Saravanan666/pypi-org huggingface dataset:
Package: famegui
FAME-GuiGraphical user interface for FAME models.
Please visit theFAME-Wikito get an explanation of FAME and its components.
This is a Beta version - see current developmentmilestones.Installationpip install fameguiYou may also usepipx. For detailed information please refer to the officialpipxdocumentation.pipx install fameguiUsageFAME-Gui is started in the console usingfameguiContributePlease read the Contributors License Agreementcla.md, sign it and send it tofame@dlr.debefore contributing.Testing changes locallyOnce some changes have been performed on the local git clone, use the following command to override your local installation with your modified copy in order to test the result:python3setup.pybdist_wheel&&pip3install--force-reinstall--no-dependencies./dist/*.whlCode styleWe use the code formatting libraryblack.
The maximum line length is defined as 120 characters.
Therefore, before committing, runblack --line-length 120 ..
Package: fameprotobuf
FAME-ProtobufGoogle Protocol Buffer (protobuf) definitions define the structure of binary input and output files for FAME applications.
Please visit theWiki for FAMEto get an explanation of FAME and its components.FAME-Protobuf connects FAME-Io to applications based on FAME-Core. Thus, both depend on FAME-Protobuf.RepositoryThe repository is split into three source code parts:protobuf definitions reside insrc/main/resources,derived Python classes for FAME-Io reside insrc/main/python.derived Java classes for FAME-Core reside intarget/generated-java-sources, andInstallation instructionsUse this Maven dependency:<dependency>
  <groupId>de.dlr.gitlab.fame</groupId>
  <artifactId>protobuf</artifactId>
  <version>1.3.0</version>
</dependency>PackagingCompileThepom.xmlis configured to allow automated compilation of the protobuf definitions to Python and Java classes.Maven buildIn the cloned repository of fame-protobuf, compile and package fame-protobuf locally to your Maven repository:mvn packageDeploy to PyPIFAME-Protobuf is packaged to PyPI.
We usepoetryfor packaging.
Packaging requires these steps:install poetry:pip install poetryrun the packaging script:python packaging.pybuild wheel:poetry buildpublish:poetry publishContributePlease read the Contributors License Agreement (cla.md), sign it and send it tofame@dlr.debefore contributing.
Package: family_parkrun
Family Parkrun SummaryTo use:Ensure you havePythoninstalled.Create a file containing a newline-separated list of runner ID numbers.
These are the numbers at the end of the URL for their personal page.Runfamily-parkrunwith the-dargument, followed by the filename
of your runner list, and wait for it to download.pythonfamily-parkrun-drunners.txtViewsummary.htmlin your browser.
Package: familysearch-python-sdk-opensource
This is a reboot/spinoff ofhttps://github.com/familysearch-devnet/python-fs-stack.May or may not have all of the latest FamilySearch endpoints in place, but it is flexible enough to allow for additional endpointsIt is designed for Python 3 and 2.7. Python 2.6 is the earliest supported version, but bugfixes are low-priority. 2.5 and earlier are not guaranteed to work, or supported, and are considered legacy.To get started, visithttp://elderamevans.github.io/familysearch-python-sdk-opensource.
Package: familytree
familytreemakerThis program creates family tree graphs from simple text files.The input file format is very simple, you describe persons of your family line
by line, children just have to follow parents in the file. Persons can be
repeated as long as they keep the same name or id. An example is given in the
fileLouisXIVfamily.txt.Note: This branch version is optimized for chinese. If other language for
input text file is used, command option gender (-g) has to be used to
specify words of male and female, since their default values are set
as "男" and "女" instead of origin "M" and "F".InstallationSimply clone the repo or install package familytree by pip as shown below:pipinstallfamilytreeThis script outputs a graph descriptor in DOT format. To make the image
containing the graph, you will need a graph drawer such as [GraphViz]1.Usageusage: familytreemaker.py [-h] [-a ANCESTOR] [-g GENDER] [-v INFOLEVEL]
                          [-o OUTFILE]
                          INPUTFILE

Generates a family tree graph from a simple text file

positional arguments:
  INPUTFILE     the formatted text file representing the family

optional arguments:
  -h, --help    show this help message and exit
  -a ANCESTOR   make the family tree from an ancestor (if omitted, the program
                will try to find an ancestor)
  -g GENDER     customized gender string, for example: "男,女" or "M,F"
  -v INFOLEVEL  Information level (0/1/2) to output. (0 - only name and
                surfname will be output; 1 - time of birthday and deathday
                will be invisable; 2 - all information will be output)
  -o OUTFILE    file name for outputThe sample family descriptorLouisXIVfamily.txtis here to show you the
usage. Simply run:python familytreemaker.py -a "王灿文" -v2 -o LouisXIVfamily.dot LouisXIVfamily.txt && \
    dot -Tsvg -o LouisXIVfamily.svg LouisXIVfamily.dotIf package familytree has been installed by pip, commands below can be performed to populate:python-mfamilytree.familytreemaker-a"王灿文"-v2-oLouisXIVfamily.dotLouisXIVfamily.txt&&\dot-Tsvg-oLouisXIVfamily.svgLouisXIVfamily.dotIt will generate the tree from the infos inLouisXIVfamily.txt, starting from王灿文and saving the image inLouisXIVfamily.svg.
Package: famn-opt
FAM-N (Fast Amplitude Modulation - N pulses) is a composite pulse that exploits the phenomenon of inversion/saturation of satellite transitions into the central transition to enhance the efficiency of the conversion pulse in MQMAS experiments, improving the overall sensitivity of the experiment. This program determines the number of individual pulse and their respective durations required for an optimal conversion under the experimental conditions defined by the user.See "Manual/quickstart.pdf" for instructionsMore information:
Colaux, H.; Dawson, D. M. & Ashbrook, S. E.,
Efficient Amplitude-Modulated Pulses for Triple-to Single-Quantum Coherence Conversion in MQMAS NMR,
The Journal of Physical Chemistry A, 2014FAMN optimisation program requires SIMPSON to be installed in order to function. SIMPSON is
released under the conditions of the GNU General Public License.
SIMPSON can be obtained fromhttp://nmr.au.dk/software/simpson/More information:
Bak, M.; Rasmussen, J.; Nielsen, N. et al.,
SIMPSON: a general simulation program for solid-state NMR spectroscopy
Journal of Magnetic Resonance, 2000, 147, 296----------------------------------------------------------------FAMN optimisation program (C) 2013-2019 Henri Colaux
FAMN optimisation program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3.FAMN optimisation program is distributed in the hopeq that it will be useful, but without any warranty; without even the implied warranty of
merchantability or fitness for a particular purpose.  See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with FAMN optimisation program.  If not, seehttp://www.gnu.org/licenses/.----------------------------------------------------------------Henri Colaux
e-mail:henri.colaux@normalesup.org
Package: fa-models
fa-modelsA set of models often used for trading services. The library offers merely models and is database agnostic.SponsorsFreya Alpha,
The Kára System,
Spark & Hale Robotic IndustriesGeneralRun and compiled for Python 3.9.13.
Expected to run for Python 3+DevelopmentTestingrun tests withpytest -s -vvto see all the details. This is extremely important for the library. It could make multiple pods fail.Installation as Consuming DeveloperSimply run:pip install fa-modelsImport in modules without the dash (e.g.):from famodels.signal import SignalSetup as ContributorCreate the virtul environment:py -m venv .venvStart the Environment:./.venv/Scripts/activate(or allow VS Code to start it). Usedeactivateto stop it.Upgrade the PIP.python -m pip install --upgrade pipAll the required libraries must be listed in requirements.txt and installed bypython -m pip install -r .\requirements.txtFor Dev usepython -m pip install -r .\requirements-dev.txtTo cleanup the environment run:pip3 freeze > to-uninstall.txtand thenpip3 uninstall -y -r to-uninstall.txtorpip3 install pip-autoremoveBuild LibraryPrerequisite: make sure that you give your Operating System user the right to modify files in the python directory. The directory where pyhton is installed.
Usepython setup.py bdist_wheelto create the dist, build and .eggs folder.Reference from a different projectIn order to use your own version of the project - to maybe contribute to the library - simply clone the code from github into new directory. Then add the path of that new directory to the requirements.txt file of your project. Then change in fa-models whatever you recommend to improve. Don't forget the Open-Closed Principle: extend only (unless it requires a breaking change)Releasing a new versionSimply commit and push into MAIN. Make sure you have tested well.Github Actions (.github/workflows/testing_and_deployment.yml) will deploy the file to pypi.org.Manual and old processDelete any old files in the /dist and build folder of your local environment.
Update your pip:python -m pip install --upgrade pipInstall the tools build, twine and bumpver:python -m pip install build twine bumpverUpgrade the setuptools:pip install --upgrade setuptoolsBump the version in pyproject.toml:bumpver update --patchThis will commit a new version to GitHub.Build the project:python -m buildCheck the distribution:twine check dist/*Upload to test-pypi to validate:twine upload -r testpypi dist/*Login with username: (password should be known)If the test-upload was successful, finally, upload to pypi production:twine upload dist/*Done.(P.S. Do not forget to update the library in your projects:pip install --upgrade fa-models)bumpver update --patch
Package: famudy-data
famudy-dataLight-weight Python access to multi-view data from NeRSemble dataset.The main data resides on the cluster in/cluster/doriath/tkirschstein/data/famudy/full1. Installationpipinstallgit+ssh://git@github.com/tobias-kirschstein/famudy-data.git2. SetupCreate a file in your home directory:~/.config/famudy/.envwith content:FAMUDY_PROCESSED_CAPTURE_DATA_PATH_NEW="/cluster/doriath/tkirschstein/data/famudy/full"Additionally, if you mounted rohan locally and want to access the data from your local machine, use another environment variable:FAMUDY_REMOTE_PROCESSED_CAPTURE_DATA_PATH_NEW="<<<PATH_TO_LOCAL_ROHAN_MOUNT>>>/cluster/doriath/tkirschstein/data/famudy/full"For example, using WSL2 on Wndows with rohan mounted into/mnt/rohan,<<<PATH_TO_LOCAL_ROHAN_MOUNT>>>would be//wsl.localhost/Ubuntu/mnt/.Usagefromfamudy.dataimportFamudySequenceDataManagerparticipant_id=...sequence_name=...data_manager=FamudySequenceDataManager(participant_id,sequence_name,downscale_factor=2)Imagestimestep=...cam_id=...image=data_manager.load_image(timestep,cam_id)Pointclouds, Depth maps or Normal mapstimestep=...cam_id=...depth_map=data_manager.load_depth_map(timestep,cam_id,n_cameras=16)normal_map=data_manager.load_normal_map(timestep,cam_id,n_cameras=16)points,colors,normals=data_manager.load_point_cloud(timestep,n_cameras=16)# n_cameras=16 indicates that COLMAP was ran on all 16 camerasBackground maskstimestep=...cam_id=...alpha_map=data_manager.load_robust_matting_alpha_image(timestep,cam_id)Camera extrinsics/intrinsicsExtrinsics are inOpenCVformat andworld2cam:world_2_cam_poses=data_manager.load_calibration_result().params_result.get_poses()intrinsics=data_manager.load_calibration_result().params_result.get_intrinsics()Note: the intrinsics is given wrt to the full resolution recordings (2200x3208). However, in the interest of storage, images and depth maps are downscaled by a factor of 2 (yielding 1100x1604). Hence, the intrinsics most likely needs to be rescaled as well byintrinsics.rescale(0.5).Segmentation maskstimestep=...cam_id=...segmentation_mask=data_manager.load_bisenet_segmentation_mask(timestep,cam_id)LandmarksThe most reliable landmarks are those detected from the front camera (serial =222200037/ cam_id =8)cam_id=...landmarks=data_manager.load_2d_landmarks(cam_id)FLAME trackingflame_params=data_manager.load_3DMM_tracking("FLAME2023_v2")
Package: fanalysis
fanalysisfanalysis is a Python module for Factorial Analysis distributed under the 3-Clause BSD license.With this fanalysis package, you can perform:Simple Correspondence AnalysisMultiple Correspondence AnalysisPrincipal Components AnalysisThose statistical methods can be used in two ways:as descriptive methods ("datamining approach")as reduction methods in scikit-learn pipelines ("machine learning approach")InstallationDependenciesfanalysis requires:Python 3
NumPy >= 1.11.0
Matplotlib >= 2.0.0
Scikit-learn >= 0.18.0
Pandas >= 0.19.0User installationYou can install fanalysis using pip:pip install fanalysisRunning the testsAfter installation, you can launch the test suite from outside the source directory:python -m unittestThe philosophy of the unit tests consists in comparing the outputs of fanalysis (with various combinations of parameters) with the outputs of the R FactoMineR package.DocumentationThe docstring is written in english.Tutorials are available in french:https://github.com/OlivierGarciaDev/fanalysis/blob/master/doc/ca_tutorial.ipynb
https://github.com/OlivierGarciaDev/fanalysis/blob/master/doc/mca_tutorial.ipynb
https://github.com/OlivierGarciaDev/fanalysis/blob/master/doc/pca_tutorial.ipynbAuthorOlivier Garcia (o.garcia.dev@gmail.com)
Package: fanchart
fanchartDocumentationGit HomepagePip RepositoryOverviewThefanchartlibrary provides functionality to create fan charts inPython.The termfan chartwas coined by theBank of Englandin 1996. Since then, the  BoE
has used these charts to illustrate its forecasts for inflation.The BoE introduced the fan charts aiming to communicate a more accurate representation of their forecast for medium term inflation. In particular, the charts have two key objectives:To convey the uncertainty in their forecasts. This is, to focus attention on  the forecast distribution, rather than only on small changes to the central projection.To promote discussion of the risks to the economic outlook, and thus contribute to a wider debate about economic policy. Fan charts help to make it clear that monetary policy is about making decisions instead of knowing the exact rate of inflation in two years time.For more details on the history of fan charts visitFan ChartsInstallationFanchart is available onpypiand can be
installed as follows.pip install fanchartDependenciesFanchart relies heavily ontwopiecefor the implementation of theTwo-Piece normaldistributionmatplotlibfor creating visualisationsCompatibilityFanchart is tested on Python versions 3.8, 3.9, and 3.10Getting Started:exclamation: 📝Install the latest version of fanchart before starting.This library provides two main functionsfanandfan_single. In order to be able to make our first fan charts with
these functions, we need to load some data via the functions.load_boe_history()which loads the historical data for inflation (CPI)load_boe_parameters()which loads the parameters for the quarterly projectionsfrom fanchart import load_boe_history, load_boe_parameters

history = load_boe_history()
parameters = load_boe_parameters()After this, we are ready to use our main functions.Thefanfunction illustrates the distribution of all the forecasts available; and it has the option to display the historical values for reference.from fanchart import fan

    probs = [0.05, 0.20, 0.35, 0.65,0.80,  0.95]
    fan(pars=parameters, probs=probs, historic=history[history.Date >= '2018'])Thefan_singlefunction illustrates the pdf/cdf of one forecast distributionfrom fanchart import fan_single

    probs = [0.05, 0.20, 0.35, 0.65,0.80,  0.95]
    fan_single(loc=9.53, sigma=1.68, gamma=1.0, probs=probs, kind='pdf')from fanchart import fan_single

    probs = [0.05, 0.20, 0.35, 0.65,0.80,  0.95]
    fan_single(loc=9.53, sigma=1.68, gamma=1.0, probs=probs, kind='cdf')Thanks for Visiting! ✨Connect with me via:🦜Twitter👩🏽‍💼Linkedin📸Instagram👾Personal Website⭐️If you like this projet, please give it a star!⭐️
Package: fancy-abak
Fancy AbakCLI tool to use ABAKFancy AbakAvailable CommandsRequirementsInstallationMacOSAvailable Commands$abak--help
Usage:abak[OPTIONS]COMMAND[ARGS]...AbakUI,NEVERAGAIN!

Options:--helpShowthismessageandexit.

Commands:clientFindclientstoassigntimesheetentriesconfigGroupofcommandstomanagethejiractlcommandlinecontextContextoperationsforAbak.doGPTPoweredcommandto"do"somethingloginopenOpensabakonyourdefaultbrowser-Pleasedon'tusethis:)projectFindprojectstoassigntimesheetentriestimesheetCommandstomanagetimesheetentriesRequirements(Tested and built using) Python >3.11InstallationPackage is available in PyPi:pipinstallfancy-abakMacOSPackage is available in brew:brewtapgcarrarom/fancygui
brewinstallgcarrarom/fancygui/fancy-abak
Package: fancybbox
FancybboxA package that allows to build Lib is a package that enables users to create visually appealing bounding boxes for object detection in image processing deep learning projects.With a current version of 0.0.10, the package is constantly being updated and improved. The package offers a variety of features, including the ability to customize the appearance of bounding boxes with different colors, line widths, and styles.
Additionally, it provides users with the flexibility to adjust the size and shape of the bounding boxes to meet their specific needs.'Developed by Prashant Verma (c) 2023This project still very much experimental and may change significantly.InstallInstall with all dependencies:pipinstallfancybboxExamples of How To Use (Alpha Version)Creating A Sample detection functionimportcv2fromfancy_bboximportFancyBox# Load an imageimage=cv2.imread("images/img_1.jpg")# Detect an object and get its bounding box coordinatesx,y,w,h=[100,100,200,200]# Create a FancyBox object and draw it on the imagefancy_box=FancyBox(x,y,w,h,border_thickness=1,border_color=(0,255,0))image_with_box=fancy_box.target_angle_bbox(image)# Display the image with the boxcv2.imshow("Fancy Box Example",image_with_box)cv2.waitKey(0)
Package: fancybox
FancyBoxFancy Box is a Python module that allows you to print beautiful message boxes on command line. There are predefined message boxes, you can use or you can create you own custom box.Installationpip3 install fancyboxUsesSimple messagesimport fancybox as fb

fb.success("Test Successful")
fb.error("IOError occured")
fb.info("Setup Completed")
fb.warning("name is NULL")Box with title and descriptionCreate customize boxfrom fancybox import Box
box= Box()
box.bgcolor= 2
box.fgcolor= 15
box.border= 11
box.makebox("NAMES")
box.addbox("Python")
box.addbox("Linux")
box.addbox("Windows")Happy Programming
Package: fancyboxmd
Fancybox markdown extensionThis markdown extension createsfancybox images.Markdown!![Title](image.png "Description")HTML<figure><adata-caption="Description"data-fancybox="gallery"href="image.png"><imgalt="Title"src="image.png"title="Title"width="600px"/></a><figcaption>Description</figcaption></figure>Using it with mkdocsEnable it in mkdocs.ymlmarkdown_extensions:
  - fancyboxmdEnable fancybox in mkdocs.yml# Css
extra_css:
  - https://cdn.jsdelivr.net/gh/fancyapps/fancybox@3.5.7/dist/jquery.fancybox.min.css

# Extra javascript
extra_javascript:
  - https://cdn.jsdelivr.net/npm/jquery@3.5.1/dist/jquery.min.js
  - https://cdn.jsdelivr.net/gh/fancyapps/fancybox@3.5.7/dist/jquery.fancybox.min.jsConfigurationIt supports one configuration optionpreview_widthwhich defaults to600px.
Package: fancy-btc-price
Fancy BTC PriceSimple program that shows the BTC price taken from the Money on Chain API RestRefrencesSource code in GithubPackage from Python package index (PyPI)Money on ChainRequirementsPython 3.6+InstallationFrom the Python package index (PyPI)Run:$ pip3 install fancy-btc-priceFrom sourceDownload fromGithubStanding inside the folder, run:$ pip3 install -r requirements.txtFor install the dependencies and then run:$ pip3 install .Runuser@host:~$ fancy_btc_price
1₿=21685$
user@host:~$More optionsuser@host:~$ fancy_btc_price --help
Usage: fancy_btc_price [OPTIONS]

  Simple program that shows the BTC price

  Taken from the Money on Chain API Rest (moneyonchain.com)

Options:
  -v, --verbose                Enables verbose mode
  --version                    Show version and exit
  -m, --max-age INTEGER RANGE  Max price age without showing a warning (in
                               minutes)  [default: 3; 1<=x<=120]
  -h, --help                   Show this message and exit.
user@host:~$Why?Mainly to be used by theGnome Shell Extension Executor. This Gnome extension execute multiple shell commands periodically with separate intervals and display the output in gnome top bar.Fancy BTC Price was developed some time ago but due to the need to install a new desktop pc, it was published to simplify this task.
Package: fancy_cache
django-fancy-cache==================(c) Peter Bengtsson, mail@peterbe.com, 2013About django-fancy-cache------------------------A Django `cache_page` decorator on steroids.Unlike the stock `django.views.decorators.cache.change_page` thisdecorator makes it possible to set a `key_prefixer` that is acallable. This callable is passed the request and if it returns `None`the page is not cached.Also, you can set another callable called `post_process_response`(which is passed the response and the request) which can do someadditional changes to the response before it's set in cache.Lastly, you can set `post_process_response_always=True` so that the`post_process_response` callable is always called, even when theresponse is coming from the cache.How to use it-------------In your Django views:from fancy_cache import cache_page@cache_page(60 * 60)def myview(request):return render(request, 'page1.html')def prefixer(request):if request.method != 'GET':return Noneif request.GET.get('no-cache'):return Nonereturn 'myprefix'@cache_page(60 * 60, key_prefixer=prefixer)def myotherview(request):return render(request, 'page2.html')def post_processor(response, request):response.content += '<!-- this was post processed -->'return response@cache_page(60 * 60,key_prefixer=prefixer,post_process_response=post_processor)def yetanotherotherview(request):return render(request, 'page3.html')Running the test suite----------------------The simplest way is to simply run:$ pip install -r requirements.txt$ fab testOr to run it without `fab` you can simply run:$ export PYTHONPATH=`pwd`$ export DJANGO_SETTINGS_MODULE=fancy_cache.tests.settings$ django-admin.py test
Package: fancy-collections
fancy-collectionsThis package provide some useful classes to store data in a structured way.
Up to now mostly advanced dictionaries are provided.Installation and requirementsfancy-collectionsrequires Python 3.7+ to run and can be installed by runningpipinstallfancy-collectionsUsage>>>fromfancy_collectionsimportSliceDict,DictOfPandas,DictOfSeriesSee alsohttps://pypi.org/project/sliceable-dict/
Package: fancy-decorators
fancy_decoratorsContains helpful decorators for common data-science tasksFree software: MIT licenseDocumentation:https://fancy-decorators.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2022-11-03)First release on PyPI.