geodatascience.jl

### A Pluto.jl notebook ###
# v0.19.19

using Markdown
using InteractiveUtils

# ╔═╡ 15926938-04a3-478a-b525-260b3107afbc
begin
	# main package
	using GeoStats

	# visualization recipes
	using GeoStatsViz

	# auxiliary package
	using CSV
	using DataFrames
	using Statistics
	using GeoTables
	using PlutoTeachingTools
	import PlutoUI

	# import Makie's WebGL backend
	import WGLMakie as Mke

	# workaround for https://github.com/fonsp/Pluto.jl/issues/2309
	PlutoRunner.pluto_showable(
		::MIME"application/vnd.pluto.table+object",
		::Data) = true

	# add table of contents to the side
	PlutoUI.TableOfContents(title="Contents")
end

# ╔═╡ d812a6c8-b676-439c-90d6-c8a0b47c29d6
html"""
<div style="
position: absolute;
width: calc(100% - 30px);
border: 50vw solid #444444;
border-top: 500px solid #048282;
border-bottom: none;
box-sizing: content-box;
left: calc(-50vw + 15px);
top: -500px;
height: 500px;
pointer-events: none;
"></div>
<div style="
height: 500px;
width: 100%;
background: #444444;
color: #fff;
padding-top: 68px;
">
<span style="
font-family: Rubik, serif;
font-weight: 700;
font-feature-settings: 'lnum', 'pnum';
"> <p style="
font-size: 1.5rem;
opacity: .8;
"><em>JuliaEO 2023 Workshop:</em></p>
<p style="text-align: center; font-size: 2rem;">
<em>Geodata Science & Geostatistical Learning</em>
</p>
<p style="
font-size: 1.5rem;
text-align: center;
opacity: .8;
"><em>presented by Arpeggeo® Technologies</em></p>
<center>
<a href="https://arpeggeo.tech"><img src="https://i.imgur.com/xgq72Z7.png" height=200></a>
</center>
<style>
body {
overflow-x: hidden;
}
</style>"""

# ╔═╡ e551d2ac-60a5-44e5-b5fa-fdf96e5c9df3
md"""
# Module I: Advanced Geodata Science

**Instructor:** [Júlio Hoffimann](https://juliohm.github.io)
**E-mail:** [juliohm@arpeggeo.tech](mailto:juliohm@arpeggeo.tech)
"""

# ╔═╡ 5e8bbb4f-bb1f-42a3-bc62-b291327c82e1
ChooseDisplayMode()

# ╔═╡ fcf7fc2b-c46c-46c4-a3f9-d9fa73d53680
html"""
<img src="https://github.com/JuliaEarth/GeoStats.jl/blob/master/docs/src/assets/logo-text.svg?raw=true">
"""

# ╔═╡ 61bcb8bf-6715-44a2-a0cb-cd48a8b64967
html"""
<head>
    <style>
    {
        box-sizing: border-box;
    }
    /* Set additional styling options for the columns*/
    .column {
    float: left;
    width: 50%;
    }

    .row:after {
    content: "";
    display: table;
    clear: both;
    }
    </style>
</head>
<body>
<div class="row">
        <div class="column";">
            <h2>Objectives</h2>
            <ol>
  				<li>Introduce important fundamental concepts</li>
  				<li>Learn the "right way" of doing geodata science</li>
  				<li>Practice concepts and tools with exercises</li>
				<li>Design sophisticated geospatial pipelines</li>
			</ol>
        </div>
        <div class="column";">
            <img src="https://i.imgur.com/0Y2bG17.jpg" width=250>
        </div>
</div>
</body>
"""

# ╔═╡ c67f15b9-c8b2-420b-980a-7713372a4746
md"""
## What is geospatial data?

Very generally, (discrete) **geo**spatial data is the combination of:

1. a **table** of attributes (or features) with
2. a discretization of a geospatial **domain**

The concept of **table** is widespread. We support any table implementing the [Tables.jl](https://github.com/JuliaData/Tables.jl) interface, including named tuples, dataframes, SQL databases, Excel spreadsheets, Apache Arrow, etc.

Given a **domain** (or region) in physical space, we can discretize it into *elements*. We support any domain implementing the [Meshes.jl]() interface, including Cartesian grids, point sets, collections of geometries and general unstructured meshes.
"""

# ╔═╡ e260b763-5bb3-4e8e-9e22-70761b2a39ca
md"""
## All you need is `georef` 🌎 ✅

We provide the **`georef`** function to combine tables with domains:

$$\textbf{georef}\text{(table, domain)} \mapsto \text{data}$$

And the functions **`values`** and **`domain`** to retrieve the table and domain of the data:

$$\begin{align*}\textbf{values}\text{(data)} &\mapsto \text{table}\\ \textbf{domain}\text{(data)} &\mapsto \text{domain}\end{align*}$$
"""

# ╔═╡ 380c8b96-d9d7-4ce7-bdc3-bcb2ae3fd173
md"""
### Basic example

Consider the following table with `100` measurements of variables `a` and `b`:
"""

# ╔═╡ e2bf778d-057d-4289-977c-67bbfba7013e
table = DataFrame(a = rand(100), b = rand(100))

# ╔═╡ 6c2806f4-40cf-4ceb-8f8e-343ad6a22510
md"""
Let's assume that the measurements are associated with random points in 2D space:
"""

# ╔═╡ 4f755fff-f8e6-4850-b657-32186d619084
pointdata = georef(table, rand(Point2, 100))

# ╔═╡ 13ffa2c5-a694-4dda-bddf-8dbd796af973
pointdata |> viewer

# ╔═╡ b4154684-adaf-4a3e-bc87-98e31b8492c4
md"""
Alternatively, they could be associated with quadrangle geometries in a Cartesian grid:
"""

# ╔═╡ 41180c1a-867d-4e3f-b1de-9674b7fd24d8
griddata = georef(table, CartesianGrid(10, 10))

# ╔═╡ feae2a69-5768-4036-8d75-bd95da78f759
griddata |> viewer

# ╔═╡ 3bf6d731-4fbe-4795-ab55-f00bf8058560
md"""
In all cases, we can always retrieve the underlying table of attributes and geospatial domain:
"""

# ╔═╡ 7664fd5d-44c3-4c70-9291-9ae78b1f9cee
griddata |> values

# ╔═╡ 0ddcbf23-3250-44c6-9c9e-bd5c9a7e0192
griddata |> domain

# ╔═╡ 04ffa2af-1bdc-473b-8ef4-f071225e410b
md"""
### The Bonnie data set

Let's try what we learned with a real data set...

```
The Bonnie Project Example is under copyright of Transmin Metallurgical Consultants, 2019. It is issued under the Creative Commons Attribution-ShareAlike 4.0 International Public License.
```
"""

# ╔═╡ 88d0ab55-ed76-4e63-86cf-02a0a81ccacc
csv = CSV.File("data/bonnie.csv")

# ╔═╡ 89d2f22e-e0ac-4927-ac62-064b42987905
md"""
#### Exercise

Read the [georef](https://juliaearth.github.io/GeoStats.jl/stable/data.html) documentation and find the correct method to georeference this table with coordinates `EAST`, `NORTH` and `RL`.
"""

# ╔═╡ 9d023c57-26e0-4eaa-830a-0745ea45e426
answer(csv) = missing

# ╔═╡ 08485d26-c599-4836-af95-44115c33c611
begin
	scored1 = false
	a = answer(csv)
	if ismissing(a)
		still_missing()
	elseif a isa Data &&
	       domain(a) isa PointSet{3} &&
		   names(values(a) |> DataFrame) == ["Auppm","Agppm","Cuppm","Asppm","Sper","CODE","OX","ISBD"]
		scored1 = true
		correct()
	else
		keep_working()
	end
end

# ╔═╡ ca1b4dbc-0b09-4d12-9973-ca2963df0a74
hint(md"Come on, it is super easy... One line of code...")

# ╔═╡ 6c2b4ecb-86f8-4b17-a1f9-7c055589ccba
md"""
The result will show below when you get the right answer.
"""

# ╔═╡ d279f14c-ab74-46f3-8ec0-42b4058fda0e
begin
	bonnie = scored1 ? answer(csv) : nothing
end

# ╔═╡ a199cb3b-0b90-4b1b-b1e7-f80a7596ac42
md"""
### Like a DataFrame...

We can index rows and columns of geospatial data. These operations are optimized to avoid unncessary copies of geometries:
"""

# ╔═╡ 097a7ce4-832e-4982-b762-9903f143ffa1
griddata[1:3,:]

# ╔═╡ 3a7fc3b9-e613-469e-b806-bb739de8f3dd
griddata[1,:]

# ╔═╡ 626db287-dd44-4119-866e-1ba63f6f5b46
griddata[:,:a]

# ╔═╡ 5985148a-0801-4822-9409-bebdd4eaca70
griddata[:,:geometry]

# ╔═╡ 1961d39a-2088-4036-b969-c58b9fba7a95
md"""
## Geospatial transforms 🌎 🔁 🌍 🔁 🗾

Data sets often come with missing values, poorly formatted variable names, among other issues. We provide **geospatial transforms** that can be used to pre-process geospatial data in more sophisticated workflows.

In the following basic example, we define two such transforms that

1. drop rows with missing values and
2. rename variables to more readable names

These transforms are placed into a sequential pipeline with the `→` (`\to`) operator:
"""

# ╔═╡ e6b7fcf1-3a51-4cc2-b2af-b961f52f43d2
pipe₀ = DropMissing() → Rename(:Auppm => :Au, :Agppm => :Ag, :Cuppm => :Cu,
	                           :Asppm => :As, :Sper => :S, :CODE => :geo,
							   :OX => :litho, :ISBD => :ρ)

# ╔═╡ f3f6fb26-0341-4491-8858-23002788bc13
md"""
We load the Bonnie data set again:
"""

# ╔═╡ abdecbd1-b034-49c8-9e51-6ff41da56ef2
raw = georef(csv, (:EAST,:NORTH,:RL))

# ╔═╡ 62da1d70-3ab1-4513-a1c1-cab794e3517c
md"""
and apply the pipeline to it:
"""

# ╔═╡ 95199a0a-c4c0-458e-be73-0722022d9959
data = raw |> pipe₀

# ╔═╡ b21a8e7e-2aa1-4fa6-b459-779d2795f171
md"""
### Building pipelines
"""

# ╔═╡ 770afbad-9aac-4443-a64a-f8660ec71b5b
md"""
#### Exercise

Read the [transforms](https://juliaearth.github.io/GeoStats.jl/stable/transforms.html) documentation to find all available transforms and use Pluto's live docs to learn more about their options. Create a pipeline named `pipe₁` that

- replaces all missing values by the value zero
- coerces the types of variables `geo` and `litho` to `Multiclass`
"""

# ╔═╡ 43b3a2b5-29a2-414f-bc61-505cad078ea2
pipe₁ = Identity()

# ╔═╡ 3b8fd905-000f-4b22-af1b-221fdda43fd6
hint(md"`Coalesce` and `Coerce` are your friends.")

# ╔═╡ c87eec82-0a83-48fe-819d-a7b7af15ccab
md"""
Create a pipeline named `pipe₂` that

- selects variables `Au`, `Ag`, `Cu`, `As`, `S`
- performs principal component analysis
"""

# ╔═╡ 796b0ad0-97f2-43ce-a3c8-ad3233c068c4
pipe₂ = Identity()

# ╔═╡ 0c6b352f-5d4f-4dc3-8e96-c9b1f4d758a5
hint(md"`Select` columns before the analysis.")

# ╔═╡ 6dbcc263-4d42-4ad6-b87e-953b6594f7ed
md"""
Create a pipeline named `pipe₃` that

- selects variable `ρ`
- computes the z-score
"""

# ╔═╡ b1fc7dfc-8373-4f9c-8317-68f846e089da
pipe₃ = Identity()

# ╔═╡ 3e766129-f47d-4325-a6d8-21c2969e6df1
hint(md"`Select` columns before computing z-scores.")

# ╔═╡ 64b0b863-ff35-41ee-aac5-a3c28871400d
md"""
Create a pipeline named `pipe₄` that

- standardizes the coordinates to $[-1,1]^3$
- detrends all variables with polynomial of degree `1`
"""

# ╔═╡ a4567d04-3127-4f5c-9a2a-eda343ea8f0d
pipe₄ = Identity()

# ╔═╡ f6418489-e896-42ed-b21b-54eceddc54f4
hint(md"No more hints, you can do it! 😉")

# ╔═╡ 41a0a95a-0f19-4032-b29b-2be1409a101c
md"""
We can combine these pipelines into an advanced geospatial pipeline using `→` (`\to`) and `⊔` (`\sqcup`):
"""

# ╔═╡ 5fcc9839-1071-4343-be3b-e67a6fe04634
pipe = pipe₁ → pipe₂ ⊔ pipe₃ → pipe₄

# ╔═╡ 75c7c69a-f3e7-4069-9e71-539efe570a1f
md"""
Pipelines are executed in parallel whenever possible, and the geospatial domain is just forwarded when transforms only involve attributes (or features):
"""

# ╔═╡ c1db6c8b-93f9-40e0-8ad4-23d68d0e8f8e
data |> pipe

# ╔═╡ a336fb47-d38c-4743-9603-12ff0df29349
md"""
### More examples

A few more examples to illustrate the power of geospatial transforms and some important optimizations implemented behind the scenes:
"""

# ╔═╡ 8d0c982e-852b-4d1b-8bfd-34721b5c017d
data |> Coerce(:litho => Multiclass) |> OneHot(:litho)

# ╔═╡ 878f4824-1b4e-438b-ae24-83d3e4a9dbdd
md"""
Notice how filtering and sampling geospatial data doesn't create unnecessary copies of geometries:
"""

# ╔═╡ 74f8593c-8f8a-4de8-8198-4bc91055cb85
data |> Filter(row -> row.Au > 0.5 && row.Cu > 0) |> domain

# ╔═╡ 22db0757-4015-457c-91b9-84be73f6d8ed
data |> Sample(100, replace=false) |> domain

# ╔═╡ 346ad531-e73f-455b-8ba4-4186449096b5
md"""
We can easily revert pipelines over geospatial data:
"""

# ╔═╡ d4610837-6753-4009-b9d0-da4d2bcb05f0
p = Select(1:5) → PCA()

# ╔═╡ fa2c8ca3-f087-4f68-91b5-1ad9f7368e66
newdata, cache = apply(p, data)

# ╔═╡ b3f4986d-73f4-4d1d-8a13-bd60a502b041
revert(p, newdata, cache)

# ╔═╡ 5a50e2b6-4d5b-4a8b-86e6-e125487e88f4
md"""
## Advanced geodata science 🌎 📊 📈 📉

As geodata scientists, we often need to formulate questions that involve both the attributes and the geometries of geospatial data. Answering these questions with traditional data science software can be extremely painful.
"""

# ╔═╡ 05ab16f1-f26c-459c-9eab-e5059b93b04f
md"""
### Geospatial split-apply-combine

We provide a **geospatial split-apply-combine** with the macros **`@groupby`**, **`@transform`** and **`@combine`**.

To illustrate the functionality, let's start by transforming the point geometries into boxes. We define our transformation function:
"""

# ╔═╡ 0099c2d3-68e6-4e25-a970-f987bcd21b41
box(point) = Box(point - Vec(2.,2.,2.), point + Vec(2.,2.,2.))

# ╔═╡ 3541c307-52fd-4ac5-9a26-14778c1846da
md"""
and apply it to the "`geometry`" column:
"""

# ╔═╡ 48fe8aa0-166b-4d2b-b79e-55af3d46a29e
blocks = @transform(data, :geometry = box(:geometry))

# ╔═╡ ee629463-f164-4f4d-96f5-00cb6c742b86
md"""
Notice how the new geometry column is correctly interpreted as a `GeometrySet`:
"""

# ╔═╡ b0f5d306-ed68-4027-96ca-5e3facdf5097
blocks |> domain

# ╔═╡ cef05884-1b63-4f9d-b3a5-5f18f51f52cb
md"""
Let's assume that we are interested in computing the mean and standard deviation of gold grade (`Au`) inside each geology (`geo`). We can write the following:
"""

# ╔═╡ 851edf21-a5d8-4f29-b2b2-bc5ef1289540
@chain blocks begin
	@groupby(:geo)
	@combine(:μ = mean(:Au), :σ = std(:Au))
end

# ╔═╡ 4ece65f2-5e99-46e5-9c5d-8881e86d1c1a
md"""
The **`@chain`** macro simply takes geospatial data as input and feeds it into the other macros in sequence. Alternatively, we could have obtained the same result into two steps.

First, compute the groups:
"""

# ╔═╡ 20603dda-e08a-4d09-bbdf-dd5b9426cb4b
groups = @groupby(blocks, :geo)

# ╔═╡ de1bd127-3984-4bd6-bf4f-879f4f3b01f3
md"""
and then, compute the statistics:
"""

# ╔═╡ 31e34bcf-e307-4349-a1bc-8382c47df190
@combine(groups, :μ = mean(:Au), :σ = std(:Au))

# ╔═╡ 1a979cb0-b58c-4bcc-82d2-3c5ef7fef844
md"""
#### Exercise

We are interested in the total mass of gold (`Au`) that will be mined from each lithology (`litho`). Implement the following split-apply-combine:

1. `@groupby`: group geospatial data into different lithologies
2. `@transform`: compute the mass of gold ($\rho\times Au \times V(block)$)
3. `@combine`: sum the mass of all samples inside each lithology

You can commute steps (1) and (2) to debug intermediate results.
"""

# ╔═╡ f19c0254-854e-4010-bceb-542c923edd78
mined = missing

# ╔═╡ f905706b-cddf-4df6-bda1-fab5d76ce22a
md"""
What is the total mass to be mined?
"""

# ╔═╡ 7a551ec9-e922-4ccb-a2e4-cb4655855a9f
mass = missing

# ╔═╡ d9130545-c499-4ab2-bae0-6c646b184256
begin
	scored2 = false
	if ismissing(mass)
		still_missing()
	elseif mass isa Number && mass ≈ 1.46547827118592e6
		scored2 = true
		correct()
	else
		keep_working()
	end
end

# ╔═╡ bac17589-8f36-43f6-b950-d268f916d748
hint(md"The function `volume` can be used to compute volumes.")

# ╔═╡ 12786110-d6ff-4734-8cb5-7aefe5b7d86a
md"""
We can directly visualize these new attributes over arbitrarily complex geospatial groups:
"""

# ╔═╡ c239b627-9d8c-4228-99db-b4c38556ff68
# mined |> viewer

# ╔═╡ 7e6409b8-d1a6-44c1-9004-65da69d507d0
md"""
## Additional resources

The [GeoStats.jl](https://github.com/JuliaEarth/GeoStats.jl) framework is quite comprehensive. We won't have the time to cover it properly here, but I would like to mention a few packages that you may find useful at AIRCentre:

- [GeoTables.jl](https://github.com/JuliaEarth/GeoTables.jl): Load and save geospatial tables from known file formats (*.shp, *.gjson, etc.).
- [INMET.jl](https://github.com/JuliaClimate/INMET.jl): Download data from the Instituto Nacional de Metereologia (INMET).
- [CDSAPI.jl](https://github.com/JuliaClimate/CDSAPI.jl): Download data from the Climate Data Store (CDS).

To give an example, here is how you can load and visualize a shapefile:
"""

# ╔═╡ e7f9997a-4316-48fa-9710-482e1e906350
shp = GeoTables.load("data/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp")

# ╔═╡ b02c5a10-10d0-4e9b-86aa-503340312726
shp |> viewer

# ╔═╡ c57f4c80-b17d-4c54-b034-d0da66857bd0
md"""
### Coffee break 😉

#### Questions? Ideas? Let's chat!

![break](https://media2.giphy.com/media/Rmu0SUVH8l1du/giphy.gif?cid=ecf05e4708wn9ubdd3bw9t4ysdkdq4xx6quse2wt5r3b65yx&rid=giphy.gif&ct=g)
"""

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deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "boost_jll"]
git-tree-sha1 = "fd7da49fae680c18aa59f421f0ba468e658a2d7a"
uuid = "e0b8ae26-5307-5830-91fd-398402328850"
version = "0.16.0+0"

[[deps.TiffImages]]
deps = ["ColorTypes", "DataStructures", "DocStringExtensions", "FileIO", "FixedPointNumbers", "IndirectArrays", "Inflate", "Mmap", "OffsetArrays", "PkgVersion", "ProgressMeter", "UUIDs"]
git-tree-sha1 = "7e6b0e3e571be0b4dd4d2a9a3a83b65c04351ccc"
uuid = "731e570b-9d59-4bfa-96dc-6df516fadf69"
version = "0.6.3"

[[deps.TranscodingStreams]]
deps = ["Random", "Test"]
git-tree-sha1 = "e4bdc63f5c6d62e80eb1c0043fcc0360d5950ff7"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.9.10"

[[deps.Transducers]]
deps = ["Adapt", "ArgCheck", "BangBang", "Baselet", "CompositionsBase", "DefineSingletons", "Distributed", "InitialValues", "Logging", "Markdown", "MicroCollections", "Requires", "Setfield", "SplittablesBase", "Tables"]
git-tree-sha1 = "c42fa452a60f022e9e087823b47e5a5f8adc53d5"
uuid = "28d57a85-8fef-5791-bfe6-a80928e7c999"
version = "0.4.75"

[[deps.TransformsBase]]
deps = ["AbstractTrees"]
git-tree-sha1 = "2412fb54902b0063c69c2bcfbec6b571120cc856"
uuid = "28dd2a49-a57a-4bfb-84ca-1a49db9b96b8"
version = "0.1.2"

[[deps.Tricks]]
git-tree-sha1 = "6bac775f2d42a611cdfcd1fb217ee719630c4175"
uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
version = "0.1.6"

[[deps.TriplotBase]]
git-tree-sha1 = "4d4ed7f294cda19382ff7de4c137d24d16adc89b"
uuid = "981d1d27-644d-49a2-9326-4793e63143c3"
version = "0.1.0"

[[deps.TupleTools]]
git-tree-sha1 = "3c712976c47707ff893cf6ba4354aa14db1d8938"
uuid = "9d95972d-f1c8-5527-a6e0-b4b365fa01f6"
version = "1.3.0"

[[deps.TypedTables]]
deps = ["Adapt", "Dictionaries", "Indexing", "SplitApplyCombine", "Tables", "Unicode"]
git-tree-sha1 = "ec72e7a68a6ffdc507b751714ff3e84e09135d9e"
uuid = "9d95f2ec-7b3d-5a63-8d20-e2491e220bb9"
version = "1.4.1"

[[deps.URIs]]
git-tree-sha1 = "ac00576f90d8a259f2c9d823e91d1de3fd44d348"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.4.1"

[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.UnicodeFun]]
deps = ["REPL"]
git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
version = "0.4.1"

[[deps.Unitful]]
deps = ["ConstructionBase", "Dates", "LinearAlgebra", "Random"]
git-tree-sha1 = "d670a70dd3cdbe1c1186f2f17c9a68a7ec24838c"
uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
version = "1.12.2"

[[deps.Variography]]
deps = ["Bessels", "Distances", "GeoStatsBase", "InteractiveUtils", "LinearAlgebra", "Meshes", "NearestNeighbors", "Optim", "Printf", "Random", "Setfield", "Tables", "Transducers", "Unitful"]
git-tree-sha1 = "3e80f568b877ef73e1ec245ca362af0037bd8d46"
uuid = "04a0146e-e6df-5636-8d7f-62fa9eb0b20c"
version = "0.15.2"

[[deps.WGLMakie]]
deps = ["Colors", "FileIO", "FreeTypeAbstraction", "GeometryBasics", "Hyperscript", "ImageMagick", "JSServe", "LinearAlgebra", "Makie", "Observables", "RelocatableFolders", "ShaderAbstractions", "SnoopPrecompile", "StaticArrays"]
git-tree-sha1 = "306e4755e868695c59bd8ee1e9ed578efd0a99d5"
uuid = "276b4fcb-3e11-5398-bf8b-a0c2d153d008"
version = "0.8.3"

[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.2"

[[deps.WidgetsBase]]
deps = ["Observables"]
git-tree-sha1 = "30a1d631eb06e8c868c559599f915a62d55c2601"
uuid = "eead4739-05f7-45a1-878c-cee36b57321c"
version = "0.1.4"

[[deps.WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "de67fa59e33ad156a590055375a30b23c40299d3"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "0.5.5"

[[deps.WorkerUtilities]]
git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
version = "1.6.1"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "93c41695bc1c08c46c5899f4fe06d6ead504bb73"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.10.3+0"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+0"

[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.2+0"

[[deps.ZygoteRules]]
deps = ["MacroTools"]
git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.2"

[[deps.boost_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "7a89efe0137720ca82f99e8daa526d23120d0d37"
uuid = "28df3c45-c428-5900-9ff8-a3135698ca75"
version = "1.76.0+1"

[[deps.isoband_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4"
version = "0.2.3+0"

[[deps.libaom_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4"
uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
version = "3.4.0+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.2.0+0"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[deps.libgeotiff_jll]]
deps = ["Artifacts", "JLLWrappers", "LibCURL_jll", "Libdl", "Libtiff_jll", "PROJ_jll", "Pkg"]
git-tree-sha1 = "13dfba87a1fe301c4b40f991d0ec990bbee59bbe"
uuid = "06c338fa-64ff-565b-ac2f-249532af990e"
version = "100.700.100+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"

[[deps.libsixel_jll]]
deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Pkg", "libpng_jll"]
git-tree-sha1 = "d4f63314c8aa1e48cd22aa0c17ed76cd1ae48c3c"
uuid = "075b6546-f08a-558a-be8f-8157d0f608a5"
version = "1.10.3+0"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+1"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.48.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+0"

[[deps.snappy_jll]]
deps = ["Artifacts", "JLLWrappers", "LZO_jll", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "985c1da710b0e43f7c52f037441021dfd0e3be14"
uuid = "fe1e1685-f7be-5f59-ac9f-4ca204017dfd"
version = "1.1.9+1"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"
"""

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