Saravanan666/pypi-org huggingface dataset:
Package: prodify
ProdifyTBDGetting Started•Features & Screenshots•Documentation•Support•Report a Bug•FAQ•Known Issues•Contribution•ChangelogEvery project should contain a short description here to help users understand what it does. This description should cover a maximum of 4 lines. If there is a UI, you can also put a screenshot under this description.Highlights📄  README template with predefined structure.🗃  Labeling and issue organization system.📝  Contribution guideline template.Getting StartedThis section should contain the simplest and most basic way to run and use the project, preferably with one command.Support & FeedbackThis project is maintained byBenjamin Räthlein,Lukas Masuch, andJan Kalkan. Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly so that more people can benefit from it.TypeChannel🚨Bug Reports🎁Feature Requests👩‍💻Usage Questionstbd📢Announcementstbd❓Other RequestsFeaturesUse this section for advertising the most important features and advantages of the project. Also, add screenshots or examples to showcase important features. The main purpose of this section is marketing.DocumentationEither put the documentation here or use a deployed documentation site via mkdocs and link to the documentation.Please refer toour documentationfor information about deployment or usage.FAQThis is the example description of an faq item(click to expand...)Known IssuesThis is the example description of a known issue(click to expand...)ContributorsTODO: sourcererhall of fameis deprecated, find something different?ContributionPull requests are encouraged and always welcome. Read ourcontribution guidelinesand check outhelp-wantedissues.Submit Github issues for anyfeature request and enhancement,bugs, ordocumentationproblems.By participating in this project, you agree to abide by itsCode of Conduct.Thedevelopment sectionbelow contains information on how to build and test the project after you have implemented some changes.DevelopmentRequirements:DockerandActare required to be installed on your machine to execute the containerized build process.To simplify the process of building this project from scratch, we provide build-scripts - based onuniversal-build- that run all necessary steps (build, check, test, and release) within a containerized environment. To build and test your changes, execute the following command in the project root folder:act-b-jbuildRefer to ourcontribution guidesfor more detailed information on our build scripts and development process.LicensedMIT. Created and maintained with ❤️  by developers from Berlin.
Package: prodiguer_client
prodiguer-client================Client side prodiguer python library.What is prodiguer-client ?--------------------------------------prodiguer-client is a python client library for interacting with prodiguer web services.  It supports pcmdi-metrics management.Why prodiguer-client ?--------------------------------------To simplify & streamline interaction with prodiguer web services.Who uses prodiguer-client ?--------------------------------------IPSL researchers & engineers.What are the contents of prodiguer-client ?--------------------------------------prodiguer_client		Python library.shell			        Bash scripts.
Package: prodigyopt
Prodigy: An Expeditiously Adaptive Parameter-Free LearnerThis is the official repository ofour paperthat proposed the Prodigy optimizer. Currently, the code is only in PyTorch.Prodigy: An Expeditiously Adaptive Parameter-Free Learner
K. Mishchenko, A. Defazio
Paper:https://arxiv.org/pdf/2306.06101.pdfHow to citeIf you find our work useful, please consider citing our paper.@article{mishchenko2023prodigy,
    title={Prodigy: An Expeditiously Adaptive Parameter-Free Learner},
    author={Mishchenko, Konstantin and Defazio, Aaron},
    journal={arXiv preprint arXiv:2306.06101},
    year={2023},
    url={https://arxiv.org/pdf/2306.06101.pdf}
}
Package: prodigy-tui
prodigy-tuiA TUI forProdigy, made withTextual.This project is currently mainly meant as a demo. Partially because doing annotations via the terminal is relatively experimental, but also because I cannot guarantee full feature parity with Prodigy just yet. The only implemented interface right now istextcat.manualbut I may implement more in the future.That said, this tooldoesintegrate with Prodigy. For example, when you run this:> python -m prodigy-tui textcat.manual --helpYou're going to get a very familiar help text.usage: textcat.manual [-h] [--label LABEL] dataset source

Interface for binary text classification from the terminal!

positional arguments:
  dataset               dataset to write annotations into (str)
  source                path to text source (Path)

optional arguments:
  -h, --help            show this help message and exit
  --label LABEL, -l LABEL
                        category label to apply, only binary is supported (str)To start annotating binary text examples, you can run something like:prodigy-tui textcat.manual reddit-data examples.jsonl --label positiveAnd this will start saving annotations into thereddit-datadataset.InstallationYou will need to install this tool in an environment that already has Prodi.gy installed. Given such an environment, you can use pip.python -m pip install "prodigy-tui @ git+https://github.com/koaning/prodigy-tui.git"FeaturesWorks over SSH, no need to share a browser.Keyboard shortcuts are the same.You could even click the buttons if you really wanted to though.FeedbackAs mentioned before, this is a bit of an experiment. But it seems interesting to explore. Maybe data annotation is something thatcanhappen via the terminal and maybe there's something that's very developer friendly about that.ShoutoutProps to the folks over attextualize. Much of the heavy lifting in UI-land is handled by their stack.
Package: prodimopy
prodimopyPython package for reading and plotting ProDiMo results.Any bug reports or feature requests are very welcome.
If you want to contribute some code please contact me (Christian Rab)Notebook examplesIf you want to take a look before installing you can try prodimopy
on the web in a binder environment:On your left hand side you will see the notebooks, just open one and try it!DocumentationPlease check out the documentation! Click on the badge!
Package: prodistribution
prodistributiona python package to work with Gaussian and Binomial DistributionsThis package provides funtions toGaussianDistributions andBinomialDistributionsCalculatemeanCalculateStandard DeviationCalculateProbability DensityfunctionAdd togethertwoGaussian / Binomial distributionsPlottingInstallationpip install prodistributionLicenseThis software is licenced underMIT
Package: prodius
prodiusCalculating cartesian product is not easy if not usingitertools.product().
Prodius library allows to perform the same but also allowing iterables to
be callables that return iterable. Provided callables will be called when
iterable is needed to calculate cartesian product.itertools.product()ofsen raise MemoryError when one of iterables is too
large. No matter matter the size of iterable, prodius will never write
whole iterator into memory.IssuesIterables cannot be more than 11.Maybe be slower thanitertools.product().InstallingProdius can be installed with pip in your command-line.pipinstallprodiusUsageProdius is just easy just like builtinitertools.product().Realise that this wont offer any benefit as iterator are passed direcly.importprodiusiterables=[range(10),range(10)]foriteminprodius.product(*iterables,repeat=2):print(item)Prodius also allows iterables to contain callable that returns iterable.The difference is that functions were used as iterables which provide
iterables on demand.importprodiusiterables=[lambda:range(10),lambda:range(10)]foriteminprodius.product(*iterables,repeat=2):print(item)This comparesitertools.product()withprodius.product().importprodius# prodius.product() is used to calculate cartesian product.# No exception or MemoryError expected even if iterables are large.iterables=[lambda:range(10000000000),lambda:range(10000000000)]product=prodius.product(*iterables)# works as espectedimportitertools# itertools.product() is used to calculate cartesian product.# MemoryError expected as iterables are too large.iterables=[range(10000000000),range(10000000000)]product=itertools.product(*iterables)# MemoryErrorProdius also gives control of over items of iterable to be used in cartesian
product. That could be accomplised usingitertools.cycle()by returning
different iterable each time function is called.importprodiusimportitertoolsfoods_numbers_list=[[1,2],["apple","orange"]]foods_numbers_cycle=itertools.cycle(foods_numbers_list)defnumbers_foods():returnnext(foods_numbers_cycle)foriteminprodius.product([100,200],numbers_foods):print(item)# (100, 1)# (100, 2)# (200, 'apple')# (200, 'orange')Notice that100was matched with numbers(1, 2)while200matched with('apple', 'orange').Thats a fantastic trick with prodius even if it does not have realworld
application but who knows.LicenseMIT license
Package: prod-manager
ProdManager APIprod-manageris a Python package providing access to theProdManagerAPI.Featuresprod-managerenables you to:write Pythonic code to manage yourProdManagerresources.pass arbitrary parameters to theProdManagerAPI. Simply followProdManager’s docs on what parameters are available.access arbitrary endpoints as soon as they are available onProdManager, by using lower-level API methods.use persistent requests sessions for authentication, proxy and certificate handling.flexible handling of paginated responses, including lazy iterators.Installationprod-manageris compatible with Python 3.7+.Usepipto install the latest stable version ofprod-manager:$pipinstall--upgradeprod-managerThe current development version is available on GitLab.com, and can be installed directly from the git repository:$pipinstallgit+https://gitlab.com/prod-manager/prod-manager-api.gitBug reportsPlease report bugs and feature requests athttps://gitlab.com/prod-manager/prod-manager-api/-/issues.DocumentationThe full documentation for CLI and API is available onGitLab Pages.Build the docsWe usemkdocsto manage our environment and build the documentation :$poetryinstall--onlydocs
$mkdocsbuildContributingFor guidelines for contributing toprod-manager, refer toCONTRIBUTING.md.
Package: prodmx
## ProdMX : Protein Functional Domain based on Compressed Sparse MatricesProdMX is a tool with user-friendly utilities developed to facilitate high-throughput analysis of protein functional domains and domain architectures. The ProdMX employs a compressed sparse matrix algorithm to reduce computational resources and time used to perform the matrix manipulation during functional domain analysis.### DependenciesPython 3.5 or newer and the following packages:[pandas](https://github.com/pandas-dev/pandas)[h5py](https://github.com/h5py/h5py)[numpy](https://github.com/numpy/numpy)[tqdm](https://github.com/tqdm/tqdm)[scipy](https://github.com/scipy/scipy)### Installation from source` git clonehttps://github.com/visanuwan/prodmxpython-mpip install prodmx `### UsageGenerally, the use of the ProdMX tool starts with constructing the compressed sparse matrix of either protein functional domains or domain architectures in a command-line environment. The input of ProdMX is a tab-delimited file containing two columns of genome labels and the path to their HMMER results.Protein functional domain`prodmx-buildDomain[-h][-v][-iINPUT][-oOUTPUT][-k]`Domain architecture`prodmx-buildArchitecture[-h][-v][-iINPUT][-oOUTPUT][-k]`For the detail of commands and examples, see the example of analyses using ProdMX in [Jupyter Notebook.](test/prodmx_example.ipynb)
Package: prodos
prodososandglobextensions with advanced path joining.InstallpipinstallprodosUsagefromprodosimportprodglob,pathprod,prodmakeprint('pathprod: creating a product of path items.')forfinpathprod('base',('a','b'),'something',('c','d')):print(f)print()print('prodglob: globbing over the product of path items.')forfinprodglob('base',('a','b'),'something',('c','d')):print(f)fs=pathprod('base',('a','b'),'something',('c','d'))print('how many files exist?',sum(os.path.exists(f)forfinfs))print()print('prodmake: making directories from the product of path items.')fs=prodmake('base',('a','b'),'something',('c','d'))print('how many files exist?',sum(os.path.exists(f)forfinfs))print()print('prodglob: globbing over the product of path items.')forfinprodglob('base',('a','b'),'something',('c','d')):print(f)Outputs:pathprod: creating a product of path items.
base/a/something/c
base/a/something/d
base/b/something/c
base/b/something/d

prodglob: globbing over the product of path items.
how many files exist? 0

prodmake: making directories from the product of path items.
how many files exist? 4

prodglob: globbing over the product of path items.
base/a/something/c
base/a/something/d
base/b/something/c
base/b/something/d
Package: prodot
ProdotA new way to deal with dictionaries and lists in python.
Treat data like classes, or by reading/writing data with json paths>>>fromprodotimportProObject>>>my_obj=ProObject({'foo':{'bar':['eggs']}})>>>my_obj.foo.bar.n1.get_value()'eggs'>>>my_obj['$.foo.bar.[0]'].get_value()'eggsGetting startedinstallpip install prodotUsageImport the pro object from the prodot library. You can create a new empty dictionary, or start with a filled one# No parameters instances an empty dictionary>>>my_new_obj=ProObject()# The pro object can be initialized with a dictionary>>>my_dict_obj=ProObject({"foo":["bar","eggs"]})# The pro object can also initialize with a list>>>my_list_obj=ProObject([[1,2,3],["a","b","c"],[{"foo":"bar"},{"bar":"eggs"}]])Dot notation usageBy using the pro-object you can use the dictionary as a class>>>my_json={..."userData":{..."name":"John",..."age":"38",..."shoppingCart":[...{"cellphone":999.99},...{"notebook":2999.99},...{"wireless keyboard":299.99}...]...}...}>>>my_new_obj=ProObject(my_json)>>>shoppingCart=my_new_obj.userData.shoppingCartThe ProObject will return another instance of the ProObject with the main_object attribute as being the selected path.>>>type(shoppingCart)<class'prodot.pro_object.ProObject'>To get the raw value of the object, you can use the.get_value()function.>>>shoppingCart.get_value()[{"cellphone":999.99},{"notebook":2999.99},{"wireless keyboard":299.99}]# or by using the get_value directly at the path>>>my_new_obj.userData.shoppingCart.get_value()[{"cellphone":999.99},{"notebook":2999.99},{"wireless keyboard":299.99}]>>>type(shoppingCart.get_value())<class'dict'>You can also add new information to the instancied object# n3 means list index 3 (will be added as 4th item)>>>my_new_obj.userData.shoppingCart.n3={"monitor":699.99}>>>my_new_obj.userData.shoppingCart.get_value()[{"cellphone":999.99},{"notebook":2999.99},{"wireless keyboard":299.99},{"monitor":399.99}]Python doesn't accept list indexes to be used as a class attribute. For solve this problem, list indexes start with thenletter (like n0, n1, ...)Json path usageIf you prefere or need to use json paths, it is possible to write and retrieve information using json path strings.>>>my_new_obj['$.userData.shoppingCart[4]']={'FunStation 6 Series T':'699,99'}>>>my_new_obj['$.userData.shoppingCart'].get_value()[{"cellphone":999.99},{"notebook":2999.99},{"wireless keyboard":299.99},{"monitor":399.99},{'FunStation 6 Series T':'699,99'}]Note that by using a json path string, list indexes must not havenas their first value.You can view all possible json_paths from object by using theget_all_pathsattribute.>>>my_new_obj.get_all_paths()<generatorobjectPathObject.all_paths_from_main_objectat0x7f6a012b5c80>>>>list(my_new_obj.get_all_paths())['.userData','.userData.name','.userData.age','.userData.shoppingCart','.userData.shoppingCart[0]','.userData.shoppingCart[0].cellphone','.userData.shoppingCart[1]','.userData.shoppingCart[1].notebook','.userData.shoppingCart[2]','.userData.shoppingCart[2].wireless keyboard','.userData.shoppingCart[3]','.userData.shoppingCart[3].monitor','.userData.shoppingCart[4]','.userData.shoppingCart[4].FunStation 6 Series T']
Package: product2vec
product2vecproduct2vec is a Python library that implements Product2Vec model. It is capable of finding complements and substitutes among products given shopping baskets. Current implementation is based on the original paper:https://ssrn.com/abstract=3519358Installationpip install product2vecUsage>>> from product2vec import Product2Vec
>>> # toy dataset with two baskets and 4 products
>>> data = [
...     ['coffee', 'cookies', 'chocolate'],
...     ['tea', 'cookies', 'chocolate'],
... ]
>>> prod2vec = Product2Vec(vector_size=3, min_count=1, sample=0, seed=1, workers=1)
>>> _ = prod2vec.fit(data)
>>> prod2vec.show_substitutes(product='tea', topn=2)
[('coffee', 0.024425969), ('chocolate', 0.023691988)]
>>> prod2vec.show_complements(product='cookies', topn=2)
[('chocolate', 0.5030633), ('coffee', 0.5007087)]Refer tousage_example.ipynbwhich can be found in GitHub repository for short model description and elaborate usage.ContributingIf you spot any bugs or have suggestions don't hesitate to open an issue.
Package: producteev
This is a Python wrapper for the Producteev API.InstallationTo install producteev, simply:$ pip install producteevOr:$ easy_install producteevOr, if you absolutely must:$ sudo python setup.py installUsageCreate a new client instance:from producteev import Producteev

client = Producteev("api_key_from_producteev","api_secret_from_producteev")Login in as a user:client.login("username","password")Get the server time:client.timeGet as list of tasks:client.tasks.listGet as list of lables:client.labels.listTestsFor run tests:$ python setup.py testThis option ask you about your API_KEY, SECRET_KEY, Username and Password for
run tests with your account (after testing, your account will remain as before).
If you don’t want write this info on each test, you can add it in tests.cfgDocsFor build docs:$ python setup.py docs [format]Available formats:html(default),pdf,latexLicenseCopyright (c) 2012 Martín García <newluxfero@gmail.com>Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the “Software”), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: producthunt
producthunt.pyProduct Hunt API wrapper for pythonInstallationpip install producthuntinitFirst, initialize the ProductHunt class with your API key.fromproducthuntimportProductHuntapi_key='YOUR_API_KEY_HERE'ph=ProductHunt(api_key)Get Daily ProductsFetch daily new products.daily_products=ph.get_daily()forproductindaily_products:print(f"ID:{product['ID']}")print(f"Name:{product['Name']}")print(f"Tagline:{product['Tagline']}")Get Product DetailsFetch the details of a specific product using its slug.product_details=ph.get_product_details("some-product-slug")ifproduct_details:print(f"Name:{product_details['Name']}")print(f"Tagline:{product_details['Tagline']}")print(f"Description:{product_details['Description']}")print(f"Website:{product_details['Website']}")else:print("Product not found.")Get Posts by TopicFetch posts by topic:products=ph.get_posts_by_topic('artificial-intelligence')forproductinproducts:print(f"ID:{product['ID']}")print(f"Name:{product['Name']}")print(f"Tagline:{product['Tagline']}")LicenseThis project is licensed under the terms of the MIT license.
Package: product-id-extractor-yimian
A extractor to extract product id from an provided url of specific platforms.Installationpip install product-id-extractor-yimianExamplesfromproduct_id_extractorimportExtractor# Initialize Extractor Object# Leave platform parameter None to use auto detectionexample=Extractor("https://item.taobao.com/item.htm?spm=a230r.1.14.141.34655b0c4rO8sl&id=637790875524&ns=1&abbucket=16#detail",platform='')# specify platform if needed by passing it to Extractor objectexample.platform='taobao'# call Extractor.extract() function to get product_idexample.extract()print(example)# 637790875524NotesOnly support URL of items from following platforms:taobaotmalljdsuningamazon
Package: productivity
ProductivityNOTE: This is in very early stages of development.Python ≥3.8 driver and command-line tool forAutomationDirect Productivity Series PLCs.Installationpip install productivityUsagePLC ConfigurationThis driver uses Modbus TCP/IP for communication. Unlike the ClickPLC, modbus
addresses need to be manually configured in the Productivity PLC firmware (seemanual).To use this driver, go toWrite Program → Tag Database, scroll down to the values
you care about, and double click theMod Startcell of each value to assign an address.
Then, go toFile → Export → Tagsto export a csv file. The file is used here so
you don't need to remember the addresses.Command LineTo print the tags and their values, simply call the library with the PLC IP address and the tags file.$ productivity the-plc-ip-address path/to/tags.csvUse --set to set values on the PLC:$ productivity the-plc-ip-address path/to/tags.csv -s "{int_test: 4, float_test: 4.45, string_test: foo}"The --set parameter takesYAML, a simple data format that allows you to easily set multiple tags at once.Seeproductivity --helpfor more.PythonThis driver uses Python ≥3.5's async/await syntax to asynchronously communicate with
a Productivity2000 PLC. For example (note that asyncio.run() requires Python >=3.7):importasynciofromproductivityimportProductivityPLCasyncdefrun():asyncwithProductivityPLC('the-plc-ip-address','path/to/tags.csv')asplc:print(awaitplc.get())asyncio.run(run())It is also possible to set tag values:asyncdefrun():asyncwithProductivityPLC('the-plc-ip-address','path/to/tags.csv')asplc:awaitplc.set(start=True,setpoint=1.1)asyncio.run(run())
Package: productivity-stack
Python Productivity StackThis is a simple meta-package that does nothing except indexing and grouping other packages. This is just something I'm doing to make life easy for meThe Stack and InstallationMost of the "code" of this repository lives insetup.cfg. The stack has been broken down into the following partsProjectLibrariesblack,pyscaffold,pre-commitInstall Steppip install productivity-stack[project]MLLibrariesScikit-Learn,PyTorch,NumPy,PyTorch Lightning,Pandas,NotebookInstall Steppip install productivity-stack[ml](Computer) VisionLibrariesScikit-Image,KorniaInstall Steppip install productivity-stack[vision]WebLibrariesFastAPI,Streamlit,AIOHTTP,DashInstall Steppip install productivity-stack[web]Software developmentLibrariesBox,aiomultiprocess,Dask,rootpath,tqdm,RichInstall Steppip install productivity-stack[sdev]MLOpsLibrariesBoto3,Sagemaker,MLflow,python-terraform,Prefect,s3fsInstall Steppip install productivity-stack[mlops]Documentation toolingLibrariesSphinx,Graphviz,MkDocsInstall Steppip install productivity-stack[docs]General OpsLibrariesBandit,RadonInstall Steppip install productivity-stack[ops]Contribution GuidelinesCheckCONTRIBUTING.rst. tl;dr - Always super happy to accept contributions!NoteThis project has been set up using PyScaffold 4.2.2. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.