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e161958bab
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12.0
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bedlam-name-fixes
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gitignore
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user/anddt/network/strongswan
user/anddt/network/umurmur
user/anddt/network/yt-dlp
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user/anddt/office/gnumeric
user/anddt/office/sigil
user/anddt/system/FontAwesome
user/anddt/system/conky
user/anddt/system/fakeroot
user/anddt/system/hplip-plugin
user/anddt/system/postgresql
user/anddt/system/testdisk
user/anddt/system/worker
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user/chris.willing/updates
user/dslackw/updates
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willysr
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Heinz Wiesinger
63daf9f79a
All: Support $PRINT_PACKAGE_NAME env var
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| README | 99722947e4 development/julia: Fix README. | před 4 roky |
| doinst.sh | 77b7439f32 development/julia: Added (Dynamic Programming Language). | před 10 roky |
| julia.SlackBuild | 63daf9f79a All: Support $PRINT_PACKAGE_NAME env var | před 3 roky |
| julia.info | 4ccb99d447 development/julia: Updated for version 1.3.1. | před 4 roky |
| slack-desc | 77b7439f32 development/julia: Added (Dynamic Programming Language). | před 10 roky |
README
Julia is a high-level, high-performance dynamic programming language
for technical computing with syntax that is familiar to users of other
technical computing environments.
It provides a sophisticated compiler, distributed parallel execution,
numerical accuracy, and an extensive mathematical function library.
The library, largely written in Julia itself, also integrates mature,
best-of-breed C and Fortran libraries for linear algebra, random number
generation, signal processing, and string processing.
In addition, the Julia developer community is contributing a number
of external packages through Julia's built-in package manager at a
rapid pace.
IJulia, a collaboration between the IPython and Julia communities,
provides a powerful browser-based graphical notebook interface to Julia.
Julia programs are organized around multiple dispatch; by defining
functions and overloading them for different combinations of argument
types, which can also be user-defined.
A Summary of Features:
* Multiple dispatch: providing ability to define function behavior
across many combinations of argument types
* Dynamic type system: types for documentation, optimization, and
dispatch
* Good performance, approaching that of statically-compiled languages
like C
* Built-in package manager
* Lisp-like macros and other metaprogramming facilities
* Call Python functions: use the PyCall package
* Call C functions directly: no wrappers or special APIs
* Powerful shell-like capabilities for managing other processes
* Designed for parallelism and distributed computation
* Coroutines: lightweight "green" threading
* User-defined types are as fast and compact as built-ins
* Automatic generation of efficient, specialized code for different
argument types
* Elegant and extensible conversions and promotions for numeric and
other types
* Efficient support for Unicode, including but not limited to UTF-8
* MIT licensed: free and open source
for technical computing with syntax that is familiar to users of other
technical computing environments.
It provides a sophisticated compiler, distributed parallel execution,
numerical accuracy, and an extensive mathematical function library.
The library, largely written in Julia itself, also integrates mature,
best-of-breed C and Fortran libraries for linear algebra, random number
generation, signal processing, and string processing.
In addition, the Julia developer community is contributing a number
of external packages through Julia's built-in package manager at a
rapid pace.
IJulia, a collaboration between the IPython and Julia communities,
provides a powerful browser-based graphical notebook interface to Julia.
Julia programs are organized around multiple dispatch; by defining
functions and overloading them for different combinations of argument
types, which can also be user-defined.
A Summary of Features:
* Multiple dispatch: providing ability to define function behavior
across many combinations of argument types
* Dynamic type system: types for documentation, optimization, and
dispatch
* Good performance, approaching that of statically-compiled languages
like C
* Built-in package manager
* Lisp-like macros and other metaprogramming facilities
* Call Python functions: use the PyCall package
* Call C functions directly: no wrappers or special APIs
* Powerful shell-like capabilities for managing other processes
* Designed for parallelism and distributed computation
* Coroutines: lightweight "green" threading
* User-defined types are as fast and compact as built-ins
* Automatic generation of efficient, specialized code for different
argument types
* Elegant and extensible conversions and promotions for numeric and
other types
* Efficient support for Unicode, including but not limited to UTF-8
* MIT licensed: free and open source