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lisa | RxPY | |
---|---|---|
6 | 4 | |
198 | 4,680 | |
0.5% | 0.9% | |
9.7 | 0.0 | |
9 days ago | 2 months ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | 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.
lisa
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So, I wrote a Maybe monad in Python 3
You might be interested in that: https://github.com/ARM-software/lisa/blob/master/lisa/monad.py
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Parca Agent rewrites eBPF in-kernel C code in Rust (using Aya-rs)
This is to replace the current flow purely based on pandas dataframe and offline trace.dat parsing used in LISA: https://github.com/ARM-software/lisa (collecting a trace.dat is nice for debugging but limits to small durations, and pandas does not allow running computations in constant memory, which is an issue for very big traces)
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Languages with integrated dependency injection
The module added by this PR seems to be a pretty good fit: https://github.com/ARM-software/lisa/pull/1722
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What tools are missing in Python?
I made that thing taking some vague inspiration from SML module system: https://github.com/ARM-software/lisa/pull/1722/files
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The pipe-operator to python |>
import builtins from operator import add import functools # These functions can be found at: # https://github.com/ARM-software/lisa/blob/master/lisa/utils.py#L147 # Note: my implementation of curry() seems to be broken wrt named parameters (or for parameters with defaults, haven't looked at the details) for some reason but for this example it does not matter from lisa.utils import compose, curry def even(x): return x % 2 == 0 # The builtin functions don't have a signature, which will upset curry() so we # redefine it here def map(f, iterable): return builtins.map(f, iterable) def filter(f, iterable): return builtins.filter(f, iterable) # Swapped init and iterable to be curry-friendly def reduce(f, init, iterable): return functools.reduce(f, iterable, init) def pipeline(*items): # Add a currying layer so that we spare the user the need to do it return compose(*(curry(f)(*args) for (f, *args) in items)) # x = filter(even, list) |> map(lambda x: x+1) |> reduce(+) f = pipeline( (filter, even), (map, lambda x: x+1), (reduce, add, 0), ) l = [1,2,3,4] x = f(l) print(x)
RxPY
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Should python add a pipeline operator?
Awaiting that why not simply using RxPy:
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Arquitetura orientada a eventos com lambda functions no Python
Neste tutorial, exploraremos a biblioteca RxPY, que é a biblioteca mais popular atualmente disponível para escrever sistemas reativos.
- How to build always listening/responding application?
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The pipe-operator to python |>
As one of the developers of RxPy, I think this would allow all streaming libraries to cohabit together, like AsyncIO did for asynchronous programming in python.
What are some alternatives?
PyInstaller - Freeze (package) Python programs into stand-alone executables
PyFunctional - Python library for creating data pipelines with chain functional programming
parca-agent - eBPF based always-on profiler auto-discovering targets in Kubernetes and systemd, zero code changes or restarts needed!
rxray - Ray distributed computing integration for RxPY
awesome-functional-python - A curated list of awesome things related to functional programming in Python.
blazon - A python library for assuring data structure and format via schemas like JSON Schema
proposals - Tracking ECMAScript Proposals
datoviz - ⚡ High-performance GPU interactive scientific data visualization with Vulkan
django-sockpuppet - Build reactive applications with the django tooling you already know and love.
function-pipe - Tools for extended function composition and pipelines in Python