Saravanan666/pypi-org huggingface dataset:
Package: prophetess-netbox
🧙‍♀️ Prophetess Netbox PluginProphetessplugin for loading data intoNetbox🚀 Installationpipinstallprophetess-netbox🔧 ConfigurationNetboxLoadertakes several required and optional configuration options. The full configuration break down is presented below:host:'http://localhost:8000'api_key:123keyupdate_method:updateendpoint:dcimmodel:sitespk:-slug-cf_id:"{site}"fk:region:endpoint:dcimmodel:regionspk:-cf_sf_id:"{region}"LoaderKeyValuesDescriptionhoststringFully qualified URL to root of Netbox installapi_keystringValid API key for accessing Netbox resourcesupdate_method(update, partial_update)When updating existing records, which method to use.updatewill send all fields,partial_updatewill only submit changed values, or skip the update if no values have been updatedendpointstringThe root API group to use, eg: dcim, ipam, tenant, etcmodelstringWhich Model of the endpoint to manipulatepkstring or list (pk)How to identify a unique record from endpoint and model. SeePKfkobject (fk)Mapping of any record fields that are related to other data models. SeeFKPKPK, Primary Key(s), are a list of strings or dictionaries (objects) on how to check if a record to be loaded exists. When using a dictionary the key is used for lookup against the API and the value is a Python formatted string. This allows for flexibility in mapping parsed record to what Netbox assumes.For example, given the parsed record of:{'slug':'nb-slug','region':'custom-field-lookup','name':'test',}With a PK config of-slug-cf_region:'{region}'The resulting lookup would be:?slug=nb-slug&cf_region=custom-field-lookupFKFK, Forigen Key(s), allow for mapping of string values from an extractor to record ids in netbox. The are a dictionary of record key to a mapping of configuration for lookup. Records can be linked across any endpoint and model within a single Netbox instance.Given the following record:{'slug':'nb-slug','region':'region-slug','name':'test',}When parsed with the following FK configuration:region:endpoint:dcimmodel:regionspk:-slugWould produce, assuming there was a region with the slugregion-slugthat had a record ID of 12:{'slug':'nb-slug','region':12,'name':'test',}If no FK record is found,Noneis set instead.ConfigKeyValuesDescriptionendpointstringThe root API group to use, eg: dcim, ipam, tenant, etcmodelstringWhich Model of the endpoint to manipulatepkstring or list (pk)How to identify a unique record from endpoint and model. SeePK🧰 DevelopmentPlease fork this project and create a new branch to submit any changes. While not required, it's highly recommended to first create an issue to propose the change you wish to make. Keep pull requests well scoped to one change / feature.This project usestox+pytestto unit test and lint code. Use the following commands to validate your changes aren't breaking:tox--cov-reportterm-missing
tox-elint🎉 Special Thanks❤️Charles Butler❤️Erick Daniszewski
Package: prophetess-null
🧙‍♀️ Prophetess Null PluginProphetessplugin for debugging data🚀 Installationpipinstallprophetess-null🔧 ConfigurationThere is currently no configuration for any of the provided Null modulesExtractorLoaderTransformer🧰 DevelopmentPlease fork this project and create a new branch to submit any changes. While not required, it's highly recommended to first create an issue to propose the change you wish to make. Keep pull requests well scoped to one change / feature.This project usestox+pytestto unit test and lint code. Use the following commands to validate your changes aren't breaking:tox--cov-reportterm-missing
tox-elint
Package: prophetess-salesforce
🧙‍♀️ Prophetess Salesforce PluginProphetessplugin for extracting data fromSalesforce🚀 Installationpipinstallprophetess-salesforce🔧 ConfigurationSalesforceExtractortakes several required configuration options. The full configuration break down is presented below:key:|------BEGIN PRIVATE KEY-----...-----END PRIVATE KEY-----client_id:"yourclient.idfromsfdc"user:marco@vapor.ioinstance:na02query:|-SELECT Id, NameFROM AccountWHERE Type IN ('Customer', 'Partner')ExtractorKeyValuesDescriptionuserstringSalesforce user which thekeyis assigned tokeystringUser private key for signing requests to SFDC APIclient_idstringSFDC API Client IDinstancestringWhich SFDC instance to connect toquerystringThe Salesforce Object Query Languge (SOQL) for record extraction🧰 DevelopmentPlease fork this project and create a new branch to submit any changes. While not required, it's highly recommended to first create an issue to propose the change you wish to make. Keep pull requests well scoped to one change / feature.This project usestox+pytestto unit test and lint code. Use the following commands to validate your changes aren't breaking:tox--cov-reportterm-missing
tox-elint🎉 Special Thanks❤️Kyler Burkeoriginal author ofSFClient❤️Charles Butler
Package: prophet-freddy
Prophet: Automatic Forecasting ProcedureProphet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.Prophet isopen source softwarereleased byFacebook's Core Data Science team.Full documentation and examples available at the homepage:https://facebook.github.io/prophet/Important linksHTML documentation:https://facebook.github.io/prophet/docs/quick_start.htmlIssue tracker:https://github.com/facebook/prophet/issuesSource code repository:https://github.com/facebook/prophetImplementation of Prophet in R:https://cran.r-project.org/package=prophetOther forecasting packagesRob Hyndman'sforecast packageStatsmodelsInstallationpipinstallprophetNote:  Installation requires PyStan, which has itsown installation instructions.
On Windows, PyStan requires a compiler so you'll need tofollow the instructions.
The key step is installing a recentC++ compilerInstallation using Docker and docker-compose (via Makefile)Simply typemake buildand if everything is fine you should be able tomake shellor alternative jump directly tomake py-shell.To run the tests, inside the containercd python/prophetand thenpython -m unittestExample usage>>>fromprophetimportProphet>>>m=Prophet()>>>m.fit(df)# df is a pandas.DataFrame with 'y' and 'ds' columns>>>future=m.make_future_dataframe(periods=365)>>>m.predict(future)
Package: propheto
Website•DocsPropheto - Open ML PlatformOverviewPropheto is a flexible, high-performance framework for deploying, managing, and monitoring models. Using Propheto, data scientists can quickly and easily deploy their models as complete, fully functioning microservices. Propheto allows:Integration first to support all major data systems, ML frameworks, amd MLOps toolsNo vendor lock in so you can use all the tools and systems your team needsReal-time or batch prediction endpoints with minimal overhead and maximum parallelismEasy debugging, testing, and versioning for model pipelinesSecurity first architecture with portability across cloud and on-prem environmentsOpen-core but designed for enterprise (automated logging, monitoring, and documentation)With only a few simple commands in their IDE (jupyter notebooks, VS Code, Pycharm, etc.) data scientists can deploy models in their cloud architecture with all of the logging, tracking, scaling, and reporting required.The Propheto package works with all major system architectures so that it can easily integrate into the production data and software applications and can easily be managed by other DevOps or software engineering resources. However, Propheto also makes it such that data scientists can self-manage these resources without any burden and without working through these other teams.Ready to get started? checkout ourQuickstart guideorsample notebookto see Propheto in action.InstallationPropheto is hosted onpypiand can be installed by simply usingpippipinstallprophetoGet in TouchThere are several ways to get in touch with us:Open an issue at:https://github.com/Propheto-op/prophetoEmail us at:hello@propheto.ioContributingThanks so much for your interest in supporting our project. We welcome any contributions to Propheto. We will be putting together a contribution guide but in the meantime please feel free to reach out to us or submit PRs as you see fit.LicensePropheto is licensed under Apache 2.0
Package: prophet-prebuilt
Prophet-prebuiltA fork of Facebook's prophet model that comes with pre-built wheels for Linux, MacOS, Windows. Nothing fancy here, I just forkedmainthat has this PR:https://github.com/facebook/prophet/pull/2010#issuecomment-1081934109and added a step to publish to PyPi. Hopefully, this fork is no longer needed with the official release of Prophet version 1.1.I also removed the pystan requirement and will only install CMDSTAN and use CMDSTAN backend. Other than that, there are no modifications to the source code.Installationpipinstallprophet-prebuiltExample usageFor more information, consult the offical Prophet page:https://github.com/facebook/prophet>>>fromprophetimportProphet>>>m=Prophet()>>>m.fit(df)# df is a pandas.DataFrame with 'y' and 'ds' columns>>>future=m.make_future_dataframe(periods=365)>>>m.predict(future)
Package: prophy
Prophy is a statically typed, binary, tag-free, unpacked serialization protocol.You can define message schema:struct MyMsg
{
    u16 x<>;
};generate codecs for chosen languages:prophyc --python_out . --cpp_full_out . --cpp_out . test.prophyand serialize data in Python:>>> import test
>>> msg = test.MyMsg()
>>> msg.x[:] = [1, 2, 3]
>>> msg.encode('<')
'\x03\x00\x00\x00\x01\x00\x02\x00\x03\x00\x00\x00'
>>> print msg
x: 1
x: 2
x: 3in C++:#include <iostream>
#include <iterator>
#include "test.ppf.hpp"

int main()
{
    prophy::generated::MyMsg msg{{1, 2, 3}};
    std::vector<uint8_t> data = msg.encode();
    std::copy(data.begin(), data.end(), std::ostream_iterator<unsigned>(std::cout, " "));
    std::cout << '\n' << msg.print();
    return 0;
}3 0 0 0 1 0 2 0 3 0 0 0
x: 1
x: 2
x: 3again in C++ (half hand-rolled, compiler-dependent, but fastest option):#include <iostream>
#include <iterator>
#include <cstdlib>
#include "test.pp.hpp"

int main()
{
    MyMsg* msg = static_cast<MyMsg*>(std::malloc(12));
    msg->num_of_x = 3;
    msg->x[0] = 1;
    msg->x[1] = 2;
    msg->x[2] = 3;
    std::copy((uint8_t*)msg, ((uint8_t*)msg) + 12, std::ostream_iterator<unsigned>(std::cout, " "));
    std::cout << '\n';
    return 0;
}3 0 0 0 1 0 2 0 3 0 0 0Documentation:http://prophy.readthedocs.orgIssues:https://github.com/aurzenligl/prophy/issues
Package: propinfer
Property Inference AttacksIn this repository, we propose a modular framework to run Property Inference Attacks on Machine Learning models.InstallationYou can get this package directly from pip:python -m pip install propinferPlease note that PyTorch is required to run this framework. Please find installation instructions corresponding to youhere.UsageThis framework is made modular for any of your experiments: you simply should define subclasses ofGeneratorandModelto represent your data source and your evaluated model respectively.From these, you can create a specific experiment configuration file. We suggest usinghydrafor your configurations, but parameters can also be passed in a standarddict.Alternatively, you can extend the Experiment class.Threat models and attacksWhite-BoxIn this threat model, we have access to the model's parameters directly. In this case, [1] defines three different attacks:Simple meta-classifier attackSimple meta-classifier attack, with layer weights' sortingDeepSets attackThey are respectively designated by the keywordsNaive,SortandDeepSets.Grey- and Black-BoxIn this threat model, we have only query access to the model (we do not know its parameters). In the scope of the Grey-Box threat model, we know the model's architecture and hyperparameters - in the scope of Black-Box we do not.For the Grey-Box case, [2] describes two simple attacks:The Loss Test (represented by theLossTestkeyword)The Threshold Test (represented by theThresholdTestkeyword)[3] also proposes a meta-classifier-based attack, that we use for both the Grey-Box and Black-Box cases: these are respectively represented by theGreyBoxandBlackBoxkeywords. For the latter case, we simply default on a pre-defined model architecture.Running an experimentTo run an experiment, you have to instanciate an Experiment object using a specific Generator and Model, and then run both targets and shadows before performing an attack.It is possible to provide a list as a model hyperparameter: in that case, the framework will automatically optimise between the given options.References[1] Karan Ganju, Qi Wang, Wei Yang, Carl A. Gunter, and Nikita Borisov. 2018. Property Inference Attacks on Fully Connected Neural Networks using Permutation Invariant Representations. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS '18). Association for Computing Machinery, New York, NY, USA, 619–633. DOI:https://doi.org/10.1145/3243734.3243834[2] Anshuman Suri, David Evans. 2021. Formalizing Distribution Inference Risks. 2021 Workshop on Theory and Practice of Differential Privacy, ICML.https://arxiv.org/abs/2106.03699[3] Wanrong Zhang, Shruti Tople, Olga Ohrimenko. 2021. Leakage of Dataset Properties in Multi-Party Machine Learning.https://arxiv.org/abs/2006.07267
Package: propka
PROPKA predicts the pKa values of ionizable groups in proteins (version 3.0) and
protein-ligand complexes (version 3.1 and later) based on the 3D structure.For proteins without ligands both version should produce the same result.The method is described in the following papers, which you should cite
in publications:Sondergaard, Chresten R., Mats HM Olsson, Michal Rostkowski, and Jan
H. Jensen. “Improved Treatment of Ligands and Coupling Effects in
Empirical Calculation and Rationalization of pKa Values.” Journal of
Chemical Theory and Computation 7, no. 7 (2011): 2284-2295.Olsson, Mats HM, Chresten R. Sondergaard, Michal Rostkowski, and Jan
H. Jensen. “PROPKA3: consistent treatment of internal and surface
residues in empirical pKa predictions.” Journal of Chemical Theory
and Computation 7, no. 2 (2011): 525-537.Seehttp://propka.org/for the PROPKA web server.
Package: proplot
A succinctmatplotlibwrapper for making beautiful,
publication-quality graphics.DocumentationThe documentation ispublished on readthedocs.InstallationProplot is published onPyPiandconda-forge. It can be installed withpiporcondaas follows:pipinstallproplotcondainstall-cconda-forgeproplotLikewise, an existing installation of proplot can be upgraded
to the latest version with:pipinstall--upgradeproplotcondaupgradeproplotTo install a development version of proplot, you can usepip installgit+https://github.com/lukelbd/proplot.gitor clone the repository and runpip install-e.inside theproplotfolder.
Package: propobject
Structure to handle the
_proprerties, _side_properties, _derived_properties
tricks.A class inheriting from BaseObject could have the following
global-class variables:` PROPERTIES         =["a","b"]SIDE_PROPERTIES    = ["t"] DERIVED_PROPERTIES = ["whynot"] `if so, object created from this class or any inheriting class
could have access to the self._properties[“a”], self._properties[“b”]
self._side_properties[“t”] or self._derived_properties[“whynot”]
parameters.BaseObject have a basic .copy() method.
Package: propor
ProporProject porter, easy and quick command-based startup, support file and folder transfer.Installpip3 install propor --upgradeUsageFirst, enter the project directory and open the command line interface, then enter the command:# propor-get 192.168.0.1 root 123456 project
>>> propor-get <host> <username> <password> <remote_path>
>>> propor-put <host> <username> <password> <remote_path>only remote host systems are supported:Linux/MacOSpropor-get: transfer the remote host specified directory to the current directory.propor-put: transfer the current directory to the remote host specified directory.host: remote host.username: remote user.password: remote login password.remote_path: remote host specified directory.in the program, you can use thefrom propor import download, uploadimport method.
Package: proportional-navigation
Proportional NavigationA package to easily doproportional navigation. This navigation equation is popular for target-persuader Line-of-Sight applications, such as missiles. The popularity stems partially from it's simple implementation, and effectiveness.Explanation on notationFrom the below figure, it is clear that we are using velocity/heading model for each body (proportional_navigation.HeadingVelocity(psi,x,y,V)). Such that V is the magnitude of velocity, psi is heading relative to world x-axis, and x and y of the body is relative to the world axes. However,proportional_navigation.GlobalVelocity(x,y,xd,yd)also exists for convenience.Installationgitclonehttps://github.com/iwishiwasaneagle/proportional_navigationcdproportional_navigation
pythonsetup.pyinstallpipinstallproportional_navigation
Package: proposalsampler
proposalsamplerExperimental project. For given proposal or equation it generate its implementation (context or sample space $\Omega$ or model (like model for theory in logic therms))
at which this proposal (or equation) is true.
For example below, given the sent "abelian(G) \and subgroup(H, G,) => abelian(H)", the program will generate some groups (described with they generators (like in GAP)) for G, H, at which this proposal is true (i.e. $\Omega = {{H_{i}, G_{i}} | where i \leq n}$ where $n$ is count of samples to be generated).
Only part of that "arguments" of the proposal can be given ($\Theta$) such that only remained part ($\Psi$) ought to be generated (so $\Omega = \Theta + \Psi$).The main idea is that such samples can give you better understanding of a proposal itself, especially if such proposal have been taken from some abstract math theory.sampling proposals:from tokentranslator.db_models.model_main import TokenizerDB
from tokentranslator.env.clause.clause_main import Clause

model = TokenizerDB()

# switch to clauses db:
model.change_dialect_db("cs")

clause = Clause("abelian(G) \\and subgroup(H, G,) => abelian(H)", db=model)
clause.parser.parse()

# now import and use sampling:
from proposalsampler.sampling.slambda.tests_slambda_main import test_ventries 
from proposalsampler.sampling.slambda import slambda_main as sm

# for this example test_ventries[3] is init data for proposal clause:
test_ventries[3] =
{'G': ('(1,4)(2,3)', '(1,3)(2,4)'),
 "['s', 1, 0, 0]": True,
 "['s', 1, 1, 0]": True,
 'idd': "['s']",
 'successors_count': 0}

# so group G is given but group H is not, and must be found
# during sampling:
sampler = sm.Sampler(clause.net_out, test_ventries[3])

sampler.run()
# if successors found, they entries would look like:

{'G': ('(1,4)(2,3)', '(1,3)(2,4)'),  'H': ('(1,3)(2,4)', '(1,4)(2,3)')", ['s', 1, 1, 0]": True, "['s', 1, 0, 0]": True, 'idd': "['s', 4, 3, 0]", 'successors_count': 0, 'checked_nodes': ["['s']", "['s', 1]", "['s', 1, 1, 0]", "['s', 1, 0, 0]", "['s', 0, 0]"], 'failure_statuses': {}, 'parent_idd': "['s', 4, 3]", "['s']": True, "['s', 0, 0]": True, "['s', 1]": True}

# so group H found and in this case H = Group('(1,3)(2,4)', '(1,4)(2,3)') (= G but proposal is still holding) 
# if no results were generated, try run again.sampling equations:>>> e = Equation("f(a*x+b*y)=a*f(x)+b*f(y)")
>>> e.parser.parse()
>>> e.sampling.sympy.sampling_vars()
>>> # or e.sampling.sympy.sampling_subs()

>>> e.sampling.sympy.show_rand()
>>> # or "".join(e.eq_tree.flatten('rand_sympy'))

sin(0.243*0.570+0.369*0.078)=0.243*sin(0.570)+0.369*sin(0.078)Requirementspip install -r requirements.txtInstallation and runningpip install proposalsamplerGUI:There is also a GUI for this project athttps://github.com/tokentranslator-group/tokentranslator-guiTests:seetests/test_list.txtReferences:Sampling:Probabilistic Models of Cognition:https://probmods.org/(eng: George Pólya: Mathematics and Plausible Reasoning, Princeton University Press 1954, 2 volumes (Vol. 1: Induction and Analogy in Mathematics, Vol. 2: Patterns of Plausible Inference)(ru: Джордж Пойа: Математика и правдоподобные рассуждения)
Package: propose
ProposeBuild truthtables with your command line.OperandsF <-> G | biconditional
F -> G  | implication
F xor G | exclusive or
F + G   | or
F * G   | and
!F      | notInstallationInstall Propose with PIP$ pip3 install proposeUsagePass an inline formula:$ propose "a * b"Pass a file instead:$ propose --file formula.proposeTo get a list of all available commands and options:$ propose --helpDevelopmentClone this repository$ git clone git@github.com:jonhue/propose.gitInstall Propose with Pip. You may want to use a virtualenv$ make setupRebuild & reinstall Propose:$ make reloadRun tests:$ make spec
Package: props
PropsProperty-based testing for Python à laQuickCheck.for_allfor_alltakes a list of generators (see below) and a property. It
then tests the property for arbitrary values of the generators.Here’s an example testing the commutative and associative properties ofints:for_all(int,int)(lambdaa,b:a+b==b+a)for_all(int,int)(lambdaa,b:a*b==b*a)for_all(int,int,int)(lambdaa,b,c:c*(a+b)==a*c+b*c)PropertiesProperties are functions which take instances of generators and returnTrueif their condition is met:defprop_associative(a,b,c):returna+(b+c)==(a+b)+cfor_all(int,int,int)(prop_associative)for_all(float,float,float)(prop_associative)# Warning: float isn't actually associative!Properties can also fail early by raisingAssertionError:defprop_list_append_pop(list,element):ifelementnotinlist:list.append(element)assertelementinlistlist.pop()returnelementnotinlistreturnelementinlistfor_all(list,int)(prop_list_append_pop)GeneratorsNote:These are not the same as Python generators. We should rename
them. Generaters? Blech.A generator is a specification of a set of possible Python objects. A
generator is either:One of the following built-in types:None,bool,int,float,long,complex,str,tuple,set,list, ordictA class that implements theArbitraryInterfaceOr constructed using the generator combinators.Combinatorsmaybe_aGenerates either an arbitrary value of the specified generator orNone.maybe_anAn alias formaybe_a. Provided for syntactic convenience.one_ofGenerates an arbitrary value of one of the specified generators.tuple_ofGenerates a tuple by generating values for each of the specified
generators.set_ofGenerates a homogeneous set of the specified generator. You can
generate non-homogeneous sets usingset.list_ofGenerates a homogeneous list of the specified generator. You can
generate non-homogeneous lists usinglist.dict_ofGenerates a homogeneous dict of the specified generators using
kwargs. You can generate non-homogeneous dicts usingdict.arbitraryarbitrarytakes a generator and returns a single instance of the
generator.ArbitraryInterfaceWe provide a mixin with one classmethod,arbitrary, which raisesNotImplementedError. To implement generators for your own classes,
please inherit from ArbitraryInterface and provide an implementation forarbitrary.Here’s an example implementation of a Binary Tree class:classBinaryTree(ArbitraryInterface):...@classmethoddefarbitrary(cls):returnarbitrary(one_of(Leaf,Node))classLeaf(BinaryTree):...@classmethoddefarbitrary(cls):returncls(...)# an instance of Leaf.classNode(BinaryTree):...@classmethoddefarbitrary(cls):returncls(...# This is equivalent:arbitrary(BinaryTree),# to this:BinaryTree.arbitrary())# an instance of Node with two subtrees.AbstractTestArbitraryInterfaceWe also provide anAbstractTestArbitraryInterfacewith you can mixin
to your test cases for each class that implementsArbitraryInterfaceto ensure thearbitrarymethod is implemented:classTestBinaryTree(AbstractTestArbitraryInterface,TestCase):defsetUp(self):self.cls=BinaryTreeTo Doall built in types:http://docs.python.org/2/library/stdtypes.htmlrangesimport some faker generators for more semantic random values