Great!** The value of c = $c. So you now have a simple computer!
Now go back above and change the value of **a = $a** to **a = $(a + 3)** and press **`Shift-Enter`**.
What is the new value of **c**? Notice how all the values get updated in this notebook!
""")
else
keep_working()
end
# ╔═╡ f26d50da-d46b-11ea-0c2d-77ca13532b3d
if pizzas == people * avg / slices
almost(md"Yes that is right! But we should round $pizzas up to an integer, otherwise the restaurant will be confused.
Try `ceil(...)`!")
elseif pizzas == ceil(people * avg / slices)
correct(md"Yes that is right, that's a lot of pizza! Excellent, you figured out we need to round up the number of pizzas!")
else
keep_working()
end
# ╔═╡ 5c4a5f22-d471-11ea-260f-9338d8bfa2d6
if A != pi * r^2
keep_working(md"Let's fix the above cell before we move on! Find the formula to calculate the area using **pi** and **r**.")
else
correct(md"""**Great!** You figured it out. Keep going.""")
end
# ╔═╡ a07e5c3e-d476-11ea-308c-718f8f128334
if A2 != pi * (12 / 2)^2
hint(md"Keep trying to get the right answer.
**Hint**: you need to multiply the radius by 2 to convert it into the diameter.")
else
correct()
end
# ╔═╡ 1ba2c208-d4ff-11ea-0a8e-e75bf7e1c3e6
if !isapprox(best_value, xl)
hint(md"No need to copy these digits yourself - what should we assign to **best_value**?")
else
correct()
end
# ╔═╡ 6ae748b2-d503-11ea-1c51-6b2df24fd212
if cuts2 != 4
hint(md"The cuts must go all the way across the pizza!")
else
correct(md"Awesome!")
end
# ╔═╡ a679bddc-d506-11ea-143a-6d4dcd70e918
if cuts3 == 6
almost(md"""Close but not quite. Who said that pizza slices need to look like pizza slices?""")
elseif cuts3 == 7
correct(md"You got it right. Now for something harder...")
else
hint(md"Try drawing it out on a piece of paper.")
end
# ╔═╡ 5df7eefc-d507-11ea-0d1f-45b224a04774
if cuts4 == 11
correct(md"That was a tough question. How did you figure it out? You tried hard.")
elseif cuts4 < 10
hint(md"Draw it out on a piece of paper. You can make more pieces with 4 cuts.")
elseif cuts4 < 11
hint(md"Getting close but you can make more pieces with 4 cuts.")
else
hint(md"That is too high. Only straight lines!")
end
# ╔═╡ e80986c6-d509-11ea-12e3-f79a54b5ab31
if pieces(n) == n * (n + 1) / 2 + 1
md"""_Testing..._
**For $n cuts, you predict $(pieces(n)) pieces.**
$(correct(md"Well done!"))"""
else
md"""_Testing..._
**For $n cuts, you predict $(pieces(n)) pieces.**
$(keep_working(md"The answer should be $(Int(n*(n+1)/2+1))."))"""
end
# ╔═╡ Cell order:
# ╟─03664f5c-d45c-11ea-21b6-91cd647a07aa
# ╠═14158eb0-d45c-11ea-088f-330e45412320
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# ╟─a38cb92e-d45e-11ea-2959-05be909befb2
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# ╠═3da812c6-d460-11ea-0170-79fbb6a4347c
# ╟─4dff4b5e-d461-11ea-29c8-d548fdb5f08b
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# ╟─8700d986-d475-11ea-0d0e-790448cf92ba
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# ╟─5a6d1a8e-dcbc-11ea-272a-6f769c8d309c
| Jupyter2Pluto | https://github.com/vdayanand/Jupyter2Pluto.jl.git |
|
[
"MIT"
] | 0.2.0 | 9432c80cf46350d8a37b39baa119352170a6e769 | code | 66 | include("test_pluto2jupyter.jl")
include("test_jupyter2pluto.jl")
| Jupyter2Pluto | https://github.com/vdayanand/Jupyter2Pluto.jl.git |
|
[
"MIT"
] | 0.2.0 | 9432c80cf46350d8a37b39baa119352170a6e769 | code | 2735 | using Test
using JSON
using Random
import Jupyter2Pluto: PlutoCell, JupyterMarkdownCell, JupyterCodeCell, generate_code
const markdown_arr = [
"# Data structures\n",
"\n",
"Once we start working with many pieces of data at once, it will be convenient for us to store data in structures like arrays or dictionaries (rather than just relying on variables).<br>\n",
"\n",
"Types of data structures covered:\n",
"1. Tuples\n",
"2. Dictionaries\n",
"3. Arrays\n",
"\n",
"<br>\n",
"As an overview, tuples and arrays are both ordered sequences of elements (so we can index into them). Dictionaries and arrays are both mutable.\n",
"We'll explain this more below!"
]
##TODO: fix remove \n from the last jupyter cell
const code_arr = ["# First, restore fibonacci\n", "fibonacci[1] = 1\n", "fibonacci"]
Random.seed!(1234)
function test_jupyter2pluto()
#@info join(markdown_arr)
jmark_cell = JupyterMarkdownCell(join(markdown_arr))
pmc = PlutoCell(jmark_cell)
@testset "test pluto markdown codegen" begin
@test string(pmc) == """# ╔═╡ 196f2941-2d58-45ba-9f13-43a2532b2fa8
md\"\"\"
# Data structures
Once we start working with many pieces of data at once, it will be convenient for us to store data in structures like arrays or dictionaries (rather than just relying on variables).<br>
Types of data structures covered:
1. Tuples
2. Dictionaries
3. Arrays
<br>
As an overview, tuples and arrays are both ordered sequences of elements (so we can index into them). Dictionaries and arrays are both mutable.
We'll explain this more below!\"\"\"
"""
end
pcc = PlutoCell(JupyterCodeCell(1, code_arr))
@testset "Pluto code block codegen" begin
@test string(pcc) == """# ╔═╡ bd85187e-0531-4a3e-9fea-713204a818a2
begin
# First, restore fibonacci
fibonacci[1] = 1
fibonacci
end
"""
end
@test string(generate_code(PlutoCell[pmc, pcc])) == """### A Pluto.jl notebook ###
# ╔═╡ 196f2941-2d58-45ba-9f13-43a2532b2fa8
md\"\"\"
# Data structures
Once we start working with many pieces of data at once, it will be convenient for us to store data in structures like arrays or dictionaries (rather than just relying on variables).<br>
Types of data structures covered:
1. Tuples
2. Dictionaries
3. Arrays
<br>
As an overview, tuples and arrays are both ordered sequences of elements (so we can index into them). Dictionaries and arrays are both mutable.
We'll explain this more below!\"\"\"
# ╔═╡ bd85187e-0531-4a3e-9fea-713204a818a2
begin
# First, restore fibonacci
fibonacci[1] = 1
fibonacci
end
# ╔═╡ Cell order:
# ╟─196f2941-2d58-45ba-9f13-43a2532b2fa8
# ╠═bd85187e-0531-4a3e-9fea-713204a818a2
"""
end
test_jupyter2pluto()
| Jupyter2Pluto | https://github.com/vdayanand/Jupyter2Pluto.jl.git |
|
[
"MIT"
] | 0.2.0 | 9432c80cf46350d8a37b39baa119352170a6e769 | code | 5061 | using Jupyter2Pluto
using Test
using JSON
const line_markdown = "The diameter of a"
const multi_markdown = """The diameter of a pizza is often stated on a menu so let's define a **formula** to calculate the area of a pizza given the diameter **d**.
We do this by writing a formula like this: `area(d) = pi * (d/2)^2`
Let's write that below:"""
const line_markdown_wrap = "md\"$line_markdown\""
const multiline_markdown_wrap = """md\"\"\"$multi_markdown\"\"\"
"""
const code_line = """
area(d) = pi * (d / 2)^2
"""
function test_pluto_parser()
multiline_markdown_str = "2ed4bb92-d45c-11ea-0b31-2d8e32ce7b44\n$multiline_markdown_wrap"
line_markdown_str = "03664f5c-d45c-11ea-21b6-91cd647a07aa\n$line_markdown_wrap"
pmark_line_cell = Jupyter2Pluto.parse_pluto_cell(line_markdown_str)
pmark_multiline_cell = Jupyter2Pluto.parse_pluto_cell(multiline_markdown_str)
@testset "parse pluto line markdown cell" begin
@test pmark_line_cell.cell_id == Base.UUID("03664f5c-d45c-11ea-21b6-91cd647a07aa")
@test pmark_line_cell.content == line_markdown
end
@testset "parse pluto multiline markdown cell" begin
@test pmark_multiline_cell.cell_id == Base.UUID("2ed4bb92-d45c-11ea-0b31-2d8e32ce7b44")
@test pmark_multiline_cell.content == multi_markdown
end
pcode_cell = Jupyter2Pluto.parse_pluto_cell("""14158eb0-d45c-11ea-088f-330e45412320
$code_line""")
@testset "parse pluto code cell" begin
@test pcode_cell.cell_id == Base.UUID("14158eb0-d45c-11ea-088f-330e45412320")
@test pcode_cell.content == code_line
end
orders = Jupyter2Pluto.parse_pluto_end("""Cell order:
# ╟─03664f5c-d45c-11ea-21b6-91cd647a07aa
# ╠═14158eb0-d45c-11ea-088f-330e45412320
# ╠═2ed4bb92-d45c-11ea-0b31-2d8e32ce7b44
# ╟─e5e0a0da-d45c-11ea-1042-e9b5d0654d4f
""")
@testset "Pluto cell order" begin
@test orders == ["03664f5c-d45c-11ea-21b6-91cd647a07aa",
"14158eb0-d45c-11ea-088f-330e45412320",
"2ed4bb92-d45c-11ea-0b31-2d8e32ce7b44",
"e5e0a0da-d45c-11ea-1042-e9b5d0654d4f"]
@test Jupyter2Pluto.order(Jupyter2Pluto.PlutoCell[pmark_line_cell, pmark_multiline_cell, pcode_cell], orders) == [pmark_line_cell, pcode_cell, pmark_multiline_cell]
@test Jupyter2Pluto.order(Jupyter2Pluto.PlutoCell[pmark_line_cell, pmark_multiline_cell, pcode_cell], reverse(orders)) == [pmark_multiline_cell, pcode_cell, pmark_line_cell]
end
jmark_line_cell = Jupyter2Pluto.JupyterCell(pmark_line_cell)
jmark_multiline_cell = Jupyter2Pluto.JupyterCell(pmark_multiline_cell)
jcodecell = Jupyter2Pluto.JupyterCell(pcode_cell)
@testset "Pluto to jupyter conversion" begin
@test jmark_line_cell.content == line_markdown
@test jmark_multiline_cell.content == multi_markdown
@test jcodecell.content == split(code_line, "\n")
@test jcodecell.execution_count != 0
end
@testset "Jupyter to dict" begin
jmark_line_dict = Dict(jmark_line_cell)
@test jmark_line_dict["cell_type"] == "markdown"
@test isempty(jmark_line_dict["metadata"])
@test jmark_line_dict["source"] == [line_markdown*"\n"]
jmark_multiline_dict = Dict(jmark_multiline_cell)
@test jmark_multiline_dict["cell_type"] == "markdown"
@test isempty(jmark_multiline_dict["metadata"])
@test jmark_multiline_dict["source"] == string.(split(multi_markdown, "\n")).*"\n"
jcode_dict = Dict(jcodecell)
@test jcode_dict["cell_type"] == "code"
@test isempty(jcode_dict["metadata"])
@test jcode_dict["execution_count"] != 0
code_lines = string.(split(code_line, "\n"))
for (index, line) in enumerate(jcode_dict["source"])
if (line == "\n")
@test false
end
if !isempty(line)
@test code_lines[index]*"\n" == line
else
@test code_lines[index] == line
end
end
end
end
function test_pluto2jupyter()
test_notebook = joinpath(@__DIR__, "Basic mathematics.jl")
pluto2jupyter(test_notebook)
notebook = JSON.parsefile(test_notebook*".ipynb")
@testset "check converted " begin
@test notebook["nbformat"] == 4
@test notebook["nbformat_minor"] == 2
@test notebook["metadata"]["kernelspec"]["display_name"] == "Julia $(VERSION)"
@test notebook["metadata"]["kernelspec"]["language"] == "julia"
@test notebook["metadata"]["kernelspec"]["name"] == "julia-$(VERSION.major).$(VERSION.minor)"
@test notebook["metadata"]["language_info"]["file_extension"] == ".jl"
@test notebook["metadata"]["language_info"]["mimetype"] == "application/julia"
@test notebook["metadata"]["language_info"]["name"] == "julia"
@test string(notebook["metadata"]["language_info"]["version"]) == string(VERSION)
@test !isempty(notebook["cells"])
end
end
function main()
test_pluto_parser()
test_pluto2jupyter()
end
main()
| Jupyter2Pluto | https://github.com/vdayanand/Jupyter2Pluto.jl.git |
|
[
"MIT"
] | 0.2.0 | 9432c80cf46350d8a37b39baa119352170a6e769 | docs | 510 |
# Jupyter2Pluto
### convert Jupyter notebook into [Pluto.jl](https://github.com/fonsp/Pluto.jl) notebook
``` julia
] add http://github.com/vdayanand/Jupyter2Pluto.jl
using Jupyter2Pluto
jupyter2pluto("sample.ipynb")
```
Pluto notebook `sample.jl` will be created in the working directory
### convert [Pluto.jl](https://github.com/fonsp/Pluto.jl) notebook into Jupyter notebook
``` julia
pluto2jupyter("sample.jl")
```
Jupyter notebook `sample.ipynb` will be created in the working directory
| Jupyter2Pluto | https://github.com/vdayanand/Jupyter2Pluto.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 2496 | module RxCiphers
include("cipher.jl") # -> charspace.jl (NCharSpace & Txt)
include("dlm charspace.jl")
include("array functions.jl")
include("tuco.jl")
include("periodic substitution.jl") # -> substitution.jl (Substitution)
include("permutation.jl")
include("matrix transposition.jl")
include("columnar.jl")
include("cracks.jl") # -> genetics.jl
export crack_Caesar, crack_Affine
export crack_Vigenere, crack_Periodic_Affine
# cipher
export Encryption, AbstractCipher
export push!, iterate
export apply, apply!, invert, invert!
export Lambda, Retokenise, Reassign
# permutation
export Permutation, invPermutation
# matrix transposition
export MatrixTransposition
# columnar
export Columnar
# periodic substitution
export PeriodicSubstitution, Vigenere, Periodic_Affine
export length, getindex, iterate, setindex!, ==, show
export apply, invert!
# substitution
export Substitution, frequency_matched_Substitution, Atbash, Caesar, Affine
export apply, length, +, -, ==, show, getindex, iterate, invert!, shift!, switch, switch!, mutate, mutate!
# tuco
export quadgramlog, bigramlog, poogramfart, orthodot, bandwidth
export monogram_freq, bigram_freq, bigram_scores, quadgram_scores, poogram_scores
export appearances, frequencies, vector_frequencies
export ioc, periodic_ioc
export bbin_probabilities
export find_period
export divisors, factorise
export blocks, block_apply_stats, rolling, rolling_average, char_distribution, KMP_appearances, repeat_units
export substructure_variance, substructure_sigma
# charspace
export NCharSpace, CharSpace, Alphabet, Decimal, Hexadecimal
export union
export getindex, ^, ==, +, show, length, iterate, setindex!, lastindex, copy
export Txt, tokenise, untokenise, tokenise!, untokenise!, checktoken
export nchar!, nchar
export TokeniseError, checktokenised
# dlm charspace
export DLMSpace
# array functions
export switch, switch!
export safe_reshape_2D
export checkperm
export affine
export normalise!
# text samples
include("samples.jl")
export TxtSamples
end
# CURRENTLY SUPPORTED
# Monoalphabetic Substitution (inc. Caesar, Affine, Atbash)
# Polyalphabetic Substitution (inc. Vigenere, PAffine)
# Permutation Transposition
# TO ADD
# Fix the include tree it is horrible
# Matrix Transposition (and Columnar)
# Stupid Transpositions (Scytale, Redefence)
# Square Ciphers
# Nihilist set (subst. transp.)
# Keystream (generator; Autokey; OTP)
# Grid Shift (therefore Cadenus)
# Diagonal cipher
# Convolution (token safe)
# bruteforce + optimise function | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 4100 | using FFTW
# Switches two entries at posa posb in any Vector
"""
switch!(self::AbstractVector, posa::Int, posb::Int) -> AbstractVector
Switches the entries at `posa` and `posb` in `self`.
"""
function switch!(self::AbstractVector, posa::Int, posb::Int) ::AbstractVector
self[posa], self[posb] = self[posb], self[posa]
return self
end
"""
switch!(v::AbstractVector, posa::Int, posb::Int) -> AbstractVector
Switches the entries at `posa` and `posb` in `v`.
"""
switch(v::Vector{Int}, posa::Int, posb::Int) ::Vector{Int} = switch!(copy(v), posa, posb)
function splice!(self::AbstractVector, ind::Int, start::Int, finish::Int) ::AbstractVector
if start <= finish
if start <= ind <= finish
error("Insertion index must not be within spliced segment")
end
if ind < start
permute!(self, [1:ind - 1 ; start:finish ; ind:start - 1 ; finish + 1:lastindex(self)])
else
permute!(self, [1:start - 1 ; finish + 1:ind - 1 ; start:finish ; ind:lastindex(self)])
end
else
if !(finish < ind < start)
error("Insertion index must not be within spliced segment")
end
permute!(self, [finish + 1:ind - 1 ; start:lastindex(self) ; 1:finish; ind:start - 1])
end
return self
end
function splice(self::AbstractVector, ind::Int, start::Int, finish::Int) ::AbstractVector
if start <= finish
if start <= ind <= finish
error("Insertion index must not be within spliced segment")
end
if ind < start
return self[[1:ind - 1 ; start:finish ; ind:start - 1 ; finish + 1:lastindex(self)]]
else
return self[[1:start - 1 ; finish + 1:ind - 1 ; start:finish ; ind:lastindex(self)]]
end
else
if !(finish < ind < start)
error("Insertion index must not be within spliced segment")
end
return self[[finish + 1:ind - 1 ; start:lastindex(self) ; 1:finish; ind:start - 1]]
end
end
# Pads vector to reshape cleanly into matrix
function safe_reshape_2D(vector::Vector{T}, dim::Int, null_token::Int) ::Matrix{T} where T
r = length(vector) % dim
if r != 0
vector = copy(vector)
append!(vector, fill(null_token, dim - r))
end
return reshape(vector, (dim, :))
end
# checks if vector is permutation and returns vector
"""
checkperm(vector::Vector{Int}) -> Vector{Int}
Throws `ArgumentError` if `vector` is not a permutation, returns `vector`.
"""
function checkperm(vector::Vector{Int}) ::Vector{Int}
if !isperm(vector)
e = ArgumentError("not a permutation")
throw(e)
end
return vector
end
# generates affine pattern with coefficoent a and bias b
function affine(a::Int, b::Int, size::Int) ::Vector{Int}
if gcd(a, size) != 1
println("WARNING: Affine parameter $(a)x + $(b) (mod $(size)) is a singular transformation and cannot be inverted")
end
s = collect(1:size)
s *= a
s .+= (b - a) # Shifted to account for one-base indexing, standardising
s = mod.(s, size)
s .+= 1
return s
end
# circularly shifts vector so that item is at the start
function circorigin(a::Vector{T}, item::T) ::Vector{T} where T
ind = findfirst(==(item), a)
return circshift(a, 1 - ind)
end
# NORMALISATION ####################################
"""
normalise!(arr::Array{AbstractFloat} [, dims]) -> Array{AbstractFloat}
Divides by the sum of `arr` to make the new sum `1.`, if `dims` are given, `arr` is normalised only in those dims.
"""
function normalise!(arr::Array{AbstractFloat}, dims) ::Array{AbstractFloat}
arr ./= sum(arr; dims = dims)
return arr
end
function normalise!(arr::Array{AbstractFloat}) ::Array{AbstractFloat}
arr /= sum(arr)
return arr
end
# CONVOLUTION ######################################
function Conv1D_reals(a::Vector{T}, b::Vector{T}) where T <: Real
N = length(a)
M = length(b)
a = [a ; zeros(M - 1)] # N + M - 1
b = [b ; zeros(N - 1)] # N + M - 1
return ifft(fft(a) .* fft(b))
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 18008 | import Base.==, Base.length, Base.+, Base.*, Base.^, Base.iterate, Base.getindex, Base.setindex!, Base.show, Base.lastindex, Base.copy
const NULL_TOKEN = 0
abstract type AbstractCharSpace end
"""
NCharSpace{N}
A characterspace, encoding substrings `::String` as tokens `::Int`.
`N` is an integer parameter: the length of encoded substrings in characters.
# Fields
- `charmap::Vector{String}` maps tokens to substrings `W.charmap[token]` gives `substring`
- `tokenmap::Dict{String, Int}` maps substrings to assigned tokens `W.tokenmap[substring]` gives `token`
- `units::Vector{String}` stores the most reduced version of encoded substrings
- `unit_length::Int` stores the length of an individual unit
- `linear::LinearIndices` maps a composite n-token to n unit tokens
- `cartesian::CartesianIndices` maps n unit tokens to a single composite n-token
- `size::Int` is the number of encoded substrings / the number of tokens
- `tokens::Vector{Int}` stores all of the encoding tokens, used for iteration
"""
struct NCharSpace{N} <: AbstractCharSpace
charmap::Vector{String}
tokenmap::Dict{String, Int}
units::Vector{String}
unit_length::Int
linear::LinearIndices{N, NTuple{N, Base.OneTo{Int64}}}
cartesian::CartesianIndices{N, NTuple{N, Base.OneTo{Int64}}}
size::Int
tokens::Vector{Int}
# DO NOT USE UNSAFE
function NCharSpace{N}(charmap::Vector{String}, tokenmap::Dict{String, Int}, units::Vector{String}, unit_length::Int, size::Int, tokens::Vector{Int}) where N
if !((N isa Int) && (N > 0))
e = DomainError("Parameter must be an Natural number")
throw(e)
end
shape = Tuple(length(units) for _ in 1:N)
linear = LinearIndices(shape)
cartesian = CartesianIndices(shape)
new{N}(charmap, tokenmap, units, unit_length, linear, cartesian, size, tokens)
end
# DO NOT USE UNSAFE
end
findparam(W::NCharSpace{N}) where N = N
"""
Creates an `NCharSpace{1}` (the most reduced version) from the arguments
"""
function CharSpace(chars::Vector{String}) ::NCharSpace{1}
uchars = unique(chars)
size = length(uchars)
first_size = length(uchars[1])
if !all(x -> length(x) == first_size, uchars) # check all sizes are the same
e = ArgumentError("Characters have different lengths")
throw(e)
end
tokenmap = Dict{String, Int}()
for (i, j) in enumerate(uchars)
tokenmap[j] = i
end
units = copy(uchars)
NCharSpace{1}(uchars, tokenmap, units, first_size, size, collect(1:size))
end
"""
^(W::NCharSpace{1}, n::Int) -> NCharSpace{n}
Returns a composite version of `W`, encoding substrings made of `n` unit substrings to single tokens.
# Examples
```julia-repl
julia> Decimal
10-element CharSpace:
["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
julia> W = Decimal^2
100-element 2-char CharSpace:
["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
julia> tokenise.(["1", "2"], Ref(Decimal))
2-element Vector{Int64}:
2
3
julia> W.linear[2,3]
22
julia> W.charmap[22]
"12"
```
"""
function ^(W::NCharSpace{1}, n::Int) ::NCharSpace{n}
size = W.size ^ n
charmap = Vector{String}(undef, size)
tokenmap = Dict{String, Int}()
for (i, c) in enumerate(Iterators.product(ntuple(i -> W.charmap, n)...))
string = join(c)
charmap[i] = string
tokenmap[string] = i
end
return NCharSpace{n}(charmap, tokenmap, W.units, W.unit_length, size, collect(1:size))
end
function charspacereduce(W::NCharSpace{N}) ::NCharSpace{1} where N
if N == 1
return W
else
return CharSpace(W.units)
end
end
"""
nchar(W::NCharSpace{N}, n::Int) -> NCharSpace{n}
Produces the order `n` `NCharSpace` from `W.units`
"""
function nchar(W::NCharSpace{N}, n::Int) where N
if n == 1
return charspacereduce(W)
elseif N == 1
return W ^ N
else
return charspacereduce(W) ^ n
end
end
############################################################################################
CharSpace(chars::Vector{Char}) = CharSpace(string.(chars))
CharSpace(chars::String) = CharSpace(string.(collect(chars)))
CharSpace(W::NCharSpace{N}) where N = charspacereduce(W)
==(W1::NCharSpace{A}, W2::NCharSpace{B}) where {A, B} = (A == B) && (W1.charmap == W2.charmap) && (W1.units == W2.units)
"""
union(W1, W2) -> NCharSpace{1}
Combines the unit substrings of `W1` and `W2` (must be in reduced form) to create a new characterspace with the union.
"""
function union(W1::NCharSpace{1}, W2::NCharSpace{1}) ::NCharSpace{1}
if W1.unit_length != W2.unit_length
e = ArgumentError("CharSpaces have different substring lengths")
throw(e)
end
return CharSpace([W1.charmap ; W2.charmap])
end
"""
tokenise(s::String, W::AbstractCharSpace) -> Int, Nothing
Returns the token assigned to `s`
Returns `nothing` if `s` is not in `W`
# Examples
```julia-repl
julia> tokenise("C", Alphabet)
3
julia> tokenise("1", Alphabet)
julia> tokenise("01", Decimal^2)
11
```
"""
function tokenise(char::String, W::AbstractCharSpace) ::Union{Nothing, Int}
try return W.tokenmap[char]
catch err
if err isa KeyError
return nothing
else
throw(err)
end
end
end
"""
untokenise(token::Int, W::AbstractCharSpace) -> String
Returns the substring that is assigned `token` in `W`
# Examples
```julia-repl
julia> untokenise(3, Alphabet)
"C"
julia> untokenise(15, Decimal^2)
"41"
```
"""
function untokenise(int::Int, W::AbstractCharSpace) ::String
return W.charmap[int]
end
+(W1::NCharSpace{1}, W2::NCharSpace{1}) ::NCharSpace{1} = union(W1, W2)
function show(io::IO, W::NCharSpace{N}) where N
show(io, W.charmap)
end
function show(io::IO, ::MIME"text/plain", W::NCharSpace{N}) where N
if N == 1
insert = ""
else
insert = " $(N)-char"
end
println(io, "$(W.size)-element", insert, " CharSpace:")
show(io, W.units)
end
"""
checktoken(token::Int, W::AbstractCharSpace)
Throws `ArgumentError` if the token does not exist in `W`, otherwise returns `token`
"""
function checktoken(token::Int, W::AbstractCharSpace)
b = 0 <= token <= W.size
if b
return token
else
e = ArgumentError("Token does not exist in character space")
throw(e)
end
end
##########################################################################################
"""
Txt
A wrapper for strings that handles tokenisation
# Fields
- `raw` stores the latest string version of the text, is updated on construction and `untokenise`
- `cases` is a `BitVector` containing the case of each corresponding `Char` in the `raw`, initialised only on construction
- `charspace` stores the `NCharSpace{N}` of the text
- `tokenised` stores the token values, is updated under most cipher calls
- `frozen` stores a `Dict` of `index => string` for substrings that are not tokenised, so they may be reinserted
"""
mutable struct Txt
raw::String
case_sensitive::Bool
cases::Union{Nothing, BitVector}
charspace::Union{Nothing, AbstractCharSpace} where N
tokenised::Union{Nothing, Vector{Int}}
frozen::Union{Nothing, Dict{Int, String}}
is_tokenised::Bool
end
"""
Txt(text::String, case_sensitive::Bool = false)
Constructs Txt wrapper for `text` in the untokenised state
# Examples
```julia-repl
julia> Txt("A bright sunny day.")
19-character Txt:
"A bright sunny day."
```
"""
Txt(text::String, case_sensitive::Bool = false) = Txt(text, case_sensitive, isuppercase.(collect(text)), nothing, nothing, nothing, false)
Txt(vec::Vector{Int}, cspace::AbstractCharSpace) = Txt("", false, nothing, cspace, vec, nothing, true)
const TokeniseError = ErrorException("Txt is not tokenised")
"""
checktokenised(txt::Txt) -> Vector{Int}
If `txt` is not tokenised (`txt.is_tokenised == false`), throws a `TokeniseError`.
Otherwise, it returns the token vector `txt.tokenised`.
"""
function checktokenised(txt::Txt) ::Vector{Int}
if txt.is_tokenised
return txt.tokenised
end
throw(TokeniseError)
end
length(txt::Txt) ::Int = length(checktokenised(txt))
function ==(T1::Txt, T2::Union{Txt, Vector{Int}}) ::Bool
if !T1.is_tokenised
throw(TokeniseError)
end
if T2 isa Vector{Int}
V2 = T2
elseif !T2.is_tokenised
throw(TokeniseError)
else
V2 = T2.tokenised
end
return T1.tokenised == V2
end
==(T1::Vector{Int}, T2::Txt) ::Bool = ==(T2, T1)
iterate(txt::Txt) = iterate(checktokenised(txt))
iterate(txt::Txt, state::Int) = iterate(txt.tokenised, state)
function getindex(txt::Txt, args) ::Union{Int, Txt}
if !txt.is_tokenised
throw(TokeniseError)
end
if args isa Int
return txt.tokenised[args]
end
t = copy(txt)
t.tokenised = getindex(t.tokenised, args)
return t
end
setindex!(txt::Txt, X, i::Int) = setindex!(checktokenised(txt), X, i)
lastindex(txt::Txt) = lastindex(checktokenised(txt))
function copy(txt::Txt) ::Txt
if txt.is_tokenised
return Txt(txt.raw, txt.case_sensitive, txt.cases, txt.charspace, copy(txt.tokenised), txt.frozen, txt.is_tokenised)
else
return Txt(txt.raw, txt.case_sensitive, txt.cases, txt.charspace, txt.tokenised, txt.frozen, txt.is_tokenised)
end
end
function show(io::IO, txt::Txt)
if txt.is_tokenised
show(io, txt.tokenised)
else
show(io, txt.raw)
end
end
function show(io::IO, ::MIME"text/plain", txt::Txt)
if txt.is_tokenised
if txt.charspace isa NCharSpace
N = findparam(txt.charspace)
if N == 1
insert = ""
else
insert = " ($(N)-gram tokens)"
end
elseif txt.charspace isa DLMSpace
insert = " (Delimited tokens)"
end
println(io, "$(length(txt))-token", txt.case_sensitive ? " case-sensitive" : "", " Txt", insert, ":")
show(io, txt.tokenised)
else
println(io, "$(length(txt.raw))-character", txt.case_sensitive ? " case-sensitive" : "", " Txt:")
show(io, txt.raw)
end
end
function txtnchar!(txt::Txt, n::Int) ::Txt
if !txt.is_tokenised
throw(TokeniseError)
end
if length(txt.tokenised) % n != 0
e = ArgumentError("Txt length is not divisible by $n")
throw(e)
end
W = txt.charspace ^ n
txt.tokenised = [W.linear[ txt.tokenised[i - n + 1:i]... ] for i in n:n:lastindex(txt.tokenised)]
txt.charspace = W
return txt
end
function txtnchar(txt::Txt, n::Int) ::Txt
if !txt.is_tokenised
throw(TokeniseError)
end
if length(txt.tokenised) % n != 0
e = ArgumentError("Txt length is not divisible by $n")
throw(e)
end
W = txt.charspace ^ n
new_tokenised = [W.linear[ txt.tokenised[i - n + 1:i]... ][1] for i in n:n:lastindex(txt.tokenised)]
return Txt(txt.raw, txt.case_sensitive, txt.cases, W, new_tokenised, txt.frozen, txt.is_tokenised)
end
function txtreduce!(txt::Txt) ::Txt
if !txt.is_tokenised
throw(TokeniseError)
end
W = nchar(txt.charspace, 1)
new_tokenised = Vector{Int}()
for i in txt.tokenised
append!(new_tokenised, Tuple(txt.charspace.cartesian[i]))
end
txt.tokenised = new_tokenised
txt.charspace = W
return txt
end
function txtreduce(txt::Txt) ::Txt
if !txt.is_tokenised
throw(TokeniseError)
end
W = nchar(txt.charspace, 1)
new_tokenised = Vector{Int}()
for i in txt.tokenised
append!(new_tokenised, Tuple(txt.charspace.cartesian[i]))
end
return Txt(txt.raw, txt.case_sensitive, txt.cases, W, new_tokenised, txt.frozen, txt.is_tokenised)
end
"""
nchar(txt::Txt, n::Int) -> Txt
Retokenises `txt` with `n` character substrings, assigning a `NCharSpace{N}` of order `N = n`.
If `txt.charspace` has order greater than 1, first the text is reduced (`reduce(txt)`).
# Examples
```julia-repl
julia> example
24-character Txt:
"Literally George Orwell."
julia> tokenise!(example)
21-token Txt:
[12, 9, 20, 5, 18, 1, 12, 12, 25, 7 … 15, 18, 7, 5, 15, 18, 23, 5, 12, 12]
julia> nexample = nchar(example, 3)
7-token Txt (3-gram tokens):
[13064, 447, 16522, 9575, 2878, 15329, 7727]
julia> untokenise(nexample[1], nexample.charspace)
"LIT"
```
"""
function nchar(txt::Txt, n::Int) ::Txt
N = findparam(txt.charspace)
if n == 1
return txtreduce(txt)
elseif N == 1
return txtnchar(txt, n)
else
return txtnchar(txtreduce(txt), n)
end
end
function nchar!(txt::Txt, n::Int)
N = findparam(txt.charspace)
if n == 1
return txtreduce!(txt)
elseif N == 1
return txtnchar!(txt, n)
else
return txtnchar!(txtreduce!(txt), n)
end
end
"""
tokenise(txt::Txt, W::NCharSpace{1} = Alphabet) -> Vector{Int}, Dict{Int, String}
Returns the token vector of `txt`, where raw substrings have been encoded as tokens (integers),
according to the mapping of the characterspace `W` and returns the `frozen` dictionary, which stores the location of substrings that do not get tokenised
"""
function tokenise(txt::Txt, W::NCharSpace{1} = Alphabet) ::Tuple{Vector{Int}, Dict{Int, String}}
if !txt.case_sensitive && join(W.charmap) != uppercase(join(W.charmap))
error("Case-insensitive text cannot be tokenised by a case-sensitive CharSpace")
end
tokenised = Vector{Int}()
text = txt.case_sensitive ? txt.raw : uppercase(txt.raw)
frozen = Dict{Int, String}()
while length(text) > 0
char_index = findfirst(startswith.(text, W.charmap))
if isnothing(char_index) # if text doesn't start with any of W.chars
frozen[length(tokenised)] = get(frozen, length(tokenised), "") * text[1]
i = nextind(text, 1)
text = text[i:end] # shave text by 1
continue
end
push!(tokenised, char_index) # add token to tokenised
text = text[W.unit_length + 1:end] # shave text by 1 unit
end
return tokenised, frozen
end
"""
untokenise(txt::Txt, W::NCharSpace; kwargs) -> String
Returns the concatenation of the substrings for tokens in `txt.tokenised`.
# Keyword Arguments
- `restore_frozen = true` controls whether frozen substrings are reinserted
- `restore_case = true` controls whether the original cases are reapplied
"""
function untokenise(txt::Txt, W::NCharSpace{1}; restore_frozen::Bool = true, restore_case::Bool = true) ::String
if !txt.is_tokenised
throw(TokeniseError)
end
if isnothing(txt.frozen)
restore_frozen = false
end
if isnothing(txt.cases)
restore_case = false
end
if !restore_frozen
raw = ""
n = 0
for token in txt.tokenised
if !(1 <= token <= W.size)
continue
end
char = W.charmap[token]
if restore_case && !txt.case_sensitive
raw *= txt.cases[1 + length(raw)] ? uppercase(char) : lowercase(char)
else
raw *= char
end
end
else
raw = get(txt.frozen, 0, "")
n = 0
for token in txt.tokenised
if !(1 <= token <= W.size)
continue
end
char = W.charmap[token]
if restore_case && !txt.case_sensitive
raw *= txt.cases[1 + length(raw)] ? uppercase(char) : lowercase(char)
else
raw *= char
end
raw *= get(txt.frozen, n += 1, "")
end
end
return raw
end
untokenise(txt::Txt; restore_frozen::Bool = true, restore_case::Bool = true) ::String = untokenise(txt, txt.charspace; restore_frozen = restore_frozen, restore_case = restore_case)
untokenise(txt::Txt, nothing::Nothing; restore_frozen::Bool = true, restore_case::Bool = true) ::String = untokenise(txt, txt.charspace; restore_frozen = restore_frozen, restore_case = restore_case)
untokenise(txt::Txt, W::NCharSpace{N} where N; restore_frozen::Bool = true, restore_case::Bool = true) ::String = untokenise(nchar(txt, 1))
"""
tokenise!(txt::Txt, W::NCharSpace{1} = Alphabet)
Tokenises `txt` in place, updating the `tokenised`, `frozen`, `is_tokenised`, `charspace` fields.
# Examples
```julia-repl
julia> t = Txt("Mr Wood moment")
14-character Txt:
"Mr Wood moment"
julia> tokenise!(t)
12-token Txt:
[13, 18, 23, 15, 15, 4, 13, 15, 13, 5, 14, 20]
julia> t.is_tokenised
true
```
"""
function tokenise!(txt::Txt, W::NCharSpace{1} = Alphabet) ::Txt
tokenised, frozen = tokenise(txt, W)
txt.charspace = W
txt.frozen = frozen
txt.is_tokenised = true
txt.tokenised = tokenised
return txt
end
"""
untokenise!(txt::Txt, W::Union{AbstractCharSpace, Nothing} = nothing; kwargs)
Unokenises `txt` in place, updating the `raw::String` field and `is_tokenised` and clearing `tokenised`
# Examples
```julia-repl
julia> t
12-token Txt:
[13, 18, 23, 15, 15, 4, 13, 15, 13, 5, 14, 20]
julia> untokenise!(t)
14-character Txt:
"Mr Wood moment"
```
"""
function untokenise!(txt::Txt, W::Union{AbstractCharSpace, Nothing} = nothing; restore_frozen::Bool = true, restore_case::Bool = true) ::Txt
raw = untokenise(txt, W, restore_case = restore_case)
txt.charspace = nothing
txt.tokenised = nothing
txt.frozen = nothing
txt.cases = isuppercase.(collect(raw))
txt.is_tokenised = false
txt.raw = raw
return txt
end
Alphabet = CharSpace("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
# Bigram_CharSpace = Alphabet ^ 2
# Trigram_CharSpace = Alphabet ^ 3
# Quadgram_CharSpace = Alphabet ^ 4
Decimal = CharSpace("0123456789")
Hexadecimal = CharSpace("0123456789ABCDEF")
# Polybius_CharSpace = CharSpace("12345") ^ 2
# ADFGX_CharSpace = CharSpace("ADFGX") ^ 2
# ADFGVX_CharSpace = CharSpace("ADFGVX") ^ 2
# ADFCube_CharSpace = CharSpace("ADF") ^ 3
# Binary5_CharSpace = CharSpace("01") ^ 5
# Ternary3_CharSpace = CharSpace("012") ^ 3 | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 4411 | include("charspace.jl")
import Base.push!, Base.iterate, Base.insert!, Base.copy, Base.append!, Base.getindex, Base.setindex!, Base.show, Base.length
abstract type AbstractCipher end
# OVERRIDES:
# preferably override apply(cipher, vector; safety_checks)
# if Txt obj necessary, override apply!(cipher, txt)
function apply!(C::T, txt::Txt) ::Txt where T <: AbstractCipher
if !txt.is_tokenised
error("Cannot apply Cipher to untokenised Txt")
end
txt.tokenised = apply(C, txt.tokenised; safety_checks = txt)
return txt
end
function apply(C::T, txt::Txt) ::Txt where T <: AbstractCipher
new_txt = copy(txt)
apply!(C, new_txt)
return new_txt
end
(C::T where T <: AbstractCipher)(txt::Txt) = apply(C, txt)
invert(C::AbstractCipher) = invert!(deepcopy(C))
# Type unsafe general inverter "lazy"
function switch_invert_tag!(x)
x.inverted = !x.inverted
return x
end
mutable struct Encryption
ciphers::Vector{AbstractCipher}
function Encryption(layers::Vector{T}) where T <: AbstractCipher
new(layers)
end
end
Encryption(args::AbstractCipher...) = Encryption(collect(args))
function copy(E::Encryption)
return Encryption(deepcopy(E.ciphers))
end
length(E::Encryption) = length(E.ciphers)
getindex(E::Encryption, inds) = getindex(E.ciphers, inds)
setindex!(E::Encryption, val::AbstractCipher, inds) = setindex!(E.ciphers, val, inds)
function push!(E::Encryption, C::AbstractCipher)
push!(E.ciphers, C)
return E
end
function insert!(E::Encryption, index::Int, C::AbstractCipher)
insert!(E.ciphers, index, C)
return E
end
function append!(E1::Encryption, E2::Vararg{Encryption, N}) ::Encryption where N
append!(E1.ciphers, (i.ciphers for i in E2)...)
return E1
end
iterate(E::Encryption) = iterate(E.ciphers)
iterate(E::Encryption, state::Int) = iterate(E.ciphers, state)
function show(io::IO, E::Encryption)
show(io, E.ciphers)
end
function show(io::IO, ::MIME"text/plain", E::Encryption)
println(io, "$(length(E))-element Encryption:")
show(io, E.ciphers)
end
(C::AbstractCipher)(D::AbstractCipher) = Encryption([D, C])
(C::AbstractCipher)(E::Encryption) = push!(copy(E), C)
(C::AbstractCipher)(n::Nothing) = C
(E::Encryption)(C::AbstractCipher) = insert!(copy(E), 1, C)
(E1::Encryption)(E2::Encryption) = append!(copy(E2), E1)
function invert!(E::Encryption)
invert!.(E.ciphers)
reverse!(E.ciphers)
return E
end
invert(E::Encryption) = invert!(copy(E))
function apply!(E::Encryption, txt::Txt) ::Txt
if !txt.is_tokenised
error("Cannot apply Encryption to untokenised Txt")
end
for layer in E
apply!(layer, txt)
end
return txt
end
# It is faster to create one copy and mutate than create copies every time
function apply(E::Encryption, txt::Txt) ::Txt
new_txt = copy(txt)
apply!(E, new_txt)
return new_txt
end
(E::Encryption)(txt::Txt) = apply(E, txt)
mutable struct Lambda <: AbstractCipher
func::Function
inv_func::Union{Function, Nothing}
function Lambda(f::Function, inv_func::Union{Function, Nothing} = nothing)
new(f, inv_func)
end
end
function invert!(L::Lambda)
if isnothing(L.inv_func)
error("Inverse function not given, cannot invert")
end
L.func, L.inv_func = L.inv_func, L.func
return L
end
apply(L::Lambda, v::Vector{Int}; safety_checks::Txt) = L.func.(v)
# mutable struct Retokenise <: AbstractCipher
# OldCharSpace::NCharSpace{1}
# NewCharSpace::NCharSpace{1}
# end
# function apply!(R::Retokenise, txt::Txt) ::Txt
# if !txt.is_tokenised
# error("Cannot retokenise untokenised Txt")
# end
# untokenise!(txt, R.OldCharSpace; restore_case = false, restore_frozen = false)
# tokenise!(txt, R.NewCharSpace)
# return txt
# end
# mutable struct Reassign <: AbstractCipher
# OldCharSpace::NCharSpace{1}
# NewCharSpace::NCharSpace{1}
# end
# function invert!(R::Union{Reassign, Retokenise})
# R.OldCharSpace, R.NewCharSpace = R.NewCharSpace, R.OldCharSpace
# return R
# end
# function apply!(C::Reassign, txt::Txt) ::Txt
# if !txt.is_tokenised
# error("Cannot apply Cipher to untokenised Txt")
# end
# if txt.charspace != C.OldCharSpace
# error("Txt character space does not match Reassign input")
# end
# txt.charspace = C.NewCharSpace
# return txt
# end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 7797 | import Base.show
using Combinatorics
mutable struct Columnar <: AbstractCipher
n::Int
permutation::Vector{Int}
remove_nulls::Bool
inverted::Bool
function Columnar(n::Int, permutation::Vector{Int}, remove_nulls::Bool, inverted::Bool)
if length(checkperm(permutation)) != n
error("Permutation must be of the same length as the reshape")
end
new(n, permutation, remove_nulls, inverted)
end
end
Columnar(n::Int, remove_nulls::Bool = false) = Columnar(n, collect(1:n), remove_nulls, false)
Columnar(permutation::Vector{Int}, remove_nulls::Bool = false) = Columnar(length(permutation), permutation, remove_nulls, false)
Columnar(colon::Colon, n::Int) = Tuple(permutations(collect(1:n)))
function switch(S::Columnar, posa::Int, posb::Int) ::Columnar
return Columnar(S.n, switch(S.permutation, posa, posb), S.remove_nulls, S.inverted)
end
function switch!(S::Columnar, posa::Int, posb::Int) ::Columnar
switch!(S.permutation ::Vector{Int}, posa, posb)
return S
end
function shift!(self::Columnar, shift::Int) ::Columnar
self.permutation = circshift(self.permutation, shift)
return self
end
function shift(self::Columnar, shift::Int)
shift!(deepcopy(self), shift)
end
function show(io::IO, T::Columnar)
show(io, T.permutation)
end
function show(io::IO, ::MIME"text/plain", T::Columnar)
if T.inverted
inverse_text = "Inverse "
else
inverse_text = ""
end
println(io, "$(T.n)-column ", inverse_text, "Columnar Transposition:")
show(io, T.permutation)
end
function reinsert_nulls(vect::Vector{Int}, T::Columnar; null_token::Int) ::Vector{Int}
L = length(vect)
overhang = L % T.n
if overhang == 0
return vect
end
vector = copy(vect)
null_ends = T.permutation .> overhang
row_length = ceil(Int, L / T.n)
for (i,j) in enumerate(null_ends)
if j
insert!(vector, i * row_length, null_token)
end
end
return vector
end
function unshape(vect::Vector{Int}, T::Columnar; null_token::Int) ::Array{Int}
new_tokens = reinsert_nulls(vect, T; null_token = null_token)
new_tokens = reshape(new_tokens, (:, T.n))
new_tokens = permutedims(new_tokens)
return new_tokens
end
function apply(T::Columnar, vect::Vector{Int}; safety_checks::Txt, null_token::Int = 0) ::Vector{Int}
if T.inverted # inverse application
new_tokens = unshape(vect, T; null_token = null_token)
inv_permutation = [findfirst(==(i), T.permutation) for i in 1:T.n]
new_tokens = vec(new_tokens[inv_permutation, :])
if T.remove_nulls
return filter!(!=(null_token), new_tokens)
else
return new_tokens
end
else # regular application
new_tokens = safe_reshape_2D(vect, T.n, null_token)
new_tokens = permutedims(new_tokens[T.permutation, :])
new_tokens = vec(new_tokens)
if T.remove_nulls
return filter!(!=(null_token), new_tokens)
else
return new_tokens
end
end
end
invert!(T::Columnar) = switch_invert_tag!(T)
# mutable struct Railfence <: AbstractCipher
# n::Int
# offset::Int
# inverted::Bool
# function Railfence(n::Int, offset::Int = 0, inverted::Bool = false)
# new(n - 1, mod(offset, 2 * n - 2), inverted)
# end
# end
# Railfence(n::Int, colon::Colon) = Tuple(collect(1:2*(n-1)))
# function show(io::IO, T::Railfence)
# if T.inverted
# inverse_text = "Inverse "
# else
# inverse_text = ""
# end
# println(io, "$(T.n + 1)-rail ", inverse_text, "Railfence (offset = $(T.offset))")
# end
# function unshape(vect::Vector{Int}, T::Railfence) ::Array{Int}
# new_tokens = copy(vect)
# L = length(vect) + T.offset
# reinsert_pos = Vector{Int}()
# num_rows = ceil(Int, L / T.n)
# matrix_size = (T.n + 1) * num_rows
# num_trailing = T.n - L % T.n + 1
# append!(reinsert_pos, collect(2:2:num_rows), collect(matrix_size-num_rows+1:2:matrix_size)) # alternating zeros
# append!(reinsert_pos, collect( 1 .+ (0:T.offset-1) * num_rows)) # offset zeros
# if 1 & num_rows == 0
# append!(reinsert_pos, collect( (1:num_trailing) * num_rows))
# else
# append!(reinsert_pos, collect( ((T.n+2-num_trailing):T.n+1) * num_rows))
# end
# sort!(reinsert_pos)
# for i in unique(reinsert_pos)
# insert!(new_tokens, i, 0)
# end
# new_tokens = reshape(new_tokens, (num_rows, :))
# new_tokens = permutedims(new_tokens)
# return new_tokens
# end
# function apply(T::Railfence, vect::Vector{Int}; safety_checks::Txt) ::Vector{Int}
# if T.inverted
# new_tokens = unshape(vect, T)
# for i in 2:2:lastindex(new_tokens, 2)
# new_tokens[:, i] .= reverse(new_tokens[:, i])
# end
# new_tokens = new_tokens[begin:end-1, :]
# return vec(new_tokens)
# else
# new_tokens = copy(vect)
# for _ in 1:T.offset
# insert!(new_tokens, 1, 0)
# end
# new_tokens = safe_reshape_2D(new_tokens, T.n, 0)
# new_tokens = [new_tokens; zeros(Int, 1, size(new_tokens)[2] )]
# for i in 2:2:lastindex(new_tokens, 2)
# new_tokens[:, i] .= reverse(new_tokens[:, i])
# end
# new_tokens = permutedims(new_tokens)
# return vec(new_tokens)
# end
# end
# invert!(T::Union{Columnar, Railfence}) = switch_invert_tag!(T)
# mutable struct Amsco1 <: AbstractCipher
# blocks::Vector{Int}
# permutation::Vector{Int}
# inverted::Bool
# Amsco1(blocks::Vector{Int}, permutation::Vector{Int}, inverted::Bool = false) = new(blocks, permutation, inverted)
# end
# mutable struct Amsco2 <: AbstractCipher
# blocks::Vector{Int}
# permutation::Vector{Int}
# shift::Int
# inverted::Bool
# function Amsco2(blocks::Vector{Int}, permutation::Vector{Int}, shift::Int = 1, inverted::Bool = false)
# if length(permutation) != length(blocks)
# error("Amsco2 requires a permutation with the same period as the number of block lengths")
# end
# new(blocks, permutation, shift, inverted)
# end
# end
# invert!(A::Union{Amsco1, Amsco2}) = switch_invert_tag!(A)
# function apply(A::Union{Amsco1, Amsco2}, v::Vector{Int}; safety_checks::Txt) ::Vector{Int}
# block_total = sum(A.blocks)
# period = length(A.permutation)
# rows = ceil(Int, length(v) / block_total * length(A.blocks) / period )
# mat = fill(Vector{Int}(), rows, period)
# u = copy(v)
# r = 1
# c = 0
# if !A.inverted
# while length(u) > 0
# c += 1
# if c > period
# c = 1
# r += 1
# end
# if A isa Amsco1
# i = A.blocks[((r - 1) * period + c - 1) % length(A.blocks) + 1]
# else
# i = A.blocks[(c - 1 + A.shift*(r-1)) % period + 1]
# end
# mat[r, c] = length(u) ≥ i ? u[1:i] : u[1:end]
# u = u[i+1:end]
# end
# mat = mat[:, A.permutation]
# return vcat(mat...)
# else
# u = copy(v)
# for c in A.permutation
# for r in 1:rows
# if A isa Amsco1
# i = A.blocks[((r - 1) * period + c - 1) % length(A.blocks) + 1]
# else
# i = A.blocks[(c - 1 + A.shift*(r-1)) % period + 1]
# end
# mat[r, c] = length(u) ≥ i ? u[1:i] : u[1:end]
# u = u[i+1:end]
# end
# end
# return vcat(permutedims(mat, (2, 1))...)
# end
# end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 4743 | include("genetics.jl")
import IterTools.product
############# ALL on ALPHABETIC CharSpace (to English)
# Returns element of input Vector that maximises x -> fitness(f(x))
function optimise(f::Function, inputs::AbstractVector, fitness::Function)
scores = [fitness(f(i)) for i in inputs]
return inputs[argmax(scores)]
end
function optimise!(f::Function, setinput!::Function, obj, inputs::AbstractVector, fitness::Function)
obj_copy = deepcopy(obj)
scores = Vector{Float64}(undef, length(inputs))
for (i, input) in enumerate(inputs)
setinput!(obj, input)
scores[i] = fitness(f(obj))
end
setinput!(obj_copy, inputs[argmax(scores)])
return obj_copy
end
function optimise(f::Symbol, inputs::AbstractArray, fitness::Symbol)
scores = [eval(:( $fitness($f($i...)) )) for i in inputs]
return inputs[argmax(scores)]
end
macro bruteforce(func_call::Expr, fitness::Symbol)
arguments = func_call.args[2:end]
f = func_call.args[1]
L = length(arguments)
isblank = isequal.(arguments, :(:))
test_sets = Vector(undef, L)
for arg in eachindex(arguments)
if isblank[arg]
param_extraction_args = deepcopy(arguments)
param_extraction_args[arg] = Colon()
try
test_sets[arg] = eval(:( $f($param_extraction_args...) ))
catch err
error("This function does not have parameter listing defined for this argument")
end
else
test_sets[arg] = (arguments[arg],)
end
end
return optimise(f, collect(product(test_sets...)), fitness)
end
function crack_Caesar(txt::Txt) ::Substitution
return invert!(optimise(x -> apply(x, txt), [Caesar(i, 26) for i in 1:26], x -> orthodot(x)))
end
function crack_Affine(txt::Txt) ::Substitution
coprimes = collect(1:26)
filter!(x -> gcd(26, x) == 1, coprimes)
return invert!(optimise(x -> apply(x, txt), [Affine(a, b, 26) for a in coprimes for b in 1:26], x -> orthodot(x)))
end
function crack_Substitution_genetic(txt::Txt, spawns::Int = 10, gen::Int = 350) # token vector // number of mutatated subs spawned each generation // no of generations
start = frequency_matched_substitution(txt, monogram_freq)
invert!(start)
return evolve_silent(x -> apply(x, txt), mutate, start, spawns, gen, quadgramlog)
end
function crack_Vigenere(txt::Txt; upper_period_lim::Int = 20, critical_sigma::Float64 = 0.15, pass_num::Int = 2, silent::Bool = true) ::PeriodicSubstitution
period = find_period(txt, upper_period_lim, critical_sigma; silent = silent)
if isnothing(period)
error("No period / period could not be found by fw_stdev (if periodicity is certain, try increasing tolerance)")
end
vigenere = Vigenere(period, 26) # Blank Vigenere
for pass in 1:pass_num
for i in 1:period
vigenere = optimise!(x -> apply(x::AbstractCipher, txt), (x, y) -> setindex!(x, y, i), vigenere, [Caesar(shift, 26) for shift in 1:26], quadgramlog)
end
end
invert!.(vigenere)
return vigenere
end
coprimes = collect(1:26)
filter!(x -> gcd(26, x) == 1, coprimes)
function crack_Periodic_Affine(txt::Txt; upper_period_lim::Int = 20, critical_sigma::Float64 = 0.15, pass_num::Int = 2, silent::Bool = true) ::PeriodicSubstitution
period = find_period(txt, upper_period_lim, critical_sigma; silent = silent)
if isnothing(period)
error("No period / period could not be found by fw_stdev (if periodicity is certain, try increasing tolerance)")
end
p_affine = Periodic_Affine(period, 26)
for pass in 1:pass_num
for i in 1:period
p_affine = optimise!(x -> apply(x::AbstractCipher, txt), (x, y) -> setindex!(x, y, i), p_affine, [Affine(b, a, 26) for a in 1:26 for b in coprimes], quadgramlog)
end
end
invert!.(p_affine)
return p_affine
end
function crack_Columnar(txt::Txt, n::Int)
T = reshape(txt.tokenised, (:, n))
T = permutedims(T)
follow = Matrix{Float64}(undef, n, n)
########### EACHINDEX IS BETTER
for i in 1:n
for j in 1:n
follow[i, j] = sum([ bigram_scores[i,j] for (i,j) in zip(T[i, :], T[j, :])])
end
end
permutation = Vector{Int}(undef, n)
permutation[1] = argmin(vec(maximum(follow; dims = 2))) # finds which column comes last
follow[permutation[1], :] .= -Inf
show(follow[:, 1])
for i in 2:n
permutation[i] = argmax(vec(follow[:, i-1]))
end
reverse!(permutation)
return permutation
end
function crack_Columnar(txt::Txt, upperlim = 20)
n = divisors(length(txt))
i = 1
while n[i] <= upperlim
end
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 4953 | import Base.==, Base.length, Base.+, Base.*, Base.^, Base.iterate, Base.getindex, Base.setindex!, Base.show, Base.lastindex, Base.copy
"""
DLMSpace
A characterspace, encoding substrings `::String` as tokens `::Int`.
`dlm` is the delimiter, separating substrings to be tokenised.
# Fields
- `charmap::Vector{String}` maps tokens to substrings `W.charmap[token]` gives `substring`
- `tokenmap::Dict{String, Int}` maps substrings to assigned tokens `W.tokenmap[substring]` gives `token`
- `dlm` is the delimiting string/character
- `size::Int` is the number of encoded substrings / the number of tokens
- `tokens::Vector{Int}` stores all of the encoding tokens, used for iteration
"""
struct DLMSpace <: AbstractCharSpace
charmap::Vector{String}
tokenmap::Dict{String, Int}
dlm
size::Int
tokens::Vector{Int}
function DLMSpace(substrings::Vector{SubString{String}}, dlm)
charmap = unique(substrings)
tokenmap = Dict{String, Int}()
for (i, j) in enumerate(charmap)
tokenmap[j] = i
end
size = length(charmap)
new(charmap, tokenmap, dlm, size, collect(1:size))
end
end
############################################################################################
function CharSpace(text::String, dlm, case_sensitive = false)
if case_sensitive
return DLMSpace(split(text, dlm), dlm)
else
return DLMSpace(split(uppercase(text), dlm), dlm)
end
end
==(W1::DLMSpace, W2::DLMSpace) = (W1.charmap == W2.charmap)
"""
union(W1, W2) -> DLMSpace
Combines the substrings of `W1` and `W2` to create a new characterspace with the union.
"""
function union(W1::DLMSpace, W2::DLMSpace) ::DLMSpace
if W1.dlm != W2.dlm
e = ArgumentError("DLMSpaces have different delimiters")
throw(e)
end
return DLMSpace([W1.charmap ; W2.charmap])
end
+(W1::DLMSpace, W2::DLMSpace) ::DLMSpace = union(W1, W2)
function show(io::IO, W::DLMSpace)
show(io, W.charmap)
end
function show(io::IO, ::MIME"text/plain", W::DLMSpace)
println(io, "$(W.size)-element Delimited (\"$(W.dlm)\") CharSpace:")
show(io, W.charmap)
end
##########################################################################################
function apply_case(cases::BitVector, substr::String) ::String
return String([case ? uppercase(char) : lowercase(char) for (case, char) in zip(cases, substr)])
end
"""
tokenise(txt::Txt, W::DLMSpace) -> Vector{Int}, Dict{Int, String}
Returns the token vector of `txt`, where raw substrings have been encoded as tokens (integers),
according to the mapping of the characterspace `W` and returns the `frozen` dictionary, which stores the location of substrings that do not get tokenised
"""
function tokenise(txt::Txt, W::DLMSpace) ::Tuple{Vector{Int}, Dict{Int, String}}
if !txt.case_sensitive && join(W.charmap) != uppercase(join(W.charmap))
error("Case-insensitive text cannot be tokenised by a case-sensitive CharSpace")
end
tokenised = Vector{Int}()
text = txt.case_sensitive ? txt.raw : uppercase(txt.raw)
frozen = Dict{Int, String}()
for substr in split(text, W.dlm)
if substr in W.charmap
push!(tokenised, W.tokenmap[substr])
frozen[length(tokenised)] = " "
else
frozen[length(tokenised)] = substr * " "
end
end
return tokenised, frozen
end
"""
untokenise(txt::Txt, W::DLMSpace; kwargs) -> String
Returns the concatenation of the substrings for tokens in `txt.tokenised`.
# Keyword Arguments
- `restore_frozen = true` controls whether frozen substrings are reinserted
- `restore_case = true` controls whether the original cases are reapplied
"""
function untokenise(txt::Txt, W::DLMSpace; restore_frozen::Bool = true, restore_case::Bool = true) ::String
if !txt.is_tokenised
throw(TokeniseError)
end
raw = get(txt.frozen, 0, "")
n = 0
i = 1
for token in txt.tokenised
if !(1 <= token <= W.size)
continue
end
char = W.charmap[token]
L = length(char)
if restore_case && !txt.case_sensitive
raw *= apply_case(txt.cases[i:i + L - 1], char)
else
raw *= char
end
raw *= get(txt.frozen, n += 1, "")
i += L
end
return raw
end
"""
tokenise!(txt::Txt, dlm)
Tokenises `txt` in place, updating the `tokenised`, `frozen`, `is_tokenised`, `charspace` fields.
# Examples
```julia-repl
julia> t = Txt("Mr Wood moment")
14-character Txt:
"Mr Wood moment"
julia> tokenise!(t)
12-token Txt (Delimited Chars):
[1, 2, 3]
julia> t.is_tokenised
true
```
"""
function tokenise!(txt::Txt, dlm) ::Txt
W = CharSpace(txt.raw, dlm, txt.case_sensitive)
tokenised, frozen = tokenise(txt, W)
txt.charspace = W
txt.frozen = frozen
txt.is_tokenised = true
txt.tokenised = tokenised
return txt
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 1493 | # Returns elemnt of input Vector that maximises x -> fitness(f(x)), with a floor item that is returned if no item has higher fitness
function optimise_floor(f::Function, floor, inputs::AbstractVector, fitness::Function)
floorscore = fitness(f(floor))
scores = [fitness(f(i)) for i in inputs]
p = inputs[argmax(scores)] # find item with highest fitness
dF = maximum(scores) - floorscore # find the item's fitness
return dF > 0 ? p : floor
end
# Mutates parent, spawning 'spawns' no. of mutated children, over 'generations' generations.
function evolve_silent(f::Function, mutate::Function, parent, spawns::Int, generations::Int, fitness::Function)
scores = Float64[]
for i in 1:generations
parent = optimise_floor(f, parent, [mutate(parent) for i in 1:spawns], fitness)
append!(scores, fitness(f(parent)))
end
return parent, scores # genetically evolved item // vector of scores over generations
end
# evolve_silent() but it stops if fitness exceeds the fitness_threshold
function evolve_until(f::Function, mutate::Function, parent, spawns::Int, fitness_threshold, max_runs, fitness::Function)
scores = [fitness(f(parent))]
i = 0
while scores[end] < fitness_threshold
i += 1
if i > max_runs
return parent, scores
end
parent = optimise_floor(f, parent, [mutate(parent) for i in 1:spawns], fitness)
append!(scores, fitness(f(parent)))
end
return parent, scores
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 1998 | import Base.show
mutable struct MatrixTransposition <: AbstractCipher
n::Int
remove_nulls::Bool
inverted::Bool
function MatrixTransposition(n::Int, remove_nulls::Bool = false)
new(n, remove_nulls, false)
end
end
invert!(T::MatrixTransposition) = switch_invert_tag!(T)
function reinsert_nulls(vect::Vector{Int}, T::MatrixTransposition; null_token::Int = 0) ::Vector{Int}
L = length(vect)
overhang = L % T.n
if overhang == 0
return vect
end
null_count = T.n - overhang
col_length = Int(ceil(L / T.n))
index = overhang * col_length
vector = copy(vect)
for _ in 1:null_count
index += col_length
insert!(vector, index, null_token)
end
return vector
end
function unshape(vect::Vector{Int}, T::MatrixTransposition) ::Matrix{Int}
new_tokens = reinsert_nulls(vect, T)
new_tokens = reshape(new_tokens, (:, T.n))
return new_tokens
end
function show(io::IO, T::MatrixTransposition)
println(io, "MatrixTransposition($(T.n))")
end
function show(io::IO, ::MIME"text/plain", T::MatrixTransposition)
if T.inverted
inverse_text = " (padding mode inverted)"
else
inverse_text = ""
end
println(io, "$(T.n)-column Matrix Transposition", inverse_text)
end
function apply(T::MatrixTransposition, vect::Vector{Int}; safety_checks::Txt, null_token::Int = 0) ::Vector{Int}
if T.inverted # inverse application
new_tokens = unshape(vect, T)
new_tokens = vec(permutedims(new_tokens))
if T.remove_nulls
return filter!(!=(null_token), new_tokens)
else
return new_tokens
end
else # regular application
new_tokens = safe_reshape_2D(vect, T.n, null_token)
new_tokens = permutedims(new_tokens)
new_tokens = vec(new_tokens)
if T.remove_nulls
return filter!(!=(null_token), new_tokens)
else
return new_tokens
end
end
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 3409 | include("substitution.jl")
import Base.setindex!
############ MULTI Substitutions FRAMEWORK ################################################
mutable struct PeriodicSubstitution <: AbstractCipher
subs::Vector{Substitution}
length::Int
function PeriodicSubstitution(substitutions::Vector{Substitution})
size_one = substitutions[1].size
if any([i.size != size_one for i in substitutions])
error("All Substitutions must be of the same size")
end
new(substitutions, length(substitutions))
end
end
PeriodicSubstitution(substitutions::Substitution...) = PeriodicSubstitution(substitutions)
length(S::PeriodicSubstitution) = S.length
getindex(S::PeriodicSubstitution, inds) = getindex(S.subs, inds)
iterate(S::PeriodicSubstitution) = iterate(S.subs)
iterate(S::PeriodicSubstitution, i::Integer) = iterate(S.subs, i)
setindex!(S::PeriodicSubstitution, val::Substitution, ind::Int) = setindex!(S.subs, val, ind)
==(A::PeriodicSubstitution, B::PeriodicSubstitution) = ==(A.subs, B.subs)
# Applies MultiSubstitution to Integer Vectors
function apply(S::PeriodicSubstitution, vector::Vector{Int}; safety_checks::Txt) ::Vector{Int}
vect = Vector{Int}(undef, length(vector))
for (i, j) in enumerate(S)
vect[i:S.length:end] .= apply(j, vector[i:S.length:end]; safety_checks = safety_checks)
end
return vect
end
invert!(S::PeriodicSubstitution) = invert!.(S)
function show(io::IO, S::PeriodicSubstitution)
if S.length > 8
for i in 1:8
show(io, S[i])
print("\n")
end
for _ in 1:3
print(".\n")
end
else
for sub in S.subs
show(io, sub)
print("\n")
end
end
end
function show(io::IO, ::MIME"text/plain", S::PeriodicSubstitution)
println(io, "$(S[1].size)-element $(S.length)-Periodic Substitution:")
if S.length > 8
for i in 1:8
show(io, S[i])
print("\n")
end
for _ in 1:3
print(".\n")
end
else
for sub in S.subs
show(io, sub)
print("\n")
end
end
end
# Presets
function Vigenere(vect::Vector{Int}, size::Union{Int, NCharSpace{N}} where N) ::PeriodicSubstitution
return PeriodicSubstitution([Caesar(i, size) for i in vect])
end
Vigenere(num::Int, size::Union{Int, NCharSpace{N}} where N) = Vigenere(zeros(Int, num), size)
Vigenere(vect::Vector{String}, W::NCharSpace{1}) ::PeriodicSubstitution = Vigenere(tokenise.(vect, Ref(W)) .- 1, W) # Subtracting one to standardise for 0-based indexing
Vigenere(vect::Vector{Char}, W::NCharSpace{1}) ::PeriodicSubstitution = Vigenere(string.(vect), W)
Vigenere(string::String, W::NCharSpace{1}) ::PeriodicSubstitution = Vigenere(collect(string), W)
function Periodic_Affine(vect::Vector{Tuple{Int, Int}}, size::Union{Int, NCharSpace{N}} where N) ::PeriodicSubstitution
return PeriodicSubstitution([Affine(a, b, size) for (a, b) in vect])
end
Periodic_Affine(vecta::Vector{Int}, vectb::Vector{Int}, size::Union{Int, NCharSpace{N}} where N) ::PeriodicSubstitution = length(vecta) == length(vectb) ? Periodic_Affine(collect(zip(vecta, vectb)), size) : error("Parameter vectors must have same length")
Periodic_Affine(num::Int, size::Union{Int, NCharSpace{N}} where N) = Periodic_Affine(ones(Int, num), zeros(Int, num), size) | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 3535 | import Base.show
using Combinatorics
mutable struct Permutation <: AbstractCipher
n::Int
permutation::Vector{Int}
remove_nulls::Bool
inverted::Bool
# UNSAFE DO NOT USE
function Permutation(n::Int, permutation::Vector{Int}, remove_nulls::Bool, inverted::Bool)
if length(permutation) != n
e = ArgumentError("Permutation does not match provided length, use safe constructor")
throw(e)
end
new(n, checkperm(permutation), remove_nulls, inverted)
end
# UNSAFE DO NOT USE
end
Permutation(permutation::Vector{Int}, remove_nulls::Bool = false) = Permutation(length(permutation), permutation, remove_nulls, false)
Permutation(n::Int, remove_nulls::Bool = false) = Permutation(n, collect(1:n), remove_nulls, false)
invPermutation(permutation::Vector{Int}, remove_nulls::Bool = false) = Permutation(length(permutation), permutation, remove_nulls, true)
invPermutation(n::Int, remove_nulls::Bool = false) = Permutation(n, collect(1:n), remove_nulls, true)
Permutation(colon::Colon, n::Int) = Tuple(permutations(collect(1:n)))
function invert!(P::Permutation)
switch_invert_tag!(P)
P.permutation = invperm(P.permutation)
return P
end
function switch(P::Permutation, posa::Int, posb::Int) ::Permutation
return Permutation(P.n, switch(P.permutation, posa, posb), P.remove_nulls, P.inverted)
end
function switch!(P::Permutation, posa::Int, posb::Int) ::Permutation
switch!(P.permutation, posa, posb)
return P
end
function splice!(P::Permutation, ind::Int, start::Int, finish::Int) ::Permutation
splice!(P.permutation, ind, start, finish)
return P
end
function splice(P::Permutation, ind::Int, start::Int, finish::Int) ::Permutation
return Permutation(P.n, splice(P.permutation, ind, start, finish), P.remove_nulls, P.inverted)
end
function show(io::IO, T::Permutation)
show(io, T.permutation)
end
function show(io::IO, ::MIME"text/plain", T::Permutation)
if T.inverted
inverse_text = " (padding mode inverted)"
else
inverse_text = ""
end
println(io, "$(T.n)-column Permutation", inverse_text, ":")
show(io, T.permutation)
end
function reinsert_nulls(vect::Vector{Int}, T::Permutation; null_token::Int = 0) ::Vector{Int}
L = length(vect)
overhang = L % T.n
if overhang == 0
return vect
end
vector = copy(vect)
null_ends = T.permutation .> overhang
num_full_rows = floor(Int, L / T.n)
for (i,j) in enumerate(null_ends)
if j
insert!(vector, num_full_rows * T.n + i, null_token)
end
end
return vector
end
function unshape(vect::Vector{Int}, T::Permutation) ::Matrix{Int}
new_tokens = reinsert_nulls(vect, T)
new_tokens = reshape(new_tokens, (T.n, :))
return new_tokens
end
function apply(T::Permutation, vect::Vector{Int}; safety_checks::Txt, null_token::Int = 0) ::Vector{Int}
if T.inverted # inverse application
new_tokens = unshape(vect, T)
new_tokens = vec(new_tokens[T.permutation, :])
if T.remove_nulls
return filter!(!=(null_token), new_tokens)
else
return new_tokens
end
else # regular application
new_tokens = safe_reshape_2D(vect, T.n, null_token)
new_tokens = new_tokens[T.permutation, :]
new_tokens = vec(new_tokens)
if T.remove_nulls
return filter!(!=(null_token), new_tokens)
else
return new_tokens
end
end
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 53742 | module TxtSamples
export orwell, veryorwell, frandom, random, adfgvx, jmacro, blanc, help
using RxCiphers
function help()
println("TxtSamples available:")
println("orwell - roughly 3000 character excerpt from George Orwell's 1984\n")
println("veryorwell - roughly 35000 character excerpt from George Orwell's 1984\n")
println("frandom - 2400 character string of capital letters drawn randomly from the English monogram frequency distribution\n")
println("random - 2400 character string of capital letters drawn randomly from a uniform distribution\n")
println("adfgvx - roughly 3000 character excerpt from http://www.practicalcryptography.com/ciphers/adfgvx-cipher/ Practical Cryptography's article on the ADFGVX cipher\n")
println("jmacro - roughly 3900 character excerpt from https://jkrumbiegel.com/pages/2021-06-07-macros-for-beginners/ an article on macros in Julia\n")
println("blanc - roughly 2200 character compilation of Benoit Blanc's voice lines in Glass Onion: A Knives Out Mystery")
end
orwell = Txt("It was a bright cold day in April, and the clocks were striking thirteen. Winston Smith, his chin nuzzled into his breast in an effort to escape the vile wind, slipped quickly through the glass doors of Victory Mansions, though not quickly enough to prevent a swirl of gritty dust from entering along with him.The hallway smelt of boiled cabbage and old rag mats. At one end of it a coloured poster, too large for indoor display, had been tacked to the wall. It depicted simply an enormous face, more than a metre wide: the face of a man of about forty-five, with a heavy black moustache and ruggedly handsome features. Winston made for the stairs. It was no use trying the lift. Even at the best of times it was seldom working, and at present the electric current
was cut off during daylight hours. It was part of the economy drive in preparation for Hate Week. The flat was seven flights up, and Winston, who was thirty-nine
and had a varicose ulcer above his right ankle, went slowly, resting several times on the way. On each landing, opposite the lift-shaft, the poster with the enormous face gazed from the wall. It was one of those pictures which are so contrived that the eyes follow you about when you move. BIG BROTHER IS WATCHING YOU, the caption beneath it ran.Inside the flat a fruity voice was reading out a list of figures which had something to do with the production of pig-iron. The voice came from an oblong metal plaque like a dulled mirror which formed part of the surface
of the right-hand wall. Winston turned a switch and the voice sank somewhat, though the words were still distinguishable. The instrument (the telescreen, it was called) could be dimmed, but there was no way of shutting it off completely. He moved over to the window: a smallish, frail figure, the meagreness of his body merely emphasized by the blue overalls which were the uniform of the party. His hair
was very fair, his face naturally sanguine, his skin roughened by coarse soap and blunt razor blades and the cold of the winter that had just ended.Outside, even
through the shut window-pane, the world looked cold. Down in the street little eddies of wind were whirling dust and torn paper into spirals, and though the sun was shining and the sky a harsh blue, there seemed to be no colour in anything, except the posters that were plastered everywhere. The blackmoustachio'd face gazed down from every commanding corner. There was one on the house-front immediately
opposite. BIG BROTHER IS WATCHING YOU, the caption said, while the dark eyes looked deep into Winston's own. Down at streetlevel another poster, torn at one corner, flapped fitfully in the wind, alternately covering and uncovering the single word INGSOC. In the far distance a helicopter skimmed down between the roofs, hovered for an instant like a bluebottle, and darted away again with a curving flight. It was the police patrol, snooping into people's windows. The patrols did not matter, however. Only the Thought Police mattered.")
frandom = Txt("TECUUEYDIYANPHEOTHMRRTOIOASPRLIOLYTEHTOIYTSAUSFORLASWDTNEIMEDIENIMERGRTGBETRLPWIOMSOAMEEOYECHEATDFRTEEWUTBYUOTOIIYGONFFWINRASRAILNRSRIHLYKCRUAOSLUECIESEWTSURCECAARDFFEMOWHDHMYICDOIUMAATWAEHSGATYLEIEROHCELEEGIOELMEEOAEOLHODEWNHAUPSWCNHTABRNZFHDOIBAUTRTNPOLRPYIOTORRBROHAERECLSWISIRHYNPONADEATROENASSMDTNMHMEGWROGOTSSNELEASLRDWAEWNPLTIIIATGOPRKITSMIHRRLHNORHARTTDIRAAEHREHPERITTSNNLKEMDPOPDRHNNHTEARJELEAONTYEELTDNTENSTATAHTDEMNWATLRCTAORHGTLSSHMMIIEONTTAHAUIOEDNONYONTBTRONMOCMTIHAEWBSEWDHIHPTSWIOOOOLYICHEEEIRGOESSUNIEEHODITGETCZRNEAHAATRINNAMTUAEOMNMGNALYSAISHTTSIADNAMAHFEETDEIHRSAELEPOFTEAFSSERPISNSFEWFEDINUEARRUAKANHELEELCTWUTHOHEEUUSOHTSEBPEAREETNMFIWECONRSLIKTERSTLCMCEHETKCYIEEEROAHNNTHFHAEGEGHTSWTENEEEMSAVNVOOAEASCPEWFHRSQOOGEIATSFCJNTCAFAOCSIONARIJYWADNLTBEONERLADSATRAHREMSNDSAHDRNAEIITEMSTOAESHMBAEUEAETGORHEDWDYDDDEEISIOEEYEOSNTJCGRFEOARNLMLECTRLHROLNENUNOTONELAUTHAIAOLOUIIZWHNSRRIHESCIIHTNLRESOTOWRNRDEEVOHIIIEWTWNITYROANEDLOTHTERCOTEERAEOEEONELEIDATGIATROTRCAUEEREIRLFEAOSAYHAHEAPPOTEOOHEIAPETAONEODECDADEEUCSEOONEESAHHSSUNTTTTFENUNONPMTNETNEOADOOPIIGASHTECEEGNDSIECHUKLEEAEBNKYCIEEEROADAFDWTSMHWHENTCMLHNDEECLOIHIFSKSLHAHNUEINLSNIEAOHINOHHOHIYOHATYIRTDMYANECAWPTRPEESFYPNSSAESEEGLIIALLDOLEEANHEOASADORLIUBTLTSLNCETHFNDTUEEEENDNNUAISHOAEEOOAMOANAAGUAODDCKIWIEQTDHDNWEENFOTAWELECLITROTELNBHAUONWEHLHLYUEUGCSSNROVNONLRESCERRHHOSGCHWAEAWOSSDOWBSISIHELCORERCECTRURAISDSIAIHPRNEOADIEIFSBDNSOBOPSPOGEPTAOEITRSNUSBIIOERFBEEKEDHEYMIOTVSLIEARSTYRWTEIDGIGROEHBVSDASIPAUEAOISNSTYTLOTTTRWWTFFGTFOSAPOIAMAALGEHFFLSYNTGTONEYHIRMTUDLENCSVEISTTCTNDLSNCIDYHMAUGNTEXHNSWEIKTTETNGHCEETLTCTMTENDADAHLLOFRCPFRGOEFUUOMYNVAXRIEAHSOEIREINRAFDTSUMCACRWYSLTLSALNCSDNTGSHRXDTIHEEETHHADEOVNEDORHYHEESEMTEAROEAOSETRIRFTEFMHISEANHTHANWBCMITNICAREISHNAITSASPSREISHTNAWPOENAEELNTNEUONLEGIAHIACHETTTAEEIARSIUOROCFIIHHEADAOULEMTSTFANDMTARNBISRRTEAUUISTSRLIYIECALTNNSAYEPODTLFSONOTSETAREITNDGLRSDHAEBHLHNTNRCITAAISOHTUTNOCISFNLHEIINTTRGLCPRBNLFEEEIDNNUEYELROBTFVHHIASSAFOKPNUOUTNWAAGIOPLSTLNYTSWNDCSTIASWLEAFNIEEETGCETWARTEMKOMEGALHRGDSSAYETFYPAROENTHSAWMNSHIDSLDMSRTLKAWIEUYTHANOTEPERODOUVEROSTTMOGITGTTOOUEWAVELDROEHBCINRSNETRROHNTNARGSDSHLWAETWWRRWLNUYINWNOTREYMSBEATUWTLLANNLWIIIESENGDAAGLFRESWNSLSONSODIHNYTNRNOTMTKDEEPRECGSEWTRSKFNITIOEGNCTWHDTBSCNRUEPHTTHCNEEMRCDPDHTHSNTHTESGORABSEHTPFSSMFNANARPMIRGWTHEODOVAIOUYTOPPAHCRATYLYOCUERJSATEOTAWESETTAWIKNJHEOSLTAHXHETSRLOPSLYNRBEWCXHLEEHPUPDVDGPHROHPUNAGMRRWLRTSTUC")
random = Txt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
adfgvx = Txt("In cryptography, the ADFGVX cipher was a field cipher used by the German Army during World War I. ADFGVX was in fact an extension of an earlier cipher called the ADFGX cipher. Invented by Colonel Fritz Nebel and introduced in March 1918, the
cipher was a fractionating transposition cipher which combined a modified Polybius square with a single columnar transposition. The cipher is named after the six
possible letters used in the ciphertext: A, D, F, G, V and X. These letters were
chosen deliberately because they sound very different from each other when transmitted via morse code. The intention was to reduce the possibility of operator error.From Kahn's 'The CodeBreakers':It was no less clear to the Allies that Germany planned to launch a climactic offensive in the spring. There were many signs—the new cipher itself was one. The question was: Where and when would the actual blow fall? The German high command, recognizing the incalculable military value of surprise, shrouded its plans in the tightest secrecy. Artillery was brought up in
concealment; feints were flung out here and there along the entire front to keep
the Allies off balance; the ADFGVX cipher, which had reportedly been chosen from
among many candidates by a conference of German cipher specialists, constituted an element in this overall security, as did the new Schliis-selheft. The Allies bent every effort and tapped every source of information to find out the time and place of the real assault.Georges Painvin was the French cryptanalyst tasked with
cracking the ADFGVX cipher. The intelligence he provided was vital to the French
war effort, particularly in saving Paris in 1918:At midnight on June 9 the front
from Montdidier to Compiegne erupted in a fierce, pelting hurricane of high-explosive, shrapnel, and gas shells. For three hours a German artillery concentration
that averaged one gun for no more than ten yards of front poured a continual stream of fire onto the French positions—and Ludendorff's urgent demand for ammunition became clear. But this time, for the first time since Ludendorff began his stupendous series of triumphs, there was no surprise. Painvin's manna had saved the French.The cipher presents several difficulties to the cryptanalyst. Ordinarily when breaking columnar transposition ciphers, anagramming is used to determine the
key. Once the substitution step is introduced, however, this approach becomes impossible. The letter frequencies are also modified due to the fractionating nature of the cipher, which adds further difficulties.The French cryptanalyst Painvin,
who first broke the ADVGVX cipher, only managed to break it in specific circumstances. The exact circumstances he needed only occurred on days with very heavy traffic. His techniques are described in Friedman's book 'Military Cryptanalysis - Part IV', along with several other more general techniques.Cryptanalysis of the ADFGVX cipher is also discussed in a paper called Cryptanalysis of ADFGVX encipherment systems in Proceedings of CRYPTO 84 on Advances in cryptology. Unfotunately this paper is not available to the public, however an extended abstract is available.")
jmacro = Txt("Macros change existing source code or generate entirely new code. They are not some kind of more powerful function that unlocks secret abilities of Julia, they are just a way to automatically write code that you could have written out by hand anyway. Theres just the question whether writing that code by hand is practical, not if its possible. Often, we can save users a lot of work, by hiding boilerplate code they would otherwise need to write inside our macro.Still, its good advice, especially for beginners, to think hard if macros are the right tool for thej
ob, or if run-of-the-mill functions serve the same purpose. Often, functions are
preferable because macro magic puts a cognitive burden on the user, it makes it harder to reason about what code does. Before understanding the code, they have to understand the transformation that the macro is doing, which often goes hand in
hand with non-standard syntax. That is, unless they are ok with their code having unintended consequences.Some of the magic of macros derives from the fact that they dont just generate some predefined code, they rather take the code they are applied to and transform it in useful ways. Variable names are one of the fundamental mechanisms by which we make code understandable for humans. In principle, you could replace every identifier in a working piece of code with something random, and it would still work.The computer doesnt care about the names, only humans do. But functions run after the code has been transformed into lower-level representations, and names are lost at that point.For example, in this code snippet, there is no way for the author of the function to know what the user named their variable. The function just receives a value, and as far as it is concerned, that value is namedLets look at our macro in more detail. Even though its short, it has a few interesting aspects to it.First of all, a macro runs before any code is executed. Therefore, you never have access to any runtime values in a macro. Thats something that trips many beginners up, but is crucial to understand. All the logic in the macro has to happen only using the information you can get from the expressions that the macro is applied to.The reason this happens is that macros do often need to reference values from whatever module they were defined in. For example, to add a helper function that also lives in that module to the users code. Any variable name used in the created expression is looked up in the macros parent module by default.The other reason is that it is potentially dangerous to just change or create variables in user space in a macro that knows nothing about whats going on there.Imagine the writer of the macro and the user as two people who know nothing about each other. They only interface via the small snippet of code passed to the macro. So, obviously, the macro shouldnt mess around with the users variables.Even though we could already see some interesting macro properties, maybe
you didnt start reading this article to learn about printing users their own variable names back (even though that is a very user friendly behavior in general, and many R users like their non-standard evaluation a lot for this reason).Usually, you want to modify the expression you receive, or build a new one with it, to achieve some functional purpose. Sometimes, macros are used to define domain specific languages or DSLs, that allow users to specify complex things with simple, yet non-standard expressions.This code-reasoning overhead must always be weighed against the convenience of shorter syntax.I hope you have learned a thing or two about macros and are encouraged to play around with them yourself. Usually, good ideas for macros only present themselves after interacting with Julia for a while, so if you are a beginner, give it time and become proficient with normal functions first.")
blanc = Txt("It was Birdie, who planted a remotedevice on the crossbow in revenge foryou stealing her signature wrendiamond.See the seating arrangement, ittriangulates Birdie perfectly to thatthing- which is loaded with a dummy boltand aimed straight at Mr. Bron. Ibelieve close inspection will revealsome sort of remote triggeringdevice, but more damning, that's avintage Jayhawk brand crossbow...Jayhawk, Birdie Jay!Of course there are other superfluousBLANC (cont'd)and rather clumsy clues - thehedgerows in the south garden spellthe letter B, her room is the sacralchakra which is the one blocked byguilt, blah blah blah, but the motiveyes, now the motive. On the cleverlyplanted 1998 issue of The Face withBirdie on the cover she famously worewhat became known as the wren diamond - a family heirloom Ibelieve?Mr. Bron! The large pendulous locketwhich has not left your neck, it's abit out of keeping with your breezyisland style... would you kindly openit for us?A dramatic, passionate and colorfulcrime for a fashionista, Ms. Birdie.But unfortunately this crimeclashed... with the presence ofBenoit Blanc.My god that felt so good, that justfelt solid. So satisfying. Just -like a mini crossword, the Timeshas - or - I have a chef friend, andshe speaks of trying to create theperfect bite - that felt like theperfect satisfying - bite sized...You're angry.She's quite goodMr. Bron, the truth is, I ruined yourgame on purpose, and for a very goodreason.I like the glass onion as a metaphor,an object that seems densely layered,but in reality the
center is in plainsight. Your relationships with thesepeople may seem complex but look atthe center, look at what you've donethis weekend, it's crystal clear:you
have taken seven people, each ofwhom has a real life reason to wishyou harm, gathered them together on aremote island, and placed the idea ofyour murder in their
heads. It's likeputting a loaded gun on the table andturning off the lights.So you played hardball with Lionel.Threatened to destroy his reputationif he does not
play along and power amanned rocket with klear?And with Claire too? Perhaps youthreatened to support her opponent inthe upcoming election if she doesn'tapprove your power plant?")
veryorwell = Txt("It was a bright cold day in April, and the clocks were striking thirteen. Winston Smith, his chin nuzzled into his breast in an effort to escape the vile wind, slipped quickly through the glass doors of Victory Mansions, though not quickly enough to prevent a swirl of gritty dust from entering along with him.The hallway smelt of boiled cabbage and old rag mats. At one end of it a coloured poster, too
large for indoor display, had been tacked to the wall. It depicted simply an enormous face, more than a metre wide: the face of a man of about forty-five, with a
heavy black moustache and ruggedly handsome features. Winston made for the stairs. It was no use trying the lift. Even at the best of times it was seldom working, and at present the electric current was cut off during daylight hours. It was part of the economy drive in preparation for Hate Week. The flat was seven flights
up, and Winston, who was thirty-nine and had a varicose ulcer above his right ankle, went slowly, resting several times on the way. On each landing, opposite the
lift-shaft, the poster with the enormous face gazed from the wall. It was one of
those pictures which are so contrived that the eyes follow you about when you move. BIG BROTHER IS WATCHING YOU, the caption beneath it ran.Inside the flat a fruity voice was reading out a list of figures which had something to do with the production of pig-iron. The voice came from an oblong metal plaque like a dulled mirror which formed part of the surface of the right-hand wall. Winston turned a switch and the voice sank somewhat, though the words were still distinguishable. The instrument (the telescreen, it was called) could be dimmed, but there was no way of shutting it off completely. He moved over to the window: a smallish, frail figure, the meagreness of his body merely emphasized by the blue overalls which were the uniform of the party. His hair was very fair, his face naturally sanguine,
his skin roughened by coarse soap and blunt razor blades and the cold of the winter that had just ended.Outside, even through the shut window-pane, the world looked cold. Down in the street little eddies of wind were whirling dust and torn paper into spirals, and though the sun was shining and the sky a harsh blue, there seemed to be no colour in anything, except the posters that were plastered everywhere. The black-moustachio'd face gazed down from every commanding corner. There was one on the house-front immediately opposite. BIG BROTHER IS WATCHING YOU, the
caption said, while the dark eyes looked deep into Winston's own. Down at street
level another poster, torn at one corner, flapped fitfully in the wind, alternately covering and uncovering the single word INGSOC. In the far distance a helicopter skimmed down between the roofs, hovered for an instant like a bluebottle, and
darted away again with a curving flight. It was the police patrol, snooping into
people's windows. The patrols did not matter, however. Only the Thought Police mattered.Behind Winston's back the voice from the telescreen was still babbling away about pig-iron and the overfulfilment of the Ninth Three-Year Plan. The telescreen received and transmitted simultaneously. Any sound that Winston made, above the level of a very low whisper, would be picked up by it, moreover, so long as he remained within the field of vision which the metal plaque commanded, he could be seen as well as heard. There was of course no way of knowing whether you were being watched at any given moment. How often, or on what system, the Thought Police plugged in on any individual wire was guesswork. It was even conceivable that they watched everybody all the time. But at any rate they could plug in your wire
whenever they wanted to. You had to live--did live, from habit that became instinct--in the assumption that every sound you made was overheard, and, except in darkness, every movement scrutinized.Winston kept his back turned to the telescreen. It was safer; though, as he well knew, even a back can be revealing. A kilometre away the Ministry of Truth, his place of work, towered vast and white above the
grimy landscape. This, he thought with a sort of vague distaste--this was London, chief city of Airstrip One, itself the third most populous of the provinces of Oceania. He tried to squeeze out some childhood memory that should tell him whether London had always been quite like this. Were there always these vistas of rotting nineteenth-century houses, their sides shored up with baulks of timber, their
windows patched with cardboard and their roofs with corrugated iron, their crazy
garden walls sagging in all directions? And the bombed sites where the plaster dust swirled in the air and the willow-herb straggled over the heaps of rubble; and the places where the bombs had cleared a larger patch and there had sprung up sordid colonies of wooden dwellings like chicken-houses? But it was no use, he could not remember: nothing remained of his childhood except a series of bright-lit tableaux occurring against no background and mostly unintelligible.The Ministry of Truth--Minitrue, in Newspeak [Newspeak was the official language of Oceania. For an account of its structure and etymology see Appendix.]--was startlingly different from any other object in sight. It was an enormous pyramidal structure of glittering white concrete, soaring up, terrace after terrace, 300 metres into the air. From where Winston stood it was just possible to read, picked out on its white face in elegant lettering, the three slogans of the Party: WAR IS PEACE FREEDOM IS SLAVERY IGNORANCE IS STRENGTH The Ministry of Truth contained, it was said, three thousand rooms above ground level, and corresponding ramifications below. Scattered about London there were just three other buildings of similar appearance and size. So completely did they dwarf the surrounding architecture that from the roof of Victory Mansions you could see all four of them simultaneously. They were the homes of the four Ministries between which the entire apparatus of government was divided. The Ministry of Truth, which concerned itself with news, entertainment, education, and the fine arts. The Ministry of Peace, which concerned
itself with war. The Ministry of Love, which maintained law and order. And the Ministry of Plenty, which was responsible for economic affairs. Their names, in Newspeak: Minitrue, Minipax, Miniluv, and Miniplenty.The Ministry of Love was the really frightening one. There were no windows in it at all. Winston had never been
inside the Ministry of Love, nor within half a kilometre of it. It was a place impossible to enter except on official business, and then only by penetrating through a maze of barbed-wire entanglements, steel doors, and hidden machine-gun nests. Even the streets leading up to its outer barriers were roamed by gorilla-faced
guards in black uniforms, armed with jointed truncheons.Winston turned round abruptly. He had set his features into the expression of quiet optimism which it was
advisable to wear when facing the telescreen. He crossed the room into the tiny kitchen. By leaving the Ministry at this time of day he had sacrificed his lunch in the canteen, and he was aware that there was no food in the kitchen except a hunk of dark-coloured bread which had got to be saved for tomorrow's breakfast. He
took down from the shelf a bottle of colourless liquid with a plain white label marked VICTORY GIN. It gave off a sickly, oily smell, as of Chinese rice-spirit. Winston poured out nearly a teacupful, nerved himself for a shock, and gulped it down like a dose of medicine.Instantly his face turned scarlet and the water ran out of his eyes. The stuff was like nitric acid, and moreover, in swallowing it one had the sensation of being hit on the back of the head with a rubber club. The
next moment, however, the burning in his belly died down and the world began to look more cheerful. He took a cigarette from a crumpled packet marked VICTORY CIGARETTES and incautiously held it upright, whereupon the tobacco fell out on to the floor. With the next he was more successful. He went back to the living-room and sat down at a small table that stood to the left of the telescreen. From the table drawer he took out a penholder, a bottle of ink, and a thick, quarto-sized blank book with a red back and a marbled cover.For some reason the telescreen in the living-room was in an unusual position. Instead of being placed, as was normal,
in the end wall, where it could command the whole room, it was in the longer wall, opposite the window. To one side of it there was a shallow alcove in which Winston was now sitting, and which, when the flats were built, had probably been intended to hold bookshelves. By sitting in the alcove, and keeping well back, Winston was able to remain outside the range of the telescreen, so far as sight went. He could be heard, of course, but so long as he stayed in his present position he
could not be seen. It was partly the unusual geography of the room that had suggested to him the thing that he was now about to do.But it had also been suggested
by the book that he had just taken out of the drawer. It was a peculiarly beautiful book. Its smooth creamy paper, a little yellowed by age, was of a kind that had not been manufactured for at least forty years past. He could guess, however, that the book was much older than that. He had seen it lying in the window of a frowsy little junk-shop in a slummy quarter of the town (just what quarter he did not now remember) and had been stricken immediately by an overwhelming desire to possess it. Party members were supposed not to go into ordinary shops ('dealing on the free market', it was called), but the rule was not strictly kept, because there were various things, such as shoelaces and razor blades, which it was impossible to get hold of in any other way. He had given a quick glance up and down the
street and then had slipped inside and bought the book for two dollars fifty. At
the time he was not conscious of wanting it for any particular purpose. He had carried it guiltily home in his briefcase. Even with nothing written in it, it was
a compromising possession.The thing that he was about to do was to open a diary.
This was not illegal (nothing was illegal, since there were no longer any laws),
but if detected it was reasonably certain that it would be punished by death, or
at least by twenty-five years in a forced-labour camp. Winston fitted a nib into
the penholder and sucked it to get the grease off. The pen was an archaic instrument, seldom used even for signatures, and he had procured one, furtively and with some difficulty, simply because of a feeling that the beautiful creamy paper deserved to be written on with a real nib instead of being scratched with an ink-pencil. Actually he was not used to writing by hand. Apart from very short notes, it was usual to dictate everything into the speak-write which was of course impossible for his present purpose. He dipped the pen into the ink and then faltered for just a second. A tremor had gone through his bowels. To mark the paper was the decisive act. In small clumsy letters he wrote: April 4th, 1984. He sat back. A sense of complete helplessness had descended upon him. To begin with, he did not know with any certainty that this was 1984. It must be round about that date, since he was fairly sure that his age was thirty-nine, and he believed that he had
been born in 1944 or 1945; but it was never possible nowadays to pin down any date within a year or two.For whom, it suddenly occurred to him to wonder, was he writing this diary? For the future, for the unborn. His mind hovered for a moment round the doubtful date on the page, and then fetched up with a bump against the Newspeak word DOUBLETHINK. For the first time the magnitude of what he had undertaken came home to him. How could you communicate with the future? It was of its nature impossible. Either the future would resemble the present, in which case it would not listen to him: or it would be different from it, and his predicament would be meaningless.For some time he sat gazing stupidly at the paper. The telescreen had changed over to strident military music. It was curious that he seemed not merely to have lost the power of expressing himself, but even to have forgotten
what it was that he had originally intended to say. For weeks past he had been making ready for this moment, and it had never crossed his mind that anything would be needed except courage. The actual writing would be easy. All he had to do was to transfer to paper the interminable restless monologue that had been running inside his head, literally for years. At this moment, however, even the monologue
had dried up. Moreover his varicose ulcer had begun itching unbearably. He dared
not scratch it, because if he did so it always became inflamed. The seconds were
ticking by. He was conscious of nothing except the blankness of the page in front of him, the itching of the skin above his ankle, the blaring of the music, and a slight booziness caused by the gin.Suddenly he began writing in sheer panic, only imperfectly aware of what he was setting down. His small but childish handwriting straggled up and down the page, shedding first its capital letters and finally even its full stops: April 4th, 1984. Last night to the flicks. All war films. One very good one of a ship full of refugees being bombed somewhere in the Mediterranean. Audience much amused by shots of a great huge fat man trying to swim away with a helicopter after him, first you saw him wallowing along in the water like a porpoise, then you saw him through the helicopters gunsights, then he was full of holes and the sea round him turned pink and he sank as suddenly as though
the holes had let in the water, audience shouting with laughter when he sank. then you saw a lifeboat full of children with a helicopter hovering over it. there was a middle-aged woman might have been a jewess sitting up in the bow with a little boy about three years old in her arms. little boy screaming with fright and hiding his head between her breasts as if he was trying to burrow right into her and the woman putting her arms round him and comforting him although she was blue with fright herself, all the time covering him up as much as possible as if she thought her arms could keep the bullets off him. then the helicopter planted a 20 kilo bomb in among them terrific flash and the boat went all to matchwood. then there was a wonderful shot of a child's arm going up up up right up into the air a
helicopter with a camera in its nose must have followed it up and there was a lot of applause from the party seats but a woman down in the prole part of the house suddenly started kicking up a fuss and shouting they didnt oughter of showed it
not in front of kids they didnt it aint right not in front of kids it aint until
the police turned her turned her out i dont suppose anything happened to her nobody cares what the proles say typical prole reaction they never---- Winston stopped writing, partly because he was suffering from cramp. He did not know what had made him pour out this stream of rubbish. But the curious thing was that while he
was doing so a totally different memory had clarified itself in his mind, to the
point where he almost felt equal to writing it down. It was, he now realized, because of this other incident that he had suddenly decided to come home and begin the diary today. It had happened that morning at the Ministry, if anything so nebulous could be said to happen.It was nearly eleven hundred, and in the Records Department, where Winston worked, they were dragging the chairs out of the cubicles and grouping them in the centre of the hall opposite the big telescreen, in preparation for the Two Minutes Hate. Winston was just taking his place in one of the middle rows when two people whom he knew by sight, but had never spoken to, came unexpectedly into the room. One of them was a girl whom he often passed in the corridors. He did not know her name, but he knew that she worked in the Fiction Department. Presumably--since he had sometimes seen her with oily hands and carrying a spanner--she had some mechanical job on one of the novel-writing machines. She was a bold-looking girl, of about twenty-seven, with thick hair, a freckled face, and swift, athletic movements. A narrow scarlet sash, emblem of the Junior Anti-Sex League, was wound several times round the waist of her overalls, just tightly enough to bring out the shapeliness of her hips. Winston had disliked her from the very first moment of seeing her. He knew the reason. It was because of the atmosphere of hockey-fields and cold baths and community hikes and general clean-mindedness which she managed to carry about with her. He disliked nearly all women, and especially the young and pretty ones. It was always the women, and above all the young ones, who were the most bigoted adherents of the Party, the swallowers of slogans, the amateur spies and nosers-out of unorthodoxy. But this particular girl gave him the impression of being more dangerous than most. Once when they
passed in the corridor she gave him a quick sidelong glance which seemed to pierce right into him and for a moment had filled him with black terror. The idea had
even crossed his mind that she might be an agent of the Thought Police. That, it
was true, was very unlikely. Still, he continued to feel a peculiar uneasiness, which had fear mixed up in it as well as hostility, whenever she was anywhere near him.The other person was a man named O'Brien, a member of the Inner Party and holder of some post so important and remote that Winston had only a dim idea of its nature. A momentary hush passed over the group of people round the chairs as they saw the black overalls of an Inner Party member approaching. O'Brien was a large, burly man with a thick neck and a coarse, humorous, brutal face. In spite of his formidable appearance he had a certain charm of manner. He had a trick of resettling his spectacles on his nose which was curiously disarming--in some indefinable way, curiously civilized. It was a gesture which, if anyone had still thought in such terms, might have recalled an eighteenth-century nobleman offering his snuffbox. Winston had seen O'Brien perhaps a dozen times in almost as many years.
He felt deeply drawn to him, and not solely because he was intrigued by the contrast between O'Brien's urbane manner and his prize-fighter's physique. Much more it was because of a secretly held belief--or perhaps not even a belief, merely a hope--that O'Brien's political orthodoxy was not perfect. Something in his face suggested it irresistibly. And again, perhaps it was not even unorthodoxy that was
written in his face, but simply intelligence. But at any rate he had the appearance of being a person that you could talk to if somehow you could cheat the telescreen and get him alone. Winston had never made the smallest effort to verify this guess: indeed, there was no way of doing so. At this moment O'Brien glanced at his wrist-watch, saw that it was nearly eleven hundred, and evidently decided to stay in the Records Department until the Two Minutes Hate was over. He took a chair in the same row as Winston, a couple of places away. A small, sandy-haired woman who worked in the next cubicle to Winston was between them. The girl with dark
hair was sitting immediately behind.The next moment a hideous, grinding speech, as of some monstrous machine running without oil, burst from the big telescreen at the end of the room. It was a noise that set one's teeth on edge and bristled the hair at the back of one's neck. The Hate had started.As usual, the face of Emmanuel Goldstein, the Enemy of the People, had flashed on to the screen. There were hisses here and there among the audience. The little sandy-haired woman gave a squeak of mingled fear and disgust. Goldstein was the renegade and backslider who
once, long ago (how long ago, nobody quite remembered), had been one of the leading figures of the Party, almost on a level with Big Brother himself, and then had engaged in counter-revolutionary activities, had been condemned to death, and had mysteriously escaped and disappeared. The programmes of the Two Minutes Hate varied from day to day, but there was none in which Goldstein was not the principal figure. He was the primal traitor, the earliest defiler of the Party's purity. All subsequent crimes against the Party, all treacheries, acts of sabotage, heresies, deviations, sprang directly out of his teaching. Somewhere or other he was still alive and hatching his conspiracies: perhaps somewhere beyond the sea, under
the protection of his foreign paymasters, perhaps even--so it was occasionally rumoured--in some hiding-place in Oceania itself.Winston's diaphragm was constricted. He could never see the face of Goldstein without a painful mixture of emotions. It was a lean Jewish face, with a great fuzzy aureole of white hair and a small goatee beard--a clever face, and yet somehow inherently despicable, with a kind
of senile silliness in the long thin nose, near the end of which a pair of spectacles was perched. It resembled the face of a sheep, and the voice, too, had a sheep-like quality. Goldstein was delivering his usual venomous attack upon the doctrines of the Party--an attack so exaggerated and perverse that a child should have been able to see through it, and yet just plausible enough to fill one with an
alarmed feeling that other people, less level-headed than oneself, might be taken in by it. He was abusing Big Brother, he was denouncing the dictatorship of the
Party, he was demanding the immediate conclusion of peace with Eurasia, he was advocating freedom of speech, freedom of the Press, freedom of assembly, freedom of thought, he was crying hysterically that the revolution had been betrayed--and all this in rapid polysyllabic speech which was a sort of parody of the habitual style of the orators of the Party, and even contained Newspeak words: more Newspeak words, indeed, than any Party member would normally use in real life. And all the while, lest one should be in any doubt as to the reality which Goldstein's specious claptrap covered, behind his head on the telescreen there marched the endless columns of the Eurasian army--row after row of solid-looking men with expressionless Asiatic faces, who swam up to the surface of the screen and vanished, to be replaced by others exactly similar. The dull rhythmic tramp of the soldiers' boots formed the background to Goldstein's bleating voice.Before the Hate had proceeded for thirty seconds, uncontrollable exclamations of rage were breaking out from half the people in the room. The self-satisfied sheep-like face on the screen, and the terrifying power of the Eurasian army behind it, were too much to be borne: besides, the sight or even the thought of Goldstein produced fear and anger automatically. He was an object of hatred more constant than either Eurasia or Eastasia, since when Oceania was at war with one of these Powers it was generally at peace with the other. But what was strange was that although Goldstein was hated and despised by everybody, although every day and a thousand times a day, on platforms, on the telescreen, in newspapers, in books, his theories were refuted, smashed, ridiculed, held up to the general gaze for the pitiful rubbish that they were--in spite of all this, his influence never seemed to grow less. Always there
were fresh dupes waiting to be seduced by him. A day never passed when spies and
saboteurs acting under his directions were not unmasked by the Thought Police. He was the commander of a vast shadowy army, an underground network of conspirators dedicated to the overthrow of the State. The Brotherhood, its name was supposed
to be. There were also whispered stories of a terrible book, a compendium of all
the heresies, of which Goldstein was the author and which circulated clandestinely here and there. It was a book without a title. People referred to it, if at all, simply as THE BOOK. But one knew of such things only through vague rumours. Neither the Brotherhood nor THE BOOK was a subject that any ordinary Party member would mention if there was a way of avoiding it.In its second minute the Hate rose
to a frenzy. People were leaping up and down in their places and shouting at the
tops of their voices in an effort to drown the maddening bleating voice that came from the screen. The little sandy-haired woman had turned bright pink, and herm
outh was opening and shutting like that of a landed fish. Even O'Brien's heavy face was flushed. He was sitting very straight in his chair, his powerful chest swelling and quivering as though he were standing up to the assault of a wave. The dark-haired girl behind Winston had begun crying out 'Swine! Swine! Swine!' and suddenly she picked up a heavy Newspeak dictionary and flung it at the screen. It struck Goldstein's nose and bounced off; the voice continued inexorably. In a lucid moment Winston found that he was shouting with the others and kicking his heel
violently against the rung of his chair. The horrible thing about the Two Minutes Hate was not that one was obliged to act a part, but, on the contrary, that it was impossible to avoid joining in. Within thirty seconds any pretence was always
unnecessary. A hideous ecstasy of fear and vindictiveness, a desire to kill, to torture, to smash faces in with a sledge-hammer, seemed to flow through the whole
group of people like an electric current, turning one even against one's will into a grimacing, screaming lunatic. And yet the rage that one felt was an abstract, undirected emotion which could be switched from one object to another like the flame of a blowlamp. Thus, at one moment Winston's hatred was not turned against Goldstein at all, but, on the contrary, against Big Brother, the Party, and the Thought Police; and at such moments his heart went out to the lonely, derided heretic on the screen, sole guardian of truth and sanity in a world of lies. And yet the very next instant he was at one with the people about him, and all that was said of Goldstein seemed to him to be true. At those moments his secret loathing of Big Brother changed into adoration, and Big Brother seemed to tower up, an invincible, fearless protector, standing like a rock against the hordes of Asia, and Goldstein, in spite of his isolation, his helplessness, and the doubt that hung about his very existence, seemed like some sinister enchanter, capable by the mere
power of his voice of wrecking the structure of civilization.It was even possible, at moments, to switch one's hatred this way or that by a voluntary act. Suddenly, by the sort of violent effort with which one wrenches one's head away from the pillow in a nightmare, Winston succeeded in transferring his hatred from the face on the screen to the dark-haired girl behind him. Vivid, beautiful hallucinations flashed through his mind. He would flog her to death with a rubber truncheon.
He would tie her naked to a stake and shoot her full of arrows like Saint Sebastian. He would ravish her and cut her throat at the moment of climax. Better than before, moreover, he realized WHY it was that he hated her. He hated her because she was young and pretty and sexless, because he wanted to go to bed with her and
would never do so, because round her sweet supple waist, which seemed to ask you
to encircle it with your arm, there was only the odious scarlet sash, aggressive
symbol of chastity.The Hate rose to its climax. The voice of Goldstein had become an actual sheep's bleat, and for an instant the face changed into that of a sheep. Then the sheep-face melted into the figure of a Eurasian soldier who seemed to be advancing, huge and terrible, his sub-machine gun roaring, and seeming to spring out of the surface of the screen, so that some of the people in the front row actually flinched backwards in their seats. But in the same moment, drawing a deep sigh of relief from everybody, the hostile figure melted into the face of Big
Brother, black-haired, black-moustachio'd, full of power and mysterious calm, and so vast that it almost filled up the screen. Nobody heard what Big Brother was saying. It was merely a few words of encouragement, the sort of words that are uttered in the din of battle, not distinguishable individually but restoring confidence by the fact of being spoken. Then the face of Big Brother faded away again, and instead the three slogans of the Party stood out in bold capitals: WAR IS PEACE FREEDOM IS SLAVERY IGNORANCE IS STRENGTH But the face of Big Brother seemed to persist for several seconds on the screen, as though the impact that
it had made on everyone's eyeballs was too vivid to wear off immediately. The little sandy-haired woman had flung herself forward over the back of the chair in front of her. With a tremulous murmur that sounded like 'My Saviour!' she extended
her arms towards the screen. Then she buried her face in her hands. It was apparent that she was uttering a prayer.At this moment the entire group of people broke into a deep, slow, rhythmical chant of 'B-B!...B-B!'--over and over again, very
slowly, with a long pause between the first 'B' and the second--a heavy, murmurous sound, somehow curiously savage, in the background of which one seemed to hear
the stamp of naked feet and the throbbing of tom-toms. For perhaps as much as thirty seconds they kept it up. It was a refrain that was often heard in moments of
overwhelming emotion. Partly it was a sort of hymn to the wisdom and majesty of Big Brother, but still more it was an act of self-hypnosis, a deliberate drowning
of consciousness by means of rhythmic noise. Winston's entrails seemed to grow cold. In the Two Minutes Hate he could not help sharing in the general delirium, but this sub-human chanting of 'B-B!...B-B!' always filled him with horror. Of course he chanted with the rest: it was impossible to do otherwise. To dissemble your feelings, to control your face, to do what everyone else was doing, was an instinctive reaction. But there was a space of a couple of seconds during which the expression of his eyes might conceivably have betrayed him. And it was exactly at this moment that the significant thing happened--if, indeed, it did happen.Momentarily he caught O'Brien's eye. O'Brien had stood up. He had taken off his spectacles and was in the act of resettling them on his nose with his characteristic gesture. But there was a fraction of a second when their eyes met, and for as long as it took to happen Winston knew--yes, he KNEW!--that O'Brien was thinking the same thing as himself. An unmistakable message had passed. It was as though their two minds had opened and the thoughts were flowing from one into the other through
their eyes. 'I am with you,' O'Brien seemed to be saying to him. 'I know precisely what you are feeling. I know all about your contempt, your hatred, your disgust. But don't worry, I am on your side!' And then the flash of intelligence was gone, and O'Brien's face was as inscrutable as everybody else's.That was all, and he was already uncertain whether it had happened. Such incidents never had any sequel. All that they did was to keep alive in him the belief, or hope, that others besides himself were the enemies of the Party. Perhaps the rumours of vast underground conspiracies were true after all--perhaps the Brotherhood really existed! It was impossible, in spite of the endless arrests and confessions and executions,
to be sure that the Brotherhood was not simply a myth. Some days he believed in it, some days not. There was no evidence, only fleeting glimpses that might mean anything or nothing: snatches of overheard conversation, faint scribbles on lavatory walls--once, even, when two strangers met, a small movement of the hand which
had looked as though it might be a signal of recognition. It was all guesswork: very likely he had imagined everything. He had gone back to his cubicle without looking at O'Brien again. The idea of following up their momentary contact hardly crossed his mind. It would have been inconceivably dangerous even if he had known
how to set about doing it. For a second, two seconds, they had exchanged an equivocal glance, and that was the end of the story. But even that was a memorable event, in the locked loneliness in which one had to live.Winston roused himself and
sat up straighter. He let out a belch. The gin was rising from his stomach.His eyes re-focused on the page. He discovered that while he sat helplessly musing he had also been writing, as though by automatic action. And it was no longer the same cramped, awkward handwriting as before. His pen had slid voluptuously over the
smooth paper, printing in large neat capitals-- DOWN WITH BIG BROTHER DOWN WITH BIG BROTHER DOWN WITH BIG BROTHER DOWN WITH BIG BROTHER DOWN WITH BIG BROTHER over and over again, filling half a page.He could not help feeling a twinge of panic. It was absurd, since the writing of those particular words was not more dangerous than the initial act of opening the diary, but for a moment he was tempted to tear out the spoiled pages and abandon the enterprise altogether.He did
not do so, however, because he knew that it was useless. Whether he wrote DOWN WITH BIG BROTHER, or whether he refrained from writing it, made no difference. Whether he went on with the diary, or whether he did not go on with it, made no difference. The Thought Police would get him just the same. He had committed--would still have committed, even if he had never set pen to paper--the essential crime that contained all others in itself. Thoughtcrime, they called it. Thoughtcrime was not a thing that could be concealed for ever. You might dodge successfully for a while, even for years, but sooner or later they were bound to get you.It was always at night--the arrests invariably happened at night. The sudden jerk out of sleep, the rough hand shaking your shoulder, the lights glaring in your eyes, the ring of hard faces round the bed. In the vast majority of cases there was no trial, no report of the arrest. People simply disappeared, always during the night. Your name was removed from the registers, every record of everything you had ever done was wiped out, your one-time existence was denied and then forgotten. You were abolished, annihilated: VAPORIZED was the usual word.For a moment he was seized by a kind of hysteria. He began writing in a hurried untidy scrawl: theyll shoot me i don't care theyll shoot me in the back of the neck i dont care down with big brother they always shoot you in the back of the neck i dont care down with
big brother---- He sat back in his chair, slightly ashamed of himself, and laid down the pen. The next moment he started violently. There was a knocking at the door.Already! He sat as still as a mouse, in the futile hope that whoever it was might go away after a single attempt. But no, the knocking was repeated. The worst
thing of all would be to delay. His heart was thumping like a drum, but his face, from long habit, was probably expressionless. He got up and moved heavily towards the door.")
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 5694 | import Base.length, Base.show, Base.+, Base.-, Base.==, Base.getindex, Base.iterate
import Random.shuffle!
#=
The Substitution object holds a Vector, where the ith entry is the token that Substitutes i in the cipher
DO NOT USE INNER CONSTRUCTOR
=#
# S[i] returns j if i -> j
mutable struct Substitution <: AbstractCipher
mapping::Vector{Int}
size::Int
tokens::Vector{Int}
# DO NOT USE
function Substitution(vect::Vector{Int}, size::Int, tokens::Vector{Int}, check_vect::Bool = true) # UNSAFE
if check_vect
checkperm(vect)
end
return new(vect, size, tokens)
end
# DO NOT USE
end
# Unsafe outer constructors
unsafe_Substitution(vect::Vector{Int}, size::Int) ::Substitution = Substitution(vect, size, collect(1:size)) # Tokenless UNSAFE
unsafe_Substitution(vect::Vector{Int}, W::NCharSpace{N} where N) ::Substitution = Substitution(vect, W.size, W.tokens) # UNSAFE
# Safe outer constructors
Substitution(vect::Vector{Int}) ::Substitution = unsafe_Substitution(vect, length(vect)) # sizeless
function Substitution(size::Int) ::Substitution # Identity
v = collect(1:size)
return Substitution(v, size, copy(v), false)
end
function Substitution(chars::Vector{String}, W::NCharSpace{1}) ::Substitution # Construct Substitution from permutation of chars
if !issetequal(chars, W.charsmap)
error("Substitutions must contain all tokenised characters only once")
end
return Substitution([W.tokenmap[i] for i in chars], W.size, W.tokens, false)
end
Substitution(W::NCharSpace{N} where N) ::Substitution = Substitution(W.size)
# Finds forwards substitution matching tokens sorted by frequency
function frequency_matched_Substitution(txt::Txt, ref_frequencies::Vector{Float64} = monogram_freq)
if length(ref_frequencies) != txt.charspace.size
error("Reference frequencies must be provided for each token (length equal to CharSpace size)")
end
f = sortperm(vector_frequencies(txt)) # ranking of empirical frequencies in ascending order
ref_frequencies = sortperm(ref_frequencies) # ranking of expected frequencies in ascending order
return Substitution([f[findfirst(==(i), ref_frequencies)] for i in txt.charspace.tokens], txt.charspace.size, txt.charspace.tokens, false) # starts with letters arranged by frequencies against ref_frequencies
end
length(S::Substitution) ::Int = S.size
function show(io::IO, S::Substitution)
show(io, S.mapping)
end
function show(io::IO, ::MIME"text/plain", S::Substitution)
println(io, "$(S.size)-element Substitution:")
show(io, S.mapping)
end
getindex(S::Substitution, i::Int) ::Int = getindex(S.mapping, i)
iterate(S::Substitution) = iterate(S.mapping)
iterate(S::Substitution, i::Integer) = iterate(S.mapping, i)
# Checks whether A and B are identical Substitutions
==(a::Substitution, b::Substitution) ::Bool = (a.mapping == b.mapping)
# if S: i -> j invert(S): j -> i
function invert!(S::Substitution) ::Substitution
S.mapping = invperm(S.mapping)
return S
end
# Compounds two Substitutions to make one new
function +(a::Substitution, b::Substitution) ::Substitution
if a.size != b.size
error("Substitutions must have the same length")
end
Substitution([b[i] for i in a])
end
-(a::Substitution, b::Substitution) ::Substitution = a + invert(b)
# Applies Substitution to Integer Vectors
function apply(S::Substitution, vect::Vector{Int}; safety_checks::Txt) ::Vector{Int}
if safety_checks.charspace.size != S.size
println("WARNING: Substitution size does not match size of Character Space")
end
return getindex.(Ref(S), vect)
end
function shift!(self::Substitution, shift::Int) ::Substitution
self.mapping = circshift(self.mapping, shift)
return self
end
# Switches two entries in a Substitution
function switch(S::Substitution, posa::Int, posb::Int) ::Substitution # SAFE
return Substitution(switch(S.mapping, posa, posb), S.size, S.tokens, false)
end
function switch!(S::Substitution, posa::Int, posb::Int) ::Substitution
switch!(S.mapping ::Vector{Int}, posa, posb)
return S
end
# Switches a random place in a Substitution, within the slice tuple, with another spot
function mutate(S::Substitution, slice::Tuple{Int, Int} = (1, length(S))) ::Substitution
switch(S, rand(slice[1]:slice[2]), rand(1:length(S)))
end
function mutate!(S::Substitution, slice::Tuple{Int, Int} = (1, length(S))) ::Substitution
switch!(S, rand(slice[1]:slice[2]), rand(1:length(S)))
end
# Presets
function Atbash(size::Int) ::Substitution
s = Substitution(size)
reverse!(s.mapping)
return s
end
Atbash(W::NCharSpace{N} where N) ::Substitution = Atbash(W.size)
function Caesar(shift::Int, size::Int) ::Substitution
s = Substitution(size)
shift!(s, mod(-shift, size))
return s
end
Caesar(shift::Int, W::NCharSpace{N} where N) ::Substitution = Caesar(shift::Int, W.size)
Caesar(colon::Colon, size::Int) ::Tuple = Tuple(1:size)
function Affine(a::Int, b::Int, size::Int) ::Substitution
if gcd(a, size) != 1
println("WARNING: Affine parameter $(a)x + $(b) (mod $(size)) is a singular transformation and cannot be inverted")
end
s = Substitution(size)
s.mapping *= a
s.mapping .+= (b - a) # Shifted to account for one-base indexing, standardising
s.mapping = mod.(s.mapping, size)
s.mapping .+= 1
return s
end
Affine(a::Int, b::Int, W::NCharSpace{N} where N) ::Substitution = Affine(a, b, W.size)
Affine(colon::Colon, b::Int, size::Int) ::Tuple = Tuple(filter!(x -> gcd(x, size) == 1, collect(1:size)))
Affine(a::Int, colon::Colon, size::Int) ::Tuple = Tuple(1:size) | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 14823 | #=
TUCO is a drug lord.
TUCO handles all statistical stuff, including fitness statistics
=#
using Statistics
jldstem = joinpath(@__DIR__, "..", "jld2")
using JLD2
@load abspath(joinpath(jldstem, "quadgram_scores.jld2")) quadgram_scores
@load abspath(joinpath(jldstem, "bigram_scores.jld2")) bigram_scores
@load abspath(joinpath(jldstem, "poogramfart_scores.jld2")) poogram_scores
@load abspath(joinpath(jldstem, "monogram_frequencies.jld2")) monogram_freq
@load abspath(joinpath(jldstem, "bigram_frequencies.jld2")) bigram_freq
# FREQUENCY ########################################
function appearances(token::Int, txt::Txt) ::Int
if !txt.is_tokenised
error("Cannot count token appearances in untokenised Txt")
end
return count(==(token), txt.tokenised)
end
function appearances(txt::Txt) ::Vector{Int}
if !txt.is_tokenised
error("Cannot count token appearances in untokenised Txt")
end
return [count(==(token), txt.tokenised) for token in txt.charspace.tokens]
end
function vector_frequencies(txt::Txt) ::Vector{Float64}
return appearances(txt) / length(txt)
end
function frequencies(txt::Txt) ::Dict{String, Float64}
freq = vector_frequencies(txt)
return Dict(txt.charspace.chars .=> freq)
end
function bandwidth(txt::Txt) ::Int
return length(unique(txt.tokenised))
end
# ORDERLESS MEASURES #########################################
# Index of Coincidence
function ioc(txt::Txt) ::Float64
len = length(txt)
tallies = appearances(txt)
summed = sum(tallies .* (tallies .- 1)) # sum of n(n-1)
return summed / (len * (len-1)) # n(n-1)/N(N-1) un-normalised, doesnt account for size of W
end
# Periodic index of Coincidence
function periodic_ioc(txt::Txt, n::Int64) ::Float64
avgIOC = 0
for i in 1:n
avgIOC += ioc(txt[i:n:end])
end
return avgIOC / n
end
# Cosine similarity of frequencies
function orthodot(txt::Txt, ref_frequencies::Vector{Float64} = monogram_freq; ordered = true) ::Float64
frequencies = vector_frequencies(txt)
if !ordered
frequencies = sort(frequencies)
ref_frequencies = sort(ref_frequencies)
end
magnitude = sum(frequencies .^ 2) * sum(ref_frequencies .^ 2)
return sum(frequencies .* ref_frequencies) / sqrt(magnitude)
end
# BATCH BINOMIAL ###################################################################################
function batch_binomial_pd(X::Vector{Int}, N::Int, p::Float64) ::Vector{Float64}
mu = N * p
var = 2 * mu * (1 - p) # 2 carried through to reduce operations
M = 7e2 - (maximum(X) - mu)^2 / var
PX = [exp( M - (x - mu)^2 / var ) for x in X]
PX = safe_normalise(PX)
#PX[PX .== 0] .= 1e-300
return PX
end
function safe_normalise(a::Vector) ::Vector
s = sum(a)
if s == 0 | isnan(s)
L = length(a)
return fill(1 / L, (L,))
end
return a / s
end
# batch binomial probabilities A[i,j] that token j represents i (forwards i -> j)
function bbin_probabilities(txt::Txt, ref_frequencies::Vector{Float64} = monogram_freq) ::Matrix{Float64}
tallies = appearances(txt)
L = length(txt)
return permutedims(hcat([batch_binomial_pd(tallies, L, i) for i in ref_frequencies]...))
end
# PERIOD FINDING ALOGRITHM ############################################
# front weighted standard deviation: rooted avg of square difference of adjacent values, weighted by a decreasing geometric series
function fw_var(data::Vector, r = 0.5) ::Float64
if r == 1
return var(data)
end
weights = [r ^ i for i in 1:length(data)]
# weight data in geometric series
w_total = r * (1 - r ^ length(data)) / (1 - r)
# Calculate sum of weights
mu = sum(weights .* data) / w_total
# find mean
var = sum( (data .- mu).^2 .* weights ) / w_total
return var
end
fw_std(data::Vector, r = 0.5) ::Float64 = sqrt(fw_var(data, r))
function find_period(data::Vector{Float64}, upper_lim::Int, sigma_threshold::Float64; weight_ratio ::Float64 = 0.65, silent::Bool = false) ::Union{Nothing, Int}
upper_lim = min(upper_lim, length(data) - 1)
# test until period > upper_lim
threshold = fw_std(data, weight_ratio) * sigma_threshold
for n in 1:upper_lim
avg_std = 0.
for i in 1:n
avg_std += fw_std(data[i:n:end], weight_ratio)
# Compare n-length chunks of data to themselves, find std
end
avg_std /= n
if avg_std < threshold # if the periodic std is below the threshold
if !silent
println("ρ: ", round(avg_std / fw_std(data, weight_ratio); sigdigits = 3), " σ")
end
return n
break
end
end
return nothing
end
find_period(txt::Txt, upper_lim::Int, sigma_threshold::Float64; weight_ratio ::Float64 = 0.65, silent::Bool = false) = find_period(periodic_ioc.(Ref(txt), collect(1:upper_lim)), upper_lim, sigma_threshold; weight_ratio = weight_ratio, silent = silent)
# NUMERICAL ANALYSIS ################################################
# Finding divisors
function divisors(number::Int) ::Vector{Int}
# Step 1: find all divisors
divisors = []
for int in 2:(number >> 1)
if number % int == 0
push!(divisors, int)
end
end
push!(divisors, number)
return divisors
end
# Prime Factorisation Algorithm
function factorise(number::Int) ::Vector{Int}
# Step 2 repeatedly divide from bottom up (2, 3, 5, 7...)
factors = []
for int in divisors(number)
while number % int == 0
push!(factors, int)
number /= int
end
if number == 1
return factors
end
end
end
# TEXT BLOCKING ANALYSIS ######################################
function blocks(txt::Txt, size::Int)
return [txt[i - size + 1:i] for i in size:size:lastindex(txt)]
end
function block_apply_stats(f::Function, txt::Txt, block_size::Int)
data = f.(blocks(txt, block_size))
mean = mean(data)
return mean , stdm(data, mean)
end
function rolling(f::Function, txt::Txt, window::Int) ::Vector
L = length(txt)
values = Vector(undef, L)
wing = round(Int, window / 2)
########## IMPLEMENT EACHINDEX
for i in 1:L
start = i - wing
final = i + wing
if start < 1
if final > L
error("Window too large")
end
start = 1
final = start + 2 * wing
elseif final > L
final = L
start = final - 2 * wing
end
values[i] = f(txt[start:final])
end
return values
end
# STRING ANALYSIS ###################################
# Rolling average of data, sampled by window
rolling_average(data::Vector, window::Int) ::Vector{Float64} = real.(Conv1D_reals(data, ones(window) / window))
function char_distribution(txt::Txt, token::Int, window::Int) ::Vector{Float64}
if !txt.is_tokenised
error("Txt must be tokenised to perform statistical tests")
end
shortend = length(txt) - window
out = Vector{Float64}(undef, shortend) # appearances of token in window
for start in 1:shortend
out[start] = appearances(token, txt[start:start+window]) / window
end
return rolling_average(out, window)
end
function KMP_appearances(unit::Vector{T}, sequence::Vector{T}) where T
search_index = 1
ref_index = 1
target_length = length(unit)
L = lastindex(sequence)
valid_pos = Vector{Int}()
while search_index <= L # through whole text
if unit[ref_index] == sequence[search_index] # current ind matches
if ref_index == target_length # current ind is last ind
push!(valid_pos, search_index - ref_index + 1) # save ind
search_index += 1 # skip ahead search by unit length
ref_index = 1 # reset window
else # current ind is not last ind
ref_index += 1 # check next ind
search_index += 1
end
else # current ind does not match
search_index += 1
ref_index = 1
end
end
return valid_pos
end
function repeat_units(txt::Txt, min_size::Int = 2) ::Dict{Vector{Int}, Int}
if !txt.is_tokenised
error("Txt must be tokenised to find repeat units")
end
vect = txt.tokenised
L = lastindex(vect)
window_start = 1
window_end = min_size
last_number = 0
repeats = Dict{Vector{Int}, Int}()
min_start_lengths = ones(Int, L) * min_size
while window_start <= L - 2 * min_size + 1 # while window still repeatable
window = vect[window_start:window_end]
repeat_pos = KMP_appearances(window, vect[window_end + 1:end])
number = length(repeat_pos)
window_length = window_end - window_start + 1
for i in repeat_pos # for places with the same start
min_start_lengths[window_end + i] = window_length + 1 # start checking with length longer than now
end
if number == 0 # if no repeats still exist
if last_number != 0 # if the last one had repeats
repeats[window[begin:end - 1]] = last_number + 1 # take the last window
for i in 1:window_length - min_size # start following checks with longer window sizes
min_start_lengths[window_start + i] = window_length - i
end
end
window_start += 1 # advance search
# set end so that length is minimum start
window_end = window_start + min_start_lengths[window_start] - 1
last_number = 0
else # if repeats still exist
window_end += 1 # elongate window
last_number = number
end
end
return repeats
end
# CODEPENDENCE ANALYSIS ###################################
# raw Substructure Variance
function substructure_variance(txt::Txt, n::Int, ref_frequencies::Vector{Float64} = monogram_freq)
L = length(txt)
txt_chunks = [txt[chunk_end - n + 1:chunk_end] for chunk_end in n:n:lastindex(txt)]
N = ceil(Int, L / n)
avg_variance = 0.0
for token in txt.charspace.tokens
f = ref_frequencies[token]
var = var(appearances.(token, txt_chunks))
avg_variance += f * var
end
return avg_variance
end
# Auto wrapped function to find sigma error in substruct (hypothesis test)
function substructure_sigma(txt::Txt, n::Int, ref_frequencies::Vector{Float64} = monogram_freq)
L = length(txt)
txt_chunks = [txt[chunk_end - n + 1:chunk_end] for chunk_end in n:n:lastindex(txt)]
N = ceil(Int, L / n)
avg_sigma_dev = 0.0
for token in txt.charspace.tokens
f = ref_frequencies[token]
actual_var = var(appearances.(token, txt_chunks))
expected_var = n * f * (1-f) * (1 - 1/N)
sigma = expected_var / sqrt(N-1)
avg_sigma_dev += f * (actual_var - expected_var) / sigma
end
return avg_sigma_dev
end
# FITNESS FUNCTIONS ########################################
function quadgramlog(txt::Txt; quadgram_scores::Array{Float64, 4} = quadgram_scores) ::Float64
if txt.charspace != Alphabet
error("Quadgramlog fitness only works on Alphabet")
end
L = length(txt) - 3
score = 0.
for i in 1:L
score += quadgram_scores[ txt[i], txt[i+1], txt[i+2], txt[i+3] ]
end
return score / L
end
function bigramlog(T::Txt; bigram_scores::Array{Float64, 2} = bigram_scores)
if T.charspace != Alphabet
error("Bigramlog fitness only works on Alphabet")
end
sum = 0.
L = length(T) - 1
for i in 1:L
sum += bigram_scores[T[i], T[i+1]]
end
return sum / L
end
function bibigramlog_arr(txt::Txt; bibigram_scores::Array{Float64, 2} = bibigram_scores_arr) ::Float64
if txt.charspace != Bigram_CharSpace
error("Bibigramlog fitness only works on Bigram_CharSpace")
end
L = length(txt) - 1
score = 0.0
for i in 1:L
score += bibigram_scores[ txt.tokenised[i], txt.tokenised[i + 1] ]
end
return score / L
end
# 93% improvement
function poogramfart(txt::Txt; poogram_scores::Dict{Int, Float64} = poogram_scores) ::Float64
if txt.charspace != Alphabet
error("Poogramfart fitness only works on Alphabet")
end
L = length(txt) - 3
score = 0.0
for i in 1:L
score += poogram_scores[ generate_structure_tag(txt.tokenised[i:i+3]) ]
end
return score / L
end
# Essentially a hashing algorithm
function generate_structure_tag(quadgram::Vector{Int}) ::Int
tag = 0
for (i, item) in enumerate(quadgram)
ident = 0
for j in 1:4
ident |= ((quadgram[j] == item) << (j-1))
end
tag |= (ident << (4 * (i-1)))
end
return tag
end
# const nullfitness = log10(0.1/4224127912)
# eng_quadgrams = Dict()
# alphabet = collect("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
# open("english_quadgrams.txt") do file
# for ln in eachline(file)
# i,j = split(ln, " ")
# i = [findfirst(x -> isequal(x, letter), alphabet) for letter in i]
# j = parse(Int64, j)
# eng_quadgrams[i] = log10(j/4224127912) # where 4224127912 is the total number of quadgrams
# end
# end
# @save "jld2/quadgram_score_dict.jld2" quadgram_scores = eng_quadgrams
# quadgram_scores_arr = Array{Float64}(undef, 26, 26, 26, 26)
# for i in 1:26
# for j in 1:26
# for k in 1:26
# for l in 1:26
# quadgram_scores_arr[i,j,k,l] = get(quadgram_scores, [i, j, k, l], nullfitness)
# end
# end
# end
# end
# using JLD2
# @load "jld2/english_monogram_frequencies.jld2" eng
# # Vector with v[i] = j is the token index and j is the frequency
# fart_matrix = Dict{Int, Float64}()
# quadgram_scores_arr = 10 .^ quadgram_scores_arr
# function determine_struct(quadgram::Vector{Int})
# (i,j,k,l) = quadgram
# structure = Vector{Int}(undef, 4)
# iter = 1
# while any(quadgram .!= 0)
# first_token = quadgram[findfirst(!=(0), quadgram)]
# indices = findall(==(first_token), quadgram)
# structure[indices] .= iter
# quadgram[indices] .= 0
# iter += 1
# end
# return structure
# end
# for i in 1:26
# for j in 1:26
# for k in 1:26
# for l in 1:26
# structure = generate_structure_tag([i,j,k,l])
# value = get(fart_matrix, structure, nothing)
# if isnothing(value)
# fart_matrix[structure] = 0.0
# end
# fart_matrix[structure] += quadgram_scores_arr[i,j,k,l]
# end
# end
# end
# end
| RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 1088 | using FFTW
include("cipher.jl")
function Conv1D_reals(a::Vector{T}, b::Vector{T}) where T <: Real
N = length(a)
M = length(b)
a = [a ; zeros(M - 1)] # N + M - 1
b = [b ; zeros(N - 1)] # N + M - 1
return ifft(fft(a) .* fft(b))
end
Conv1D(a::Vector{Int}, b::Vector{Int}) = round.(Int, real.(Conv1D_reals(a, b)))
function invConv1D_reals(conv::Vector{T}, filter::Vector{T}) where T <: Real
K = length(conv)
M = length(filter)
filter = [filter ; zeros(K - M)]
return ifft(fft(conv) ./ fft(filter))[1 : K - M + 1]
end
invConv1D(a::Vector{Int}, b::Vector{Int}) = round.(Int, real.(invConv1D_reals(a, b)))
#######################################################################################
mutable struct Convolution <: AbstractCipher
filter::Vector{Int}
inverted::Bool
function Convolution(filter::Vector{Int})
new(filter, false)
end
end
apply(Q::Convolution, v::Vector{Int}; safety_checks::Txt) = Q.inverted ? invConv1D(v, Q.filter) : Conv1D(v, Q.filter)
invert!(Q::Convolution) = switch_invert_tag!(Q) | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 1771 | include("cipher.jl")
mutable struct Diagonal <: AbstractCipher
n::Int
skew::Bool
reverse::Bool
inverted::Bool
function Diagonal(n::Int, skew::Bool = false, reverse::Bool = false)
new(n, skew, reverse, false)
end
end
invert!(D::Diagonal) = switch_invert_tag!(D)
using LinearAlgebra
function get_diagonals(matrix::Matrix) ::Vector
(a,b) = size(matrix)
return [diag(matrix, n) for n in 1-a:b-1]
end
function fuse_diagonals(a::Int, b::Int, diagonals::Vector{Vector{Int}})
diagm(a, b, [num => diag for (num, diag) in zip(1-a:b-1, diagonals)]...)
end
function apply(D::Diagonal, vect::Vector{Int}; safety_checks::Txt)
if D.inverted
new_tokens = copy(vect)
a = D.n
b = Int(length(vect) / D.n)
diagonals = Vector{Vector{Int}}(undef, a + b - 1)
diag_total = a + b - 1
max_diag = min(a,b)
for i in 1:diag_total
if i < max_diag
diagonals[i] = [popat!(new_tokens, 1) for _ in 1:i]
elseif i > diag_total - max_diag
diagonals[i] = [popat!(new_tokens, 1) for _ in 1:a + b - i]
else
diagonals[i] = [popat!(new_tokens, 1) for _ in 1:max_diag]
end
end
if D.reverse
reverse!.(diagonals)
end
new_tokens = fuse_diagonals(a, b, diagonals)
if D.skew
reverse(new_tokens; dims = 1)
end
return vec(new_tokens)
else
new_tokens = reshape(vect, (D.n, :))
if D.skew
reverse(new_tokens; dims = 1)
end
new_tokens = get_diagonals(new_tokens)
if D.reverse
reverse!.(new_tokens)
end
return vcat(new_tokens...)
end
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 1676 | include("cipher.jl")
mutable struct GridShift <: AbstractCipher
shifts::Vector{Int}
col_length::Int
end
function shifter(v::Vector{Int}, shifts::Vector{Int})
v = reshape(v, (length(shifts), :))
for (i,j) in enumerate(shifts)
v[i, :] = circshift(v[i, :], j)
end
return vec(v)
end
function apply(S::GridShift, vect::Vector{Int}; safety_checks::Txt)
new_tokens = copy(vect)
L = length(vect)
block_l = length(S.shifts) * S.col_length
if L % block_l != 0
error("idk just fix")
end
blocks = [vect[1 + b - block_l:b] for b in block_l:block_l:lastindex(vect)]
new_tokens = vcat([shifter(i, S.shifts) for i in blocks]...)
return new_tokens
end
mutable struct InvJaggedGridShift <: AbstractCipher
shifts::Vector{Int}
col_length::Int
end
function invshifter(v::Vector{Int}, shifts::Vector{Int}, col_length::Int)
blanks = [ones(Int, (length(shifts), col_length)) zeros(Int, (length(shifts), col_length))]
for (i,j) in enumerate(shifts)
blanks[i, :] = circshift(blanks[i, :], j)
end
blanks[findall(==(1.), blanks)] .= v
for (i,j) in enumerate(shifts)
blanks[i, :] = circshift(blanks[i, :], -j)
end
return vec(v)
end
function apply(S::InvJaggedGridShift, vect::Vector{Int}; safety_checks::Txt)
new_tokens = copy(vect)
L = length(vect)
block_l = length(S.shifts) * S.col_length
if L % block_l != 0
error("idk just fix")
end
blocks = [vect[1 + b - block_l:b] for b in block_l:block_l:lastindex(vect)]
new_tokens = vcat([invshifter(i, S.shifts, S.col_length) for i in blocks]...)
return new_tokens
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 4149 | include("cipher.jl")
import Base.show
const valid_continuation_modes = Set(("blank", "repeat", "autokey"))
mutable struct Keystream <: AbstractCipher
stem::Vector{Int}
stem_length::Int
mode::String
inverted::Bool
function Keystream(vect::Vector{Int}, continuation_mode::String = "blank")
if !(continuation_mode in valid_continuation_modes)
error("Invalid key continuation mode argument")
end
new(vect, length(vect), continuation_mode, false)
end
end
Keystream(txt::Txt, continuation_mode::String = "blank") ::Keystream = txt.is_tokenised ? Keystream(txt.tokenised, continuation_mode) : error("Txt must be tokenised to create Keystream")
Keystream(vect::Vector{String}, W::NCharSpace{1}, continuation_mode::String = "blank") ::Keystream = Keystream(tokenise.(vect, Ref(W)) .- 1, continuation_mode) # Subtracting one to standardise for 0-based indexing
Keystream(vect::Vector{Char}, W::NCharSpace{1}, continuation_mode::String = "blank") ::Keystream = Keystream(string.(vect), W, continuation_mode)
Keystream(string::String, W::NCharSpace{1}, continuation_mode::String = "blank") ::Keystream = W.n == 1 ? Keystream(collect(string), W, continuation_mode) : error("Character Space must be 1-gram for String argument to be tokenised")
function show(io::IO, K::Keystream)
show(io, K.stem)
end
function show(io::IO, ::MIME"text/plain", K::Keystream)
if K.inverted
inverse_text = "Inverse "
else
inverse_text = ""
end
println(io, "$(K.stem_length)-token ", inverse_text, "Keystream (end continuation = ", K.mode, "):")
show(io, K.stem)
end
function invert!(K::Keystream) ::Keystream
K.inverted = !K.inverted
return K
end
function apply(K::Keystream, v::Vector{Int}; safety_checks::Txt) ::Vector{Int}
new_v = copy(v)
L = length(v)
if K.inverted
if K.mode == "blank"
stopindex = min(K.stem_length, L)
new_v[begin : stopindex] .-= K.stem[begin : stopindex]
elseif K.mode == "repeat"
num_full_cycles = floor(Int, L / K.stem_length)
remainder = L % K.stem_length
for i in (0:num_full_cycles - 1) * K.stem_length
active_indices = i + 1 : i + K.stem_length
new_v[active_indices] .-= K.stem
end
new_v[end - remainder + 1 : end] .-= K.stem[begin : remainder]
elseif K.mode == "autokey"
diff = L - K.stem_length
if diff <= 0
new_v .-= K.stem[begin : L]
else
new_v[begin:K.stem_length] .-= K.stem
num_full_cycles = floor(Int, L / K.stem_length)
remainder = L % K.stem_length
for i in (1:num_full_cycles - 1) * K.stem_length
new_v[i + 1 : i + K.stem_length] .-= new_v[i + 1 - K.stem_length : i]
end
new_v[end - remainder + 1 : end] .-= new_v[end - remainder + 1 - K.stem_length : end - K.stem_length]
end
end
else
if K.mode == "blank"
stopindex = min(K.stem_length, L)
new_v[begin : stopindex] .+= K.stem[begin : stopindex]
elseif K.mode == "repeat"
num_full_cycles = floor(Int, L / K.stem_length)
remainder = L % K.stem_length
for i in (0:num_full_cycles - 1) * K.stem_length
active_indices = i + 1 : i + K.stem_length
new_v[active_indices] .+= K.stem
end
new_v[end - remainder + 1 : end] .+= K.stem[begin : remainder]
elseif K.mode == "autokey"
diff = L - K.stem_length
if diff <= 0
new_v .+= K.stem[begin : L]
else
new_v[begin:K.stem_length] .+= K.stem
new_v[K.stem_length + 1 : L] .+= v[begin : diff]
end
end
end
new_v .-= 1
new_v = mod.(new_v, safety_checks.charspace.size)
new_v .+= 1
return new_v
end
Autokey(args...) = Keystream(args..., "autokey")
| RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 1865 | include("cipher.jl")
mutable struct NihilistTransposition <: AbstractCipher
row_perm::Vector{Int}
col_perm::Vector{Int}
read_dim::Int
inverted::Bool
function NihilistTransposition(row_perm::Vector{Int}, col_perm::Vector{Int}, read_dim::Int = 1)
new(row_perm, col_perm, read_dim, false)
end
end
invert!(N::NihilistTransposition) = switch_invert_tag!(N)
function apply(N::NihilistTransposition, v::Vector{Int}; safety_checks::Txt)
vect = copy(v)
L = length(vect)
block_l = length(N.row_perm) * length(N.col_perm)
if L % block_l != 0
error("idk just fix")
end
blocks = [vect[1 + b - block_l:b] for b in block_l:block_l:lastindex(vect)]
if N.inverted
return vcat([nihilist_trans_dec(i, N.row_perm, N.col_perm, N.read_dim) for i in blocks]...)
else
return vcat([nihilist_trans_enc(i, N.row_perm, N.col_perm, N.read_dim) for i in blocks]...)
end
end
function nihilist_trans_enc(plaintext::Vector, permrow, permcolumn, read_dim)
len1 = length(permcolumn)
len2 = length(permrow)
plainmatrix = reshape(plaintext, (len1, len2))
plainmatrix = plainmatrix[:, permrow]
plainmatrix = plainmatrix[permcolumn, :]
if read_dim == 2
plainmatrix = permutedims(plainmatrix)
end
return vec(plainmatrix)
end
function nihilist_trans_dec(ciphertext::Vector, permrow, permcolumn, order="rows")
len1 = length(permcolumn)
len2 = length(permrow)
invpermrow = [findfirst(==(i), permrow) for i in 1:len2]
invpermcolumn = [findfirst(==(i), permcolumn) for i in 1:len1]
ciphematrix = reshape(ciphertext, (len1, len2))
if read_dim == 2
ciphematrix = permutedims(ciphematrix)
end
ciphematrix = ciphematrix[invpermcolumn, :]
ciphematrix = ciphematrix[:, invpermrow]
return vec(ciphematrix)
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 1891 | include("cipher.jl")
mutable struct Nihilist3D <: AbstractCipher
row_perm::Vector{Int}
col_perm::Vector{Int}
z_perm::Vector{Int}
inverted::Bool
function Nihilist3D(row_perm::Vector{Int}, col_perm::Vector{Int}, z_perm::Vector{Int})
new(row_perm, col_perm, z_perm, false)
end
end
invert!(N::Nihilist3D) = switch_invert_tag!(N)
function apply(N::Nihilist3D, v::Vector{Int}; safety_checks::Txt)
vect = copy(v)
L = length(vect)
block_l = length(N.row_perm) * length(N.col_perm) * length(N.z_perm)
if L % block_l != 0
error("idk just fix")
end
blocks = [vect[1 + b - block_l:b] for b in block_l:block_l:lastindex(vect)]
if N.inverted
return vcat([nihilist_3D_dec(i, N.row_perm, N.col_perm, N.z_perm) for i in blocks]...)
else
return vcat([nihilist_3D_enc(i, N.row_perm, N.col_perm, N.z_perm) for i in blocks]...)
end
end
function nihilist_3D_enc(plaintext::Vector, permrow, permcolumn, permz)
len1 = length(permcolumn)
len2 = length(permrow)
len3 = length(permz)
plainmatrix = reshape(plaintext, (len1, len2, len3))
plainmatrix = plainmatrix[:, permrow, :]
plainmatrix = plainmatrix[permcolumn, :, :]
plainmatrix = plainmatrix[:, :, permz]
return vec(plainmatrix)
end
function nihilist_3D_dec(ciphertext::Vector, permrow, permcolumn, permz)
len1 = length(permcolumn)
len2 = length(permrow)
len3 = length(permz)
invpermrow = [findfirst(==(i), permrow) for i in 1:len2]
invpermcolumn = [findfirst(==(i), permcolumn) for i in 1:len1]
invpermz = [findfirst(==(i), permz) for i in 1:len3]
ciphematrix = reshape(ciphertext, (len1, len2, len3))
ciphematrix = ciphematrix[:, :, invpermz]
ciphematrix = ciphematrix[invpermcolumn, :, :]
ciphematrix = ciphematrix[:, invpermrow, :]
return vec(ciphematrix)
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 6663 | using RxCiphers
include("square.jl")
const fixed_playfairmap = [0, 28, 29, 30, 26, 136, 127, 128, 129, 130, 141, 252, 253, 254, 255, 146, 377, 378, 379, 380, 126, 502, 503, 504, 505, 52, 0, 54, 55, 51, 151, 162, 153, 154, 155, 276, 167, 278, 279, 280, 401, 172, 403,
404, 405, 526, 152, 528, 529, 530, 77, 78, 0, 80, 76, 176, 177, 188, 179, 180, 301, 302, 193, 304, 305, 426, 427, 198, 429, 430, 551, 552, 178, 554, 555, 102, 103, 104, 0, 101, 201, 202, 203, 214, 205, 326, 327, 328, 219, 330, 451, 452, 453, 224, 455, 576, 577, 578, 204,
580, 2, 3, 4, 5, 0, 226, 227, 228, 229, 240, 351, 352, 353, 354, 245, 476, 477, 478, 479, 250, 601, 602, 603, 604, 230, 256, 7, 8, 9, 10, 0, 158, 159, 160, 156, 266, 257, 258, 259, 260, 271, 382, 383, 384, 385, 251, 507, 508, 509, 510, 31, 282, 33, 34, 35, 182, 0, 184, 185, 181, 281, 292, 283, 284, 285, 406, 297, 408, 409, 410, 531, 277,
533, 534, 535, 56, 57, 308, 59, 60, 207, 208, 0, 210, 206, 306, 307, 318, 309, 310, 431, 432, 323, 434, 435, 556, 557, 303, 559, 560, 81, 82, 83, 334, 85, 232, 233, 234, 0, 231, 331, 332, 333, 344, 335, 456, 457, 458, 349, 460, 581, 582, 583, 329, 585, 106, 107, 108, 109, 360, 132, 133, 134, 135, 0, 356, 357, 358, 359, 370, 481, 482, 483, 484, 375, 606, 607, 608, 609, 355, 381, 12, 13, 14, 15, 386, 137, 138, 139, 140, 0, 288, 289, 290, 286, 396, 387, 388, 389, 390, 376, 512, 513, 514, 515, 36, 407, 38, 39, 40, 161, 412, 163, 164, 165, 312, 0, 314, 315, 311, 411, 422, 413, 414, 415, 536, 402, 538, 539, 540, 61, 62, 433, 64, 65, 186, 187, 438, 189, 190, 337, 338, 0, 340, 336, 436, 437, 448, 439, 440, 561, 562, 428, 564, 565, 86, 87, 88, 459, 90, 211, 212, 213, 464, 215, 362, 363, 364, 0, 361, 461, 462, 463, 474, 465, 586, 587, 588, 454, 590, 111, 112, 113, 114, 485, 236, 237, 238, 239, 490, 262, 263, 264, 265, 0, 486, 487, 488, 489, 500, 611, 612, 613, 614, 480, 506, 17, 18, 19, 20, 511, 142, 143, 144, 145, 516, 267, 268, 269, 270, 0, 418, 419, 420, 416, 501, 517, 518, 519, 520, 41, 532, 43, 44, 45, 166, 537, 168, 169, 170, 291, 542, 293, 294, 295, 442, 0, 444, 445, 441, 541, 527, 543, 544, 545, 66, 67, 558, 69, 70, 191, 192, 563, 194, 195, 316, 317, 568, 319, 320, 467, 468, 0, 470, 466, 566, 567, 553, 569, 570, 91, 92, 93, 584, 95, 216, 217, 218, 589, 220, 341, 342, 343, 594, 345, 492, 493, 494, 0, 491, 591, 592, 593, 579, 595, 116, 117, 118, 119, 610, 241, 242, 243, 244,
615, 366, 367, 368, 369, 620, 392, 393, 394, 395, 0, 616, 617, 618,
619, 605, 6, 22, 23, 24, 25, 11, 147, 148, 149, 150, 16, 272, 273, 274, 275, 21, 397, 398, 399, 400, 0, 548, 549, 550, 546, 46, 32, 48,
49, 50, 171, 37, 173, 174, 175, 296, 42, 298, 299, 300, 421, 47, 423, 424, 425, 572, 0, 574, 575, 571, 71, 72, 58, 74, 75, 196, 197, 63, 199, 200, 321, 322, 68, 324, 325, 446, 447, 73, 449, 450, 597, 598, 0, 600, 596, 96, 97, 98, 84, 100, 221, 222, 223, 89, 225, 346, 347, 348, 94, 350, 471, 472, 473, 99, 475, 622, 623, 624, 0, 621, 121, 122, 123, 124, 110, 246, 247, 248, 249, 115, 371, 372, 373, 374, 120, 496, 497, 498, 499, 125, 522, 523, 524, 525, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
const AtoY = CharSpace(collect("ABCDEFGHIJKLMNOPQRSTUVWXY"))^2
const BiAlph = Alphabet ^ 2
mutable struct Playfair <: AbstractCipher
key::SquareKey
splittoken::Int # confusingly with Alphabet tokenisation
splitmap::Dict{Int, Tuple{Int, Int}}
forbidden::Int
fromsquaremap::Vector{Int}
outcharspace::NCharSpace{2}
inverted::Bool
function Playfair(key::SquareKey, spl::Int)
if length(key) != 25
error("Input key must be of length 25 (omit tokens to be merged)")
end
W_base = CharSpace(key)
W = W_base ^ 2
fromsquaremap = Vector{Int}(undef, 25)
for (tok, str) in enumerate(W_base.charmap)
p = findfirst(==(str), Alphabet.charmap)
fromsquaremap[tok] = p
end
forbiddenbigram = BiAlph.reducemap[spl, spl]
splits = Dict{Int, Tuple{Int, Int}}()
for i in 1:25
isobigram = BiAlph.reducemap[i, i]
splitbigram = (BiAlph.reducemap[i, spl], BiAlph.reducemap[spl, i])
splits[isobigram] = splitbigram
end
return new(key, spl, splits, forbiddenbigram, fromsquaremap, W, false)
end
end
function replace_insert(vector::Vector{Int}, dict::Dict)
out = Vector{Int}()
for i in vector
append!(out, get(dict, i, i))
end
return out
end
function apply(P::Playfair, vector::Vector{Int}; safety_checks::Txt, playfairmap::Vector{Int} = fixed_playfairmap)
# Split isobigrams
# Apply fixed map
if P.inverted
else
if P.forbidden in vector
e = ErrorException("$(P.outcharspace.charmap[P.forbidden]) bigram cannot be split by $(P.outcharspace.charmap[P.splittoken])")
throw(e)
end
new_tokens = replace_insert(vector, P.splitmap) # process in BiAlph
new_tokens = [BiAlph.reducemap[i] for i in new_tokens] # return to monograms
# reassign characters to square space
# return to bigrams
new_tokens = [playfairmap[i] for i in new_tokens] # execute map
# return to monograms
new_tokens = [P.fromsquaremap[i] for i in new_tokens] # return to Alphabet
end
return new_tokens
end
# cart25 = CartesianIndices(Array{Int}(undef, (5,5)))
# linear25 = LinearIndices(Array{Int}(undef, (5,5)))
# fixed_playfairmap = zeros(Int, 675)
# for (bigram, str) in enumerate(AtoY.charmap)
# (tokenA, tokenB) = AtoY.reducemap[bigram]
# if tokenA == tokenB
# continue
# end
# A = cart25[tokenA]
# B = cart25[tokenB]
# if A[1] == B[1]
# # increment
# A += CartesianIndex(0, 1)
# B += CartesianIndex(0, 1)
# # wrap around
# if B[2] == 6
# B = CartesianIndex(B[1], 1)
# elseif A[2] == 6
# A = CartesianIndex(A[1], 1)
# end
# elseif A[2] == B[2]
# # increment
# A += CartesianIndex(1, 0)
# B += CartesianIndex(1, 0)
# # wrap around
# if B[1] == 6
# B = CartesianIndex(1, B[2])
# elseif A[1] == 6
# A = CartesianIndex(1, A[2])
# end
# else
# # switch corners
# a2, b2 = B[2], A[2]
# A = CartesianIndex(A[1], a2)
# B = CartesianIndex(B[1], b2)
# end
# tokenA = linear25[A]
# tokenB = linear25[B]
# newbigram = AtoY.reducemap[tokenA, tokenB]
# fixed_playfairmap[bigram] = newbigram
# end
| RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 1327 | include("square.jl")
mutable struct Polybius <: AbstractCipher
key::SquareKey
indices::Vector{String}
outcharspace::NCharSpace{2}
inverted::Bool
function Polybius(key::SquareKey, indices::Vector{String} = ["1", "2", "3", "4", "5"])
W = CharSpace(indices)
return new(key, indices, W ^ 2, false)
end
end
invert!(P::Polybius) = RxCiphers.switch_invert_tag!(P)
function apply!(P::Polybius, txt::Txt) ::Txt
if !txt.is_tokenised
throw(TokeniseError)
end
if P.inverted
# take text
# nchar
nchar!(txt, 2)
if txt.charspace != P.outcharspace
println("WARNING: Txt character space does not match Polybius output space")
end
# reassign to key.inspace
txt.charspace = key.charspace
# map from square
txt.tokenised = invmapsquare(txt.tokenised, P.key)
# bye bye
else
if txt.charspace != P.key.charspace
println("WARNING: Txt character space does not match Polybius input space")
end
# take text
# map to square
txt.tokenised = mapsquare(txt.tokenised, P.key)
# reassign to NCharSpace{2}
txt.charspace = P.outcharspace
# reduce
reduce!(txt)
# bye bye
end
return txt
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 2576 | using RxCiphers
import Base.show
function vchecksquare(vector::Vector) ::Int
L = length(vector)
n = isqrt(L)
if n ^ 2 != L
e = ErrorException("Vector is not a square length")
throw(e)
end
return n
end
mutable struct SquareKey
charspace::NCharSpace{1}
key::Vector{Int}
size::Int
displaykey::Matrix{String}
tosquare::Dict{Int, Int}
fromsquare::Dict{Int, Int}
function SquareKey(key::Vector{Int}, charspace::NCharSpace{1}, merged::NTuple{N, Pair{Int, Int}} where N)
# check that merged is good
if !allunique(key)
e = ArgumentError("All square key entries must be unique")
throw(e)
end
n = vchecksquare(key)
display = fill("", (n, n))
tosquare = Dict{Int, Int}()
fromsquare = Dict{Int, Int}()
for squaretoken in 1:n^2
exttoken = key[squaretoken]
fromsquare[squaretoken] = exttoken
tosquare[exttoken] = squaretoken
display[squaretoken] = charspace.charmap[exttoken]
end
for (mergedtoken, mergedest) in merged
squaretoken = tosquare[mergedest]
tosquare[mergedtoken] = squaretoken
display[squaretoken] *= "/$(charspace.charmap[mergedtoken])"
end
return new(charspace, key, n, display, tosquare, fromsquare)
end
end
# if merged not provided
# auto merge excluded tokens
function SquareKey(key::Vector{Int}, charspace::NCharSpace{1}) ::SquareKey
# check it's not ridiculous to merge
if charspace.size > 2 * length(key)
e = ArgumentError("Too many tokens remain to be merged, choose a larger square")
throw(e)
end
# you know exactly how long the vector should be, preallocate.
tomerge = Vector{Int}()
for i in 1:charspace.size
if !(i in key)
push!(tomerge, i)
end
end
merged = Tuple(i => j for (i, j) in zip(tomerge, key))
return SquareKey(key, charspace, merged)
end
SquareKey(txt::Txt, merged::Vararg{Pair{Int, Int}, N} where N) = SquareKey(checktokenised(txt), txt.charspace, merged)
function show(io::IO, S::SquareKey)
show(io, S.displaykey)
end
function show(io::IO, ::MIME"text/plain", S::SquareKey)
println(io, "$(S.size)-Square Key:")
display(S.displaykey)
end
function mapsquare(vect::Vector{Int}, square::SquareKey)
return [square.tosquare[i] for i in vect]
end
function invmapsquare(vect::Vector{Int}, square::SquareKey)
return [square.fromsquare[i] for i in vect]
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 2476 | include("substitution.jl")
include("transposition.jl")
function issquare(int::Int) ::Bool
return isqrt(int) ^ 2 == int
end
Replace(map::Pair{Int, Int}) = Lambda(x -> x == map[1] ? map[2] : x, identity)
function Square(square_key::Vector{Int}, replace::Vector{Pair{Int, Int}}; square_indices::Vector{Char})
if !issquare(length(square_key))
error("Input key must be of square integer length (omit tokens to be merged)")
end
if any([i[1] in square_key for i in replace])
error("Tokens in the square key cannot be merged to other tokens (replace must involve only omitted tokens)")
end
if length(square_indices) != isqrt(length(square_key))
error("Number of indices squared must match the square size")
end
omitted = [i[1] for i in replace]
S = Substitution([square_key ; omitted])
invert!(S)
square = nothing
for pair in replace
square = Replace(pair)(square)
end
square = S(square)
return square
end
function Polybius(square_key::Vector{Int}, replace::Pair{Int, Int}; square_indices::Vector{Char} = ['1', '2', '3', '4', '5'])
if length(square_key) != 25
error("Input key must be of length 25 (omit tokens to be merged)")
end
if replace[1] in square_key
error("Tokens in the square key cannot be merged to other tokens (replace must involve only omitted tokens)")
end
if length(square_indices) != 5
error("Polybius requires five indices exactly")
end
charspace = CharSpace(square_indices) ^ 2
polybius = Square(square_key, [replace]; square_indices = square_indices)
polybius = Reassign(Alphabet, charspace)(polybius)
return polybius
end
function ADFGX(square_key::Vector{Int}, replace::Pair{Int, Int}, columnar_args...; square_indices::Vector{Char} = ['A', 'D', 'F', 'G', 'X'])
if length(square_key) != 25
error("Input key must be of length 25 (omit tokens to be merged)")
end
if replace[1] in square_key
error("Tokens in the square key cannot be merged to other tokens (replace must involve only omitted tokens)")
end
if length(square_indices) != 5
error("Polybius requires five indices exactly")
end
charspace = CharSpace(square_indices)
adfgx = Polybius(square_key, replace; square_indices = square_indices)
adfgx = Retokenise(charspace ^ 2, charspace)(adfgx)
adfgx = Columnar(columnar_args...)(adfgx)
return adfgx
end
| RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 333 | include("tuco.jl")
include("transposition.jl")
mutable struct Unshaper <: AbstractCipher
columnar::ColumnarType
end
Unshaper(n::Int) = Unshaper(Columnar(n))
function apply(u::Unshaper, vect::Vector{Int}; safety_checks::Txt)
new_tokens = unshape(vect, u.columnar)
new_tokens = vec(new_tokens)
return new_tokens
end | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 3792 | using RxCiphers
using Test
using JLD2
include("test results.jl")
@testset "RxCiphers.jl" begin
using .TxtSamples
# NCharSpace
t = orwell.raw
T = Txt(t)
@test tokenise!(T).tokenised == orwell_tokens
@test untokenise!(T).raw == t
W_test = CharSpace(["1", "2", "3"])
W_squared = W_test ^ 2
@test W_squared.charmap == w_squared_charmap
@test W_squared.linear[2, 3][1] == 8
@test Tuple(W_squared.cartesian[8]) == (2, 3)
@test W_squared.units == W_test.charmap
@test nchar(W_squared, 1) == W_test
# Remains:
# Txt nchar
# union
# Base. overloads
# Substitution
S = Substitution(26)
@test S.mapping == collect(1:26)
@test S.tokens == S.mapping
@test S.size == 26
@test Atbash(15).mapping == atbash_15_mapping
Caesar_test = Caesar(2, 26)
@test Caesar_test.mapping == caesar_2_26_mapping
Affine_test = Affine(3, 5, Alphabet)
@test Affine_test.mapping == affine_3_5_26_mapping
tokenise!(orwell)
caesar_enc = apply(Caesar_test, orwell)
@test caesar_enc.tokenised == caesar_2_26_orwell_tokens
@test crack_Caesar(caesar_enc) == Caesar_test
affine_enc = apply(Affine_test, orwell)
@test affine_enc.tokenised == affine_3_5_26_orwell_tokens
@test crack_Affine(affine_enc) == Affine_test
invert!(Affine_test)
@test Affine_test.mapping == affine_3_5_26_mapping_inv
@test Caesar(2, 10) + Caesar(3, 10) == Caesar(5, 10)
@test frequency_matched_Substitution(orwell).mapping == freq_match_orwell_mapping
# Remains:
# shift, switch, mutate
# Base. overloads
# Periodic Substitution
tokenise!(orwell)
Vigenere_test = Vigenere("ABBA", Alphabet)
@test [i.mapping for i in Vigenere_test] == vigenere_ABBA_mappings
Periodic_Affine_test = Periodic_Affine([1,3,5], [7,6,5], 26)
@test [i.mapping for i in Periodic_Affine_test] == periodic_affine_123_765_mappings
vigenere_enc = Vigenere_test(orwell)
@test vigenere_enc.tokenised == vigenere_ABBA_orwell_tokens
@test crack_Vigenere(vigenere_enc) == Vigenere_test
periodic_affine_enc = Periodic_Affine_test(orwell)
@test periodic_affine_enc.tokenised == periodic_affine_123_765_orwell_tokens
@test crack_Periodic_Affine(periodic_affine_enc) == Periodic_Affine_test
# Permutation
Permutation_test = Permutation([1,5,3,2,4], true)
permutation_enc = Permutation_test(orwell)
@test permutation_enc.tokenised == permutation_15324_orwell_tokens
invert!(Permutation_test)
@test Permutation_test(permutation_enc).tokenised == orwell_tokens
# MatrixTransposition
MTrans_test = MatrixTransposition(6, true)
mtrans_enc = MTrans_test(orwell)
@test mtrans_enc.tokenised == mtrans_6_orwell_tokens
invert!(MTrans_test)
@test MTrans_test(mtrans_enc).tokenised == orwell_tokens
# Columnar
Columnar_test = Columnar([1,4,3,2], true)
columnar_enc = Columnar_test(orwell)
@test columnar_enc.tokenised == columnar_1432_orwell_tokens
invert!(Columnar_test)
@test Columnar_test(columnar_enc).tokenised == orwell_tokens
# Remains:
# Base. overloads
# AbstractCipher & Encryption
# Remains:
# __constr__
# push!, iterate
# apply, invert
# Lambda, Retokenise, Reassign
# Tuco
# Remains:
# __gramlogs, orthodot
# appearances, frequencies
# ioc, periodic_ioc
# bbin_probabilities
# find_period (retested in Periodic Substitution)
# divisors, factorise
# blocks, block_apply_stats, rolling, rolling_average, char_distribution
# substructure_sigma, substructure_variance
# Array functions
# Remains:
# switch, switch!
# safe_reshape_2D
# checkperm
# affine
# normalise!
end
| RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | code | 70870 | const orwell_tokens = [9, 20, 23, 1, 19, 1, 2, 18, 9, 7, 8, 20, 3, 15, 12, 4, 4, 1, 25, 9, 14, 1, 16, 18, 9, 12, 1, 14, 4, 20, 8, 5, 3,
12, 15, 3, 11, 19, 23, 5, 18, 5, 19, 20, 18, 9, 11, 9, 14, 7, 20, 8, 9, 18, 20, 5, 5, 14, 23, 9, 14, 19, 20, 15, 14, 19, 13, 9, 20, 8, 8, 9, 19, 3, 8, 9, 14, 14, 21, 26, 26, 12, 5, 4, 9, 14, 20, 15, 8, 9, 19, 2, 18, 5, 1, 19, 20, 9, 14, 1, 14, 5, 6, 6, 15, 18, 20, 20, 15, 5, 19, 3, 1, 16, 5, 20, 8, 5, 22, 9, 12, 5, 23, 9, 14, 4, 19, 12, 9,
16, 16, 5, 4, 17, 21, 9, 3, 11, 12, 25, 20, 8, 18, 15, 21, 7, 8, 20, 8, 5, 7, 12, 1, 19, 19, 4, 15, 15, 18, 19, 15, 6, 22, 9, 3, 20, 15, 18, 25, 13, 1, 14, 19, 9, 15, 14, 19, 20, 8, 15, 21, 7, 8, 14, 15, 20, 17, 21, 9, 3, 11, 12, 25, 5, 14, 15, 21, 7, 8, 20, 15, 16, 18, 5, 22, 5, 14, 20, 1, 19, 23, 9, 18, 12, 15, 6, 7, 18, 9, 20, 20, 25, 4, 21, 19, 20, 6, 18, 15, 13, 5, 14, 20, 5, 18, 9, 14, 7, 1, 12, 15, 14, 7, 23, 9, 20, 8, 8, 9, 13, 20, 8, 5, 8, 1,
12, 12, 23, 1, 25, 19, 13, 5, 12, 20, 15, 6, 2, 15, 9, 12, 5, 4, 3, 1, 2, 2, 1, 7, 5, 1, 14, 4, 15, 12, 4, 18, 1,
7, 13, 1, 20, 19, 1, 20, 15, 14, 5, 5, 14, 4, 15, 6, 9, 20, 1, 3, 15, 12, 15, 21, 18, 5, 4, 16, 15, 19, 20, 5, 18, 20, 15, 15, 12, 1, 18, 7, 5, 6, 15, 18, 9, 14, 4, 15, 15, 18, 4, 9, 19, 16, 12, 1, 25, 8, 1, 4, 2, 5, 5, 14, 20,
1, 3, 11, 5, 4, 20, 15, 20, 8, 5, 23, 1, 12, 12, 9, 20, 4, 5, 16, 9, 3, 20, 5, 4, 19, 9, 13, 16, 12, 25, 1, 14, 5, 14, 15, 18, 13, 15, 21, 19, 6, 1, 3, 5, 13, 15, 18, 5, 20, 8, 1, 14, 1, 13, 5, 20, 18, 5, 23, 9, 4, 5, 20, 8, 5,
6, 1, 3, 5, 15, 6, 1, 13, 1, 14, 15, 6, 1, 2, 15, 21, 20, 6, 15, 18, 20, 25, 6, 9, 22, 5, 23, 9, 20, 8, 1, 8, 5, 1, 22, 25, 2, 12, 1, 3, 11, 13, 15, 21, 19, 20, 1, 3, 8, 5, 1, 14, 4, 18, 21, 7, 7, 5, 4, 12, 25, 8, 1, 14, 4, 19,
15, 13, 5, 6, 5, 1, 20, 21, 18, 5, 19, 23, 9, 14, 19, 20, 15, 14, 13, 1, 4, 5, 6, 15, 18, 20, 8, 5, 19, 20, 1, 9,
18, 19, 9, 20, 23, 1, 19, 14, 15, 21, 19, 5, 20, 18, 25, 9, 14, 7, 20, 8, 5, 12, 9, 6, 20, 5, 22, 5, 14, 1, 20, 20, 8, 5, 2, 5, 19, 20, 15, 6, 20, 9, 13, 5, 19, 9, 20, 23, 1, 19, 19, 5, 12, 4, 15, 13, 23, 15, 18, 11, 9, 14, 7, 1, 14, 4, 1, 20, 16, 18, 5, 19, 5, 14, 20, 20, 8, 5, 5, 12, 5, 3, 20, 18, 9, 3, 3, 21, 18, 18, 5, 14, 20, 23, 1, 19, 3, 21, 20, 15, 6, 6, 4, 21, 18, 9, 14, 7, 4, 1, 25, 12, 9, 7, 8, 20, 8, 15, 21, 18, 19, 9, 20, 23, 1, 19, 16, 1, 18, 20, 15, 6, 20, 8, 5, 5, 3, 15, 14, 15, 13, 25, 4, 18, 9, 22, 5, 9, 14, 16, 18, 5, 16, 1, 18, 1, 20, 9, 15, 14, 6, 15, 18, 8, 1, 20, 5, 23, 5, 5, 11, 20, 8, 5, 6, 12, 1, 20, 23, 1, 19, 19, 5, 22, 5, 14, 6, 12, 9, 7, 8, 20, 19, 21, 16, 1, 14, 4, 23, 9, 14, 19, 20, 15, 14, 23, 8, 15, 23, 1, 19, 20, 8, 9, 18, 20, 25, 14, 9, 14, 5, 1, 14, 4, 8, 1, 4, 1, 22, 1, 18, 9, 3, 15, 19, 5, 21, 12, 3, 5, 18, 1, 2, 15, 22, 5, 8, 9, 19, 18, 9, 7, 8, 20, 1, 14, 11, 12, 5, 23, 5, 14, 20, 19, 12, 15, 23, 12, 25, 18, 5, 19, 20, 9, 14, 7, 19, 5, 22, 5, 18, 1, 12, 20, 9, 13, 5, 19, 15, 14, 20, 8, 5, 23, 1, 25, 15, 14, 5, 1, 3, 8, 12, 1, 14, 4, 9, 14, 7, 15, 16, 16, 15, 19, 9, 20, 5, 20, 8, 5,
12, 9, 6, 20, 19, 8, 1, 6, 20, 20, 8, 5, 16, 15, 19, 20, 5, 18, 23, 9, 20, 8, 20, 8, 5, 5, 14, 15, 18, 13, 15, 21, 19, 6, 1, 3, 5, 7, 1, 26, 5, 4, 6, 18, 15, 13, 20, 8, 5, 23, 1, 12, 12, 9, 20, 23, 1, 19, 15, 14, 5, 15, 6, 20, 8, 15, 19, 5, 16, 9, 3, 20, 21, 18, 5, 19, 23, 8, 9, 3, 8, 1, 18, 5, 19, 15, 3, 15, 14, 20, 18, 9, 22, 5, 4, 20, 8, 1, 20, 20, 8, 5, 5, 25, 5, 19, 6, 15, 12, 12, 15, 23, 25, 15, 21, 1, 2, 15, 21, 20, 23, 8, 5, 14, 25, 15, 21, 13, 15, 22, 5, 2, 9, 7, 2, 18, 15, 20, 8, 5, 18, 9, 19, 23, 1, 20, 3, 8, 9, 14, 7, 25, 15, 21, 20, 8, 5, 3, 1, 16, 20, 9, 15, 14, 2, 5, 14, 5, 1, 20, 8, 9, 20, 18, 1, 14, 9, 14, 19, 9, 4, 5, 20, 8, 5, 6, 12, 1, 20, 1, 6, 18, 21, 9, 20, 25, 22, 15, 9, 3, 5, 23, 1, 19, 18, 5, 1, 4, 9, 14, 7, 15, 21, 20, 1, 12, 9, 19, 20, 15, 6, 6, 9, 7, 21, 18,
5, 19, 23, 8, 9, 3, 8, 8, 1, 4, 19, 15, 13, 5, 20, 8, 9, 14, 7, 20, 15, 4, 15, 23, 9, 20, 8, 20, 8, 5, 16, 18, 15, 4, 21, 3, 20, 9, 15, 14, 15, 6, 16, 9, 7, 9, 18, 15, 14, 20, 8, 5, 22, 15, 9, 3, 5, 3, 1, 13, 5, 6, 18, 15, 13, 1, 14, 15, 2, 12, 15, 14, 7, 13, 5, 20, 1, 12, 16, 12, 1, 17, 21, 5, 12, 9, 11, 5, 1, 4, 21, 12, 12, 5, 4, 13, 9, 18, 18, 15, 18, 23, 8, 9, 3, 8, 6, 15, 18, 13, 5, 4, 16, 1, 18, 20, 15, 6, 20, 8, 5, 19, 21, 18, 6, 1, 3, 5, 15, 6, 20, 8, 5, 18, 9, 7, 8, 20, 8, 1, 14, 4, 23, 1, 12, 12, 23, 9, 14, 19, 20, 15, 14, 20, 21, 18, 14, 5, 4, 1, 19, 23, 9, 20, 3, 8, 1, 14, 4, 20, 8, 5, 22, 15, 9, 3, 5, 19, 1, 14, 11, 19, 15, 13, 5, 23, 8, 1, 20, 20, 8, 15, 21, 7,
8, 20, 8, 5, 23, 15, 18, 4, 19, 23, 5, 18, 5, 19, 20, 9, 12, 12, 4, 9, 19, 20, 9, 14, 7, 21, 9, 19, 8, 1, 2, 12, 5, 20, 8, 5, 9, 14, 19, 20, 18, 21, 13, 5, 14, 20, 20, 8, 5, 20, 5, 12, 5, 19, 3, 18, 5, 5, 14, 9, 20, 23, 1, 19, 3, 1, 12, 12, 5, 4, 3, 15, 21, 12, 4, 2, 5, 4, 9, 13, 13, 5, 4, 2, 21, 20, 20, 8, 5, 18, 5, 23, 1, 19, 14, 15, 23,
1, 25, 15, 6, 19, 8, 21, 20, 20, 9, 14, 7, 9, 20, 15, 6, 6, 3, 15, 13, 16, 12, 5, 20, 5, 12, 25, 8, 5, 13, 15, 22, 5, 4, 15, 22, 5, 18, 20, 15, 20, 8, 5, 23, 9, 14, 4, 15, 23, 1, 19, 13, 1, 12, 12, 9, 19, 8, 6, 18, 1, 9, 12, 6,
9, 7, 21, 18, 5, 20, 8, 5, 13, 5, 1, 7, 18, 5, 14, 5, 19, 19, 15, 6, 8, 9, 19, 2, 15, 4, 25, 13, 5, 18, 5, 12, 25, 5, 13, 16, 8, 1, 19, 9, 26, 5, 4, 2, 25, 20, 8, 5, 2, 12, 21, 5, 15, 22, 5, 18, 1, 12, 12, 19, 23, 8, 9, 3, 8, 23, 5, 18, 5, 20, 8, 5, 21, 14, 9, 6, 15, 18, 13, 15, 6, 20, 8, 5, 16, 1, 18, 20, 25, 8, 9, 19, 8, 1, 9, 18, 23, 1,
19, 22, 5, 18, 25, 6, 1, 9, 18, 8, 9, 19, 6, 1, 3, 5, 14, 1, 20, 21, 18, 1, 12, 12, 25, 19, 1, 14, 7, 21, 9, 14, 5, 8, 9, 19, 19, 11, 9, 14, 18, 15, 21, 7, 8, 5, 14, 5, 4, 2, 25, 3, 15, 1, 18, 19, 5, 19, 15, 1, 16, 1, 14, 4, 2,
12, 21, 14, 20, 18, 1, 26, 15, 18, 2, 12, 1, 4, 5, 19, 1, 14, 4, 20, 8, 5, 3, 15, 12, 4, 15, 6, 20, 8, 5, 23, 9, 14, 20, 5, 18, 20, 8, 1, 20, 8, 1, 4, 10, 21, 19, 20, 5, 14, 4, 5, 4, 15, 21, 20, 19, 9, 4, 5, 5, 22, 5, 14, 20, 8, 18, 15, 21, 7, 8, 20, 8, 5, 19, 8, 21, 20, 23, 9, 14, 4, 15, 23, 16, 1, 14, 5, 20, 8, 5, 23, 15, 18, 12, 4, 12, 15, 15, 11, 5, 4, 3, 15, 12, 4, 4, 15, 23, 14, 9, 14, 20, 8, 5, 19, 20, 18, 5, 5, 20, 12, 9, 20, 20, 12, 5, 5, 4, 4, 9, 5, 19, 15, 6, 23, 9, 14, 4, 23, 5, 18, 5, 23, 8, 9, 18, 12, 9, 14, 7, 4, 21, 19, 20, 1, 14, 4, 20, 15, 18, 14, 16, 1, 16, 5, 18, 9, 14, 20, 15, 19, 16, 9, 18, 1, 12, 19, 1, 14, 4, 20, 8, 15, 21, 7, 8, 20, 8, 5, 19, 21, 14,
23, 1, 19, 19, 8, 9, 14, 9, 14, 7, 1, 14, 4, 20, 8, 5, 19, 11, 25, 1, 8, 1, 18, 19, 8, 2, 12, 21, 5, 20, 8, 5, 18, 5, 19, 5, 5, 13, 5, 4, 20, 15, 2, 5, 14, 15, 3, 15, 12, 15, 21, 18, 9, 14, 1, 14, 25, 20, 8, 9, 14, 7, 5, 24, 3,
5, 16, 20, 20, 8, 5, 16, 15, 19, 20, 5, 18, 19, 20, 8, 1, 20, 23, 5, 18, 5, 16, 12, 1, 19, 20, 5, 18, 5, 4, 5, 22, 5, 18, 25, 23, 8, 5, 18, 5, 20, 8, 5, 2, 12, 1, 3, 11, 13, 15, 21, 19, 20, 1, 3, 8, 9, 15, 4, 6, 1, 3, 5, 7, 1, 26, 5, 4, 4, 15, 23, 14, 6, 18, 15, 13, 5, 22, 5, 18, 25, 3, 15, 13, 13, 1, 14, 4, 9, 14, 7, 3, 15, 18, 14, 5, 18,
20, 8, 5, 18, 5, 23, 1, 19, 15, 14, 5, 15, 14, 20, 8, 5, 8, 15, 21, 19, 5, 6, 18, 15, 14, 20, 9, 13, 13, 5, 4, 9,
1, 20, 5, 12, 25, 15, 16, 16, 15, 19, 9, 20, 5, 2, 9, 7, 2, 18, 15, 20, 8, 5, 18, 9, 19, 23, 1, 20, 3, 8, 9, 14, 7, 25, 15, 21, 20, 8, 5, 3, 1, 16, 20, 9, 15, 14, 19, 1, 9, 4, 23, 8, 9, 12, 5, 20, 8, 5, 4, 1, 18, 11, 5, 25, 5, 19, 12, 15, 15, 11, 5, 4, 4, 5, 5, 16, 9, 14, 20, 15, 23, 9, 14, 19, 20, 15, 14, 19, 15, 23, 14, 4, 15, 23, 14, 1,
20, 19, 20, 18, 5, 5, 20, 12, 5, 22, 5, 12, 1, 14, 15, 20, 8, 5, 18, 16, 15, 19, 20, 5, 18, 20, 15, 18, 14, 1, 20, 15, 14, 5, 3, 15, 18, 14, 5, 18, 6, 12, 1, 16, 16, 5, 4, 6, 9, 20, 6, 21, 12, 12, 25, 9, 14, 20, 8, 5, 23, 9, 14, 4, 1, 12, 20, 5, 18, 14, 1, 20, 5, 12, 25, 3, 15, 22, 5, 18, 9, 14, 7, 1, 14, 4, 21, 14, 3, 15, 22, 5, 18, 9, 14, 7, 20, 8, 5, 19, 9, 14, 7, 12, 5, 23, 15, 18, 4, 9, 14, 7, 19, 15, 3, 9, 14, 20, 8, 5, 6, 1, 18, 4, 9, 19, 20, 1, 14, 3, 5, 1, 8, 5, 12, 9, 3, 15, 16, 20, 5, 18, 19, 11, 9, 13, 13, 5, 4, 4, 15, 23, 14, 2, 5, 20, 23, 5, 5, 14, 20, 8, 5, 18, 15, 15, 6, 19, 8, 15, 22, 5, 18, 5, 4, 6, 15, 18, 1, 14, 9, 14, 19, 20, 1, 14, 20, 12, 9, 11, 5, 1, 2, 12, 21, 5, 2, 15, 20, 20, 12, 5, 1, 14, 4, 4, 1, 18, 20, 5, 4, 1, 23, 1, 25, 1, 7, 1, 9, 14, 23, 9, 20, 8, 1, 3, 21, 18, 22, 9, 14, 7, 6, 12, 9, 7, 8, 20, 9, 20, 23, 1, 19, 20, 8, 5, 16, 15, 12, 9, 3, 5, 16, 1, 20, 18, 15, 12, 19, 14, 15, 15, 16, 9, 14, 7, 9, 14, 20, 15, 16, 5, 15, 16, 12, 5, 19, 23, 9, 14, 4, 15, 23, 19, 20, 8, 5, 16, 1, 20, 18, 15, 12, 19, 4, 9, 4, 14, 15, 20, 13, 1, 20, 20, 5, 18, 8, 15, 23, 5, 22, 5, 18, 15, 14, 12, 25, 20, 8, 5, 20, 8, 15, 21, 7, 8, 20, 16, 15, 12, 9, 3, 5, 13, 1, 20, 20, 5, 18, 5, 4]
atbash_15_mapping = reverse!(collect(1:15))
caesar_2_26_mapping = circshift(collect(1:26), -2)
affine_3_5_26_mapping = [6, 9, 12, 15, 18, 21, 24, 1, 4, 7, 10, 13, 16, 19, 22, 25, 2, 5, 8, 11, 14, 17, 20, 23, 26, 3]
affine_3_5_26_mapping_inv = [8, 17, 26, 9, 18, 1, 10, 19, 2, 11, 20, 3, 12, 21, 4, 13, 22, 5, 14, 23, 6, 15, 24, 7, 16, 25]
caesar_2_26_orwell_tokens = [11, 22, 25, 3, 21, 3, 4, 20, 11, 9, 10, 22, 5, 17, 14, 6, 6, 3, 1, 11, 16, 3, 18, 20, 11, 14, 3, 16, 6, 22, 10, 7, 5, 14, 17, 5, 13, 21, 25, 7, 20, 7, 21, 22, 20, 11, 13, 11, 16, 9, 22, 10, 11, 20, 22, 7, 7, 16, 25, 11, 16, 21, 22, 17, 16, 21, 15, 11, 22, 10, 10, 11, 21, 5, 10, 11, 16, 16, 23, 2, 2, 14, 7, 6, 11, 16, 22, 17, 10, 11, 21, 4, 20, 7, 3, 21, 22, 11, 16, 3, 16, 7, 8, 8, 17, 20, 22, 22, 17, 7, 21, 5, 3, 18, 7, 22, 10, 7, 24, 11, 14, 7, 25, 11, 16, 6, 21, 14, 11, 18, 18, 7, 6, 19, 23, 11, 5, 13, 14, 1, 22, 10, 20, 17, 23, 9, 10, 22, 10, 7, 9, 14, 3, 21,
21, 6, 17, 17, 20, 21, 17, 8, 24, 11, 5, 22, 17, 20, 1, 15, 3, 16, 21, 11, 17, 16, 21, 22, 10, 17, 23, 9, 10, 16,
17, 22, 19, 23, 11, 5, 13, 14, 1, 7, 16, 17, 23, 9, 10, 22, 17, 18, 20, 7, 24, 7, 16, 22, 3, 21, 25, 11, 20, 14, 17, 8, 9, 20, 11, 22, 22, 1, 6, 23, 21, 22, 8, 20, 17, 15, 7, 16, 22, 7, 20, 11, 16, 9, 3, 14, 17, 16, 9, 25, 11, 22, 10, 10, 11, 15, 22, 10, 7, 10, 3, 14, 14, 25, 3, 1, 21, 15, 7, 14, 22, 17, 8, 4, 17, 11, 14, 7, 6, 5, 3, 4, 4,
3, 9, 7, 3, 16, 6, 17, 14, 6, 20, 3, 9, 15, 3, 22, 21, 3, 22, 17, 16, 7, 7, 16, 6, 17, 8, 11, 22, 3, 5, 17, 14, 17, 23, 20, 7, 6, 18, 17, 21, 22, 7, 20, 22, 17, 17, 14, 3, 20, 9, 7, 8, 17, 20, 11, 16, 6, 17, 17, 20, 6, 11, 21, 18, 14, 3, 1, 10, 3, 6, 4, 7, 7, 16, 22, 3, 5, 13, 7, 6, 22, 17, 22, 10, 7, 25, 3, 14, 14, 11, 22, 6, 7, 18, 11, 5, 22, 7, 6, 21, 11, 15, 18, 14, 1, 3, 16, 7, 16, 17, 20, 15, 17, 23, 21, 8, 3, 5, 7, 15, 17, 20, 7, 22, 10, 3, 16,
3, 15, 7, 22, 20, 7, 25, 11, 6, 7, 22, 10, 7, 8, 3, 5, 7, 17, 8, 3, 15, 3, 16, 17, 8, 3, 4, 17, 23, 22, 8, 17, 20, 22, 1, 8, 11, 24, 7, 25, 11, 22, 10, 3, 10, 7, 3, 24, 1, 4, 14, 3, 5, 13, 15, 17, 23, 21, 22, 3, 5, 10, 7, 3, 16, 6, 20, 23, 9, 9, 7, 6, 14, 1, 10, 3, 16, 6, 21, 17, 15, 7, 8, 7, 3, 22, 23, 20, 7, 21, 25, 11, 16, 21, 22, 17, 16, 15, 3, 6, 7, 8, 17, 20, 22, 10, 7, 21, 22, 3, 11, 20, 21, 11, 22, 25, 3, 21, 16, 17, 23, 21, 7, 22, 20, 1, 11, 16, 9, 22, 10, 7, 14, 11, 8, 22, 7, 24, 7, 16, 3, 22, 22, 10, 7, 4, 7, 21, 22, 17, 8, 22, 11, 15, 7, 21, 11, 22, 25, 3, 21, 21, 7, 14, 6, 17, 15, 25, 17, 20, 13, 11, 16, 9, 3, 16, 6, 3, 22, 18, 20, 7, 21, 7, 16, 22, 22, 10, 7, 7, 14, 7, 5, 22, 20, 11, 5, 5, 23, 20, 20, 7, 16, 22, 25, 3, 21, 5, 23, 22, 17, 8, 8, 6, 23, 20, 11, 16, 9, 6, 3, 1, 14, 11, 9, 10, 22, 10, 17, 23, 20, 21, 11, 22, 25, 3, 21, 18, 3, 20, 22, 17, 8, 22, 10, 7, 7, 5, 17, 16, 17, 15,
1, 6, 20, 11, 24, 7, 11, 16, 18, 20, 7, 18, 3, 20, 3, 22, 11, 17, 16, 8, 17, 20, 10, 3, 22, 7, 25, 7, 7, 13, 22, 10, 7, 8, 14, 3, 22, 25, 3, 21, 21, 7, 24, 7, 16, 8, 14, 11, 9, 10, 22, 21, 23, 18, 3, 16, 6, 25, 11, 16, 21, 22, 17, 16, 25, 10, 17, 25, 3, 21, 22, 10, 11, 20, 22, 1, 16, 11, 16, 7, 3, 16, 6, 10, 3, 6, 3, 24, 3, 20, 11, 5, 17, 21, 7, 23, 14, 5, 7, 20, 3, 4, 17, 24, 7, 10, 11, 21, 20, 11, 9, 10, 22, 3, 16, 13, 14, 7, 25, 7, 16, 22, 21, 14, 17, 25, 14, 1, 20, 7, 21, 22, 11, 16, 9, 21, 7, 24, 7, 20, 3, 14, 22, 11, 15, 7, 21, 17, 16, 22, 10, 7, 25, 3, 1, 17, 16, 7, 3, 5, 10, 14, 3, 16, 6, 11, 16, 9, 17, 18, 18, 17, 21, 11, 22, 7, 22, 10, 7, 14, 11, 8, 22, 21, 10, 3, 8, 22, 22, 10, 7, 18, 17, 21, 22, 7, 20, 25, 11, 22, 10, 22, 10, 7, 7, 16, 17, 20, 15, 17, 23, 21, 8, 3, 5, 7, 9, 3, 2, 7, 6, 8, 20, 17, 15, 22, 10, 7, 25, 3, 14, 14, 11, 22, 25, 3, 21, 17, 16, 7, 17, 8, 22, 10, 17, 21, 7, 18, 11, 5, 22, 23, 20, 7, 21, 25, 10, 11, 5, 10, 3, 20, 7, 21, 17, 5, 17, 16, 22, 20, 11, 24, 7, 6, 22, 10, 3, 22, 22, 10, 7, 7, 1, 7, 21, 8, 17, 14, 14, 17, 25, 1, 17, 23, 3, 4, 17, 23, 22, 25, 10, 7, 16, 1, 17, 23, 15, 17, 24, 7, 4, 11, 9, 4, 20, 17, 22, 10, 7, 20, 11, 21, 25, 3, 22, 5, 10, 11, 16, 9, 1, 17, 23, 22, 10, 7, 5, 3, 18, 22, 11, 17, 16, 4, 7, 16, 7, 3, 22, 10, 11, 22, 20, 3, 16, 11, 16, 21, 11, 6, 7, 22, 10, 7, 8, 14, 3, 22, 3, 8, 20, 23, 11, 22, 1, 24, 17, 11, 5, 7, 25, 3, 21, 20, 7, 3, 6, 11, 16, 9, 17, 23, 22, 3, 14, 11, 21, 22, 17, 8, 8, 11, 9, 23, 20, 7, 21, 25, 10, 11, 5, 10, 10, 3, 6, 21, 17, 15, 7, 22, 10, 11, 16, 9, 22, 17, 6, 17, 25, 11, 22, 10, 22, 10, 7, 18, 20, 17, 6, 23, 5, 22, 11, 17, 16, 17, 8, 18, 11, 9, 11, 20, 17, 16, 22, 10, 7, 24, 17, 11, 5, 7, 5, 3, 15, 7, 8, 20, 17, 15, 3, 16, 17, 4, 14, 17, 16, 9, 15, 7, 22, 3, 14, 18, 14, 3, 19, 23, 7, 14, 11, 13, 7, 3, 6, 23, 14, 14, 7, 6, 15, 11, 20, 20, 17, 20, 25, 10, 11, 5, 10, 8, 17, 20, 15, 7, 6, 18, 3, 20, 22, 17, 8, 22, 10, 7, 21, 23, 20, 8, 3, 5, 7, 17, 8, 22, 10, 7, 20, 11, 9, 10, 22, 10, 3, 16, 6, 25, 3, 14, 14, 25, 11, 16, 21, 22, 17, 16, 22, 23, 20, 16, 7, 6, 3, 21, 25, 11, 22, 5, 10, 3, 16, 6, 22, 10, 7, 24, 17, 11, 5, 7, 21, 3, 16, 13, 21, 17, 15, 7, 25, 10, 3, 22, 22, 10, 17, 23, 9, 10, 22, 10, 7, 25, 17, 20, 6, 21, 25, 7, 20, 7, 21, 22, 11, 14, 14, 6, 11, 21, 22, 11, 16, 9, 23, 11, 21, 10, 3, 4, 14, 7, 22, 10, 7, 11, 16, 21, 22, 20, 23, 15, 7, 16, 22, 22, 10, 7, 22, 7, 14, 7, 21, 5, 20, 7, 7, 16, 11, 22, 25, 3, 21, 5, 3, 14, 14, 7, 6, 5, 17, 23, 14, 6, 4, 7, 6, 11, 15, 15, 7, 6, 4, 23,
22, 22, 10, 7, 20, 7, 25, 3, 21, 16, 17, 25, 3, 1, 17, 8, 21, 10, 23, 22, 22, 11, 16, 9, 11, 22, 17, 8, 8, 5, 17,
15, 18, 14, 7, 22, 7, 14, 1, 10, 7, 15, 17, 24, 7, 6, 17, 24, 7, 20, 22, 17, 22, 10, 7, 25, 11, 16, 6, 17, 25, 3,
21, 15, 3, 14, 14, 11, 21, 10, 8, 20, 3, 11, 14, 8, 11, 9, 23, 20, 7, 22, 10, 7, 15, 7, 3, 9, 20, 7, 16, 7, 21, 21, 17, 8, 10, 11, 21, 4, 17, 6, 1, 15, 7, 20, 7, 14, 1, 7, 15, 18, 10, 3, 21, 11, 2, 7, 6, 4, 1, 22, 10, 7, 4, 14,
23, 7, 17, 24, 7, 20, 3, 14, 14, 21, 25, 10, 11, 5, 10, 25, 7, 20, 7, 22, 10, 7, 23, 16, 11, 8, 17, 20, 15, 17, 8, 22, 10, 7, 18, 3, 20, 22, 1, 10, 11, 21, 10, 3, 11, 20, 25, 3, 21, 24, 7, 20, 1, 8, 3, 11, 20, 10, 11, 21, 8, 3,
5, 7, 16, 3, 22, 23, 20, 3, 14, 14, 1, 21, 3, 16, 9, 23, 11, 16, 7, 10, 11, 21, 21, 13, 11, 16, 20, 17, 23, 9, 10, 7, 16, 7, 6, 4, 1, 5, 17, 3, 20, 21, 7, 21, 17, 3, 18, 3, 16, 6, 4, 14, 23, 16, 22, 20, 3, 2, 17, 20, 4, 14, 3, 6, 7, 21, 3, 16, 6, 22, 10, 7, 5, 17, 14, 6, 17, 8, 22, 10, 7, 25, 11, 16, 22, 7, 20, 22, 10, 3, 22, 10, 3, 6, 12,
23, 21, 22, 7, 16, 6, 7, 6, 17, 23, 22, 21, 11, 6, 7, 7, 24, 7, 16, 22, 10, 20, 17, 23, 9, 10, 22, 10, 7, 21, 10,
23, 22, 25, 11, 16, 6, 17, 25, 18, 3, 16, 7, 22, 10, 7, 25, 17, 20, 14, 6, 14, 17, 17, 13, 7, 6, 5, 17, 14, 6, 6,
17, 25, 16, 11, 16, 22, 10, 7, 21, 22, 20, 7, 7, 22, 14, 11, 22, 22, 14, 7, 7, 6, 6, 11, 7, 21, 17, 8, 25, 11, 16, 6, 25, 7, 20, 7, 25, 10, 11, 20, 14, 11, 16, 9, 6, 23, 21, 22, 3, 16, 6, 22, 17, 20, 16, 18, 3, 18, 7, 20, 11, 16, 22, 17, 21, 18, 11, 20, 3, 14, 21, 3, 16, 6, 22, 10, 17, 23, 9, 10, 22, 10, 7, 21, 23, 16, 25, 3, 21, 21, 10, 11, 16, 11, 16, 9, 3, 16, 6, 22, 10, 7, 21, 13, 1, 3, 10, 3, 20, 21, 10, 4, 14, 23, 7, 22, 10, 7, 20, 7, 21, 7, 7, 15, 7, 6, 22, 17, 4, 7, 16, 17, 5, 17, 14, 17, 23, 20, 11, 16, 3, 16, 1, 22, 10, 11, 16, 9, 7, 26, 5, 7, 18, 22, 22, 10, 7, 18, 17, 21, 22, 7, 20, 21, 22, 10, 3, 22, 25, 7, 20, 7, 18, 14, 3, 21, 22, 7, 20, 7, 6, 7, 24, 7, 20, 1,
25, 10, 7, 20, 7, 22, 10, 7, 4, 14, 3, 5, 13, 15, 17, 23, 21, 22, 3, 5, 10, 11, 17, 6, 8, 3, 5, 7, 9, 3, 2, 7, 6,
6, 17, 25, 16, 8, 20, 17, 15, 7, 24, 7, 20, 1, 5, 17, 15, 15, 3, 16, 6, 11, 16, 9, 5, 17, 20, 16, 7, 20, 22, 10, 7, 20, 7, 25, 3, 21, 17, 16, 7, 17, 16, 22, 10, 7, 10, 17, 23, 21, 7, 8, 20, 17, 16, 22, 11, 15, 15, 7, 6, 11, 3, 22, 7, 14, 1, 17, 18, 18, 17, 21, 11, 22, 7, 4, 11, 9, 4, 20, 17, 22, 10, 7, 20, 11, 21, 25, 3, 22, 5, 10, 11, 16,
9, 1, 17, 23, 22, 10, 7, 5, 3, 18, 22, 11, 17, 16, 21, 3, 11, 6, 25, 10, 11, 14, 7, 22, 10, 7, 6, 3, 20, 13, 7, 1, 7, 21, 14, 17, 17, 13, 7, 6, 6, 7, 7, 18, 11, 16, 22, 17, 25, 11, 16, 21, 22, 17, 16, 21, 17, 25, 16, 6, 17, 25,
16, 3, 22, 21, 22, 20, 7, 7, 22, 14, 7, 24, 7, 14, 3, 16, 17, 22, 10, 7, 20, 18, 17, 21, 22, 7, 20, 22, 17, 20, 16, 3, 22, 17, 16, 7, 5, 17, 20, 16, 7, 20, 8, 14, 3, 18, 18, 7, 6, 8, 11, 22, 8, 23, 14, 14, 1, 11, 16, 22, 10, 7,
25, 11, 16, 6, 3, 14, 22, 7, 20, 16, 3, 22, 7, 14, 1, 5, 17, 24, 7, 20, 11, 16, 9, 3, 16, 6, 23, 16, 5, 17, 24, 7, 20, 11, 16, 9, 22, 10, 7, 21, 11, 16, 9, 14, 7, 25, 17, 20, 6, 11, 16, 9, 21, 17, 5, 11, 16, 22, 10, 7, 8, 3, 20, 6, 11, 21, 22, 3, 16, 5, 7, 3, 10, 7, 14, 11, 5, 17, 18, 22, 7, 20, 21, 13, 11, 15, 15, 7, 6, 6, 17, 25, 16, 4, 7, 22, 25, 7, 7, 16, 22, 10, 7, 20, 17, 17, 8, 21, 10, 17, 24, 7, 20, 7, 6, 8, 17, 20, 3, 16, 11, 16, 21, 22, 3, 16, 22, 14, 11, 13, 7, 3, 4, 14, 23, 7, 4, 17, 22, 22, 14, 7, 3, 16, 6, 6, 3, 20, 22, 7, 6, 3, 25, 3, 1, 3, 9, 3, 11, 16, 25, 11, 22, 10, 3, 5, 23, 20, 24, 11, 16, 9, 8, 14, 11, 9, 10, 22, 11, 22, 25, 3, 21, 22, 10, 7, 18, 17, 14, 11, 5, 7, 18, 3, 22, 20, 17, 14, 21, 16, 17, 17, 18, 11, 16, 9, 11, 16, 22, 17, 18, 7, 17, 18, 14, 7, 21, 25, 11, 16, 6, 17, 25, 21, 22, 10, 7, 18, 3, 22, 20, 17, 14, 21, 6, 11, 6, 16, 17, 22, 15, 3, 22, 22, 7, 20, 10, 17, 25,
7, 24, 7, 20, 17, 16, 14, 1, 22, 10, 7, 22, 10, 17, 23, 9, 10, 22, 18, 17, 14, 11, 5, 7, 15, 3, 22, 22, 7, 20, 7,
6]
affine_3_5_26_orwell_tokens = [4, 11, 20, 6, 8, 6, 9, 5, 4, 24, 1, 11, 12, 22, 13, 15, 15, 6, 26, 4, 19, 6, 25, 5, 4, 13, 6, 19, 15, 11, 1, 18,
12, 13, 22, 12, 10, 8, 20, 18, 5, 18, 8, 11, 5, 4, 10, 4, 19, 24, 11, 1, 4, 5, 11, 18, 18, 19, 20, 4, 19, 8, 11, 22, 19, 8, 16, 4, 11, 1, 1, 4, 8, 12, 1, 4, 19, 19, 14, 3, 3, 13, 18, 15, 4, 19, 11, 22, 1, 4, 8, 9, 5, 18, 6, 8, 11, 4, 19, 6, 19, 18, 21, 21, 22, 5, 11, 11, 22, 18, 8, 12, 6, 25, 18, 11, 1, 18, 17, 4, 13, 18, 20, 4, 19, 15, 8,
13, 4, 25, 25, 18, 15, 2, 14, 4, 12, 10, 13, 26, 11, 1, 5, 22, 14, 24, 1, 11, 1, 18, 24, 13, 6, 8, 8, 15, 22, 22,
5, 8, 22, 21, 17, 4, 12, 11, 22, 5, 26, 16, 6, 19, 8, 4, 22, 19, 8, 11, 1, 22, 14, 24, 1, 19, 22, 11, 2, 14, 4, 12, 10, 13, 26, 18, 19, 22, 14, 24, 1, 11, 22, 25, 5, 18, 17, 18, 19, 11, 6, 8, 20, 4, 5, 13, 22, 21, 24, 5, 4, 11,
11, 26, 15, 14, 8, 11, 21, 5, 22, 16, 18, 19, 11, 18, 5, 4, 19, 24, 6, 13, 22, 19, 24, 20, 4, 11, 1, 1, 4, 16, 11, 1, 18, 1, 6, 13, 13, 20, 6, 26, 8, 16, 18, 13, 11, 22, 21, 9, 22, 4, 13, 18, 15, 12, 6, 9, 9, 6, 24, 18, 6, 19, 15, 22, 13, 15, 5, 6, 24, 16, 6, 11, 8, 6, 11, 22, 19, 18, 18, 19, 15, 22, 21, 4, 11, 6, 12, 22, 13, 22, 14, 5, 18, 15, 25, 22, 8, 11, 18, 5, 11, 22, 22, 13, 6, 5, 24, 18, 21, 22, 5, 4, 19, 15, 22, 22, 5, 15, 4, 8, 25, 13, 6, 26, 1, 6, 15, 9, 18, 18, 19, 11, 6, 12, 10, 18, 15, 11, 22, 11, 1, 18, 20, 6, 13, 13, 4, 11, 15, 18, 25, 4, 12, 11, 18, 15, 8, 4, 16, 25, 13, 26, 6, 19, 18, 19, 22, 5, 16, 22, 14, 8, 21, 6, 12, 18, 16, 22, 5, 18, 11, 1, 6, 19, 6, 16, 18, 11, 5, 18, 20, 4, 15, 18, 11, 1, 18, 21, 6, 12, 18, 22, 21, 6, 16, 6, 19, 22, 21, 6, 9, 22, 14, 11, 21, 22, 5, 11, 26, 21, 4, 17, 18, 20, 4, 11, 1, 6, 1, 18, 6, 17, 26, 9, 13, 6, 12, 10, 16, 22, 14, 8, 11, 6, 12, 1, 18, 6, 19, 15, 5, 14, 24, 24, 18, 15, 13, 26, 1, 6, 19, 15, 8, 22, 16, 18, 21, 18, 6, 11, 14, 5, 18, 8, 20, 4, 19, 8, 11, 22, 19, 16, 6, 15, 18, 21, 22, 5, 11, 1, 18, 8, 11, 6, 4, 5, 8, 4, 11, 20, 6, 8, 19, 22, 14, 8, 18, 11, 5, 26,
4, 19, 24, 11, 1, 18, 13, 4, 21, 11, 18, 17, 18, 19, 6, 11, 11, 1, 18, 9, 18, 8, 11, 22, 21, 11, 4, 16, 18, 8, 4,
11, 20, 6, 8, 8, 18, 13, 15, 22, 16, 20, 22, 5, 10, 4, 19, 24, 6, 19, 15, 6, 11, 25, 5, 18, 8, 18, 19, 11, 11, 1,
18, 18, 13, 18, 12, 11, 5, 4, 12, 12, 14, 5, 5, 18, 19, 11, 20, 6, 8, 12, 14, 11, 22, 21, 21, 15, 14, 5, 4, 19, 24, 15, 6, 26, 13, 4, 24, 1, 11, 1, 22, 14, 5, 8, 4, 11, 20, 6, 8, 25, 6, 5, 11, 22, 21, 11, 1, 18, 18, 12, 22, 19,
22, 16, 26, 15, 5, 4, 17, 18, 4, 19, 25, 5, 18, 25, 6, 5, 6, 11, 4, 22, 19, 21, 22, 5, 1, 6, 11, 18, 20, 18, 18, 10, 11, 1, 18, 21, 13, 6, 11, 20, 6, 8, 8, 18, 17, 18, 19, 21, 13, 4, 24, 1, 11, 8, 14, 25, 6, 19, 15, 20, 4, 19, 8, 11, 22, 19, 20, 1, 22, 20, 6, 8, 11, 1, 4, 5, 11, 26, 19, 4, 19, 18, 6, 19, 15, 1, 6, 15, 6, 17, 6, 5, 4, 12, 22, 8, 18, 14, 13, 12, 18, 5, 6, 9, 22, 17, 18, 1, 4, 8, 5, 4, 24, 1, 11, 6, 19, 10, 13, 18, 20, 18, 19, 11, 8, 13,
22, 20, 13, 26, 5, 18, 8, 11, 4, 19, 24, 8, 18, 17, 18, 5, 6, 13, 11, 4, 16, 18, 8, 22, 19, 11, 1, 18, 20, 6, 26,
22, 19, 18, 6, 12, 1, 13, 6, 19, 15, 4, 19, 24, 22, 25, 25, 22, 8, 4, 11, 18, 11, 1, 18, 13, 4, 21, 11, 8, 1, 6, 21, 11, 11, 1, 18, 25, 22, 8, 11, 18, 5, 20, 4, 11, 1, 11, 1, 18, 18, 19, 22, 5, 16, 22, 14, 8, 21, 6, 12, 18, 24,
6, 3, 18, 15, 21, 5, 22, 16, 11, 1, 18, 20, 6, 13, 13, 4, 11, 20, 6, 8, 22, 19, 18, 22, 21, 11, 1, 22, 8, 18, 25,
4, 12, 11, 14, 5, 18, 8, 20, 1, 4, 12, 1, 6, 5, 18, 8, 22, 12, 22, 19, 11, 5, 4, 17, 18, 15, 11, 1, 6, 11, 11, 1,
18, 18, 26, 18, 8, 21, 22, 13, 13, 22, 20, 26, 22, 14, 6, 9, 22, 14, 11, 20, 1, 18, 19, 26, 22, 14, 16, 22, 17, 18, 9, 4, 24, 9, 5, 22, 11, 1, 18, 5, 4, 8, 20, 6, 11, 12, 1, 4, 19, 24, 26, 22, 14, 11, 1, 18, 12, 6, 25, 11, 4, 22, 19, 9, 18, 19, 18, 6, 11, 1, 4, 11, 5, 6, 19, 4, 19, 8, 4, 15, 18, 11, 1, 18, 21, 13, 6, 11, 6, 21, 5, 14, 4, 11, 26, 17, 22, 4, 12, 18, 20, 6, 8, 5, 18, 6, 15, 4, 19, 24, 22, 14, 11, 6, 13, 4, 8, 11, 22, 21, 21, 4, 24, 14, 5, 18, 8, 20, 1, 4, 12, 1, 1, 6, 15, 8, 22, 16, 18, 11, 1, 4, 19, 24, 11, 22, 15, 22, 20, 4, 11, 1, 11, 1, 18, 25, 5, 22, 15, 14, 12, 11, 4, 22, 19, 22, 21, 25, 4, 24, 4, 5, 22, 19, 11, 1, 18, 17, 22, 4, 12, 18, 12, 6, 16, 18, 21, 5, 22, 16, 6, 19, 22, 9, 13, 22, 19, 24, 16, 18, 11, 6, 13, 25, 13, 6, 2, 14, 18, 13, 4, 10, 18, 6, 15, 14, 13, 13, 18, 15, 16, 4, 5, 5, 22, 5, 20, 1, 4, 12, 1, 21, 22, 5, 16, 18, 15, 25, 6, 5, 11, 22, 21, 11, 1, 18, 8, 14, 5,
21, 6, 12, 18, 22, 21, 11, 1, 18, 5, 4, 24, 1, 11, 1, 6, 19, 15, 20, 6, 13, 13, 20, 4, 19, 8, 11, 22, 19, 11, 14,
5, 19, 18, 15, 6, 8, 20, 4, 11, 12, 1, 6, 19, 15, 11, 1, 18, 17, 22, 4, 12, 18, 8, 6, 19, 10, 8, 22, 16, 18, 20, 1, 6, 11, 11, 1, 22, 14, 24, 1, 11, 1, 18, 20, 22, 5, 15, 8, 20, 18, 5, 18, 8, 11, 4, 13, 13, 15, 4, 8, 11, 4, 19,
24, 14, 4, 8, 1, 6, 9, 13, 18, 11, 1, 18, 4, 19, 8, 11, 5, 14, 16, 18, 19, 11, 11, 1, 18, 11, 18, 13, 18, 8, 12, 5, 18, 18, 19, 4, 11, 20, 6, 8, 12, 6, 13, 13, 18, 15, 12, 22, 14, 13, 15, 9, 18, 15, 4, 16, 16, 18, 15, 9, 14, 11, 11, 1, 18, 5, 18, 20, 6, 8, 19, 22, 20, 6, 26, 22, 21, 8, 1, 14, 11, 11, 4, 19, 24, 4, 11, 22, 21, 21, 12, 22, 16, 25, 13, 18, 11, 18, 13, 26, 1, 18, 16, 22, 17, 18, 15, 22, 17, 18, 5, 11, 22, 11, 1, 18, 20, 4, 19, 15, 22, 20,
6, 8, 16, 6, 13, 13, 4, 8, 1, 21, 5, 6, 4, 13, 21, 4, 24, 14, 5, 18, 11, 1, 18, 16, 18, 6, 24, 5, 18, 19, 18, 8, 8, 22, 21, 1, 4, 8, 9, 22, 15, 26, 16, 18, 5, 18, 13, 26, 18, 16, 25, 1, 6, 8, 4, 3, 18, 15, 9, 26, 11, 1, 18, 9, 13, 14, 18, 22, 17, 18, 5, 6, 13, 13, 8, 20, 1, 4, 12, 1, 20, 18, 5, 18, 11, 1, 18, 14, 19, 4, 21, 22, 5, 16, 22, 21, 11, 1, 18, 25, 6, 5, 11, 26, 1, 4, 8, 1, 6, 4, 5, 20, 6, 8, 17, 18, 5, 26, 21, 6, 4, 5, 1, 4, 8, 21, 6, 12, 18, 19, 6, 11, 14, 5, 6, 13, 13, 26, 8, 6, 19, 24, 14, 4, 19, 18, 1, 4, 8, 8, 10, 4, 19, 5, 22, 14, 24, 1, 18, 19, 18, 15, 9, 26, 12, 22, 6, 5, 8, 18, 8, 22, 6, 25, 6, 19, 15, 9, 13, 14, 19, 11, 5, 6, 3, 22, 5, 9, 13, 6, 15, 18, 8, 6, 19, 15, 11, 1, 18, 12, 22, 13, 15, 22, 21, 11, 1, 18, 20, 4, 19, 11, 18, 5, 11, 1, 6, 11, 1, 6, 15, 7, 14, 8,
11, 18, 19, 15, 18, 15, 22, 14, 11, 8, 4, 15, 18, 18, 17, 18, 19, 11, 1, 5, 22, 14, 24, 1, 11, 1, 18, 8, 1, 14, 11, 20, 4, 19, 15, 22, 20, 25, 6, 19, 18, 11, 1, 18, 20, 22, 5, 13, 15, 13, 22, 22, 10, 18, 15, 12, 22, 13, 15, 15,
22, 20, 19, 4, 19, 11, 1, 18, 8, 11, 5, 18, 18, 11, 13, 4, 11, 11, 13, 18, 18, 15, 15, 4, 18, 8, 22, 21, 20, 4, 19, 15, 20, 18, 5, 18, 20, 1, 4, 5, 13, 4, 19, 24, 15, 14, 8, 11, 6, 19, 15, 11, 22, 5, 19, 25, 6, 25, 18, 5, 4, 19, 11, 22, 8, 25, 4, 5, 6, 13, 8, 6, 19, 15, 11, 1, 22, 14, 24, 1, 11, 1, 18, 8, 14, 19, 20, 6, 8, 8, 1, 4, 19, 4, 19, 24, 6, 19, 15, 11, 1, 18, 8, 10, 26, 6, 1, 6, 5, 8, 1, 9, 13, 14, 18, 11, 1, 18, 5, 18, 8, 18, 18, 16, 18, 15,
11, 22, 9, 18, 19, 22, 12, 22, 13, 22, 14, 5, 4, 19, 6, 19, 26, 11, 1, 4, 19, 24, 18, 23, 12, 18, 25, 11, 11, 1, 18, 25, 22, 8, 11, 18, 5, 8, 11, 1, 6, 11, 20, 18, 5, 18, 25, 13, 6, 8, 11, 18, 5, 18, 15, 18, 17, 18, 5, 26, 20, 1, 18, 5, 18, 11, 1, 18, 9, 13, 6, 12, 10, 16, 22, 14, 8, 11, 6, 12, 1, 4, 22, 15, 21, 6, 12, 18, 24, 6, 3, 18, 15, 15, 22, 20, 19, 21, 5, 22, 16, 18, 17, 18, 5, 26, 12, 22, 16, 16, 6, 19, 15, 4, 19, 24, 12, 22, 5, 19, 18, 5, 11, 1, 18, 5, 18, 20, 6, 8, 22, 19, 18, 22, 19, 11, 1, 18, 1, 22, 14, 8, 18, 21, 5, 22, 19, 11, 4, 16, 16, 18, 15, 4, 6, 11, 18, 13, 26, 22, 25, 25, 22, 8, 4, 11, 18, 9, 4, 24, 9, 5, 22, 11, 1, 18, 5, 4, 8, 20, 6, 11, 12, 1, 4, 19, 24, 26, 22, 14, 11, 1, 18, 12, 6, 25, 11, 4, 22, 19, 8, 6, 4, 15, 20, 1, 4, 13, 18, 11, 1, 18, 15, 6, 5, 10, 18,
26, 18, 8, 13, 22, 22, 10, 18, 15, 15, 18, 18, 25, 4, 19, 11, 22, 20, 4, 19, 8, 11, 22, 19, 8, 22, 20, 19, 15, 22, 20, 19, 6, 11, 8, 11, 5, 18, 18, 11, 13, 18, 17, 18, 13, 6, 19, 22, 11, 1, 18, 5, 25, 22, 8, 11, 18, 5, 11, 22, 5, 19, 6, 11, 22, 19, 18, 12, 22, 5, 19, 18, 5, 21, 13, 6, 25, 25, 18, 15, 21, 4, 11, 21, 14, 13, 13, 26, 4, 19, 11, 1, 18, 20, 4, 19, 15, 6, 13, 11, 18, 5, 19, 6, 11, 18, 13, 26, 12, 22, 17, 18, 5, 4, 19, 24, 6, 19, 15, 14, 19,
12, 22, 17, 18, 5, 4, 19, 24, 11, 1, 18, 8, 4, 19, 24, 13, 18, 20, 22, 5, 15, 4, 19, 24, 8, 22, 12, 4, 19, 11, 1,
18, 21, 6, 5, 15, 4, 8, 11, 6, 19, 12, 18, 6, 1, 18, 13, 4, 12, 22, 25, 11, 18, 5, 8, 10, 4, 16, 16, 18, 15, 15, 22, 20, 19, 9, 18, 11, 20, 18, 18, 19, 11, 1, 18, 5, 22, 22, 21, 8, 1, 22, 17, 18, 5, 18, 15, 21, 22, 5, 6, 19, 4,
19, 8, 11, 6, 19, 11, 13, 4, 10, 18, 6, 9, 13, 14, 18, 9, 22, 11, 11, 13, 18, 6, 19, 15, 15, 6, 5, 11, 18, 15, 6,
20, 6, 26, 6, 24, 6, 4, 19, 20, 4, 11, 1, 6, 12, 14, 5, 17, 4, 19, 24, 21, 13, 4, 24, 1, 11, 4, 11, 20, 6, 8, 11,
1, 18, 25, 22, 13, 4, 12, 18, 25, 6, 11, 5, 22, 13, 8, 19, 22, 22, 25, 4, 19, 24, 4, 19, 11, 22, 25, 18, 22, 25, 13, 18, 8, 20, 4, 19, 15, 22, 20, 8, 11, 1, 18, 25, 6, 11, 5, 22, 13, 8, 15, 4, 15, 19, 22, 11, 16, 6, 11, 11, 18,
5, 1, 22, 20, 18, 17, 18, 5, 22, 19, 13, 26, 11, 1, 18, 11, 1, 22, 14, 24, 1, 11, 25, 22, 13, 4, 12, 18, 16, 6, 11, 11, 18, 5, 18, 15]
freq_match_orwell_mapping = [15, 2, 23, 12, 5, 7, 16, 18, 9, 26, 11, 4, 21, 14, 1, 25, 24, 19, 8, 20, 3, 22, 6, 17, 13, 10]
vigenere_ABBA_mappings = [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26], [2, 3, 4, 5, 6,
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 1], [2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 1], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26]]
periodic_affine_123_765_mappings = [[8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 1, 2, 3, 4, 5, 6, 7], [7, 10, 13, 16,
19, 22, 25, 2, 5, 8, 11, 14, 17, 20, 23, 26, 3, 6, 9, 12, 15, 18, 21, 24, 1, 4], [6, 11, 16, 21, 26, 5, 10, 15, 20, 25, 4, 9, 14, 19, 24, 3, 8, 13, 18, 23, 2, 7, 12, 17, 22, 1]]
vigenere_ABBA_orwell_tokens = [9, 21, 24, 1, 19, 2, 3, 18, 9, 8, 9, 20, 3, 16, 13, 4, 4, 2, 26, 9, 14, 2, 17, 18, 9, 13, 2, 14, 4, 21, 9, 5, 3,
13, 16, 3, 11, 20, 24, 5, 18, 6, 20, 20, 18, 10, 12, 9, 14, 8, 21, 8, 9, 19, 21, 5, 5, 15, 24, 9, 14, 20, 21, 15,
14, 20, 14, 9, 20, 9, 9, 9, 19, 4, 9, 9, 14, 15, 22, 26, 26, 13, 6, 4, 9, 15, 21, 15, 8, 10, 20, 2, 18, 6, 2, 19,
20, 10, 15, 1, 14, 6, 7, 6, 15, 19, 21, 20, 15, 6, 20, 3, 1, 17, 6, 20, 8, 6, 23, 9, 12, 6, 24, 9, 14, 5, 20, 12,
9, 17, 17, 5, 4, 18, 22, 9, 3, 12, 13, 25, 20, 9, 19, 15, 21, 8, 9, 20, 8, 6, 8, 12, 1, 20, 20, 4, 15, 16, 19, 19, 15, 7, 23, 9, 3, 21, 16, 18, 25, 14, 2, 14, 19, 10, 16, 14, 19, 21, 9, 15, 21, 8, 9, 14, 15, 21, 18, 21, 9, 4, 12, 12, 25, 6, 15, 15, 21, 8, 9, 20, 15, 17, 19, 5, 22, 6, 15, 20, 1, 20, 24, 9, 18, 13, 16, 6, 7, 19, 10, 20, 20, 26, 5, 21, 19, 21, 7, 18, 15, 14, 6, 14, 20, 6, 19, 9, 14, 8, 2, 12, 15, 15, 8, 23, 9, 21, 9, 8, 9, 14, 21, 8, 5, 9, 2, 12, 12, 24, 2, 25, 19, 14, 6, 12, 20, 16, 7, 2, 15, 10, 13, 5, 4, 4, 2, 2, 2, 2, 8, 5, 1, 15, 5, 15, 12, 5, 19, 1, 7, 14, 2, 20, 19, 2, 21, 15, 14, 6, 6, 14, 4, 16, 7, 9, 20, 2, 4, 15, 12, 16, 22, 18, 5, 5, 17, 15, 19, 21,
6, 18, 20, 16, 16, 12, 1, 19, 8, 5, 6, 16, 19, 9, 14, 5, 16, 15, 18, 5, 10, 19, 16, 13, 2, 25, 8, 2, 5, 2, 5, 6, 15, 20, 1, 4, 12, 5, 4, 21, 16, 20, 8, 6, 24, 1, 12, 13, 10, 20, 4, 6, 17, 9, 3, 21, 6, 4, 19, 10, 14, 16, 12, 26,
2, 14, 5, 15, 16, 18, 13, 16, 22, 19, 6, 2, 4, 5, 13, 16, 19, 5, 20, 9, 2, 14, 1, 14, 6, 20, 18, 6, 24, 9, 4, 6, 21, 8, 5, 7, 2, 3, 5, 16, 7, 1, 13, 2, 15, 15, 6, 2, 3, 15, 21, 21, 7, 15, 18, 21, 26, 6, 9, 23, 6, 23, 9, 21, 9, 1, 8, 6, 2, 22, 25, 3, 13, 1, 3, 12, 14, 15, 21, 20, 21, 1, 3, 9, 6, 1, 14, 5, 19, 21, 7, 8, 6, 4, 12, 26, 9, 1, 14, 5, 20, 15, 13, 6, 7, 5, 1, 21, 22, 18, 5, 20, 24, 9, 14, 20, 21, 15, 14, 14, 2, 4, 5, 7, 16, 18, 20, 9, 6, 19, 20, 2, 10, 18, 19, 10, 21, 23, 1, 20, 15, 15, 21, 20, 6, 20, 18, 26, 10, 14, 7, 21, 9, 5, 12, 10, 7, 20, 5, 23, 6,
14, 1, 21, 21, 8, 5, 3, 6, 19, 20, 16, 7, 20, 9, 14, 6, 19, 9, 21, 24, 1, 19, 20, 6, 12, 4, 16, 14, 23, 15, 19, 12, 9, 14, 8, 2, 14, 4, 2, 21, 16, 18, 6, 20, 5, 14, 21, 21, 8, 5, 6, 13, 5, 3, 21, 19, 9, 3, 4, 22, 18, 18, 6, 15,
20, 23, 2, 20, 3, 21, 21, 16, 6, 6, 5, 22, 18, 9, 15, 8, 4, 1, 26, 13, 9, 7, 9, 21, 8, 15, 22, 19, 19, 9, 21, 24,
1, 19, 17, 2, 18, 20, 16, 7, 20, 8, 6, 6, 3, 15, 15, 16, 13, 25, 5, 19, 9, 22, 6, 10, 14, 16, 19, 6, 16, 1, 19, 2, 20, 9, 16, 15, 6, 15, 19, 9, 1, 20, 6, 24, 5, 5, 12, 21, 8, 5, 7, 13, 1, 20, 24, 2, 19, 19, 6, 23, 5, 14, 7, 13,
9, 7, 9, 21, 19, 21, 17, 2, 14, 4, 24, 10, 14, 19, 21, 16, 14, 23, 9, 16, 23, 1, 20, 21, 8, 9, 19, 21, 25, 14, 10, 15, 5, 1, 15, 5, 8, 1, 5, 2, 22, 1, 19, 10, 3, 15, 20, 6, 21, 12, 4, 6, 18, 1, 3, 16, 22, 5, 9, 10, 19, 18, 10, 8, 8, 20, 2, 15, 11, 12, 6, 24, 5, 14, 21, 20, 12, 15, 24, 13, 25, 18, 6, 20, 20, 9, 15, 8, 19, 5, 23, 6, 18, 1, 13, 21, 9, 13, 6, 20, 15, 14, 21, 9, 5, 23, 2, 26, 15, 14, 6, 2, 3, 8, 13, 2, 14, 4, 10, 15, 7, 15, 17, 17, 15, 19,
10, 21, 5, 20, 9, 6, 12, 9, 7, 21, 19, 8, 2, 7, 20, 20, 9, 6, 16, 15, 20, 21, 5, 18, 24, 10, 20, 8, 21, 9, 5, 5, 15, 16, 18, 13, 16, 22, 19, 6, 2, 4, 5, 7, 2, 1, 5, 4, 7, 19, 15, 13, 21, 9, 5, 23, 2, 13, 12, 9, 21, 24, 1, 19, 16, 15, 5, 15, 7, 21, 8, 15, 20, 6, 16, 9, 4, 21, 21, 18, 6, 20, 23, 8, 10, 4, 8, 1, 19, 6, 19, 15, 4, 16, 14, 20, 19, 10, 22, 5, 5, 21, 8, 1, 21, 21, 8, 5, 6, 26, 5, 19, 7, 16, 12, 12, 16, 24, 25, 15, 22, 2, 2, 15, 22, 21, 23, 8, 6, 15, 25, 15, 22, 14, 15, 22, 6, 3, 9, 7, 3, 19, 15, 20, 9, 6, 18, 9, 20, 24, 1, 20, 4, 9, 9, 14, 8, 26, 15, 21, 21, 9, 5, 3, 2, 17, 20, 9, 16, 15, 2, 5, 15, 6, 1, 20, 9, 10, 20, 18, 2, 15, 9, 14, 20, 10, 4, 5, 21, 9, 5, 6, 13, 2, 20, 1, 7, 19, 21, 9, 21, 26, 22, 15, 10, 4, 5, 23, 2, 20, 18, 5, 2, 5, 9, 14, 8, 16, 21, 20, 2, 13, 9, 19, 21, 16, 6, 6, 10, 8, 21, 18, 6, 20, 23, 8, 10, 4, 8, 8, 2, 5, 19, 15, 14, 6, 20, 8, 10, 15, 7, 20, 16, 5, 15, 23, 10, 21, 8, 20, 9, 6, 16, 18, 16, 5, 21, 3, 21, 10, 15, 14, 16, 7, 16, 9, 8, 10, 18, 15, 15, 21, 8, 5, 23, 16, 9, 3,
6, 4, 1, 13, 6, 7, 18, 15, 14, 2, 14, 15, 3, 13, 15, 14, 8, 14, 5, 20, 2, 13, 16, 12, 2, 18, 21, 5, 13, 10, 11, 5, 2, 5, 21, 12, 13, 6, 4, 13, 10, 19, 18, 15, 19, 24, 8, 9, 4, 9, 6, 15, 19, 14, 5, 4, 17, 2, 18, 20, 16, 7, 20, 8, 6, 20, 21, 18, 7, 2, 3, 5, 16, 7, 20, 8, 6, 19, 9, 7, 9, 21, 8, 1, 15, 5, 23, 1, 13, 13, 23, 9, 15, 20, 20, 15, 15, 21, 21, 18, 15, 6, 4, 1, 20, 24, 9, 20, 4, 9, 1, 14, 5, 21, 8, 5, 23, 16, 9, 3, 6, 20, 1, 14, 12, 20, 15, 13, 6, 24, 8, 1, 21, 21, 8, 15, 22, 8, 8, 20, 9, 6, 23, 15, 19, 5, 19, 23, 6, 19, 5, 19, 21, 10, 12, 12, 5, 10, 19, 20, 10, 15, 7, 21, 10, 20, 8, 1, 3, 13, 5, 20, 9, 6, 9, 14, 20, 21, 18, 21, 14, 6, 14, 20, 21, 9, 5, 20, 6, 13, 5, 19, 4, 19, 5, 5, 15, 10, 20, 23, 2, 20, 3, 1, 13, 13, 5, 4, 4, 16, 21, 12, 5, 3, 5, 4, 10, 14, 13, 5, 5, 3, 21, 20,
21, 9, 5, 18, 6, 24, 1, 19, 15, 16, 23, 1, 26, 16, 6, 19, 9, 22, 20, 20, 10, 15, 7, 9, 21, 16, 6, 6, 4, 16, 13, 16, 13, 6, 20, 5, 13, 26, 8, 5, 14, 16, 22, 5, 5, 16, 22, 5, 19, 21, 15, 20, 9, 6, 23, 9, 15, 5, 15, 23, 2, 20, 13,
1, 13, 13, 9, 19, 9, 7, 18, 1, 10, 13, 6, 9, 8, 22, 18, 5, 21, 9, 5, 13, 6, 2, 7, 18, 6, 15, 5, 19, 20, 16, 6, 8,
10, 20, 2, 15, 5, 26, 13, 5, 19, 6, 12, 25, 6, 14, 16, 8, 2, 20, 9, 26, 6, 5, 2, 25, 21, 9, 5, 2, 13, 22, 5, 15, 23, 6, 18, 1, 13, 13, 19, 23, 9, 10, 3, 8, 24, 6, 18, 5, 21, 9, 5, 21, 15, 10, 6, 15, 19, 14, 15, 6, 21, 9, 5, 16,
2, 19, 20, 25, 9, 10, 19, 8, 2, 10, 18, 23, 2, 20, 22, 5, 19, 26, 6, 1, 10, 19, 8, 9, 20, 7, 1, 3, 6, 15, 1, 20, 22, 19, 1, 12, 13, 26, 19, 1, 15, 8, 21, 9, 15, 6, 8, 9, 20, 20, 11, 9, 15, 19, 15, 21, 8, 9, 5, 14, 6, 5, 2, 25, 4, 16, 1, 18, 20, 6, 19, 15, 2, 17, 1, 14, 5, 3, 12, 21, 15, 21, 18, 1, 1, 16, 18, 2, 13, 2, 4, 5, 20, 2, 14, 4, 21, 9, 5, 3, 16, 13, 4, 15, 7, 21, 8, 5, 24, 10, 14, 20, 6, 19, 20, 8, 2, 21, 8, 1, 5, 11, 21, 19, 21, 6, 14, 4, 6,
5, 15, 21, 21, 20, 9, 4, 6, 6, 22, 5, 15, 21, 8, 18, 16, 22, 7, 8, 21, 9, 5, 19, 9, 22, 20, 23, 10, 15, 4, 15, 24, 17, 1, 14, 6, 21, 8, 5, 24, 16, 18, 12, 5, 13, 15, 15, 12, 6, 4, 3, 16, 13, 4, 4, 16, 24, 14, 9, 15, 21, 8, 5, 20, 21, 18, 5, 6, 21, 12, 9, 21, 21, 12, 5, 6, 5, 4, 9, 6, 20, 15, 6, 24, 10, 14, 4, 24, 6, 18, 5, 24, 9, 9, 18, 13, 10, 14, 7, 5, 22, 19, 20, 2, 15, 4, 20, 16, 19, 14, 16, 2, 17, 5, 18, 10, 15, 20, 15, 20, 17, 9, 18, 2, 13, 19, 1, 15, 5, 20, 8, 16, 22, 7, 8, 21, 9, 5, 19, 22, 15, 23, 1, 20, 20, 8, 9, 15, 10, 14, 7, 2, 15, 4, 20, 9, 6, 19, 11, 26, 2, 8, 1, 19, 20, 8, 2, 13, 22, 5, 20, 9, 6, 18, 5, 20, 6, 5, 13, 6, 5, 20, 15, 3, 6, 14, 15, 4, 16, 12, 15,
22, 19, 9, 14, 2, 15, 25, 20, 9, 10, 14, 7, 6, 25, 3, 5, 17, 21, 20, 8, 6, 17, 15, 19, 21, 6, 18, 19, 21, 9, 1, 20, 24, 6, 18, 5, 17, 13, 1, 19, 21, 6, 18, 5, 5, 6, 22, 5, 19, 26, 23, 8, 6, 19, 5, 20, 9, 6, 2, 12, 2, 4, 11, 13,
16, 22, 19, 20, 2, 4, 8, 9, 16, 5, 6, 1, 4, 6, 7, 1, 1, 6, 4, 4, 16, 24, 14, 6, 19, 16, 13, 5, 23, 6, 18, 25, 4, 16, 13, 13, 2, 15, 4, 9, 15, 8, 3, 15, 19, 15, 5, 18, 21, 9, 5, 18, 6, 24, 1, 19, 16, 15, 5, 15, 15, 21, 8, 5, 9, 16, 21, 19, 6, 7, 18, 15, 15, 21, 9, 13, 14, 6, 4, 9, 2, 21, 5, 12, 26, 16, 16, 16, 16, 20, 9, 20, 6, 3, 9, 7, 3, 19, 15, 20, 9, 6, 18, 9, 20, 24, 1, 20, 4, 9, 9, 14, 8, 26, 15, 21, 21, 9, 5, 3, 2, 17, 20, 9, 16, 15, 19, 1, 10, 5, 23, 8, 10, 13, 5, 20, 9, 6, 4, 1, 19, 12, 5, 25, 6, 20, 12, 15, 16, 12, 5, 4, 5, 6, 5, 16, 10, 15, 20, 15, 24, 10, 14, 19, 21, 16, 14, 19, 16, 24, 14, 4, 16, 24, 14, 1, 21, 20, 20, 18, 6, 6, 20, 12, 6, 23, 5, 12, 2, 15, 15, 20, 9, 6, 18, 16, 16, 20, 20, 5, 19, 21, 15, 18, 15, 2, 20, 15, 15, 6, 3, 15, 19, 15, 5, 18, 7, 13, 1, 16, 17, 6, 4, 6, 10, 21, 6, 21, 13, 13, 25, 9, 15, 21, 8, 5, 24, 10, 14, 4, 2, 13, 20, 5, 19, 15, 1, 20, 6, 13, 25, 3, 16, 23,
5, 18, 10, 15, 7, 1, 15, 5, 21, 14, 4, 16, 22, 5, 19, 10, 14, 7, 21, 9, 5, 19, 10, 15, 7, 12, 6, 24, 15, 18, 5, 10, 14, 7, 20, 16, 3, 9, 15, 21, 8, 5, 7, 2, 18, 4, 10, 20, 20, 1, 15, 4, 5, 1, 9, 6, 12, 9, 4, 16, 16, 20, 6, 19, 19, 11, 10, 14, 13, 5, 5, 5, 15, 23, 15, 3, 5, 20, 24, 6, 5, 14, 21, 9, 5, 18, 16, 16, 6, 19, 9, 16, 22, 5, 19, 6,
4, 6, 16, 19, 1, 14, 10, 15, 19, 20, 2, 15, 20, 12, 10, 12, 5, 1, 3, 13, 21, 5, 3, 16, 20, 20, 13, 6, 1, 14, 5, 5, 1, 18, 21, 6, 4, 1, 24, 2, 25, 1, 8, 2, 9, 14, 24, 10, 20, 8, 2, 4, 21, 18, 23, 10, 14, 7, 7, 13, 9, 7, 9, 21, 9, 20, 24, 2, 19, 20, 9, 6, 16, 15, 13, 10, 3, 5, 17, 2, 20, 18, 16, 13, 19, 14, 16, 16, 16, 9, 15, 8, 9, 14, 21, 16, 16, 5, 16, 17, 12, 5, 20, 24, 9, 14, 5, 16, 23, 19, 21, 9, 5, 16, 2, 21, 18, 15, 13, 20, 4, 9, 5, 15, 15, 20, 14, 2, 20, 20, 6, 19, 8, 15, 24, 6, 22, 5, 19, 16, 14, 12, 26, 21, 8, 5, 21, 9, 15, 21, 8, 9, 20, 16, 16, 13, 9, 3,
6, 14, 1, 20, 21, 6, 18, 5, 5]
periodic_affine_123_765_orwell_tokens = [16, 12, 12, 8, 9, 6, 9, 6, 20, 14, 2, 23, 10, 23, 9, 11, 16, 6, 6, 5, 19, 8, 26, 13, 16, 14, 6, 21, 16, 23, 15, 19, 16, 19, 23, 16, 18, 9, 12, 12, 6, 26, 26, 12, 13, 16, 11, 20, 21, 25, 23, 15, 5, 13, 1, 19, 26, 21, 21, 20, 21, 9, 23, 22, 20, 18, 20, 5, 23, 15, 2, 20, 26, 13, 15, 16, 20, 19, 2, 4, 1, 19, 19, 21, 16, 20, 23, 22, 2, 20, 26,
10, 13, 12, 7, 18, 1, 5, 19, 8, 20, 26, 13, 22, 24, 25, 12, 23, 22, 19, 18, 10, 7, 3, 12, 12, 15, 12, 18, 20, 19,
19, 12, 16, 20, 21, 26, 14, 20, 23, 26, 26, 11, 3, 2, 16, 13, 4, 19, 1, 23, 15, 6, 24, 2, 25, 15, 1, 2, 26, 14, 14, 6, 26, 9, 21, 22, 23, 13, 26, 23, 5, 3, 5, 16, 1, 23, 13, 6, 17, 6, 21, 9, 20, 22, 20, 18, 1, 2, 24, 2, 25, 15,
21, 23, 23, 24, 15, 20, 10, 11, 9, 6, 19, 19, 22, 15, 10, 15, 12, 24, 23, 6, 26, 3, 19, 19, 1, 7, 18, 4, 5, 13, 19, 23, 5, 14, 6, 20, 1, 12, 22, 11, 15, 18, 1, 22, 13, 22, 17, 26, 21, 12, 26, 25, 5, 19, 14, 7, 9, 22, 20, 10, 4,
5, 23, 15, 2, 20, 20, 12, 15, 12, 2, 6, 19, 14, 12, 8, 1, 18, 20, 19, 9, 1, 23, 5, 9, 23, 20, 19, 19, 21, 10, 7, 11, 9, 7, 10, 12, 7, 19, 11, 23, 9, 11, 6, 6, 14, 17, 6, 1, 9, 6, 1, 23, 19, 12, 19, 19, 11, 23, 5, 16, 12, 6, 10,
23, 9, 22, 15, 13, 12, 16, 3, 22, 9, 23, 12, 6, 23, 22, 23, 9, 8, 6, 10, 12, 22, 24, 25, 5, 19, 11, 23, 24, 25, 16, 20, 26, 26, 9, 8, 1, 15, 8, 16, 11, 12, 19, 19, 1, 7, 16, 18, 19, 21, 1, 23, 23, 15, 19, 12, 8, 14, 9, 16, 12, 21, 12, 26, 20, 10, 12, 26, 11, 9, 20, 20, 26, 9, 6, 7, 19, 12, 20, 24, 25, 17, 24, 2, 9, 5, 8, 13, 26, 20, 23, 13, 12, 12, 15, 8, 20, 6, 20, 19, 23, 25, 19, 12, 16, 16, 26, 1, 2, 26, 13, 7, 16, 12, 23, 5, 8, 17, 6, 21, 23, 5, 8, 10, 24, 2, 12, 5, 22, 6, 23, 6, 22, 20, 3, 19, 12, 16, 12, 15, 8, 2, 26, 8, 18, 22, 9, 14, 6, 10, 11, 14, 22, 15, 18, 1, 7, 16, 15, 19, 6, 21, 16, 13, 2, 25, 10, 12, 16, 9, 6, 2, 6, 21, 16, 18, 22, 17, 26, 13, 19, 6, 1, 15, 13, 12, 9, 12, 16, 20, 18, 1, 23, 19, 20, 7, 21, 12, 22, 24, 25, 12, 15, 12, 9, 23, 8, 5, 13, 26, 5, 23, 4, 7, 18, 21, 23, 2, 26, 19, 23, 25, 1, 20, 21, 25, 23, 15, 19, 9, 16, 22, 23, 12, 18, 26, 21, 7, 23, 1, 2, 26, 9, 19, 18, 1,
23, 5, 1, 5, 14, 12, 9, 20, 1, 21, 6, 26, 9, 26, 19, 16, 24, 20, 21, 24, 25, 11, 20, 21, 25, 6, 21, 16, 6, 1, 26,
13, 12, 9, 26, 21, 12, 23, 15, 19, 26, 19, 19, 16, 1, 6, 20, 10, 13, 2, 25, 6, 26, 21, 12, 12, 8, 9, 16, 2, 12, 24, 13, 22, 21, 2, 6, 20, 21, 25, 21, 8, 1, 9, 16, 25, 15, 1, 2, 24, 2, 6, 18, 16, 12, 12, 8, 9, 3, 8, 6, 23, 22, 22, 23, 15, 19, 26, 10, 23, 19, 22, 17, 22, 11, 6, 20, 3, 19, 20, 21, 26, 13, 12, 26, 6, 25, 7, 23, 16, 23, 19, 13,
23, 13, 15, 7, 23, 12, 21, 26, 12, 11, 23, 15, 19, 5, 19, 7, 23, 4, 7, 18, 26, 19, 7, 12, 20, 5, 19, 5, 10, 15, 12, 18, 2, 26, 6, 21, 16, 12, 16, 20, 18, 1, 23, 19, 4, 2, 24, 4, 7, 18, 1, 2, 20, 25, 12, 22, 21, 5, 19, 12, 7, 19, 11, 2, 6, 11, 7, 7, 8, 6, 20, 10, 23, 18, 12, 15, 9, 10, 19, 13, 8, 10, 24, 3, 19, 15, 16, 9, 13, 16, 25, 15, 1,
7, 19, 18, 14, 26, 4, 19, 19, 1, 9, 9, 22, 21, 9, 6, 6, 26, 26, 12, 20, 21, 25, 18, 12, 18, 26, 25, 7, 9, 1, 5, 14, 12, 9, 24, 21, 12, 15, 12, 21, 6, 6, 23, 19, 12, 7, 16, 15, 14, 6, 21, 16, 20, 21, 25, 24, 23, 26, 24, 26, 5, 23, 12, 12, 15, 12, 14, 20, 13, 12, 18, 15, 7, 5, 1, 12, 15, 12, 26, 24, 26, 12, 26, 25, 21, 20, 1, 2, 23, 15, 19, 26, 21, 23, 13, 20, 23, 2, 26, 22, 6, 10, 19, 10, 8, 4, 26, 11, 22, 13, 22, 17, 23, 15, 19, 12, 8, 14, 9, 16, 12, 12, 8, 9, 24, 21, 19, 24, 13, 12, 15, 22, 9, 26, 23, 5, 16, 1, 15, 13, 12, 9, 12, 15, 5, 16, 15, 7, 13, 12, 9, 24,
10, 23, 19, 1, 6, 20, 3, 19, 21, 1, 2, 6, 1, 12, 15, 12, 19, 22, 12, 9, 5, 22, 14, 9, 22, 21, 22, 22, 15, 6, 9, 23, 2, 1, 21, 15, 12, 20, 22, 22, 15, 14, 22, 18, 26, 9, 5, 10, 9, 6, 24, 1, 2, 26, 25, 5, 18, 4, 7, 23, 10, 2, 20,
21, 25, 22, 22, 15, 23, 15, 19, 16, 8, 26, 23, 16, 23, 19, 9, 19, 19, 12, 7, 23, 15, 5, 23, 25, 7, 19, 16, 20, 18, 16, 16, 26, 1, 2, 26, 13, 14, 6, 1, 7, 5, 25, 15, 20, 1, 1, 7, 22, 5, 16, 12, 21, 6, 26, 6, 26, 8, 16, 20, 21, 25, 24, 2, 12, 6, 19, 5, 18, 1, 23, 5, 13, 5, 10, 2, 6, 26, 26, 21, 15, 16, 13, 15, 15, 7, 21, 26, 23, 14, 12, 12, 15, 16, 20, 10, 1, 23, 21, 22, 21, 20, 1, 2, 23, 15, 19, 3, 25, 23, 21, 2, 13, 23, 16, 23, 19, 22, 22, 3, 16, 25, 20, 25, 23, 19, 1, 2, 26, 3, 23, 20, 10, 19, 16, 8, 17, 26, 13, 6, 24, 20, 7, 19, 22, 10, 9, 22, 20, 10, 20, 19, 23, 8, 14, 3, 19, 7, 8, 2, 19, 9, 16, 11, 26, 8, 16, 2, 19, 14, 26, 11, 17, 20, 25, 6, 24, 25, 21, 15, 16, 13, 15, 13, 23, 13, 20, 19, 21, 23, 7, 13, 1, 23, 5, 1, 2, 26, 26, 15, 13, 13, 7, 16, 12, 23, 5, 1, 2, 26, 25, 5, 10, 15, 12, 15, 8, 20, 21, 4, 7, 9, 19, 21, 20, 21, 9, 23, 22, 20, 23, 2, 6, 19, 12, 16, 6, 26, 21, 20, 1, 13, 15, 8, 20, 21, 1, 2, 26, 3, 23, 20, 10, 19, 18, 8, 20, 4, 26, 23, 14, 12, 21, 15, 8, 12, 23, 15, 23, 2, 14, 2, 23, 15, 19, 12, 22, 6, 21, 26, 21, 26, 25, 19, 18, 1, 5, 9, 19, 16, 20, 26, 12, 20, 21, 25, 2, 16, 9, 15, 8, 10, 9, 12, 12, 15, 12, 5, 19, 26, 12, 13, 2, 17, 26, 21, 12, 23, 15, 19, 23, 12, 14, 26, 26, 13, 13, 12, 19, 19, 16, 12, 12, 8, 9, 16, 8, 14, 9, 12, 16, 16, 22, 15, 9, 11, 10, 26, 11, 5, 14, 20, 19, 21, 9, 15, 23, 1, 2, 26, 25, 19, 12, 8, 9, 19, 22, 21, 6, 6, 23, 5, 26, 2, 2, 1, 12, 20, 21, 25, 20, 1, 23, 5, 13, 13, 24, 20, 26, 9, 12, 12, 26, 19, 1, 15, 12, 17, 24, 3, 19, 21, 22, 18, 26, 25, 12, 24, 1, 2, 26, 4, 5, 19, 11, 23, 12, 8, 9, 14, 8, 14, 9, 16, 9, 15, 13, 6, 6,
16, 14, 5, 16, 25, 2, 25, 19, 23, 15, 19, 14, 12, 7, 10, 25, 19, 19, 12, 9, 18, 22, 22, 15, 16, 9, 11, 22, 16, 22, 20, 19, 13, 12, 14, 22, 12, 17, 3, 15, 7, 18, 16, 4, 26, 11, 10, 22, 1, 2, 26, 9, 14, 2, 12, 23, 7, 12, 6, 6, 19, 14, 18, 4, 2, 20, 10, 2, 12, 12, 6, 26, 1, 2, 26, 2, 20, 20, 13, 23, 13, 20, 23, 5, 1, 2, 26, 23, 7, 13, 1, 1, 15, 16, 9, 15, 8, 5, 13, 4, 7, 18, 3, 19, 13, 6, 22, 6, 16, 6, 15, 16, 9, 5, 8, 13, 26, 21, 7, 23, 2, 6, 6, 19, 14,
22, 26, 7, 19, 14, 15, 20, 21, 19, 15, 16, 9, 18, 18, 5, 19, 25, 23, 2, 14, 2, 26, 21, 19, 21, 9, 1, 16, 22, 7, 13, 26, 19, 18, 22, 7, 3, 8, 20, 21, 9, 14, 2, 21, 12, 13, 8, 4, 24, 25, 10, 9, 8, 16, 26, 26, 7, 19, 11, 12, 15, 12, 13, 24, 19, 16, 24, 13, 12, 15, 12, 21, 20, 21, 12, 26, 25, 12, 15, 8, 12, 15, 8, 16, 25, 2, 9, 23, 12, 20, 21,
12, 16, 24, 2, 12, 18, 16, 16, 26, 12, 18, 26, 21, 12, 15, 25, 23, 2, 14, 2, 23, 15, 19, 18, 15, 15, 23, 4, 5, 19, 11, 23, 12, 23, 7, 19, 12, 12, 15, 12, 21, 24, 25, 14, 21, 19, 23, 24, 18, 19, 21, 10, 23, 9, 11, 16, 24, 4, 20,
20, 21, 12, 15, 12, 9, 23, 25, 19, 26, 1, 14, 20, 1, 12, 9, 12, 19, 21, 11, 5, 26, 26, 23, 5, 4, 5, 19, 11, 21, 26, 25, 19, 12, 15, 5, 13, 19, 5, 19, 14, 16, 2, 26, 12, 6, 21, 16, 23, 22, 6, 19, 23, 7, 3, 12, 6, 20, 21, 12, 24,
26, 26, 20, 25, 7, 9, 26, 7, 19, 11, 12, 15, 22, 15, 10, 15, 12, 15, 12, 9, 2, 21, 21, 6, 26, 9, 15, 16, 20, 20, 21, 25, 6, 21, 16, 23, 15, 19, 18, 18, 1, 6, 15, 7, 13, 26, 2, 11, 19, 15, 26, 1, 2, 26, 25, 19, 18, 12, 19, 14, 12, 16, 23, 22, 10, 26, 21, 23, 16, 22, 14, 24, 2, 6, 20, 21, 7, 19, 6, 12, 15, 16, 20, 10, 12, 24, 16, 12, 26, 23,
1, 2, 26, 23, 23, 18, 1, 19, 13, 26, 12, 15, 8, 12, 12, 12, 6, 26, 23, 14, 6, 26, 12, 26, 25, 19, 21, 12, 18, 26,
25, 1, 12, 15, 19, 13, 12, 12, 15, 12, 10, 9, 8, 13, 4, 20, 23, 2, 26, 12, 6, 10, 2, 20, 22, 16, 5, 8, 13, 26, 14, 7, 1, 12, 16, 21, 22, 21, 19, 13, 6, 24, 20, 19, 7, 12, 6, 22, 10, 23, 14, 20, 7, 19, 11, 5, 19, 14, 13, 24, 25,
20, 26, 25, 12, 15, 12, 6, 26, 4, 7, 18, 22, 20, 26, 22, 20, 23, 15, 19, 15, 22, 15, 18, 12, 22, 13, 22, 20, 23, 16, 17, 14, 12, 16, 20, 8, 12, 26, 19, 1, 24, 23, 26, 24, 26, 5, 23, 12, 10, 20, 14, 10, 13, 22, 12, 15, 12, 6, 20, 26, 21, 6, 1, 13, 15, 16, 20, 10, 6, 23, 2, 1, 2, 26, 10, 7, 3, 1, 5, 24, 21, 9, 6, 16, 16, 12, 15, 5, 9, 12, 12, 15, 12, 16, 6, 25, 11, 26, 6, 19, 18, 19, 23, 24, 18, 19, 21, 11, 19, 26, 23, 5, 19, 1, 23, 12, 16, 20, 18, 1, 23, 19, 26, 23, 12, 21, 16, 24, 4, 20, 6, 1, 9, 23, 25, 19, 26, 1, 14, 26, 3, 19, 9, 8, 20, 24, 1, 2, 26, 25, 26, 24, 26, 12, 26, 25, 12, 24, 25, 20, 6, 1, 23, 19, 12, 13, 24, 25, 20, 26, 25, 22, 9, 8, 26, 3, 12, 16, 5, 16, 12, 5, 2, 14, 9, 6, 5, 19, 1, 2, 26, 4, 5, 19, 11, 7, 9, 1, 19, 13, 21, 7, 23, 12, 14, 22, 10, 23, 7, 12, 6, 20, 21, 25, 6, 21, 16, 2, 21, 13, 24, 3, 19, 13, 16, 20, 10, 1, 2, 26, 26, 5, 19, 14, 14, 26, 4, 23, 13, 11, 5, 19, 14, 9, 24, 10, 5, 19, 1, 2, 26, 13, 7, 13, 11, 5, 18, 1, 7, 19, 10, 19, 6, 15, 19, 9, 16, 13, 24, 23, 12, 26, 25, 9, 4, 16, 17, 14, 12, 16, 21, 22, 21, 19, 9, 19, 23, 4, 19, 26, 21, 12, 15, 12, 6, 24, 22, 22, 18, 15, 23, 7, 12, 6, 26, 11, 22, 24, 25, 7, 19, 16, 20, 18, 1, 7, 19, 1, 14, 20, 18, 19, 6, 9, 14, 2, 12, 10, 24, 1, 12, 9, 12, 7, 19, 11, 16, 6, 25, 12, 26, 11, 7, 12, 8, 1, 6, 14, 7, 20, 21, 21, 20, 1, 2, 6, 10, 15, 13, 3, 5, 19, 14, 22, 9, 16, 25, 15,
1, 5, 23, 4, 7, 18, 1, 2, 26, 23, 23, 9, 16, 13, 26, 23, 7, 23, 25, 23, 9, 26, 20, 24, 22, 26, 20, 21, 25, 20, 21, 12, 24, 23, 19, 24, 23, 14, 26, 26, 21, 20, 21, 16, 24, 4, 9, 23, 15, 19, 3, 8, 12, 13, 22, 14, 18, 11, 5, 21, 21, 23, 23, 20, 7, 23, 1, 19, 13, 15, 23, 12, 12, 18, 26, 25, 23, 19, 19, 1, 23, 15, 19, 23, 15, 23, 2, 14, 2, 23, 23, 23, 9, 16, 13, 26, 20, 7, 23, 1, 19, 13, 12, 16]
columnar_1432_orwell_tokens = [9, 19, 9, 3, 4, 14, 9, 4, 3, 11, 18, 18, 14, 9, 5, 14, 14, 20, 19, 14, 26, 9, 8, 18, 20, 14, 15, 15, 1, 8, 12, 14, 9, 4, 3, 20, 21, 8, 1, 15, 15, 3, 25, 19, 19, 21, 15, 9, 25, 21, 15, 22, 1, 18, 7, 20, 19, 15, 20, 14, 15, 9, 9, 5, 12, 19, 20, 15, 4, 2, 1, 12, 7, 19, 14, 4, 20, 12, 5, 19, 20, 1, 6, 14, 18, 16, 8, 5, 1, 4, 8, 12, 4, 3, 19, 12, 5, 13, 6, 13, 20, 1, 18, 4, 5, 5, 13, 6, 21, 18, 9, 9, 8, 25, 3, 21, 3, 14, 7, 12, 14, 13, 1, 5, 14, 14, 5, 20, 20, 19, 1, 21, 18, 7, 12, 5, 1, 5, 20, 9, 9, 19, 4, 15, 14, 4, 18, 14, 5, 3, 3, 18, 23, 21, 6, 9, 1, 7, 15, 9, 19, 20, 8, 15, 25, 22, 16, 1, 9, 15, 20, 5, 5, 20, 19, 14, 7, 21, 4, 19, 23, 1, 9, 14, 1, 1, 1, 15, 12, 1, 5, 18, 20, 12, 14, 15, 18, 9, 5, 1, 13, 14, 23, 14, 8,
4, 15, 19, 20, 9, 8, 20, 15, 18, 8, 5, 13, 6, 7, 4, 13, 23, 9, 19, 15, 15, 9, 18, 8, 1, 15, 20, 5, 1, 5, 19, 12, 15, 15, 8, 15, 22, 7, 20, 9, 20,
14, 21, 3, 9, 5, 20, 18, 14, 5, 6, 1, 9, 15, 23, 5, 14, 20, 19, 6, 18, 8, 8, 15, 8, 20, 23, 20, 18, 3, 14, 9, 15, 5, 3, 13, 15, 15, 14, 20, 12, 5, 5, 12, 13, 15, 9, 15, 4, 20, 8, 18, 5, 8, 7, 1, 1, 9, 15, 18, 1, 20, 14, 5, 3, 14, 13, 1, 15, 20, 15, 23, 19, 12, 20, 21, 1, 20, 14, 21, 20, 20,
19, 5, 23, 1, 4, 12, 4, 5, 20, 18, 19, 1, 19, 20, 9, 6, 16, 5, 5, 5, 5, 20, 9, 23, 1, 19, 1, 9, 5, 13, 18, 19, 8, 15, 5, 25, 8, 26, 25, 2, 15, 1,
23, 8, 5, 21, 15, 6, 16, 25, 8, 23, 5, 1, 9, 3, 20, 12, 1, 9, 9, 9, 21, 14, 25, 18, 15, 14, 21, 1, 2, 5, 4, 3, 15, 5, 20, 8, 1, 19, 4, 21, 4, 5, 18, 8, 19, 23, 15, 14, 5, 12, 15, 3, 4, 9, 5, 5, 9, 5, 9, 6, 4, 5, 18, 7, 20, 20, 16, 18, 15, 18, 1, 8, 8, 19, 1, 9, 7, 20, 11, 1, 2, 20, 5, 13, 15, 15, 15, 14, 20, 7, 5, 8, 19, 19, 20, 5, 19, 5, 5, 8, 20, 12, 13, 20, 9, 1, 1, 4, 6, 5, 25, 13, 9, 15, 18, 18, 19, 15, 5, 19, 15, 13, 9, 12, 16,
20, 7, 20, 9, 20, 14, 21, 3, 9, 1, 8, 20, 1, 25, 15, 4, 16, 15, 19, 19, 4, 1, 18, 12, 12, 20, 16, 5, 18, 15, 15, 18, 16, 6, 21, 9, 5, 4, 5, 20, 3, 18, 1, 14, 5, 7, 19, 12, 18, 7, 9, 5, 4, 1, 1, 9, 20, 11, 5, 23, 20, 14, 18, 19, 5, 6, 14, 20, 12, 1, 5, 20, 14, 18, 1, 1, 14, 8, 18, 7, 7, 20, 20, 15, 5, 18, 14, 9, 14, 5, 5, 14, 19, 16, 15, 9, 20, 20, 15, 5, 12, 5, 21, 16, 3, 20, 5, 1, 18, 20, 4, 9, 18, 14, 5, 3, 5, 20, 9, 8, 5, 9, 15, 9, 9, 9, 26, 4, 15, 2, 19, 1, 6, 20, 3, 20, 9, 9, 12, 5, 9, 25, 15, 20, 12, 4, 19, 9, 18, 14, 14, 15, 14, 21, 12, 15, 20, 5, 20, 9, 6, 20, 21, 18, 14, 9, 12, 23, 8, 8, 12, 25, 12, 2, 5, 2, 5, 15, 1, 20, 15, 14, 9, 15, 18, 15, 18, 12, 5, 9, 15, 19, 25, 2, 20, 5, 20, 1, 20, 9, 4, 16, 14, 18, 19, 5, 5, 14, 20, 9, 8, 3, 1, 15, 15, 15, 6, 23, 1, 22, 1, 15, 1, 1, 21, 4, 1, 15, 5, 18, 9, 15, 4, 18, 19, 18, 23, 15, 20, 14, 5, 20, 14, 8, 19, 20, 19, 1, 12, 23, 9, 14, 16, 5, 8, 5, 9, 18, 20, 3, 6, 18, 4, 9, 8, 19, 1, 18, 20, 3, 13, 9, 14, 16, 20, 6, 1, 5, 8, 1, 19, 5, 9, 19, 14, 14, 14, 23, 8, 25, 5, 8, 22, 3, 21, 18, 22, 19, 8, 11, 5, 12, 25, 20, 19, 18, 9, 15, 5, 15, 3, 14, 7, 15, 5, 12, 19, 20, 16, 5, 20, 5, 18, 19, 5, 5, 15, 5,
12, 1, 5, 8, 16, 21, 23, 8, 19, 14, 22, 8, 8, 5, 12, 25, 2, 23, 25, 15, 9, 15, 18, 1, 9, 15, 5, 20, 2, 1, 20, 9, 4, 5, 20, 21, 22, 5, 18, 9, 21, 9, 6, 21, 23, 8, 19, 20, 7, 15, 8, 16, 21, 15, 16, 18, 8, 9, 1, 18, 14, 15, 5, 16, 21, 11, 21, 4, 18, 8, 6, 5, 18, 20, 21, 3, 20, 9, 8, 23, 23, 20, 21, 4, 9, 1, 8, 9, 1, 15, 8, 8, 8, 23, 19, 5, 12, 19, 7, 8, 5, 9, 18, 14, 5, 5, 5, 20, 3, 5, 21, 5, 13, 21, 5, 1, 23, 6, 20, 7, 6, 13, 20, 8, 22, 22, 15, 23, 15, 13, 9, 18, 6, 18, 5, 7, 5, 6, 2, 13, 12, 16, 9, 2, 5, 5, 18, 19, 3, 18, 5, 6, 15, 5, 20, 19, 18, 22, 6, 8, 1, 1, 1, 19, 21, 8, 11, 15, 5, 2, 1, 19, 1, 12, 18, 18, 4, 14, 5, 4, 8, 14, 20, 8, 21, 14, 15, 9, 22, 8, 7, 5, 20, 4, 1, 8, 18, 15, 4, 4, 14, 8, 18, 12, 12, 4, 15, 14,
18, 9, 14, 19, 4, 14, 5, 20, 9, 19, 20, 7, 5, 23, 8, 14, 4, 19, 8, 8, 5, 18, 5, 20, 14, 12, 9, 25, 14, 3, 20, 15, 18, 1, 18, 1, 18, 22, 23, 5, 2,
11, 19, 8, 6, 7, 4, 14, 13, 18, 13, 4, 3, 5, 5, 1, 5, 8, 21, 18, 9, 4, 5, 16, 9, 9, 15, 18, 1, 9, 15, 5, 20, 19, 23, 5, 4, 5, 12, 5, 5, 20, 14, 14, 14, 14, 20, 20, 5, 15, 18, 20, 15, 20, 3, 5, 1, 4, 6, 25, 8, 14, 20, 1, 25, 5, 7, 21, 22, 14, 5, 7, 15, 14, 3, 8, 18, 20, 5, 12, 16, 19, 13, 15, 5, 5, 5, 6, 22, 4, 1, 19, 20, 5, 21, 20, 1, 1, 4, 25, 9, 20, 21, 14, 9, 9, 19, 16, 3, 20, 19, 16, 9, 16, 12, 9, 23, 5, 18, 4, 15, 20, 8, 22, 14,
8, 15, 20, 9, 1, 18, 23, 2, 8, 12, 25, 16, 1, 8, 15, 23, 19, 11, 20, 20, 23, 20, 13, 8, 8, 21, 5, 20, 19, 1, 14, 6, 20, 19, 5, 22, 23, 19, 16, 21, 12, 18, 8, 7, 19, 18, 22, 15, 1, 15, 8, 8, 17, 11, 14, 8, 18, 14, 23, 15, 9, 4, 6, 5, 18, 1, 7, 8, 20, 1, 1, 5, 6, 12, 1, 7, 4, 18, 1, 20, 5, 6,
3, 21, 16, 5, 15, 7, 18, 15, 9, 1, 4, 14, 11, 15, 23, 9, 16, 5, 13, 1, 15, 21, 3, 18, 1, 5, 23, 20, 1, 6, 14, 2, 6, 25, 5, 8, 1, 12, 13, 20, 5, 18, 5, 8, 19, 6, 21, 23, 20, 1, 15, 5, 9, 20, 14, 5, 9, 8, 6, 5, 20, 5, 6, 5, 23, 5, 13, 11, 1, 20, 19, 20, 12, 18, 21, 14, 19, 15, 21, 7, 12, 20, 18, 23, 1, 6, 5, 15, 18, 9, 5, 1, 14, 8, 23, 20, 12, 1, 22, 12, 20, 1, 9, 15, 15, 20, 20, 14, 4, 1, 9, 5, 5, 15, 9, 7, 14, 23, 19, 12, 19, 7, 5, 20, 19, 8, 25, 1, 1, 14, 16, 20, 5, 20, 6, 5, 20, 9, 8, 15, 21, 3, 26, 18, 8, 12, 23, 14, 20, 5, 20, 19, 3, 5, 15, 9, 20, 20, 25, 15, 23, 1, 20, 14, 13, 2, 18, 5, 23, 8, 25, 8, 16, 14, 5, 9, 14, 9, 8, 1, 18, 25, 3, 19, 4, 15, 12, 15, 7, 19, 3, 4, 5, 14, 4, 20, 5, 4, 9, 6, 9, 20, 15, 3, 6, 1, 12, 13, 12, 17, 9, 4, 5, 18, 23, 8, 13, 1, 6, 19, 1, 6, 18, 20, 4, 12, 19, 20, 5, 23, 8, 20, 15, 19, 19, 23, 20, 7, 5, 4, 18, 9, 9, 14, 19, 12, 5,
20, 5, 8, 12, 18, 9, 19, 12, 15, 2, 13, 2, 8, 23, 15, 15, 21, 14, 15, 15, 5, 25, 15, 15, 20, 5, 4, 19, 12, 6, 12, 21, 8, 1, 14, 15, 19, 25, 5, 13, 19, 4, 8, 21, 5, 12, 9, 5, 8, 9, 13, 8, 18, 9, 9, 19, 25, 18, 6, 14, 18, 25, 7, 5, 19, 18, 8, 4, 15, 5, 16, 2, 20, 15, 1, 1, 8, 12, 20, 9, 18, 20, 10, 5, 4, 19, 5, 20, 21, 8, 21, 14, 16, 20, 15, 12, 5, 12, 23, 20, 20, 20, 20, 4, 19, 9, 5, 8, 9, 21, 14, 18, 16, 14, 16, 12, 4, 21, 8, 14, 19,
9, 14, 5, 1, 19, 21, 5, 5, 4, 5, 15, 18, 14, 9, 24, 20, 16, 5, 8, 5, 12, 5, 5, 25, 18, 5, 3, 21, 3, 4, 5, 5, 23, 15, 5, 15, 14, 7, 14, 8, 23, 14,
20, 15, 6, 20, 5, 20, 15, 19, 2, 18, 5, 23, 8, 25, 8, 16, 14, 4, 12, 5, 11, 19, 11, 5, 14, 9, 15, 23, 23, 19, 5, 22, 14, 5, 19, 20, 1, 5, 14, 12,
5, 20, 12, 20, 9, 12, 14, 12, 22, 14, 4, 15, 9, 8, 14, 23, 9, 15, 20, 1, 19, 3, 5, 15, 18, 13, 4, 2, 5, 8, 15, 15, 5, 18, 14, 14, 11, 12, 15, 5, 4, 5, 1, 1, 9, 3, 9, 12, 20, 1, 5, 9, 1, 12, 15, 7, 15, 16, 23, 15, 8, 20, 19, 14, 1, 18, 5, 15, 20, 8, 8, 12, 13, 5, 20, 1, 7, 15, 1, 1, 12, 20, 12, 19, 5, 9, 7, 18, 14, 19, 19, 8, 3, 14, 12, 14, 9, 5, 9, 5, 18, 5, 16, 5, 5, 4, 16, 17, 11, 8, 7, 5, 19, 15, 6, 20, 13, 9, 20, 7, 20, 3, 5, 7, 16, 5, 19, 12, 18, 25, 20, 13, 5, 7, 14, 20, 13, 8, 23, 13, 15, 9, 3, 1, 14, 4, 13, 1, 5, 15, 1, 15, 4, 20, 15, 18, 15, 4, 4, 12, 1, 5, 3, 20, 5, 12, 5, 20, 9, 25, 14, 15, 1, 15, 8, 13, 5, 5, 6, 15, 1, 1, 20, 20, 22, 20, 5, 2, 11, 19, 8, 4, 7, 25, 4, 5, 20, 19, 19, 13, 6, 8, 1, 9, 19, 19, 25, 20, 9, 22, 20, 2, 15, 13, 20, 19, 15, 18, 7, 1, 5, 20, 5, 20, 3, 5, 1, 20, 4, 14, 25, 8, 21, 20, 16, 15, 5, 14, 4, 5, 18, 18, 15, 18, 5, 11, 6, 23, 5, 6, 8, 16, 23, 20, 8, 19, 18, 9, 14, 4, 18, 19, 3, 2, 8, 9, 1, 5, 20, 23, 5, 14, 22, 12, 5, 20, 1, 5, 12, 9, 16, 9, 8, 6, 1, 8, 19, 23, 20, 14, 15, 1, 1, 6, 20, 1, 20, 15, 6, 19, 3, 5, 9, 18, 3, 18, 4, 20, 5, 6, 15, 21, 21, 5, 21, 5, 2, 8, 19, 3, 7, 20, 1, 15, 14, 8, 1, 19, 20, 12, 6, 20, 9, 1, 1, 7, 1, 20, 9, 5, 9, 1, 13, 9, 15, 9, 8, 15, 20, 15, 7, 14, 22, 5, 5, 13, 2, 7, 1, 1, 12, 1, 12, 9, 18, 3, 18, 16, 15, 5, 6, 15, 5, 8, 14, 12, 14, 14, 14, 19, 3, 4, 22, 5, 11, 5, 20, 21, 8, 18, 5, 20, 4, 9, 9, 2, 8, 19, 13, 20, 5, 3, 14, 1, 12, 3, 4, 9, 4, 20, 5, 14, 25, 8, 9, 20,
3, 12, 12, 13, 4, 18, 8, 14, 1, 12, 8, 9, 7, 20, 5, 5, 19, 9, 4, 18, 5, 1, 5, 20, 12, 22, 12, 8, 23, 20, 14, 18, 20, 1, 8, 1, 1, 18, 9, 19, 5, 21, 12, 14, 14, 19, 14, 7, 5, 3, 19, 1, 4, 14, 26, 12, 19, 20, 15, 6, 23, 5, 1, 4, 20, 5, 20, 5, 14, 15, 20, 8, 9, 23, 5, 23, 4, 11, 15, 15, 14, 19,
5, 20, 5, 5, 23, 23, 23, 12, 4, 1, 15, 1, 9, 19, 1, 14, 15, 20, 21, 19, 14, 1, 8, 25, 18, 12, 8, 19, 5, 2, 3, 21, 1, 8, 5, 16, 5, 20, 20, 23, 16,
20, 4, 18, 5, 8, 1, 15, 1, 15, 3, 26, 15, 18, 22, 3, 1, 14, 18, 20, 5, 15, 14, 8, 5, 14, 13, 1, 25, 15, 5, 2, 8, 19, 3, 7, 20, 1, 15, 9, 9, 8, 18, 5, 15, 4, 9, 23, 20, 15, 15, 20, 5, 5, 1, 8, 15, 18, 14, 14, 18, 6, 16, 9, 12, 14, 23, 1, 18, 5, 15, 9, 14, 3, 18, 20, 9, 5, 4, 19, 14, 6, 9, 14, 8, 3, 5, 9, 4, 14, 23, 20, 15, 8, 18, 15, 9, 1, 9, 2, 2, 12, 4, 20, 23, 7, 23, 1, 22, 6, 8, 23, 8, 12, 16, 15, 15, 14, 20, 15, 19, 4, 20, 1, 12,
4, 13, 5, 23, 18, 25, 20, 7, 15, 5, 20, 4]
permutation_15324_orwell_tokens = [9, 19, 23, 20, 1, 1, 7, 18, 2, 9, 8, 12, 3, 20, 15, 4, 9, 1, 4, 25, 14, 9, 16, 1, 18, 12, 20, 14, 1, 4, 8, 15, 3, 5, 12, 3, 5, 19, 11, 23, 18, 18, 19, 5, 20, 9, 7, 9, 11, 14, 20, 20, 9, 8, 18, 5, 9, 14, 5, 23, 14, 14, 20, 19, 15, 19, 8, 9, 13, 20, 8, 8, 19, 9, 3, 9, 26, 14, 14, 21, 26, 9, 5, 12, 4, 14, 9, 15, 20, 8, 19, 1, 18, 2, 5, 19, 1, 9, 20, 14, 14, 15, 6, 5, 6, 18, 5, 20, 20, 15, 19, 5, 1, 3, 16, 20, 9, 5, 8, 22, 12, 14, 23, 5, 9, 4, 16, 12, 19, 9, 16, 21, 4, 5, 17, 9, 25, 11, 3, 12, 20, 21, 18, 8, 15, 7, 5, 20, 8, 8, 7, 19, 1, 12, 19, 4, 19, 15, 15, 18, 15, 3, 22, 6, 9, 20, 13, 18, 15, 25, 1, 15, 19, 14, 9, 14, 15, 20, 19, 8, 21, 15, 8, 7, 14, 20, 3, 21, 17, 9, 11, 14, 25, 12, 5, 15, 20, 7, 21, 8, 15, 22, 18, 16, 5, 5, 19, 20, 14, 1, 23, 15, 18, 9, 12, 6, 20, 18, 7, 9, 20, 19, 4, 25, 21, 20, 13, 18, 6, 15, 5, 18, 20, 14, 5, 9, 12, 7, 14, 1, 15, 9, 7, 14, 23, 20, 13, 8, 8, 9, 20, 1, 5, 8, 8, 12, 25, 23, 12, 1, 19, 20, 5, 13, 12, 15, 9, 2, 6, 15, 12, 1, 4, 5, 3, 2, 5, 1, 2, 7, 1, 12, 4, 14, 15, 4, 13, 1, 18, 7, 1, 20, 19, 20, 1, 15, 14, 5, 14, 5, 4, 20, 6, 15, 9, 1, 15, 15, 3, 12, 21, 16, 5, 18, 4, 15, 18, 20, 19, 5, 20, 1, 15, 15, 12, 18, 15,
5, 7, 6, 18, 15, 14, 9, 4, 15, 19, 4, 18, 9, 16, 8, 1, 12, 25, 1, 5, 2, 4, 5, 14, 11, 1, 20, 3, 5, 20, 20, 4, 15, 8, 12, 23, 5, 1, 12, 5, 20, 9, 4, 16, 5, 3, 9, 20, 4, 16, 9, 19, 13, 12, 5, 1, 25, 14, 14, 15, 18, 15, 13, 21, 3, 6, 19, 1, 5, 5, 15, 13, 18, 20, 1, 1, 8, 14, 13, 5, 20, 5, 18, 23, 20, 4, 9, 5, 8, 3, 6, 5, 1, 5, 13, 6, 15, 1, 1, 1, 15, 14, 6, 2, 6, 21, 15, 20, 15, 6, 20, 18, 25, 9, 9, 5, 22, 23, 20, 5, 1, 8, 8, 1, 12, 25,
22, 2, 1, 15, 11, 3, 13, 21, 3, 20, 19, 1, 8, 4, 1, 5, 14, 18, 5, 7, 21, 7, 4, 1, 25, 12, 8, 14, 13, 19, 4, 15, 5, 20, 5, 6, 1, 21, 23, 5, 18, 19, 9, 15, 19, 14, 20, 14, 5, 1, 13, 4, 6, 8, 18, 15, 20, 5, 9, 20, 19, 1, 18, 23, 9, 19, 20, 1, 21, 14, 19, 15, 19, 25, 20, 5, 18, 9, 8, 7, 14, 20,
5, 20, 9, 12, 6, 5, 1, 5, 22, 14, 20, 2, 8, 20, 5, 5, 6, 20, 19, 15, 20, 19, 13, 9, 5, 9, 19, 23, 20, 1, 19, 15, 12, 5, 4, 13, 11, 15, 23, 18, 9,
14, 7, 14, 1, 4, 18, 20, 1, 16, 5, 20, 5, 19, 14, 20, 12, 5, 8, 5, 5, 9, 20, 3, 18, 3, 18, 21, 3, 18, 5, 1, 20, 14, 23, 19, 15, 21, 3, 20, 6, 18,
4, 6, 21, 9, 1, 7, 14, 4, 25, 8, 9, 12, 7, 20, 18, 15, 8, 21, 19, 1, 20, 9, 23, 19, 20, 1, 16, 18, 15, 5, 20, 6, 8, 5, 15, 15, 3, 14, 13, 9, 4, 25, 18, 22, 16, 9, 5, 14, 18, 18, 16, 5, 1, 1, 14, 9, 20, 15, 6, 1, 18, 15, 8, 20, 5, 23, 5, 5, 11, 6, 8, 20, 5, 12, 1, 20, 1, 23, 19, 5, 5, 19, 22, 14, 7, 12, 6, 9, 8, 16, 19, 20, 21, 1, 9, 4, 14, 23, 14, 14, 20, 19, 15, 23, 1, 15, 8, 23, 19, 18, 8, 20, 9, 20, 14, 14, 25, 9, 5, 8, 14, 1, 4, 1, 1, 1, 4, 22, 18, 19, 3, 9, 15, 5, 5, 12, 21, 3, 18, 22, 2, 1, 15, 5, 18, 9, 8, 19, 9, 1, 8, 7, 20, 14, 23, 12, 11, 5, 5, 12, 20, 14, 19, 15, 18, 12, 23, 25, 5, 14, 20, 19, 9, 7, 5, 5, 19, 22, 18, 9, 12, 1, 20, 13, 14, 19, 5, 15, 20, 1, 5, 8, 23, 25, 1, 14, 15, 5, 3, 14, 12, 8, 1, 4, 15, 14, 9, 7, 16, 9, 15, 16, 19, 20, 5, 20, 5, 8, 12, 19, 6, 9, 20, 8, 20, 6, 1, 20, 8, 19, 16, 5, 15, 20, 9, 18, 5, 23, 20, 5, 20, 8, 8, 5, 13, 15, 14, 18, 15, 1, 19, 21, 6, 3, 26, 7, 5, 1, 5, 15, 6, 4, 18, 13, 23, 8, 20, 5, 1, 20, 12, 12, 9, 23, 14, 19, 1, 15, 5, 8, 6, 15, 20, 15, 9, 5, 19, 16,
3, 5, 21, 20, 18, 19, 3, 8, 23, 9, 8, 19, 18, 1, 5, 15, 20, 15, 3, 14, 18, 4, 22, 9, 5, 20, 20, 1, 8, 20, 8, 5, 5, 5, 25, 19, 12, 15, 6, 12, 15, 21, 25, 23, 15, 1, 20, 15, 2, 21, 23, 25, 5, 8, 14, 15, 22, 13, 21, 15, 5, 2, 9, 2, 7, 18, 5, 20, 15, 8, 18, 1, 19, 9, 23, 20, 14, 8, 3, 9, 7, 20,
15, 25, 21, 8, 16, 3, 5, 1, 20, 2, 15, 9, 14, 5, 20, 5, 14, 1, 8, 1, 20, 9, 18, 14, 9, 14, 9, 19, 4, 5, 20, 5, 8, 6, 1, 1, 12, 20, 6, 20, 21, 18,
9, 25, 3, 15, 22, 9, 5, 18, 1, 23, 19, 5, 14, 4, 1, 9, 7, 1, 21, 15, 20, 12, 15, 19, 9, 20, 6, 21, 9, 6, 7, 18, 8, 19, 5, 23, 9, 1, 8, 3, 8, 4, 5, 15, 19, 13, 20, 7, 9, 8, 14, 20, 23, 4, 15, 15, 9, 8, 8, 20, 20, 5, 4, 18, 16, 15, 21, 15, 20, 3, 9, 14, 9, 6, 15, 16, 7, 14, 18, 9, 15, 20, 15,
5, 8, 22, 9, 1, 5, 3, 3, 13, 15, 6, 5, 18, 13, 2, 14, 1, 15, 12, 13, 14, 15, 7, 5, 16, 1, 20, 12, 12, 5, 17, 1, 21, 12, 1, 11, 9, 5, 4, 5, 12, 21, 12, 4, 18, 9, 13, 18, 15, 9, 23, 18, 8, 3, 18, 6, 8, 15, 13, 1, 4, 5, 16, 18, 20, 15, 20, 6, 8, 18, 19, 5, 21, 6, 15, 3, 1, 5, 6, 18, 8, 20, 5, 9, 8, 8, 7, 20, 1, 1, 4, 14, 23, 12, 14, 23, 12, 9, 19, 20, 15, 20, 14, 21, 4, 14, 18, 5, 1, 20, 23, 19, 9, 3, 4, 1, 8, 14, 20, 15, 5, 8, 22, 9, 1, 5, 3, 19, 14, 13, 19, 11, 15, 5, 20, 8, 23, 1, 20, 7, 15, 8, 21, 8, 23, 8, 20, 5, 15, 23, 4, 18, 19, 5, 20, 5, 18, 19, 9, 9, 12, 12, 4, 19, 7, 9, 20, 14, 21, 1, 19, 9, 8, 2, 8, 5, 12, 20, 5, 20, 14, 9, 19, 18, 14, 13, 21, 5, 20, 20, 8, 20, 5, 5, 3, 5, 12, 19, 18, 9, 5, 5, 14, 20, 3, 1, 23,
19, 1, 4, 12, 12, 5, 3, 4, 21, 15, 12, 2, 13, 4, 5, 9, 13, 21, 4, 5, 2, 20, 18, 8, 20, 5, 5, 14, 1, 23, 19, 15, 15, 1, 23, 25, 6, 20, 8, 19, 21, 20, 9, 14, 9, 7, 20, 3, 6, 15, 6, 15, 5, 16, 13, 12, 20, 8, 12, 5, 25, 5, 5, 15, 13, 22, 4, 18, 22, 15, 5, 20, 5, 20, 15, 8, 23, 15, 14, 9, 4, 23,
1, 19, 1, 13, 12, 8, 9, 12, 19, 6, 12, 1, 18, 9, 6, 18, 7, 9, 21, 5, 13, 8, 20, 5, 5, 5, 7, 1, 18, 14, 15, 19, 5, 19, 6, 2, 9, 8, 19, 15, 5, 25, 4, 13, 18, 5, 12, 5, 25, 13, 19, 8, 16, 1, 9, 2, 5, 26, 4, 25, 2, 8, 20, 5, 12, 22, 5, 21, 15, 5, 12, 1, 18, 12, 19, 3, 8, 23, 9, 8, 5, 5, 23, 18,
20, 14, 5, 8, 21, 9, 13, 15, 6, 18, 15, 5, 20, 6, 8, 16, 25, 18, 1, 20, 8, 1, 19, 9, 8, 9, 19, 23, 18, 1, 22, 6, 18, 5, 25, 1, 9, 18, 9, 8, 19, 5, 1, 6, 3, 14, 18, 20, 1, 21, 1, 19, 12, 12, 25, 1, 9, 7, 14, 21, 14, 19, 8, 5, 9, 19, 18, 9, 11, 14, 15, 5, 7, 21, 8, 14, 25, 4, 5, 2, 3, 19, 1, 15, 18, 5, 16, 15, 19, 1, 1, 12, 4, 14, 2, 21, 1, 20, 14, 18, 26, 12, 18, 15, 2, 1, 1, 5, 4, 19, 14, 5, 20, 4, 8, 3, 15, 12, 15, 4, 6, 23, 8, 20, 5, 9, 18, 20, 14, 5, 20, 8, 1, 8, 20, 1, 19, 10, 4, 21, 20, 5, 14, 5, 4, 4, 19, 21, 15, 20, 9, 22, 5, 4, 5, 5, 18, 20, 14, 8, 15, 20, 7, 21, 8, 8,
21, 19, 5, 8, 20, 4, 9, 23, 14, 15, 14, 16, 23, 1, 5, 23, 8, 20, 5, 15, 12, 12, 18, 4, 15, 4, 11, 15, 5, 3, 4, 12, 15, 4, 15, 14, 14, 23, 9, 20, 20, 5, 8, 19, 18, 12, 5, 5, 20, 9, 5, 20, 20, 12, 5, 5, 4, 4, 9, 19, 9, 6, 15, 23, 14, 18, 23, 4, 5, 5, 18, 8, 23, 9, 12, 4, 14, 9, 7, 21, 14, 20,
19, 1, 4, 14, 15, 20, 18, 16, 18, 16, 1, 5, 9, 19, 20, 14, 15, 16, 12, 18, 9, 1, 19, 20, 14, 1, 4, 8, 8, 21, 15, 7, 20, 21, 5, 8, 19, 14, 19, 1, 23, 19, 8, 14, 14, 9, 9, 7, 20, 14, 1, 4, 8, 25, 19, 5, 11, 1, 19, 1, 8, 18, 8, 5, 12, 2, 21, 20, 5, 5, 8, 18, 19, 5, 5, 5, 13, 4, 5, 15, 20, 2, 14, 12, 3, 15, 15, 15, 14, 18, 21, 9, 1, 8, 25, 14, 20, 9, 24, 7, 14, 5, 3, 20, 16, 5, 20, 8, 19, 16, 5, 15, 20, 20, 18, 5, 19, 8, 5, 20, 1, 23, 18, 1, 16, 5, 12, 19, 5, 5, 20, 18, 4, 18, 22, 5, 5, 25, 18, 8, 23, 5, 5, 2, 8, 20, 5, 12, 13, 3, 1, 11, 15, 1, 19, 21, 20, 3, 4, 9, 8, 15, 6, 7, 3,
1, 5, 1, 4, 5, 26, 4, 15, 18, 14, 23, 6, 15, 5, 5, 13, 22, 18, 13, 3, 25, 15, 13, 9, 14, 1, 4, 14, 18, 3, 7, 15, 14, 8, 18, 5, 20, 5, 1, 5, 18, 23, 19, 15, 14, 15, 5, 14, 8, 8, 20, 5, 15, 6, 19, 21, 5, 18, 9, 14, 15, 20, 13, 9, 5, 13, 4, 1, 25, 5, 20, 12, 15, 19, 16, 16, 15, 9, 9, 5, 20, 2,
7, 20, 18, 2, 15, 8, 19, 18, 5, 9, 23, 8, 20, 1, 3, 9, 15, 7, 14, 25, 21, 3, 8, 20, 5, 1, 15, 20, 16, 9, 14, 4, 1, 19, 9, 23, 5, 9, 8, 12, 20, 1,
5, 8, 4, 18, 5, 5, 11, 25, 19, 11, 15, 12, 15, 5, 5, 4, 4, 5, 16, 15, 14, 9, 20, 23, 20, 14, 9, 19, 15, 23, 19, 14, 15, 14, 14, 15, 4, 23, 1, 18,
19, 20, 20, 5, 5, 20, 5, 12, 22, 14, 12, 5, 1, 15, 18, 8, 20, 5, 16, 5, 19, 15, 20, 18, 14, 15, 20, 18, 1, 5, 15, 20, 14, 3, 5, 18, 15, 14, 18, 16, 12, 6, 1, 16, 9, 4, 5, 6, 20, 12, 21, 6, 12, 25, 8, 14, 9, 20, 5, 4, 9, 23, 14, 1, 18, 20, 12, 5, 14, 12, 20, 1, 5, 25, 5, 15, 3, 22, 18, 1, 14, 9, 7, 14, 3, 21, 4, 14, 15, 9, 5, 22, 18, 14, 5, 20, 7, 8, 19, 12, 14, 9, 7, 5, 4, 15, 23, 18, 9, 15, 7, 14, 19, 3, 8, 14, 9, 20, 5, 4, 1, 6, 18, 9, 14, 20, 19, 1, 3, 5, 1, 5, 8, 12, 16, 3, 9, 15, 20, 11, 18, 5, 19, 9, 4, 13, 13, 5, 4, 2, 23, 15, 14, 5, 5, 23, 20, 5, 14, 18, 8, 20, 5, 15, 8, 6, 15, 19, 15, 5, 5, 22, 18, 4, 1, 15, 6, 18, 14, 20, 14, 9, 19, 1, 9, 20, 14, 12, 11, 12, 1, 5, 2, 21, 20, 2, 5, 15, 20, 14, 5, 12, 1, 4, 20, 1, 4, 18, 5, 1, 1, 4, 23, 25, 9, 7, 1, 1, 14, 8, 9, 23, 20, 1, 22, 21, 3, 18, 9, 12, 7, 14, 6, 9, 9, 8, 7, 20, 20, 20, 1, 23, 19, 8, 12, 16, 5, 15, 9, 1, 5, 3, 16, 20, 19, 15, 18, 12, 14, 9, 15, 15, 16, 14, 20, 9, 7, 14, 15, 16, 5, 16, 15, 12, 9, 19, 5, 23, 14, 19, 15, 4, 23, 20, 1, 5, 8, 16, 20, 19, 15, 18, 12, 4, 15, 4, 9, 14, 20, 20, 1, 13, 20, 5, 23, 8, 18, 15, 5, 15, 5, 22, 18, 14, 8, 25, 12, 20, 5, 21, 8, 20, 15, 7, 15, 20, 8, 16, 12, 13, 3, 9, 5, 1, 18, 20, 20, 5, 5, 4]
w_squared_charmap = ["11", "21", "31", "12", "22", "32", "13", "23", "33"]
mtrans_6_orwell_tokens = [9, 2, 3, 25, 9, 8, 11, 19, 14, 20, 14, 13, 19, 21, 9, 19, 20, 6, 15, 5, 12, 19, 4, 12, 21, 7, 15, 22, 25, 15, 21, 17, 25, 8, 22, 23, 7, 4, 15, 18, 15, 8, 5, 1, 20, 12, 2, 4, 7, 20, 4, 3, 5, 5, 1, 18, 18, 1, 5, 11, 8, 9, 3, 13, 5, 21, 13, 1, 18, 20, 5, 14, 21, 25, 9, 1, 3, 20, 14, 5, 14, 6, 5, 20, 5, 5,
19, 14, 18, 8, 5, 20, 20, 5, 19, 13, 14, 20, 14, 12, 3, 14, 21, 21, 1, 20, 9, 1, 8, 15, 22, 5, 9, 8, 5, 12, 19, 12, 21, 9, 23, 20, 14, 4, 1, 5, 1, 9, 20, 23, 15, 19, 5, 20, 14, 25, 8, 14, 19, 5, 8, 5, 18, 8, 13, 3, 4, 8, 9, 14, 15, 20, 8, 5, 20, 20, 5, 15, 15, 20, 15, 2, 20, 23, 14, 8, 9, 5, 18, 9, 6, 18, 15, 19, 14, 12, 6, 19, 8, 5, 20, 20, 18, 9, 9, 20, 3, 6, 15, 13, 12, 9, 12, 18, 9, 13, 20, 19, 5, 18, 1, 12, 15, 5, 20,
20, 3, 19, 1, 7, 15, 18, 12, 14, 1, 5, 21, 8, 19, 9, 1, 15, 4, 2, 18, 15, 19, 14, 6, 5, 5, 15, 20, 4, 1, 6, 9, 8, 18, 15,
15, 5, 8, 4, 2, 5, 23, 5, 21, 13, 16, 9, 23, 25, 9, 14, 12, 7, 9, 18, 14, 15, 15, 2, 1, 1, 4, 12, 5, 18, 1, 5, 21, 5, 18,
8, 23, 16, 5, 12, 3, 23, 5, 20, 5, 19, 4, 8, 7, 14, 16, 14, 18, 4, 8, 14, 9, 14, 11, 19, 20, 5, 15, 15, 14, 9, 5, 16, 19,
5, 19, 5, 8, 5, 13, 3, 1, 5, 6, 5, 13, 7, 18, 23, 15, 15, 15, 5, 12, 19, 7, 5, 20, 25, 3, 14, 8, 5, 25, 11, 16, 9, 19, 23, 18, 22, 20, 19, 18, 5, 18, 5, 21, 20, 4, 14, 3, 14, 14, 9, 19, 23, 7, 20, 4, 3, 9, 18, 5, 2, 14, 15, 5, 18, 20, 11, 5, 5, 18, 1, 14, 3, 7, 20, 20, 9, 18, 15, 14, 16, 14, 8, 15, 14, 20, 5, 12, 8, 16, 13, 5, 20, 18, 15, 9, 12, 5, 19, 20, 7, 5, 19, 9, 3, 26, 14, 2, 9, 6, 5, 20, 5, 12, 17, 25, 7, 12, 15, 9, 13, 14, 7, 21, 5, 20, 5, 9, 18, 21, 13, 9, 14, 8, 8, 25, 15, 5, 1, 15, 13, 15, 15, 15, 4, 18, 18, 9, 4, 25, 5, 5, 5, 20,
20, 16, 14, 19, 15, 14, 5, 8, 15, 15, 20, 6, 20, 22, 11, 1, 4, 4, 4, 5, 19, 15, 6, 19, 9, 15, 25, 5, 22, 8, 15, 19, 19, 23, 7, 16, 20, 5, 3, 20, 20, 18, 25, 8, 20, 18, 5, 13, 5, 16, 15, 1, 11, 1, 5, 9, 16, 14, 8, 8, 9, 8, 18, 21, 2, 19, 1, 5, 23, 20, 22, 9, 20, 15, 12, 7, 9, 12, 1, 16, 23, 5, 15, 5, 6, 5, 20, 5, 19, 21, 9, 19, 18, 8, 5, 12, 21, 23, 21, 9, 8, 1, 7, 5, 15, 1, 1, 4, 12, 21, 9, 18, 7, 9, 9, 23, 1, 20, 15, 8, 15, 15, 7, 8, 5, 18, 2, 5, 1, 11, 12, 18, 3, 5, 15, 21, 15, 9,
14, 23, 14, 4, 3, 8, 5, 15, 20, 8, 18, 5, 4, 7, 2, 9, 13, 5,
3, 20, 12, 21, 9, 21, 5, 23, 8, 7, 3, 20, 13, 22, 8, 15, 12,
18, 7, 5, 5, 6, 4, 12, 1, 2, 12, 18, 8, 18, 14, 15, 1, 19, 1, 6, 19, 1, 12, 21, 19, 15, 5, 1, 1, 12, 26, 4, 20, 4, 23, 20, 4, 14, 20, 22, 15, 5, 9, 1, 23, 15, 15, 14, 19, 12, 5, 15, 23, 9, 4, 4, 1, 20, 1, 20, 20, 23, 14, 4, 25, 8, 8, 5, 2, 12,
1, 14, 16, 15, 20, 18, 20, 22, 5, 2, 15, 8, 3, 4, 18, 18, 1,
3, 20, 1, 14, 21, 14, 4, 25, 9, 2, 18, 3, 15, 1, 19, 9, 4, 5, 5, 9, 14, 15, 14, 5, 5, 8, 20, 14, 3, 6, 4, 12, 8, 1, 1, 15, 7, 3, 14, 9, 15, 19, 8, 9, 5, 3, 19, 4, 5, 20, 6, 18, 1, 1,
5, 2, 1, 20, 25, 23, 21, 6, 9, 8, 3, 15, 16, 20, 12, 4, 5, 12, 15, 5, 22, 25, 15, 15, 1, 4, 23, 9, 12, 14, 1, 3, 23, 18, 20, 5, 20, 20, 8, 26, 20, 18, 14, 15, 19, 8, 23, 9, 21, 20, 8, 1, 18, 3, 1, 19, 8, 9, 14, 15, 14, 18, 9, 19, 5, 14, 7, 9, 1, 19, 6, 4, 7, 12, 1, 14, 6, 12, 16, 20, 7, 14, 9, 8, 14, 4,
23, 4, 5, 12, 15, 6, 18, 1, 23, 5, 6, 6, 6, 9, 8, 25, 13, 3,
18, 12, 19, 1, 23, 14, 15, 20, 20, 21, 9, 12, 5, 5, 6, 9, 5,
15, 1, 18, 20, 3, 21, 23, 15, 9, 12, 15, 23, 20, 5, 25, 9, 1, 14, 20, 20, 20, 22, 7, 1, 19, 15, 9, 14, 1, 9, 12, 15, 18, 14, 14, 12, 9, 5, 13, 8, 14, 1, 15, 20, 9, 6, 15, 9, 5, 21, 7, 18, 23, 23, 15, 5, 18, 3, 15, 9, 1, 25, 12, 1, 8, 13, 7, 5,
20, 25, 3, 14, 20, 14, 5, 1, 9, 3, 5, 15, 19, 7, 8, 4, 8, 4,
20, 4, 14, 9, 5, 3, 15, 12, 20, 17, 5, 5, 15, 8, 4, 6, 18, 6, 7, 4, 9, 20, 1, 8, 5, 19, 13, 20, 20, 4, 19, 9, 21, 12, 14,
5, 20, 18, 23, 12, 12, 13, 20, 23, 1, 21, 9, 15, 5, 15, 5, 5, 23, 12, 1, 21, 13, 14, 8, 25, 25, 19, 25, 21, 1, 9, 5, 9, 6, 18, 8, 19, 1, 6, 20, 25, 9, 19, 21, 4, 18, 16, 21, 15, 5, 8, 15, 9, 8, 10, 4, 19, 5, 21, 19, 14, 14, 15, 15, 12, 9, 20, 9, 4, 6, 5, 18, 21, 20, 16, 15, 12, 8, 8, 1, 9, 20, 1, 2, 5, 13, 5, 15, 14, 7, 20, 19, 8, 5, 5, 5, 18, 12, 21, 9, 5, 4, 15, 25, 14, 15, 8, 19, 20, 19, 20, 9, 15, 20, 18, 9, 8, 21, 16,
1, 12, 1, 19, 4, 14, 19, 23, 1, 5, 12, 5, 5, 1, 15, 12, 6, 12, 5, 12, 20, 22, 1, 15, 7, 14, 18, 15, 5, 19, 1, 15, 11, 4, 20, 8, 19, 5, 14, 14, 1, 15, 14, 5, 1, 9, 18, 12, 20, 5, 5, 12, 9, 15, 5, 15, 16, 19, 20, 18, 5, 20, 21, 12, 20, 1, 7, 4, 1, 14, 12, 5, 9, 8, 14, 15, 8, 9, 12, 15, 5, 1, 18, 3, 5, 9, 16, 9, 8, 20, 19, 19, 20, 14, 20, 14, 3, 15, 16, 20, 12, 20, 20, 14, 7, 23, 13, 12, 13, 2, 3, 5, 4, 20, 5, 9, 15, 15, 15, 5, 4, 19, 1, 20, 20, 1, 5, 4, 25, 18, 1, 5, 13, 9, 6, 1, 1, 15, 22, 1, 2, 15, 8, 21, 25, 15, 20, 9, 13, 18, 1, 23, 19, 14,
9, 14, 2, 20, 20, 12, 18, 14, 5, 8, 20, 18, 1, 6, 14, 9, 21,
1, 15, 3, 4, 14, 18, 6, 5, 8, 23, 5, 8, 14, 20, 23, 18, 5, 4, 3, 3, 22, 9, 11, 20, 25, 14, 18, 5, 5, 5, 14, 16, 5, 6, 20,
19, 20, 14, 19, 1, 15, 1, 1, 6, 16, 5, 8, 3, 22, 20, 5, 15, 2, 5, 15, 2, 18, 3, 15, 1, 2, 8, 9, 20, 20, 20, 5, 1, 21, 20,
21, 9, 19, 9, 15, 8, 21, 15, 18, 22, 1, 13, 15, 1, 21, 1, 4,
18, 6, 16, 20, 6, 20, 8, 23, 14, 21, 19, 1, 22, 1, 5, 8, 8, 19, 20, 19, 9, 5, 19, 14, 5, 5, 1, 5, 4, 13, 20, 1, 25, 20, 20, 13, 12, 22, 18, 23, 1, 9, 9, 18, 5, 5, 9, 13, 5, 9, 20, 5,
12, 3, 20, 6, 20, 20, 1, 22, 9, 1, 21, 19, 14, 11, 7, 2, 19,
1, 14, 18, 19, 5, 6, 14, 1, 21, 5, 9, 14, 7, 8, 4, 5, 18, 11, 4, 14, 18, 20, 4, 23, 18, 12, 19, 15, 5, 19, 19, 15, 5, 19,
14, 8, 8, 12, 18, 5, 14, 21, 25, 5, 20, 20, 1, 16, 18, 18, 5, 1, 19, 15, 7, 15, 13, 3, 4, 18, 5, 15, 8, 5, 9, 1, 16, 5, 15, 19, 9, 20, 20, 9, 5, 18, 12, 4, 20, 20, 14, 20, 20, 1, 18,
18, 20, 18, 1, 9, 25, 23, 20, 5, 5, 14, 22, 20, 7, 4, 3, 6, 20, 8, 16, 9, 15, 23, 5, 8, 4, 9, 20, 2, 20, 4, 4, 7, 20, 22,
9, 23, 16, 16, 19, 14, 16, 19, 23, 1, 4, 13, 8, 18, 8, 7, 9,
20, 19, 8, 4, 16, 4, 15, 18, 11, 9, 23, 14, 8, 14, 5, 8, 1, 14, 20, 1, 22, 14, 16, 3, 18, 8, 19, 15, 15, 19, 8, 15, 11, 21, 18, 1, 15, 20, 6, 20, 1, 9, 20, 12, 5, 15, 1, 1, 18, 19, 5, 20, 21, 19, 15, 6, 15, 16, 4, 1, 15, 12, 16, 19, 1, 13, 3, 20, 5, 4, 1, 13, 2, 18, 5, 8, 12, 21, 5, 7, 8, 13, 21, 14, 1,
20, 9, 1, 5, 7, 6, 1, 5, 9, 23, 4, 11, 4, 19, 5, 18, 18, 19,
6, 7, 7, 18, 19, 6, 15, 18, 16, 1, 15, 23, 5, 1, 14, 20, 4, 15, 1, 20, 1, 1, 15, 5, 5, 7, 12, 19, 18, 7, 1, 19, 23, 1, 4,
16, 20, 20, 20, 20, 8, 15, 6, 26, 13, 12, 19, 20, 9, 19, 1, 15, 5, 20, 19, 23, 15, 14, 22, 18, 9, 8, 21, 16, 5, 9, 14, 8,
1, 25, 23, 4, 20, 15, 18, 3, 15, 14, 23, 5, 3, 6, 15, 15, 13, 1, 14, 12, 5, 4, 13, 23, 15, 1, 8, 1, 8, 20, 1, 19, 18, 23,
14, 15, 14, 23, 15, 5, 23, 9, 20, 19, 20, 20, 20, 12, 5, 19,
4, 2, 5, 8, 19, 15, 20, 15, 16, 25, 5, 20, 9, 19, 19, 12, 5,
1, 19, 19, 5, 13, 26, 8, 15, 12, 8, 8, 15, 8, 25, 9, 5, 18, 3, 18, 1, 5, 9, 8, 25, 5, 14, 20, 2, 1, 3, 20, 20, 20, 19, 4,
4, 20, 8, 21, 15, 20, 12, 5, 4, 20, 5, 20, 9, 9, 5, 9, 20, 18, 18, 16, 1, 21, 19, 19, 7, 5, 1, 21, 5, 4, 15, 18, 20, 24, 8, 5, 20, 12, 5, 25, 20, 3, 20, 4, 1, 23, 5, 15, 9, 14, 18, 14, 5, 6, 13, 20, 16, 2, 20, 23, 14, 8, 9, 4, 20, 11, 15, 5, 15, 15, 4, 19, 12, 14, 16, 20, 15, 14, 16, 20, 9, 9, 5, 12, 18, 4, 5, 8, 12, 9, 9, 1, 1, 5, 20, 13, 23, 5, 18, 15, 6, 14, 12, 12, 20, 4, 1, 1, 8, 9, 7, 1, 15, 1, 14, 7, 5, 23, 19, 20, 9, 1, 15, 15, 5, 8, 3, 5, 1, 20, 1, 18, 20, 3, 5, 9, 18, 9, 19, 9, 14, 4, 9, 19, 5, 20, 16, 9, 4, 5, 11, 15, 5, 4, 6, 18, 9, 15, 20, 12, 7, 5, 19, 6, 25, 18, 5, 12, 20, 8, 23, 12, 9, 2, 14, 1, 1, 14, 1, 18, 20, 12, 15, 15, 12, 2, 3, 20, 12, 9, 9, 14, 15, 5, 8, 20, 5, 3, 1, 15, 20, 23, 5, 1, 19, 1, 7, 1, 5, 18, 19, 4, 8, 18, 19, 20, 20, 20, 20, 19, 13, 1, 15, 9, 1,
5, 5, 9, 5, 3, 4, 4, 8, 19, 16, 20, 14, 9, 18, 20, 18, 5, 6,
19, 6, 19, 23, 14, 19, 25, 14, 22, 19, 18, 8, 8, 5, 12, 5, 19, 12, 15, 1, 3, 9, 15, 8, 19, 8, 5, 20, 18, 1, 5, 20, 12, 15, 8, 3, 23, 18, 14, 4, 8, 6, 25, 21, 25, 5, 15, 19, 9, 20, 20, 14, 20, 19, 5, 6, 22, 1, 9, 1, 6, 5, 8, 13, 7, 9, 16, 20, 16, 14, 9, 5, 14, 7, 16, 12, 21, 9, 8, 18, 18, 5, 3, 5, 8, 12, 20, 14, 9, 4, 9, 11, 8, 21, 23, 5, 12, 9, 8, 8, 18, 20, 5, 14, 3, 3, 5, 4, 5, 14, 6, 9, 6, 12, 8, 4, 15, 14, 13, 8, 6, 20, 7, 19, 2, 18, 16, 5, 5, 22, 19, 23, 5, 18, 5, 8, 18, 18, 8, 5, 1, 14, 8, 14, 5, 3, 19, 4, 18, 12, 14, 15, 8, 5, 8, 20, 15, 5, 8, 20, 20, 23, 8, 4, 4, 15, 8, 5, 12, 5, 14, 23, 14, 1, 14, 9, 9, 14, 7, 21, 8, 1, 19, 18, 5, 19, 20, 3, 9, 8, 3, 5, 18, 23, 1, 4, 23, 8, 11, 1, 6, 26, 14, 22, 13, 14, 5, 5, 5, 8, 18, 13, 5, 15, 9, 8, 1, 7, 5, 15, 23, 8, 5, 15, 5, 23, 14, 15, 20, 5, 15, 15, 15, 14, 5, 16, 6, 14, 14, 18, 25, 9, 21, 18, 5, 5, 14, 14, 18, 14, 12, 5, 13, 14, 5, 15, 22, 15, 19, 9, 21, 12, 1, 23, 9, 1, 14, 8, 19, 12, 20, 15, 9, 15, 9, 20, 18, 4, 20, 23, 14, 20, 20, 5, 18] | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"MIT"
] | 0.4.1 | 5e8e2c2b2057935d2128bc1c98f8f50c7aae5f60 | docs | 484 | # RxCiphers
[](https://github.com/rxwop/RxCiphers.jl/actions/workflows/CI.yml?query=branch%3Amaster)
[](https://codecov.io/gh/rxwop/RxCiphers.jl)
Credit to:
https://jkrumbiegel.com/pages/2021-06-07-macros-for-beginners/
http://www.practicalcryptography.com/ciphers/adfgvx-cipher/
for text samples | RxCiphers | https://github.com/rxwop/RxCiphers.jl.git |
|
[
"Apache-2.0"
] | 0.1.2 | 17337ed2faf970a7af883c8af86c2f6aff4417b0 | code | 172 | push!(LOAD_PATH,"../src/")
using Documenter, GnuplotLite
makedocs(sitename="GnuplotLite documentation")
deploydocs(
repo = "github.com/jhidding/GnuplotLite.jl.git",
) | GnuplotLite | https://github.com/jhidding/GnuplotLite.jl.git |
|
[
"Apache-2.0"
] | 0.1.2 | 17337ed2faf970a7af883c8af86c2f6aff4417b0 | code | 4391 | module GnuplotLite
export gnuplot, send, save
""" struct Gnuplot
This `struct` is not exported, as it is not supposed to be created by a user,
other than through calling [`gnuplot()`](@ref), or [`save()`](@ref).
Messages can be sent to a Gnuplot instance using the [`send()`](@ref) method:
```julia
gnuplot() do g
g |> send("plot sin(x)")
end
```
"""
struct Gnuplot
channel :: Channel{String};
end
""" struct Msg
This wraps the actions that are created using the different variants of the
[`send()`](@ref) method.
Messages can be composed (unlike `∘`, going left to right) using the `*`
operator.
"""
struct Msg
action :: Function
end
(a::Msg)(g::Gnuplot) = a.action(g)
Base.:*(a::Msg, b::Msg) = Msg(b.action ∘ a.action)
"""
gnuplot(persist = true, echo = false)
The main constructor for a `Gnuplot` instance. This starts the `gnuplot`
process and creates a channel to which commands can be sent.
The `persist` argument make sure that any interactive window remains open, even
if we have closed the pipe to the Gnuplot process.
The `echo` argument is for debugging. If enabled, all commands send to this
instance are also echoed to `stdout`.
"""
function gnuplot(;persist::Bool = true, echo::Bool = false)
cmd = persist ? `gnuplot -persist` : `gnuplot`
channel = Channel{String}() do channel
open(cmd, stdout; write=true) do input
for line in channel
echo && println("> ", line)
write(input, line * "\n")
end
end
end
Gnuplot(channel)
end
""" close(::Gnuplot)
Close the Gnuplot process by closing the underlying channel. The actual process
my continue living if terminals are left open.
"""
Base.close(g::Gnuplot) = close(g.channel)
""" gnuplot(::Function; kwargs...)
Variant of the `gnuplot` constructor for use with `do` syntax. Makes sure to
close the channel (and thereby) the process.
"""
function gnuplot(f::Function; kwargs...)
g = gnuplot(kwargs...)
f(g)
close(g.channel)
end
""" send(::String)
Send a literal message to a Gnuplot instance. This is supposed
to be used using a pipe (`|>`) operator.
Returns a [`Msg`](@ref).
"""
function send(msg::String)
function (g::Gnuplot)
put!(g.channel, msg)
g
end |> Msg
end
""" send(::Pair{String,Matrix{T}}) where T <: Number
Send a matrix to Gnuplot, storing it in a variable. This variant assumes the
matrix gets integer indices from `[0..N-1]`, as described in the Gnuplot
documentation for uniform matrices.
Returns a [`Msg`](@ref).
"""
function send(data::Pair{String,Matrix{T}}) where
{T <: Number}
function (g::Gnuplot)
g |> send("\$$(data[1]) << EOD")
for row in eachrow(data[2])
g |> send(join(string.(row), " "))
end
g |> send("EOD")
end |> Msg
end
""" send(::Pair{String,@NamedTuple{x::T,y::T,z::U}}) where
{T <: AbstractVector{<:Real}, U <: AbstractMatrix{<:Real}}
Send a matrix to Gnuplot, storing it in a variable. This variant also sends
axis information along with the matrix, as described in the Gnuplot
documentation for nonuniform matrices.
Returns a [`Msg`](@ref).
Example:
x = y = -1:0.1:1
z = x.^2 .- y'.^2
gnuplot() do gp
gp |>
send("data" => (x=collect(x), y=collect(y), z=z)) |>
send("splot \$data w l")
end
"""
function send(data::Pair{String,@NamedTuple{x::T,y::T,z::U}}) where
{T <: AbstractVector{<:Real}, U <: AbstractMatrix{<:Real}}
(k, (x, y, z)) = data
function (g::Gnuplot)
g |>
send("\$$k << EOD") |>
send("$(length(x)) $(join(string.(x), " "))")
for (y, row) in zip(y, eachrow(z))
g |> send("$y $(join(string.(row), " "))")
end
g |> send("EOD")
end |> Msg
end
""" save(path::String)
Create a phony [`Gnuplot`](@ref) instance, without the underlying process
attached. Instead, write messages to file. You can then later turn the file
into a plot by running it with Gnuplot from the command line, or run a
`pipeline` from Julia.
"""
function save(path::String)
channel = Channel() do channel
open(path, "w") do output
for line in channel
write(output, line * "\n")
end
end
end
Gnuplot(channel)
end
end # module
| GnuplotLite | https://github.com/jhidding/GnuplotLite.jl.git |
|
[
"Apache-2.0"
] | 0.1.2 | 17337ed2faf970a7af883c8af86c2f6aff4417b0 | docs | 1316 | # GnuplotLite.jl
Responsive, composable, no-nonsense interface to Gnuplot. This has the following design goals:
- Responsiveness: this should be the thinnest of possible wrappers. The biggest drag to plotting in Julia is the number of lines of code that sit in between the programmer and the plotter.
- Composability: it should be easy to extend GnuplotLite.
- Transparency: it should be easy to reason about how data is entered into Gnuplot.
That being said, there are so many plotting packages in Julia. When should you use this?
- Use `GnuplotLite` if you are in love with Gnuplot and want nothing to sit in between you and it.
```julia
using GnuplotLite
gnuplot() do gp
gp |>
send("set term svg background 'white'") |>
send("set output 'sine.svg'") |>
send("plot sin(x)")
end
```
You can specialize writing data to Gnuplot by overloading the `send()` method.
```julia
x = -4:0.15:4
y = x
z = sinc.(sqrt.(x.^2 .+ y'.^2))
gnuplot() do gp
gp |>
send("data" => (x=collect(x),y=collect(y),z=z)) |>
send("plot \$data matrix nonuniform with image")
end
```
Check out the [full documentation](https://jhidding.github.io/GnuplotLite.jl).
## License
Copyright 2022, Johan Hidding, Netherlands eScience Center.
Licensed under the Apache 2.0 license, see LICENSE.
| GnuplotLite | https://github.com/jhidding/GnuplotLite.jl.git |
|
[
"Apache-2.0"
] | 0.1.2 | 17337ed2faf970a7af883c8af86c2f6aff4417b0 | docs | 48 | # API
```@autodocs
Modules = [GnuplotLite]
```
| GnuplotLite | https://github.com/jhidding/GnuplotLite.jl.git |
|
[
"Apache-2.0"
] | 0.1.2 | 17337ed2faf970a7af883c8af86c2f6aff4417b0 | docs | 4284 | # Introduction to GnuplotLite.jl
This module is a Julia wrapper for Gnuplot. This has the following design goals:
- Responsiveness: this should be the thinnest of possible wrappers. The biggest drag to plotting in Julia is the number of lines of code that sit in between the programmer and the plotter.
- Composability: it should be easy to extend `GnuplotLite`.
- Transparency: it should be easy to reason about how data is entered into Gnuplot.
That being said, there are so many plotting packages in Julia. When should you use this?
- Use `GnuplotLite` if you are in love with Gnuplot and want nothing to sit in between you and it.
## Alternatives
Plotting in Julia is a notoriously hard problem. Why? A core issue with many plotting packages in Julia is that these packages become too big, and then compiling all the necessary code makes things slow. So the problem is a combination of the one-shot nature of plotting and the complexity of the packages involved.
A related problem with plotting packages in any language, is that two problems get conflated: data analysis and visualization. This makes for very nice interfaces a la GGPlot (which I find brilliant), however, on the implementation side this needlessly complicates things. The focus on visualizing data-frames has left it very hard to work with grid based data in most modern packages (plotly, ggplot, vegalite, bokeh, you name it).
- `Plots.jl` is a large package that provides a uniform interface over a lot of backends. I have recently tried `Plots` with the most recommended backends: `GR`, `Plotly`, `Pyplot`, `Gaston`. The `GR` backend worked really well, but it was really hard to tweak the output towards publication quality. In other words, plotting was relatively fast but hideous to the limit of being buggy. All the other backends that I tried gave me installation issues.
- `Gadfly.jl` looks very promising, but is very slow to respond with larger data sets.
- `Gnuplot.jl` is a thin wrapper around Gnuplot. My problem with this package is that it focusses heavily on use with a macro `@gp`. Because of this, code in `Gnuplot.jl` suffers from a lack of composability. It is also not so straight forward to see exactly how `@gp` code translates to `gnuplot` commands when arrays are used.
- `Gaston.jl` is a slightly thicker wrapper around Gnuplot, mirroring Gnuplot syntax in Julia functions. I could not make it work.
## Tutorial
Start a Gnuplot session by running `gnuplot()`. Then send it messages using `|> send(...)`.
```julia
using GnuplotLite
g = gnuplot()
g |> send("plot sin(x)")
close(g)
```
Note that using `gnuplot()` this way may leave the actual Gnuplot process dangling. There is `do`-notation to prevent that:
```julia
gnuplot() do g
g |> send("plot sin(x)")
end
```
Messages can be composed:
```@example 1
using GnuplotLite
setup(output) =
send("set term svg background 'white' size 600,400 dynamic " *
"mouse standalone") *
send("set output '$output'")
gnuplot() do g
g |> setup("sine.svg") |> send("plot sin(x)")
end
```
You may enter data into Gnuplot by sending a pair of string and $x, y, z$ data. This gets translated into an inline data definition.
```@example 1
settings = """
set pm3d lighting primary 0.5 specular 0.3
set pm3d depthorder border lc 'black' lw 0.3
set style fill solid 1.00 noborder
set ticslevel 0
set autoscale fix
set tmargin 0
unset colorbox
"""
x = -4:0.15:4
y = x
z = sinc.(sqrt.(x.^2 .+ y'.^2))
plot = setup("sinc3d.svg") *
send(settings) *
send("set title 'radial sinc function'") *
send("data" => (x=x, y=y, z=z)) *
send("splot \$data nonuniform matrix " *
"using 1:2:3:(0xffad00) t'' " *
"with pm3d lc rgb variable")
gnuplot() |> plot |> close
```
## Under the hood
There are two objects in `GnuplotLite`: the `Gnuplot` instance and `Msg`. The `Gnuplot` instance contains a channel to which you can send Gnuplot commands. A `Msg` contains a function of type `::Gnuplot -> ::Gnuplot` that then actually sends the commands to the channel. By abstracting the messages into a function we can stay efficient while being fully composable.
Because of the composability we can easily extend `GnuplotLite` to interact with data the way *you* want.
| GnuplotLite | https://github.com/jhidding/GnuplotLite.jl.git |
|
[
"MIT"
] | 0.3.5 | 3e53af9fa1ce5e05ed4201758f5518b6268303b9 | code | 647 | using Documenter,GCIdentifier
makedocs(sitename = "GCIdentifier.jl",
format = Documenter.HTML(
# Use clean URLs, unless built as a "local" build
canonical = "https://ClapeyronThermo.github.io/GCIdentifier.jl/",
),
warnonly = Documenter.except(),
authors = "Pierre J. Walker and Andrés Riedemann.",
pages = ["Home" => "index.md",
"Group Assignment" => "group_search.md",
"Finding Missing Groups" => "missing_groups.md",
"Custom Groups" => "custom_groups.md",
"API" => "api.md"]
)
deploydocs(repo="github.com/ClapeyronThermo/GCIdentifier.jl.git")
| GCIdentifier | https://github.com/ClapeyronThermo/GCIdentifier.jl.git |
|
[
"MIT"
] | 0.3.5 | 3e53af9fa1ce5e05ed4201758f5518b6268303b9 | code | 827 | module GCIdentifierChemicalIdentifiersExt
using ChemicalIdentifiers
using GCIdentifier
const GC = GCIdentifier
function GC.get_groups_from_name(component::String,groups;connectivity = false)
groups = GC.get_grouplist(groups)
return GC.get_groups_from_name(component,groups,connectivity=connectivity)
end
function GC.get_groups_from_name(component::String,groups::Vector{GC.GCPair};connectivity=false,check = true)
res = search_chemical(component)
smiles = res.smiles
gcpairs = get_groups_from_smiles(smiles,groups;connectivity=connectivity,check = check)
if connectivity == true
(smiles,groups_found,connectivity) = gcpairs
return (component,groups_found,connectivity)
else
(smiles,groups_found) = gcpairs
return (component,groups_found)
end
end
end #module
| GCIdentifier | https://github.com/ClapeyronThermo/GCIdentifier.jl.git |
|
[
"MIT"
] | 0.3.5 | 3e53af9fa1ce5e05ed4201758f5518b6268303b9 | code | 586 | module GCIdentifierClapeyronExt
using Clapeyron
using GCIdentifier
const GC = GCIdentifier
GC.get_grouplist(m::Clapeyron.EoSModel) = GC.get_grouplist(typeof(m))
GC.get_grouplist(::Type{T}) where T <: Clapeyron.UNIFAC = GC.UNIFACGroups
GC.get_grouplist(::Type{T}) where T <: Clapeyron.SAFTgammaMie = GC.SAFTgammaMieGroups
GC.get_grouplist(::Type{T}) where T <: Clapeyron.JobackIdeal = GC.JobackGroups
GC.get_grouplist(::Type{T}) where T <: Clapeyron.HomogcPCPSAFT = GC.gcPPCSAFTGroups
GC.get_grouplist(::Type{T}) where T <: Clapeyron.HeterogcPCPSAFT = GC.gcPPCSAFTGroups
end #module
| GCIdentifier | https://github.com/ClapeyronThermo/GCIdentifier.jl.git |
|
[
"MIT"
] | 0.3.5 | 3e53af9fa1ce5e05ed4201758f5518b6268303b9 | code | 1644 | module GCIdentifier
using Combinatorics
@static if !isdefined(Base,:eachsplit)
eachsplit(str::AbstractString, dlm; limit::Integer=0, keepempty::Bool=true) = split(str,dlm;limit,keepempty)
eachsplit(str::AbstractString; limit::Integer=0, keepempty::Bool=false) = split(str;limit,keepempty)
end
split_2(str) = NTuple{2}(eachsplit(str, limit=2))
split_2(str,dlm) = NTuple{2}(eachsplit(str,dlm, limit=2))
#TODO: windows support with MolecularGraph
import MolecularGraph
include("prelude.jl")
include("group_search.jl")
include("missing_groups.jl")
include("database/database.jl")
"""
get_groups_from_name(name::String,groups;connectivity = false)
Given a molecule name and a group list (`groups::Vector{GCPair}`), returns a list of groups and their corresponding amount.
If `connectivity` is `true`, then it will additionally return a vector containing the amount of bonds between each pair.
Note: Can only be used if the ChemicalIdentifiers package is also installed and loaded (`using ChemicalIdentifiers`).
## Examples
```julia
julia> get_groups_from_name("ethanol",UNIFACGroups)
("ethanol", ["CH3" => 1, "CH2" => 1, "OH(P)" => 1])
julia> get_groups_from_name("ethanol",JobackGroups,connectivity = true)
("ethanol", ["-CH3" => 1, "-CH2-" => 1, "-OH (alcohol)" => 1], [("-CH3", "-CH2-") => 1, ("-CH2-", "-OH (alcohol)") => 1])
```
"""
function get_groups_from_name end #overload this if ChemicalIdentifiers is loaded.
if !isdefined(Base,:get_extension)
using Clapeyron,ChemicalIdentifiers
include("../ext/GCIdentifierClapeyronExt.jl")
include("../ext/GCIdentifierChemicalIdentifiersExt.jl")
end
end # module
| GCIdentifier | https://github.com/ClapeyronThermo/GCIdentifier.jl.git |
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