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python3-matplotlib-3.10.0-3.lbn36.x86_64: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.

python3-matplotlib-3.10.0-3.lbn36.x86_64: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, web application servers, and various graphical user interface toolkits. Matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.

python3-marshmallow-sqlalchemy-1.0.0-1.lbn36.noarch: ********************** marshmallow-sqlalchemy **********************|pypi- package| |build-status| |docs| |marshmallow3| |black|Homepage: < integration with the marshmallow < (de)serialization library.Declare your models .. code- block:: python import sqlalchemy as sa from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import scoped_session, sessionmaker, relationship,...

python3-jupyterlab_pygments-0.1.2-5.fc36.noarch: This package contains a syntax coloring theme for pygments making use of the JupyterLab CSS variables.

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.

python3-jupyter-polymake-0.16-18.20180129.7049940.fc36.noarch: This package contains a Jupyter kernel for polymake.

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)

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)

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):

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):

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):

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.

python3-jupyter-core-5.7.2-1.lbn36.noarch: There is no reason to install this package on its own.

python3-jupyter-core-5.7.2-1.lbn36.noarch: There is no reason to install this package on its own.

python3-jupyter-console-6.6.3-1.lbn36.noarch: Jupyter Console A terminal-based console frontend for Jupyter kernels. This code is based on the single-process IPython terminal. Install with pip: pip install jupyter-console Install with conda: conda install -c conda-forge jupyter_console Start: jupyter console Help: jupyter console -h Jupyter Console allows for console-based interaction with non-python Jupyter kernels such as IJulia, IRKernel. To start the console with a particular kernel, ask for it by name:: jupyter console --kernel=julia-0.4 A list of available kernels can be seen with:: jupyter kernelspec list Release build: $ pip install pep517 $ python -m pep517.build . Resources Project Jupyter website Documentation for Jupyter Console [PDF] Documentation for Project Jupyter [PDF] Issues Technical support - Jupyter Google Group About the Jupyter Development Team The Jupyter Development Team is the set of all contributors to the Jupyter project. This includes all of the Jupyter subprojects. The core team that coordi

python3-jupyter-console-6.6.3-1.lbn36.noarch: Jupyter Console A terminal-based console frontend for Jupyter kernels. This code is based on the single-process IPython terminal. Install with pip: pip install jupyter-console Install with conda: conda install -c conda-forge jupyter_console Start: jupyter console Help: jupyter console -h Jupyter Console allows for console-based interaction with non-python Jupyter kernels such as IJulia, IRKernel. To start the console with a particular kernel, ask for it by name:: jupyter console --kernel=julia-0.4 A list of available kernels can be seen with:: jupyter kernelspec list Release build: $ pip install pep517 $ python -m pep517.build . Resources Project Jupyter website Documentation for Jupyter Console [PDF] Documentation for Project Jupyter [PDF] Issues Technical support - Jupyter Google Group About the Jupyter Development Team The Jupyter Development Team is the set of all contributors to the Jupyter project. This includes all of the Jupyter subprojects. The core team that coordi

python3-jupyter-client-8.4.0-1.lbn36.noarch: Jupyter Client jupyter_client contains the reference implementation of the Jupyter protocol. It also provides client and kernel management APIs for working with kernels. It also provides the jupyter kernelspec entrypoint for installing kernelspecs for use with Jupyter frontends. Development Setup The Jupyter Contributor Guides provide extensive information on contributing code or documentation to Jupyter projects. The limited instructions below for setting up a development environment are for your convenience. Coding You'll need Python and pip on the search path. Clone the Jupyter Client git repository to your computer, for example in /my/project/jupyter_client cd /my/projects/ git clone git@github.com:jupyter/jupyter_client.git Now create an editable install and download the dependencies of code and test suite by executing: cd /my/projects/jupyter_client/ pip install -e ".[test]" pytest The last command runs the test suite to verify the setup. During development, you can pass file

python3-jupyter-client-8.4.0-1.lbn36.noarch: Jupyter Client jupyter_client contains the reference implementation of the Jupyter protocol. It also provides client and kernel management APIs for working with kernels. It also provides the jupyter kernelspec entrypoint for installing kernelspecs for use with Jupyter frontends. Development Setup The Jupyter Contributor Guides provide extensive information on contributing code or documentation to Jupyter projects. The limited instructions below for setting up a development environment are for your convenience. Coding You'll need Python and pip on the search path. Clone the Jupyter Client git repository to your computer, for example in /my/project/jupyter_client cd /my/projects/ git clone git@github.com:jupyter/jupyter_client.git Now create an editable install and download the dependencies of code and test suite by executing: cd /my/projects/jupyter_client/ pip install -e ".[test]" pytest The last command runs the test suite to verify the setup. During development, you can pass file

python3-gunicorn-23.0.0-1.lbn36.noarch: Gunicorn Gunicorn ‘Green Unicorn’ is a Python WSGI HTTP Server for UNIX. It’s a pre-fork worker model ported from Ruby’s Unicorn project. The Gunicorn server is broadly compatible with various web frameworks, simply implemented, light on server resource usage, and fairly speedy. Feel free to join us in #gunicorn on Libera.chat. Documentation The documentation is hosted at https:/docs.gunicorn.org. Installation Gunicorn requires Python 3.x >= 3.7. Install from PyPI: $ pip install gunicorn Usage Basic usage: $ gunicorn [OPTIONS] APP_MODULE Where APP_MODULE is of the pattern $(MODULE_NAME):$(VARIABLE_NAME). The module name can be a full dotted path. The variable name refers to a WSGI callable that should be found in the specified module. Example with test app: $ cd examples $ gunicorn --workers=2 test:app Contributing See our complete contributor’s guide for more details. License Gunicorn is released under the MIT License. See the LICENSE file for more details.

python3-graphene-sqlalchemy-2.3.0-1.lbn36.noarch: Please read UPGRADE-v2.0.md < to learn how to upgrade to Graphene 2.0.--|Graphene Logo| Graphene-SQLAlchemy |Build Status| |PyPI version| |Coverage Status| A SQLAlchemy < integration for Graphene < For instaling graphene, just run this command in your shell.. code:: bash pip install "graphene-sqlalchemy>2.0"Examples Here is a simple SQLAlchemy model:

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