gonum
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gonum | Testify | |
---|---|---|
24 | 64 | |
7,260 | 22,019 | |
1.5% | 1.6% | |
8.3 | 8.6 | |
6 days ago | 6 days ago | |
Go | Go | |
BSD 3-clause "New" or "Revised" License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gonum
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How to set up interface to accept multi-dimension array?
But if you want to see what can be done for numeric stuff, check out gonum. Personally, I still wouldn't use Go, and I rather suspect it's still pretty easy to reach for something like what you're trying to do and not find it because Go just can't write that type sensibly, but you can at least see what is available, written by people who disagree with me about Go not being a great language for this.
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packages similar to Pandas
Numpy functionality is largely covered by https://www.gonum.org/ but for pandas I'm not sure if there is an equivalent as widely accepted. However, you might try https://github.com/rocketlaunchr/dataframe-go which I have not tried but it looks like it covers some of what you're looking for
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What libraries are missing?
Math libraries. It's just gonum right now. Missing things that often require people to link C or Python libs. E.g. https://github.com/gonum/gonum/issues/354
- Gonum Numerical Packages
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SIMD Accelerated vector math
Maybe this way you could avoid having Mul, Mul_Inplace, Mul_Into variants. Gonum mostly follows the same pattern.
- Modern hardware is fast, so let's choose the slowest language to balance it out
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graph: A generic Go library for creating graph data structures and performing operations on them. It supports different kinds of graphs such as directed graphs, acyclic graphs, or trees.
How does this compare to gonum graph? https://github.com/gonum/gonum/tree/master/graph
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From Python to NumPy
Go is quite a bit cleaner than Python and its concurrency/parallelism primitives can be well suited to scientific workloads.
You may want to have a look at Gonum (https://www.gonum.org), and the Go HEP package developed by CERN (https://go-hep.org).
I was also surprised to see DSP and pretty sophisticated packages, although I never used them: https://awesome-go.com/science-and-data-analysis
And of course Go has Jupyter integration, it's almost like running a script thanks to its fast compilation time.
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Go for science?
You should check out this https://github.com/gonum/gonum
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What makes concurrency in Go better than multiprocesing/multithreading in Python?
No, using CPU extensions and GPUs is a different thing than doing multitasking. There is Gonum but it is still slower than Numpy: https://github.com/gonum/gonum/issues/511
Testify
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What 3rd-party libraries do you use often/all the time?
github.com/stretchr/testify
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Testing calls to Daily's REST API in Go
I then verify that there are no issues with writing the body with require.NoError() from the testify toolkit. This will ensure the test fails if something happens to go wrong at this point.
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Gopher Pythonista #1: Moving From Python To Go
For testing purposes, Go provides a go test command that automatically discovers tests within your application and supports features such as caching and code coverage. However, if you require more advanced testing capabilities such as suites or mocking, you will need to install a toolkit like testify. Overall, while Go provides a highly effective testing experience, it's worth noting that writing tests in Python using pytest is arguably one of the most enjoyable testing experiences I have encountered across all programming languages.
- Why elixir over Golang
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How to start a Go project in 2023
Things I can't live without in a new Go project in no particular order:
- https://github.com/golangci/golangci-lint - meta-linter
- https://goreleaser.com - automate release workflows
- https://magefile.org - build tool that can version your tools
- https://github.com/ory/dockertest/v3 - run containers for e2e testing
- https://github.com/ecordell/optgen - generate functional options
- https://golang.org/x/tools/cmd/stringer - generate String()
- https://mvdan.cc/gofumpt - stricter gofmt
- https://github.com/stretchr/testify - test assertion library
- https://github.com/rs/zerolog - logging
- https://github.com/spf13/cobra - CLI framework
FWIW, I just lifted all the tools we use for https://github.com/authzed/spicedb
We've also written some custom linters that might be useful for other folks: https://github.com/authzed/spicedb/tree/main/tools/analyzers
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Do you wrap testing libraries?
Im thinking in wrap or not the library https://github.com/stretchr/testify to do my tests.
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[Go] How to unit test for exception handling?
Are you limited to the std lib, or can you use testify? You can require things like require.Error()
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Tools besides Go for a newbie
IDE: use whatever make you productive. I personally use vscode. VCS: git, as golang communities use github heavily as base for many libraries. AFAIK Linter: use staticcheck for linting as it looks like mostly used linting tool in go, supported by many also. In Vscode it will be recommended once you install go plugin. Libraries/Framework: actually the standard libraries already included many things you need, decent enough for your day-to-day development cycles(e.g. `net/http`). But here are things for extra: - Struct fields validator: validator - Http server lib: chi router , httprouter , fasthttp (for non standard http implementations, but fast) - Web Framework: echo , gin , fiber , beego , etc - Http client lib: most already covered by stdlib(net/http), so you rarely need extra lib for this, but if you really need some are: resty - CLI: cobra - Config: godotenv , viper - DB Drivers: sqlx , postgre , sqlite , mysql - nosql: redis , mongodb , elasticsearch - ORM: gorm , entgo , sqlc(codegen) - JS Transpiler: gopherjs - GUI: fyne - grpc: grpc - logging: zerolog - test: testify , gomock , dockertest - and many others you can find here
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Is gomock still maintained and recommended?
To answer OP directly, I am largely quite happy with mockery (and testify) to write expressive tests.
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Golang, GraphQL y Postgress
Como herramientas te recomiendo: FastJson https://github.com/valyala/fastjson : Si necesitas leer jsons Testify https://github.com/stretchr/testify : Para mockear y testear
What are some alternatives?
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
ginkgo - A Modern Testing Framework for Go
gosl - Linear algebra, eigenvalues, FFT, Bessel, elliptic, orthogonal polys, geometry, NURBS, numerical quadrature, 3D transfinite interpolation, random numbers, Mersenne twister, probability distributions, optimisation, differential equations.
GoConvey - Go testing in the browser. Integrates with `go test`. Write behavioral tests in Go.
Stats - A well tested and comprehensive Golang statistics library package with no dependencies.
gomega - Ginkgo's Preferred Matcher Library
gonum/plot - A repository for plotting and visualizing data
gomock - GoMock is a mocking framework for the Go programming language.
PiHex - PiHex Library, written in Go, generates a hexadecimal number sequence in the number Pi in the range from 0 to 10,000,000.
gotest.tools - A collection of packages to augment the go testing package and support common patterns.
goraph - Package goraph implements graph data structure and algorithms.
go-cmp - Package for comparing Go values in tests