Saravanan666/pypi-org huggingface dataset:
Package: obj2uni
obj2uni=======This packages defines the single function, `obj2uni`, which returnsUnicode strings for sequences of any objects. Each stringhas the length equal to the length of the corresponding sequence.Multiple sequences share the same character mapping.This function can be useful for other packages which operate on strings faster,e.g. for computing Levenshtein distance.```Python>>> from obj2uni import obj2uni>>> obj2uni((1, 2, 3, None), ('test', 'ham', 'spam', 3.14159))(('\x01\x02\x03\x00', '\x07\x05\x06\x04'),{'\x00': None,'\x01': 1,'\x02': 2,'\x03': 3,'\x04': 3.14159,'\x05': 'ham','\x06': 'spam','\x07': 'test'})```Released under MIT license. Copyright © 2016 Mail.Ru Group LLC.
Package: obj2xgl
obj2xglobj2xglconverts a 3D model stored as a Wavefront (.obj) file into a C file
which can be linked into a program. Thexin the name means thatobj2xglsupports not just OpenGL, but also other 3D graphic toolkits.Supported toolkitsOpenGLImmediate modeCall lists (SOON!)Nintendo DS and 3DS (vialibnds)Immediate modeCall lists (SOON!)Nintendo Wii and Gamecube (vialibogc) (SOON!)ExamplesPlease see theexamplesdirectory for a few examples on how to use this program.Useful links:OpenGL ES reference documentation
Package: obj2xml
Convert objects to XML quickly and easily.FeaturesEasily convert objects using an XML template.Supports node lists and sub-documents.Objects don’t need to know about XML paths, flat objects can become deep XML documents.Dynamically inject XML at creation time.ExamplesSimple document:from obj2xml import XML_Object, XML_Property, XML_TextProperty


class MyExampleXML(XML_Object):
    version = XML_Property(['root', 'version'], 1.0)
    test_value = XML_Property(['root', 'test-value'], 'Default Value')
    no_default_value = XML_Property(['root', 'no-default'])

    text_node = XML_TextProperty(['root', 'text-node-1'])
    manual_text_node = XML_Property(['root', 'text-node-2', '_text'])

obj = MyExampleXML()
obj.test_value = 'Another value'
obj.no_default_value = 'This value was hidden before'
obj.a_text_value = 'Over-rode the default text'
obj.text_node = 'This is some text'
obj.manual_text_node = 'This is some more text'

print(obj)

<?xml version="1.0" ?>
<root no-default="This value was hidden before" test-value="Another value" version="1.0">
  <text-node-2>This is some more text</text-node-2>
  <text-node-1>This is some text</text-node-1>
</root>For further examples, look in theexamplesdirectory.Dependenciespydict2xmlAuthorsAdam Griffiths
Package: objaverse
🪐 Objaverse-XLThis repository contains scripts to download and process Objaverse-XL.Objaverse-XL is an open dataset of over 10 million 3D objects!With it, we train Zero123-XL, a foundation model for 3D, observing incredible 3D generalization abilities: 🧵👇Scale ComparisonObjaverse 1.0 was released back in December. It was a step in the right direction, but still relatively small with 800K objects.Objaverse-XL is over an order of magnitude larger and much more diverse!Unlocking GeneralizationCompared to the original Zero123 model, Zero123-XL improves remarkably in 0-shot generalization abilities, even being able to perform novel view synthesis on sketches, cartoons, and people!A ton more examples in the📝 paper:)Image → 3DWith the base Zero123-XL foundation model, we can perform image → 3D usingDreamFusion, having the model guide a NeRF to generate novel views!https://github.com/allenai/objaverse-rendering/assets/28768645/571852cd-dc02-46ce-b2bb-88f64a67d0acText → 3DText-to-3D comes for free with text → image models, such as with SDXL here, providing the initial image!https://github.com/allenai/objaverse-rendering/assets/28768645/96255b42-8158-4c7a-8308-7b0f1257ada8Scaling TrendsBeyond that, we show strong scaling trends for both Zero123-XL andPixelNeRF!TutorialCheck out theGoogle Colab tutorialto download Objaverse-XL .Polycam data is available by Polycam to academic researchers for non-commercial use upon request and approval from Polycam. For access please fill outthis form.Blender RenderingBlender rendering scripts are available in thescripts/rendering directory!LicenseThe use of the dataset as a whole is licensed under the ODC-By v1.0 license. Individual objects in Objaverse-XL are licensed under different licenses.CitationTo cite Objaverse-XL, please cite our📝 arXivpaper with the following BibTeX entry:@article{objaverseXL,title={Objaverse-XL: A Universe of 10M+ 3D Objects},author={Matt Deitke and Ruoshi Liu and Matthew Wallingford and Huong Ngo andOscar Michel and Aditya Kusupati and Alan Fan and Christian Laforte andVikram Voleti and Samir Yitzhak Gadre and Eli VanderBilt andAniruddha Kembhavi and Carl Vondrick and Georgia Gkioxari andKiana Ehsani and Ludwig Schmidt and Ali Farhadi},journal={arXiv preprint arXiv:2307.05663},year={2023}}Objaverse 1.0 is available on 🤗Hugging Face at@allenai/objaverse. To cite it, use:@article{objaverse,title={Objaverse: A Universe of Annotated 3D Objects},author={Matt Deitke and Dustin Schwenk and Jordi Salvador and Luca Weihs andOscar Michel and Eli VanderBilt and Ludwig Schmidt andKiana Ehsani and Aniruddha Kembhavi and Ali Farhadi},journal={arXiv preprint arXiv:2212.08051},year={2022}}
Package: objaverse-xl
🪐 Objaverse-XLThis repository contains scripts to download and process Objaverse-XL.Objaverse-XL is an open dataset of over 10 million 3D objects!With it, we train Zero123-XL, a foundation model for 3D, observing incredible 3D generalization abilities: 🧵👇Scale ComparisonObjaverse 1.0 was released back in December. It was a step in the right direction, but still relatively small with 800K objects.Objaverse-XL is over an order of magnitude larger and much more diverse!Unlocking GeneralizationCompared to the original Zero123 model, Zero123-XL improves remarkably in 0-shot generalization abilities, even being able to perform novel view synthesis on sketches, cartoons, and people!A ton more examples in the📝 paper:)Image → 3DWith the base Zero123-XL foundation model, we can perform image → 3D usingDreamFusion, having the model guide a NeRF to generate novel views!https://github.com/allenai/objaverse-rendering/assets/28768645/571852cd-dc02-46ce-b2bb-88f64a67d0acText → 3DText-to-3D comes for free with text → image models, such as with SDXL here, providing the initial image!https://github.com/allenai/objaverse-rendering/assets/28768645/96255b42-8158-4c7a-8308-7b0f1257ada8Scaling TrendsBeyond that, we show strong scaling trends for both Zero123-XL andPixelNeRF!TutorialCheck out theGoogle Colab tutorialto download Objaverse-XL (work in progress).Blender RenderingBlender rendering scripts are available in thescripts/rendering directory!LicenseThe use of the dataset as a whole is licensed under the ODC-By v1.0 license. Individual objects in Objaverse-XL are licensed under different licenses.CitationTo cite Objaverse-XL, please cite our📝 arXivpaper with the following BibTeX entry:@article{objaverseXL,title={Objaverse-XL: A Universe of 10M+ 3D Objects},author={Matt Deitke and Ruoshi Liu and Matthew Wallingford and Huong Ngo andOscar Michel and Aditya Kusupati and Alan Fan and Christian Laforte andVikram Voleti and Samir Yitzhak Gadre and Eli VanderBilt andAniruddha Kembhavi and Carl Vondrick and Georgia Gkioxari andKiana Ehsani and Ludwig Schmidt and Ali Farhadi},journal={arXiv preprint arXiv:2307.05663},year={2023}}Objaverse 1.0 is available on 🤗Hugging Face at@allenai/objaverse. To cite it, use:@article{objaverse,title={Objaverse: A Universe of Annotated 3D Objects},author={Matt Deitke and Dustin Schwenk and Jordi Salvador and Luca Weihs andOscar Michel and Eli VanderBilt and Ludwig Schmidt andKiana Ehsani and Aniruddha Kembhavi and Ali Farhadi},journal={arXiv preprint arXiv:2212.08051},year={2022}}
Package: objax
ObjaxTutorials|Install|Documentation|PhilosophyThis is not an officially supported Google product.Objax is an open source machine learning framework that accelerates research and learning thanks to a
minimalist object-oriented design and a readable code base.
Its name comes from the contraction of Object andJAX-- a popular high-performance
framework.
Objax is designedby researchers for researcherswith a focus on simplicity and understandability.
Its users should be able to easily read, understand, extend, and modify it to fit their needs.This is the developer repository of Objax, there is very little user documentation
here, for the full documentation go toobjax.readthedocs.io.You can find READMEs in the subdirectory of this project, for example:Sample CodeWriting documentationUser installation guideYou install Objax usingpipas follows:pipinstall--upgradeobjaxObjax supports GPUs but assumes that you already have some version of CUDA
installed. Here are the extra steps required to install CUDA-enabled jaxlib
(jaxlib releases require CUDA 11.2 or newer):RELEASE_URL="https://storage.googleapis.com/jax-releases/jax_cuda_releases.html"JAX_VERSION=`python3-c'import jax; print(jax.__version__)'`pipuninstall-yjaxlib
pipinstall-f$RELEASE_URLjax[cuda]==$JAX_VERSIONFor more installation options, seehttps://github.com/google/jax#pip-installation-gpu-cudaUseful environment configurationsHere are a few useful options:# Prevent JAX from taking the whole GPU memory# (useful if you want to run several programs on a single GPU)exportXLA_PYTHON_CLIENT_PREALLOCATE=falseTesting your installationYou can test your installation by running the code below:importjaximportobjaxprint(f'Number of GPUs{jax.device_count()}')x=objax.random.normal(shape=(100,4))m=objax.nn.Linear(nin=4,nout=5)print('Matrix product shape',m(x).shape)# (100, 5)x=objax.random.normal(shape=(100,3,32,32))m=objax.nn.Conv2D(nin=3,nout=4,k=3)print('Conv2D return shape',m(x).shape)# (100, 4, 32, 32)Typically if you get errors running this using CUDA, it probably means your
installation of CUDA or CuDNN has issues.Runing code examplesClone the code repository:gitclonehttps://github.com/google/objax.gitcdobjax/examplesCiting ObjaxTo cite this repository:@software{objax2020github,
  author = {{Objax Developers}},
  title = {{Objax}},
  url = {https://github.com/google/objax},
  version = {1.2.0},
  year = {2020},
}Developer documentationHere is information aboutdevelopment setupand aguide on adding new code.
Package: objbrowser
Extensible Python object inspection tool implemented in Qt.Displays objects as trees and allows you to inspect their attributes
recursively (e.g. browse through a list of dictionaries). You can add
your own inspection methods as new columns to the tree view, or as radio buttons
to the details pane. Altering existing inspection methods is possible as well.Requires: PySide or PyQt5Installation: pip install objbrowserExample use:from objbrowser import browse
a = 16; b = 'hello'
browse(locals())For more examples see:https://github.com/titusjan/objbrowser
Package: objcache
obj-cacheOverviewSimple Redis-like interface to storing arbitrary Python objects. A high-level wrapper around ZoDB.Getting StartedInstallobjcache:pip install objcacheA simple example:fromobjcacheimportObjectCachecache=ObjectCache('cache.zodb',('a','b'))cache.store(5)# Later sessioncache=ObjectCache('cache.zodb',('a','b'))result=cache.get()print(result)5LinksSee thedocumentation here.AuthorCreated by Nick DeRobertis. MIT License.
Package: objcrypt
objcrypteasily encrypt and decrypt python object and JSON objects with AES CBC 32 Block Encryptioninstallationpip install objcrypt

or

python3 setup.py installusageimport objcrypt, json

crypter = objcrypt.Crypter('key', 'cbc')
dictionary = {
  'test': 'test value'
}
encrypted_dict = crypter.encrypt_object(dictionary)

# encrypted_dict now has encrypted values

json_dict = json.loads(dictionary)
enc_json = crypter.encrypt_json(json_dict)

# enc_json is now encrypted

dec_dict = crypter.decrypt_object(encrypted_dict)

# decoded now

dec_json = crypter.decrypt_json(enc_json)

# decoded json object now
Package: objc-types-decoder
objc-types-decoderA type decoder for Objective-C types.It translates the encoded Objective-C type notation, the notation that the@encodefunction returns, into a readable
form that tries to be as close as possible to the original type definition.For example, lets look at the following@encode:NSLog(@"%s",@encode(float**));// "^^f" will be printed.Using our decoder, we can "reverse" the process:fromobjc_types_decoder.decodeimportdecodeprint(decode('^^f'))# 'float * *' will be printed.InstallationIn order to install this package, just use a regularpipinstallation:pipinstallobjc_types_decoderUsageIn order to use the decoder, just run the main with your desired encoded type:>>py-mobjc_types_decoder^f
float*You can also decode by importing it in your python code:>>fromobjc_types_decoder.decodeimportdecode>>decode('{NSObject=#}')'struct NSObject { Class x0; }'Sometimes, you might want to keep the tail of the parsed data. For this case, you can usedecode_with_tail.>>fromobjc_types_decoder.decodeimportdecode_with_tail>>decode_with_tail('fyour boat')('float','your boat')
Package: obj-det-metrics
About this RepoThis repo contains simple code to compute object detection metrics. The code in this repo can be packaged and published to PyPI via Poetry.Getting StartedFor package usersInstall the package with one of the following commands:# Poetry userspoetryaddobj-det-metrics# Pip userspipinstallobj-det-metricsRefer to thebin/directory for examples of using the package.For contributorsInstall Poetry (refer to thedocumentationfor installation steps)Fork this repo and do git clone.Runpoetry installto install dependency packages.AcknowledgementThe codes for computing APs and mAP were adapted frommAPrepo by Cartuchoandmapcalcrepo by LeMuecke.
Package: objdict-hephhay
OBJDICTAn object wrapper for dict that allows for using object type notation with dictionaries.Installationpip install objdict-hephhayDescriptionAn object style implementation for dict that allows for using object type dot(.) notation with dictionaries.All dict operations such as .pop(), .items(), .contains() etc' will also work with ObjDictExamplefrom objdict_hephhay import ObjDict
object = ObjDict(<dict>)

object.<dict key>usageParametersdict : int
Dictionary to convertobj__depth : int
depth of conversion to ObjDictReturnsobjdict.ObjDict
An instance of ObjDictExamples>>>  obj = ObjDict(foo = 1, bar = 2)
<ObjDict : {"foo" : 1, "bar" : 2}>
>>> dictionary = {"foo" : 1, "bar" : 2}
>>> obj1 = ObjDict(dictionary)
>>> obj1.foo
1
>>> dictionary_2 = {"foo" : 1, "bar" : 2, "baz" : {"alice" : 3, "bob" : {"steph" : 5}}}
>>> obj2 = ObjDict(dictionary_2, obj__depth = 1)
>>> obj2.bar
2
>>> obj2.baz
<ObjDict : {'alice': 3, 'bob': {'steph': 5}}>
Package: objdiff
Take 2 sets of containers and provide a (deep) delta between themThis module is used by the author to diff yaml files and on disk file trees
and best work out how to transition from one state to another (mainly for
work with containers).Note:Requires Python 3.3 or greater due to use of ‘yield from’How?Objdiff usesdifflibbuilt into python for lists and tuples (basically sorted
things) and implements its own comparison code for dictionaries. User types are
detected via thecollections.abc.Mappingtype and instance comparison and are treated as dictionaries (ie unsorted item
=> value mappings)What does this look like?>>> import objdiff
>>> a = {'a': 1, 'b':[1,2,3], 'c':None}
>>> b = {'a': 1, 'b':[1,4], 'c':'hello'}
>>> objdiff.obj_diff(a, b)
<generator object obj_diff at 0xb6a3da80>We return an iterator and make use of yield from so you can process large trees
of objects efficiently and incremental>>> from pprint import pprint
>>> pprint(list(objdiff.obj_diff(a, b)))
[modified(path=['c'], old=None, new='hello'),
 modified(path=['b'], old=[1, 2, 3], new=[1, 4]),
 equal(path=['a'], old=1, new=1)]Expanding out the generator we get back a bunch of tuples containing the
command value, key path, before and after value>>> c = {'a':{1: None, 2: 2, 3: 3}, 'b': None}
>>> d = {'a':{1: 1, 2: 2}, 'b': {'1':{}, '2':{'2':2}}}
>>> pprint(list(objdiff.obj_diff(c, d)))
[modified(path=['b'], old=None, new={'1': {}, '2': {'2': 2}}),
 modified(path=['a', 1], old=None, new=1),
 equal(path=['a', 2], old=2, new=2),
 deleted(path=['a', 3], val=3)]Note:in the above how you get a full list of keys to the destined object after
the command value.In total there are 4 types of command, as listed below with one internal type
that can be ignored.addeddeletedmodifiedequal (internal, scalar values are equal)More documentation can be found atBlitz works docsChangelog1.2.3 2016-08-26Actually include module with release1.2.2 2016-03-28Split out CHANGELOG to seperate fileAdd documentationAdd doctestsProtection against PYTHONHASHSEED randomisation for doctestsobjdiff arg names where renamed to make relationship clearer1.2.1 2016-03-26Add ‘version guard’ to prevent install on python older than version 3.31.2 2016-03-26Documentation updates‘deep_update’ function1.1 2016-03-24Cleanups of codeDocumentation updatesMore infrastructure in src module1.0 2016-03-24Initial releaseWorking objdiffdiffing of lists and dicts functions
Package: object-api
The Object APIObjectAPI provides a concise negative-boilerplate paradigm for creating full-stack web applications with Python. It is built on top ofFastAPI,SQLModel, andpydantic.FeaturesActive record pattern for database accessAutomatic get_by_id lookup for instance methods with route decoratorsAutomatic CRUD routesScheduled service methodsManaged DB sessions for service methods and for each requestInstallationpipinstallobject-apiUsagefromobject_apiimportApp,Entity,RouterBuilder,ServiceBuilderapp=App()classUser(Entity):classMeta:service=ServiceBuilder()router=RouterBuilder()new_private=["pass"]name:strpass:strage:int@service.servicemethod@classmethoddefremove_inactive(cls):foruserinUser.get_all():ifuser.age>100:user.delete()@router.route()defget_name(self):returnself.name@router.post("/change_name")defchange_name(self,name:str):self.name=nameself.save()app=App()app.run()Documentationhttps://github.com/ComputaCo/object-apiLicenseMIT
Package: object-bucket
Object BucketAn easy and fun way to store python objects.Table of contentsDescriptionInstallationUsageDescriptionObject Bucket is a python package that allows you to store python objects permanently in a more user friendly way.InstallationThe object-bucket package can be installed by using pip.pipinstallobject-bucketUsageCreating new bucket.fromobject_bucketimportBuckettest_bucket=Bucket("name-of-the-bucket")Adding droplets to the bucket, droplets are considered as objects that you want to save permanently.test_obj=[1,2,3,4]test_bucket.add_droplet("droplet-name",test_obj)Trying to add a droplet with the same name will cause an error.Adding multiple droplets.
To add multiple droplets you have to have a dictionary that contains all the names and objects of the droplet.
To add the dictionary you can use theadd_dropletsmethod.droplets={"one":1,"two":2,"three":[2,3,4]}test_bucket.add_droplets(droplets)Modifying a dropletnew_obj={1:"a"}test_bucket.modify_droplet("droplet-name",new_obj)Trying to modify a droplet that does not exists will cause an error.Saving a bucketAll the things mentioned above will not be added or saved permanently, to do so it is necessary to save the bucket.test_bucket.save_bucket()Retrieving values from a bucket.fromobject_bucketimportBuckettest_bucket=Bucket("name-of-the-bucket")a=test.bucker.get_droplet("droplet-name")print(a)# {1: "a"}Trying to get a droplet that does not exists will cause an error.Get all the runtime dropletsdrop1=[1,2,3,4]drop2="Hello"drop3={1:"a",2:"b"}test_bucket.add_droplet("drop1",drop1)test_bucket.add_droplet("drop2",drop2)test_bucket.add_droplet("drop3",drop3)# to get all the dropletsa=test_bucket.get_all_droplets()print(a)# output{"drop1":[1,2,3,4],"drop2":"Hello","drop3":{1:"a",2:"b"}}Deleting a bucket
To delete the bucket and to clear the runtime storage of all the droplets.test_bucket.delete_bucket()You can also delete a bucket usingremove_bucketfunctionfromobject_bucketimportremove_bucketremove_bucket("name-of_bucket_to_be_removed")Using the context manager.It might be a hastle to remember to save to bucket, so you can use the context manager to avoid using thesave_bucketmethod.Note: UsingBucket().delete_bucketinside the context manager is useless as at the end the file will be saved automatically.fromobject_bucketimportBucketwithBucket("name-of-the-bucket")asb:# code to executeb.add_droplet("name",1)# ...etcb.delete_bucket()# wont work as the file will be again saved,# but the runtime contents will be clearedSome more stuffYou can use the if statement to check whether a bucket is empty or notfromobject_bucketimportBuckett=Bucket("name")ift:print("Hello")# -> does not print anything as bucket is emptyt.add_droplet("demo",1)ift:print("Hello 2")# -> prints "hello 2" as the bucket has at least one dropletTo get the number of droplets in a bucket you can use thelenmethodfromobject_bucketimportBuckett=Bucket("name")print(len(t))# -> 0t.add_droplet("demo",[1,2,3])print(len(t))# -> 1
Package: object_by_name
Get an object by name or dotted path, import if it necessary.Installpip install object_by_nameUsage>>> from object_by_name import object_by_name>>> object_by_name('test_package')
<module 'test_package' from ...>>>> object_by_name('test_package.test_module')
<module 'test_package.test_module' from ...>>>> object_by_name('test_package.test_module.TestClass')
<class 'test_package.test_module.TestClass'>>>> object_by_name('test_package.test_module.instance')
<test_package.test_module.TestClass object at 0x...>>>> object_by_name('test_package.test_module.instance.method')
<bound method TestClass.method of <test_package.test_module.TestClass ...>>
Package: object-cache
descriptionThis module caches the processing result of the function in the storage, and if the cache hits, skips the processing in the function and returns the result. Create “.object_cache” in the current path and cache it.installpipinstallobject-cachecode exampleimporttimefromobject_cacheimportobject_cache@object_cachedeffactorial(a):result=1foriinrange(2,a+1):result*=ireturnresultfor_inrange(5):start=time.time()factorial(100000)print("elapsed time",time.time()-start)clear cacherm-fr.object_cache