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python3-jupyter-kernel-singular-0.9.9-8.fc36.noarch
This package contains a Jupyter kernel for Singular, to enable using
Jupyter as the front end for Singular.
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python3-jupyter-kernel-test-0.6.0-1.lbn36.noarch
jupyter_kernel_test is a tool for testing Jupyter kernels. It tests kernels
for successful code execution and conformance with the Jupyter Messaging Protocol
(currently 5.0).
Install
Install it with pip (python3.4 or greater required):
pip3 install jupyter_kernel_test
Usage
To use it, you need to write a (python) unittest file containing code
samples in the relevant language which test various parts of the messaging protocol.
A short example is given below, and you can also refer to the
test_ipykernel.py and test_irkernel.py files for complete examples.
Some parts of the messaging protocol are relevant only to the browser-based
notebook (rich display) or console interfaces (code completeness,
history searching). Only parts of the spec for which you provide code samples
are tested.
Run this file directly using python, or use nosetests or py.test to find
and run it.
Example
import unittest
import jupyter_kernel_test
class MyKernelTests(jupyter_kernel_test.KernelTests):
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python3-jupyter-packaging-0.12.3-1.lbn36.noarch
Jupyter Packaging
Tools to help build and install Jupyter Python packages that require a pre-build step that may include JavaScript build steps.
Install
pip install jupyter-packaging
Usage
There are three ways to use jupyter-packaging in another package.
In general, you should not depend on jupyter_packaging as a runtime dependency, only as a build dependency.
As a Build Requirement
Use a pyproject.toml file as outlined in pep-518.
An example:
[build-system]
requires = ["jupyter_packaging>=0.10,<2"]
build-backend = "setuptools.build_meta"
Below is an example setup.py using the above config.
It assumes the rest of your metadata is in setup.cfg.
We wrap the import in a try/catch to allow the file to be run without jupyter_packaging
so that python setup.py can be run directly when not building.
from setuptools import setup
try:
from jupyter_packaging import wrap_installers, npm_builder
builder = npm_builder()
cmdclass = wrap_installers(pre_develop=builder, pre_dist=builder)
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python3-jupyter-polymake-0.16-18.20180129.7049940.fc36.noarch
This package contains a Jupyter kernel for polymake.
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python3-jupyter-sphinx-0.3.2-3.fc36.noarch
Jupyter-Sphinx enables running code embedded in Sphinx documentation and
embedding output of that code into the resulting document. It has
support for rich output such as images and even Jupyter interactive
widgets.
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python3-kfp-server-api-1.8.5-1.lbn36.noarch
This file contains REST API specification for Kubeflow Pipelines. The file is
autogenerated from the swagger definition. noqa: E501
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python3-kubernetes-24.2.0-4.fc36.noarch
Python client for the kubernetes API.
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python3-kylinpy-2.8.4-1.lbn36.noarch
Apache Kylin Python Client Library
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python3-lazy-object-proxy-1.7.1-2.fc36.x86_64
A fast and thorough lazy object proxy.
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python3-libcst-0.4.9-1.lbn36.x86_64
LibCST parses Python 3.0 -> 3.11 source code as a CST tree that keeps all formatting details (comments, whitespaces, parentheses, etc). It’s useful for building automated refactoring (codemod) applications and linters.
LibCST creates a compromise between an Abstract Syntax Tree (AST) and a traditional Concrete Syntax Tree (CST). By carefully reorganizing and naming node types and fields, we’ve created a lossless CST that looks and feels like an AST.
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