Saravanan666/pypi-org huggingface dataset:
Package: object-freezer
object-freezerPython module for freezing objects.Installation$ pip install object-freezerThen import this module as:importobject_freezerasofExamplesA list is frozen into a tuple:>>>l=[1,2,3]>>>hash(l)Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>TypeError:unhashabletype:'list'>>>fl=of.freeze(l)>>>hash(fl)2528502973977326415>>>fl(1,2,3)A dictionary is frozen into adeepfrozendictinstance:>>>importobject_freezerasof>>>d={'a':'A','b':'B'}>>>hash(d)Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>TypeError:unhashabletype:'dict'>>>fd=of.freeze(d)>>>hash(fd)3871628092596788623>>>fd<deepfrozendict{'a':'A','b':'B'}>If an element of a list is not hashable, it will also be frozen:>>>l=[[1,2],{3:4}]>>>hash(l)Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>TypeError:unhashabletype:'list'>>>fl=of.freeze(l)>>>hash(fl)5879964472677921951>>>fl((1,2),<deepfrozendict{3:4}>)If a value of a dictionary is not hashable, it will be frozen:>>>importobject_freezerasof>>>d={'a':['A',0],'b':{'B':1}}>>>hash(d)Traceback(mostrecentcalllast):File"<stdin>",line1,in<module>TypeError:unhashabletype:'dict'>>>fd=of.freeze(d)>>>hash(fd)6230706721132225338>>>fd<deepfrozendict{'a':('A',0),'b':<deepfrozendict{'B':1}>}>Known issues & notesAdictwith one(key, value)pair has the same hash value as atuple (key, value)Since a list is frozen into a tuple, it is obvious thatof.freeze([key, value])andof.freeze((key, value))are the same and thus have the same hash value. However,of.freeze({key: value})also always has the same hash value.>>>d={1:2}>>>t=(1,2)>>>fd=of.freeze(d)>>>ft=of.freeze(t)>>>hash(fd)==hash(ft)TrueIt is not a major issue sincefd == ftreturnsFalse. Therefore, for example, you can usefdandftas a distinct dictionary keys.>>>fd==ftFalse>>>{fd:0,ft:1}{<deepfrozendict{1:2}>:0,(1,2):1}LicenseMITTO-DO'sTest against various Python versions.
Package: objectfs
PyVFS is a simple pure Python VFS library. It consists of
several layers, allowing to use different low-level protocol
implementations. Now you can choose between 9p (9p2000.u)
and FUSE (you should have python-py9p or python-fuse
installed).The library has several options to control access to the
filesystem, from FUSE restrictions to PKI client authentication
in 9p2000 protocol. You can mount your FS with TCP/IP or
UNIX sockets, or simply browse it with 9p clients without
mounting.The simplest example. Environment variables:export PYVFS_PROTO=9p
export PYVFS_ADDRESS=/tmp/socketYour script:# import server
from pyvfs.utils import Server
# create it
srv = Server()
# start it in foreground
srv.run()Client side, 9p + UNIX socket:$ sudo mount -t 9p -o trans=unix /tmp/socket /mntObjectFSObjectFS (pyvfs.objectfs) is a library that allows you
to export your Python objects on a dynamic filesystem.
ObjectFS integration is extremely simple and engages only
the decorator import and usage. The developer should not
care about almost any of fs-related issues. Objects of the
decorated classes will automatically appear as file trees
on a dynamic filesystem with read/write access.Example. Environment variables:export PYVFS_PROTO=fuse
export PYVFS_MOUNTPOINT=~/mntYour script:# simply import the library in your code
from pyvfs.objectfs import export
# decorate a class
@export
class MyClass(object):
    some codeClient side (already mounted!):# ls -l ~/mntPyVFSAlso you can write your own applications with PyVFS. E.g.,
one can utilize file I/O as an RPC interface, or use a
dynamic filesystem for runtime service configuration.More details in the documentation and examples.Linkshome:https://github.com/svinota/pyvfsbugs:https://github.com/svinota/pyvfs/issuesdocs:http://peet.spb.ru/pyvfs/wiki:https://github.com/svinota/pyvfs/wikipypi:http://pypi.python.org/pypi/objectfs/list:https://groups.google.com/forum/#!forum/pyvfsChanges0.2.9 – Mesoarcheanunicode literals fixed0.2.8 – Paleoarcheandirectory listing fixes0.2.7 – Eoarchaeansupport authentication optionssupport setuid, setgid, sticky bits0.2.6 – Hadeaninitial pypi release
Package: object-get
object-getUse "." to get the value of a JSON object. Just like JavaScripthat gets the value of an objectInstallpipinstallobject-getUsagefromobject_getimportgetobj={'age':18,'name':{'first':'John','last':'Doe'},'languages':['python','java',['c','c++'],['js','javascript','typescript']]}print(get(obj,'age'))# => 18print(get(obj,'weight'))# => Noneprint(get(obj,'weight',100))# => 100- print(obj['name']['first'])+ print(get(obj, 'name.first'))# => John- print(obj['name']['last'])+ print(get(obj, 'name.last'))# => Doe- print(obj['languages'][0])+ print(get(obj, 'languages[0]'))# => python- print(obj['languages'][2][1])+ print(get(obj, 'languages[2][1]'))# => c++- print(obj['languages'][3][2])+ print(get(obj, 'languages[3][2]'))# => typescript
Package: objectgraph
Objectgraph is a library that provides a class representing
basic graphs with nodes and edges between nodes.Nodes can be arbitrary objects with an “identifier” attribute
(which should be hashable). Edges can have arbitrary attributes.The model for edges is slighly non-standard: There can be multiple
edges between nodes, but all edges with the same attributes are
collapsed into one edge.There isdocumentation at readthedocsHistoricObjectgraph is a complete rewrite of the ObjectGraph class inaltgraph,
using lessons learned in that project but with a complete new
Python 3 code base and full test coverage.
Package: objectifiedetree
OverviewWouldn’t it be nice to use dot notation for ElementTrees? This package
allows for:tree=ET.fromstring('<root><a><b c="asdf" /></a></root>')a=tree.ab=a.bb.attrib['c']=="asdf"# Truetree.awill be a`Element<https://docs.python.org/3.4/library/xml.etree.elementtree.html#xml.etree.ElementTree.Element>`__
with the extra`__getattr__<https://github.com/arve0/objectifiedetree/blob/master/objectifiedetree/__init__.py#L11-12>`__
method. This means that you can use the element as you would do
normally, but names in your XML that crases with python’s methods or
attributes must be accessed throughtree.find(xpath).This package uses the python implementation of etree, which makes it
slower than etree found in CPython. An alternative would be to
monkey-patch the built-in withforbiddenfruit, but I
haven’t looked into this.objectifiedetreehas copied the python implementation of etree fromCPython 3.4
Lib/xml/etreeand will probably only work with Python 3.4.InstallationInstall usingpip…pipinstallobjectifiedetreeExamplefromobjectifiedetreeimport*tree=ET.parse('/path/to/file.xml')# dot notation :-)el=tree.xpath.to.your.element# use normal etree attributesprint(el.attrib)# access name crashesattrib_el=el.find('./attrib')DevelopmentInstall dependencies and link development version of objectifiedetree to
pip:gitclonehttps://github.com/arve0/objectifiedetreecdobjectifiedetreepipinstall-rrequirements.txt# install dependencies and objectifiedetree-packageTestingtoxBuild documentation locallyTo build the documentation:pipinstall-rdocs/requirements.txtmakedocs
Package: objectify-json
Objectify JSONMake accessing JSON like data more convenient.FeaturesAccess dict value via dot.(data.a.b.c).Always returnObjectifyJSONtype, which holds the data with type of dict, list or any other primitive types.Usex._datato get the real data.Always returnObjectifyJSON(None)if doesn't exist.An CLI tool namedobjectto process JSON data.Installpip3 install objectify-jsonExampleSeetest.pyFunctions to process data in batchThe return value is alwaysObjectifyJOSNtoo!The return value of lambda funtions will always be unwrapped to primitive types.Most of thefn_*functions accept optionalunwrapparameter to enable passing the underlying value as primitive types to lambda. Default is False.When used in CLI tool, if it failed getting the property by property name, e.g.map, it will retry to get function with prefixfn_added to the name. This will simplify writting the CLI command.CommonFollowing methods ofObjectifyJOSNaccept optionalunwrapto unwrapObjectifyJOSNdata to the underlying built-in data, the default value isFalse.fn_map(fn, unwrap=False):mapon the iteratorfn_reduce(fn, initializer=None, unwrap=False):reduceon the iterator, lambda as the first positional parameter, optionalinitializerparameter will be passed to built-inreduce.fn_lambda(fn, unwrap=False): value in-and-outfn_filter(fn, unwrap=False):filteron the iteratorDictfn_keys(): Return keys as list.fn_values(): Return values as list.fn_items(): Return items as list. Element has the typetuple, e.g.("key", "value").fn_include_keys(keys): Filter dict. Keep thekeysyou give.fn_exclude_keys(keys): Filter dict. Remove thekeysyou give.fn_filter_by_value(fn): Filter dict. Filter by the lambda you give, which accept the value of dict item.fn_filter_by_kv(fn): Filter dict. Filter by the lambda you give, which acceptkeyandvaluetwo variables.fn_update(key, fn, unwrap=False): Update dict value. The lambda you give accept the origin value and return a new value.fn_items_update(fn, unwrap=False): Update dict value. The lambda you give acceptkeyandvaluetwo variables and return a new value.fn_rename(mapping): Update dict key. Themappingis a list of two-elements list.Listfn_sort(fn): Sort the list in place. The lambda you give will be passed askeyargument to thesortmethod of list.fn_dedup(fn=None, all=True): Dedup the elements in list. IfallifFalse, the duplication will checked by comparing current value between last value, else will compare to all appeared before.
Package: objectio
The Object IO Library and CommandObject stores (S3, Google, Azure, Minio, Swift, etc.) all have their own
command line interfaces with their own conventions for accessing objects.
This library and command provides a simple, uniform interface to object
store facilities, both from the command line and from within Python.Installation$pipinstallobjectioFor the Github version:$pipinstallgit+https://github.com/tmbdev/objectio.gitDocumentationReadTheDocsCommand Line Usage$objcataz://container/blobname$objcatgs://bucket/blobname$objcats3://bucket/blobname$objcatfile:/path$catfile|objputgs://bucket/blobnamePython Usageimportobjectiowithobjectio.gopen("gs://bucket/blob","rb")asstream:contents=stream.read()New ProtocolsYou can define new schemes by creating a./objectio.yamlor~/.objectio.yamlfile. For example:schemes:random:read:cmd:["cat","/dev/random"]Future ExtensionsThe intention is to keep this library simple and always allow command line
programs to be configured for I/O as needed by end users.Additional functionality:better error handling of non-zero return codesobj dir az://container --format csvobj del gs://container/blobpossibly port to GoNote that for Python, running I/O in a separate process ispreferableto using
Python-native libraries, since the latter do not run concurrently. For a Go
language implementation, protocol implementations using built in native client
libraries are potentially useful.
Package: objectionloltotxt
objectiontotxtA really really simple program to convert objection files fromhttps://objection.lol/to readable txt files.I am not experienced with creating projects or programming stuff for public use so if there are any problems i would appreciate feedback.UsageFor giving filenames of the .objection files$ objtoxt --filename file1 file2 file3...For giving the directory where the .objection files are at$ objtotxt -d directory1 directory2...Help:usage: objtotxt [-h] (-f file [file ...] | -d directory [directory ...])

Convert .objection files to txt

optional arguments:
  -h, --help            show this help message and exit
  -f file [file ...], --filename file [file ...]
                        Filename of the .objection file
  -d directory [directory ...], --directory directory [directory ...]
                        convert the .objection files in a directory to readable text file=======Currently only works for courtroom recordings/ objection entries with nicknamespypi :https://pypi.org/project/objectionloltotxt/
Package: objectionpy
Objection.pyA modular library for creatingobjection.lolprojects beyond what's possible manually.OverviewThe .objection format is good for distribution, but doesn't work as well for production or automation.This library focuses on providing helpful IDE suggestions, simple readability, and a class-based structure making up every piece of an objection. This structure can then compile to objection.lol's complicated JSON format.This way, complex objections with gameplay systems that use large amounts of repetitive actions become simple to generate.scene=Scene()scene.frames.append(Frame(char=FrameCharacter(character=preset.Characters.Defense.PhoenixWright,poseSubstr='Point',pairOffset=(-20,0),),pairChar=FrameCharacter(character=preset.Characters.Defense.MiaFey,poseSubstr='Point',pairOffset=(20,0),),fade=Fade(direction=FadeDirection.OUT,target=FadeTarget.BACKGROUND,duration=1000,color=Color('#000'),),text=f'{preset.Sound.Yell}Objection!',))scene.compile()InstallationInstalling objection.py with pip:pip install objectionpyDocumentationView the documentation pageTODOComplete: galleryAssign option for custom charactersComplete: improve tests for scene features and for objection JSON loading
Package: objectiv-bach
Objectiv Bach: Pandas-like analytics modeling translated to SQLBach is a Python-based analytics modeling library with a Pandas-like interface. It translates all operations and models to SQL, running directly on your full dataset. All DataFrames & models can be converted to an SQL statement with just a single command, to use in other tools like dbt or BI.Bach is part ofObjectiv, an open-source data collection & modeling platform that helps data teams run product analytics from their notebooks. Bach therefore works with any dataset that embraces Objectiv'sopen analytics taxonomy, so you can take pre-built models from theopen model hubto quickly build deep analyses for a wide range of product & marketing use cases.InstallationTo install Bach, use the following command:pipinstallobjectiv-bach# just the Bach library, supports PostgreSQL out of the boxpipinstallobjectiv-bach[bigquery]# for Google BigQuery supportpipinstallobjectiv-bach[athena]# for AWS Athena supportSee how toget started in your notebookfor detailed installation instructions.UsageExample notebooks with instructionsBach API referenceSupportVisit Objectiv Docs for instructions & FAQsJoin Objectiv on Slack to get helpRequest a feature or report an issue on GithubFound a security issue?Please don’t use the issue tracker but contact us directly. SeeSECURITY.mdfor details.ContributingIf you want to contribute to Objectiv Bach or use it as a base for custom development, take a look atCONTRIBUTING.mdfor detailed development instructions. For more unformation about our contribution process and where you can fit in, check out ourContribution Guidein the Docs.LicenseThis repository is part of the source code for Objectiv, which is released under the Apache 2.0 License. Please refer toLICENSE.mdfor details.Copyright (c) 2021-2022 Objectiv B.V. All rights reserved.
Package: objective
As a result of frustration working with colander, I decided to create something similar…Objective is a de/serialization tool with following features:declarative definition of objectives.no confusion about how to define something, it is always the same.reuse definitions by inheritance.optional validation.make it simple to override certain parts.UsageSimple mapping and validationimportobjectiveimportdatetimenow=datetime.datetime(2001,9,11)classUserObjective(objective.Mapping):name=objective.Item(objective.Unicode,missing=objective.Ignore)email=objective.Item(objective.Unicode,validator=objective.Email())password=objective.Item(objective.Unicode)since=objective.Item(objective.UtcDateTime,missing=now)serialized={'email':'foo@example.com','password':'foobar'}deserialized=UserObjective.deserialize(serialized)assertdeserialized=={'password':u'foobar','email':u'foo@example.com','since':datetime.datetime(2001,9,11,0,0)}bad_serialized={'name':'foobar'}try:deserialized=UserObjective().deserialize(bad_serialized)exceptobjective.Invalidase:assertisinstance(e,objective.exc.InvalidChildren)assert{(x.node.__name__,x.__class__,x.value)forxine.children}=={('password',objective.exc.MissingValue,objective.values.Undefined),('email',objective.exc.InvalidValue,'baz')}Mapping complex structuresimportobjectiveclassProductRequestObjective(objective.BunchMapping):@objective.Item()classbody(objective.Mapping):name=objective.Item(objective.Unicode)root=objective.Item(objective.Unicode)@objective.Item()classsemantics(objective.List):items=objective.Item(objective.Unicode)@objective.Item()classmatch(objective.Mapping):_id=objective.Item(objective.Field)Issues, thoughts, ideasobjective is far from complete. There are tons of possible validators I can imagine.I tried my best (mostly in terms of time) to test.If you find bugs or you have a great idea, I am totally open to implement it.
Package: objective-sdk
Objective, Inc. Python LibraryThis is the official python library for the Objective APIs. It provides convenient methods for using Objective's APIs.DocumentationOur documentation can be found atdocs.objective.inc.Install and configure API keyTo get an API key, create an account atapp.objective.inc.pipinstallobjective-sdkQuickstartUpload your objectsPush schemaless JSON objects to the Object Store. This enables their contents to be indexed for search.fromobjectiveimportClientclient=Client(api_key="sk_...")objects=[]objects.append({"id":"1","object":{"title":"Sevendayz Men's Shady Records Eminem Hoodie Hoody Black Medium","brand":"sevendayz"},},)client.object_store.upsert_objects(objects)which will upsert your objects and let you know once the operation is complete:Successfully upserted 1 object.You can useclient.object_store.delete_objects(["1"])to delete this object from the object store.Create an index and wait for indexing to be completedCreate a search Index, which triggers the objects in the Object Store to be indexed.
See ourdocsfor all options.fromobjectiveimportClientclient=Client(api_key="sk_...")index=client.indexes.create_index(index_type="text-neural-base",fields={"searchable":["title","brand"]})index.status(watch=True)which returns information on how the objects are being processed and indexed:Index idx_wXRUedqJkPof successfully created.
Progress: [---------------------------------------------------------------] 0%SearchOnce the objects are live in the index, issue search requests.fromobjectiveimportClientimportjsonclient=Client(api_key="sk_...")index=client.indexes.get_index(<<INSERTINDEXIDHERE>>)results=index.search(query="rapper hoodies",object_fields="*")print(json.dumps(results,indent=4))which outputs the following search result format:{"results":[{"id":1,"object":{"title":"Sevendayz Men's Shady Records Eminem Hoodie Hoody Black Medium","brand":"sevendayz"}}],"pagination":{"pages":1,"page":1,"next":null}}Congratulations, you just indexed and searched your first objects. To learn more and see more example code, check out ourdocs.DevelopmentUse python build to build this package:python-mpipinstall--upgradepip# Install dependenciespipinstall.# Build package:pipinstallbuildpython-mbuild
Package: objective-weights-mcda
objective-weights-for-mcdaThis is Python 3 library dedicated for multi-criteria decision analysis with criteria weights determined by objective weighting methods.
The documentation is providedhereInstallationDownloading and installation ofobjective-weights-mcdapackage can be done with using pippip install objective-weights-mcdaMethodsmcda_methodsincludes:vikorwith VIKOR methodOther modules include:additionsincluderank_preferencemethod for ranking alternatives according to MCDA scorecorrelationsinclude:Spearman rank correlation coefficientspearman,Weighted Spearman rank correlation coefficientweighted_spearman,Pearson correlation coefficientpearson_coeffnormalizationswith methods for decision matrix normalization:linear_normalization- Linear normalization,minmax_normalization- Minimum- Maximum normalization,max_normalization- Maximum normalization,sum_normalization- Sum normalization,vector_normalization- Vector normalizationweighting_methodsinclude 11 objective weighting methods for determination of criteria weights (significance) without decision-maker involvement:equal_weighting- Equal weighting methodentropy_weighting- Entropy weighting methodstd_weighting- Standard deviation weighting methodcritic_weighting- CRITIC weighting methodgini_weighting- Gini coefficient-based weighting methodmerec_weighting- MEREC weighting methodstat_var_weighting- Statistical variance weighting methodcilos_weighting- CILOS weighting methodidocriw_weighting- IDOCRIW weighting methodangle_weighting- Angle weighting methodcoeff_var_weighting- Coefficient of variation weighting methodExamples of usage ofobjective_weights_mcdaare provided onGitHubinexamplesLicenseThis package calledobjective-weights-mcdawas created by Aleksandra Bączkiewicz. It is licensed under the terms of the MIT license.NoteThis project is under active development.
Package: objectiv-modelhub
Objectiv open model hubTheopen model hubis a library of pre-built analytics models & functions to build & run deep analyses for a wide range of product & marketing use cases. It’s part ofObjectiv, an open-source data collection & modeling platform that helps data teams run product analytics from their notebooks. All models & operations can run directly on a full dataset collected with the Objectiv Tracker without cleaning or transformation, as the data adheres to thestrict event typing & validation of an open taxonomy.Under the hood, the open model hub is powered byBach, a Python-based analytics modeling library with a Pandas-like interface. Bach translates all operations and models to SQL, running directly on your full dataset. All DataFrames & models can be converted to an SQL statement with just a single command, to use in other tools like dbt or BI.Installationpip install objectiv-modelhubSee how toget started in your notebookfor detailed installation instructions.UsageExample notebooks with instructionsOpen Model Hub API referenceSupportVisit Objectiv Docs for instructions & FAQsJoin Objectiv on Slack to get helpRequest a feature or report an issue on GithubFound a security issue?Please don’t use the issue tracker but contact us directly. SeeSECURITY.mdfor details.ContributingIf you want to contribute to the open model hub or use it as a base for custom development, take a look atCONTRIBUTING.mdfor detailed development instructions. For more unformation about our contribution process and where you can fit in, check out ourContribution Guidein the Docs.LicenseThis repository is part of the source code for Objectiv, which is released under the Apache 2.0 License. Please refer toLICENSE.mdfor details.Copyright (c) 2021-2022 Objectiv B.V. All rights reserved.
Package: objectlist
ObjectListUse methods and method chaining on lists of objects.
Also allowing clean syntax for parallel processing.ExamplesAs an example we define an ObjectList of Path objects.fromobjectlistimportObjectListfrompathlibimportPathpath_list=[Path('foo/a'),Path('foo/bar/b'),Path('foo/bar/c')]path_objectlist=ObjectList(path_list)Getting object attributes and propertiesIf we would like to get the parents of all paths[WindowsPath('foo'), WindowsPath('foo/bar'), WindowsPath('foo/bar')]then using a normal list it is necessary to use a loop:[p.parentforpinpath_list]but using an ObjectList this can be done bypath_objectlist.parentObjectLists can be easily converted to strings:path_objectlist.parent.str['foo', 'foo\\bar', 'foo\\bar']Using object methods and passing argumentsObject methods can also be usedpath_objectlist.with_suffix('.txt')[WindowsPath('foo/a.txt'), WindowsPath('foo/bar/b.txt'), WindowsPath('foo/bar/c.txt')]Using parallel processingParallel processing can be activated on existing ObjectLists by using .parallelpath_objectlist.parallel.with_suffix('.txt')Alternatively parallel processing can be activated when the ObjectList is defined:path_objectlist=ObjectList(path_list,use_parallel_processing=True,number_of_cores=None,parallel_processing_kwargs=dict(verbose=0),return_none_if_all_none=True)