Saravanan666/pypi-org huggingface dataset:
Package: fab-addon-geoalchemy
fab_addon_geoalchemyImplementation of GeoAlchemy fields for Flask Appbuilder.It automatically transforms POINT Geometry columns to widgets with a field for latitude and longitude
and a leaflet map. The map has a pointer for the location, and there is two way binding where dragging
the pointer changes the value in the input fields and vice-versa.
Later on, support for line and polygon columns may be added as well (pull requests welcome).InstallationRunpip install fab-addon-geoalchemy.UsageYou can find examples in the unittests in the tests folder.
But in short, this is what you need to do:Add the following to your config.py:ADDON_MANAGERS = ['fab_addon_geoalchemy.manager.GeoAlchemyManager']And then use this for your models and views:from sqlalchemy import Column, String
from fab_addon_geoalchemy.views import GeoModelView
from fab_addon_geoalchemy.models import GeoSQLAInterface, Geometry

class Observation(Model):
    id = Column(Integer, primary_key=True)
    name = Column(String)
    location = Column(Geometry(geometry_type='POINT', srid=4326))

    def __repr__(self):
        if self.name:
            return self.name
        else:
            return 'Person Type %s' % self.id


class ObservationView(GeoModelView):
    datamodel = GeoSQLAInterface(Observation)
    add_columns = ['name', 'location']This will automatically create the LatLonWidget in the form, and process the data to transform the latitude and longitude values into a point geometry in the database. The srid form the column is used by the field for the conversion of the text coordinates to the geometry. No validation is done if the entered coordinates are actually valid for the specified SRID.
Package: fab-admin
Fab-admin a flask_appbuilder app skeletonQuick start:pip install fab_adminInit stepcd to a empty folderfab_admin clonefab_admin run --port 8080FeaturesFront-end based on VUE IVIEWRedisRQScheduleSSEGraphQLMailAddonDepends on:flaskclickcoloramaflask-sqlalchemyflask-loginflask-openidflask-wtformflask-BabelDeploy and install:python setup.py sdist --formats=gztar,zipMake it Public::python setup.py sdist upload -r pypitwine upload pypi
Package: fab-classic
fab-classicis a Python (2.7 or 3.4+) library and command-line tool
for streamlining the use of SSH for application deployment or systems
administration tasks.fab-classic is forked fromFabric 1.14and is intended to add only bug fixes and compatibility patches, for projects
already using Fabric-1.x in some way. It includes python3 compatibility patches
fromFabric3.UpstreamFabricis now on version 2.x. Fabric-2.x is python3 compatible,
and comesafterFabric3. Fabric-2.x has significant compatibility-breaking
changes, in order to fix some long-standing limitations.Installfab-classic ison PyPI,
so you canpip installfab-classicBe sure topip uninstall fabricfirst if you happen to have it installed,
becausefab-classicuses the same import name “fabric” and entrypoint (tool)
name “fab”.Starting with version 1.16,fab-classicdepends onparamiko-nginstead ofparamiko. Both of those packages
are imported with the nameparamiko, and unfortunately that means that you
should make sure you uninstallparamikobeforeparamiko-ngis installed,
or you will get strange issues (even if things seem to work at first).Starting with version 1.18, you can switch back to depending on the package
namedparamikoby setting the environment variablePARAMIKO_REPLACE=1while installingfab-classic:PARAMIKO_REPLACE=1 pip install --no-binary fab-classic fab-classic==1.18.0(paramiko-ngalso supportsPARAMIKO_REPLACE,
seeparamiko-ng#installation)DocumentationAPI Documentation:https://ploxiln.github.io/fab-classic/Changelog:https://github.com/ploxiln/fab-classic/releasesFor a quick command reference, runfab--helpUsage Introductionfab-classic provides a basic suite of operations for executing local or remote shell
commands (normally or viasudo) and uploading/downloading files, as well as
auxiliary functionality such as prompting the running user for input, or
aborting execution.Typical use involves creating a Python module containing one or more functions,
then executing them via thefabcommand-line tool. Below is a small but
complete “fabfile” containing a single task:fromfabric.apiimportrundefhost_type():run('uname -s')If you save the above asfabfile.py(the default module thatfabloads), you can run the tasks defined in it on one or more
servers, like so:$ fab -H localhost,linuxbox host_type
[localhost] run: uname -s
[localhost] out: Darwin
[linuxbox] run: uname -s
[linuxbox] out: Linux

Done.
Disconnecting from localhost... done.
Disconnecting from linuxbox... done.In addition to use via thefabtool, Fabric’s components may be imported
into other Python code, providing a Pythonic interface to the SSH protocol
suite at a higher level than that provided by theparamiko-nglibrary
(which Fabric itself uses).
Package: fabcohort
fabcohortA small demo library for a fab_cohort about cohort analysisInstallationpip install fabcohortGet startedHow to do cohort analysis with this lib:FUNCTION1:Cohort analysis by segmentsPandas df.head(5) should look like -user_iddatesegmentcount5fb507360cd5c02023-04-01A,B1weg507360cwfw32023-03-01A,16001ef966c13w32023-02-01C,D1weg507360cwfw32023-04-01B,D16001ef966c13w32023-03-01A,B1fromfab_cohortimportCohort# Instantiate a Cohort objectcohort=Cohort()# Call the count_cohort_segments method, e.g., MS for month start, W-MON for week startresult=cohort.count_cohort_segments(df,frequency)# (Optional) if you have multiple segments just parse itresult[['segment1','segment2']]=result['segment'].str.split(',',expand=True)result.drop('segment',axis=1,inplace=True)FUNCTION3:Convert the count to percentage# once the above result is obtained# Call the count_cohort methodresult_pct=cohort.to_pct(result)# (Optional) if you have multiple segments just parse itresult[['segment1','segment2']]=result['segment'].str.split(',',expand=True)result.drop('segment',axis=1,inplace=True)
Package: fabdb
fabdb-cliA Command Line Interface and general-purpose library for queryingfabdbin python.Usage:warning: Currently under construction :warning:fromfabdb.clientimportFabDBClientclient=FabDBClient()print("Single card by ID:")nimblism=client.get_card("nimblism-yellow")print(nimblism.name)print()print("Search Cards:")ranger_cards=client.search_cards(class_="ranger",pitch=2)forcinranger_cards:# automatically handles requesting additional pages as neededprint(c)print()print("Find a deck:")deck=client.get_deck("bYDmozyB")print(deck.name)print(deck.cards[0])Coming SoonCLI interfaceTextual output formattersInteractive/expanded searchLocal card cache
Package: fabdeploit
AboutUsing git for deployments is a great solution for agile development processes and is
implemented by different people with many different flavours. Any solution has one
thing in common: You have to move the whole history of your project over to your server.
This may be ok for some setups, but has - besides giving many internals to your
clients - some disadvantages like:Rollbacks are more complex, as you need to know which commit is the last release
(may be solved using tags)You have to copy the whole repository over to the server, so you cannot skip any
files or add new ones only needed on the server (like aggregated CSS/JS)fabdeploit tries to solve these issues by using a seprate release branch, not wired
to the normal git branches and history. This release branch will only contain release
history (one commit for every release/deployment) and allows you to use filters to
change the contents of the commit tree. This way you are able to have a very slick
deployment process only containing what is necessary, you may even add new files not
contained in your normal history.In addition fabdeploit contains helpers for professional deployment of some common
CMS/Frameworks we use. This will help you implementing a clean process of common deployments,
including enabling maintenance mode, running database migrations, clearing the caches, …DocumentationStill not as much as intended, but growing, this is a goal for 1.0. Please feel
free to look at the example/ directory or the code itself in addition to the
documentation.Seehttp://fabdeploit.readthedocs.org/.
Package: faber-ml
faberwhat is faber?faber is a lightweight pipelining tool for machine learning and data projectswhy though?so our pipelines look clean - like the belowfromfaberimportnodefromdata_science.nodes.quick_mlimport*ml_pipe=[node(prepare_ml,['df_processed','ml_params'],['df_master_train','df_master_test'],),node(train_models,['df_master_train','ml_params'],['ml'],),node(get_best_params,['ml'],['best_params']),]and our data is connected externally and defined in a yaml file; meaning if a data location changes only one place is changed
. wildcards are also supported with {{}}df_raw:filepath:data/00_raw/df_raw.csvread_func:read_csv_pandasdf_processed:filepath:data/01_master/df_processed_{{run_number}}.csvread_func:read_csv_pandaswrite_func:write_csv_pandasoutput_profile:filepath:data/02_insight/output_profile.csvread_func:read_csv_pandaswrite_func:write_csv_pandasml:filepath:data/models/ml_object.pickleread_func:read_picklewrite_func:write_pickleinstall usinggitclonehttps://github.com/pinata-brad/faber.gitcdfaber
python3setup.pysdistbdist_wheel&&pipinstalldist/faber-0.0.3.tar.gz
Package: fabexp-helper
fabexp-helperA CLI to help create new fabric project templates in an effort to standardize experiment structure.Usagefabexp --add (-a) --name (-n) <fabric project name> --local (-l) --from-template (-f) TEMPLATE_NAMEReplace  with a desired name--add: required to add project--name: optional, default: myFabricExperiment--local: add folder for executing local scripts, default: False--from_template: create project from template. Names are any valid jupyter example name.Find project name from folder of jupyter examples OR your own notebookMust give path to file (relative or absolute) if customExamples: hello_fabric, create_l2network_basic (jupyter examples)Only including examples fromfablib_apifolder for now.
Once you run the above command, you should see a new set of folders that serve as a template
for your fabric project.
Package: fabez
A deploy tool for Centos, based on Fabric.FAQChinese VersionRelease Note0.9.xfrom 0.9.0, we change package name to cabric0.8.xsupport create tornado app easilysupport mongo shard featuresupport local web operation,like django’s  manage.py collectstaticsupport supervisorsupport statsdsupport send release-note use mailsupport smtp serversupport phpsupport more config typeimprove install python lxmlsupport tenginesupport nginxsupport python3,pypysupport python complex package.e.g:pillowsupport redissupport mongodbsupport mysqlcore featureadd usergit clone/git pull codeupload private | public key…
Package: fab-geoalchemy
THIS PACKAGE HAS BEEN DEPRECATED.In order to use the proper flask appbuilder addon infrastructure, this package has been deprecated in favor of flask-addon-geoalchemy. This package won’t be updated.fab_geoalchemyImplementation of GeoAlchemy fields for Flask Appbuilder.It automatically transforms POINT Geometry columns to widgets with a field for latitude and longitude.
The goal is to add a leaflet widget and add support for line and polygon as well.InstallationRunpip installfab-geoalchemy.UsageYou can find examples in the unittests in the tests folder.
But in short, this is what you need to do:from fab_geoalchemy.views import GeoModelView
from fab_geoalchemy.interface import GeoSQLAInterface

class Observation(Model):
    id = Column(Integer, primary_key=True)
    name = Column(String)
    location = Column(Geometry(geometry_type='POINT', srid=4326))

    def __repr__(self):
        if self.name:
            return self.name
        else:
            return 'Person Type %s' % self.id


class ObservationView(GeoModelView):
    datamodel = GeoSQLAInterface(Observation)
    add_columns = ['name', 'location']This will automatically create the LatLonWidget in the form, and process the data to transform the latitude and longitude values into a point geometry in the database. The srid form the column is used by the field for the conversion of the text coordinates to the geometry. No validation is done if the entered coordinates are actually valid for the specified SRID.
Package: fabia
Fabia is a biclustering algorithm based on matrix factorization, first
published in:FABIA: Factor Analysis for Bicluster Acquisition, Sepp Hochreiter et al,  Bioinformatics 2010This implementation conforms to the scikit-learn biclustering API.
It requires scipy 0.16 or higher, scikit-learn >= 0.16 as well as Cython to work.Licensethis package is licensed under a GPL v2 license.
Package: fabio-libpypro
pip# libpythonpro
Modulo para exemplificar construcao de projetos Python no curso PytoolsNesse Curso eh ensinado como contribuir com projetos de codigo abertoLink para o curso [Python Pro]Suportada Versao 3 do PythonPara instalar:'''console
py -3 -m venv .venv
activate
pip install - r requirements-dev.txt
'''Para conferir qualidade do codigo:
'''console
flake8
'''Topicos a serem abordados:GitVirtualenvPip
Package: fabisschomagut
Converts back and forth between different color formats [(255,0,0),#ff0000, 16711680,(255,0,255) ... ]pip install fabisschomagutTested against Windows 10 / Python 3.10 / AnacondaHow to use it# Converting from one color format to anotherfromfabisschomagutimportsubtract_color,add_color,to_rgb_hex,to_rgba_hex,to_rgba_tuple,to_rgb_tupleprint(to_rgb_tuple(color="red"))print(to_rgba_tuple(color=(255,0,0)))print(to_rgb_hex(color="#ff0000"))print(to_rgba_hex(color="0xFF0000"))print(to_rgb_tuple(color=0xFF0000,invert=True))print(to_rgba_tuple(color="red",invert=True))print(to_rgb_hex(color="red",invert=True))print(to_rgba_hex(color=(255,0,0),invert=True))print(to_rgb_tuple(color="blue",invert=True))print(to_rgba_tuple(color=(0,255,0),invert=True))print(to_rgb_hex(color="ff0000",invert=False))print(to_rgba_hex(color="#FFF000",invert=False))print(to_rgb_tuple(color="BLUE",invert=False))print(add_color(color1="BLUE",color2=(0,255,0),invert=False))print(subtract_color(color1="pink",color2="#200000",invert=False))print(to_rgb_hex(color=16746513,invert=False))print(subtract_color(color1="pink",color2="0x200000",invert=False))print(subtract_color(color1="ffc0cb",color2=0x200000,invert=False))print(subtract_color(color1=0xFFC0CB,color2="#200000",invert=True))print(to_rgb_hex(subtract_color(color1="pink",color2="200000",invert=True)))# (255, 0, 0)# (255, 0, 0, 255)# 0xff0000# (255, 0, 0, 255)# 0xff0000ff# #FF0000# (0, 0, 255)# (0, 0, 255, 255)# 0x0000ff# (0, 0, 255, 255)# 0x0000ffff# (255, 0, 0)# (0, 255, 0, 255)# 0xff0000# (255, 240, 0, 255)# 0xfff000ff# (0, 0, 255)# (0, 255, 255)# (223, 192, 203)# #FF8811# 0xff8811# (223, 192, 203)# #200000# (223, 192, 203)# #FFC0CB# (203, 192, 223)# 0xcbc0df
Package: fabistrano
fabistrano============Fabistrano is a set of [Fabric](http://docs.fabfile.org/en/1.6/) tasks that allows you to do [Capistrano](https://github.com/capistrano/capistrano) style deployments for python web apps.## What is a _capistrano style_ deployments?Capistrano deploys code under versioned directories and then symlinks the latest one as the *current* release.A sample structure would look like this:```app.com|| - releases| - 1366924759| - 1366927898| - 1367155641| - shared| - current```## Why use this stlye of deployment?Using fabistrano you get the following benefits:- Scripted and ordered deployments- Very low downtime when deploying new code- Easy roll backs## Usage1. Install fabistrano:` pip install fabistrano `2. In your `fabfile.py` you need to import fabistrano and set the environment variables:```from fabistrano import deployenv.hosts = ["HOST"] # Replace with your host name or IPenv.base_dir = '/www' # Set to your apps directoryenv.app_name = 'app_name.com' # This will deploy the app to /www/app_name.com/env.git_clone = 'GIT_PATH' # Your git urlenv.restart_cmd = 'kill -HUP `supervisorctl pid gunicorn`' # Restart command# or# env.wsgi_path = "app_name/apache.wsgi" # Relative path to the wsgi file to be touched on restart```3. Run `setup` to create the directory structure:` fab deploy.setup `4. Deploy the app:` fab deploy `5. Setup your web server to point to the `current` directory.And you should be good to go!## Current statusThis tool is under active development and you might see errors.## LicenseThe template itself is available under the "Simplified" BSD license and can befreely modified under the terms of that license. You can see the[full license text](https://github.com/dlapiduz/fabistrano/blob/master/LICENSE>)in the Github repository for additional detail.Applications created using this template are not considered derivatives works.Applications created using this template can be freely licensed under whateveryou as the author chooses. They may be either open or closed source.
Package: fabix
Fabix is a serie of functions built on top of fabric and cuisine to easily
deploy python web projects. It includes tasks to manage a Django installation,
compile python, install libs, etc.Supported OSsCurrently, only Ubuntu is supported but patches supporting other OS/distros are
welcome :).RequirementsFabric1.4.2cuisine0.2.6RoadmapSupport framework installations (Django, Tornado, CherryPy, etc.)Add support for RPM-based distrosContributingJust do the usual thing: fork & send a pull request. Please, respect the coding
style around you and try to respectPEP 8guidelines.LicenseThis project is licensed under MIT license. For details, please see file MIT-LICENSE.
Package: fabkit
FabkitThis library is a group of common helpers forFabric 1.xused by myself in my projects.It was inspired by similarfabtoolsbut it's targeting to be working on Arch LinuxInstallationTo install the latest release from PyPI$pipinstallfabkitRequirementsPython 3.6+Arch Linux remote target (but most utils are generic and should work on other Linux systems)Examplestbd.
Package: fablab-lib
fablab_libPurposefablab_lib is a package designed to facilitate communication with Programmable Logic Controllers (PLCs) through industrial protocols.FeaturesEnables seamless interaction with PLCs.Provides an interface for sending and receiving data.Supports various industrial protocols for communication.InstallationYou can install fablab_lib via pip:
This project is licensed under the MIT License - see theLICENSEfile for details.To install the 'fablab_lib' package, you can use the following command:pip install fablab_libNoteTo install this lib in Raspberry Pi (Python >= 3.7), you need install following libs before:sudo apt-get install python3-pandassudo apt-get install python3-numpy
Package: fable-circuits
Fast Approximate BLock Encodings (FABLE)FABLE can synthesize quantum circuits for approximate block-encodings of matrices. A block-encoding is the embedding of a matrix in the leading block of of a larger unitary matrix.FABLE is a quantum representation for dense, unstructured matrices. The gate complexity of FABLE circuits scales linear in the number of matrix elements, which is optimal for the unstructured case. FABLE includes a circuit compression algorithm that can significantly reduce the gate complexity and works particularly well if there is certain structure available in the matrix to be block encoded.We provide two reference implementations of the FABLE algorithm:a Python implementation built on top ofQiskita MATLAB implementation built on top ofQCLABQiskit - Python ImplementationFABLE can be installed from PyPI as follows:pip install fable-circuitsAfter installation, it can be loaded and used as follows:fromfableimportfableimportnumpyasnpfromqiskitimportAersimulator=Aer.get_backend("unitary_simulator")# generate a random matrix and block encode itn=3N=2**nA=np.random.randn(N,N)circ,alpha=fable(A,0)result=simulator.run(circ).result()unitary=result.get_unitary(circ)np.linalg.norm(alpha*N*unitary.data[0:N,0:N]-A)/np.linalg.norm(A)QCLAB - MATLAB ImplementationIn order to run the MATLAB implementation of FABLE:InstallQCLABClone FABLE and addfable-qclabdirectory to your MATLAB path.After installation, FABLE can be run for a target matrixAas either:logging = true ;
[circuit, OA, alpha, info] = fable( A, 'cutoff', 1e-4, logging ) ;
[circuit, OA, alpha, info] = fable( A, 'percentage', 80, logging ) ;The first option ('cutoff') ignores coefficients smaller than1e-4in absolute value, the second option
('percentage') applies an 80% compression and only retains the 20% largest coefficients. The'percentage'andloggingoptions are only available in the MATLAB version of FABLE.ReferenceCite the following reference for FABLE:FABLE: Fast Approximate Quantum Circuits for Block-Encodings, Daan Camps, Roel Van Beeumen, 2022 2022 IEEE International Conference on Quantum Computing and Engineering (QCE),arXiv:2205.00081.Developers - Lawrence Berkeley National LaboratoryDaan Camps-dcamps@lbl.govRoel Van Beeumen-rvanbeeumen@lbl.govAboutFABLE Copyright (c) 2022, The Regents of the University of California,
through Lawrence Berkeley National Laboratory (subject to receipt of
any required approvals from the U.S. Dept. of Energy). All rights reserved.If you have questions about your rights to use or distribute this software,
please contact Berkeley Lab's Intellectual Property Office atIPO@lbl.gov.NOTICE.  This Software was developed under funding from the U.S. Department
of Energy and the U.S. Government consequently retains certain rights.  As
such, the U.S. Government has been granted for itself and others acting on
its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
Software to reproduce, distribute copies to the public, prepare derivative
works, and perform publicly and display publicly, and to permit others to do so.
Package: fabled-rest
Fabled RESTPackage designed to make creating API SDKs easier.Free software: MIT licenseDocumentation:https://fabled-rest.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-10-05)First release on PyPI.
Package: fable-py
F# and Fable (Python) support for JupyterFable Python is an F# kernel for Jupyter based onFableandIPythonKernel. Fable is a transpiler
that convertsF#to Python (and JavaScript).This work is work-in-progress and related tohttps://github.com/fable-compiler/Fable/issues/2339https://github.com/fable-compiler/Fable/pull/2345InstallMake sure you have a recent version of .NET installed on your machine:https://dotnet.microsoft.com/downloadYou also need to install thefable-py.NET tool globally (and make sure it's available in PATH environment)dotnettoolinstall-gfable-py--version4.0.0-alpha-026

pipinstallfable-py
python-mfable_pyinstallTo use the very latest changes (for development):gitclonehttps://github.com/dbrattli/Fable.Jupyter.gitcdFable.Jupyter
pythonsetup.pydevelop
python-mfable_pyinstallUsageYou can use Fable Python in the Jupyter notebook by selecting the "F#
(Fable Python)" kernel. To start Jupyter run e.g:jupyternotebook# orjupyterlabMagic commandsYou can inspect the generated Python code by executing%pythonin a cell:%pythonYou can inspect the maintained F# program by executing%fsharpin a cell:%fsharpF# ProgramThe kernel works by maintaining an F# programFable.fsbehind the
scenes. This program lives in a separatetmpfolder for each instance
of the kernel.Sometimes the generated F# program might become invalid because of the
submitted code fragments (this can happen with a Python notebook as well).
The way to recover is to reset the kernel. That will reset the F#
program that is running behind the notebook. To reset the kernel select
on the menu:Kernel -> RestartorKernel -> Restart & Clear Output.or you can use the reset command:%resetIf you need additional package references you currently need to add them
manually to theFable.fsprojproject file. TODO: handle#r nuget "...commands from within the notebook.