Saravanan666/pypi-org huggingface dataset:
Package: typeform-advancedapi
TypeForm_AdvancedAPI written in Pythonby Anton Kaiserpip install typeform-advancedapiInitializefrom typeform_advancedapi import *
API_KEY = ""
TypeForm = TypeForm_AdvancedAPI.CTypeForm(API_KEY)Get Form by ID <-- returns a CForm Objectformobject = TypeForm.getformByID("XXXXXX")Get Workspaces <-- returns an array with CWorkspace Objectsfor workspace in TypeForm.getWorkspaces():
	print(workspace.getID())
	print(workspace.getName())
	for form in workspace.getForms():
		print(form.getFormID())
		print(form.getTitle())Get Workspace by ID <-- returns the requested CWorkspace Objectworkspaceobject = TypeForm.getWorkspaceByID("XXXXXXXX")getFormResponse <-- returns a CResponse Array from the Formresponselist = TypeForm.getFormResponse(formobject)
OR
responselist = TypeForm.getFormResponse(formobject,true) <-- This will replace every choice answer with the index instead of the namegetFormResponse - After date  <-- returns a CResponse Array from the Formdate = "2019-04-29T00%3A00%3A00"
responselist = TypeForm.getFormResponse(formobject,false,date)Objects:CWorkspace:
getID() - Returns a String
getName() - Returns a String
getForms() - Returns a CForm arrayCForm:
getFormID() - Returns a String
getTitle() - Returns a String
getLastUpdatedAt() - Returns a String
getFormFields() - Returns a CFormField Array
GetQuestionFromRef(QuestionREF) - Returns a StringCFormField:
getID() - Returns a String
getType() - Returns a String
getRef() - Returns a String
getTitle() - Returns a String
getProperties() - Returns a JSON ObjectCResponse:
getLandingID() - Returns a String
getToken() - Returns a String
getResponseID() - Returns a String
getLandedAt() - Returns a String
getSubmittedAt() - Returns a String
getMetadata() - Returns a CMetadata Object
getAnswers() - Returns a CAnswer ArrayCAnswer:
getField() - Returns a String
getType() - Returns a String
getResult() - Returns a StringCMetadata:
getUserAgent() - Returns a String
getPlatform() - Returns a String
getReferer() - Returns a String
getNetworkID() - Returns a String
getBrowser() - Returns a StringExample: Dumping Questions from a Form with QuestionID, QuestionformID = "XXXXX"
form = TypeForm.getformByID(formID)
responselist = TypeForm.getFormResponse(form)
counter = 1
for answer in responselist[0].getAnswers():
	print(row.ExternalSurveyID + " Question " + str(counter) + "> " + answer.getField().getID() + " - " + form.GetQuestionFromRef(answer.getField().getRef()) + " | " + answer.getType())
	counter += 1
Package: typeform-python
typeform-pythonTypeForm API wrapper written in python.Installingpip install typeform-pythonUsagefrom typeform.client import Client

client = Client('CLIENT_KEY', 'CLIENT_SECRET')Get authorization urlurl = client.authorization_url('REDIRECT_URI', ['forms:write', 'forms:read'])Exchange the code for a tokentoken = client.exchange_code('REDIRECT_URI', 'CODE')Set the tokenclient.set_access_token('TOKEN')Get form IDclient.get_form_uid('FORM_URL')Get form informationclient.get_form_information('FORM_UID')Get form questionsclient.get_form_questions('FORM_UID')Get form metadataclient.get_form_metadata('FORM_UID', 'SINCE', 'UNTIL')Get all formsclient.get_forms()Create Webhookclient.create_webhook('WEBHOOK_URL', 'WEBHOOK_TAG', 'FORM_UID')View Webhookclient.view_webhook('WEBHOOK_TAG', 'FORM_UID')Delete Webhookclient.delete_webhook('WEBHOOK_TAG', 'FORM_UID')RequirementsrequestsTODOcreate_formUpdate_formdelete_formget_custom_form_messagesupdate_custom_messagescreate_imageget_images_collectionget_imagedelete_imageget_image_by_sizeget_background_by_sizeget_choice_image_by_sizecreate_themeget_themesupdate_themesdelete_themesContributingWe are always grateful for any kind of contribution including but not limited to bug reports, code enhancements, bug fixes, and even functionality suggestions.You can report any bug you find or suggest new functionality with a newissue.If you want to add yourself some functionality to the wrapper:Fork it (https://github.com/GearPlug/typeform-python)Create your feature branch (git checkout -b my-new-feature)Commit your changes (git commit -am 'Adds my new feature')Push to the branch (git push origin my-new-feature)Create a new Pull Request
Package: typegame
Typegamea small dash app for creating quizes for programming educationinstallpip install typegameorpip install git+https://github.com/endremborza/typegame.gitfor latest versionuserun withpython -m typegame \
    --host=0.0.0.0 \
    --port=6999 \
    --quiz-path=./quizes \
    --answer-path=./answers(all parameters are optional, these are the defaults)optional parameters:--value-game: asks for values, not just type of last line
Package: typegen
typegen Is a Typed Dict GeneratorWhat it works for?Basic dictionariesNested dictionariesDictionaries with Lists if the items are of same typeAny level of nesting as long as list items are of same typeNoneType values default toOptional[Any]When it failsEach generation gets tested by pytype. In the case it fails it will let you know and provide a link totypegen Issues.In many cases of failure:It created the type, but didn't import something needed like a datetime.I'm looking for a clever way to handle the importing, but this should be a quick fix for now.Usage:usage: typegen [-h] -f FILE -v VAR -t TYPE_NAME [-o OUTPUT]

Import a variable from a file.

options:
  -h, --help            show this help message and exit
  -f FILE, --file FILE  Path to the python file (without .py extension)
  -v VAR, --var VAR     Variable name in the file
  -t TYPE_NAME, --type-name TYPE_NAME
                        Name for the type
  -o OUTPUT, --output OUTPUT
                        Path to the output file (default: out/<name of variable>.py)Example:File: tests/fish_obj.pyfish = {
    'fish': {
        "health":3,
        "location":{
            "city": "belo",
            "state": "kansas"
        },
        "items": [
            {"item_id": 1, "value":4, "name": "scales", "variations": ["weak", "strong"]},
            {"item_id": 2, "value":4, "name": "knife", "variations": ["weak", "strong"]},
            {"item_id": 1, "value":4, "name": "scales", "variations": ["weak", "strong"]}  
        ]
        
    }
}Runtypegen -f tests/fish_obj -v fish -t FishTypeOutput:File: out/FishType.pyfrom typing import TypedDict, List, Optional, Any

class ItemsList(TypedDict):
    item_id: int
    value: int
    name: str
    variations: List[str]

class Location(TypedDict):
    city: str
    state: str

class Fish(TypedDict):
    health: int
    location: Location
    items: List[ItemsList]

class FishType(TypedDict):
    fish: FishMaintenanceI will work to patch big issues with generating TypedDicts but will not expand the scope of the project for now.
Package: typegenie
TypeGenieAPIClientPython API client to access TypeGenie API.DocumentationCheck out detailed API documentation athttps://api.typegenie.netInstallationRun the following to install:pip install typegenieUsingtypegenie-cliOnce a model has been deployed, you can usetypegenie-clito test the model quality.typegenie-cli-dsi<DATASET-ID>-dpi<DEPLOYMENT-ID>-u<ACCOUNT-USER-ID>-p<PASSWORD>-ui<USER-ID>--multilinewhere<USER-ID>is the id of the user that exists within the deployment<DEPLOYMENT-ID>The tool
randomly samples dialogues from the given dataset, and shows messages with different colors.Usectr+cto start a new dialogue andctr+ctwice to exit.
Package: typegenie-test-version
TypeGenieAPIClientPython API client to access TypeGenie API.DocumentationCheck out detailed API documentation athttps://api.typegenie.netInstallationRun the following to install:pip install typegenieUsingtypegenie-cliOnce a model has been deployed, you can usetypegenie-clito test the model quality.typegenie-cli-dsi<DATASET-ID>-dpi<DEPLOYMENT-ID>-u<ACCOUNT-USER-ID>-p<PASSWORD>-ui<USER-ID>--multilinewhere<USER-ID>is the id of the user that exists within the deployment<DEPLOYMENT-ID>The tool
randomly samples dialogues from the given dataset, and shows messages with different colors.Usectr+cto start a new dialogue andctr+ctwice to exit.
Package: typeguard
This library provides run-time type checking for functions defined withPEP 484argument (and return) type
annotations, and any arbitrary objects. It can be used together with static type
checkers as an additional layer of type safety, to catch type violations that could only
be detected at run time.Two principal ways to do type checking are provided:Thecheck_typefunction:likeisinstance(), but supports arbitrary type annotations (within limits)can be used as acast()replacement, but with actual checking of the valueCode instrumentation:entire modules, or individual functions (via@typechecked) are recompiled, with
type checking code injected into themautomatically checks function arguments, return values and assignments to annotated
local variablesfor generator functions (regular and async), checks yield and send valuesrequires the original source code of the instrumented module(s) to be accessibleTwo options are provided for code instrumentation:the@typecheckedfunction:can be applied to functions individuallythe import hook (typeguard.install_import_hook()):automatically instruments targeted modules on importno manual code changes required in the target modulesrequires the import hook to be installed before the targeted modules are importedmay clash with other import hooksSee thedocumentationfor further information.
Package: typeguards
This is a Python library ofTypeGuardfunctions that can be used for static type
narrowing, and runtime type checking.is_json({"str_key":"str_val","int_key":1,"float_key":1.0,"bool_key":True,"none_key":None,"list_key":[1,2,"3",{"foo":"bar"}],"dict_key":{"foo":"bar"},},)# TrueInstallationpipinstalltypeguardsUsageAssert that an object is valid JSON.fromtypeguards.jsonimportis_jsonassertis_json({"str_key":"str_val","int_key":1,"float_key":1.0,"bool_key":True,"none_key":None,"list_key":[1,2,"3",{"foo":"bar"}],"dict_key":{# dict values can be nested infinitely"foo":"bar",},},)# OKassertis_json("a string")# AssertionErrorassertis_json([1,2,3])# AssertionErrorAssert that an object conforms to a JSON schema.fromtypingimportList,NamedTuple,TypedDictfromtypeguards.jsonimportis_json_schemaclassHobbySchema(NamedTuple):name:stris_fun:boolclassUserSchema(TypedDict):id:intusername:strhobbies:List[HobbySchema]# Nested schemaassertis_json_schema({"id":7,"username":"charlotte","hobbies":[{"name":"Hyrule Warriors: Age of Calamity","is_fun":True}],},UserSchema,)# OKassertis_json_schema({# No id, but still conforms to schema"username":"oscar","hobbies":[{"name":"Whacking things","is_fun":True}],},UserSchema,)# OKassertis_json_schema({"bad-id":123,# Doesn't conform to schema"username":"narvin","hobbies":[{"name":"Watching coding videos","is_fun":False}],},UserSchema,)# AssertionError
Package: typehintjson
JSON Decoder / Encoder for type hintsSimple, 2 file python module that parses JSON into dataclasses / enums
and vice versa. Ignores extra fields, and works with default values for
missing fields.Requires Python 3.8.0 or higherFor security sake, you have to know the type you're expecting to deserialize
into.Unions will be resolved in order (i.e. Union[int, MyType, None] will first
try parsing as int, then as MyType, then as None),
so if a value is MULTIPLE valid types, it might not pick the right one.Installationpip install typehintjsonUsageimportjsonimporttypingasTfromtypehintjsonimportparse_as_type,dataclass_to_dictionaryfromdataclassesimportdataclass@dataclassclassChild:x:T.Union[int,str]y:int=4@dataclassclassParent:a:intc:Childobj=Parent(a=2,c=Child(x="meow"))print(repr(parse_as_type(json.loads(json.dumps(dataclass_to_dictionary(obj))),Parent)))# Parent(a=2, c=Child(x='meow', y=4))
Package: typeid-python
TypeID PythonA Python implementation ofTypeIDsusing PythonTypeIDs are a modern,type-safe, globally unique identifier based on the upcoming
UUIDv7 standard. They provide a ton of nice properties that make them a great choice
as the primary identifiers for your data in a database, APIs, and distributed systems.
Read more about TypeIDs in theirspec.This particular implementation provides an pip package that can be used by any Python project.InstallationPyPI:pip install typeid-pythonPoetry:poetry add typeid-pythonUsageBasicCreate TypeID Instance:fromtypeidimportTypeIDtypeid=TypeID()print(typeid.prefix)# ""print(typeid.suffix)# "01h45ytscbebyvny4gc8cr8ma2" (encoded uuid7 instance)typeid=TypeID(prefix="user")print(typeid.prefix)# "user"print(str(typeid))# "user_01h45ytscbebyvny4gc8cr8ma2"Create TypeID from string:fromtypeidimportTypeIDtypeid=TypeID.from_string("user_01h45ytscbebyvny4gc8cr8ma2")print(str(typeid))# "user_01h45ytscbebyvny4gc8cr8ma2"Create TypeID from uuid7:fromtypeidimportTypeIDfromuuid6importuuid7uuid=uuid7()# UUID('01890bf0-846f-7762-8605-5a3abb40e0e5')prefix="user"typeid=TypeID.from_uuid(prefix=prefix,suffix=uuid)print(str(typeid))# "user_01h45z113fexh8c1at7axm1r75"CLI-toolInstall dependencies:pip install typeid-python[cli]To generate a new TypeID, run:$python3-mtypeid.clinew-pprefixprefix_01h2xcejqtf2nbrexx3vqjhp41To decode an existing TypeID into a UUID run:$python3-mtypeid.clidecodeprefix_01h2xcejqtf2nbrexx3vqjhp41type: prefixuuid: 0188bac7-4afa-78aa-bc3b-bd1eef28d881And to encode an existing UUID into a TypeID run:$python3-mtypeid.cliencode0188bac7-4afa-78aa-bc3b-bd1eef28d881--prefixprefixprefix_01h2xcejqtf2nbrexx3vqjhp41
Package: type_infer
MindsDB Type InferWebsite|Docs|Community Slack|Contribute|HacktoberfestAutomated type inference for Machine Learning pipelines.In the context of tabular data,type_inferaims for optimal interpretation of each column’s data type for ML use cases. For example, strings with date or time format would be classified as timestamps, or integers as categorical if there is a sufficiently small set of unique values in the column.DocumentationDocumentation link
Package: type-info
type-infoProvide some oop api fortypingmodule.Support python 3.6+, tested on3.6and3.7.Usagefromtype_infoimportget_type_infotype_info=get_type_info(typing.Dict[str,int])asserttype_info.generic_typeistyping.Dictasserttype_info.generic_args==(str,int)asserttype_info.dynamic_typeisdictread unittests for more examples.
Package: type-intersections
type-intersectionsA naive prototype of type intersections implementation.Installationpipinstalltype-intersectionsIntroType intersections are a way for a programmer to say that some variable is of typeAand typeBat the same time which is only possible if it inherits fromAandBor ifAandBare both structural types with overloaded subclass checks such asSizedorHashable.Current issuesOrdering checksThe current implementation assumes that intersections are unordered which makes some sense for two reasons:I have not been able to come up with an algorithm that can intuitively do structural and ordered intersection issubclass checks at the same time (see the XFAIL testcase). Not sure if that is even possible but I haven't tried for long.TypeScriptdoesn't even tryto handle order in intersectionsThe issue arises when we try to decide what to use in issubclass checks: the class itself or itsmro().Let's say that a classCinherits methodfoo()that is defined in both its parents:AandB. Let's also say thatCdefines the method__len__that its parents do not have. If we have an intersectionA & B & Sized, what algorithm would we use to check thatCuses the correctfoo()and that it is Sized?classA:deffoo(self,a):print(a)classB:deffoo(self,a,b):print(a,b)classC(A,B):# Makes C Sizeddef__len__(self):return83If we try to just checkissubclass(C, A) and issubclass(C, B) and issubclass(C, Sized), then we are omitting order information and cannot definitively tell thatCwill inheritfoo()fromA.An alternative would be to go throughC's MRO in order: checking that each next item in the MRO is a subclass of each argument in the intersection like so:def__subclasscheck__(self,cls):args=self.__args__fortype_incls.mro():whileargs:ifissubclass(type_,args[0]):args.pop(0)else:breakreturnnotargsBut we quickly realize that the first item in MRO is the class itself which is definitely an instance ofA,B, andSized, thus keeping our check unordered. The naive way to resolve this would be to remove the class itself from its MRO before we start checking:def__subclasscheck__(self,cls):args=self.__args__[1:]fortype_incls.mro():whileargs:ifissubclass(type_,args[0]):args.pop(0)else:breakreturnnotargsBut nowissubclass(C, Intersection[A, B, Sized])isFalsebecause__len__is defined onCwhich we skip in the MRO checks.Another option would be to use both algorithms: unordered algorithm for structural checks and ordered algorithm for inheritance-based checks. But we cannot distinguish between the checks because classes can have their__subclasscheck__redefined in all sorts of complex ways and because even some structural checks care about order: such as in the example I gave above from TypeScript.In other words, we can't check that some classCinherits from classesAandBin this specific order (i.e. not fromBandA)andcheck that it implementsSizedat the same time. The only solution I see is to abandon either structural checks or order in inheritance checks. Order seems a lot less necessary and fundamental so I decided to abandon it for now for my naive implementation.
Package: typeit
Typeittypeitinfers Python types from a sample JSON/YAML data, and provides you with the tools
for serialising and parsing it. It also provides you with smart constructors for arbitrarily nested data structures.
The library works superb on Python 3.7 and above.Start using it by generating types for your JSON payloads:$echo'{"first-name": "Hello", "initial": null, "last_name": "World"}'|typeitgenThe snipped above produces output similar to this:fromtypingimportAny,NamedTuple,Optional,SequencefromtypeitimportTypeConstructorclassMain(NamedTuple):first_name:strinitial:Optional[Any]last_name:stroverrides={Main.first_name:'first-name',}mk_main,serialize_main=TypeConstructor&overrides^MainUse these functions to construct and serialize your payloads:payload={"first-name":"Hello","initial":None,"last_name":"World"}data=mk_main(payload)assertisinstance(data,Main)assertserialize_main(data)==payloadDocumentationDocumentation is hosted on ReadTheDocs:https://typeit.readthedocs.io/en/develop/Test frameworkRun existing test suite with$ make testChangelogSeeCHANGELOG.
Package: typejudge
Judgement as a Service.Typejudge will automatically check forsemvercompliance based on type hints.  If the type signature for a function changes,
it will judge this to be an API change and recommend a major version bump.This is largely inspired by Elm’s package manager, which also enforces semver.InstallationNote: typejudge only works on python 3.5 and higher.Install with pip:$ pip install typejudgeUsage$ typejudge --help
usage: typejudge [-h] [-o OUT] [-f FILE] MODULE [VERSION]

judge your types

positional arguments:
  MODULE                module to import and check
  VERSION               current version of the package

optional arguments:
  -h, --help            show this help message and exit
  -o OUT, --out OUT     save current type definitions to this file
  -f FILE, --file FILE  load type definitions from this fileExample usageSuppose we’ve got a module that contains some type annotations on the publicly
exported API, testmodule.py:defgreeting(name:str)->str:return'Hello '+nameSave the types somewhere:$typejudge-otestmodule.jsontestmoduleMake some small change totestmodule.py, add a new function:defgreeting2(name:str,name2:str)->str:return'Hello '+name+' and 'name2Typejudge will recommend this is a minor release:$ typejudge -f testmodule.json testmodule
minorThe same, but with a known current version number:$typejudge-ftestmodule.jsontestmodule0.3.20.4.0Make a change to existing type signatures:fromtypingimportListdefgreeting(names:List[str])->str:return'Hello '+' '.join(names)Typejudge will now recommend this is a major release:$typejudge-ftestmodule.jsontestmodulemajorWith no changes to the API’s types, typejudge will recommend a patch release.Example usage with bumpversionTypejudge works quite well with bumpversion. Doing this is probably
inadvisable, but you can entirely automate releases.Assuming similar files as in the previous section, set up a config file for
bumpversion, something like:$ cat .bumpversion.cfg[bumpversion]current_version=2.0.3commit=Truetag=True[bumpversion:file:setup.py]And a setup.py:$catsetup.pyimportsetuptoolssetuptools.setup(name="testpackage",version="2.0.3",description="Test stuff",)Then run something along these lines to cut a new release:$bumpversion$(typejudge-ftestmodule.jsontestmodule)You’ll also want to save the state of the API at this point, so you can compare
it at the next release:$typejudge-otestmodule.jsontestmoduleObviously use some discretion when releasing in this way. Just because the
types of your API remain the same, it doesn’t necessarily mean that your code
is backwards compatible. Typejudge suggests the smallest version increment you
should make.
Package: typekev-mlnd-probability
# MLND Probability ClassesThis package exposes two probability distribution classes, Gaussian and Binomial, and was made as part of the Udacity Machine Learning Engineer Nanodegree.This package was created for educational purposes.# Use ofGaussianandBinomialThe classes can be imported as so:from typekev_mlnd_probability import Gaussianandfrom typekev_mlnd_probability import BinomialBoth classes are initialized with the following attributes:mean: The mean or averagestdev: Standard deviationdata: The list of data in which to calculate the other attributesBoth classes also contain aread_data_filemethod.TheGaussianandBinomialclasses also contain unique attributes and methods. Though names may overlap, implementation will differ.##Gaussianattributes and methodsTheGaussianclass contains the following additional methods:calculate_meancalculate_stdevplot_histogrampdfplot_histogram_pdfThe magic methods__add__and__repr__have also been implemented.##Binomialattributes and methodsTheBinomialclass contains the following additional attributes:n: The size of the data setp: The probabilityTheBinomialclass contains the following additional methods:calculate_meancalculate_stdevreplace_stats_with_dataplot_barpdfplot_bar_pdfThe magic methods__add__and__repr__have also been implemented.
Package: type-lens
type-lensProjectStatusCI/CDQualityCommunityMetaAboutThis project is a template repository forJoltprojects. It is designed to be a starting point for
any project that is a part of the Jolt organization.UsageInstall copier$ copier copy gh:jolt-org/project-template $new-project-nameAnswer questions.New project checklistCreate thehttps://github.com/jolt-org/type-lensrepository.If using docs: Create thehttps://github.com/jolt-org/type-lens-docs-previewrepository.If not using docs: remove.github/workflows/docs-preview.yaml.Update theREADME.mdfile with the project-specific information.Initialize git repository:$ git initStage the files:$ git add ."Install pre-commit hooks:$ pre-commit installRun pre-commit hooks:$ pre-commit run --all-filesStage any files that were modified by the pre-commit hooks:$ git add .Commit the changes:$ git commit -m "Initial commit"Add the remote:$ git remote add origin git@github.com:jolt-org/type-lens.gitPush the changes:$ git push -u origin mainContributingAllJoltprojects will always be a community-centered, available for contributions of any size.Before contributing, please review thecontribution guide.If you have any questions, reach out to us onDiscord, our org-wideGitHub discussionspage,
or theproject-specific GitHub discussions page.AJolt OrganizationProject