Saravanan666/pypi-org huggingface dataset:
Package: faciesteller
faciestellerfaciesteller is a Python package as Techlog plug-in, which automate facies classifaction from borehole image.installationTo install faciesteller, you need to navigate to Python3 folder of Techlog using command prompt.
The usual Techlog Python3 folder address likes this: "C:\Program Files\Schlumberger\Techlog Version\Python3".
Code in: python.exe -m pip install faciesteller and hit execute, that is it.usageThe model has been trained with Resnet50 using local FMI image and geolgical interpretation. It might be used in same geological area with caution, while local expertise is very important and advised though.To successfully predict faices, need to have the training model (model.h5) in the following address:
C:\Users\username\AppData\Roaming\Schlumberger\Techlog\model\model.h5As for pratical manipulation, it requires to open a new python eiditor in Techlog and paste below coding inside, add three rows for inputs in paramters area. The rows are CSV path for depth data, img path for FMI image and well name.Code to use in python script area:from faciesteller.faciesteller import FaciesClassifierfc = FaciesClassifier(csv_path, img_path, well_name)
df_csv, img_array = fc.load_data()
print("DataFrame shape: ", df_csv.shape,
"\nImagearray shape: ", img_array.shape)df_output = fc.imagetochunk(df_csv, img_array)df_predict = fc.predict(df_output)
print(df_predict)contributingIf you'd like to contribute to faciesteller, please contact me.licenseMy Package is licensed under the MIT License. See LICENSE for more information.
Package: facile
Python constraint programming libraryOCaml Facile library is an excellent tool for solving constraint programming problems in OCaml. Facile stands for "Functional Constraint Library". Besides, "facile" means "easy" in French!This code is a Cython-based wrapping in Python of basic functionalities of this library.The documentation (installation, quickstart and examples) has been moved toreadthedocs.org.Things left to doFind a way to better parametrize the resolution process (heuristics, variable choice order, etc.)Study the feasibility of building your own constraints in pure Python.
Package: facile-gitlab-trigger-easy
Facile GitLab HelperThis library provides help to microservices inside gitLab C.I pipelines, integrating different repositories within different pipelines and projects.Getting StartedOpen the terminal and install the given library:pipinstallfacile-gitlab-trigger-easyExecute the command below to make sure the library has been installed correctly:facile-trigger-easy--helpThe list of commands below should be displayed:usage:facile-trigger-easy-aGITLAB_API_TOKEN[-hGIT_LAB_HOST][--help][-tTARGET_BRANCH]-pPROJECT_ID
Package: facile-new-business-lib
New Business libThis Robot Selenium library which owns a couple of keywords called by Facile New Business Projects centralizing most used keywords within those projects.Getting StartedGo to the terminal and install the given library:pipinstallfacile-new-business-libAfter that go to the robot project and import the given libraryLibraryfacilenewbusinesslibPrerequisitesYou need Python 3.5 or later to run mypy. You can have multiple Python versions (2.x and 3.x) installed on the same system without problems.DemoThis is a alpha version which contains different driver instances in order to understand different browsers flows convering future troubleshootings
Package: faciljsondb
facil-json-dbIniciarPara iniciar tu base de datos debes usar la clase BaseDeDatos() que te ayudara a modificar tu base de datos y la creara en caso de que no existaimportfaciljsondbdb=faciljsondb("nombre")leerEn este modulo hay dos funciones leer (leer y leer_async) esta funcion no retorna nada solo imprime en pantalla tu db.leerimportfaciljsondbdb=faciljsondb("nombre")leer()asyncimportfaciljsondbimportasynciodb=faciljsondb("nombre")asyncio.run(leer([tiempo1,][tiempo2]))obtenerTe devuelve el json almacenado.obtenerimportfaciljsondbdb=faciljsondb("nombre")obtener()asyncimportfaciljsondbimportasynciodb=faciljsondb("nombre")asyncio.run(obtener([tiempo1,][tiempo2]))escribirSirve para escribir el json.escribirimportfaciljsondbdb=faciljsondb("nombre")escribir(json)asyncimportfaciljsondbimportasynciodb=faciljsondb("nombre")asyncio.run(escribir(json,[tiempo1,][tiempo2]))
Package: facilyst
FacilystFacil • ystFacilis - Latin for easy, ready, or quick.Analyst - Leaving this blank because I don't think anyone knows.As a software developer in machine learning, I often find myself in the awkward position of building features and expanding use cases without easy access to data to test them with.
Sometimes it could be as simple as a Pandas DataFrame with different data types just so I have something basic to throw at my code, and in other cases I might need a sinusoidal wave with varying frequency
and amplitude consisting of an upwards trend projected over a staggered datetime horizon.Instead of having to constantly look up the right functions in order to meet these data needs, and then search through StackOverflow or documentation to figure out
how to plot it in a visually appealing way, I wanted a library that could provide a simple interface that made it easy to facilitate the work of an analyst/developer.InstallationInstall fromPyPIpip install facilyst
Package: facio
Facio: /ˈfa.ki.oː/ - Latin, meaning to make, do, act, perform, cause, bring about.What is it?Are you forever creating new projects, re creating the same standard cruft over
and over? Then Facio maybe for you, it allows you to create your standard
project skeleton once and create new projects from that standard skeleton over
and over, easily.It can be as simple or as complicated as you need it to be. You can write logic
into your skeleton, store it in agitormercurialrepository, you can
even write hooks to be run before or after your skeletons been built. Did I
mention you can have as many templates as you like and quickly reference them
by name or pick from a list.Facio aims to hopefully fix your standard project skeleton woes.facio my_new_project -t django_skeletonSupportsPython 2.6, 2.7, 3.2, 3.3FeaturesSupport for multiple templatesStore templates ingitormercurialrepositoriesAdd template logic usingJinja2Add extra context-variables to your templatesAbility to add before and after hooks that are called before or after the
project is created.Bundled hooks include:Create python virtual environmentsRun pythonsetup.py install | developGenerate Django Secret key for usage in Django settings modulesDocumentationDocumentation forFaciocan be found on here onRead the Docs.
Package: facleaning
facleaningImproved display of persian textDownloadInstallation through pypi.pip install facleaning --userYou can also install from this repository.git clone https://github.com/goldaaa/facleaning.gitThen, install the library withpython setup.py installImportingImport kivyir commands, menus, and the shellfrom facleaning import cleaning
from facleaning.LanguageCheck import reverse_parse, find_text, fa, enCommandsCommands that can be usedfrom facleaning.LanguageCheck import reverse_parse, find_text, fa, en

text = 'این متن توسط goldaaa برای تست نوشته شده است'

reverse_parse(text, search='fa')  # جستجوی کلمه های فارسی برای تغییر جهت کلمه

reverse_parse(text, search='en')  # جستجوی کلمه های انگلیسی برای تغییر جهت کلمه


# Search for other items requested via regex to change the direction of the word 
# جستجوی سایر موارد خواسته شده از طریق regex برای تغییر جهت کلمه
reverse_parse(
    text=text,
    search=lambda word: find_text(word, "[A-Z a-z 1-9 ۱-۹]"),
    reverse_text=True
)

test_fa = (
    'lang fa word (تست): ', fa('تست'),
    '| lang fa word (goldaaa): ', fa('goldaaa')
)
test_en = (
    'lang en word (تست): ', en('تست'),
    '| lang en word (goldaaa): ', en('goldaaa')
)


from facleaning import cleaning
cleaning(
    text=text,
    lang='fa',                  # language fa or ar
    reverse_text=False,         # Change the direction of the entire text | تغییر جهت کل متن
    reverse_other_char=True,    # Change direction of signs | تغییر جهت علامت ها
)If you are interested in financial support, you can send a message through Gmail if you have any questions.اگر قصد حمایت مالی یا سوال دارید می توانید از طریق جیمیل پیام ارسال کنید.gmail:goldaaa.program@gmail.comgithub facleaning
Package: facond
DemoRun the demo which uses Django server side:pip install --user facond[demo]
~/.local/bin/facond runserverResources**Documentation** graciously hostedbyRTFDMailing list graciously hostedbyGoogleForSecurityissues, please contactyourlabs-security@googlegroups.comGit graciously hostedbyGitHub,Package graciously hostedbyPyPi,Continuous integration graciously hostedbyCircleCI**Online paid support** provided via HackHands,Dual-licenseIt is released with the Creative Commons Attribution-NonCommercial 3.0 Unported
License, but a commercial license can be made available, please get in touch
with the author by email (see setup.py) if you are interrested.Commercial license the money goes to YourLabs hacker club, a non-profit
foundation to promote the role of hackers in the process of making our society
more fair and free, while using their skills to develop local economy and give
internet back to the people.That being said, sponsors are: BetaGouv, Hitachi and YourLabs Business Service.WhyWe’ve been inventing this over and over again for years. Thefirst time I
invented this was in 2009and
honnestly my python, django and javascript skills were pretty weak back then.
Since then, I’ve seen users asking this, paying me as a consultant for this,
makingpull requests to have this in a per-app basis. It’s about
time we have a generic solution that works for all kinds of fields, and not
just the ones of the apps we maintain.With lots of passion and love.∞
Package: facrud-router
facrud-routerSource
Code:https://github.com/drtnn/facrud-routerRequirementsPython 3.9+facrud-router stands on the shoulders of giants:FastAPIPydanticSQLAlchemyInstallation$pipinstallfacrud-router---> 100%ExampleCreate itCreate a filemain.pywith:importuuidfromdataclassesimportdataclassfromdataclassesimportfieldimportuvicornfromfacrud_routerimportModelCRUDRouterfromfastapiimportFastAPIfrompydanticimportBaseModel,FieldfromsqlalchemyimportColumn,Stringfromsqlalchemy.dialects.postgresqlimportUUIDfromapp.api.depsimportget_sessionfromapp.api.depsimportauthentication_schemeapp=FastAPI(title="FastAPI CURD Router Demo",version="0.1.2",description="FastAPI CRUD Router for SQLAlchemy",openapi_url="/openapi.json",docs_url="/",)@dataclassclassUser:__sa_dataclass_metadata_key__="sa"__tablename__="user"id:uuid.UUID=field(init=False,default_factory=uuid.uuid4,metadata={"sa":Column(UUID(as_uuid=True),primary_key=True,default=uuid.uuid4)},)username:str=field(metadata={"sa":Column(String(255),nullable=False,index=True)})full_name:str=field(metadata={"sa":Column(String(255),nullable=False)})classUserRequestSchema(BaseModel):username:str=Field(title="Username",max_length=255)full_name:str=Field(title="User Full Name",max_length=255)classUserResponseSchema(BaseModel):id:uuid.UUID=Field(title="User Id")username:str=Field(title="Username",max_length=255)full_name:str=Field(title="User Full Name",max_length=255)router=ModelCRUDRouter(prefix="user",model=User,identifier_type=uuid.UUID,get_session=get_session,get_authentication=authentication_scheme,request_schema=UserRequestSchema,response_schema=UserResponseSchema)app.include_router(router.api_router)if__name__=="__main__":uvicorn.run(app)Run itRun the server with:$uvicornmain:app--reloadINFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)INFO:     Started reloader process [28720]INFO:     Started server process [28722]INFO:     Waiting for application startup.INFO:     Application startup complete.Check itYou already created an API that:GET/user/{user_id}retrieves User object by UUID.GET/userreturns list of User objects.POST/usercreates User object.DELETE/user/{user_id}deletes User object by UUID.PUT/user/{user_id}updates User object by UUID.PATCH/user/{user_id}partial updates User object by UUID.Interactive API docsNow go tohttp://127.0.0.1:8000.You will see the automatic interactive API documentation (provided
bySwagger UI):LicenseThis project is licensed under the terms of the Apache License 2.0.
Package: facsimile
Facsimile=========Contents--------.toc 0Introduction------------Facsimile is designed to create exact environment copies for local developmentinstances and deployed staging and production environments.It supports multiple hosts, as well as multiple geographic regions.You can use either by making a separate repo for a conglomerate, or by puttingdefinitions in config/facsimile.jsonInternals---------### Definitions* project : highest level of facsimile config, can contain other subprojects* modules : project wide pieces, these are used to specify any sections that might be used between subprojects, or need separate passwords, etc* instances :An instance is a set of machines, acting on concert toproduce the full experience of using the platform. Another meaning ofinstance, in the deploy sense, is the set of configurations (including extradirectory, instance.json, passwd.json generated file, etc) that allow theinstance to be updated using the deploy scripts.instance data is shared across projects* targets : instances may be broken into different node types (e.g., frontend, database, app) called targetsrename:An instance in a daemonlevel sense refers to the fact that we sometimes run more than one copy of thesame code, off the same binary filename - usually with different arguments.For now we are doing this purely for an HA/LB or sharding perspective, but we mayadd further uses as well (such as hot spares - our existing HA is loadbalanced).* definition : defines a project down to each instance, contains no state info. Definitions can be inherited infinately to avoid redefining anything* state : configured and generated information that is instance specific.  For example, passwords, directoriesrm:per instance specific configuration for the deployment system to set up an install.* environment : environment specific* subinstance : sometimes, the 1:1 nature of the instance term becomes 1:2, or1:3.. or 1:n. For example, iceberg: we don't want to separately maintain theconfigurations for dev4.ch1's instance, dev0.ch0's instance, anddev5.ch1's instance separately, and we want to keep the passwords anduiid in sync, for clarity.  However, there are subtle differences that shouldbe tracked. This is like an overlay, in a way - but it's for the wholeinstance (in a deploy sense), not just one of the software packages.### Directory Layout* SRC/source checkouts - not separated by instance, assumes version tags are enough* BUILD/out of source build trees, subdirectoried under $instance/* define/instance definition files* extra/extra files to be deployed* tmpl/tmpl files to be rendered and then deployedTemplates---------Available structures are `instance` and `module`config file templates should be kept with the source repository as much as possible, so changes are versioned along with the code that uses themModules-------name: module namegenconfig: should facs genarate a config for thiswrite_sql: <remove>db::Class Overrides---------------_init_<system>Notes-----if project name matches a directory in deploy, rsync --delete is used to clean up the deploy
Package: fact4astro
Fundamental Astrometric Catalog Tools (FACT)A python package for processing fundamental astrometric catalogs.The package consists of several parts, whose names and functions are listed
below.cat_parser: read commonly used astrometric catalogs, for example,
ICRF3 catalogs.cat_tools: compare different catalogs and compute the position and proper
motion differencessolve_parser: read the output files of the VLBI global solution
generated by the Calc/Solve software.vlbi_tools: parse different kinds of VLBI products mainly available atParis Observatory Geodetic VLBI Center(OPAR)vsh: catalog analyses based on the vector spherical harmonics (VSH)The mannual is under development. Comments are welcomed.
Package: factanal
FactanalPython wrapper replicating the known factor analysis output from the factanal R function.
The only supported input is a pandas data frame. Formulas as input are currently
not supported. A covariance matrix is always computed from the input data frame.
Setting control variables for maximum likelihood estimation is currently not
supported.Further information on R's factanal function for factor analysis:https://www.rdocumentation.org/packages/stats/versions/3.6.2/topics/factanalMore information on the factanal output and examples:https://data.library.virginia.edu/getting-started-with-factor-analysis/Exampleimport pandas as pd
import random
from factanal.wrapper import factanal

pdf = pd.DataFrame({"v1": [random.randint(0, 100) for _ in range (30)],
                    "v2": [random.randint(0, 100) for _ in range (30)],
                    "v3": [random.randint(0, 100) for _ in range (30)],
                    "v4": [random.randint(0, 100) for _ in range (30)],
                    "v5": [random.randint(0, 100) for _ in range (30)],
                    "v6": [random.randint(0, 100) for _ in range (30)],
                    "v7": [random.randint(0, 100) for _ in range (30)],
                    "v8": [random.randint(0, 100) for _ in range (30)]})

fa_res = factanal(pdf, factors=4, scores='regression', rotation='promax', 
                  verbose=True, return_dict=True)


Uniquenesses: 
   v1    v2    v3    v4    v5    v6    v7    v8 
0.861 0.005 0.666 0.005 0.611 0.223 0.812 0.885 

Loadings:
   Factor1 Factor2 Factor3 Factor4
v1 -0.136                   0.326 
v2  0.983           0.169   0.104 
v3          0.128           0.575 
v4          0.999                 
v5 -0.114           0.199  -0.553 
v6 -0.204          -0.825   0.197 
v7 -0.264           0.317         
v8                  0.313   0.106 

               Factor1 Factor2 Factor3 Factor4
SS loadings      1.127   1.048   0.953   0.807
Proportion Var   0.141   0.131   0.119   0.101
Cumulative Var   0.141   0.272   0.391   0.492

Factor Correlations:
        Factor1 Factor2 Factor3 Factor4
Factor1  1.0000  0.0380 -0.0526  0.1918
Factor2  0.0380  1.0000  0.0675 -0.0599
Factor3 -0.0526  0.0675  1.0000 -0.0671
Factor4  0.1918 -0.0599 -0.0671  1.0000

Test of the hypothesis that 4 factors are sufficient.
The chi square statistic is 0.37 on 2 degrees of freedom.
The p-value is 0.833Installationpip install factanalDependenciesThe only dependency is the rpy2 library.In addition to that, R must be installed on your system and accessible to rpy2.More information on rpy2:https://rpy2.github.io/doc/latest/html/index.htmlDownload R here:https://www.r-project.org/MiscFactanal for python is MIT licensed.
Package: fact-checking
Fact checkingThis generative model - trained on FEVER - aims to predict whether a claim is consistent with the provided evidence.Installation and simple usageOne quick way to install it is to typepipinstallfact_checkingand then use the following code:fromtransformersimport(GPT2LMHeadModel,GPT2Tokenizer,)fromfact_checkingimportFactChecker_evidence="""Justine Tanya Bateman (born February 19, 1966) is an American writer, producer, and actress . She is best known for her regular role as Mallory Keaton on the sitcom Family Ties (1982 -- 1989). Until recently, Bateman ran a production and consulting company, SECTION 5 . In the fall of 2012, she started studying computer science at UCLA."""_claim='Justine Bateman is a poet.'tokenizer=GPT2Tokenizer.from_pretrained('gpt2')fact_checking_model=GPT2LMHeadModel.from_pretrained('fractalego/fact-checking')fact_checker=FactChecker(fact_checking_model,tokenizer)is_claim_true=fact_checker.validate(_evidence,_claim)print(is_claim_true)which gives the outputFalseProbabilistic output with replicasThe output can include a probabilistic component, obtained by iterating a number of times the output generation.
The system generates an ensemble of answers and groups them by Yes or No.For example, one can askfromtransformersimport(GPT2LMHeadModel,GPT2Tokenizer,)fromfact_checkingimportFactChecker_evidence="""Jane writes code for Huggingface."""_claim='Jane is an engineer.'tokenizer=GPT2Tokenizer.from_pretrained('gpt2')fact_checking_model=GPT2LMHeadModel.from_pretrained('fractalego/fact-checking')fact_checker=FactChecker(fact_checking_model,tokenizer)is_claim_true=fact_checker.validate_with_replicas(_evidence,_claim)print(is_claim_true)with output{'Y':0.95,'N':0.05}Score on FEVERThe predictions are evaluated on a subset of the FEVER dev dataset,
restricted to the SUPPORTING and REFUTING options:precisionrecallF10.940.980.96These results should be taken with many grains of salt. This is still a work in progress,
and there might be leakage coming from the underlining GPT2 model unnaturally raising the scores.
Package: facterpy
facterpy========Python library to provide a cached and dictionary-like interface to [Puppet's facter utility](http://puppetlabs.com/puppet/related-projects/facter).The facter script is run to gather facts, and YAML output is used if pyYAML is installed.The library is compatibile with Python 2 and Python 3 and *nix and OS X, although testing hasn't been extensive.Usage------```>>> import facter>>> f = facter.Facter()>>> f["architecture"]'x86_64'>>> f.lookup("uptime_seconds")195106>>> f.lookup("uptime_seconds")195106>>> f.lookup("uptime_seconds", cache=False)195234>>> f = facter.Facter(use_yaml=False)>>> f.lookup("uptime_seconds")'195301'>>> f.get("not a fact", "mere opinion")'mere opinion'```More methods exist. Documentation to come.Install---------```pip install facter```or```pip install facter[yaml]```to include pyYAMLRequirements-------------Required:- facter - must be installed through system packages or Ruby Gems- [six](https://pypi.python.org/pypi/six/) - Python 2 and 3 compatibility (marked in setup.py)Optional:- [pyYAML](http://pyyaml.org/wiki/PyYAML) - For YAML parsing of resultsProject State--------------This is an alpha/early version and it hasn't been tested much. Please report any issues observed.
Package: fact-explorer
Fact ExplorerWelcome to fact_explorer. You can find more extensive documentation over atreadthedocs.This is a companion project tofactcast <https://github.com/factcast/factcast>_ an event store written in Java.
You can also check out other event sourcing related project in python over atpyfactcast <https://pypi.org/project/pyfactcast/>_
as well ascryptoshred <https://pypi.org/project/cryptoshred/>_.This project arose mainly out of the necessity to work with events directly instead of through projections. This is useful to:Enable searching for 'random' events quickly during debuggingChecking events during migrationsMany other things (you should probably not do if you are an event purist)Contributions are welcome. Just get in touch.QuickstartSimplypip install fact-exporterand get going. The cli is available asfact-exporterand
you can runfact-exporter --helpto get up to speed on what you can do.DevelopmentThis project usespoetryfor dependency management andpre-commitfor local checks.SponsorsThis project is sponsored byPRISMA
Package: factful.py
Factful.pyA Package which can give you Random Facts in an easy way!LinksDiscord Support ServerGitHub ProfileFactful.js NPMQuick Example1. Getting Random Facts# Import factful.py Moduleimportfactful# Define facts functionfacts=factful.facts()# Getting random fact from random cateogaryprint(facts["all"])# Getting random facts from a paticular cateogary!# All Cateogaries: "computer", "food", "emoji", "space"print(facts["computer"])print(facts["food"])print(facts["emoji"])print(facts["space"])We also have this module asJavascript NPM Package!https://www.npmjs.com/package/factful.js. You can join our Discord Server for support!© Science Spot | Decimal Development - 2020