From 76343aa3f999be225afabc0a362ed343369167ff Mon Sep 17 00:00:00 2001 From: KronosTheLate Date: Tue, 19 Mar 2024 09:02:09 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20JuliaPac?= =?UTF-8?q?kageComparisons/JuliaPackageComparisons.github.io@ea00e106394cd?= =?UTF-8?q?811672df5e18bead8991afe8584=20=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- PR84/404.html | 2 +- PR84/Manifest.toml | 8 +- .../acceleration/loop_acceleration/index.html | 2 +- .../acceleration/multithreading/index.html | 2 +- .../data_structure/dataframes/index.html | 2 +- .../data_structure/graphs/index.html | 2 +- PR84/comparisons/fileio/json/index.html | 2 +- .../fileio/saving_files/index.html | 2 +- .../index.html | 2 +- PR84/comparisons/graphics/plotting/index.html | 2 +- PR84/comparisons/interoperability/index.html | 2 +- .../interoperability/python/index.html | 2 +- .../machine_learning/index.html | 2 +- .../machine_learning_datasets/index.html | 2 +- .../neural_networks/index.html | 2 +- PR84/comparisons/math/bspline/index.html | 2 +- .../math/differentiation/index.html | 2 +- PR84/comparisons/math/einsum/index.html | 2 +- .../math/linear_solvers/index.html | 2 +- .../math/nonlinear_solvers/index.html | 2 +- PR84/comparisons/math/quaternions/index.html | 2 +- PR84/comparisons/metrics/index.html | 2 +- PR84/comparisons/physics/units/index.html | 2 +- .../probabilistic_programming/index.html | 2 +- PR84/comparisons/signal_processing/index.html | 2 +- .../simulation/agentbasedmodelling/index.html | 2 +- .../simulation/control_systems/index.html | 2 +- PR84/comparisons/testing/profiling/index.html | 2 +- .../testing/property_based_testing/index.html | 2 +- PR84/comparisons/testing/runtime/index.html | 2 +- .../uncertainty_propagation/index.html | 2 +- PR84/comparisons/utility/notebooks/index.html | 2 +- .../utility/package_templates/index.html | 2 +- PR84/comparisons/utility/piping/index.html | 2 +- .../utility/redefinable_structs/index.html | 2 +- .../utility/types_compactification/index.html | 2 +- .../web/static_websites/index.html | 2 +- PR84/comparisons/web/web_apps/index.html | 2 +- PR84/contributing/index.html | 2 +- PR84/index.html | 2 +- PR84/libs/lunr/lunr_index.js | 2 +- PR84/search/index.html | 2 +- PR84/sitemap.xml | 78 +++++++++---------- 43 files changed, 84 insertions(+), 84 deletions(-) diff --git a/PR84/404.html b/PR84/404.html index a25dc58..781d076 100644 --- a/PR84/404.html +++ b/PR84/404.html @@ -1 +1 @@ - 404
Julia
Package
Comparisons
Page source | GitHub repository

404


The requested page was not found



Click here to go back to the homepage.
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + 404
Julia
Package
Comparisons
Page source | GitHub repository

404


The requested page was not found



Click here to go back to the homepage.
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/Manifest.toml b/PR84/Manifest.toml index 490398c..43c9f75 100644 --- a/PR84/Manifest.toml +++ b/PR84/Manifest.toml @@ -80,9 +80,9 @@ version = "0.10.2" [[deps.HTTP]] deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] -git-tree-sha1 = "db864f2d91f68a5912937af80327d288ea1f3aee" +git-tree-sha1 = "995f762e0182ebc50548c434c171a5bb6635f8e4" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" -version = "1.10.3" +version = "1.10.4" [[deps.IOCapture]] deps = ["Logging", "Random"] @@ -273,9 +273,9 @@ deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [[deps.TranscodingStreams]] -git-tree-sha1 = "3caa21522e7efac1ba21834a03734c57b4611c7e" +git-tree-sha1 = "a09c933bebed12501890d8e92946bbab6a1690f1" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.10.4" +version = "0.10.5" weakdeps = ["Random", "Test"] [deps.TranscodingStreams.extensions] diff --git a/PR84/comparisons/acceleration/loop_acceleration/index.html b/PR84/comparisons/acceleration/loop_acceleration/index.html index 58f9c30..01b71c6 100644 --- a/PR84/comparisons/acceleration/loop_acceleration/index.html +++ b/PR84/comparisons/acceleration/loop_acceleration/index.html @@ -1 +1 @@ - Loop acceleration
Julia
Package
Comparisons
Page source | GitHub repository

Loop acceleration

Relevant Packages: LoopVectorization.jl, ThreadsX.jl, FLoops.jl, Folds.jl, FoldsThreads.jl

This page might be more appropriately named something along the lines of "accelerated computing" or "multithreading". The optimal way to slice the domains into pages is not yet decided.

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Loop acceleration
Julia
Package
Comparisons
Page source | GitHub repository

Loop acceleration

Relevant Packages: LoopVectorization.jl, ThreadsX.jl, FLoops.jl, Folds.jl, FoldsThreads.jl

This page might be more appropriately named something along the lines of "accelerated computing" or "multithreading". The optimal way to slice the domains into pages is not yet decided.

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/acceleration/multithreading/index.html b/PR84/comparisons/acceleration/multithreading/index.html index 56f9eda..5a21dd0 100644 --- a/PR84/comparisons/acceleration/multithreading/index.html +++ b/PR84/comparisons/acceleration/multithreading/index.html @@ -1 +1 @@ - Multithreading
Julia
Package
Comparisons
Page source | GitHub repository

Multithreading

Relevant packages: Threads.jl, Polyester.jl, ThreadsX.jl Relevant org: https://github.com/JuliaSIMD Probably multithreading, loop acceleration, SIMD and vectorization should be a single section. What is right name?

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Multithreading
Julia
Package
Comparisons
Page source | GitHub repository

Multithreading

Relevant packages: Threads.jl, Polyester.jl, ThreadsX.jl Relevant org: https://github.com/JuliaSIMD Probably multithreading, loop acceleration, SIMD and vectorization should be a single section. What is right name?

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/data_structure/dataframes/index.html b/PR84/comparisons/data_structure/dataframes/index.html index 25dc15c..465962e 100644 --- a/PR84/comparisons/data_structure/dataframes/index.html +++ b/PR84/comparisons/data_structure/dataframes/index.html @@ -1 +1 @@ - DataFrames
Julia
Package
Comparisons
Page source | GitHub repository

Dataframes

Relevant packages: DataFrames.jl, InMemoryDatasets.jl, JuliaDB.jl, Tidier.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + DataFrames
Julia
Package
Comparisons
Page source | GitHub repository

Dataframes

Relevant packages: DataFrames.jl, InMemoryDatasets.jl, JuliaDB.jl, Tidier.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/data_structure/graphs/index.html b/PR84/comparisons/data_structure/graphs/index.html index d4a9f5c..75de80f 100644 --- a/PR84/comparisons/data_structure/graphs/index.html +++ b/PR84/comparisons/data_structure/graphs/index.html @@ -1 +1 @@ - Graphs
Julia
Package
Comparisons
Page source | GitHub repository

Graphs

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

For now this discourse post summarized the graphs ecosystem well. It was written in May of 2023.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Graphs
Julia
Package
Comparisons
Page source | GitHub repository

Graphs

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

For now this discourse post summarized the graphs ecosystem well. It was written in May of 2023.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/fileio/json/index.html b/PR84/comparisons/fileio/json/index.html index 9e3fe01..faf636a 100644 --- a/PR84/comparisons/fileio/json/index.html +++ b/PR84/comparisons/fileio/json/index.html @@ -1 +1 @@ - JSON
Julia
Package
Comparisons
Page source | GitHub repository

JSON

  1. Overview
  2. Packages
    1. JSON.jl
    2. JSON2.jl
    3. JSON3.jl
    4. JSONBase.jl
    5. LazyJSON.jl
    6. BSON.jl
    7. JSONRPC.jl
  3. Related posts
  4. Star History

Overview

  • Use JSON3.jl or JSON.jl for most cases.

    • JSON3.jl has faster implementation.

    • JSON.jl has long history. If you need to load JSON on old Julia versions (e.g. v1.0), JSON.jl will be suitable.

  • Use BSON.jl for Binary JSON.

  • Use JSONRPC.jl for JSON-RPC 2.0.

A quote from the later linked release-announcement for JSON3.jl helps us understand why there are so many packages:

Let’s cut right to the chase and answer the elephant questions in the proverbial discourse room: why do we need another JSON package in Julia? what does it offer distinct from what JSON.jl, JSON2.jl, or LazyJSON.jl offer? why spend time and effort developing something that’s “already solved”? JSON3.jl was born from the spark of three separate ideas, and a vision that they could come together to make the best, most performant, simple, yet powerful JSON integration for Julia possible. It also exists as a way to “prove out” these ideas before trying to potentially upstream improvements into a more canonically named package like JSON.jl. I fully believe the package is ready for full-time use and reliance, but similar to JSON2.jl, it exists as a way to try out a different JSON integration API to potentially make things better, faster, easier.

Packages

JSON.jl

GitHub Repo stars deps JSON Downloads
GitHub last commit (branch) version Coverage

JSON2.jl

GitHub Repo stars deps JSON2 Downloads
GitHub last commit (branch) version Coverage

This package is not maintained. Use JSON3.jl instead.

JSON3.jl

GitHub Repo stars deps JSON3 Downloads
Stable Dev GitHub last commit (branch) version Coverage

From its README:

Yet another JSON package for Julia; this one is for speed and slick struct mapping

JSONBase.jl

GitHub Repo stars
GitHub last commit (branch) Coverage

quinnj (a founder of JSON3.jl) also provides JSONBase.jl, but its is not registered yet.

LazyJSON.jl

GitHub Repo stars deps LazyJSON Downloads
GitHub last commit (branch) version Coverage

BSON.jl

GitHub Repo stars deps BSON Downloads
GitHub last commit (branch) version Coverage

JSONRPC.jl

GitHub Repo stars deps JSONRPC Downloads
GitHub last commit (branch) version Coverage

From its README:

An implementation for JSON RPC 2.0. See the specification for details.

Currently, only JSON RPC 2.0 is supported. This package can act as both a client & a server.

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + JSON
Julia
Package
Comparisons
Page source | GitHub repository

JSON

  1. Overview
  2. Packages
    1. JSON.jl
    2. JSON2.jl
    3. JSON3.jl
    4. JSONBase.jl
    5. LazyJSON.jl
    6. BSON.jl
    7. JSONRPC.jl
  3. Related posts
  4. Star History

Overview

  • Use JSON3.jl or JSON.jl for most cases.

    • JSON3.jl has faster implementation.

    • JSON.jl has long history. If you need to load JSON on old Julia versions (e.g. v1.0), JSON.jl will be suitable.

  • Use BSON.jl for Binary JSON.

  • Use JSONRPC.jl for JSON-RPC 2.0.

A quote from the later linked release-announcement for JSON3.jl helps us understand why there are so many packages:

Let’s cut right to the chase and answer the elephant questions in the proverbial discourse room: why do we need another JSON package in Julia? what does it offer distinct from what JSON.jl, JSON2.jl, or LazyJSON.jl offer? why spend time and effort developing something that’s “already solved”? JSON3.jl was born from the spark of three separate ideas, and a vision that they could come together to make the best, most performant, simple, yet powerful JSON integration for Julia possible. It also exists as a way to “prove out” these ideas before trying to potentially upstream improvements into a more canonically named package like JSON.jl. I fully believe the package is ready for full-time use and reliance, but similar to JSON2.jl, it exists as a way to try out a different JSON integration API to potentially make things better, faster, easier.

Packages

JSON.jl

GitHub Repo stars deps JSON Downloads
GitHub last commit (branch) version Coverage

JSON2.jl

GitHub Repo stars deps JSON2 Downloads
GitHub last commit (branch) version Coverage

This package is not maintained. Use JSON3.jl instead.

JSON3.jl

GitHub Repo stars deps JSON3 Downloads
Stable Dev GitHub last commit (branch) version Coverage

From its README:

Yet another JSON package for Julia; this one is for speed and slick struct mapping

JSONBase.jl

GitHub Repo stars
GitHub last commit (branch) Coverage

quinnj (a founder of JSON3.jl) also provides JSONBase.jl, but its is not registered yet.

LazyJSON.jl

GitHub Repo stars deps LazyJSON Downloads
GitHub last commit (branch) version Coverage

BSON.jl

GitHub Repo stars deps BSON Downloads
GitHub last commit (branch) version Coverage

JSONRPC.jl

GitHub Repo stars deps JSONRPC Downloads
GitHub last commit (branch) version Coverage

From its README:

An implementation for JSON RPC 2.0. See the specification for details.

Currently, only JSON RPC 2.0 is supported. This package can act as both a client & a server.

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/fileio/saving_files/index.html b/PR84/comparisons/fileio/saving_files/index.html index dff6038..3b062d6 100644 --- a/PR84/comparisons/fileio/saving_files/index.html +++ b/PR84/comparisons/fileio/saving_files/index.html @@ -1 +1 @@ - Saving files
Julia
Package
Comparisons
Page source | GitHub repository

Saving files

There are many formats of files you may which to solve. This pages covers them all.

Relevant packages: JLD.jl, JLD2.jl, Parquet.jl, Parquet2.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Saving files
Julia
Package
Comparisons
Page source | GitHub repository

Saving files

There are many formats of files you may which to solve. This pages covers them all.

Relevant packages: JLD.jl, JLD2.jl, Parquet.jl, Parquet2.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/geometry/triangulations_or_tessellations/index.html b/PR84/comparisons/geometry/triangulations_or_tessellations/index.html index 33486a4..c85c217 100644 --- a/PR84/comparisons/geometry/triangulations_or_tessellations/index.html +++ b/PR84/comparisons/geometry/triangulations_or_tessellations/index.html @@ -1 +1 @@ - Triangulations and Tessellations
Julia
Package
Comparisons
Page source | GitHub repository

Triangulations and Tessellations

Two common methods for partitioning space into individual elements are triangulations and tessellations. This section is dedicated to packages that implement such methods.

Short summary

DelaunayTriangulation.jl is the most supported package for Delaunay triangulations and Voronoi tessellations in two dimensions. Delaunator.jl might be faster for unconstrained triangulations if you do not need exact arithmetic. In higher dimensions, you need Delaunay.jl if n>3n > 3, or TetGen.jl is great if n=3n=3.

List of packages with short descriptions

DelaunayTriangulation.jl

GitHub Repo stars deps DelaunayTriangulation Downloads
Stable Dev GitHub last commit (branch) version Coverage
A pure Julia library for constructing planar triangulations with support for both unconstrained and constrained triangulations (including domains with holes, disjoint domains, etc.), mesh refinement, Voronoi tessellations, clipped and centroidal Voronoi tessellations, and dynamic updates. Uses exact geometric predicates and supports custom types.

VoronoiDelaunay.jl

GitHub Repo stars deps VoronoiDelaunay Downloads
GitHub last commit (branch) version Coverage
A pure Julia library that constructs planar triangulations and tessellations, although no support for constrained triangulations / mesh refinement or clipped / centroid tessellations. Restricts points to [1,2]×[1,2][1, 2] \times [1, 2].

VoronoiCells.jl

GitHub Repo stars deps VoronoiCells Downloads
GitHub last commit (branch) version Coverage
A pure Julia library that extends VoronoiDelaunay.jl. This package provides useful tools for constructing and working with Voronoi tessellations. Supports clipping Voronoi cells to a specified rectangle. Like VoronoiDelaunay.jl, restricts points to [1,2]×[1,2][1, 2] \times [1, 2].

Delaunay.jl

GitHub Repo stars deps Delaunay Downloads
GitHub last commit (branch) version
Wraps Python's main Delaunay triangulation library, scipy.spatial.Delaunay, for computing Delaunay triangulations in RN\mathbb R^N. I don't believe constrained triangulations or mesh refinement is available here.

MiniQhull.jl

GitHub Repo stars deps MiniQhull Downloads
GitHub last commit (branch) version Coverage
Wraps Qhull for computing unconstrained Delaunay triangulations in RN\mathbb R^N. No support is provided for mesh refinement.

DirectQhull.jl

GitHub Repo stars deps DirectQhull Downloads
GitHub last commit (branch) version
Similar to MiniQhull.jl, although also provides support for convex hulls and Voronoi tessellations from Qhull.

Delaunator.jl

GitHub Repo stars deps Delaunator Downloads
Stable Dev GitHub last commit (branch) version
A pure Julia library modelled after the JavaScript Delaunator library. This package can construct unconstrained triangulations of planar point sets. No support is available for constrained triangulations or mesh refinement, although support exists for computing the dual Voronoi tessellation. Centroidal tessellations are not implemented, although the Voronoi cells can be clipped to a bounding box.

TriangleMesh.jl

GitHub Repo stars deps TriangleMesh Downloads
Stable Dev GitHub last commit (branch) version Coverage
Interfaces to Shewchuk's Triangle library.

Triangulate.jl

GitHub Repo stars deps Triangulate Downloads
Stable Dev GitHub last commit (branch) version Coverage
Interfaces to Shewchuk's Triangle library.

Triangle.jl

GitHub Repo stars deps Triangle Downloads
GitHub last commit (branch) version Coverage
Interfaces to Shewchuk's Triangle library.

TetGen.jl

GitHub Repo stars deps TetGen Downloads
Stable Dev GitHub last commit (branch) version Coverage
This is for Delaunay tetrahedralisation, wrapping TetGen.

GMT.jl

GitHub Repo stars deps GMT Downloads
Stable Dev GitHub last commit (branch) version Coverage
A wrapper of GMT, allowing for unconstrained Delaunay triangulations in two dimensions, and for spherical triangulation, i.e. triangulation of points lying on a sphere.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Triangulations and Tessellations
Julia
Package
Comparisons
Page source | GitHub repository

Triangulations and Tessellations

Two common methods for partitioning space into individual elements are triangulations and tessellations. This section is dedicated to packages that implement such methods.

Short summary

DelaunayTriangulation.jl is the most supported package for Delaunay triangulations and Voronoi tessellations in two dimensions. Delaunator.jl might be faster for unconstrained triangulations if you do not need exact arithmetic. In higher dimensions, you need Delaunay.jl if n>3n > 3, or TetGen.jl is great if n=3n=3.

List of packages with short descriptions

DelaunayTriangulation.jl

GitHub Repo stars deps DelaunayTriangulation Downloads
Stable Dev GitHub last commit (branch) version Coverage
A pure Julia library for constructing planar triangulations with support for both unconstrained and constrained triangulations (including domains with holes, disjoint domains, etc.), mesh refinement, Voronoi tessellations, clipped and centroidal Voronoi tessellations, and dynamic updates. Uses exact geometric predicates and supports custom types.

VoronoiDelaunay.jl

GitHub Repo stars deps VoronoiDelaunay Downloads
GitHub last commit (branch) version Coverage
A pure Julia library that constructs planar triangulations and tessellations, although no support for constrained triangulations / mesh refinement or clipped / centroid tessellations. Restricts points to [1,2]×[1,2][1, 2] \times [1, 2].

VoronoiCells.jl

GitHub Repo stars deps VoronoiCells Downloads
GitHub last commit (branch) version Coverage
A pure Julia library that extends VoronoiDelaunay.jl. This package provides useful tools for constructing and working with Voronoi tessellations. Supports clipping Voronoi cells to a specified rectangle. Like VoronoiDelaunay.jl, restricts points to [1,2]×[1,2][1, 2] \times [1, 2].

Delaunay.jl

GitHub Repo stars deps Delaunay Downloads
GitHub last commit (branch) version
Wraps Python's main Delaunay triangulation library, scipy.spatial.Delaunay, for computing Delaunay triangulations in RN\mathbb R^N. I don't believe constrained triangulations or mesh refinement is available here.

MiniQhull.jl

GitHub Repo stars deps MiniQhull Downloads
GitHub last commit (branch) version Coverage
Wraps Qhull for computing unconstrained Delaunay triangulations in RN\mathbb R^N. No support is provided for mesh refinement.

DirectQhull.jl

GitHub Repo stars deps DirectQhull Downloads
GitHub last commit (branch) version
Similar to MiniQhull.jl, although also provides support for convex hulls and Voronoi tessellations from Qhull.

Delaunator.jl

GitHub Repo stars deps Delaunator Downloads
Stable Dev GitHub last commit (branch) version
A pure Julia library modelled after the JavaScript Delaunator library. This package can construct unconstrained triangulations of planar point sets. No support is available for constrained triangulations or mesh refinement, although support exists for computing the dual Voronoi tessellation. Centroidal tessellations are not implemented, although the Voronoi cells can be clipped to a bounding box.

TriangleMesh.jl

GitHub Repo stars deps TriangleMesh Downloads
Stable Dev GitHub last commit (branch) version Coverage
Interfaces to Shewchuk's Triangle library.

Triangulate.jl

GitHub Repo stars deps Triangulate Downloads
Stable Dev GitHub last commit (branch) version Coverage
Interfaces to Shewchuk's Triangle library.

Triangle.jl

GitHub Repo stars deps Triangle Downloads
GitHub last commit (branch) version Coverage
Interfaces to Shewchuk's Triangle library.

TetGen.jl

GitHub Repo stars deps TetGen Downloads
Stable Dev GitHub last commit (branch) version Coverage
This is for Delaunay tetrahedralisation, wrapping TetGen.

GMT.jl

GitHub Repo stars deps GMT Downloads
Stable Dev GitHub last commit (branch) version Coverage
A wrapper of GMT, allowing for unconstrained Delaunay triangulations in two dimensions, and for spherical triangulation, i.e. triangulation of points lying on a sphere.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/graphics/plotting/index.html b/PR84/comparisons/graphics/plotting/index.html index c358a48..266f61f 100644 --- a/PR84/comparisons/graphics/plotting/index.html +++ b/PR84/comparisons/graphics/plotting/index.html @@ -1 +1 @@ - Plotting
Julia
Package
Comparisons
Page source | GitHub repository

Plotting

This section is not yet well written. See this issue for thoughts on reworking this section.

Julia has a large number of available plotting libraries. They differ in a number of way, but the most important difference relate to native Julia vs wrapper, speed, interactivity, output file formats, animations, and more. There are so many packages that it will not make sense to compare each package to each other package, so this section mainly focuses on providing a description of each package.

  1. Summary
  2. Description of Each Package
    1. Plots.jl
    2. Makie.jl
    3. AlgebraOfGraphics.jl
    4. Gadfly.jl
    5. Vega.jl
    6. Deneb.jl
    7. PlotlyLight.jl
    8. GR.jl
    9. PGFPlotsX.jl
    10. UnicodePlots.jl
    11. Gaston.jl
    12. GMT.jl
    13. Gnuplot.jl

Summary

Plots.jl was for a long time the most used package for a reason. It's very flexible, integrates with the most Julia packages so you'll find it all throughout other docs, and it has many of the advantages of the other libraries through its backend system. Thus if you needed Latex output, use the pgfplots backend. If you needed a webpage, use the Plotly backend. Unicodeplots backend when you want text output. Or the GR default for the basics. With Julia v1.9 its startup time is much improved (and it's like sub second on v1.10 beta), which was its major complaint before. If you're going to use one plotting library and don't care too much about every little detail, then Plots.jl is a good one to go with. It's definitely not the best in any of the cases, animations are better in Makie, Latex is better in PGFPlotsX, etc., but it's capable everywhere.

Makie.jl has surpassed Plots.jl in the number of stars on github, and the two are competing as the main general-purpose plotting packaged. It scales well and its getting all of the niceties of Plots.jl. I wouldn't learn it first if you're new to Julia (right now, though that will likely change by 2024). But if you need animations or want to add custom buttons to a window (make a quick GUI-like thing), Makie is unmatched. If it makes its standard plotting interface a bit simpler, gets a few more integrations, and thus matches Plots.jl in simplicity, it may hit a "best of most worlds" soon.

Otherwise it's a bit domain specific. If you were using Plots.jl and needed more flexibility for publication-quality plots, PGFPlotsX.jl can help. Or if you prefer grammar of graphics, AlgebraOfGraphics.jl is good. If you're a stats person you may find Gadfly or VegaLite familiar, though I wouldn't recommend them first because these don't satisfy general user needs (try making a plot of an FEM output and see what I mean).

All of these are pretty good. You have a lot of options. In the end, pick the one that suits your needs best.

Description of Each Package

Plots.jl

GitHub Repo stars deps Plots Downloads
Stable Dev GitHub last commit (branch) version
Plots.jl is the most used. It's probably the most documented, used in the most tutorials, and is used in many videos.

  • Pros: Its main draw is that it has a lot of plugins to other packages through its recipes system, which means that a lot of odd things like plot(sol::ODESolution) or showing the sparsity of a BandedMatrix just works. With all of these integrations, it's normally what I would recommend first to newcomers since they will generally get the most done with the least work. It has a backend system, so you can make it generate plots via GR (the default), Plotly (i.e. make webpages), pyplot, PGFPlots (Latex output), UnicodePlots (i.e. output plots as text). This ease of use and flexibility is what its main draw is.

  • Cons: Its downside has traditionally been its startup time, though it's nearly a second now so that's fine. Its main downside now is mostly that it's not as configurable as something like Makie, and it's not as optimized if you get up to millions of points. Its flexibility means it's not just for standard plots but also for animations, building small graphical user interfaces, and building small apps.

Makie.jl

GitHub Repo stars deps Makie Downloads
Stable Dev GitHub last commit (branch) version
Makie.jl is probably the second most popular. It's natively Julia so it's cool in that aspect, you can see code all the way down.

  • Pros: It's very optimized for large plots, especially with GPU acceleration via the OpenGL backend (GLMakie). It has a lot of examples these days.

  • Cons: Its downside is that it's a bit less "first user friendly", given that its flexibility means there's a lot more options. It has a recipe system now but it's fairly new and not well-integrated with most of the ecosystem, so it's not as seamless as Plots, though by 2024 I would assume that would largely be fixed. It has the longest startup time, used to be in minutes but now it's like 5-10 seconds.

AlgebraOfGraphics.jl

GitHub Repo stars deps AlgebraOfGraphics Downloads
Stable Dev GitHub last commit (branch) version
AlgebraOfGraphics.jl is a grammar of graphics front-end to Makie. This essentially means it has an API that looks and acts like R's ggplot2. Thus it has largely the same pros and cons as Makie, since it's just calling Makie under the hood, but with the pro of being more familiar to users coming from R.

Gadfly.jl

GitHub Repo stars deps Gadfly Downloads
Stable Dev GitHub last commit (branch) version
Gadfly.jl is a grammar of graphics based library.

  • Pros: It's very familiar to a ggplot2 user. Its default theme is pretty nice looking.

  • Cons: It's a bit high on startup time, closer to Makie than Plots. Also, it's pretty feature poor. In particular, it is missing 3D plots, animations, the ability to make interactive apps with buttons, etc. For these reasons more and more people are adopting AlgebraOfGraphics, but if you're just doing some standard statistics it's fine.

Vega.jl

GitHub Repo stars deps Vega Downloads
GitHub last commit (branch) version
Vega.jl and VegaLite are of the same camp as Gadfly in the focus towards "standard" statistics and data science, but using wrappers to Javascript libraries.

  • Pro: Fast startups

  • Cons Similar to Gadfly, little to no flexibility (making apps, animations, ...) and integration with Julia libraries beyond Queryverse.

Deneb.jl

GitHub Repo stars deps Deneb Downloads
Stable Dev GitHub last commit (branch) version Coverage
Deneb.jl is another Julia alternative to build Vega-Lite plots. It provides an elegant julian API for creating Vega-Lite visualizations with high inspiration from Python's Altair.

  • Pros: Deneb.jl gives access to Vega-Lite's high-level grammar of interactive graphics. It can produce beautiful plots with high levels of interactivity using a concise, declarative syntax. See the gallery for a showcase of compelling interactive plots.

  • Cons: Limited to what Vega-Lite can achieve, for instance there is no support yet for 3D plotting. It might face challenges with larger datasets, since it doesn't produce plots consisting of pixels, but rather plots consisting of data along with a specification. For scalability with larger datasets, VegaFusion provides a solution, but Deneb.jl has yet to leverage this potential.

PlotlyLight.jl

GitHub Repo stars deps PlotlyLight Downloads
GitHub last commit (branch) version Coverage
PlotlyLight.jl is a no-frills wrapper to Plotly.

  • Pro: No startup time

  • Cons: Requires reading the Plotly docs to know how to use it and has little flexibility or integration into Julia libraries.

GR.jl

GitHub Repo stars deps GR Downloads
GitHub last commit (branch) version Coverage
GR.jl is a front end to a C library GR. It's actually used as the default front-end from Plots.jl. Many more people use it from Plots.jl than directly due to the integrations and docs, but it is nice for some things on its own.

  • Pros: It's fast, scales fairly well, has a fast startup time, has a nice GUI for investigating results, integrates well with ITerm, very flexible.

  • Cons: It's docs are bit difficult, and it doesn't have any integrations with Julia libraries.

PGFPlotsX.jl

GitHub Repo stars deps PGFPlotsX Downloads
Stable Dev GitHub last commit (branch) version Coverage
PGFPlotsX.jl is a front-end to generate plots for Latex.

  • Pros: Fast startup, output to Latex which makes it easy to then further modify in publication documents.

  • Cons: Its interface is wonky, even if you are familiar with the pgfplots Latex package. This makes quite hard to use and teach. Very few integrations with Julia libraries (Measurements and Colors only?). Lacking flexibility in terms of animations and making apps, though it's quite flexible in its ability to modify the plots and make weird things.

UnicodePlots.jl

GitHub Repo stars deps UnicodePlots Downloads
GitHub last commit (branch) version Coverage
UnicodePlots.jl is very simple, fast startup, and plots to text. Its downside of course is that text is the only output it has.

Gaston.jl

GitHub Repo stars deps Gaston Downloads
Stable Dev GitHub last commit (branch) version
Gaston.jl a front-end to gnuplot.

  • Fast startup.

  • Pretty basic, lacking flexibility and integrations with Julia packages. Requires gnuplot so limitations on where it can be installed.

GMT.jl

GitHub Repo stars deps GMT Downloads
Stable Dev GitHub last commit (branch) version Coverage
GMT.jl is "generic mapping tools". It has some plotting tools highlighted here.

  • Pros: Has good examples in the docs. Nice extra tools for maps.

  • Cons: Missing some standard plot types, limited integrations with other Julia packages.

Gnuplot.jl

GitHub Repo stars deps Gnuplot Downloads
Stable Dev GitHub last commit (branch) version Coverage
Gnuplot.jl uses gnuplot under the hood.

  • Pros: Instant startup, has some interesting data science integrations for things like named datasets, very complete set of plots

  • Cons: Not the most complete documentation, requires Linux with gnuplot.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Plotting
Julia
Package
Comparisons
Page source | GitHub repository

Plotting

This section is not yet well written. See this issue for thoughts on reworking this section.

Julia has a large number of available plotting libraries. They differ in a number of way, but the most important difference relate to native Julia vs wrapper, speed, interactivity, output file formats, animations, and more. There are so many packages that it will not make sense to compare each package to each other package, so this section mainly focuses on providing a description of each package.

  1. Summary
  2. Description of Each Package
    1. Plots.jl
    2. Makie.jl
    3. AlgebraOfGraphics.jl
    4. Gadfly.jl
    5. Vega.jl
    6. Deneb.jl
    7. PlotlyLight.jl
    8. GR.jl
    9. PGFPlotsX.jl
    10. UnicodePlots.jl
    11. Gaston.jl
    12. GMT.jl
    13. Gnuplot.jl

Summary

Plots.jl was for a long time the most used package for a reason. It's very flexible, integrates with the most Julia packages so you'll find it all throughout other docs, and it has many of the advantages of the other libraries through its backend system. Thus if you needed Latex output, use the pgfplots backend. If you needed a webpage, use the Plotly backend. Unicodeplots backend when you want text output. Or the GR default for the basics. With Julia v1.9 its startup time is much improved (and it's like sub second on v1.10 beta), which was its major complaint before. If you're going to use one plotting library and don't care too much about every little detail, then Plots.jl is a good one to go with. It's definitely not the best in any of the cases, animations are better in Makie, Latex is better in PGFPlotsX, etc., but it's capable everywhere.

Makie.jl has surpassed Plots.jl in the number of stars on github, and the two are competing as the main general-purpose plotting packaged. It scales well and its getting all of the niceties of Plots.jl. I wouldn't learn it first if you're new to Julia (right now, though that will likely change by 2024). But if you need animations or want to add custom buttons to a window (make a quick GUI-like thing), Makie is unmatched. If it makes its standard plotting interface a bit simpler, gets a few more integrations, and thus matches Plots.jl in simplicity, it may hit a "best of most worlds" soon.

Otherwise it's a bit domain specific. If you were using Plots.jl and needed more flexibility for publication-quality plots, PGFPlotsX.jl can help. Or if you prefer grammar of graphics, AlgebraOfGraphics.jl is good. If you're a stats person you may find Gadfly or VegaLite familiar, though I wouldn't recommend them first because these don't satisfy general user needs (try making a plot of an FEM output and see what I mean).

All of these are pretty good. You have a lot of options. In the end, pick the one that suits your needs best.

Description of Each Package

Plots.jl

GitHub Repo stars deps Plots Downloads
Stable Dev GitHub last commit (branch) version
Plots.jl is the most used. It's probably the most documented, used in the most tutorials, and is used in many videos.

  • Pros: Its main draw is that it has a lot of plugins to other packages through its recipes system, which means that a lot of odd things like plot(sol::ODESolution) or showing the sparsity of a BandedMatrix just works. With all of these integrations, it's normally what I would recommend first to newcomers since they will generally get the most done with the least work. It has a backend system, so you can make it generate plots via GR (the default), Plotly (i.e. make webpages), pyplot, PGFPlots (Latex output), UnicodePlots (i.e. output plots as text). This ease of use and flexibility is what its main draw is.

  • Cons: Its downside has traditionally been its startup time, though it's nearly a second now so that's fine. Its main downside now is mostly that it's not as configurable as something like Makie, and it's not as optimized if you get up to millions of points. Its flexibility means it's not just for standard plots but also for animations, building small graphical user interfaces, and building small apps.

Makie.jl

GitHub Repo stars deps Makie Downloads
Stable Dev GitHub last commit (branch) version
Makie.jl is probably the second most popular. It's natively Julia so it's cool in that aspect, you can see code all the way down.

  • Pros: It's very optimized for large plots, especially with GPU acceleration via the OpenGL backend (GLMakie). It has a lot of examples these days.

  • Cons: Its downside is that it's a bit less "first user friendly", given that its flexibility means there's a lot more options. It has a recipe system now but it's fairly new and not well-integrated with most of the ecosystem, so it's not as seamless as Plots, though by 2024 I would assume that would largely be fixed. It has the longest startup time, used to be in minutes but now it's like 5-10 seconds.

AlgebraOfGraphics.jl

GitHub Repo stars deps AlgebraOfGraphics Downloads
Stable Dev GitHub last commit (branch) version
AlgebraOfGraphics.jl is a grammar of graphics front-end to Makie. This essentially means it has an API that looks and acts like R's ggplot2. Thus it has largely the same pros and cons as Makie, since it's just calling Makie under the hood, but with the pro of being more familiar to users coming from R.

Gadfly.jl

GitHub Repo stars deps Gadfly Downloads
Stable Dev GitHub last commit (branch) version
Gadfly.jl is a grammar of graphics based library.

  • Pros: It's very familiar to a ggplot2 user. Its default theme is pretty nice looking.

  • Cons: It's a bit high on startup time, closer to Makie than Plots. Also, it's pretty feature poor. In particular, it is missing 3D plots, animations, the ability to make interactive apps with buttons, etc. For these reasons more and more people are adopting AlgebraOfGraphics, but if you're just doing some standard statistics it's fine.

Vega.jl

GitHub Repo stars deps Vega Downloads
GitHub last commit (branch) version
Vega.jl and VegaLite are of the same camp as Gadfly in the focus towards "standard" statistics and data science, but using wrappers to Javascript libraries.

  • Pro: Fast startups

  • Cons Similar to Gadfly, little to no flexibility (making apps, animations, ...) and integration with Julia libraries beyond Queryverse.

Deneb.jl

GitHub Repo stars deps Deneb Downloads
Stable Dev GitHub last commit (branch) version Coverage
Deneb.jl is another Julia alternative to build Vega-Lite plots. It provides an elegant julian API for creating Vega-Lite visualizations with high inspiration from Python's Altair.

  • Pros: Deneb.jl gives access to Vega-Lite's high-level grammar of interactive graphics. It can produce beautiful plots with high levels of interactivity using a concise, declarative syntax. See the gallery for a showcase of compelling interactive plots.

  • Cons: Limited to what Vega-Lite can achieve, for instance there is no support yet for 3D plotting. It might face challenges with larger datasets, since it doesn't produce plots consisting of pixels, but rather plots consisting of data along with a specification. For scalability with larger datasets, VegaFusion provides a solution, but Deneb.jl has yet to leverage this potential.

PlotlyLight.jl

GitHub Repo stars deps PlotlyLight Downloads
GitHub last commit (branch) version Coverage
PlotlyLight.jl is a no-frills wrapper to Plotly.

  • Pro: No startup time

  • Cons: Requires reading the Plotly docs to know how to use it and has little flexibility or integration into Julia libraries.

GR.jl

GitHub Repo stars deps GR Downloads
GitHub last commit (branch) version Coverage
GR.jl is a front end to a C library GR. It's actually used as the default front-end from Plots.jl. Many more people use it from Plots.jl than directly due to the integrations and docs, but it is nice for some things on its own.

  • Pros: It's fast, scales fairly well, has a fast startup time, has a nice GUI for investigating results, integrates well with ITerm, very flexible.

  • Cons: It's docs are bit difficult, and it doesn't have any integrations with Julia libraries.

PGFPlotsX.jl

GitHub Repo stars deps PGFPlotsX Downloads
Stable Dev GitHub last commit (branch) version Coverage
PGFPlotsX.jl is a front-end to generate plots for Latex.

  • Pros: Fast startup, output to Latex which makes it easy to then further modify in publication documents.

  • Cons: Its interface is wonky, even if you are familiar with the pgfplots Latex package. This makes quite hard to use and teach. Very few integrations with Julia libraries (Measurements and Colors only?). Lacking flexibility in terms of animations and making apps, though it's quite flexible in its ability to modify the plots and make weird things.

UnicodePlots.jl

GitHub Repo stars deps UnicodePlots Downloads
GitHub last commit (branch) version Coverage
UnicodePlots.jl is very simple, fast startup, and plots to text. Its downside of course is that text is the only output it has.

Gaston.jl

GitHub Repo stars deps Gaston Downloads
Stable Dev GitHub last commit (branch) version
Gaston.jl a front-end to gnuplot.

  • Fast startup.

  • Pretty basic, lacking flexibility and integrations with Julia packages. Requires gnuplot so limitations on where it can be installed.

GMT.jl

GitHub Repo stars deps GMT Downloads
Stable Dev GitHub last commit (branch) version Coverage
GMT.jl is "generic mapping tools". It has some plotting tools highlighted here.

  • Pros: Has good examples in the docs. Nice extra tools for maps.

  • Cons: Missing some standard plot types, limited integrations with other Julia packages.

Gnuplot.jl

GitHub Repo stars deps Gnuplot Downloads
Stable Dev GitHub last commit (branch) version Coverage
Gnuplot.jl uses gnuplot under the hood.

  • Pros: Instant startup, has some interesting data science integrations for things like named datasets, very complete set of plots

  • Cons: Not the most complete documentation, requires Linux with gnuplot.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/interoperability/index.html b/PR84/comparisons/interoperability/index.html index fee8cc1..c3dfeee 100644 --- a/PR84/comparisons/interoperability/index.html +++ b/PR84/comparisons/interoperability/index.html @@ -1 +1 @@ - Interoperability
Julia
Package
Comparisons
Page source | GitHub repository

Interoperability

While Julia is great, it is not the only programming language around. This section will compare packages that allow interoperability between Julia and other programming languages. This typically means calling Julia from another language, or calling another language from Julia.

There are some organizations for inteoperabilities in Julia:

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Interoperability
Julia
Package
Comparisons
Page source | GitHub repository

Interoperability

While Julia is great, it is not the only programming language around. This section will compare packages that allow interoperability between Julia and other programming languages. This typically means calling Julia from another language, or calling another language from Julia.

There are some organizations for inteoperabilities in Julia:

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/interoperability/python/index.html b/PR84/comparisons/interoperability/python/index.html index 32937ab..9b38eeb 100644 --- a/PR84/comparisons/interoperability/python/index.html +++ b/PR84/comparisons/interoperability/python/index.html @@ -1 +1 @@ - Python
Julia
Package
Comparisons
Page source | GitHub repository

Python

  1. Packages
    1. PyCall.jl
    2. PythonCall.jl
  2. Star History

Packages

PyCall.jl

GitHub Repo stars deps PyCall Downloads
Stable Dev GitHub last commit (branch) version Coverage

PythonCall.jl

GitHub Repo stars deps PythonCall Downloads
Stable Dev GitHub last commit (branch) version Coverage

PythonCall.jl describes the differences from PyCall.jl in its README.

What about PyCall?

The existing package PyCall is another similar interface to Python. Here we note some key differences, but a more detailed comparison is in the documentation.

  • PythonCall supports a wider range of conversions between Julia and Python, and the conversion mechanism is extensible.

  • PythonCall by default never copies mutable objects when converting, but instead directly wraps the mutable object. This means that modifying the converted object modifies the original, and conversion is faster.

  • PythonCall does not usually automatically convert results to Julia values, but leaves them as Python objects. This makes it easier to do Pythonic things with these objects (e.g. accessing methods) and is type-stable.

  • PythonCall installs dependencies into a separate Conda environment for each Julia project. This means each Julia project can have an isolated set of Python dependencies.

  • PythonCall supports Julia 1.6.1+ and Python 3.7+ whereas PyCall supports Julia 0.7+ and Python 2.7+.

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Python
Julia
Package
Comparisons
Page source | GitHub repository

Python

  1. Packages
    1. PyCall.jl
    2. PythonCall.jl
  2. Star History

Packages

PyCall.jl

GitHub Repo stars deps PyCall Downloads
Stable Dev GitHub last commit (branch) version Coverage

PythonCall.jl

GitHub Repo stars deps PythonCall Downloads
Stable Dev GitHub last commit (branch) version Coverage

PythonCall.jl describes the differences from PyCall.jl in its README.

What about PyCall?

The existing package PyCall is another similar interface to Python. Here we note some key differences, but a more detailed comparison is in the documentation.

  • PythonCall supports a wider range of conversions between Julia and Python, and the conversion mechanism is extensible.

  • PythonCall by default never copies mutable objects when converting, but instead directly wraps the mutable object. This means that modifying the converted object modifies the original, and conversion is faster.

  • PythonCall does not usually automatically convert results to Julia values, but leaves them as Python objects. This makes it easier to do Pythonic things with these objects (e.g. accessing methods) and is type-stable.

  • PythonCall installs dependencies into a separate Conda environment for each Julia project. This means each Julia project can have an isolated set of Python dependencies.

  • PythonCall supports Julia 1.6.1+ and Python 3.7+ whereas PyCall supports Julia 0.7+ and Python 2.7+.

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/machine_learning/machine_learning/index.html b/PR84/comparisons/machine_learning/machine_learning/index.html index eac1918..9e7140d 100644 --- a/PR84/comparisons/machine_learning/machine_learning/index.html +++ b/PR84/comparisons/machine_learning/machine_learning/index.html @@ -1 +1 @@ - Machine Learning
Julia
Package
Comparisons
Page source | GitHub repository

Machine Learning

This section is concerned with general machine learning packages. A separate section (link to section) exists for packages that only concern themselves with the sub-domain of Neural Networks. Relevant packages: MLJ.jl, SciKitLearn.jl, KNet.jl, HorseML.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Machine Learning
Julia
Package
Comparisons
Page source | GitHub repository

Machine Learning

This section is concerned with general machine learning packages. A separate section (link to section) exists for packages that only concern themselves with the sub-domain of Neural Networks. Relevant packages: MLJ.jl, SciKitLearn.jl, KNet.jl, HorseML.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/machine_learning/machine_learning_datasets/index.html b/PR84/comparisons/machine_learning/machine_learning_datasets/index.html index 45a92a4..b1b5481 100644 --- a/PR84/comparisons/machine_learning/machine_learning_datasets/index.html +++ b/PR84/comparisons/machine_learning/machine_learning_datasets/index.html @@ -1 +1 @@ - Machine Learning Datasets
Julia
Package
Comparisons
Page source | GitHub repository

Machine Learning Datasets

Relevant packages: OpenML.jl, MLDatasets.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Machine Learning Datasets
Julia
Package
Comparisons
Page source | GitHub repository

Machine Learning Datasets

Relevant packages: OpenML.jl, MLDatasets.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/machine_learning/neural_networks/index.html b/PR84/comparisons/machine_learning/neural_networks/index.html index a083996..4ffb21d 100644 --- a/PR84/comparisons/machine_learning/neural_networks/index.html +++ b/PR84/comparisons/machine_learning/neural_networks/index.html @@ -1 +1 @@ - Neural Networks
Julia
Package
Comparisons
Page source | GitHub repository

Neural Networks

Relevant packages: Flux.jl, Lux.jl, GradValley.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Neural Networks
Julia
Package
Comparisons
Page source | GitHub repository

Neural Networks

Relevant packages: Flux.jl, Lux.jl, GradValley.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/math/bspline/index.html b/PR84/comparisons/math/bspline/index.html index 5fea0e0..b5dcbf3 100644 --- a/PR84/comparisons/math/bspline/index.html +++ b/PR84/comparisons/math/bspline/index.html @@ -1 +1 @@ - B-spline
Julia
Package
Comparisons
Page source | GitHub repository

B-spline

There are several Julia packages for B-spline

This page is still work-in-progress.

  1. Packages
    1. BSplines.jl
    2. BasicBSpline.jl
    3. BSplineKit.jl
    4. NURBS.jl
  2. Star History

Packages

BSplines.jl

GitHub Repo stars deps BSplines Downloads
Stable Dev GitHub last commit (branch) version Coverage

BasicBSpline.jl

GitHub Repo stars deps BasicBSpline Downloads
Stable Dev GitHub last commit (branch) version Coverage

BSplineKit.jl

GitHub Repo stars deps BSplineKit Downloads
Stable Dev GitHub last commit (branch) version Coverage

NURBS.jl

GitHub Repo stars deps NURBS Downloads
Stable Dev GitHub last commit (branch) version Coverage

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + B-spline
Julia
Package
Comparisons
Page source | GitHub repository

B-spline

There are several Julia packages for B-spline

This page is still work-in-progress.

  1. Packages
    1. BSplines.jl
    2. BasicBSpline.jl
    3. BSplineKit.jl
    4. NURBS.jl
  2. Star History

Packages

BSplines.jl

GitHub Repo stars deps BSplines Downloads
Stable Dev GitHub last commit (branch) version Coverage

BasicBSpline.jl

GitHub Repo stars deps BasicBSpline Downloads
Stable Dev GitHub last commit (branch) version Coverage

BSplineKit.jl

GitHub Repo stars deps BSplineKit Downloads
Stable Dev GitHub last commit (branch) version Coverage

NURBS.jl

GitHub Repo stars deps NURBS Downloads
Stable Dev GitHub last commit (branch) version Coverage

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/math/differentiation/index.html b/PR84/comparisons/math/differentiation/index.html index ada25c9..eb17a5d 100644 --- a/PR84/comparisons/math/differentiation/index.html +++ b/PR84/comparisons/math/differentiation/index.html @@ -1 +1 @@ - Differentiation
Julia
Package
Comparisons
Page source | GitHub repository

Differentiation

There is a large number of Julia packages that provide differentiation functionality. The 3 main types of differentiation are symbolic, numerical, and automatic. Particularly automatic differentiation is a field in which there is a lot of research, and therefore a lot of new and experimental packages.

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

For now, a good resource is juliadiff.org.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Differentiation
Julia
Package
Comparisons
Page source | GitHub repository

Differentiation

There is a large number of Julia packages that provide differentiation functionality. The 3 main types of differentiation are symbolic, numerical, and automatic. Particularly automatic differentiation is a field in which there is a lot of research, and therefore a lot of new and experimental packages.

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

For now, a good resource is juliadiff.org.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/math/einsum/index.html b/PR84/comparisons/math/einsum/index.html index 833f241..f32615d 100644 --- a/PR84/comparisons/math/einsum/index.html +++ b/PR84/comparisons/math/einsum/index.html @@ -1 +1 @@ - Einsum
Julia
Package
Comparisons
Page source | GitHub repository

Einsum

In linear algebra and mathematical physics, there is a notational convention called Einstein Notation. The python package numpy implements a function called einsum, which is the first hit when googling the term. Several Julia packages exists that implement einsum functionality.

  1. Packages
    1. Tullio.jl
    2. Einsum.jl
    3. OMEinsum.jl
    4. TensorOperations.jl
    5. TensorCast.jl
    6. ArrayMeta.jl
    7. Tortilla.jl
  2. Star History

Packages

Tullio.jl

GitHub Repo stars deps Tullio Downloads
GitHub last commit (branch) version Coverage
This package implements a macro @tullio, which it describes as

a very flexible einsum macro. It understands many array operations written in index notation – not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting.

Einsum.jl

GitHub Repo stars deps Einsum Downloads
GitHub last commit (branch) version
From README.md of this package:

This package exports a single macro @einsum, which implements similar notation to the Einstein summation convention to flexibly specify operations on Julia Arrays, similar to numpy's einsum function (but more flexible!).

OMEinsum.jl

GitHub Repo stars deps OMEinsum Downloads
Stable Dev GitHub last commit (branch) version Coverage
From README.md of this package:

This is a repository for the Google Summer of Code project on Differentiable Tensor Networks. It implements one function that both computer scientists and physicists love, the Einstein summation

TensorOperations.jl

GitHub Repo stars deps TensorOperations Downloads
Stable Dev GitHub last commit (branch) version Coverage

TensorCast.jl

GitHub Repo stars deps TensorCast Downloads
Stable Dev GitHub last commit (branch) version Coverage

ArrayMeta.jl

GitHub Repo stars
GitHub last commit (branch)

ArrayMeta.jl exists, but the package is not maintained and registered.

Tortilla.jl

Tortilla.jl was announced in JuliaCon2018, but the package is not public yet.

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Einsum
Julia
Package
Comparisons
Page source | GitHub repository

Einsum

In linear algebra and mathematical physics, there is a notational convention called Einstein Notation. The python package numpy implements a function called einsum, which is the first hit when googling the term. Several Julia packages exists that implement einsum functionality.

  1. Packages
    1. Tullio.jl
    2. Einsum.jl
    3. OMEinsum.jl
    4. TensorOperations.jl
    5. TensorCast.jl
    6. ArrayMeta.jl
    7. Tortilla.jl
  2. Star History

Packages

Tullio.jl

GitHub Repo stars deps Tullio Downloads
GitHub last commit (branch) version Coverage
This package implements a macro @tullio, which it describes as

a very flexible einsum macro. It understands many array operations written in index notation – not just matrix multiplication and permutations, but also convolutions, stencils, scatter/gather, and broadcasting.

Einsum.jl

GitHub Repo stars deps Einsum Downloads
GitHub last commit (branch) version
From README.md of this package:

This package exports a single macro @einsum, which implements similar notation to the Einstein summation convention to flexibly specify operations on Julia Arrays, similar to numpy's einsum function (but more flexible!).

OMEinsum.jl

GitHub Repo stars deps OMEinsum Downloads
Stable Dev GitHub last commit (branch) version Coverage
From README.md of this package:

This is a repository for the Google Summer of Code project on Differentiable Tensor Networks. It implements one function that both computer scientists and physicists love, the Einstein summation

TensorOperations.jl

GitHub Repo stars deps TensorOperations Downloads
Stable Dev GitHub last commit (branch) version Coverage

TensorCast.jl

GitHub Repo stars deps TensorCast Downloads
Stable Dev GitHub last commit (branch) version Coverage

ArrayMeta.jl

GitHub Repo stars
GitHub last commit (branch)

ArrayMeta.jl exists, but the package is not maintained and registered.

Tortilla.jl

Tortilla.jl was announced in JuliaCon2018, but the package is not public yet.

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/math/linear_solvers/index.html b/PR84/comparisons/math/linear_solvers/index.html index 4bab595..180a604 100644 --- a/PR84/comparisons/math/linear_solvers/index.html +++ b/PR84/comparisons/math/linear_solvers/index.html @@ -1 +1 @@ - Linear Solvers
Julia
Package
Comparisons
Page source | GitHub repository

Linear Solvers

This section will be split into two categories; Numerical linear solvers, and symbolic linear solvers.

Numerical Linear Solvers

A linear problem is of the form Ax=bAx=b for some matrix AA, known vector bb and unknown vector xx. This can be solved by A\b in base Julia, which is good enough in many cases. If you need more control over the solver algorithm, there are dedicated packages that provide such functionality.

The most complete one is LinearSolve.jl, which is part of the SciML ecosystem. It takes the role of a meta-package, and build on top of other packages that implement the actual algorithms. The benefit is that you can define the problem once, and then solve it with a number of different solvers by changing a keyword argument.

Specific solvers providing their own API's include Paradiso.jl, MKL.jl, BandedMatrices.jl, and more.

See https://discourse.julialang.org/t/solving-sparse-linear-systems-fast/83071/9 for a relevant discussion on the options for solving numerical linear equations.

Symbolic Linear Solvers

You can use Symbolics.jl to solve a single or multiple equations in one or more variables. See the documentation for solve_for for more detail.

You can also use SymPy.jl

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Linear Solvers
Julia
Package
Comparisons
Page source | GitHub repository

Linear Solvers

This section will be split into two categories; Numerical linear solvers, and symbolic linear solvers.

Numerical Linear Solvers

A linear problem is of the form Ax=bAx=b for some matrix AA, known vector bb and unknown vector xx. This can be solved by A\b in base Julia, which is good enough in many cases. If you need more control over the solver algorithm, there are dedicated packages that provide such functionality.

The most complete one is LinearSolve.jl, which is part of the SciML ecosystem. It takes the role of a meta-package, and build on top of other packages that implement the actual algorithms. The benefit is that you can define the problem once, and then solve it with a number of different solvers by changing a keyword argument.

Specific solvers providing their own API's include Paradiso.jl, MKL.jl, BandedMatrices.jl, and more.

See https://discourse.julialang.org/t/solving-sparse-linear-systems-fast/83071/9 for a relevant discussion on the options for solving numerical linear equations.

Symbolic Linear Solvers

You can use Symbolics.jl to solve a single or multiple equations in one or more variables. See the documentation for solve_for for more detail.

You can also use SymPy.jl

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/math/nonlinear_solvers/index.html b/PR84/comparisons/math/nonlinear_solvers/index.html index 6c52424..e68494b 100644 --- a/PR84/comparisons/math/nonlinear_solvers/index.html +++ b/PR84/comparisons/math/nonlinear_solvers/index.html @@ -1 +1 @@ - Nonlinear Solvers
Julia
Package
Comparisons
Page source | GitHub repository

Nonlinear Solvers

This section is still missing a lot of content. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions. Table of contents:

  1. Numerical Nonlinear Solvers
    1. Nonlinear Least Squares Solvers
      1. Feature comparison
    2. Performance evaluation
      1. Small, dense problems
    3. Medium sized, sparse problems
  2. Symbolic Nonlinear Solvers

This section will be split into two categories; Numerical nonlinear solvers, and symbolic nonlinear solvers.

Numerical Nonlinear Solvers

The most complete one is NonlinearSolve.jl, which is part of the SciML ecosystem. It takes the role of a meta-package, and build on top of other packages that implement the actual algorithms. The benefit is that you can define the problem once, and then solve it with a number of different solvers by changing a keyword argument.

The JuMP.dev framework provides a simple grammar for defining optimization cost functions, or models. It allows non-linear models to be defined, which can then be optimized using any of the compatible solvers, such as Ipopt.jl

There are also a number of other packages that provide nonlinear solver algorithms. Several of them are part of the JuliaNLSolvers organization, such as NLSolve.jl and Optim.jl. There is also Roots.jl and SIAMFANLEquations.jl.

Finally, there are a number of packages that specialize in optimizing nonlinear least-squares functions, discussed below.

Nonlinear Least Squares Solvers

Nonlinear Least Squares (NLLS) solvers are a particular class of numerical nonlinear solvers that optimize problems of the form:

arg min⁡x 12∑iρi(∥fi(x)∥2),s. to cj(x)<bj ∀j, ck(x)=ek ∀k,\begin{aligned}\argmin_{\mathbf{x}} ~&~ \frac12 \sum_i \rho_i\left(\| f_i(\mathbf{x})\|^2\right), \\ \mathrm{s.~to} ~&~ c_j(\mathbf{x}) < b_j ~~\forall j, \\ ~&~ c_k(\mathbf{x}) = e_k ~~\forall k, \end{aligned}

where

  • x\mathbf{x} is the set of variables to be optimized over.

  • fi()f_i() are (potentially multi-dimensional) nonlinear functions of x\mathbf{x}, whose squared norms are to be minimized.

  • ρi()\rho_i() are monotonically increasing robustification functions, that can be used to downweight larger errors.

  • cj()c_j() are linear or nonlinear scalar functions of x\mathbf{x} on the output of which bound constraints bjb_j are placed.

  • ck()c_k() are linear or nonlinear scalar functions of x\mathbf{x} on the output of which equality constraints eke_k are placed.

A number of packages and solvers exist for this class of problem:

More general nonlinear solvers can also often be used to optimize NLLS problems, e.g.

However, the more specialized packages tend to offer better performance.

Feature comparison

Different packages and solvers offer different features. Here's a summary of the important ones:

IpopttrontrunkCaNNOLeS.jlNLLSsolver.jlLeastSquaresOptim.jl
Registered✅✅✅✅✅✅
Uses JuMP model definition✅✅✅✅❌❌
Bound constraints✅✅❌❌❌✅
Equality constraints✅❌❌✅❌❌
Non-linear constraints✅❌❌✅❌❌
Robustified cost functions❌❌❌❌✅❌
Non-Euclidean variables❌❌❌❌✅❌
Dense auto-differentiation✅✅✅✅✅✅
Supports sparsity✅✅✅✅✅✅
Sparse auto-differentiation✅✅✅✅✅❌

  • Registered: The solver can be installed automatically using Julia's package manager.

  • Uses JuMP model definition: The JuMP.dev framework provides a simple grammar for defining optimization cost functions, or models. Some solvers support these models.

  • Bound constraints: Solvers can require some function output to be above or below a certain value.

  • Equality constraints: Solvers can require some function output to equal to a certain value.

  • Non-linear constraints: Functions used in constraints can be some nonlinear function of the variables.

  • Robustified cost functions: A scalar, monotonic function, ρ:R+→R+\rho : \mathbb{R}^+ \rightarrow \mathbb{R}^+ can be used to downweight larger errors.

  • Non-Euclidean variables: Variables can exist on a non-linear manifold in a higher dimensional space, e.g. 3D rotations represented by a 9-element 3x3 matrix.

  • Dense auto-differentiation: The solver supports auto-differentiation of a dense Jacobian of the cost function.

  • Supports sparsity: The solver can exploit sparsity within the Jacobian to optimize very large, sparse problems.

  • Sparse auto-differentiation: The solver supports auto-differentiation of a sparse Jacobian of the cost function.

Performance evaluation

Different solvers provide different performance on different problems, so any evaluation is subjective. Here, performance is evaluated on unconstrained, unrobustified problems, some small and dense, others larger and sparse. Only solvers able to optimize all problems are included. Performance is evaluated by the time taken to optimize the cost function. This script was used to evaluate the algorithms, on an Apple M1 Pro CPU. Except where timings are omitted, solvers converged to the global optimum.

Small, dense problems
Medium sized, sparse problems

Symbolic Nonlinear Solvers

A nonlinear symbolic problem could for example be to solve x2=4x^2=4 for xx. The current state of symbolic nonlinear solving in native Julia is unfortunately quite poor. There is an open issue for Symbolics.jl to add such functionality, but for now, the best option is to use SymPy.jl.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Nonlinear Solvers
Julia
Package
Comparisons
Page source | GitHub repository

Nonlinear Solvers

This section is still missing a lot of content. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions. Table of contents:

  1. Numerical Nonlinear Solvers
    1. Nonlinear Least Squares Solvers
      1. Feature comparison
    2. Performance evaluation
      1. Small, dense problems
    3. Medium sized, sparse problems
  2. Symbolic Nonlinear Solvers

This section will be split into two categories; Numerical nonlinear solvers, and symbolic nonlinear solvers.

Numerical Nonlinear Solvers

The most complete one is NonlinearSolve.jl, which is part of the SciML ecosystem. It takes the role of a meta-package, and build on top of other packages that implement the actual algorithms. The benefit is that you can define the problem once, and then solve it with a number of different solvers by changing a keyword argument.

The JuMP.dev framework provides a simple grammar for defining optimization cost functions, or models. It allows non-linear models to be defined, which can then be optimized using any of the compatible solvers, such as Ipopt.jl

There are also a number of other packages that provide nonlinear solver algorithms. Several of them are part of the JuliaNLSolvers organization, such as NLSolve.jl and Optim.jl. There is also Roots.jl and SIAMFANLEquations.jl.

Finally, there are a number of packages that specialize in optimizing nonlinear least-squares functions, discussed below.

Nonlinear Least Squares Solvers

Nonlinear Least Squares (NLLS) solvers are a particular class of numerical nonlinear solvers that optimize problems of the form:

arg min⁡x 12∑iρi(∥fi(x)∥2),s. to cj(x)<bj ∀j, ck(x)=ek ∀k,\begin{aligned}\argmin_{\mathbf{x}} ~&~ \frac12 \sum_i \rho_i\left(\| f_i(\mathbf{x})\|^2\right), \\ \mathrm{s.~to} ~&~ c_j(\mathbf{x}) < b_j ~~\forall j, \\ ~&~ c_k(\mathbf{x}) = e_k ~~\forall k, \end{aligned}

where

  • x\mathbf{x} is the set of variables to be optimized over.

  • fi()f_i() are (potentially multi-dimensional) nonlinear functions of x\mathbf{x}, whose squared norms are to be minimized.

  • ρi()\rho_i() are monotonically increasing robustification functions, that can be used to downweight larger errors.

  • cj()c_j() are linear or nonlinear scalar functions of x\mathbf{x} on the output of which bound constraints bjb_j are placed.

  • ck()c_k() are linear or nonlinear scalar functions of x\mathbf{x} on the output of which equality constraints eke_k are placed.

A number of packages and solvers exist for this class of problem:

More general nonlinear solvers can also often be used to optimize NLLS problems, e.g.

However, the more specialized packages tend to offer better performance.

Feature comparison

Different packages and solvers offer different features. Here's a summary of the important ones:

IpopttrontrunkCaNNOLeS.jlNLLSsolver.jlLeastSquaresOptim.jl
Registered✅✅✅✅✅✅
Uses JuMP model definition✅✅✅✅❌❌
Bound constraints✅✅❌❌❌✅
Equality constraints✅❌❌✅❌❌
Non-linear constraints✅❌❌✅❌❌
Robustified cost functions❌❌❌❌✅❌
Non-Euclidean variables❌❌❌❌✅❌
Dense auto-differentiation✅✅✅✅✅✅
Supports sparsity✅✅✅✅✅✅
Sparse auto-differentiation✅✅✅✅✅❌

  • Registered: The solver can be installed automatically using Julia's package manager.

  • Uses JuMP model definition: The JuMP.dev framework provides a simple grammar for defining optimization cost functions, or models. Some solvers support these models.

  • Bound constraints: Solvers can require some function output to be above or below a certain value.

  • Equality constraints: Solvers can require some function output to equal to a certain value.

  • Non-linear constraints: Functions used in constraints can be some nonlinear function of the variables.

  • Robustified cost functions: A scalar, monotonic function, ρ:R+→R+\rho : \mathbb{R}^+ \rightarrow \mathbb{R}^+ can be used to downweight larger errors.

  • Non-Euclidean variables: Variables can exist on a non-linear manifold in a higher dimensional space, e.g. 3D rotations represented by a 9-element 3x3 matrix.

  • Dense auto-differentiation: The solver supports auto-differentiation of a dense Jacobian of the cost function.

  • Supports sparsity: The solver can exploit sparsity within the Jacobian to optimize very large, sparse problems.

  • Sparse auto-differentiation: The solver supports auto-differentiation of a sparse Jacobian of the cost function.

Performance evaluation

Different solvers provide different performance on different problems, so any evaluation is subjective. Here, performance is evaluated on unconstrained, unrobustified problems, some small and dense, others larger and sparse. Only solvers able to optimize all problems are included. Performance is evaluated by the time taken to optimize the cost function. This script was used to evaluate the algorithms, on an Apple M1 Pro CPU. Except where timings are omitted, solvers converged to the global optimum.

Small, dense problems
Medium sized, sparse problems

Symbolic Nonlinear Solvers

A nonlinear symbolic problem could for example be to solve x2=4x^2=4 for xx. The current state of symbolic nonlinear solving in native Julia is unfortunately quite poor. There is an open issue for Symbolics.jl to add such functionality, but for now, the best option is to use SymPy.jl.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/math/quaternions/index.html b/PR84/comparisons/math/quaternions/index.html index 5accb93..e13429e 100644 --- a/PR84/comparisons/math/quaternions/index.html +++ b/PR84/comparisons/math/quaternions/index.html @@ -1 +1 @@ - Quaternions
Julia
Package
Comparisons
Page source | GitHub repository

Quaternions

Quaternions are best known for their suitability as representations of 3D rotational orientation. They can also be viewed as an extension of complex numbers.

Many softwares such as SciPy and ROS, they treat the order of a quaternion as xi+yj+zk+wxi+yj+zk+w, but not w+xi+yj+zkw+xi+yj+zk. One reason for that is that the order in swizzling is xyzw.

However, most Julia packages use w+xi+yj+zkw+xi+yj+zk order. This is for consistency with Base.Complex's x+yix+yi order.

  1. Packages for Quaternions
    1. Quaternions.jl
    2. Quaternionic.jl
  2. Packages that define their own Quaternions
    1. Makie.jl
    2. ReferenceFrameRotations.jl
    3. Grassmann.jl
  3. Related discourse posts

Packages for Quaternions

Quaternions.jl

GitHub Repo stars deps Quaternions Downloads
Stable Dev GitHub last commit (branch) version Coverage
Quaternions.jl is the most popular Julia package for quaternions.

  • This package implements Quaternions.Quaternion which is much similar to Base.Complex.

  • This package focus on some basic operations for quaternions. Most implemented methods are added to Base functions.

  • Other operations such as rotations are not implemented in this package. Use Rotations.jl instead.

  • The arguments for Quaternions.Quaternion is ordered in w+xi+yj+zkw+xi+yj+zk.

Historically, this package was not actively maintained before 2022.

Quaternionic.jl

GitHub Repo stars deps Quaternionic Downloads
Stable Dev GitHub last commit (branch) version Coverage
Quaternionic.jl is another Julia package for quaternions.

  • This package exports AbstractQuaternion as well as three concrete subtypes: Quaternion for arbitrary quaternions, Rotor for quaternions with unit magnitude, and QuatVec for quaternions with zero scalar part (corresponding to ordinary three-vectors). These allows specializations for faster and/or more precise results in those special cases.

  • Each of the types parametrizes the type T of its components, as inQuaternionic.Quaternion{T}, but does not require T <: Real. As a result, this package can handle biquaternions for example.

  • This package exports imx, imy, and imz (similar to Base.im), and their unicode counterparts 𝐢, 𝐣, and 𝐤.

  • Special care is taken to ensure that functions such as log, exp, sqrt, etc., are accurate and smooth near singularities and branch cuts, and to ensure that they are differentiable at those points. In particular ChainRules and ForwardDiff are explicitly supported.

  • Methods are directly included to permit transformation to and from various representations of rotations, such as Euler angles, spherical coordinates, axis-angle, and rotation-matrix representations.

  • Several functions are included to find the optimal rotation to align two sets of points, or the optimal rotor to align two sets of rotors, and to measure distances between quaternions or rotors.

  • This package also enables various ways of dealing with quaternion-valued functions of time, including

    • Linear interpolation, or slerp

    • Quadratic interpolation, or squad

    • Conversion to and from angular velocity as a function of time

    • Conversion to the "minimal-rotation" frame

  • The arguments for Quaternionic.Quaternion is ordered in w+xi+yj+zkw+xi+yj+zk.

Packages that define their own Quaternions

Makie.jl

GitHub Repo stars deps Makie Downloads
Stable Dev GitHub last commit (branch) version
Makie.jl has its own Quaternion type, but this should be replaced with Quaternions.Quaternion. Please check comment in Makie.jl/src/utilities/quaternions.jl. Note that Makie.Quaternion uses xi+yj+zk+wxi+yj+zk+w order.

ReferenceFrameRotations.jl

GitHub Repo stars deps ReferenceFrameRotations Downloads
Stable Dev GitHub last commit (branch) version Coverage
ReferenceFrameRotations.jl also has its own Quaternion type. There was an issue#25 to be compatible with Quaternions.jl.

Grassmann.jl

GitHub Repo stars deps Grassmann Downloads
Stable Dev GitHub last commit (branch) version Coverage
Grassmann.jl does not implement Quaternion as a struct, but quaternions are realized as an alias Grassmann.Quaternion (alias for Spinor{V, T, 4} where {V, T}).

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Quaternions
Julia
Package
Comparisons
Page source | GitHub repository

Quaternions

Quaternions are best known for their suitability as representations of 3D rotational orientation. They can also be viewed as an extension of complex numbers.

Many softwares such as SciPy and ROS, they treat the order of a quaternion as xi+yj+zk+wxi+yj+zk+w, but not w+xi+yj+zkw+xi+yj+zk. One reason for that is that the order in swizzling is xyzw.

However, most Julia packages use w+xi+yj+zkw+xi+yj+zk order. This is for consistency with Base.Complex's x+yix+yi order.

  1. Packages for Quaternions
    1. Quaternions.jl
    2. Quaternionic.jl
  2. Packages that define their own Quaternions
    1. Makie.jl
    2. ReferenceFrameRotations.jl
    3. Grassmann.jl
  3. Related discourse posts

Packages for Quaternions

Quaternions.jl

GitHub Repo stars deps Quaternions Downloads
Stable Dev GitHub last commit (branch) version Coverage
Quaternions.jl is the most popular Julia package for quaternions.

  • This package implements Quaternions.Quaternion which is much similar to Base.Complex.

  • This package focus on some basic operations for quaternions. Most implemented methods are added to Base functions.

  • Other operations such as rotations are not implemented in this package. Use Rotations.jl instead.

  • The arguments for Quaternions.Quaternion is ordered in w+xi+yj+zkw+xi+yj+zk.

Historically, this package was not actively maintained before 2022.

Quaternionic.jl

GitHub Repo stars deps Quaternionic Downloads
Stable Dev GitHub last commit (branch) version Coverage
Quaternionic.jl is another Julia package for quaternions.

  • This package exports AbstractQuaternion as well as three concrete subtypes: Quaternion for arbitrary quaternions, Rotor for quaternions with unit magnitude, and QuatVec for quaternions with zero scalar part (corresponding to ordinary three-vectors). These allows specializations for faster and/or more precise results in those special cases.

  • Each of the types parametrizes the type T of its components, as inQuaternionic.Quaternion{T}, but does not require T <: Real. As a result, this package can handle biquaternions for example.

  • This package exports imx, imy, and imz (similar to Base.im), and their unicode counterparts 𝐢, 𝐣, and 𝐤.

  • Special care is taken to ensure that functions such as log, exp, sqrt, etc., are accurate and smooth near singularities and branch cuts, and to ensure that they are differentiable at those points. In particular ChainRules and ForwardDiff are explicitly supported.

  • Methods are directly included to permit transformation to and from various representations of rotations, such as Euler angles, spherical coordinates, axis-angle, and rotation-matrix representations.

  • Several functions are included to find the optimal rotation to align two sets of points, or the optimal rotor to align two sets of rotors, and to measure distances between quaternions or rotors.

  • This package also enables various ways of dealing with quaternion-valued functions of time, including

    • Linear interpolation, or slerp

    • Quadratic interpolation, or squad

    • Conversion to and from angular velocity as a function of time

    • Conversion to the "minimal-rotation" frame

  • The arguments for Quaternionic.Quaternion is ordered in w+xi+yj+zkw+xi+yj+zk.

Packages that define their own Quaternions

Makie.jl

GitHub Repo stars deps Makie Downloads
Stable Dev GitHub last commit (branch) version
Makie.jl has its own Quaternion type, but this should be replaced with Quaternions.Quaternion. Please check comment in Makie.jl/src/utilities/quaternions.jl. Note that Makie.Quaternion uses xi+yj+zk+wxi+yj+zk+w order.

ReferenceFrameRotations.jl

GitHub Repo stars deps ReferenceFrameRotations Downloads
Stable Dev GitHub last commit (branch) version Coverage
ReferenceFrameRotations.jl also has its own Quaternion type. There was an issue#25 to be compatible with Quaternions.jl.

Grassmann.jl

GitHub Repo stars deps Grassmann Downloads
Stable Dev GitHub last commit (branch) version Coverage
Grassmann.jl does not implement Quaternion as a struct, but quaternions are realized as an alias Grassmann.Quaternion (alias for Spinor{V, T, 4} where {V, T}).

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/metrics/index.html b/PR84/comparisons/metrics/index.html index 63998c1..5c08c22 100644 --- a/PR84/comparisons/metrics/index.html +++ b/PR84/comparisons/metrics/index.html @@ -1 +1 @@ - Metrics
Julia
Package
Comparisons
Page source | GitHub repository

Metrics

A metric can refer to many things, but a fitting description (from wikipedia) goes as follows:

An adjective indicating relation to measurement in general, or a noun describing a specific type of measurement

In the world of programming languages, a typical use-case for metrics is for estimating performance of some algorithm. There are therefore many examples and implementations related to machine learning.

Below is a list of packages that implement metrics.

If in doubt, a good bet is StatisticalMeasures.jl. It is a new effort to provide a wide set of metrics in a consistent manner, born from the JuliaAI ecosystem.

This section could still use a lot of love. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Metrics
Julia
Package
Comparisons
Page source | GitHub repository

Metrics

A metric can refer to many things, but a fitting description (from wikipedia) goes as follows:

An adjective indicating relation to measurement in general, or a noun describing a specific type of measurement

In the world of programming languages, a typical use-case for metrics is for estimating performance of some algorithm. There are therefore many examples and implementations related to machine learning.

Below is a list of packages that implement metrics.

If in doubt, a good bet is StatisticalMeasures.jl. It is a new effort to provide a wide set of metrics in a consistent manner, born from the JuliaAI ecosystem.

This section could still use a lot of love. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/physics/units/index.html b/PR84/comparisons/physics/units/index.html index 5575101..1595bed 100644 --- a/PR84/comparisons/physics/units/index.html +++ b/PR84/comparisons/physics/units/index.html @@ -1 +1 @@ - Units
Julia
Package
Comparisons
Page source | GitHub repository

Units

Attaching physical units directly to numbers allows things such as automatic unit conversions, and automatic unit checking. There are several packages that add such functionality to Julia.

  1. Unitful.jl
  2. DynamicQuantities.jl
  3. UnitSystems.jl

Unitful.jl

GitHub Repo stars deps Unitful Downloads
Stable Dev GitHub last commit (branch) version Coverage

The most mature package for working with units is Unitful.jl. Is handles a wide variety of units, unit conversion, and unit checking. It describes it's goals as follows:

Unitful is a Julia package for physical units. We want to support not only SI units but also any other unit system. We also want to minimize or in some cases eliminate the run-time penalty of units. There should be facilities for dimensional analysis. All of this should integrate easily with the usual mathematical operations and collections that are found in Julia base.

There is a rich ecosystem around Unitful that implements things such as plotting recipes, and specific units for specific fields.

DynamicQuantities.jl

GitHub Repo stars deps DynamicQuantities Downloads
Stable Dev GitHub last commit (branch) version

A newer package for working with units is DynamicQuantities.jl. It describes itself as follows:

DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods.

UnitSystems.jl

GitHub Repo stars deps UnitSystems Downloads
GitHub last commit (branch) version Coverage

While it is not recommended above previously mentioned alternatives, there is also UnitSystems. The author can not speak to it's ability.

This section is not yet written well. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Units
Julia
Package
Comparisons
Page source | GitHub repository

Units

Attaching physical units directly to numbers allows things such as automatic unit conversions, and automatic unit checking. There are several packages that add such functionality to Julia.

  1. Unitful.jl
  2. DynamicQuantities.jl
  3. UnitSystems.jl

Unitful.jl

GitHub Repo stars deps Unitful Downloads
Stable Dev GitHub last commit (branch) version Coverage

The most mature package for working with units is Unitful.jl. Is handles a wide variety of units, unit conversion, and unit checking. It describes it's goals as follows:

Unitful is a Julia package for physical units. We want to support not only SI units but also any other unit system. We also want to minimize or in some cases eliminate the run-time penalty of units. There should be facilities for dimensional analysis. All of this should integrate easily with the usual mathematical operations and collections that are found in Julia base.

There is a rich ecosystem around Unitful that implements things such as plotting recipes, and specific units for specific fields.

DynamicQuantities.jl

GitHub Repo stars deps DynamicQuantities Downloads
Stable Dev GitHub last commit (branch) version

A newer package for working with units is DynamicQuantities.jl. It describes itself as follows:

DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods.

UnitSystems.jl

GitHub Repo stars deps UnitSystems Downloads
GitHub last commit (branch) version Coverage

While it is not recommended above previously mentioned alternatives, there is also UnitSystems. The author can not speak to it's ability.

This section is not yet written well. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/probabilistic_programming/index.html b/PR84/comparisons/probabilistic_programming/index.html index a80cffc..25a5092 100644 --- a/PR84/comparisons/probabilistic_programming/index.html +++ b/PR84/comparisons/probabilistic_programming/index.html @@ -1 +1 @@ - Probabilistic Programming
Julia
Package
Comparisons
Page source | GitHub repository

Probabilistic Programming

Turing.jl, Gen.jl, SOSS.jl, KissABC.jl, RxInfer.jl

Sampler

Hamiltonian Monte Carlo (gradient based)

Adaptive MCMC (without gradient)

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Probabilistic Programming
Julia
Package
Comparisons
Page source | GitHub repository

Probabilistic Programming

Turing.jl, Gen.jl, SOSS.jl, KissABC.jl, RxInfer.jl

Sampler

Hamiltonian Monte Carlo (gradient based)

Adaptive MCMC (without gradient)

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/signal_processing/index.html b/PR84/comparisons/signal_processing/index.html index affa3d7..aa2b444 100644 --- a/PR84/comparisons/signal_processing/index.html +++ b/PR84/comparisons/signal_processing/index.html @@ -1 +1 @@ - Signal Processing
Julia
Package
Comparisons
Page source | GitHub repository

Signal Processing

General packages

DSP.jl, SignalsBase.jl, SampledSignals.jl, SignalAnalysis.jl

Specific domains

Fourier Transformations

FFTW.jl

Peak Finding

By "peak" we refer to a numerical value larger than any immediate neighbour. A simple example is a vector such as [1, 2, 3, 1], which has a peak at it's third index. Functions also have peaks, but in a continuous context. Multidimensional arrays/functions also have peaks, which are significantly harder to find.

Peaks.jl, FindPeaks.jl, Images.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

M/EEG

PyMNE.jl is a wrapper-toolbox around the popular mne-python M/EEG analysis toolbox

NeuroAnalyzer.jl is a non-registered, but feature strong package for the analysis of EEG data (https://neuroanalyzer.org/).

Unfold.jl is a toolbox to analyze event-based timeseries using regression with a focus on EEG (Electroencephalography) data. It is based on regression, and includes simulation related toolboxes like (UnfoldSim.jl), plotting (UnfoldMakie.jl). It supports deconvolution, hierarchical and non-linear (spline)-effects.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Signal Processing
Julia
Package
Comparisons
Page source | GitHub repository

Signal Processing

General packages

DSP.jl, SignalsBase.jl, SampledSignals.jl, SignalAnalysis.jl

Specific domains

Fourier Transformations

FFTW.jl

Peak Finding

By "peak" we refer to a numerical value larger than any immediate neighbour. A simple example is a vector such as [1, 2, 3, 1], which has a peak at it's third index. Functions also have peaks, but in a continuous context. Multidimensional arrays/functions also have peaks, which are significantly harder to find.

Peaks.jl, FindPeaks.jl, Images.jl

This section is not yet written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

M/EEG

PyMNE.jl is a wrapper-toolbox around the popular mne-python M/EEG analysis toolbox

NeuroAnalyzer.jl is a non-registered, but feature strong package for the analysis of EEG data (https://neuroanalyzer.org/).

Unfold.jl is a toolbox to analyze event-based timeseries using regression with a focus on EEG (Electroencephalography) data. It is based on regression, and includes simulation related toolboxes like (UnfoldSim.jl), plotting (UnfoldMakie.jl). It supports deconvolution, hierarchical and non-linear (spline)-effects.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/simulation/agentbasedmodelling/index.html b/PR84/comparisons/simulation/agentbasedmodelling/index.html index c73d5f3..0875b16 100644 --- a/PR84/comparisons/simulation/agentbasedmodelling/index.html +++ b/PR84/comparisons/simulation/agentbasedmodelling/index.html @@ -1 +1 @@ - Agent Based Modelling
Julia
Package
Comparisons
Page source | GitHub repository

Agent Based Modelling

Julia has an extensive suite of various packages targeting various applications for agent based modelling (ABM).

  1. Agents.jl
  2. Vahana.jl
  3. MEDYAN.jl
  4. CellBasedModels.jl
  5. EasyABM.jl

Agents.jl

GitHub Repo stars deps Agents Downloads
Stable Dev GitHub last commit (branch) version Coverage
Agents.jl is a pure-Julia general purpose framework for ABM. It is currently the most popular framework in terms of numer of users and is the ABM framework that has the longest actively-developed period in the list (within the registered Julia packages ecosystem). Although its main focus is providing a framework for fast prototyping and flexibility in generating and then altering the ABM, it has been heavily optimized for performance as well. Some of its key features can be summarized as:

  1. It is fast (faster than MASON, NetLogo, or Mesa or any other general purpose alternative we have compared it with)

  2. It is simple: has a short learning curve and requires writing minimal code when compared to other general purpose alternatives

  3. Has an extensive interface of thousands of out-of-the box possible agent actions

  4. Straightforwardly allows simulations on Open Street Maps

  5. Allows both traditional discrete-time ABM simulations as well as continuous time "event queue based" ABM simulations.

Agents.jl is also extended by some domain-specific frameworks such as MicrobeAgents.jl.

Vahana.jl

GitHub Repo stars deps Vahana Downloads
Stable Dev GitHub last commit (branch) version
Vahana.jl is an ABM framework tailored for the development of large-scale agent-based models, based on a synchronous graph dynamical system approach. One of its notable strengths lies in its parallel execution capabilities, making it suitable for supercomputer clusters and for handling large datasets or agent populations (an aspect for which there are few alternatives even outside the Julia community). Therefore, a major focus of Vahana's development has been on CPU performance and a small memory footprint. Furthermore, Vahana is optimized for representing complex network structures, making it a good choice for network-centric models.

On the downside, while Vahana supports spatial information, it is limited to discrete n-dimensional rasters, and it is less suitable for simulations where agents need to move extensively within these spaces. The requirement to express models as graphs is unconventional and may require a paradigm shift for those accustomed to more traditional agent-based modeling approaches. In addition, some ABMs, e.g., such as those where only a single agent can occupy a cell in a grid and can move (as in the Schelling model), are not easy to formulate in a parallel version at all.

You can learn more about Vahana.jl in:

MEDYAN.jl

Mechanochemical Dynamics of Active Networks (MEDYAN) is an efficient and scalable computational model for mechanochemical simulations of active matter networks. Our goal is to be able to simulate both the mechanics and chemistry of the cytoskeleton in a whole cell at the minute time scale while still keeping track of the stochastic chemistry of individual proteins. MEDYAN.jl is not released yet and is still a work in progress but there are currently some basic tutorials.

When fully released, MEDYAN.jl's source code will be downloadable for scientific use, but it probably won't be on GitHub or in the general registry. What is likely is that components of MEDYAN.jl that might be useful as stand-alone packages will be separated and published on GitHub medyan-dev organization.

CellBasedModels.jl

GitHub Repo stars deps CellBasedModels Downloads
Stable Dev GitHub last commit (branch) version
CellBasedModels.jl is an ABM package that has in mind its application to continuum space ABMs. Its main target is to solve physical models that are, at least partially, defined in terms of differential equations. Characteristics:

  • Work with parallelized CPU capabilities or Nvidia GPUs through CUDA.jl. This makes it very efficient.

  • Hybrid ABM/Continuum Model. Incorporation of a continuum medium that can evolve with PDEs to allow ABM+Coninuum medium models.

  • Wide compatibility to use DifferentialEquations.jl as solvers of the differential equation. DifferentialEquations.jl is fully integrated into the package in a way that goes beyond the type of integration that Agents.jl supports.

  • Provides extensive documentation and examples.

  • Although the applicability is very general to continuum space ABMs, the examples provided so far are all from biophysical problems.

There are plans to in the future merge/port GPU functionalities of CellBasedModels.jl with Agents.jl.

EasyABM.jl

GitHub Repo stars deps EasyABM Downloads
Stable Dev GitHub last commit (branch) version Coverage
EasyABM.jl is general purpose ABM framework with a philosophy/style that is more oriented towards NetLogo and tries to offer a simple approach to ABMs for people without experience in Julia, at the expense of performance, flexibility, and total number of features.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Agent Based Modelling
Julia
Package
Comparisons
Page source | GitHub repository

Agent Based Modelling

Julia has an extensive suite of various packages targeting various applications for agent based modelling (ABM).

  1. Agents.jl
  2. Vahana.jl
  3. MEDYAN.jl
  4. CellBasedModels.jl
  5. EasyABM.jl

Agents.jl

GitHub Repo stars deps Agents Downloads
Stable Dev GitHub last commit (branch) version Coverage
Agents.jl is a pure-Julia general purpose framework for ABM. It is currently the most popular framework in terms of numer of users and is the ABM framework that has the longest actively-developed period in the list (within the registered Julia packages ecosystem). Although its main focus is providing a framework for fast prototyping and flexibility in generating and then altering the ABM, it has been heavily optimized for performance as well. Some of its key features can be summarized as:

  1. It is fast (faster than MASON, NetLogo, or Mesa or any other general purpose alternative we have compared it with)

  2. It is simple: has a short learning curve and requires writing minimal code when compared to other general purpose alternatives

  3. Has an extensive interface of thousands of out-of-the box possible agent actions

  4. Straightforwardly allows simulations on Open Street Maps

  5. Allows both traditional discrete-time ABM simulations as well as continuous time "event queue based" ABM simulations.

Agents.jl is also extended by some domain-specific frameworks such as MicrobeAgents.jl.

Vahana.jl

GitHub Repo stars deps Vahana Downloads
Stable Dev GitHub last commit (branch) version
Vahana.jl is an ABM framework tailored for the development of large-scale agent-based models, based on a synchronous graph dynamical system approach. One of its notable strengths lies in its parallel execution capabilities, making it suitable for supercomputer clusters and for handling large datasets or agent populations (an aspect for which there are few alternatives even outside the Julia community). Therefore, a major focus of Vahana's development has been on CPU performance and a small memory footprint. Furthermore, Vahana is optimized for representing complex network structures, making it a good choice for network-centric models.

On the downside, while Vahana supports spatial information, it is limited to discrete n-dimensional rasters, and it is less suitable for simulations where agents need to move extensively within these spaces. The requirement to express models as graphs is unconventional and may require a paradigm shift for those accustomed to more traditional agent-based modeling approaches. In addition, some ABMs, e.g., such as those where only a single agent can occupy a cell in a grid and can move (as in the Schelling model), are not easy to formulate in a parallel version at all.

You can learn more about Vahana.jl in:

MEDYAN.jl

Mechanochemical Dynamics of Active Networks (MEDYAN) is an efficient and scalable computational model for mechanochemical simulations of active matter networks. Our goal is to be able to simulate both the mechanics and chemistry of the cytoskeleton in a whole cell at the minute time scale while still keeping track of the stochastic chemistry of individual proteins. MEDYAN.jl is not released yet and is still a work in progress but there are currently some basic tutorials.

When fully released, MEDYAN.jl's source code will be downloadable for scientific use, but it probably won't be on GitHub or in the general registry. What is likely is that components of MEDYAN.jl that might be useful as stand-alone packages will be separated and published on GitHub medyan-dev organization.

CellBasedModels.jl

GitHub Repo stars deps CellBasedModels Downloads
Stable Dev GitHub last commit (branch) version
CellBasedModels.jl is an ABM package that has in mind its application to continuum space ABMs. Its main target is to solve physical models that are, at least partially, defined in terms of differential equations. Characteristics:

  • Work with parallelized CPU capabilities or Nvidia GPUs through CUDA.jl. This makes it very efficient.

  • Hybrid ABM/Continuum Model. Incorporation of a continuum medium that can evolve with PDEs to allow ABM+Coninuum medium models.

  • Wide compatibility to use DifferentialEquations.jl as solvers of the differential equation. DifferentialEquations.jl is fully integrated into the package in a way that goes beyond the type of integration that Agents.jl supports.

  • Provides extensive documentation and examples.

  • Although the applicability is very general to continuum space ABMs, the examples provided so far are all from biophysical problems.

There are plans to in the future merge/port GPU functionalities of CellBasedModels.jl with Agents.jl.

EasyABM.jl

GitHub Repo stars deps EasyABM Downloads
Stable Dev GitHub last commit (branch) version Coverage
EasyABM.jl is general purpose ABM framework with a philosophy/style that is more oriented towards NetLogo and tries to offer a simple approach to ABMs for people without experience in Julia, at the expense of performance, flexibility, and total number of features.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/simulation/control_systems/index.html b/PR84/comparisons/simulation/control_systems/index.html index 94dbcc4..70c2aa4 100644 --- a/PR84/comparisons/simulation/control_systems/index.html +++ b/PR84/comparisons/simulation/control_systems/index.html @@ -1 +1 @@ - Control Systems
Julia
Package
Comparisons
Page source | GitHub repository

Control Systems

Wikipedia states the following:

A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops.

This section covers packages that provide the tools needed to create and analyze control systems in Julia.

  1. ControlSystems.jl
  2. ModelPredictiveControl.jl
  3. Other

ControlSystems.jl

GitHub Repo stars deps ControlSystems Downloads
Stable Dev GitHub last commit (branch) version Coverage
There is a github organization called JuliaControl, the star of which is ControlSystems.jl. It is a Control Systems Toolbox for Julia, and provides a lot of common functionality for working with control systems.

ModelPredictiveControl.jl

GitHub Repo stars deps ModelPredictiveControl Downloads
Stable Dev GitHub last commit (branch) version Coverage
A new and much less used/tested package is ModelPredictiveControl.jl. I the author can not speak to its capabilities.

Other

While not directly aimed at control systems, ModelingToolkit.jl is also capable of performing some of the modeling and analysis typical for this domain. It is however a more general framework with a different focus, and so, will require more lines of code to perform similar analysis. The upside is that due to its general and flexible nature, it may be easier to extend for some projects as they grow.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Control Systems
Julia
Package
Comparisons
Page source | GitHub repository

Control Systems

Wikipedia states the following:

A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops.

This section covers packages that provide the tools needed to create and analyze control systems in Julia.

  1. ControlSystems.jl
  2. ModelPredictiveControl.jl
  3. Other

ControlSystems.jl

GitHub Repo stars deps ControlSystems Downloads
Stable Dev GitHub last commit (branch) version Coverage
There is a github organization called JuliaControl, the star of which is ControlSystems.jl. It is a Control Systems Toolbox for Julia, and provides a lot of common functionality for working with control systems.

ModelPredictiveControl.jl

GitHub Repo stars deps ModelPredictiveControl Downloads
Stable Dev GitHub last commit (branch) version Coverage
A new and much less used/tested package is ModelPredictiveControl.jl. I the author can not speak to its capabilities.

Other

While not directly aimed at control systems, ModelingToolkit.jl is also capable of performing some of the modeling and analysis typical for this domain. It is however a more general framework with a different focus, and so, will require more lines of code to perform similar analysis. The upside is that due to its general and flexible nature, it may be easier to extend for some projects as they grow.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/testing/profiling/index.html b/PR84/comparisons/testing/profiling/index.html index da68952..e092bb6 100644 --- a/PR84/comparisons/testing/profiling/index.html +++ b/PR84/comparisons/testing/profiling/index.html @@ -1 +1 @@ - Profiling
Julia
Package
Comparisons
Page source | GitHub repository

Profiling

  1. Introduction
  2. Overview
    1. The short version
    2. The long version
  3. Packages
    1. VSCode's built-in profiler
    2. ProfileView
    3. PProf
    4. StatProfilerHTML
    5. ProfileCanvas
    6. ProfileSVG
    7. ProfileVega
  4. Useful links
  5. Star history

Introduction

Profiling, in the context of programming, usually refers to the process of learning how much time your computer spends in the different parts of you program/function. In this way, you get a profile of the runtime of your program. This can be immensely useful to identify bottlenecks of your code, and allows you to spend your time optimizing the code that actually takes the most time to run. This can save you from the rather normal situation of spending a long time making a certain part of the program run e.g. 1000 times faster, when that part only makes out a fraction (say 1%) of the total runtime of the program.

If you are interested in measuring the runtime of your program, see the page comparing packages that measure runtime.

If you are interested in identifying type-instabilities in your program, see Cthulhu.jl.

Overview

The short version

  • If you are using VSCode, just put @profview in front of the expression you want to profile. No package installation required. For more detail, see VSCode's built-in profiler

  • ProfileView.jl is both the oldest and most starred package, and is therefore a safe general choice.

  • If you are using something else, there are multiple options. Some integrate well with Jupyter or Pluto notebooks, whereas others serve the profile as a locally hosted website in your browser, and others yet give you a standalone GUI window. Scroll through Packages for more detail.

If you are new to profiling and optimization of Julia code, this video is a really good resource that goes through the process, showing how to use two of the most important Julia packages in the code-optimization domain.

The long version

Julia has a standard library called Profile. It implements a "statistical profiler", meaning that samples are taken regularly during runtime. You can think of it like sampling a probability distribution - you will never perfectly know the true distribution, but with enough samples you can be pretty damn sure.

While the Profile standard library profiles the basic functionality, it is most useful when visualized. The most normal way to visualize a profile is as a "Flamegraph", which is handled by FlameGraphs.jl. However, to render than visualization, one would use yet another package. It is not important to understand the precise relationship between all the packages. Everything is neatly bundled up for you, the user, in one of the alternatives listed under Packages.

Packages

VSCode's built-in profiler

While it is not a package, using functionality from the Julia extension for VSCode is the most convenient way to profile code (if you are already using VSCode). There is a user guide on the VSCode profiler with screenshots and detailed explanation about what is displayed, how to interpret it, and how to interact with the flamegraph.

ProfileView

GitHub Repo stars deps ProfileView Downloads
Stable Dev GitHub last commit (branch) version Coverage
ProfileView.jl is a stand-alone visualizer based on GTK. It is the most starred, and the oldest and most established alternative. It should be a suitable choice for most. If however you are running code in VSCode, or a notebook like Jupyter, other alternatives might work better for you.

PProf

GitHub Repo stars deps PProf Downloads
Stable Dev GitHub last commit (branch) version Coverage
PProf.jl serves a local website for inspecting graphs, flamegraphs and more. It is the only alternative that does not build on Flamegraphs.jl, instead converting the output from the Profile standard library directly to google's pprof. The only hard downside is that PProf.jl looses the sample ordering information captured in FlameGraphs.jl, according to the Flamegraphs.jl README. But if this is not important to you, PPros features "excellent support for interaction, filtering, aggregation, and viewing source code", according to the same README.

StatProfilerHTML

GitHub Repo stars deps StatProfilerHTML Downloads
Stable Dev GitHub last commit (branch) version Coverage
StatProfilerHTML.jl produces HTML and presents some additional summaries, and also integrates well with Jupyter notebooks.

ProfileCanvas

GitHub Repo stars deps ProfileCanvas Downloads
Stable Dev GitHub last commit (branch) version Coverage
ProfileCanvas.jl is a HTML canvas based profile viewer UI, used by the Julia VS Code extension, but can also generate interactive HTML files.

ProfileSVG

GitHub Repo stars deps ProfileSVG Downloads
Stable Dev GitHub last commit (branch) version Coverage
ProfileSVG.jl renders SVG.

ProfileVega

GitHub Repo stars deps ProfileVega Downloads
Stable Dev GitHub last commit (branch) version Coverage
ProfileVega.jl uses VegaLight and integrates well with Jupyter notebooks.

Star history

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Profiling
Julia
Package
Comparisons
Page source | GitHub repository

Profiling

  1. Introduction
  2. Overview
    1. The short version
    2. The long version
  3. Packages
    1. VSCode's built-in profiler
    2. ProfileView
    3. PProf
    4. StatProfilerHTML
    5. ProfileCanvas
    6. ProfileSVG
    7. ProfileVega
  4. Useful links
  5. Star history

Introduction

Profiling, in the context of programming, usually refers to the process of learning how much time your computer spends in the different parts of you program/function. In this way, you get a profile of the runtime of your program. This can be immensely useful to identify bottlenecks of your code, and allows you to spend your time optimizing the code that actually takes the most time to run. This can save you from the rather normal situation of spending a long time making a certain part of the program run e.g. 1000 times faster, when that part only makes out a fraction (say 1%) of the total runtime of the program.

If you are interested in measuring the runtime of your program, see the page comparing packages that measure runtime.

If you are interested in identifying type-instabilities in your program, see Cthulhu.jl.

Overview

The short version

  • If you are using VSCode, just put @profview in front of the expression you want to profile. No package installation required. For more detail, see VSCode's built-in profiler

  • ProfileView.jl is both the oldest and most starred package, and is therefore a safe general choice.

  • If you are using something else, there are multiple options. Some integrate well with Jupyter or Pluto notebooks, whereas others serve the profile as a locally hosted website in your browser, and others yet give you a standalone GUI window. Scroll through Packages for more detail.

If you are new to profiling and optimization of Julia code, this video is a really good resource that goes through the process, showing how to use two of the most important Julia packages in the code-optimization domain.

The long version

Julia has a standard library called Profile. It implements a "statistical profiler", meaning that samples are taken regularly during runtime. You can think of it like sampling a probability distribution - you will never perfectly know the true distribution, but with enough samples you can be pretty damn sure.

While the Profile standard library profiles the basic functionality, it is most useful when visualized. The most normal way to visualize a profile is as a "Flamegraph", which is handled by FlameGraphs.jl. However, to render than visualization, one would use yet another package. It is not important to understand the precise relationship between all the packages. Everything is neatly bundled up for you, the user, in one of the alternatives listed under Packages.

Packages

VSCode's built-in profiler

While it is not a package, using functionality from the Julia extension for VSCode is the most convenient way to profile code (if you are already using VSCode). There is a user guide on the VSCode profiler with screenshots and detailed explanation about what is displayed, how to interpret it, and how to interact with the flamegraph.

ProfileView

GitHub Repo stars deps ProfileView Downloads
Stable Dev GitHub last commit (branch) version Coverage
ProfileView.jl is a stand-alone visualizer based on GTK. It is the most starred, and the oldest and most established alternative. It should be a suitable choice for most. If however you are running code in VSCode, or a notebook like Jupyter, other alternatives might work better for you.

PProf

GitHub Repo stars deps PProf Downloads
Stable Dev GitHub last commit (branch) version Coverage
PProf.jl serves a local website for inspecting graphs, flamegraphs and more. It is the only alternative that does not build on Flamegraphs.jl, instead converting the output from the Profile standard library directly to google's pprof. The only hard downside is that PProf.jl looses the sample ordering information captured in FlameGraphs.jl, according to the Flamegraphs.jl README. But if this is not important to you, PPros features "excellent support for interaction, filtering, aggregation, and viewing source code", according to the same README.

StatProfilerHTML

GitHub Repo stars deps StatProfilerHTML Downloads
Stable Dev GitHub last commit (branch) version Coverage
StatProfilerHTML.jl produces HTML and presents some additional summaries, and also integrates well with Jupyter notebooks.

ProfileCanvas

GitHub Repo stars deps ProfileCanvas Downloads
Stable Dev GitHub last commit (branch) version Coverage
ProfileCanvas.jl is a HTML canvas based profile viewer UI, used by the Julia VS Code extension, but can also generate interactive HTML files.

ProfileSVG

GitHub Repo stars deps ProfileSVG Downloads
Stable Dev GitHub last commit (branch) version Coverage
ProfileSVG.jl renders SVG.

ProfileVega

GitHub Repo stars deps ProfileVega Downloads
Stable Dev GitHub last commit (branch) version Coverage
ProfileVega.jl uses VegaLight and integrates well with Jupyter notebooks.

Star history

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/testing/property_based_testing/index.html b/PR84/comparisons/testing/property_based_testing/index.html index 3182022..a0499f1 100644 --- a/PR84/comparisons/testing/property_based_testing/index.html +++ b/PR84/comparisons/testing/property_based_testing/index.html @@ -1 +1 @@ - Property Based Testing
Julia
Package
Comparisons
Page source | GitHub repository

Property Based Testing

  1. Introduction
  2. Overview
  3. Packages
    1. PropCheck.jl
    2. Supposition.jl
  4. Related Posts

Introduction

Programming and mathematics have always interested similar minds, and tackled related problems. Mathematics has strong formalism in place about the properties of certain objects (scalars, vectors, matrices) under certain operations (addition, multiplication, transposition), or even properties of the operators themselves. Property Based Testing (PBT) is an attempt to use computer programming to test if certain properties hold for certain objects.

Overview

There are two main packages in this domain: PropCheck.jl, and the newer Suppositon.jl. Both packages are created by the same person, who has made a comparison page of the two packages in the FAQ of the Supposition.jl docs. That page includes this table which shows some things that PropCheck can do that Supposition can't, and this list of things that Supposition can do that PropCheck can't. The general takeaway is that PropCheck is in maintenance mode, and that Supposition should be considered a successor to PropCheck. New users should generally use Supposition.

Supposition.jl is heavily inspired by the python library Hypothesis, whereas PropCheck.jl is inspired by the Haskell library Hedgehog. For those interested in the differences beyond the surface level, the package author recommends this comparison blogpost between QuickCheck, Hedgehog and Hypothesis as a starting point.

Star History Chart

Packages

PropCheck.jl

GitHub Repo stars deps PropCheck Downloads
Stable Dev GitHub last commit (branch) version Coverage
PropCheck is the original package for Property Based Testing, and is therefore more mature and battle-tested. However, it is in maintenance mode, as the package developer has moved on to Supposition as a successor to PropCheck.

Supposition.jl

GitHub Repo stars deps Supposition Downloads
Stable Dev GitHub last commit (branch) version Coverage
Supposition.jl is very new, and has not yet proven itself. However, as the maker of the package has also created the battle-tested PropCheck, experience from that project has without a doubt gone into making Supposition, meaning that it is likely more stable and well-though out than the package age suggests.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Property Based Testing
Julia
Package
Comparisons
Page source | GitHub repository

Property Based Testing

  1. Introduction
  2. Overview
  3. Packages
    1. PropCheck.jl
    2. Supposition.jl
    3. JCheck.jl
  4. Related Posts

Introduction

Programming and mathematics have always interested similar minds, and tackled related problems. Mathematics has strong formalism in place about the properties of certain objects (scalars, vectors, matrices) under certain operations (addition, multiplication, transposition), or even properties of the operators themselves. Property Based Testing (PBT) is an attempt to use computer programming to test if certain properties hold for certain objects.

Overview

There are two main packages in this domain: PropCheck.jl, and the newer Suppositon.jl. Both packages are created by the same person, who has made a comparison page of the two packages in the FAQ of the Supposition.jl docs. That page includes this table which shows some things that PropCheck can do that Supposition can't, and this list of things that Supposition can do that PropCheck can't. The general takeaway is that PropCheck is in maintenance mode, and that Supposition should be considered a successor to PropCheck. New users should generally use Supposition.

Supposition.jl is heavily inspired by the python library Hypothesis, whereas PropCheck.jl is inspired by the Haskell library Hedgehog. For those interested in the differences beyond the surface level, the package author recommends this comparison blogpost between QuickCheck, Hedgehog and Hypothesis as a starting point.

Star History Chart

Packages

PropCheck.jl

GitHub Repo stars deps PropCheck Downloads
Stable Dev GitHub last commit (branch) version Coverage
PropCheck is the original package for Property Based Testing, and is therefore more mature and battle-tested. However, it is in maintenance mode, as the package developer has moved on to Supposition as a successor to PropCheck.

Supposition.jl

GitHub Repo stars deps Supposition Downloads
Stable Dev GitHub last commit (branch) version Coverage
Supposition.jl is very new, and has not yet proven itself. However, as the maker of the package has also created the battle-tested PropCheck, experience from that project has without a doubt gone into making Supposition, meaning that it is likely more stable and well-though out than the package age suggests.

JCheck.jl

GitHub Repo stars deps JCheck Downloads
Stable Dev GitHub last commit (branch) version Coverage
This package is mentioned for completeness. It appears to be a functional package and thereby a valid alternative, but it no longer appears maintained. Given the age, starts and activity of PropCheck, and that it's developer also made Supposition.jl, it seems likely that this package is overall worse for most users.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/testing/runtime/index.html b/PR84/comparisons/testing/runtime/index.html index 017dc8d..2af72eb 100644 --- a/PR84/comparisons/testing/runtime/index.html +++ b/PR84/comparisons/testing/runtime/index.html @@ -6,4 +6,4 @@ julia> println("Elapsed time: $(t_final - t_initial)") Elapsed time: 0.3160099983215332

Let's run it again, this time using the @time macro, which is much more convenient:

julia> @time my_function(100);
   0.000626 seconds (12 allocations: 207.406 KiB)

The results change a lot. This is mainly caused by the fact that my_function is compiled the first time we call it, but the compiled code is simply excecuted the next time. But even with the compilation done, the runtime varies:

julia> @time my_function(100);
-  0.000660 seconds (12 allocations: 207.406 KiB)

Measuring runtime is always noisy. It depends on what other things you system is doing, how hot your CPU is, and a lot of other compilated things. The thing to realize however is that all the noise sources add to the runtime. The least noisy runtime measurement is therefore the minimal runtime for some number of runs. The packages listed under Pakcages will run your code in a loop, do fancy stuff like warmup, and help you find a balance between long waiting time, and accurate results, before returning this minimal runtime.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + 0.000660 seconds (12 allocations: 207.406 KiB)

Measuring runtime is always noisy. It depends on what other things you system is doing, how hot your CPU is, and a lot of other compilated things. The thing to realize however is that all the noise sources add to the runtime. The least noisy runtime measurement is therefore the minimal runtime for some number of runs. The packages listed under Pakcages will run your code in a loop, do fancy stuff like warmup, and help you find a balance between long waiting time, and accurate results, before returning this minimal runtime.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/uncertainty_propagation/index.html b/PR84/comparisons/uncertainty_propagation/index.html index 6839fe6..f28a065 100644 --- a/PR84/comparisons/uncertainty_propagation/index.html +++ b/PR84/comparisons/uncertainty_propagation/index.html @@ -1 +1 @@ - Uncertainty Propagation
Julia
Package
Comparisons
Page source | GitHub repository

Uncertainty Propagation

The two main Julia packages that provide uncertainty propagation are Measurements.jl and MontecarloMeasurements.jl.

The main difference is that Measurements.jl uses linear error propagation theory to propagate errors, while MonteCarloMeasurements.jl uses Monte-Carlo methods, in a way that allows for propagation of probability distributions through functions, allowing nonlinear uncertainty propagation.

This section is not yet written well. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Uncertainty Propagation
Julia
Package
Comparisons
Page source | GitHub repository

Uncertainty Propagation

The two main Julia packages that provide uncertainty propagation are Measurements.jl and MontecarloMeasurements.jl.

The main difference is that Measurements.jl uses linear error propagation theory to propagate errors, while MonteCarloMeasurements.jl uses Monte-Carlo methods, in a way that allows for propagation of probability distributions through functions, allowing nonlinear uncertainty propagation.

This section is not yet written well. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/utility/notebooks/index.html b/PR84/comparisons/utility/notebooks/index.html index f5373df..a86810b 100644 --- a/PR84/comparisons/utility/notebooks/index.html +++ b/PR84/comparisons/utility/notebooks/index.html @@ -1 +1 @@ - Notebooks
Julia
Package
Comparisons
Page source | GitHub repository

Notebooks

Notebooks are a type of Integrated Development Environment (IDE), and are particularly useful for sharing and showing computations.

Relevant packages: Pluto.jl, IJulia.jl, Neptune.jl

The author of Pluto.jl gave a talk (YouTube link) at JupyterCon 2023, which is a good demonstration of how to use Pluto.jl.

This section is not yet properly written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Notebooks
Julia
Package
Comparisons
Page source | GitHub repository

Notebooks

Notebooks are a type of Integrated Development Environment (IDE), and are particularly useful for sharing and showing computations.

Relevant packages: Pluto.jl, IJulia.jl, Neptune.jl

The author of Pluto.jl gave a talk (YouTube link) at JupyterCon 2023, which is a good demonstration of how to use Pluto.jl.

This section is not yet properly written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/utility/package_templates/index.html b/PR84/comparisons/utility/package_templates/index.html index 1fd526e..0171f57 100644 --- a/PR84/comparisons/utility/package_templates/index.html +++ b/PR84/comparisons/utility/package_templates/index.html @@ -1 +1 @@ - Package Templates
Julia
Package
Comparisons
Page source | GitHub repository

Package Templates

There are several ways to create a Julia packages from templates.

  1. Packages
    1. PkgTemplates.jl
    2. PkgSkeleton.jl
    3. Pkg.jl
  2. CLI tools
    1. Ion
  3. GitHub repository templates
  4. Star History

Packages

PkgTemplates.jl

GitHub Repo stars deps PkgTemplates Downloads
Stable Dev GitHub last commit (branch) version Coverage

This package is the the most popular package generator.

PkgSkeleton.jl

GitHub Repo stars deps PkgSkeleton Downloads
Stable Dev GitHub last commit (branch) version Coverage

PkgSkeleton is another package generator.

Pkg.jl

GitHub Repo stars deps Pkg Downloads
Stable Dev GitHub last commit (branch) version Coverage

Pkg.jl does not have templates, but it can create a minimum package with generate command.

CLI tools

Ion

Ion is a CLI toolkit for Julia developers, and can generate a new package with pre-defined templates.

See Ion Tutorial for more information.

GitHub repository templates

Some people use GitHub repository templates to generate a new Julia package. Here are some examples:

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Package Templates
Julia
Package
Comparisons
Page source | GitHub repository

Package Templates

There are several ways to create a Julia packages from templates.

  1. Packages
    1. PkgTemplates.jl
    2. PkgSkeleton.jl
    3. Pkg.jl
  2. CLI tools
    1. Ion
  3. GitHub repository templates
  4. Star History

Packages

PkgTemplates.jl

GitHub Repo stars deps PkgTemplates Downloads
Stable Dev GitHub last commit (branch) version Coverage

This package is the the most popular package generator.

PkgSkeleton.jl

GitHub Repo stars deps PkgSkeleton Downloads
Stable Dev GitHub last commit (branch) version Coverage

PkgSkeleton is another package generator.

Pkg.jl

GitHub Repo stars deps Pkg Downloads
Stable Dev GitHub last commit (branch) version Coverage

Pkg.jl does not have templates, but it can create a minimum package with generate command.

CLI tools

Ion

Ion is a CLI toolkit for Julia developers, and can generate a new package with pre-defined templates.

See Ion Tutorial for more information.

GitHub repository templates

Some people use GitHub repository templates to generate a new Julia package. Here are some examples:

Star History

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/utility/piping/index.html b/PR84/comparisons/utility/piping/index.html index 5ea7620..960bbd7 100644 --- a/PR84/comparisons/utility/piping/index.html +++ b/PR84/comparisons/utility/piping/index.html @@ -1 +1 @@ - Piping
Julia
Package
Comparisons
Page source | GitHub repository

Piping

Wikipedia describes a software pipeline as a "chain of processing elements, arranged so that the output of each element is the input of the next". Julia has native piping functionality in the |> operator, allowing f(g(x)) to be written as x |> g |> f. However, once you want to do mode advanced operations like using multi-argument functions, the core functionality is lacking. A pull-request was created to address this in 2017, but progress has halted. Instead, there is now a number of different packages that implement advanced piping, in slightly different flavours.

The different packages that implement more advanced piping functionality are listed in the table of contents below

  1. Packages
    1. Chain.jl
    2. Lazy.jl
    3. Pipe.jl
  2. Star history

Packages

Chain.jl

GitHub Repo stars deps Chain Downloads
GitHub last commit (branch) version
Chain.jl is the most starred package that purely implements piping functionality. It describes itself as follows:

A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.

Lazy.jl

GitHub Repo stars deps Lazy Downloads
GitHub last commit (branch) version
Lazy.jl implements a lot of other functionality, but discussed piping in particular under this section of the readme

Pipe.jl

GitHub Repo stars deps Pipe Downloads
GitHub last commit (branch) version
Pipe.jl describes itself as follows:

An enhancement to julia piping syntax

Star history

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Piping
Julia
Package
Comparisons
Page source | GitHub repository

Piping

Wikipedia describes a software pipeline as a "chain of processing elements, arranged so that the output of each element is the input of the next". Julia has native piping functionality in the |> operator, allowing f(g(x)) to be written as x |> g |> f. However, once you want to do mode advanced operations like using multi-argument functions, the core functionality is lacking. A pull-request was created to address this in 2017, but progress has halted. Instead, there is now a number of different packages that implement advanced piping, in slightly different flavours.

The different packages that implement more advanced piping functionality are listed in the table of contents below

  1. Packages
    1. Chain.jl
    2. Lazy.jl
    3. Pipe.jl
  2. Star history

Packages

Chain.jl

GitHub Repo stars deps Chain Downloads
GitHub last commit (branch) version
Chain.jl is the most starred package that purely implements piping functionality. It describes itself as follows:

A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.

Lazy.jl

GitHub Repo stars deps Lazy Downloads
GitHub last commit (branch) version
Lazy.jl implements a lot of other functionality, but discussed piping in particular under this section of the readme

Pipe.jl

GitHub Repo stars deps Pipe Downloads
GitHub last commit (branch) version
Pipe.jl describes itself as follows:

An enhancement to julia piping syntax

Star history

Star History Chart
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/utility/redefinable_structs/index.html b/PR84/comparisons/utility/redefinable_structs/index.html index dd6a966..3f1e0c0 100644 --- a/PR84/comparisons/utility/redefinable_structs/index.html +++ b/PR84/comparisons/utility/redefinable_structs/index.html @@ -1 +1 @@ - Redefinable Structs
Julia
Package
Comparisons
Page source | GitHub repository

Redefinable Structs

Redefining a struct in base julia requires restarting the Julia process. This can incur significant recompilation time (though this is greatly reduced in Julia 1.9 and 1.10). These packages allow users to define structs that are redefinable. ProtoStructs.jl and RedefStructsRedfineStructs.jl

See also Revise.jl for a different approach to struct redefinition.

This section is not yet properly written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Redefinable Structs
Julia
Package
Comparisons
Page source | GitHub repository

Redefinable Structs

Redefining a struct in base julia requires restarting the Julia process. This can incur significant recompilation time (though this is greatly reduced in Julia 1.9 and 1.10). These packages allow users to define structs that are redefinable. ProtoStructs.jl and RedefStructsRedfineStructs.jl

See also Revise.jl for a different approach to struct redefinition.

This section is not yet properly written. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/utility/types_compactification/index.html b/PR84/comparisons/utility/types_compactification/index.html index d63301c..ca8994c 100644 --- a/PR84/comparisons/utility/types_compactification/index.html +++ b/PR84/comparisons/utility/types_compactification/index.html @@ -1 +1 @@ - Types compactification
Julia
Package
Comparisons
Page source | GitHub repository

These packages help to avoid Union-splitting performance drawbacks by merging multiple types into one.

  1. MixedStructTypes.jl
  2. SumTypes.jl
  3. Unityper.jl
  4. Expronicon.jl (with ADT.@adt)

MixedStructTypes.jl

GitHub Repo stars deps MixedStructTypes Downloads
Stable Dev GitHub last commit (branch) version Coverage

SumTypes.jl

GitHub Repo stars deps SumTypes Downloads
Stable Dev GitHub last commit (branch) version Coverage

Unityper.jl

GitHub Repo stars deps Unityper Downloads
Stable Dev GitHub last commit (branch) version Coverage

Expronicon.jl (with ADT.@adt)

GitHub Repo stars deps Expronicon Downloads
Stable Dev GitHub last commit (branch) version Coverage
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Types compactification
Julia
Package
Comparisons
Page source | GitHub repository

These packages help to avoid Union-splitting performance drawbacks by merging multiple types into one.

  1. MixedStructTypes.jl
  2. SumTypes.jl
  3. Unityper.jl
  4. Expronicon.jl (with ADT.@adt)

MixedStructTypes.jl

GitHub Repo stars deps MixedStructTypes Downloads
Stable Dev GitHub last commit (branch) version Coverage

SumTypes.jl

GitHub Repo stars deps SumTypes Downloads
Stable Dev GitHub last commit (branch) version Coverage

Unityper.jl

GitHub Repo stars deps Unityper Downloads
Stable Dev GitHub last commit (branch) version Coverage

Expronicon.jl (with ADT.@adt)

GitHub Repo stars deps Expronicon Downloads
Stable Dev GitHub last commit (branch) version Coverage
This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/web/static_websites/index.html b/PR84/comparisons/web/static_websites/index.html index 6528abf..aba801b 100644 --- a/PR84/comparisons/web/static_websites/index.html +++ b/PR84/comparisons/web/static_websites/index.html @@ -1 +1 @@ - Static Websites
Julia
Package
Comparisons
Page source | GitHub repository

Static Websites

The two main static web page packages for julia are Franklin.jl and StaticWebPages.jl. Franklin is clearly the most popular, and as of September 2023 it is getting close to 900 stars. This makes it among the most starred julia packages. Several julia projects, including this website and the documentation for Makie.jl, are built with Franklin.

StaticWebPages is less starred and less actively developed. It does however have a notable number of stars, indicating that it can get the job done. More detail can not be given by the current author, as I have not used the package myself.

This section is not yet written well. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Static Websites
Julia
Package
Comparisons
Page source | GitHub repository

Static Websites

The two main static web page packages for julia are Franklin.jl and StaticWebPages.jl. Franklin is clearly the most popular, and as of September 2023 it is getting close to 900 stars. This makes it among the most starred julia packages. Several julia projects, including this website and the documentation for Makie.jl, are built with Franklin.

StaticWebPages is less starred and less actively developed. It does however have a notable number of stars, indicating that it can get the job done. More detail can not be given by the current author, as I have not used the package myself.

This section is not yet written well. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/comparisons/web/web_apps/index.html b/PR84/comparisons/web/web_apps/index.html index 8383517..e63cade 100644 --- a/PR84/comparisons/web/web_apps/index.html +++ b/PR84/comparisons/web/web_apps/index.html @@ -1 +1 @@ - Web Apps
Julia
Package
Comparisons
Page source | GitHub repository

Web Apps

A web app is an interactive website. There are several Julia packages that help in the creation of web apps that run Julia as the backend language.

  1. Genie.jl
  2. Oxygen.jl
  3. Bonito.jl
  4. Other

Genie.jl

GitHub Repo stars deps Genie Downloads
Stable Dev GitHub last commit (branch) version
The most popular web app framework is Genie.jl. As of September 2023 it has more than 2000 stars, making it one of the most starred Julia packages out there. It describes itself as follows:

Genie is a full-stack web framework that provides a streamlined and efficient workflow for developing modern web applications. It builds on Julia's strengths (high-level, high-performance, dynamic, JIT compiled), exposing a rich API and a powerful toolset for productive web development.

Oxygen.jl

GitHub Repo stars deps Oxygen Downloads
Stable Dev GitHub last commit (branch) version Coverage
Then there is https://github.com/ndortega/Oxygen.jl. It describes itself as

A micro-framework built on top of the HTTP.jl library. Breathe easy knowing you can quickly spin up a web server with abstractions you're already familiar with.

Bonito.jl

GitHub Repo stars deps Bonito Downloads
Stable Dev GitHub last commit (branch) version Coverage
Finally, there is Bonito.jl. It describes itself as follows:

Easy way of building interactive applications from Julia. Uses Hyperscript to create HTML descriptions, and allows to execute Javascript & building of widgets. It also supports an offline mode, that exports your interactive app to a folder, and optionally records a statemap for all UI elements, so that a running Julia process isn't necessary anymore.

Other

It is worth mentioning the github organization JuliaWeb. JuliaWeb hosts a number of repositories which implement the tools needed to build web apps.

This section is not yet written well. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Web Apps
Julia
Package
Comparisons
Page source | GitHub repository

Web Apps

A web app is an interactive website. There are several Julia packages that help in the creation of web apps that run Julia as the backend language.

  1. Genie.jl
  2. Oxygen.jl
  3. Bonito.jl
  4. Other

Genie.jl

GitHub Repo stars deps Genie Downloads
Stable Dev GitHub last commit (branch) version
The most popular web app framework is Genie.jl. As of September 2023 it has more than 2000 stars, making it one of the most starred Julia packages out there. It describes itself as follows:

Genie is a full-stack web framework that provides a streamlined and efficient workflow for developing modern web applications. It builds on Julia's strengths (high-level, high-performance, dynamic, JIT compiled), exposing a rich API and a powerful toolset for productive web development.

Oxygen.jl

GitHub Repo stars deps Oxygen Downloads
Stable Dev GitHub last commit (branch) version Coverage
Then there is https://github.com/ndortega/Oxygen.jl. It describes itself as

A micro-framework built on top of the HTTP.jl library. Breathe easy knowing you can quickly spin up a web server with abstractions you're already familiar with.

Bonito.jl

GitHub Repo stars deps Bonito Downloads
Stable Dev GitHub last commit (branch) version Coverage
Finally, there is Bonito.jl. It describes itself as follows:

Easy way of building interactive applications from Julia. Uses Hyperscript to create HTML descriptions, and allows to execute Javascript & building of widgets. It also supports an offline mode, that exports your interactive app to a folder, and optionally records a statemap for all UI elements, so that a running Julia process isn't necessary anymore.

Other

It is worth mentioning the github organization JuliaWeb. JuliaWeb hosts a number of repositories which implement the tools needed to build web apps.

This section is not yet written well. If you have used or developed Julia packages in this domain, we would love your help! Please visit the "Contributing" section of the repository that hosts this website for information on contributions.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/contributing/index.html b/PR84/contributing/index.html index be980c0..c4e9fc4 100644 --- a/PR84/contributing/index.html +++ b/PR84/contributing/index.html @@ -94,7 +94,7 @@

github repository.

- Last modified: March 15, 2024. Built with Franklin.jl + Last modified: March 19, 2024. Built with Franklin.jl diff --git a/PR84/index.html b/PR84/index.html index 6b1568a..b65a972 100644 --- a/PR84/index.html +++ b/PR84/index.html @@ -1 +1 @@ - Julia Package Comparisons
Julia
Package
Comparisons
Page source | GitHub repository

Main Menu

Welcome to Julia Package Comparisons! This is a web resource created to help Julia users discover packages that might solve their problems, and to get guidance in choosing among similar packages.

The button in the top left opens a menu of the different sections. Each section is related to some specific domain, for example plotting, and contains information about Julia packages within that domain. To return to this page, click "Julia Package Comparisons" at the top of the page.

Contributing

This website is currently a work in progress. Once it is "in shape", a proper release announcement will be made on the Julia Discourse. For now, please take a look around to see if you like the project, and feel free to open issues and pull-requests in the github repository hosting this website (link in the top right) if you see something you can improve! If you just want to modify some existing content, see this guide incontributing.md for a brief description of how to do it. It is almost as easy as editing a text-file, and the maintainers will help you out if there are any problems ^_^

The goal is for the content of this website to be created by the users and contributors to the Julia packages being discussed.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Julia Package Comparisons
Julia
Package
Comparisons
Page source | GitHub repository

Main Menu

Welcome to Julia Package Comparisons! This is a web resource created to help Julia users discover packages that might solve their problems, and to get guidance in choosing among similar packages.

The button in the top left opens a menu of the different sections. Each section is related to some specific domain, for example plotting, and contains information about Julia packages within that domain. To return to this page, click "Julia Package Comparisons" at the top of the page.

Contributing

This website is currently a work in progress. Once it is "in shape", a proper release announcement will be made on the Julia Discourse. For now, please take a look around to see if you like the project, and feel free to open issues and pull-requests in the github repository hosting this website (link in the top right) if you see something you can improve! If you just want to modify some existing content, see this guide incontributing.md for a brief description of how to do it. It is almost as easy as editing a text-file, and the maintainers will help you out if there are any problems ^_^

The goal is for the content of this website to be created by the users and contributors to the Julia packages being discussed.

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/libs/lunr/lunr_index.js b/PR84/libs/lunr/lunr_index.js index 8416a1f..a5d6635 100644 --- a/PR84/libs/lunr/lunr_index.js +++ b/PR84/libs/lunr/lunr_index.js @@ -1,2 +1,2 @@ -const LUNR_DATA = {"version":"2.3.9","fields":["t","b"],"fieldVectors":[["t/0",[0,3.378]],["b/0",[0,3.573,1,0.044,2,0.09,3,0.044,4,0.016,5,0.016,6,0.016,7,0.016,8,0.015,9,0.015,10,0.019,11,0.015,12,0.015,13,0.016,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.056,20,0.015,21,0.015,22,0.016,23,0.015,24,0.061,25,0.061,26,0.015,27,0.015,28,0.015,29,0.016,30,0.015,31,0.015,32,0.016,33,0.015,34,0.016,35,0.015,36,0.056,37,0.015,38,0.015,39,0.015,40,0.019,41,0.016,42,0.015,43,0.015,44,0.015,45,0.015,46,0.016,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.019,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.022,70,0.02,71,0.016,72,0.015,73,0.02,74,0.02,75,2.822,76,2.822,77,3.143,78,2.352,79,3.143,80,4.225,81,4.225,82,0.016,83,0.016,84,0.016,85,0.016,86,0.016,87,0.016,88,0.016,89,0.016,90,0.016,91,0.016,92,0.016,93,0.02,94,0.02,95,0.016,96,0.016,97,0.016,98,0.016,99,0.016,100,0.016,101,0.016,102,0.016,103,0.016,104,0.016]],["t/1",[10,0.011,11,0.011]],["b/1",[1,0.054,2,0.102,3,0.042,4,0.015,5,0.015,6,0.015,7,0.015,8,0.014,9,0.014,10,0.022,11,0.019,12,0.019,13,0.015,14,0.014,15,0.014,16,0.014,17,0.014,18,0.014,19,0.054,20,0.014,21,0.014,22,0.015,23,0.014,24,0.06,25,0.06,26,0.014,27,0.014,28,0.014,29,0.015,30,0.014,31,0.014,32,0.015,33,0.014,34,0.015,35,0.014,36,0.054,37,0.014,38,0.014,39,0.014,40,0.019,41,0.015,42,0.014,43,0.014,44,0.014,45,0.014,46,0.015,47,0.014,48,0.014,49,0.014,50,0.014,51,0.014,52,0.014,53,0.014,54,0.019,55,0.014,56,0.021,57,0.014,58,0.019,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.014,67,0.014,68,0.014,69,0.023,70,0.021,71,0.015,72,0.014,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.021,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,105,1.521,106,3.959,107,3.348,108,3.959,109,3.959,110,3.959,111,0.952,112,3.959,113,2.404,114,3.348,115,3.348,116,1.883,117,2.204,118,1.521,119,3.959,120,0.677,121,3.959,122,0.773,123,0.278,124,0.666,125,0.83,126,0.568,127,0.83,128,0.718]],["t/2",[12,0.014]],["b/2",[1,0.054,2,0.102,3,0.042,4,0.015,5,0.015,6,0.015,7,0.015,8,0.014,9,0.014,10,0.021,11,0.019,12,0.021,13,0.015,14,0.014,15,0.014,16,0.014,17,0.014,18,0.014,19,0.054,20,0.014,21,0.014,22,0.015,23,0.014,24,0.06,25,0.06,26,0.014,27,0.014,28,0.014,29,0.015,30,0.014,31,0.014,32,0.015,33,0.014,34,0.015,35,0.014,36,0.054,37,0.014,38,0.014,39,0.014,40,0.019,41,0.015,42,0.014,43,0.014,44,0.014,45,0.014,46,0.015,47,0.014,48,0.014,49,0.014,50,0.014,51,0.014,52,0.014,53,0.014,54,0.019,55,0.014,56,0.021,57,0.014,58,0.019,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.014,67,0.014,68,0.014,69,0.023,70,0.015,71,0.015,72,0.014,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.022,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,105,1.985,107,3.385,113,2.431,120,0.528,122,0.781,123,0.281,124,0.674,125,0.839,126,0.575,127,0.839,128,0.726,129,4.004,130,4.004,131,4.004,132,4.004,133,2.229,134,4.004,135,2.431,136,2.431,137,2.431]],["t/3",[8,0.014]],["b/3",[1,0.055,2,0.103,3,0.043,4,0.015,5,0.015,6,0.015,7,0.015,8,0.019,9,0.015,10,0.019,11,0.015,12,0.015,13,0.015,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.055,20,0.015,21,0.015,22,0.015,23,0.015,24,0.061,25,0.061,26,0.015,27,0.015,28,0.015,29,0.015,30,0.015,31,0.015,32,0.015,33,0.015,34,0.015,35,0.015,36,0.055,37,0.015,38,0.015,39,0.015,40,0.019,41,0.015,42,0.015,43,0.015,44,0.015,45,0.015,46,0.015,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.021,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.023,70,0.015,71,0.015,72,0.015,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,105,1.574,120,0.54,122,0.799,123,0.288,124,0.689,125,0.858,126,0.588,127,0.858,128,0.743,138,4.096,139,4.096,140,4.096,141,4.096]],["t/4",[9,0.014]],["b/4",[1,0.055,2,0.097,3,0.043,4,0.015,5,0.015,6,0.015,7,0.015,8,0.015,9,0.021,10,0.019,11,0.015,12,0.015,13,0.015,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.055,20,0.015,21,0.015,22,0.015,23,0.015,24,0.06,25,0.06,26,0.015,27,0.015,28,0.015,29,0.015,30,0.015,31,0.015,32,0.015,33,0.015,34,0.015,35,0.015,36,0.055,37,0.015,38,0.015,39,0.015,40,0.019,41,0.015,42,0.015,43,0.015,44,0.015,45,0.015,46,0.015,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.021,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.023,70,0.015,71,0.015,72,0.015,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,120,0.537,122,1.019,123,0.286,124,0.685,125,0.853,126,0.585,127,0.853,128,0.739,142,2.091,143,2.72,144,2.72,145,3.444,146,2.091,147,1.565,148,2.473]],["t/5",[42,0.014]],["b/5",[1,0.063,2,0.11,3,0.031,4,0.011,5,0.011,6,0.018,7,0.011,8,0.011,9,0.011,10,0.015,11,0.011,12,0.011,13,0.011,14,0.011,15,0.011,16,0.011,17,0.011,18,0.011,19,0.045,20,0.011,21,0.011,22,0.011,23,0.011,24,0.052,25,0.052,26,0.011,27,0.011,28,0.011,29,0.011,30,0.011,31,0.011,32,0.011,33,0.011,34,0.011,35,0.011,36,0.045,37,0.011,38,0.011,39,0.011,40,0.015,41,0.011,42,0.024,43,0.015,44,0.011,45,0.011,46,0.011,47,0.011,48,0.011,49,0.011,50,0.011,51,0.015,52,0.011,53,0.011,54,0.015,55,0.011,56,0.015,57,0.011,58,0.015,59,0.011,60,0.011,61,0.011,62,0.011,63,0.011,64,0.011,65,0.011,66,0.011,67,0.011,68,0.011,69,0.018,70,0.011,71,0.011,72,0.018,73,0.016,74,0.016,82,0.011,83,0.016,84,0.011,85,0.011,86,0.011,87,0.011,88,0.011,89,0.011,90,0.011,91,0.011,92,0.011,93,0.018,94,0.018,95,0.011,96,0.011,97,0.011,98,0.011,99,0.011,100,0.011,101,0.011,102,0.011,103,0.011,104,0.011,111,0.721,113,1.821,118,1.895,123,0.425,124,0.504,126,0.43,137,1.821,143,2.002,144,2.002,149,2.998,150,2.998,151,2.532,152,2.002,153,6.281,154,6.045,155,1.669,156,2.181,157,1.632,158,2.002,159,1.877,160,2.998,161,2.998,162,2.002,163,1.669,164,2.998,165,2.002,166,4.931,167,2.998,168,4.247,169,4.931,170,4.17,171,2.998,172,2.998,173,1.539,174,2.579,175,2.579,176,2.23,177,1.877,178,2.998,179,2.535,180,2.998,181,2.998,182,2.998,183,2.998,184,2.998,185,2.998,186,2.532,187,3.159,188,2.998,189,4.931,190,4.931,191,2.535,192,1.632,193,2.998,194,2.23,195,1.669,196,2.535,197,2.998,198,2.535,199,2.002,200,3.591,201,2.998,202,2.535,203,2.998,204,1.749,205,1.669,206,1.235,207,1.669,208,2.535,209,2.364,210,2.23,211,2.02,212,2.998,213,2.364,214,2.002,215,3.159,216,3.159,217,2.998,218,1.821,219,2.998,220,2.535,221,2.535,222,2.998,223,2.23,224,2.998,225,1.325,226,1.334,227,2.23,228,1.426,229,2.002,230,2.002,231,1.821,232,1.821,233,2.836,234,1.235,235,2.535,236,2.998,237,2.23,238,4.247,239,2.998,240,2.998,241,1.076,242,1.669,243,1.325,244,1.669,245,1.669,246,1.152,247,2.535,248,1.539,249,2.998,250,2.535,251,1.539,252,2.002,253,3.668,254,2.998,255,1.235,256,2.998,257,1.821,258,1.669,259,2.998,260,1.076]],["t/6",[40,0.011,43,0.011]],["b/6",[1,0.054,2,0.102,3,0.042,4,0.015,5,0.015,6,0.015,7,0.015,8,0.015,9,0.015,10,0.019,11,0.015,12,0.015,13,0.015,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.054,20,0.015,21,0.015,22,0.015,23,0.015,24,0.06,25,0.06,26,0.015,27,0.015,28,0.015,29,0.015,30,0.015,31,0.015,32,0.015,33,0.015,34,0.015,35,0.015,36,0.054,37,0.015,38,0.015,39,0.015,40,0.022,41,0.015,42,0.015,43,0.019,44,0.015,45,0.015,46,0.015,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.021,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.023,70,0.019,71,0.015,72,0.015,73,0.019,74,0.021,82,0.015,83,0.015,84,0.019,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,105,1.553,120,0.533,122,0.789,123,0.284,124,0.68,125,0.847,126,0.58,127,0.847,128,0.733,177,1.786,195,2.25,261,3.417,262,4.042,263,4.042,264,4.042,265,4.042]],["t/7",[30,0.011,31,0.011]],["b/7",[1,0.058,2,0.1,3,0.03,4,0.011,5,0.011,6,0.011,7,0.011,8,0.01,9,0.01,10,0.015,11,0.01,12,0.01,13,0.011,14,0.01,15,0.01,16,0.01,17,0.01,18,0.01,19,0.043,20,0.01,21,0.01,22,0.011,23,0.01,24,0.05,25,0.05,26,0.01,27,0.01,28,0.01,29,0.011,30,0.024,31,0.023,32,0.011,33,0.01,34,0.011,35,0.01,36,0.043,37,0.015,38,0.01,39,0.01,40,0.015,41,0.011,42,0.01,43,0.01,44,0.01,45,0.01,46,0.011,47,0.01,48,0.01,49,0.01,50,0.01,51,0.01,52,0.015,53,0.01,54,0.015,55,0.01,56,0.015,57,0.01,58,0.015,59,0.01,60,0.01,61,0.01,62,0.01,63,0.01,64,0.01,65,0.01,66,0.01,67,0.01,68,0.01,69,0.018,70,0.011,71,0.011,72,0.019,73,0.015,74,0.015,78,1.578,82,0.011,83,0.011,84,0.011,85,0.011,86,0.011,87,0.011,88,0.011,89,0.011,90,0.011,91,0.011,92,0.011,93,0.015,94,0.015,95,0.011,96,0.011,97,0.015,98,0.011,99,0.011,100,0.011,101,0.011,102,0.011,103,0.011,104,0.011,116,2.274,120,0.538,123,0.287,156,1.456,157,1.568,159,1.804,192,1.568,211,1.348,221,2.397,225,1.253,241,1.717,246,2.382,248,1.456,258,3.451,266,1.968,267,2.397,268,2.272,269,2.835,270,1.893,271,2.397,272,1.893,273,2.109,274,1.253,275,3.035,276,1.721,277,4.08,278,5.771,279,6.086,280,4.042,281,4.08,282,4.685,283,4.08,284,2.835,285,2.397,286,4.08,287,2.109,288,2.109,289,4.08,290,2.397,291,2.835,292,1.348,293,1.721,294,3.89,295,5.229,296,4.78,297,4.422,298,1.253,299,2.835,300,2.835,301,2.109,302,5.541,303,5.541,304,5.229,305,4.78,306,2.109,307,2.397,308,2.397,309,2.835,310,2.397,311,4.78,312,4.422,313,4.08,314,3.493,315,3.493,316,4.422,317,2.835,318,2.109,319,1.578,320,1.348,321,3.45,322,2.397,323,2.835,324,4.042,325,2.397,326,1.253,327,2.835,328,4.08,329,4.08,330,2.397,331,3.45,332,4.08,333,4.08,334,2.835,335,2.835,336,2.835,337,2.109,338,2.835,339,1.456,340,2.835,341,2.397,342,2.397,343,2.835,344,3.193,345,4.78,346,4.78,347,2.835,348,2.835,349,2.835,350,2.835,351,2.397,352,2.109,353,2.835,354,1.089,355,2.397,356,2.397,357,2.835,358,2.835,359,1.893]],["t/8",[33,0.014]],["b/8",[1,0.061,2,0.098,3,0.017,4,0.006,5,0.006,6,0.014,7,0.006,8,0.006,9,0.006,10,0.012,11,0.006,12,0.006,13,0.006,14,0.006,15,0.006,16,0.006,17,0.006,18,0.006,19,0.028,20,0.006,21,0.006,22,0.006,23,0.006,24,0.035,25,0.041,26,0.014,27,0.006,28,0.006,29,0.006,30,0.006,31,0.006,32,0.017,33,0.023,34,0.006,35,0.006,36,0.035,37,0.006,38,0.006,39,0.015,40,0.012,41,0.006,42,0.006,43,0.006,44,0.006,45,0.006,46,0.006,47,0.006,48,0.006,49,0.006,50,0.006,51,0.006,52,0.01,53,0.006,54,0.01,55,0.006,56,0.01,57,0.015,58,0.01,59,0.006,60,0.006,61,0.006,62,0.006,63,0.006,64,0.006,65,0.006,66,0.006,67,0.006,68,0.006,69,0.012,70,0.006,71,0.006,72,0.01,73,0.012,74,0.01,78,0.893,79,1.956,82,0.006,83,0.006,84,0.006,85,0.017,86,0.01,87,0.006,88,0.006,89,0.006,90,0.012,91,0.006,92,0.006,93,0.017,94,0.012,95,0.006,96,0.006,97,0.014,98,0.006,99,0.012,100,0.006,101,0.006,102,0.012,103,0.006,104,0.006,111,1.214,113,0.974,114,1.357,117,1.464,118,1.01,120,0.212,122,0.313,123,0.423,126,0.23,133,1.464,137,0.974,142,2.189,146,0.824,147,1.638,155,0.893,158,1.072,159,2.024,177,1.708,186,0.824,192,1.858,202,1.357,204,2.315,205,1.86,207,0.893,209,3.185,214,1.072,215,1.193,216,1.193,218,0.974,225,0.709,226,0.826,227,1.956,228,2.028,229,1.756,234,1.375,235,1.357,237,1.956,241,1.199,243,1.162,244,0.893,246,0.617,247,1.357,251,0.824,252,1.072,255,1.083,258,2.993,260,0.576,261,1.357,266,0.661,276,0.974,293,1.597,301,1.956,310,1.357,314,1.072,319,1.86,320,1.251,325,1.357,326,1.708,330,2.223,331,1.357,337,1.193,339,0.824,344,2.231,351,2.223,352,2.485,356,2.223,360,1.193,361,1.604,362,1.604,363,2.58,364,1.199,365,1.072,366,1.956,367,1.357,368,3.171,369,3.371,370,4.835,371,1.604,372,0.893,373,2.373,374,1.357,375,1.604,376,1.604,377,1.604,378,5.377,379,2.223,380,4.298,381,2.483,382,3.678,383,1.604,384,1.597,385,1.604,386,3.171,387,1.604,388,1.464,389,3.872,390,3.34,391,4.579,392,3.34,393,2.223,394,3.863,395,2.629,396,1.35,397,3.266,398,3.34,399,2.873,400,1.597,401,1.604,402,4.657,403,1.597,404,1.357,405,3.266,406,1.604,407,1.604,408,1.357,409,1.604,410,3.266,411,1.193,412,2.825,413,1.708,414,1.357,415,4.835,416,1.604,417,1.072,418,1.604,419,2.485,420,1.604,421,1.604,422,1.644,423,1.357,424,2.223,425,2.223,426,1.604,427,2.629,428,2.58,429,3.171,430,0.974,431,1.956,432,1.357,433,1.604,434,2.223,435,1.604,436,3.605,437,4.263,438,1.604,439,2.223,440,1.357,441,1.604,442,1.357,443,1.357,444,1.604,445,1.604,446,2.223,447,1.604,448,3.34,449,1.604,450,3.266,451,3.34,452,1.604,453,1.357,454,3.34,455,2.629,456,3.266,457,1.756,458,1.604,459,2.189,460,1.604,461,2.589,462,3.266,463,1.464,464,4.579,465,1.604,466,1.357,467,1.715,468,1.193,469,1.357,470,4.753,471,2.629,472,1.604,473,2.223,474,1.604,475,1.604,476,0.974,477,1.357,478,1.604,479,1.193,480,1.604,481,1.072,482,2.223,483,1.604,484,1.604,485,5.507,486,2.58,487,1.604,488,1.604,489,1.604,490,2.629,491,1.604,492,1.604,493,1.072,494,1.604,495,1.715,496,1.956,497,0.763,498,1.604,499,1.357,500,0.893,501,1.604,502,1.604,503,1.357,504,1.604,505,1.604,506,1.251,507,1.604,508,1.072,509,1.604,510,1.357,511,1.604,512,2.629,513,1.604,514,1.604,515,1.604,516,1.357,517,1.357,518,1.604,519,1.604,520,3.605,521,1.357,522,1.956,523,1.604,524,2.629,525,1.756,526,0.824,527,1.597,528,2.629,529,1.357,530,1.604,531,2.629,532,1.604,533,3.863,534,3.266,535,1.604,536,0.893,537,1.357,538,1.072,539,2.223,540,1.956,541,2.223,542,1.756,543,1.604,544,1.604,545,1.357,546,2.223,547,2.629,548,1.604,549,0.974,550,1.357,551,2.629,552,3.406,553,1.193,554,1.604,555,3.863,556,1.072,557,3.863,558,3.34,559,1.604,560,1.604,561,0.763,562,1.357,563,1.357,564,1.604,565,1.193,566,1.357,567,1.604,568,1.956,569,2.223,570,1.604,571,1.604,572,1.604,573,1.357,574,1.604,575,1.604,576,1.604,577,2.825,578,1.604,579,1.604,580,1.604,581,1.604,582,1.956,583,2.629,584,1.756,585,1.604,586,1.357,587,1.604,588,1.604,589,1.604,590,2.629,591,1.604,592,1.715,593,0.974,594,2.629,595,1.604,596,0.974,597,0.893,598,1.193,599,1.604,600,0.974,601,1.604,602,1.604,603,0.893,604,1.604,605,1.604,606,1.072,607,2.223,608,1.357,609,1.604,610,0.974,611,1.604,612,2.223,613,1.193,614,1.604,615,2.629,616,1.604,617,2.629,618,3.863,619,1.072,620,1.604,621,1.604,622,2.629,623,1.604,624,0.974,625,1.604]],["t/9",[44,0.014]],["b/9",[1,0.067,2,0.097,3,0.043,4,0.015,5,0.015,6,0.015,7,0.015,8,0.015,9,0.015,10,0.019,11,0.015,12,0.015,13,0.015,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.055,20,0.015,21,0.015,22,0.015,23,0.015,24,0.06,25,0.06,26,0.015,27,0.015,28,0.015,29,0.015,30,0.015,31,0.015,32,0.015,33,0.015,34,0.015,35,0.015,36,0.055,37,0.015,38,0.015,39,0.015,40,0.019,41,0.015,42,0.015,43,0.015,44,0.021,45,0.015,46,0.015,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.019,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.021,65,0.015,66,0.015,67,0.015,68,0.015,69,0.021,70,0.015,71,0.015,72,0.015,73,0.019,74,0.019,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.015,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.015,97,0.019,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,186,2.675,290,3.43,354,1.559,372,2.259,461,2.464,526,2.675,626,4.056,627,2.71,628,2.259,629,3.018,630,2.259,631,4.057,632,4.057,633,4.057]],["t/10",[45,0.014]],["b/10",[1,0.066,2,0.097,3,0.05,4,0.013,5,0.013,6,0.013,7,0.013,8,0.013,9,0.013,10,0.017,11,0.013,12,0.013,13,0.013,14,0.013,15,0.013,16,0.013,17,0.013,18,0.013,19,0.05,20,0.013,21,0.013,22,0.013,23,0.013,24,0.056,25,0.056,26,0.013,27,0.013,28,0.013,29,0.013,30,0.013,31,0.013,32,0.013,33,0.013,34,0.013,35,0.013,36,0.05,37,0.013,38,0.013,39,0.013,40,0.017,41,0.013,42,0.013,43,0.013,44,0.013,45,0.024,46,0.013,47,0.013,48,0.013,49,0.013,50,0.013,51,0.013,52,0.017,53,0.013,54,0.017,55,0.013,56,0.017,57,0.013,58,0.017,59,0.013,60,0.013,61,0.013,62,0.013,63,0.013,64,0.013,65,0.013,66,0.013,67,0.013,68,0.013,69,0.02,70,0.013,71,0.013,72,0.013,73,0.018,74,0.018,78,1.961,82,0.013,83,0.013,84,0.013,85,0.013,86,0.013,87,0.013,88,0.013,89,0.018,90,0.013,91,0.013,92,0.013,93,0.018,94,0.018,95,0.013,96,0.013,97,0.013,98,0.013,99,0.02,100,0.013,101,0.013,102,0.013,103,0.013,104,0.013,111,0.847,151,2.437,156,1.808,163,1.961,186,1.808,199,2.352,204,1.45,211,1.675,225,1.557,226,1.491,228,1.675,230,2.352,232,2.139,233,2.352,244,1.961,246,2.063,260,1.265,268,1.961,324,2.978,339,1.808,344,2.352,373,2.643,399,2.62,461,2.882,467,1.808,566,2.978,597,1.961,600,2.139,619,2.352,628,1.961,634,3.522,635,4.747,636,4.747,637,1.961,638,5.369,639,2.62,640,2.978,641,5.998,642,3.522,643,3.522,644,3.994,645,2.978,646,2.352,647,3.522,648,4.747,649,5.072,650,4.54,651,2.62,652,2.62,653,2.139,654,1.961,655,3.522,656,2.978,657,4.014,658,3.522,659,2.978,660,2.437,661,3.522,662,3.522,663,3.522,664,2.62,665,3.522,666,3.522]],["t/11",[24,0.033,25,0.033]],["b/11",[1,0.054,2,0.108,3,0.041,4,0.015,5,0.015,6,0.015,7,0.015,8,0.014,9,0.014,10,0.019,11,0.014,12,0.014,13,0.015,14,0.014,15,0.014,16,0.014,17,0.014,18,0.014,19,0.054,20,0.014,21,0.014,22,0.015,23,0.014,24,0.065,25,0.065,26,0.014,27,0.019,28,0.019,29,0.015,30,0.014,31,0.014,32,0.015,33,0.014,34,0.015,35,0.014,36,0.054,37,0.014,38,0.014,39,0.014,40,0.019,41,0.015,42,0.014,43,0.014,44,0.014,45,0.014,46,0.015,47,0.014,48,0.014,49,0.014,50,0.014,51,0.014,52,0.014,53,0.014,54,0.019,55,0.014,56,0.021,57,0.014,58,0.019,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.014,67,0.014,68,0.014,69,0.023,70,0.015,71,0.015,72,0.014,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.024,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,105,1.519,120,0.676,122,0.771,123,0.278,124,0.665,125,0.828,126,0.567,127,0.828,128,0.717,173,2.029,199,2.639,211,1.88,404,3.342,422,1.419,667,5.122,668,3.342,669,3.952,670,3.952,671,3.952,672,3.952]],["t/12",[24,0.027,25,0.027,26,0.009]],["b/12",[1,0.055,2,0.103,3,0.043,4,0.015,5,0.015,6,0.015,7,0.015,8,0.015,9,0.015,10,0.019,11,0.015,12,0.015,13,0.015,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.055,20,0.015,21,0.015,22,0.015,23,0.015,24,0.064,25,0.064,26,0.019,27,0.015,28,0.015,29,0.015,30,0.015,31,0.015,32,0.015,33,0.015,34,0.015,35,0.015,36,0.055,37,0.015,38,0.015,39,0.015,40,0.019,41,0.015,42,0.015,43,0.015,44,0.015,45,0.015,46,0.015,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.021,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.023,70,0.015,71,0.015,72,0.015,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,105,1.574,120,0.54,122,0.799,123,0.288,124,0.689,125,0.858,126,0.588,127,0.858,128,0.743,673,4.096,674,4.096]],["t/13",[27,0.011,28,0.011]],["b/13",[1,0.055,2,0.103,3,0.043,4,0.015,5,0.015,6,0.015,7,0.015,8,0.015,9,0.015,10,0.019,11,0.015,12,0.015,13,0.015,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.055,20,0.015,21,0.015,22,0.015,23,0.015,24,0.061,25,0.061,26,0.015,27,0.019,28,0.019,29,0.015,30,0.015,31,0.015,32,0.015,33,0.015,34,0.015,35,0.015,36,0.055,37,0.015,38,0.015,39,0.015,40,0.019,41,0.015,42,0.015,43,0.015,44,0.015,45,0.015,46,0.015,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.021,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.023,70,0.015,71,0.015,72,0.015,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,105,1.574,120,0.54,122,0.799,123,0.288,124,0.689,125,0.858,126,0.588,127,0.858,128,0.743,675,4.096,676,4.096,677,4.096]],["t/14",[14,0.011,15,0.011]],["b/14",[1,0.055,2,0.103,3,0.043,4,0.015,5,0.015,6,0.015,7,0.015,8,0.015,9,0.015,10,0.019,11,0.015,12,0.015,13,0.015,14,0.021,15,0.021,16,0.015,17,0.015,18,0.015,19,0.055,20,0.015,21,0.015,22,0.015,23,0.015,24,0.061,25,0.061,26,0.015,27,0.015,28,0.015,29,0.015,30,0.015,31,0.015,32,0.015,33,0.015,34,0.015,35,0.015,36,0.055,37,0.015,38,0.015,39,0.015,40,0.019,41,0.015,42,0.015,43,0.015,44,0.015,45,0.015,46,0.015,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.019,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.022,70,0.02,71,0.015,72,0.015,73,0.02,74,0.02,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.015,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.02,94,0.02,95,0.015,96,0.015,97,0.015,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,151,2.708,257,2.511,260,1.485,320,1.967,678,1.828,679,2.762,680,4.135,681,4.135,682,4.135,683,4.135,684,4.135]],["t/15",[16,0.014]],["b/15",[1,0.059,2,0.105,3,0.041,4,0.015,5,0.015,6,0.015,7,0.015,8,0.014,9,0.014,10,0.018,11,0.014,12,0.014,13,0.015,14,0.014,15,0.014,16,0.022,17,0.014,18,0.014,19,0.053,20,0.014,21,0.014,22,0.015,23,0.014,24,0.059,25,0.059,26,0.014,27,0.014,28,0.014,29,0.015,30,0.014,31,0.014,32,0.015,33,0.014,34,0.015,35,0.014,36,0.053,37,0.014,38,0.014,39,0.014,40,0.018,41,0.015,42,0.014,43,0.014,44,0.014,45,0.014,46,0.015,47,0.014,48,0.014,49,0.014,50,0.014,51,0.014,52,0.018,53,0.014,54,0.018,55,0.014,56,0.021,57,0.014,58,0.018,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.014,67,0.014,68,0.014,69,0.023,70,0.015,71,0.015,72,0.014,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.021,86,0.015,87,0.019,88,0.019,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,120,0.518,122,0.765,123,0.276,124,0.66,125,0.822,126,0.563,127,0.822,128,0.712,142,2.014,241,1.409,255,1.615,288,2.918,326,1.734,363,2.62,364,1.409,413,1.734,653,3.096,685,1.317,686,2.918,687,2.382,688,2.918,689,2.918,690,3.923,691,3.923,692,2.62,693,3.923]],["t/16",[17,0.014]],["b/16",[1,0.055,2,0.111,3,0.036,4,0.013,5,0.013,6,0.013,7,0.013,8,0.012,9,0.012,10,0.017,11,0.017,12,0.012,13,0.013,14,0.012,15,0.012,16,0.017,17,0.023,18,0.017,19,0.049,20,0.012,21,0.012,22,0.017,23,0.012,24,0.055,25,0.055,26,0.012,27,0.012,28,0.017,29,0.013,30,0.012,31,0.012,32,0.013,33,0.012,34,0.013,35,0.012,36,0.049,37,0.012,38,0.012,39,0.012,40,0.017,41,0.013,42,0.012,43,0.012,44,0.012,45,0.017,46,0.013,47,0.012,48,0.012,49,0.012,50,0.012,51,0.012,52,0.012,53,0.012,54,0.017,55,0.012,56,0.017,57,0.012,58,0.017,59,0.012,60,0.012,61,0.012,62,0.012,63,0.012,64,0.012,65,0.012,66,0.012,67,0.012,68,0.012,69,0.02,70,0.013,71,0.013,72,0.021,73,0.017,74,0.02,82,0.013,83,0.013,84,0.013,85,0.013,86,0.013,87,0.013,88,0.013,89,0.013,90,0.013,91,0.013,92,0.013,93,0.017,94,0.017,95,0.013,96,0.013,97,0.013,98,0.013,99,0.013,100,0.013,101,0.013,102,0.013,103,0.013,104,0.013,111,0.819,116,1.619,122,0.664,125,0.713,136,2.067,151,2.382,157,2.278,170,2.878,175,2.067,176,2.532,177,1.505,211,2.206,225,2.05,231,2.067,234,1.402,242,1.895,248,1.748,260,1.222,322,2.878,381,1.748,382,3.524,428,2.274,442,2.878,446,2.878,500,1.895,526,2.382,613,2.532,637,1.895,660,1.748,678,1.505,685,1.903,694,2.878,695,2.532,696,5.929,697,3.922,698,5.276,699,3.404,700,3.922,701,3.404,702,3.404,703,4.462,704,3.404,705,4.639,706,2.817,707,3.922,708,2.067,709,2.067,710,3.404,711,3.404,712,3.404,713,3.404,714,3.404,715,3.404,716,4.639,717,2.532,718,4.639,719,3.404,720,3.404,721,3.404,722,4.639,723,3.404,724,3.404,725,3.404,726,3.404,727,4.639,728,4.639,729,3.404,730,2.878,731,2.532,732,3.404,733,3.404,734,2.878,735,3.404]],["t/17",[18,0.011,19,0.033]],["b/17",[1,0.05,2,0.101,3,0.037,4,0.013,5,0.013,6,0.013,7,0.013,8,0.013,9,0.013,10,0.017,11,0.013,12,0.013,13,0.013,14,0.017,15,0.013,16,0.013,17,0.013,18,0.024,19,0.069,20,0.013,21,0.013,22,0.013,23,0.013,24,0.056,25,0.056,26,0.013,27,0.013,28,0.013,29,0.013,30,0.013,31,0.013,32,0.013,33,0.013,34,0.013,35,0.013,36,0.056,37,0.013,38,0.017,39,0.017,40,0.017,41,0.013,42,0.013,43,0.013,44,0.013,45,0.013,46,0.013,47,0.013,48,0.013,49,0.013,50,0.013,51,0.013,52,0.013,53,0.013,54,0.017,55,0.013,56,0.017,57,0.013,58,0.017,59,0.013,60,0.013,61,0.013,62,0.013,63,0.013,64,0.013,65,0.013,66,0.013,67,0.013,68,0.013,69,0.02,70,0.013,71,0.013,72,0.013,73,0.018,74,0.018,82,0.013,83,0.013,84,0.013,85,0.013,86,0.018,87,0.013,88,0.013,89,0.013,90,0.018,91,0.013,92,0.013,93,0.021,94,0.018,95,0.013,96,0.013,97,0.018,98,0.013,99,0.013,100,0.013,101,0.013,102,0.013,103,0.013,104,0.013,105,1.345,111,1.379,123,0.332,135,2.87,136,2.125,146,1.797,155,1.948,157,1.345,159,1.547,177,1.547,195,3.19,226,1.099,234,1.946,241,1.697,243,1.547,244,1.948,266,1.441,273,2.603,274,1.547,298,1.547,359,2.337,364,1.256,413,1.547,463,1.948,467,1.797,495,1.797,596,2.125,685,1.175,686,3.515,687,3.249,708,2.125,709,2.125,736,2.603,737,2.959,738,2.87,739,2.959,740,3.499,741,2.603,742,3.499,743,2.603,744,3.499,745,2.959,746,2.959,747,3.515,748,3.499,749,2.337,750,2.959,751,2.125,752,2.959,753,2.603,754,2.125,755,2.603,756,1.664,757,2.125,758,2.959,759,2.337,760,3.499,761,3.499,762,3.499,763,3.499,764,3.499,765,2.959,766,3.499,767,2.125,768,3.996,769,2.959,770,2.959,771,3.499,772,2.959]],["t/18",[19,0.033,20,0.011]],["b/18",[1,0.048,2,0.101,3,0.035,4,0.008,5,0.008,6,0.008,7,0.008,8,0.008,9,0.008,10,0.012,11,0.008,12,0.008,13,0.008,14,0.008,15,0.008,16,0.019,17,0.008,18,0.02,19,0.07,20,0.024,21,0.008,22,0.008,23,0.008,24,0.042,25,0.042,26,0.008,27,0.008,28,0.008,29,0.008,30,0.008,31,0.008,32,0.008,33,0.008,34,0.008,35,0.008,36,0.035,37,0.02,38,0.008,39,0.008,40,0.012,41,0.008,42,0.008,43,0.008,44,0.008,45,0.008,46,0.008,47,0.008,48,0.008,49,0.008,50,0.008,51,0.008,52,0.008,53,0.008,54,0.012,55,0.008,56,0.015,57,0.008,58,0.012,59,0.008,60,0.008,61,0.008,62,0.008,63,0.008,64,0.008,65,0.008,66,0.008,67,0.008,68,0.008,69,0.018,70,0.008,71,0.008,72,0.021,73,0.012,74,0.015,78,1.189,82,0.008,83,0.008,84,0.008,85,0.012,86,0.012,87,0.012,88,0.008,89,0.008,90,0.012,91,0.008,92,0.015,93,0.012,94,0.012,95,0.008,96,0.012,97,0.017,98,0.008,99,0.008,100,0.008,101,0.008,102,0.008,103,0.008,104,0.008,111,0.794,117,3.324,120,0.282,123,0.412,124,0.359,125,0.447,126,0.307,127,0.447,128,0.387,142,1.096,146,1.096,157,0.82,163,2.247,187,2.456,192,1.269,195,2.247,205,1.189,206,2.023,207,1.838,211,1.57,216,1.588,226,1.632,229,1.426,234,1.36,241,1.631,242,1.189,245,1.189,246,1.746,257,1.297,266,0.879,270,1.426,272,1.426,274,1.784,276,1.297,282,2.792,285,1.805,297,3.413,298,0.944,339,1.096,341,3.841,354,0.82,359,1.426,360,1.588,363,1.426,364,1.631,365,1.426,366,1.588,369,3.034,381,1.096,414,1.805,422,0.767,430,1.297,450,2.792,463,1.189,470,1.805,477,2.792,495,1.096,496,2.456,506,1.016,536,1.838,537,1.805,538,1.426,539,1.805,540,1.588,582,3.003,592,1.695,596,1.297,607,1.805,619,1.426,630,1.189,653,1.297,654,1.838,678,0.944,685,2.156,686,3.379,687,2.758,708,1.297,736,1.588,737,1.805,738,3.808,739,1.805,743,1.588,746,1.805,747,3.003,749,2.205,750,1.805,751,1.297,752,1.805,753,1.588,754,1.297,755,1.588,756,2.161,757,1.297,758,1.805,759,1.426,765,4.391,767,1.297,769,1.805,770,4.579,772,1.805,773,2.205,774,1.426,775,2.135,776,4.912,777,2.135,778,2.135,779,2.135,780,4.912,781,2.135,782,3.302,783,2.135,784,2.135,785,4.037,786,2.205,787,5.594,788,4.153,789,1.588,790,3.302,791,3.302,792,2.135,793,2.135,794,2.135,795,2.135,796,1.805,797,2.792,798,2.205,799,3.302,800,3.302,801,2.135,802,2.135,803,2.135,804,2.135,805,2.135,806,2.135,807,2.135,808,3.302,809,3.302,810,2.135,811,2.135,812,3.302,813,2.135,814,2.135,815,4.543,816,2.135,817,1.805,818,2.456,819,2.135,820,2.135,821,1.297,822,2.135,823,3.302,824,2.135,825,2.135,826,3.302,827,2.135,828,3.003,829,5.193,830,2.456,831,2.135,832,4.543,833,2.135,834,1.805,835,1.805,836,1.805,837,2.135,838,2.135,839,2.135,840,2.135,841,2.135,842,2.135,843,3.302,844,4.037,845,3.302,846,2.135,847,2.135,848,2.135,849,3.302,850,2.135,851,2.135,852,2.135,853,2.135,854,2.135,855,2.135,856,5.193,857,2.135,858,2.135,859,1.588,860,1.805,861,1.426,862,2.456,863,2.135,864,3.302,865,2.135,866,1.805,867,1.805,868,2.135,869,2.135,870,4.037,871,2.135,872,1.588,873,4.912,874,2.135,875,2.135,876,1.805,877,1.426,878,2.135,879,2.135,880,2.135,881,1.588,882,2.135,883,2.135,884,2.135,885,2.135,886,2.135,887,1.805,888,2.135,889,1.805,890,2.135,891,1.426]],["t/19",[21,0.014]],["b/19",[1,0.053,2,0.111,3,0.028,4,0.01,5,0.01,6,0.01,7,0.01,8,0.01,9,0.01,10,0.014,11,0.01,12,0.01,13,0.01,14,0.01,15,0.01,16,0.017,17,0.01,18,0.014,19,0.041,20,0.01,21,0.024,22,0.01,23,0.014,24,0.049,25,0.049,26,0.01,27,0.01,28,0.01,29,0.01,30,0.01,31,0.01,32,0.01,33,0.01,34,0.01,35,0.01,36,0.049,37,0.01,38,0.01,39,0.01,40,0.014,41,0.01,42,0.01,43,0.01,44,0.01,45,0.01,46,0.01,47,0.01,48,0.01,49,0.01,50,0.01,51,0.014,52,0.02,53,0.01,54,0.014,55,0.01,56,0.014,57,0.01,58,0.014,59,0.01,60,0.01,61,0.01,62,0.01,63,0.01,64,0.01,65,0.01,66,0.01,67,0.01,68,0.01,69,0.017,70,0.01,71,0.01,72,0.02,73,0.015,74,0.015,82,0.01,83,0.01,84,0.01,85,0.01,86,0.01,87,0.01,88,0.01,89,0.01,90,0.01,91,0.01,92,0.01,93,0.015,94,0.015,95,0.01,96,0.015,97,0.01,98,0.01,99,0.01,100,0.01,101,0.01,102,0.01,103,0.01,104,0.01,111,0.644,117,2.18,118,1.029,123,0.325,135,1.627,143,2.615,144,2.615,147,1.029,155,1.491,156,1.375,157,1.957,165,1.789,177,1.184,179,2.265,186,1.375,192,1.505,198,3.311,205,1.491,214,1.789,225,1.731,231,1.627,232,1.627,234,1.103,246,1.029,251,1.375,253,2.913,266,1.612,268,2.18,274,2.251,298,2.046,301,1.992,308,2.265,314,2.615,339,2.011,354,1.029,355,2.265,359,1.789,364,0.962,373,1.491,379,2.265,384,1.627,400,1.627,403,1.627,411,1.992,419,2.913,497,1.274,506,1.274,538,1.789,540,1.992,549,1.627,584,1.789,592,1.375,597,1.491,600,2.378,610,1.627,628,1.491,639,1.992,644,2.913,646,1.789,654,1.491,678,1.184,685,1.819,694,2.265,706,2.378,708,1.627,717,2.913,741,1.992,749,1.789,751,1.627,756,1.863,759,2.615,789,1.992,797,3.914,821,1.627,828,1.992,836,2.265,866,4.785,877,1.789,892,4.629,893,2.265,894,1.992,895,2.265,896,1.992,897,2.679,898,2.679,899,2.679,900,4.942,901,3.916,902,5.092,903,2.679,904,2.679,905,2.679,906,2.679,907,2.679,908,2.679,909,5.092,910,4.629,911,2.679,912,2.265,913,2.679,914,2.679,915,1.992,916,2.679,917,3.916,918,2.679,919,2.679,920,2.679,921,2.679,922,5.092,923,2.679,924,2.679,925,2.679,926,2.679,927,2.679,928,2.679,929,2.265,930,2.913,931,2.265,932,5.092,933,2.265,934,2.679,935,2.265,936,1.789,937,2.679,938,2.679,939,2.679,940,2.679,941,2.679,942,2.679,943,2.679,944,3.311,945,2.265,946,2.679,947,2.679,948,2.265,949,2.679,950,2.679,951,2.265,952,2.679,953,2.679,954,2.265,955,2.679,956,1.992,957,3.311,958,2.679,959,3.916,960,2.679,961,2.679,962,3.916,963,2.679,964,2.679,965,2.679,966,3.916,967,2.679,968,2.679,969,2.679,970,2.679,971,2.679,972,2.679,973,2.679,974,2.265,975,2.265,976,2.679,977,2.679,978,2.679,979,3.916,980,2.679,981,4.629,982,2.265,983,3.916,984,2.679,985,2.679,986,2.679,987,2.679,988,2.679,989,2.679]],["t/20",[62,0.014]],["b/20",[1,0.05,2,0.098,3,0.038,4,0.013,5,0.013,6,0.013,7,0.013,8,0.013,9,0.013,10,0.017,11,0.013,12,0.013,13,0.013,14,0.013,15,0.013,16,0.013,17,0.013,18,0.013,19,0.05,20,0.013,21,0.013,22,0.013,23,0.013,24,0.061,25,0.061,26,0.013,27,0.013,28,0.013,29,0.013,30,0.013,31,0.013,32,0.013,33,0.013,34,0.013,35,0.013,36,0.05,37,0.013,38,0.013,39,0.013,40,0.017,41,0.013,42,0.013,43,0.013,44,0.013,45,0.013,46,0.013,47,0.013,48,0.013,49,0.013,50,0.013,51,0.013,52,0.017,53,0.013,54,0.017,55,0.013,56,0.02,57,0.013,58,0.017,59,0.013,60,0.013,61,0.013,62,0.023,63,0.013,64,0.017,65,0.013,66,0.013,67,0.013,68,0.013,69,0.022,70,0.013,71,0.013,72,0.013,73,0.018,74,0.02,82,0.013,83,0.018,84,0.013,85,0.018,86,0.013,87,0.018,88,0.018,89,0.013,90,0.013,91,0.013,92,0.013,93,0.018,94,0.018,95,0.013,96,0.018,97,0.02,98,0.013,99,0.013,100,0.013,101,0.013,102,0.013,103,0.013,104,0.013,120,0.475,123,0.381,124,0.606,125,1.009,126,0.517,127,0.754,128,0.653,146,1.848,155,2.004,157,1.851,177,2.129,196,3.043,206,1.482,228,1.712,241,1.292,243,1.591,251,2.473,255,1.482,257,2.185,292,1.712,293,2.185,339,1.848,413,1.591,422,1.292,443,3.043,506,1.712,584,2.404,610,2.925,626,2.404,629,2.677,637,2.004,747,2.677,798,2.404,835,3.043,990,2.185,991,2.677,992,2.677,993,1.848,994,3.599,995,2.677,996,3.599,997,3.599,998,3.599,999,3.599,1000,3.599,1001,3.599,1002,3.599,1003,3.599,1004,3.599,1005,3.599,1006,3.599,1007,3.599,1008,3.599,1009,3.599,1010,3.599,1011,4.816,1012,3.599,1013,3.599,1014,3.599,1015,3.599,1016,3.599,1017,3.043,1018,3.599,1019,2.404,1020,3.599,1021,3.599]],["t/21",[23,0.014]],["b/21",[1,0.064,2,0.105,3,0.035,4,0.012,5,0.012,6,0.012,7,0.012,8,0.012,9,0.012,10,0.016,11,0.012,12,0.012,13,0.012,14,0.012,15,0.012,16,0.012,17,0.012,18,0.012,19,0.048,20,0.012,21,0.012,22,0.021,23,0.025,24,0.054,25,0.054,26,0.012,27,0.012,28,0.012,29,0.012,30,0.012,31,0.012,32,0.012,33,0.016,34,0.012,35,0.012,36,0.054,37,0.012,38,0.012,39,0.016,40,0.016,41,0.012,42,0.012,43,0.012,44,0.012,45,0.012,46,0.012,47,0.012,48,0.012,49,0.012,50,0.012,51,0.012,52,0.021,53,0.012,54,0.016,55,0.016,56,0.019,57,0.012,58,0.016,59,0.012,60,0.012,61,0.016,62,0.012,63,0.012,64,0.012,65,0.012,66,0.012,67,0.012,68,0.012,69,0.022,70,0.012,71,0.012,72,0.012,73,0.017,74,0.019,76,2.2,82,0.012,83,0.012,84,0.012,85,0.012,86,0.012,87,0.017,88,0.012,89,0.012,90,0.012,91,0.012,92,0.012,93,0.017,94,0.017,95,0.012,96,0.017,97,0.019,98,0.012,99,0.012,100,0.012,101,0.012,102,0.012,103,0.012,104,0.012,120,0.435,122,0.643,123,0.231,124,0.554,125,0.69,126,0.473,127,0.69,128,0.597,146,1.691,147,1.266,155,1.834,157,1.266,192,1.743,206,1.356,207,1.834,209,1.834,218,2,228,2.157,243,2.005,246,1.266,248,1.691,268,1.834,274,2.293,280,2.785,320,2.157,354,1.266,364,1.183,380,3.029,396,2.329,402,2.785,430,2,457,2.2,473,2.785,541,2.785,556,2.2,597,1.834,627,2.2,637,2.525,644,3.374,652,2.45,653,2.754,654,1.834,678,1.456,685,1.106,689,2.45,695,2.45,706,2,756,1.567,818,2.45,821,2,861,2.2,931,2.785,936,2.2,975,3.835,993,2.329,1019,2.2,1022,3.294,1023,3.294,1024,5.187,1025,2.785,1026,3.294,1027,2.45,1028,3.294,1029,3.294,1030,2,1031,3.294,1032,3.294,1033,2.45,1034,3.294,1035,3.294,1036,2.785,1037,4.535,1038,2.785,1039,2.2,1040,3.294,1041,3.294,1042,3.294,1043,3.294,1044,2.785,1045,3.835,1046,2.785,1047,3.294,1048,3.294,1049,3.294,1050,2.785,1051,3.294,1052,2,1053,2.785]],["t/22",[63,0.011,64,0.011]],["b/22",[1,0.054,2,0.096,3,0.042,4,0.015,5,0.015,6,0.015,7,0.015,8,0.014,9,0.014,10,0.019,11,0.014,12,0.014,13,0.015,14,0.014,15,0.014,16,0.014,17,0.014,18,0.014,19,0.054,20,0.014,21,0.014,22,0.015,23,0.014,24,0.06,25,0.06,26,0.014,27,0.014,28,0.014,29,0.015,30,0.014,31,0.014,32,0.015,33,0.014,34,0.015,35,0.014,36,0.06,37,0.014,38,0.014,39,0.014,40,0.019,41,0.015,42,0.014,43,0.014,44,0.014,45,0.014,46,0.015,47,0.014,48,0.014,49,0.014,50,0.014,51,0.014,52,0.014,53,0.014,54,0.019,55,0.014,56,0.021,57,0.014,58,0.019,59,0.014,60,0.014,61,0.014,62,0.014,63,0.019,64,0.019,65,0.014,66,0.014,67,0.014,68,0.014,69,0.023,70,0.015,71,0.015,72,0.014,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.019,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,120,0.523,122,0.774,123,0.279,124,0.667,125,0.831,126,0.569,127,0.831,128,0.719,1054,3.967,1055,3.967,1056,3.967,1057,3.967,1058,3.967,1059,3.967,1060,3.967,1061,3.967,1062,3.354,1063,3.354,1064,5.135,1065,3.967,1066,3.967,1067,3.967,1068,3.967,1069,3.967,1070,3.967,1071,3.967,1072,3.967]],["t/23",[65,0.011,66,0.011]],["b/23",[1,0.05,2,0.101,3,0.037,4,0.013,5,0.013,6,0.02,7,0.013,8,0.013,9,0.013,10,0.017,11,0.013,12,0.013,13,0.013,14,0.013,15,0.017,16,0.013,17,0.013,18,0.017,19,0.05,20,0.013,21,0.013,22,0.013,23,0.013,24,0.056,25,0.056,26,0.013,27,0.013,28,0.013,29,0.013,30,0.013,31,0.013,32,0.013,33,0.017,34,0.018,35,0.013,36,0.06,37,0.013,38,0.013,39,0.013,40,0.017,41,0.013,42,0.013,43,0.013,44,0.013,45,0.017,46,0.013,47,0.013,48,0.013,49,0.013,50,0.013,51,0.013,52,0.013,53,0.013,54,0.017,55,0.013,56,0.019,57,0.013,58,0.017,59,0.013,60,0.013,61,0.013,62,0.013,63,0.013,64,0.013,65,0.017,66,0.017,67,0.013,68,0.013,69,0.022,70,0.013,71,0.013,72,0.013,73,0.018,74,0.02,82,0.013,83,0.013,84,0.013,85,0.013,86,0.013,87,0.018,88,0.013,89,0.013,90,0.013,91,0.013,92,0.013,93,0.018,94,0.018,95,0.013,96,0.018,97,0.02,98,0.013,99,0.013,100,0.013,101,0.013,102,0.013,103,0.013,104,0.013,120,0.623,122,0.683,123,0.332,124,0.589,125,0.733,126,0.502,127,0.733,128,0.635,135,2.125,207,1.948,242,1.948,243,1.547,246,1.345,251,1.797,274,1.547,288,2.603,298,1.547,315,3.156,316,2.959,352,2.603,380,2.337,384,2.87,422,1.256,497,1.664,506,1.664,536,1.948,549,2.125,582,2.603,627,2.337,654,1.948,685,1.175,687,2.125,707,2.959,788,3.996,956,2.603,990,2.125,1033,3.515,1073,3.499,1074,3.499,1075,3.499,1076,3.499,1077,3.499,1078,3.499,1079,5.985,1080,3.499,1081,3.499,1082,3.499,1083,2.959,1084,2.959,1085,3.499,1086,3.499,1087,3.499,1088,3.499,1089,3.499,1090,3.499,1091,3.499,1092,4.726,1093,3.499,1094,4.846,1095,3.499,1096,3.499,1097,2.959,1098,4.726,1099,3.499,1100,3.499,1101,2.959,1102,2.959,1103,3.499,1104,3.996,1105,3.499,1106,3.499,1107,3.499,1108,3.499,1109,3.499,1110,3.499]],["t/24",[35,0.009,36,0.027,37,0.009]],["b/24",[1,0.058,2,0.093,3,0.023,4,0.008,5,0.008,6,0.008,7,0.013,8,0.008,9,0.015,10,0.012,11,0.008,12,0.008,13,0.008,14,0.008,15,0.008,16,0.015,17,0.008,18,0.008,19,0.043,20,0.008,21,0.008,22,0.013,23,0.008,24,0.043,25,0.052,26,0.012,27,0.008,28,0.018,29,0.008,30,0.008,31,0.008,32,0.008,33,0.008,34,0.02,35,0.023,36,0.065,37,0.023,38,0.008,39,0.012,40,0.012,41,0.008,42,0.008,43,0.008,44,0.008,45,0.008,46,0.008,47,0.008,48,0.008,49,0.008,50,0.008,51,0.008,52,0.012,53,0.008,54,0.012,55,0.008,56,0.012,57,0.008,58,0.012,59,0.008,60,0.008,61,0.008,62,0.008,63,0.008,64,0.008,65,0.008,66,0.008,67,0.008,68,0.008,69,0.015,70,0.008,71,0.013,72,0.018,73,0.017,74,0.013,82,0.013,83,0.008,84,0.008,85,0.008,86,0.008,87,0.013,88,0.013,89,0.008,90,0.008,91,0.013,92,0.008,93,0.013,94,0.013,95,0.008,96,0.008,97,0.008,98,0.008,99,0.008,100,0.008,101,0.008,102,0.008,103,0.008,104,0.008,111,0.994,116,1.053,117,1.891,118,0.851,120,0.292,122,0.432,123,0.325,124,0.695,133,1.233,136,1.345,145,1.873,146,1.137,147,1.305,148,2.062,156,1.137,159,0.979,162,1.479,163,1.233,165,2.268,174,2.062,187,1.648,192,1.587,195,1.233,199,1.479,204,1.701,206,1.907,207,1.891,209,1.891,210,1.648,213,1.233,215,1.648,220,2.872,223,1.648,234,0.912,237,1.648,241,1.483,242,1.233,243,0.979,245,1.891,246,1.305,248,1.744,253,1.648,257,2.062,270,2.759,271,1.873,274,1.501,275,1.648,287,1.648,292,1.053,294,1.648,306,2.526,312,2.872,318,1.648,319,1.233,320,1.615,321,3.493,326,1.501,342,1.873,344,1.479,354,1.587,363,3.093,364,0.795,372,1.891,381,1.744,382,2.268,388,1.233,400,1.345,408,1.873,413,0.979,417,2.268,422,1.97,423,3.916,424,3.916,439,1.873,459,1.137,466,1.873,467,1.137,468,1.648,486,1.479,496,1.648,497,1.053,499,1.873,500,2.3,503,2.872,506,1.615,508,1.479,516,1.873,517,1.873,536,2.3,542,1.479,549,2.062,550,1.873,552,2.526,553,1.648,556,2.759,577,1.873,586,1.873,592,2.121,603,1.891,606,1.479,613,2.526,630,1.233,640,1.873,645,1.873,646,3.093,679,1.479,685,0.744,687,1.345,730,1.873,734,2.872,738,1.345,756,1.053,768,2.872,786,3.876,789,1.648,818,1.648,821,2.062,834,1.873,867,1.873,872,2.526,881,2.526,887,2.872,891,1.479,893,1.873,895,2.872,896,1.648,915,3.445,930,2.526,933,1.873,936,1.479,957,2.872,992,1.648,1019,1.479,1027,1.648,1083,1.873,1102,1.873,1111,3.396,1112,3.493,1113,6.189,1114,2.215,1115,5.27,1116,2.215,1117,2.215,1118,2.215,1119,1.873,1120,2.215,1121,3.396,1122,2.215,1123,1.873,1124,2.215,1125,2.215,1126,2.215,1127,2.215,1128,3.396,1129,3.396,1130,2.215,1131,2.215,1132,4.994,1133,2.215,1134,2.215,1135,3.493,1136,1.873,1137,1.873,1138,2.215,1139,4.13,1140,1.873,1141,2.215,1142,2.215,1143,2.215,1144,2.215,1145,2.215,1146,2.215,1147,2.215,1148,2.215,1149,3.396,1150,1.873,1151,2.215,1152,2.215,1153,2.215,1154,2.872,1155,1.648,1156,2.215,1157,2.215,1158,2.215,1159,2.215,1160,1.648,1161,2.215,1162,2.215,1163,2.215,1164,2.215,1165,2.215,1166,2.215,1167,4.631,1168,3.396,1169,3.396,1170,2.872,1171,3.396,1172,3.396,1173,2.215,1174,3.396,1175,1.873,1176,2.215,1177,2.215,1178,2.215,1179,2.215,1180,1.873,1181,2.215,1182,2.215,1183,2.215,1184,1.873,1185,1.873,1186,2.215,1187,2.215,1188,2.215,1189,2.215,1190,4.13,1191,1.648,1192,4.13,1193,2.215,1194,2.215,1195,2.215,1196,2.215,1197,2.215,1198,2.215,1199,2.215,1200,2.215,1201,2.215,1202,2.215,1203,3.396,1204,2.215,1205,2.215,1206,2.215,1207,2.215,1208,3.396,1209,2.215,1210,1.648,1211,2.215,1212,1.873,1213,2.215,1214,2.215,1215,2.215]],["t/25",[38,0.011,39,0.011]],["b/25",[1,0.057,2,0.098,3,0.038,4,0.014,5,0.014,6,0.014,7,0.014,8,0.013,9,0.013,10,0.018,11,0.018,12,0.013,13,0.014,14,0.013,15,0.013,16,0.013,17,0.013,18,0.013,19,0.051,20,0.013,21,0.013,22,0.014,23,0.013,24,0.057,25,0.057,26,0.013,27,0.013,28,0.013,29,0.014,30,0.013,31,0.013,32,0.014,33,0.013,34,0.014,35,0.013,36,0.051,37,0.018,38,0.023,39,0.023,40,0.018,41,0.014,42,0.013,43,0.013,44,0.013,45,0.013,46,0.014,47,0.013,48,0.013,49,0.013,50,0.013,51,0.013,52,0.013,53,0.013,54,0.018,55,0.013,56,0.018,57,0.013,58,0.018,59,0.013,60,0.013,61,0.013,62,0.013,63,0.013,64,0.013,65,0.013,66,0.013,67,0.013,68,0.013,69,0.02,70,0.014,71,0.014,72,0.013,73,0.02,74,0.018,82,0.014,83,0.014,84,0.018,85,0.018,86,0.014,87,0.014,88,0.014,89,0.014,90,0.014,91,0.014,92,0.014,93,0.018,94,0.018,95,0.014,96,0.014,97,0.018,98,0.014,99,0.014,100,0.014,101,0.014,102,0.014,103,0.014,104,0.014,111,1.169,115,3.085,120,0.481,123,0.256,159,1.613,206,2.001,225,1.613,226,1.146,230,2.437,241,1.745,255,1.502,260,1.31,267,3.085,318,2.714,319,2.031,320,1.735,382,2.437,403,2.215,417,3.246,422,1.745,500,2.031,508,2.437,526,1.873,549,2.215,561,1.735,592,1.873,597,2.031,598,2.714,629,2.714,630,2.031,660,1.873,685,1.225,786,2.437,860,3.085,889,3.085,991,2.714,993,1.873,1033,3.615,1052,2.215,1053,3.085,1094,3.085,1101,3.085,1216,2.714,1217,3.648,1218,3.648,1219,3.648,1220,3.648,1221,3.648,1222,4.86,1223,3.648,1224,4.86,1225,3.648,1226,3.648,1227,3.648,1228,3.085,1229,3.085,1230,3.648]],["t/26",[59,0.014]],["b/26",[1,0.06,2,0.106,3,0.026,4,0.009,5,0.009,6,0.009,7,0.009,8,0.009,9,0.013,10,0.013,11,0.009,12,0.009,13,0.009,14,0.009,15,0.009,16,0.009,17,0.009,18,0.009,19,0.039,20,0.009,21,0.009,22,0.009,23,0.009,24,0.046,25,0.051,26,0.009,27,0.009,28,0.009,29,0.009,30,0.009,31,0.009,32,0.009,33,0.009,34,0.009,35,0.009,36,0.051,37,0.009,38,0.009,39,0.009,40,0.016,41,0.009,42,0.009,43,0.013,44,0.009,45,0.009,46,0.009,47,0.019,48,0.009,49,0.009,50,0.009,51,0.009,52,0.013,53,0.009,54,0.013,55,0.009,56,0.018,57,0.009,58,0.013,59,0.024,60,0.009,61,0.02,62,0.009,63,0.009,64,0.021,65,0.009,66,0.016,67,0.009,68,0.009,69,0.016,70,0.014,71,0.014,72,0.009,73,0.014,74,0.014,82,0.009,83,0.009,84,0.009,85,0.009,86,0.009,87,0.014,88,0.014,89,0.014,90,0.009,91,0.009,92,0.009,93,0.019,94,0.016,95,0.009,96,0.009,97,0.009,98,0.009,99,0.009,100,0.009,101,0.009,102,0.009,103,0.018,104,0.009,111,1.06,116,1.171,117,2.453,118,1.693,120,0.325,123,0.434,128,0.447,133,1.371,147,1.693,151,1.89,152,1.645,156,1.264,157,0.946,158,2.943,162,2.458,163,1.371,165,1.645,173,1.264,176,1.832,186,1.264,191,3.726,192,2.01,194,1.832,204,1.515,206,1.014,209,2.453,213,1.371,226,0.774,229,1.645,232,1.495,233,2.458,234,1.515,244,2.453,246,0.946,248,1.264,255,1.014,258,2.453,260,1.582,266,1.014,275,2.737,276,1.495,292,1.171,315,1.645,320,1.171,354,0.946,365,2.943,367,2.082,368,3.277,369,1.645,372,1.371,388,2.049,396,1.264,400,1.495,403,1.495,413,1.088,422,1.582,432,2.082,434,2.082,436,3.726,459,1.89,461,1.495,462,2.082,463,1.371,469,2.082,476,1.495,479,2.737,486,1.645,493,1.645,495,1.264,500,3.256,520,2.082,525,1.645,526,1.264,527,1.495,536,1.371,546,2.082,556,3.265,562,4.133,565,1.832,569,2.082,592,1.264,593,1.495,596,1.495,597,1.371,608,2.082,610,2.235,619,1.645,624,2.235,628,1.371,646,2.458,650,2.082,652,1.832,664,1.832,685,1.236,692,1.645,709,1.495,745,2.082,749,2.943,751,1.495,862,1.832,876,3.112,877,1.645,894,1.832,900,2.082,929,2.082,936,1.645,990,1.495,992,1.832,1030,2.675,1046,2.082,1084,2.082,1150,3.112,1155,1.832,1160,1.832,1184,2.082,1185,2.082,1212,2.082,1231,2.462,1232,2.462,1233,2.462,1234,2.462,1235,2.462,1236,1.832,1237,2.462,1238,2.462,1239,3.68,1240,2.462,1241,2.462,1242,2.462,1243,2.462,1244,2.462,1245,5.232,1246,2.462,1247,2.462,1248,3.68,1249,3.68,1250,3.68,1251,2.462,1252,4.888,1253,2.462,1254,3.112,1255,3.112,1256,2.082,1257,2.462,1258,2.462,1259,2.462,1260,4.888,1261,2.462,1262,2.462,1263,2.462,1264,3.112,1265,2.462,1266,2.082,1267,2.462,1268,2.462,1269,4.406,1270,4.888,1271,3.68,1272,2.462,1273,2.462,1274,2.462,1275,2.462,1276,1.832,1277,1.832,1278,2.462,1279,2.462,1280,2.462,1281,2.462,1282,2.462,1283,2.462,1284,2.082,1285,3.68,1286,3.68,1287,2.462,1288,2.462,1289,2.462,1290,2.462,1291,3.68,1292,2.462,1293,2.462,1294,2.462,1295,2.462,1296,2.462,1297,2.462,1298,3.726,1299,2.462,1300,2.462,1301,2.462,1302,2.462,1303,2.462,1304,2.082,1305,2.462,1306,2.462,1307,2.462,1308,2.462,1309,2.462,1310,2.462,1311,2.462]],["t/27",[36,0.027,58,0.009,60,0.009]],["b/27",[1,0.033,2,0.11,3,0.053,4,0.012,5,0.012,6,0.012,7,0.012,8,0.011,9,0.011,10,0.016,11,0.011,12,0.011,13,0.012,14,0.011,15,0.011,16,0.011,17,0.011,18,0.011,19,0.046,20,0.011,21,0.011,22,0.012,23,0.011,24,0.053,25,0.053,26,0.011,27,0.011,28,0.011,29,0.012,30,0.011,31,0.011,32,0.012,33,0.011,34,0.012,35,0.011,36,0.061,37,0.011,38,0.011,39,0.011,40,0.016,41,0.012,42,0.011,43,0.011,44,0.011,45,0.016,46,0.012,47,0.011,48,0.011,49,0.011,50,0.011,51,0.011,52,0.011,53,0.011,54,0.016,55,0.011,56,0.016,57,0.011,58,0.023,59,0.011,60,0.022,61,0.011,62,0.011,63,0.011,64,0.018,65,0.011,66,0.011,67,0.011,68,0.011,69,0.019,70,0.019,71,0.012,72,0.011,73,0.016,74,0.016,82,0.012,83,0.012,84,0.012,85,0.012,86,0.012,87,0.012,88,0.016,89,0.012,90,0.012,91,0.016,92,0.012,93,0.016,94,0.016,95,0.012,96,0.012,97,0.012,98,0.012,99,0.012,100,0.012,101,0.012,102,0.012,103,0.012,104,0.012,111,1.059,116,1.502,120,0.417,123,0.309,124,0.531,135,1.917,144,2.942,147,1.213,152,2.109,174,1.917,175,1.917,204,1.3,213,1.758,225,1.396,226,0.992,228,2.095,248,1.621,251,2.262,255,1.814,258,2.452,266,1.814,292,1.502,298,1.396,314,2.109,326,1.396,381,1.621,386,2.349,422,1.581,429,2.349,453,2.67,457,2.109,459,1.621,461,1.917,476,1.917,525,2.109,527,1.917,553,2.349,561,2.095,563,3.724,568,2.349,606,2.109,624,2.674,628,1.758,649,3.724,651,2.349,656,2.67,660,1.621,664,2.349,668,2.67,695,3.276,706,2.674,709,1.917,738,1.917,774,2.109,828,2.349,830,2.349,862,4.082,930,2.349,1017,2.67,1025,2.67,1038,2.67,1039,2.109,1052,1.917,1097,2.67,1119,2.67,1154,2.67,1160,2.349,1191,2.349,1210,2.349,1236,2.349,1312,3.158,1313,2.349,1314,3.158,1315,2.67,1316,3.158,1317,3.158,1318,3.158,1319,3.158,1320,3.158,1321,5.072,1322,3.158,1323,2.349,1324,3.158,1325,5.772,1326,6.134,1327,5.978,1328,4.404,1329,3.158,1330,4.404,1331,3.276,1332,3.158,1333,4.404,1334,4.404,1335,3.158,1336,4.404,1337,3.158,1338,3.158,1339,3.158,1340,2.67,1341,3.724,1342,3.158,1343,3.158,1344,3.158,1345,3.158,1346,3.158]],["t/28",[61,0.014]],["b/28",[1,0.064,2,0.112,3,0.027,4,0.01,5,0.01,6,0.01,7,0.01,8,0.009,9,0.009,10,0.014,11,0.014,12,0.009,13,0.01,14,0.009,15,0.009,16,0.009,17,0.009,18,0.009,19,0.04,20,0.009,21,0.009,22,0.01,23,0.009,24,0.048,25,0.048,26,0.009,27,0.009,28,0.009,29,0.01,30,0.009,31,0.009,32,0.01,33,0.009,34,0.01,35,0.009,36,0.053,37,0.009,38,0.009,39,0.014,40,0.014,41,0.01,42,0.009,43,0.009,44,0.009,45,0.009,46,0.014,47,0.009,48,0.009,49,0.009,50,0.009,51,0.009,52,0.009,53,0.009,54,0.014,55,0.009,56,0.014,57,0.009,58,0.019,59,0.009,60,0.009,61,0.024,62,0.009,63,0.009,64,0.009,65,0.009,66,0.009,67,0.009,68,0.009,69,0.017,70,0.014,71,0.014,72,0.021,73,0.014,74,0.014,76,1.73,82,0.014,83,0.01,84,0.01,85,0.02,86,0.019,87,0.01,88,0.014,89,0.01,90,0.01,91,0.014,92,0.01,93,0.017,94,0.014,95,0.01,96,0.01,97,0.01,98,0.01,99,0.01,100,0.01,101,0.01,102,0.01,103,0.01,104,0.01,105,0.996,111,0.623,116,1.232,118,0.996,123,0.417,126,0.549,152,1.73,156,1.962,158,1.73,159,1.145,173,1.33,174,1.573,175,1.573,177,1.145,192,2.455,204,1.574,205,1.442,206,1.574,214,2.552,218,1.573,226,0.814,228,2.159,241,0.93,252,1.73,255,1.574,268,1.442,273,1.927,274,1.145,287,1.927,292,2.159,298,1.145,315,1.73,364,0.93,365,1.73,372,1.442,373,1.442,374,2.191,380,1.73,381,1.962,384,1.573,396,1.962,400,1.573,403,2.32,405,3.231,411,1.927,413,1.689,417,1.73,422,1.799,428,1.73,430,2.32,457,1.73,467,1.33,481,1.73,486,1.73,495,1.33,497,1.232,500,3.113,526,1.962,527,1.573,536,1.442,545,2.191,552,1.927,593,1.573,600,2.32,603,1.442,606,1.73,610,3.795,624,2.32,628,2.127,657,2.191,678,1.145,685,1.283,688,1.927,703,3.231,708,1.573,731,3.728,767,1.573,798,1.73,821,2.32,861,1.73,881,1.927,944,2.191,948,2.191,982,2.191,990,1.573,993,1.33,1019,1.73,1030,3.245,1104,2.191,1228,2.191,1236,2.843,1276,1.927,1284,2.191,1313,1.927,1341,3.231,1347,2.591,1348,2.591,1349,2.591,1350,2.591,1351,5.344,1352,2.591,1353,3.821,1354,2.591,1355,1.927,1356,2.591,1357,2.591,1358,5.011,1359,2.591,1360,5.344,1361,2.591,1362,2.591,1363,2.591,1364,2.591,1365,2.591,1366,2.591,1367,2.191,1368,2.191,1369,2.591,1370,2.591,1371,2.591,1372,3.821,1373,2.591,1374,2.591,1375,2.591,1376,2.591,1377,2.591,1378,2.591,1379,2.591,1380,2.591,1381,2.191,1382,2.591,1383,2.591,1384,2.591,1385,2.591,1386,3.821,1387,3.821,1388,4.54,1389,3.821,1390,2.591,1391,2.591,1392,2.591,1393,2.591,1394,2.591,1395,2.591,1396,3.821,1397,3.821,1398,3.821,1399,3.821,1400,2.591,1401,2.591,1402,2.591,1403,1.927,1404,2.591,1405,2.591,1406,3.821,1407,2.591,1408,2.591,1409,2.591,1410,2.591,1411,2.591,1412,2.591,1413,2.591,1414,2.591,1415,2.191,1416,2.191]],["t/29",[67,0.011,68,0.011]],["b/29",[1,0.059,2,0.101,3,0.041,4,0.014,5,0.014,6,0.014,7,0.014,8,0.014,9,0.014,10,0.018,11,0.014,12,0.014,13,0.014,14,0.014,15,0.014,16,0.014,17,0.014,18,0.018,19,0.053,20,0.018,21,0.014,22,0.014,23,0.014,24,0.059,25,0.059,26,0.014,27,0.014,28,0.014,29,0.014,30,0.014,31,0.014,32,0.014,33,0.014,34,0.014,35,0.014,36,0.053,37,0.014,38,0.014,39,0.014,40,0.018,41,0.014,42,0.014,43,0.014,44,0.014,45,0.014,46,0.014,47,0.014,48,0.014,49,0.014,50,0.014,51,0.014,52,0.014,53,0.014,54,0.018,55,0.014,56,0.02,57,0.014,58,0.018,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.014,67,0.022,68,0.023,69,0.023,70,0.014,71,0.014,72,0.014,73,0.019,74,0.021,82,0.014,83,0.014,84,0.014,85,0.014,86,0.014,87,0.019,88,0.014,89,0.014,90,0.014,91,0.014,92,0.021,93,0.019,94,0.019,95,0.014,96,0.019,97,0.021,98,0.014,99,0.014,100,0.014,101,0.014,102,0.014,103,0.014,104,0.014,118,1.486,120,0.51,122,0.754,123,0.395,124,0.65,125,0.81,126,0.555,127,0.81,128,0.701,133,2.152,147,1.486,226,1.215,241,1.388,266,1.592,268,2.152,326,2.232,354,1.94,388,2.152,685,1.298,1062,3.269,1063,3.269,1264,3.269,1417,5.05,1418,5.05,1419,3.866]],["t/30",[47,0.014]],["b/30",[1,0.053,2,0.101,3,0.041,4,0.015,5,0.015,6,0.015,7,0.015,8,0.014,9,0.014,10,0.018,11,0.014,12,0.014,13,0.015,14,0.014,15,0.014,16,0.014,17,0.014,18,0.014,19,0.053,20,0.014,21,0.014,22,0.015,23,0.014,24,0.059,25,0.059,26,0.014,27,0.014,28,0.014,29,0.015,30,0.014,31,0.014,32,0.015,33,0.014,34,0.015,35,0.014,36,0.053,37,0.014,38,0.014,39,0.014,40,0.018,41,0.015,42,0.014,43,0.014,44,0.014,45,0.014,46,0.015,47,0.021,48,0.014,49,0.014,50,0.014,51,0.014,52,0.018,53,0.014,54,0.018,55,0.014,56,0.021,57,0.014,58,0.018,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.014,67,0.014,68,0.014,69,0.023,70,0.015,71,0.015,72,0.014,73,0.019,74,0.021,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.019,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.019,94,0.019,95,0.015,96,0.019,97,0.021,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,105,1.507,116,1.866,120,0.518,122,0.765,123,0.398,124,0.858,125,0.822,126,0.563,127,0.822,128,0.712,148,2.382,173,2.014,209,2.184,413,1.734,476,2.382,659,3.317,688,2.918,1052,2.382,1420,3.923,1421,3.923,1422,5.663,1423,3.923,1424,3.923,1425,3.923,1426,3.923,1427,3.923,1428,3.923,1429,3.923,1430,3.317]],["t/31",[2,0.052,48,0.011]],["b/31",[1,0.062,2,0.113,3,0.04,4,0.014,5,0.014,6,0.014,7,0.014,8,0.014,9,0.014,10,0.018,11,0.014,12,0.014,13,0.014,14,0.014,15,0.014,16,0.014,17,0.014,18,0.014,19,0.052,20,0.014,21,0.014,22,0.014,23,0.014,24,0.058,25,0.058,26,0.014,27,0.014,28,0.014,29,0.014,30,0.014,31,0.014,32,0.014,33,0.014,34,0.014,35,0.014,36,0.052,37,0.014,38,0.014,39,0.014,40,0.018,41,0.014,42,0.014,43,0.014,44,0.014,45,0.014,46,0.014,47,0.014,48,0.023,49,0.014,50,0.014,51,0.014,52,0.014,53,0.014,54,0.018,55,0.014,56,0.018,57,0.014,58,0.018,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.014,67,0.014,68,0.014,69,0.021,70,0.014,71,0.014,72,0.014,73,0.022,74,0.023,78,2.103,82,0.014,83,0.014,84,0.014,85,0.014,86,0.014,87,0.018,88,0.014,89,0.014,90,0.014,91,0.014,92,0.014,93,0.021,94,0.018,95,0.014,96,0.014,97,0.014,98,0.014,99,0.014,100,0.014,101,0.014,102,0.014,103,0.014,104,0.014,111,0.909,118,1.451,123,0.265,124,0.635,151,2.554,186,1.94,255,2.048,260,1.356,319,2.103,422,2.205,468,2.81,497,1.797,506,1.797,542,2.523,561,2.366,678,1.67,756,1.797,1216,2.81,1431,3.777,1432,3.777,1433,3.777,1434,3.777,1435,3.777,1436,3.777,1437,4.973,1438,3.777,1439,4.973,1440,5.56,1441,3.777,1442,3.777,1443,3.777,1444,3.777]],["t/32",[49,0.014]],["b/32",[1,0.06,2,0.106,3,0.036,4,0.013,5,0.013,6,0.013,7,0.013,8,0.013,9,0.013,10,0.017,11,0.013,12,0.013,13,0.013,14,0.013,15,0.013,16,0.013,17,0.013,18,0.013,19,0.049,20,0.013,21,0.013,22,0.013,23,0.013,24,0.056,25,0.056,26,0.013,27,0.013,28,0.013,29,0.013,30,0.013,31,0.013,32,0.013,33,0.013,34,0.013,35,0.013,36,0.049,37,0.013,38,0.013,39,0.013,40,0.017,41,0.013,42,0.013,43,0.013,44,0.013,45,0.013,46,0.013,47,0.013,48,0.013,49,0.024,50,0.013,51,0.013,52,0.013,53,0.013,54,0.017,55,0.013,56,0.017,57,0.013,58,0.017,59,0.013,60,0.013,61,0.013,62,0.013,63,0.013,64,0.013,65,0.013,66,0.017,67,0.013,68,0.013,69,0.02,70,0.013,71,0.013,72,0.021,73,0.017,74,0.017,75,2.31,82,0.013,83,0.013,84,0.013,85,0.017,86,0.013,87,0.013,88,0.013,89,0.013,90,0.013,91,0.013,92,0.013,93,0.017,94,0.017,95,0.013,96,0.013,97,0.017,98,0.013,99,0.013,100,0.013,101,0.013,102,0.013,103,0.013,104,0.013,111,1.128,122,0.675,123,0.329,142,1.776,151,2.408,157,2.192,226,1.672,232,2.101,233,2.31,260,1.684,272,3.132,292,1.645,294,2.573,354,1.329,364,1.242,366,3.488,369,2.31,373,1.926,388,1.926,396,1.776,527,2.101,538,2.31,561,1.645,573,3.965,612,2.925,637,2.962,654,1.926,679,2.31,685,2.111,706,2.848,743,2.573,757,2.101,759,2.31,767,2.101,773,2.31,774,2.31,798,2.31,817,2.925,859,2.573,896,2.573,956,2.573,991,2.573,993,2.408,1039,3.132,1155,2.573,1276,2.573,1331,2.573,1403,2.573,1445,3.459,1446,3.459,1447,3.459,1448,3.459,1449,3.459,1450,3.459,1451,3.459,1452,5.32,1453,3.459,1454,2.573,1455,3.459,1456,3.459,1457,3.459,1458,3.459,1459,3.459,1460,3.459,1461,4.689,1462,3.459,1463,4.689,1464,4.689,1465,3.459]],["t/33",[50,0.011,51,0.011]],["b/33",[1,0.065,2,0.101,3,0.041,4,0.014,5,0.014,6,0.014,7,0.014,8,0.014,9,0.014,10,0.018,11,0.014,12,0.014,13,0.014,14,0.014,15,0.014,16,0.014,17,0.014,18,0.014,19,0.053,20,0.014,21,0.014,22,0.014,23,0.014,24,0.059,25,0.059,26,0.014,27,0.014,28,0.014,29,0.014,30,0.014,31,0.014,32,0.014,33,0.014,34,0.014,35,0.014,36,0.059,37,0.014,38,0.014,39,0.014,40,0.018,41,0.014,42,0.014,43,0.014,44,0.014,45,0.014,46,0.014,47,0.014,48,0.014,49,0.014,50,0.022,51,0.022,52,0.014,53,0.014,54,0.018,55,0.014,56,0.02,57,0.014,58,0.018,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.018,67,0.014,68,0.014,69,0.023,70,0.014,71,0.014,72,0.014,73,0.019,74,0.021,82,0.014,83,0.014,84,0.014,85,0.014,86,0.014,87,0.019,88,0.014,89,0.014,90,0.014,91,0.014,92,0.014,93,0.021,94,0.019,95,0.014,96,0.019,97,0.021,98,0.014,99,0.014,100,0.014,101,0.014,102,0.014,103,0.014,104,0.014,120,0.513,122,0.759,123,0.273,124,0.654,125,0.814,126,0.558,127,0.814,128,0.705,192,1.494,226,1.221,354,1.494,429,2.892,459,1.996,592,1.996,756,1.849,1044,3.287,1135,3.287,1430,3.287,1466,3.887,1467,3.887,1468,3.887,1469,3.887,1470,3.887,1471,3.887,1472,3.887,1473,3.887,1474,3.887,1475,3.887,1476,3.887]],["t/34",[52,0.011,53,0.011]],["b/34",[1,0.044,2,0.098,3,0.044,4,0.015,5,0.015,6,0.015,7,0.015,8,0.015,9,0.015,10,0.019,11,0.015,12,0.015,13,0.015,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.055,20,0.015,21,0.015,22,0.015,23,0.015,24,0.061,25,0.061,26,0.015,27,0.015,28,0.015,29,0.015,30,0.015,31,0.015,32,0.015,33,0.015,34,0.015,35,0.015,36,0.055,37,0.015,38,0.015,39,0.015,40,0.019,41,0.015,42,0.015,43,0.015,44,0.015,45,0.015,46,0.015,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.019,53,0.015,54,0.019,55,0.015,56,0.019,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.022,70,0.015,71,0.015,72,0.015,73,0.02,74,0.02,82,0.015,83,0.015,84,0.015,85,0.015,86,0.015,87,0.015,88,0.015,89,0.015,90,0.015,91,0.015,92,0.015,93,0.02,94,0.02,95,0.015,96,0.015,97,0.015,98,0.015,99,0.015,100,0.015,101,0.015,102,0.015,103,0.015,104,0.015,126,0.596,206,1.709,234,1.709,709,2.521,736,3.088,1045,3.51,1477,4.151,1478,4.151,1479,3.51,1480,4.151,1481,5.287,1482,4.151,1483,4.151,1484,4.151,1485,4.151]],["t/35",[55,0.011,56,0.011]],["b/35",[1,0.064,2,0.106,3,0.039,4,0.014,5,0.014,6,0.014,7,0.014,8,0.014,9,0.014,10,0.018,11,0.014,12,0.014,13,0.014,14,0.014,15,0.014,16,0.014,17,0.014,18,0.014,19,0.052,20,0.014,21,0.014,22,0.014,23,0.014,24,0.058,25,0.058,26,0.014,27,0.014,28,0.014,29,0.014,30,0.014,31,0.014,32,0.014,33,0.014,34,0.014,35,0.014,36,0.052,37,0.014,38,0.014,39,0.014,40,0.018,41,0.014,42,0.014,43,0.014,44,0.014,45,0.014,46,0.014,47,0.014,48,0.014,49,0.014,50,0.014,51,0.014,52,0.014,53,0.014,54,0.02,55,0.02,56,0.022,57,0.014,58,0.018,59,0.014,60,0.014,61,0.014,62,0.014,63,0.014,64,0.014,65,0.014,66,0.014,67,0.014,68,0.014,69,0.023,70,0.018,71,0.014,72,0.014,73,0.018,74,0.021,82,0.014,83,0.014,84,0.014,85,0.014,86,0.014,87,0.018,88,0.014,89,0.014,90,0.014,91,0.014,92,0.014,93,0.018,94,0.018,95,0.014,96,0.018,97,0.021,98,0.014,99,0.014,100,0.014,101,0.014,102,0.014,103,0.018,104,0.018,111,0.904,120,0.496,122,0.733,123,0.348,124,0.834,125,0.787,126,0.539,127,0.787,128,0.682,147,1.444,148,2.282,204,1.547,244,2.092,245,2.092,260,2.117,266,1.547,298,1.661,326,1.661,364,1.349,419,2.795,425,3.177,467,1.929,481,2.509,497,1.787,508,3.31,510,3.177,660,1.929,678,1.661,915,2.795,995,2.795,1052,2.282,1136,3.177,1486,3.757,1487,4.191,1488,3.757,1489,3.177,1490,3.177,1491,3.757,1492,3.757,1493,3.757,1494,3.757]],["t/36",[54,0.011,57,0.011]],["b/36",[1,0.065,2,0.098,3,0.034,4,0.012,5,0.012,6,0.012,7,0.012,8,0.012,9,0.012,10,0.016,11,0.012,12,0.012,13,0.012,14,0.012,15,0.012,16,0.012,17,0.012,18,0.012,19,0.047,20,0.012,21,0.012,22,0.012,23,0.012,24,0.054,25,0.054,26,0.012,27,0.012,28,0.012,29,0.012,30,0.012,31,0.012,32,0.012,33,0.012,34,0.012,35,0.012,36,0.047,37,0.012,38,0.012,39,0.012,40,0.016,41,0.012,42,0.012,43,0.012,44,0.012,45,0.012,46,0.012,47,0.012,48,0.012,49,0.012,50,0.012,51,0.012,52,0.012,53,0.012,54,0.024,55,0.012,56,0.02,57,0.022,58,0.016,59,0.012,60,0.012,61,0.012,62,0.012,63,0.012,64,0.012,65,0.012,66,0.016,67,0.012,68,0.012,69,0.022,70,0.012,71,0.012,72,0.016,73,0.019,74,0.021,82,0.012,83,0.012,84,0.012,85,0.012,86,0.012,87,0.017,88,0.012,89,0.012,90,0.012,91,0.012,92,0.012,93,0.017,94,0.017,95,0.012,96,0.017,97,0.019,98,0.012,99,0.012,100,0.012,101,0.012,102,0.012,103,0.017,104,0.012,111,0.776,118,1.239,120,0.425,122,0.629,123,0.314,124,0.863,125,0.676,126,0.642,127,0.676,128,0.811,147,1.239,148,1.958,157,1.239,159,1.425,194,2.398,204,1.328,206,1.328,208,2.726,213,1.795,223,2.398,227,2.398,234,1.328,241,1.158,246,1.239,250,2.726,258,1.795,260,1.605,272,2.153,293,1.958,306,2.398,319,1.795,337,2.398,354,1.239,364,1.158,368,3.815,389,2.726,410,2.726,456,2.726,463,1.795,493,2.153,495,2.843,497,1.534,534,3.779,561,1.534,568,2.398,593,1.958,603,2.488,626,2.153,630,1.795,637,2.855,678,1.425,717,2.398,731,2.398,754,1.958,786,3.425,796,2.726,859,2.398,993,2.295,1030,2.714,1036,2.726,1039,3.425,1050,2.726,1112,3.779,1137,2.726,1140,2.726,1170,2.726,1298,2.726,1304,2.726,1331,2.398,1381,2.726,1489,2.726,1495,3.224,1496,3.224,1497,4.469,1498,3.224,1499,3.224,1500,3.224,1501,3.224,1502,3.224,1503,3.224,1504,3.224,1505,3.224,1506,3.224,1507,3.224,1508,3.224,1509,3.224,1510,3.224,1511,3.224,1512,3.224,1513,3.224,1514,3.224,1515,3.224,1516,4.469,1517,3.224,1518,3.224,1519,3.224,1520,2.726,1521,3.224,1522,3.224,1523,3.224,1524,3.224,1525,3.224,1526,4.469]],["t/37",[1,0.027,2,0.043,3,0.027]],["b/37",[1,0.046,2,0.102,3,0.041,4,0.012,5,0.012,6,0.007,7,0.007,8,0.007,9,0.007,10,0.011,11,0.007,12,0.007,13,0.007,14,0.007,15,0.007,16,0.007,17,0.007,18,0.007,19,0.033,20,0.007,21,0.007,22,0.007,23,0.007,24,0.041,25,0.041,26,0.007,27,0.007,28,0.007,29,0.007,30,0.007,31,0.007,32,0.007,33,0.007,34,0.007,35,0.007,36,0.033,37,0.007,38,0.011,39,0.011,40,0.02,41,0.007,42,0.011,43,0.007,44,0.007,45,0.007,46,0.007,47,0.007,48,0.007,49,0.007,50,0.007,51,0.011,52,0.011,53,0.007,54,0.014,55,0.007,56,0.016,57,0.007,58,0.011,59,0.007,60,0.007,61,0.007,62,0.007,63,0.007,64,0.007,65,0.007,66,0.011,67,0.007,68,0.007,69,0.021,70,0.024,71,0.012,72,0.019,73,0.022,74,0.02,75,2.582,77,2.876,79,2.33,82,0.007,83,0.007,84,0.007,85,0.007,86,0.022,87,0.012,88,0.012,89,0.007,90,0.007,91,0.012,92,0.007,93,0.016,94,0.012,95,0.007,96,0.007,97,0.023,98,0.007,99,0.016,100,0.007,101,0.007,102,0.007,103,0.007,104,0.007,105,0.767,111,0.93,113,2.659,118,1.486,123,0.395,126,0.286,127,0.418,128,0.863,133,1.111,136,1.212,137,1.902,142,1.025,147,0.767,159,1.709,162,2.582,163,1.744,173,1.025,177,0.882,192,0.767,200,2.649,204,2.37,205,1.744,210,2.33,211,2.263,226,1.376,228,1.839,230,1.333,231,1.212,242,1.744,243,0.882,245,1.111,252,2.092,255,2.593,260,0.717,266,0.822,270,1.333,274,0.882,276,1.212,292,2.083,298,0.882,307,4.27,326,1.385,339,1.985,372,1.111,381,1.025,384,1.212,386,2.33,388,1.111,393,3.269,396,2.443,399,3.928,412,3.269,413,1.709,422,0.717,428,1.333,430,2.659,431,2.33,440,1.687,459,1.025,463,2.649,467,1.985,476,1.902,479,1.484,481,2.092,482,1.687,493,2.092,495,1.025,521,1.687,522,1.484,525,1.333,526,1.025,542,2.092,561,2.737,565,1.484,584,1.333,593,1.902,596,1.212,598,1.484,603,1.111,624,1.212,626,1.333,630,1.111,639,1.484,651,1.484,653,1.212,660,1.985,689,1.484,692,2.092,697,3.703,700,1.687,741,1.484,753,2.33,754,1.902,755,1.484,756,0.949,757,1.212,773,3.918,774,2.092,830,1.484,861,2.092,877,1.333,891,2.582,894,1.484,912,1.687,935,1.687,945,1.687,951,4.624,954,1.687,974,1.687,990,1.902,993,1.608,995,1.484,1030,1.902,1123,4.024,1175,2.649,1180,1.687,1191,1.484,1210,2.33,1216,2.876,1229,1.687,1254,1.687,1255,4.024,1256,1.687,1266,2.649,1277,3.928,1313,1.484,1315,1.687,1323,1.484,1340,3.269,1355,1.484,1367,3.269,1368,2.649,1403,1.484,1416,1.687,1454,2.876,1479,1.687,1487,1.687,1490,1.687,1520,4.024,1527,1.996,1528,3.269,1529,5.468,1530,1.996,1531,1.996,1532,1.996,1533,1.996,1534,1.996,1535,1.996,1536,1.996,1537,1.996,1538,1.996,1539,1.996,1540,3.132,1541,3.132,1542,1.687,1543,1.996,1544,1.996,1545,1.996,1546,1.996,1547,1.996,1548,1.996,1549,1.996,1550,1.996,1551,1.687,1552,1.996,1553,1.996,1554,3.132,1555,2.649,1556,1.996,1557,1.996,1558,1.996,1559,1.996,1560,1.996,1561,4.38,1562,1.996,1563,1.996,1564,1.996,1565,1.996,1566,1.996,1567,1.996,1568,1.996,1569,3.132,1570,1.996,1571,1.996,1572,1.996,1573,4.759,1574,1.996,1575,1.996,1576,3.132,1577,3.132,1578,1.996,1579,3.132,1580,1.996,1581,1.996,1582,1.996,1583,1.996,1584,1.996,1585,1.996,1586,1.996,1587,1.996,1588,1.996,1589,3.132,1590,1.996,1591,3.132,1592,1.996,1593,1.996,1594,3.866,1595,3.132,1596,1.996,1597,3.132,1598,1.996,1599,1.996,1600,1.996,1601,1.996,1602,3.132,1603,1.996,1604,1.996,1605,1.996,1606,1.996,1607,1.996,1608,3.132,1609,1.996,1610,1.996,1611,4.38,1612,1.996,1613,1.996,1614,1.996,1615,1.996,1616,3.132,1617,1.996,1618,1.996,1619,1.996,1620,1.996,1621,3.132,1622,1.996,1623,1.996,1624,1.996,1625,1.996,1626,1.996,1627,1.996,1628,1.996]],["t/38",[1,0.027,2,0.043,3,0.027]],["b/38",[1,0.066,2,0.108,3,0.056,4,0.02,5,0.013,6,0.013,7,0.013,8,0.013,9,0.013,10,0.017,11,0.013,12,0.013,13,0.013,14,0.013,15,0.013,16,0.013,17,0.013,18,0.013,19,0.05,20,0.013,21,0.013,22,0.013,23,0.013,24,0.056,25,0.056,26,0.013,27,0.013,28,0.013,29,0.013,30,0.013,31,0.013,32,0.013,33,0.017,34,0.013,35,0.013,36,0.05,37,0.013,38,0.013,39,0.013,40,0.019,41,0.013,42,0.013,43,0.013,44,0.013,45,0.013,46,0.013,47,0.013,48,0.013,49,0.013,50,0.013,51,0.013,52,0.013,53,0.013,54,0.019,55,0.013,56,0.022,57,0.013,58,0.017,59,0.013,60,0.013,61,0.013,62,0.013,63,0.013,64,0.013,65,0.013,66,0.013,67,0.013,68,0.013,69,0.021,70,0.02,71,0.013,72,0.013,73,0.02,74,0.02,75,2.345,77,2.611,82,0.013,83,0.013,84,0.013,85,0.013,86,0.013,87,0.018,88,0.013,89,0.013,90,0.013,91,0.013,92,0.013,93,0.022,94,0.02,95,0.013,96,0.018,97,0.02,98,0.013,99,0.018,100,0.013,101,0.013,102,0.013,103,0.013,104,0.013,120,0.625,126,0.68,128,0.637,137,2.132,142,1.802,143,2.345,173,1.802,174,2.132,175,2.132,195,1.954,211,1.67,213,1.954,218,2.132,225,1.552,226,1.103,231,2.132,243,1.552,245,1.954,251,1.802,293,2.132,320,1.67,326,1.552,360,2.611,373,1.954,396,1.802,397,2.968,431,2.611,459,2.432,506,1.67,522,2.611,529,2.968,561,2.253,603,1.954,627,2.345,660,1.802,679,2.345,692,2.345,738,2.876,751,2.132,754,3.255,757,2.132,767,2.132,773,3.163,872,2.611,891,3.163,1027,2.611,1277,2.611,1323,2.611,1355,2.611,1415,2.968,1454,2.611,1528,2.968,1542,2.968,1551,2.968,1555,2.968,1629,3.51,1630,3.51,1631,3.51,1632,3.51,1633,3.51,1634,3.51,1635,3.51,1636,3.51,1637,3.51,1638,3.51]],["t/39",[72,0.009,1639,2.148,1640,2.54]],["b/39",[1,0.045,2,0.091,3,0.045,4,0.016,5,0.016,6,0.016,7,0.016,8,0.015,9,0.015,10,0.019,11,0.015,12,0.015,13,0.016,14,0.015,15,0.015,16,0.015,17,0.015,18,0.015,19,0.056,20,0.015,21,0.015,22,0.016,23,0.015,24,0.062,25,0.062,26,0.015,27,0.015,28,0.015,29,0.016,30,0.015,31,0.015,32,0.016,33,0.015,34,0.016,35,0.015,36,0.056,37,0.015,38,0.015,39,0.015,40,0.019,41,0.016,42,0.015,43,0.015,44,0.015,45,0.015,46,0.016,47,0.015,48,0.015,49,0.015,50,0.015,51,0.015,52,0.015,53,0.015,54,0.019,55,0.015,56,0.019,57,0.015,58,0.019,59,0.015,60,0.015,61,0.015,62,0.015,63,0.015,64,0.015,65,0.015,66,0.015,67,0.015,68,0.015,69,0.022,70,0.016,71,0.016,72,0.015,73,0.02,74,0.02,76,2.85,82,0.016,83,0.016,84,0.016,85,0.016,86,0.016,87,0.016,88,0.016,89,0.016,90,0.016,91,0.016,92,0.016,93,0.02,94,0.02,95,0.016,96,0.016,97,0.016,98,0.016,99,0.016,100,0.016,101,0.016,102,0.016,103,0.016,104,0.016,364,1.532,600,2.591,1639,3.609]]],"invertedIndex":[["",{"_index":72,"t":{"39":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["0.000626",{"_index":1395,"t":{},"b":{"28":{}}}],["0.000660",{"_index":1405,"t":{},"b":{"28":{}}}],["0.3160099983215332",{"_index":1392,"t":{},"b":{"28":{}}}],["0.7",{"_index":665,"t":{},"b":{"10":{}}}],["1",{"_index":315,"t":{},"b":{"7":{},"23":{},"26":{},"28":{}}}],["1.0",{"_index":254,"t":{},"b":{"5":{}}}],["1.10",{"_index":1472,"t":{},"b":{"33":{}}}],["1.6.1",{"_index":662,"t":{},"b":{"10":{}}}],["1.9",{"_index":1471,"t":{},"b":{"33":{}}}],["10",{"_index":519,"t":{},"b":{"8":{}}}],["1000",{"_index":1242,"t":{},"b":{"26":{}}}],["12",{"_index":1396,"t":{},"b":{"28":{}}}],["15",{"_index":101,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["2",{"_index":316,"t":{},"b":{"7":{},"23":{}}}],["2.0",{"_index":170,"t":{},"b":{"5":{},"16":{}}}],["2.7",{"_index":666,"t":{},"b":{"10":{}}}],["2000",{"_index":1498,"t":{},"b":{"36":{}}}],["2017",{"_index":1456,"t":{},"b":{"32":{}}}],["2018",{"_index":735,"t":{},"b":{"16":{}}}],["2022",{"_index":916,"t":{},"b":{"19":{}}}],["2023",{"_index":148,"t":{},"b":{"4":{},"24":{},"30":{},"35":{},"36":{}}}],["2024",{"_index":102,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["207.406",{"_index":1398,"t":{},"b":{"28":{}}}],["3",{"_index":288,"t":{},"b":{"7":{},"15":{},"23":{}}}],["3.7",{"_index":663,"t":{},"b":{"10":{}}}],["3d",{"_index":540,"t":{},"b":{"8":{},"18":{},"19":{}}}],["3x3",{"_index":869,"t":{},"b":{"18":{}}}],["4",{"_index":986,"t":{},"b":{"19":{}}}],["404",{"_index":0,"t":{"0":{}},"b":{"0":{}}}],["5",{"_index":518,"t":{},"b":{"8":{}}}],["9",{"_index":868,"t":{},"b":{"18":{}}}],["900",{"_index":1491,"t":{},"b":{"35":{}}}],["_",{"_index":1637,"t":{},"b":{"38":{}}}],["a\\b",{"_index":744,"t":{},"b":{"17":{}}}],["abil",{"_index":541,"t":{},"b":{"8":{},"21":{}}}],["abm",{"_index":1113,"t":{},"b":{"24":{}}}],["abm+coninuum",{"_index":1202,"t":{},"b":{"24":{}}}],["abm/continuum",{"_index":1198,"t":{},"b":{"24":{}}}],["abov",{"_index":861,"t":{},"b":{"18":{},"21":{},"28":{},"37":{}}}],["absolut",{"_index":1547,"t":{},"b":{"37":{}}}],["abstract",{"_index":1515,"t":{},"b":{"36":{}}}],["abstractquaternion",{"_index":918,"t":{},"b":{"19":{}}}],["acceler",{"_index":10,"t":{"1":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["access",{"_index":566,"t":{},"b":{"8":{},"10":{}}}],["accident",{"_index":1371,"t":{},"b":{"28":{}}}],["accord",{"_index":1291,"t":{},"b":{"26":{}}}],["accur",{"_index":948,"t":{},"b":{"19":{},"28":{}}}],["accuraci",{"_index":1366,"t":{},"b":{"28":{}}}],["accustom",{"_index":1159,"t":{},"b":{"24":{}}}],["achiev",{"_index":578,"t":{},"b":{"8":{}}}],["acount",{"_index":1562,"t":{},"b":{"37":{}}}],["act",{"_index":247,"t":{},"b":{"5":{},"8":{}}}],["action",{"_index":1125,"t":{},"b":{"24":{}}}],["activ",{"_index":915,"t":{},"b":{"19":{},"24":{},"35":{}}}],["actual",{"_index":596,"t":{},"b":{"8":{},"17":{},"18":{},"26":{},"37":{}}}],["ad",{"_index":912,"t":{},"b":{"19":{},"37":{}}}],["adapt",{"_index":1068,"t":{},"b":{"22":{}}}],["adaptivemcmc.jl",{"_index":1070,"t":{},"b":{"22":{}}}],["add",{"_index":430,"t":{},"b":{"8":{},"18":{},"21":{},"28":{},"37":{}}}],["addit",{"_index":1160,"t":{},"b":{"24":{},"26":{},"27":{}}}],["address",{"_index":1455,"t":{},"b":{"32":{}}}],["adject",{"_index":994,"t":{},"b":{"20":{}}}],["adopt",{"_index":543,"t":{},"b":{"8":{}}}],["adt.@adt",{"_index":1481,"t":{},"b":{"34":{}}}],["advanc",{"_index":1452,"t":{},"b":{"32":{}}}],["advancedhmc.jl",{"_index":1066,"t":{},"b":{"22":{}}}],["advantag",{"_index":387,"t":{},"b":{"8":{}}}],["adversarialprediction.jl",{"_index":998,"t":{},"b":{"20":{}}}],["ag",{"_index":1345,"t":{},"b":{"27":{}}}],["again",{"_index":1394,"t":{},"b":{"28":{}}}],["agent",{"_index":35,"t":{"24":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["agents.jl",{"_index":1115,"t":{},"b":{"24":{}}}],["agents.jlvahana.jlmedyan.jlcellbasedmodels.jleasyabm.jl",{"_index":1114,"t":{},"b":{"24":{}}}],["aggreg",{"_index":1295,"t":{},"b":{"26":{}}}],["ahren",{"_index":733,"t":{},"b":{"16":{}}}],["aim",{"_index":1226,"t":{},"b":{"25":{}}}],["airspeedveloc",{"_index":1375,"t":{},"b":{"28":{}}}],["algebra",{"_index":694,"t":{},"b":{"16":{},"19":{}}}],["algebraofgraph",{"_index":544,"t":{},"b":{"8":{}}}],["algebraofgraphics.jl",{"_index":451,"t":{},"b":{"8":{}}}],["algorithm",{"_index":747,"t":{},"b":{"17":{},"18":{},"20":{}}}],["alia",{"_index":983,"t":{},"b":{"19":{}}}],["align",{"_index":962,"t":{},"b":{"19":{}}}],["alloc",{"_index":1397,"t":{},"b":{"28":{}}}],["allow",{"_index":354,"t":{},"b":{"7":{},"9":{},"18":{},"19":{},"21":{},"24":{},"26":{},"29":{},"32":{},"33":{},"36":{}}}],["alon",{"_index":1185,"t":{},"b":{"24":{},"26":{}}}],["along",{"_index":114,"t":{},"b":{"1":{},"8":{}}}],["alreadi",{"_index":194,"t":{},"b":{"5":{},"26":{},"36":{}}}],["altair",{"_index":564,"t":{},"b":{"8":{}}}],["alter",{"_index":1118,"t":{},"b":{"24":{}}}],["altern",{"_index":556,"t":{},"b":{"8":{},"21":{},"24":{},"26":{}}}],["although",{"_index":312,"t":{},"b":{"7":{},"24":{}}}],["alway",{"_index":1313,"t":{},"b":{"27":{},"28":{},"37":{}}}],["analysi",{"_index":1033,"t":{},"b":{"21":{},"23":{},"25":{}}}],["analyz",{"_index":1101,"t":{},"b":{"23":{},"25":{}}}],["and/or",{"_index":928,"t":{},"b":{"19":{}}}],["angl",{"_index":959,"t":{},"b":{"19":{}}}],["angular",{"_index":970,"t":{},"b":{"19":{}}}],["anim",{"_index":370,"t":{},"b":{"8":{}}}],["ann",{"_index":253,"t":{},"b":{"5":{},"19":{},"24":{}}}],["announc",{"_index":175,"t":{},"b":{"5":{},"16":{},"27":{},"28":{},"38":{}}}],["anoth",{"_index":186,"t":{},"b":{"5":{},"8":{},"9":{},"10":{},"19":{},"26":{},"31":{}}}],["answer",{"_index":181,"t":{},"b":{"5":{}}}],["anymor",{"_index":1524,"t":{},"b":{"36":{}}}],["api",{"_index":227,"t":{},"b":{"5":{},"8":{},"36":{}}}],["api'",{"_index":760,"t":{},"b":{"17":{}}}],["app",{"_index":57,"t":{"36":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["appear",{"_index":1567,"t":{},"b":{"37":{}}}],["appl",{"_index":879,"t":{},"b":{"18":{}}}],["applic",{"_index":1112,"t":{},"b":{"24":{},"36":{}}}],["approach",{"_index":1135,"t":{},"b":{"24":{},"33":{}}}],["appropri",{"_index":112,"t":{},"b":{"1":{}}}],["arbitrari",{"_index":921,"t":{},"b":{"19":{}}}],["argument",{"_index":759,"t":{},"b":{"17":{},"18":{},"19":{},"32":{}}}],["arithmet",{"_index":284,"t":{},"b":{"7":{}}}],["around",{"_index":627,"t":{},"b":{"9":{},"21":{},"23":{},"38":{}}}],["arrang",{"_index":1447,"t":{},"b":{"32":{}}}],["array",{"_index":705,"t":{},"b":{"16":{}}}],["arraymeta.jl",{"_index":727,"t":{},"b":{"16":{}}}],["arrays/funct",{"_index":1086,"t":{},"b":{"23":{}}}],["aspect",{"_index":499,"t":{},"b":{"8":{},"24":{}}}],["assum",{"_index":514,"t":{},"b":{"8":{}}}],["attach",{"_index":1022,"t":{},"b":{"21":{}}}],["attempt",{"_index":1319,"t":{},"b":{"27":{}}}],["author",{"_index":1052,"t":{},"b":{"21":{},"25":{},"27":{},"30":{},"35":{}}}],["auto",{"_index":856,"t":{},"b":{"18":{}}}],["automat",{"_index":653,"t":{},"b":{"10":{},"15":{},"18":{},"21":{},"37":{}}}],["avail",{"_index":331,"t":{},"b":{"7":{},"8":{}}}],["avalon.jl",{"_index":999,"t":{},"b":{"20":{}}}],["avoid",{"_index":1045,"t":{},"b":{"21":{},"34":{}}}],["ax=b",{"_index":740,"t":{},"b":{"17":{}}}],["axi",{"_index":961,"t":{},"b":{"19":{}}}],["b",{"_index":14,"t":{"14":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["b_j",{"_index":808,"t":{},"b":{"18":{}}}],["back",{"_index":80,"t":{},"b":{"0":{}}}],["backend",{"_index":389,"t":{},"b":{"8":{},"36":{}}}],["balanc",{"_index":1413,"t":{},"b":{"28":{}}}],["bandedmatrices.jl",{"_index":763,"t":{},"b":{"17":{}}}],["bandedmatrix",{"_index":478,"t":{},"b":{"8":{}}}],["barkermcmc.jl",{"_index":1067,"t":{},"b":{"22":{}}}],["base",{"_index":36,"t":{"24":{},"27":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["base.complex",{"_index":911,"t":{},"b":{"19":{}}}],["base.complex'",{"_index":905,"t":{},"b":{"19":{}}}],["base.im",{"_index":941,"t":{},"b":{"19":{}}}],["basic",{"_index":400,"t":{},"b":{"8":{},"19":{},"24":{},"26":{},"28":{}}}],["basicbspline.jl",{"_index":682,"t":{},"b":{"14":{}}}],["battl",{"_index":1341,"t":{},"b":{"27":{},"28":{}}}],["be",{"_index":529,"t":{},"b":{"8":{},"38":{}}}],["beauti",{"_index":570,"t":{},"b":{"8":{}}}],["becom",{"_index":1545,"t":{},"b":{"37":{}}}],["befor",{"_index":214,"t":{},"b":{"5":{},"8":{},"19":{},"28":{}}}],["beginn",{"_index":1548,"t":{},"b":{"37":{}}}],["begin{aligned}\\argmin_{\\mathbf{x",{"_index":801,"t":{},"b":{"18":{}}}],["behavior",{"_index":1219,"t":{},"b":{"25":{}}}],["believ",{"_index":221,"t":{},"b":{"5":{},"7":{}}}],["belov",{"_index":1359,"t":{},"b":{"28":{}}}],["below",{"_index":798,"t":{},"b":{"18":{},"20":{},"28":{},"32":{}}}],["benchmark",{"_index":1351,"t":{},"b":{"28":{}}}],["benchmarkci",{"_index":1376,"t":{},"b":{"28":{}}}],["benchmarktool",{"_index":1358,"t":{},"b":{"28":{}}}],["benchmarktools.jl",{"_index":1357,"t":{},"b":{"28":{}}}],["benefit",{"_index":755,"t":{},"b":{"17":{},"18":{},"37":{}}}],["best",{"_index":205,"t":{},"b":{"5":{},"8":{},"18":{},"19":{},"28":{},"37":{}}}],["bet",{"_index":1018,"t":{},"b":{"20":{}}}],["beta",{"_index":407,"t":{},"b":{"8":{}}}],["betaml.jl",{"_index":1000,"t":{},"b":{"20":{}}}],["better",{"_index":229,"t":{},"b":{"5":{},"8":{},"18":{},"26":{}}}],["between",{"_index":628,"t":{},"b":{"9":{},"10":{},"19":{},"26":{},"27":{},"28":{}}}],["beyond",{"_index":553,"t":{},"b":{"8":{},"24":{},"27":{}}}],["binari",{"_index":167,"t":{},"b":{"5":{}}}],["biophys",{"_index":1205,"t":{},"b":{"24":{}}}],["biquaternion",{"_index":937,"t":{},"b":{"19":{}}}],["bit",{"_index":437,"t":{},"b":{"8":{}}}],["blogpost",{"_index":1338,"t":{},"b":{"27":{}}}],["bonito.jl",{"_index":1516,"t":{},"b":{"36":{}}}],["book",{"_index":1565,"t":{},"b":{"37":{}}}],["born",{"_index":196,"t":{},"b":{"5":{},"20":{}}}],["both",{"_index":248,"t":{},"b":{"5":{},"7":{},"16":{},"21":{},"24":{},"26":{},"27":{}}}],["bottleneck",{"_index":1240,"t":{},"b":{"26":{}}}],["bound",{"_index":341,"t":{},"b":{"7":{},"18":{}}}],["box",{"_index":342,"t":{},"b":{"7":{},"24":{}}}],["branch",{"_index":951,"t":{},"b":{"19":{},"37":{}}}],["breath",{"_index":1512,"t":{},"b":{"36":{}}}],["brief",{"_index":1551,"t":{},"b":{"37":{},"38":{}}}],["broadcast",{"_index":714,"t":{},"b":{"16":{}}}],["browser",{"_index":1256,"t":{},"b":{"26":{},"37":{}}}],["bson.jl",{"_index":166,"t":{},"b":{"5":{}}}],["bsplinekit.jl",{"_index":683,"t":{},"b":{"14":{}}}],["bsplines.jl",{"_index":681,"t":{},"b":{"14":{}}}],["bug",{"_index":1379,"t":{},"b":{"28":{}}}],["build",{"_index":495,"t":{},"b":{"8":{},"17":{},"18":{},"26":{},"28":{},"36":{},"37":{}}}],["built",{"_index":103,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["bundl",{"_index":1275,"t":{},"b":{"26":{}}}],["button",{"_index":431,"t":{},"b":{"8":{},"37":{},"38":{}}}],["c",{"_index":595,"t":{},"b":{"8":{}}}],["c_j",{"_index":827,"t":{},"b":{"18":{}}}],["c_j(\\mathbf{x",{"_index":807,"t":{},"b":{"18":{}}}],["c_k",{"_index":831,"t":{},"b":{"18":{}}}],["c_k(\\mathbf{x",{"_index":811,"t":{},"b":{"18":{}}}],["call",{"_index":526,"t":{},"b":{"8":{},"9":{},"16":{},"25":{},"26":{},"28":{},"37":{}}}],["camp",{"_index":548,"t":{},"b":{"8":{}}}],["can't",{"_index":1328,"t":{},"b":{"27":{}}}],["cannoles.jl",{"_index":840,"t":{},"b":{"18":{}}}],["canon",{"_index":219,"t":{},"b":{"5":{}}}],["canva",{"_index":1302,"t":{},"b":{"26":{}}}],["capabl",{"_index":417,"t":{},"b":{"8":{},"24":{},"25":{},"28":{}}}],["captur",{"_index":1290,"t":{},"b":{"26":{}}}],["care",{"_index":411,"t":{},"b":{"8":{},"19":{},"28":{}}}],["carlo",{"_index":1063,"t":{},"b":{"22":{},"29":{}}}],["case",{"_index":155,"t":{},"b":{"5":{},"8":{},"17":{},"19":{},"20":{},"21":{}}}],["categori",{"_index":737,"t":{},"b":{"17":{},"18":{}}}],["caus",{"_index":1372,"t":{},"b":{"28":{}}}],["cd",{"_index":1579,"t":{},"b":{"37":{}}}],["cell",{"_index":321,"t":{},"b":{"7":{},"24":{}}}],["cellbasedmodels.jl",{"_index":1190,"t":{},"b":{"24":{}}}],["centric",{"_index":1151,"t":{},"b":{"24":{}}}],["centroid",{"_index":305,"t":{},"b":{"7":{}}}],["certain",{"_index":862,"t":{},"b":{"18":{},"26":{},"27":{}}}],["chain",{"_index":1446,"t":{},"b":{"32":{}}}],["chain.jl",{"_index":1461,"t":{},"b":{"32":{}}}],["chainrul",{"_index":952,"t":{},"b":{"19":{}}}],["chairmark",{"_index":1360,"t":{},"b":{"28":{}}}],["chairmarks.jl",{"_index":1361,"t":{},"b":{"28":{}}}],["challeng",{"_index":581,"t":{},"b":{"8":{}}}],["chang",{"_index":86,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["characterist",{"_index":1194,"t":{},"b":{"24":{}}}],["chase",{"_index":180,"t":{},"b":{"5":{}}}],["check",{"_index":975,"t":{},"b":{"19":{},"21":{}}}],["chemistri",{"_index":1172,"t":{},"b":{"24":{}}}],["choic",{"_index":1150,"t":{},"b":{"24":{},"26":{}}}],["choos",{"_index":1632,"t":{},"b":{"38":{}}}],["claim",{"_index":1363,"t":{},"b":{"28":{}}}],["class",{"_index":800,"t":{},"b":{"18":{}}}],["clearli",{"_index":1488,"t":{},"b":{"35":{}}}],["cleen",{"_index":1564,"t":{},"b":{"37":{}}}],["clever",{"_index":1352,"t":{},"b":{"28":{}}}],["cli",{"_index":1439,"t":{},"b":{"31":{}}}],["click",{"_index":77,"t":{},"b":{"0":{},"37":{},"38":{}}}],["client",{"_index":249,"t":{},"b":{"5":{}}}],["clinicaltrialutilities.jl",{"_index":1001,"t":{},"b":{"20":{}}}],["clip",{"_index":304,"t":{},"b":{"7":{}}}],["clone",{"_index":1569,"t":{},"b":{"37":{}}}],["close",{"_index":1490,"t":{},"b":{"35":{},"37":{}}}],["closer",{"_index":535,"t":{},"b":{"8":{}}}],["cluster",{"_index":1142,"t":{},"b":{"24":{}}}],["code",{"_index":500,"t":{},"b":{"8":{},"16":{},"24":{},"25":{},"26":{},"28":{}}}],["codecov",{"_index":1621,"t":{},"b":{"37":{}}}],["codecovlink",{"_index":1619,"t":{},"b":{"37":{}}}],["coher",{"_index":1538,"t":{},"b":{"37":{}}}],["collect",{"_index":1035,"t":{},"b":{"21":{}}}],["color",{"_index":611,"t":{},"b":{"8":{}}}],["come",{"_index":202,"t":{},"b":{"5":{},"8":{}}}],["command",{"_index":1216,"t":{},"b":{"25":{},"31":{},"37":{}}}],["comment",{"_index":976,"t":{},"b":{"19":{}}}],["commit",{"_index":1367,"t":{},"b":{"28":{},"37":{}}}],["common",{"_index":267,"t":{},"b":{"7":{},"25":{}}}],["commonli",{"_index":1350,"t":{},"b":{"28":{}}}],["commun",{"_index":82,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["compactif",{"_index":53,"t":{"34":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["compar",{"_index":372,"t":{},"b":{"8":{},"9":{},"24":{},"26":{},"28":{},"37":{}}}],["comparison",{"_index":3,"t":{"37":{},"38":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["comparisonperform",{"_index":778,"t":{},"b":{"18":{}}}],["compat",{"_index":789,"t":{},"b":{"18":{},"19":{},"24":{}}}],["compel",{"_index":576,"t":{},"b":{"8":{}}}],["compet",{"_index":421,"t":{},"b":{"8":{}}}],["compil",{"_index":731,"t":{},"b":{"16":{},"28":{},"36":{}}}],["complaint",{"_index":409,"t":{},"b":{"8":{}}}],["complet",{"_index":90,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["complex",{"_index":895,"t":{},"b":{"19":{},"24":{}}}],["compon",{"_index":933,"t":{},"b":{"19":{},"24":{}}}],["compromis",{"_index":1365,"t":{},"b":{"28":{}}}],["comput",{"_index":116,"t":{},"b":{"1":{},"7":{},"16":{},"24":{},"26":{},"27":{},"28":{},"30":{}}}],["con",{"_index":485,"t":{},"b":{"8":{}}}],["concern",{"_index":667,"t":{},"b":{"11":{}}}],["concis",{"_index":571,"t":{},"b":{"8":{}}}],["concret",{"_index":919,"t":{},"b":{"19":{}}}],["conda",{"_index":658,"t":{},"b":{"10":{}}}],["configur",{"_index":492,"t":{},"b":{"8":{}}}],["consid",{"_index":1332,"t":{},"b":{"27":{}}}],["consist",{"_index":584,"t":{},"b":{"8":{},"19":{},"20":{},"37":{}}}],["constrain",{"_index":297,"t":{},"b":{"7":{},"18":{}}}],["constraint",{"_index":829,"t":{},"b":{"18":{}}}],["constraints✅✅❌❌❌✅equ",{"_index":851,"t":{},"b":{"18":{}}}],["constraints✅❌❌✅❌❌non",{"_index":852,"t":{},"b":{"18":{}}}],["constraints✅❌❌✅❌❌robustifi",{"_index":853,"t":{},"b":{"18":{}}}],["construct",{"_index":295,"t":{},"b":{"7":{}}}],["contain",{"_index":1528,"t":{},"b":{"37":{},"38":{}}}],["content",{"_index":773,"t":{},"b":{"18":{},"32":{},"37":{},"38":{}}}],["contentbuild",{"_index":1531,"t":{},"b":{"37":{}}}],["contentcr",{"_index":1533,"t":{},"b":{"37":{}}}],["context",{"_index":1084,"t":{},"b":{"23":{},"26":{}}}],["continu",{"_index":1083,"t":{},"b":{"23":{},"24":{}}}],["continuum",{"_index":1192,"t":{},"b":{"24":{}}}],["contribut",{"_index":69,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["contributor",{"_index":1638,"t":{},"b":{"38":{}}}],["control",{"_index":38,"t":{"25":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["controlsystems.jl",{"_index":1222,"t":{},"b":{"25":{}}}],["controlsystems.jlmodelpredictivecontrol.jloth",{"_index":1221,"t":{},"b":{"25":{}}}],["conveni",{"_index":1276,"t":{},"b":{"26":{},"28":{},"32":{}}}],["convent",{"_index":697,"t":{},"b":{"16":{},"37":{}}}],["conventionsguidesmodifi",{"_index":1530,"t":{},"b":{"37":{}}}],["converg",{"_index":884,"t":{},"b":{"18":{}}}],["convers",{"_index":644,"t":{},"b":{"10":{},"19":{},"21":{}}}],["convert",{"_index":650,"t":{},"b":{"10":{},"26":{}}}],["convex",{"_index":335,"t":{},"b":{"7":{}}}],["convolut",{"_index":711,"t":{},"b":{"16":{}}}],["cool",{"_index":498,"t":{},"b":{"8":{}}}],["coordin",{"_index":960,"t":{},"b":{"19":{}}}],["copi",{"_index":647,"t":{},"b":{"10":{}}}],["core",{"_index":1453,"t":{},"b":{"32":{}}}],["correct",{"_index":1370,"t":{},"b":{"28":{}}}],["correspond",{"_index":926,"t":{},"b":{"19":{}}}],["cost",{"_index":787,"t":{},"b":{"18":{}}}],["costli",{"_index":1384,"t":{},"b":{"28":{}}}],["counterpart",{"_index":943,"t":{},"b":{"19":{}}}],["cours",{"_index":616,"t":{},"b":{"8":{}}}],["cover",{"_index":84,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["cpu",{"_index":881,"t":{},"b":{"18":{},"24":{},"28":{}}}],["creat",{"_index":561,"t":{},"b":{"8":{},"25":{},"27":{},"31":{},"32":{},"36":{},"37":{},"38":{}}}],["create/restructur",{"_index":1550,"t":{},"b":{"37":{}}}],["creation",{"_index":1495,"t":{},"b":{"36":{}}}],["cthulhu.jl",{"_index":1244,"t":{},"b":{"26":{}}}],["cuda.jl",{"_index":1196,"t":{},"b":{"24":{}}}],["current",{"_index":245,"t":{},"b":{"5":{},"18":{},"24":{},"35":{},"37":{},"38":{}}}],["curv",{"_index":834,"t":{},"b":{"18":{},"24":{}}}],["custom",{"_index":310,"t":{},"b":{"7":{},"8":{}}}],["cut",{"_index":179,"t":{},"b":{"5":{},"19":{}}}],["cytoskeleton",{"_index":1173,"t":{},"b":{"24":{}}}],["damn",{"_index":1267,"t":{},"b":{"26":{}}}],["data",{"_index":6,"t":{},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["datafram",{"_index":8,"t":{"3":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["dataframes.jl",{"_index":138,"t":{},"b":{"3":{}}}],["dataset",{"_index":26,"t":{"12":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["day",{"_index":507,"t":{},"b":{"8":{}}}],["deal",{"_index":965,"t":{},"b":{"19":{}}}],["decid",{"_index":121,"t":{},"b":{"1":{}}}],["declar",{"_index":572,"t":{},"b":{"8":{}}}],["deconvolut",{"_index":1108,"t":{},"b":{"23":{}}}],["dedic",{"_index":273,"t":{},"b":{"7":{},"17":{},"28":{}}}],["default",{"_index":399,"t":{},"b":{"8":{},"10":{},"37":{}}}],["defin",{"_index":756,"t":{},"b":{"17":{},"18":{},"19":{},"21":{},"24":{},"31":{},"33":{},"37":{}}}],["definit",{"_index":414,"t":{},"b":{"8":{},"18":{}}}],["definition✅✅✅✅❌❌bound",{"_index":850,"t":{},"b":{"18":{}}}],["delaun",{"_index":338,"t":{},"b":{"7":{}}}],["delaunator.jl",{"_index":281,"t":{},"b":{"7":{}}}],["delaunay",{"_index":278,"t":{},"b":{"7":{}}}],["delaunay.jl",{"_index":286,"t":{},"b":{"7":{}}}],["delaunaytriangulation.jl",{"_index":277,"t":{},"b":{"7":{}}}],["demonstr",{"_index":1429,"t":{},"b":{"30":{}}}],["deneb.jl",{"_index":555,"t":{},"b":{"8":{}}}],["dens",{"_index":780,"t":{},"b":{"18":{}}}],["depend",{"_index":657,"t":{},"b":{"10":{},"28":{}}}],["describ",{"_index":637,"t":{},"b":{"10":{},"16":{},"20":{},"21":{},"32":{},"36":{}}}],["descript",{"_index":293,"t":{},"b":{"7":{},"8":{},"20":{},"36":{},"38":{}}}],["detail",{"_index":244,"t":{},"b":{"5":{},"8":{},"10":{},"17":{},"26":{},"35":{}}}],["detect",{"_index":1587,"t":{},"b":{"37":{}}}],["dev",{"_index":1187,"t":{},"b":{"24":{}}}],["develop",{"_index":124,"t":{},"b":{"1":{},"2":{},"3":{},"4":{},"5":{},"6":{},"11":{},"12":{},"13":{},"15":{},"18":{},"20":{},"21":{},"22":{},"23":{},"24":{},"27":{},"29":{},"30":{},"31":{},"33":{},"35":{},"36":{}}}],["devic",{"_index":1220,"t":{},"b":{"25":{}}}],["dicusss",{"_index":1380,"t":{},"b":{"28":{}}}],["differ",{"_index":226,"t":{},"b":{"5":{},"8":{},"10":{},"17":{},"18":{},"25":{},"26":{},"27":{},"28":{},"29":{},"32":{},"33":{},"37":{},"38":{}}}],["differenti",{"_index":16,"t":{"15":{}},"b":{"0":{},"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":{},"27":{},"28":{},"29":{},"30":{},"31":{},"32":{},"33":{},"34":{},"35":{},"36":{},"37":{},"38":{},"39":{}}}],["differentialequations.jl",{"_index":1203,"t":{},"b":{"24":{}}}],["differentiation✅✅✅✅✅✅support",{"_index":857,"t":{},"b":{"18":{}}}],["difficult",{"_index":602,"t":{},"b":{"8":{}}}],["dimens",{"_index":280,"t":{},"b":{"7":{},"21":{}}}],["dimension",{"_index":818,"t":{},"b":{"18":{},"21":{},"24":{}}}],["direct",{"_index":1217,"t":{},"b":{"25":{}}}],["directli",{"_index":597,"t":{},"b":{"8":{},"10":{},"19":{},"21":{},"25":{},"26":{}}}],["directori",{"_index":1624,"t":{},"b":{"37":{}}}],["directqhull.jl",{"_index":334,"t":{},"b":{"7":{}}}],["discours",{"_index":143,"t":{},"b":{"4":{},"5":{},"19":{},"38":{}}}],["discov",{"_index":1630,"t":{},"b":{"38":{}}}],["discret",{"_index":1129,"t":{},"b":{"24":{}}}],["discuss",{"_index":767,"t":{},"b":{"17":{},"18":{},"28":{},"32":{},"38":{}}}],["disjoint",{"_index":300,"t":{},"b":{"7":{}}}],["display",{"_index":1280,"t":{},"b":{"26":{}}}],["distanc",{"_index":963,"t":{},"b":{"19":{}}}],["distances.jl",{"_index":1002,"t":{},"b":{"20":{}}}],["distinct",{"_index":188,"t":{},"b":{"5":{}}}],["distribut",{"_index":1264,"t":{},"b":{"26":{},"29":{}}}],["do",{"_index":545,"t":{},"b":{"8":{},"28":{}}}],["doc",{"_index":386,"t":{},"b":{"8":{},"27":{},"37":{}}}],["docs/_layout/pgwrap.html",{"_index":1627,"t":{},"b":{"37":{}}}],["docs/comparisons//.md",{"_index":1593,"t":{},"b":{"37":{}}}],["docs/comparisons///.md",{"_index":1600,"t":{},"b":{"37":{}}}],["docs/comparisons// Search ⋅ JuliaPackageComparisons
Julia
Package
Comparisons
Page source | GitHub repository

Number of results found:

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 15, 2024. Built with Franklin.jl
\ No newline at end of file + Search ⋅ JuliaPackageComparisons
Julia
Package
Comparisons
Page source | GitHub repository

Number of results found:

This website is a community effort covering a lot of ever-changing information. It will therefore never be complete or without error. If you see something wrong, or have something to contribute, please see the "Contributing" section in the github repository.

Last modified: March 19, 2024. Built with Franklin.jl
\ No newline at end of file diff --git a/PR84/sitemap.xml b/PR84/sitemap.xml index 187101f..cc2e203 100644 --- a/PR84/sitemap.xml +++ b/PR84/sitemap.xml @@ -3,235 +3,235 @@ comparisons/math/differentiation/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/utility/redefinable_structs/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/math/linear_solvers/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/web/static_websites/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/machine_learning/machine_learning_datasets/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 contributing/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/testing/profiling/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/uncertainty_propagation/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/utility/piping/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/utility/types_compactification/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/simulation/agentbasedmodelling/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/math/nonlinear_solvers/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/signal_processing/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/math/einsum/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/geometry/triangulations_or_tessellations/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/acceleration/loop_acceleration/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/acceleration/multithreading/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/machine_learning/neural_networks/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/data_structure/graphs/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/web/web_apps/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/utility/notebooks/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/fileio/saving_files/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 search/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/math/quaternions/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/testing/runtime/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/metrics/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/simulation/control_systems/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/data_structure/dataframes/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/interoperability/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/machine_learning/machine_learning/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/testing/property_based_testing/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/utility/package_templates/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/graphics/plotting/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/math/bspline/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/probabilistic_programming/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/interoperability/python/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/physics/units/index.html - 2024-03-15 + 2024-03-19 monthly 0.5 comparisons/fileio/json/index.html - 2024-03-15 + 2024-03-19 monthly 0.5