Saravanan666/pypi-org huggingface dataset:
Package: pert-estimator
Pert EstimatorPERT estimation toolOverviewNOTE: The descriptions of the numbers and calculations provided in this section (and frankly the idea for this project) were taken fromThe Clean Coderby Robert C. MartinPERT is an estimating technique which uses three numbers to compute a final estimation (expected duration) and a standard deviation. The three numbers you provide areO: Optimistic Estimate. This number is wildly optimistic. You could only get the task done this quickly if absolutely everything went right. Indeed, in order for the math to work this number should have much less than a 1% change of occurrence.N: Nominal Estimate. This is the estimate with the greatest chance of success.P: Pessimistic Estimate. Once again this is wildly pessimistic. It should include everything except hurricanes, nuclear war, stray black holes, and other catastrophes. Again, the math only works if this number has much less than a 1% change of success.CalculationsExpected Duration:(O + 4N + P) / 6This gives you the final estimation for the task given the 3 estimates provided.Standard Deviation:(P - O) / 6This gives you a measure of how uncertain the expected duration is. When this number is large, the uncertainty is large too.UsageFrom the command line:$ pert_estimator
Task name: test
Optimistic estimate: .5
Nominal estimate: 2
Pessimistic estimate: 6
Add task? (Y/N): n

+------+------------+---------+-------------+-------------------+--------------------+
| Task | Optimistic | Nominal | Pessimistic | Expected Duration | Standard Deviation |
+------+------------+---------+-------------+-------------------+--------------------+
| test |    0.5     |    2    |      6      |        2.4        |        0.9         |
+------+------------+---------+-------------+-------------------+--------------------+
Package: pertinosdk
Welcome to Pertino Python SDKExample Usage'''
Created on Jul 26, 2014

@author: lwoydziak
'''frompertinosdkimportPertinoSdk,wherefromjsonconfigfileimportEnvdeftest_whenOrgsAvailableThenCanListThem():pertinoSdk=PertinoSdk(Env().get("Pertino","login"),Env().get("Pertino","password"))organizations=pertinoSdk.listOrgs()assertlen(organizations)>0deftest_whenDevicesInOrganizationsThenCanListThem():pertinoSdk=PertinoSdk(Env().get("Pertino","login"),Env().get("Pertino","password"))organizations=pertinoSdk.listOrgs()devices=pertinoSdk.listDevicesIn(organizations[0])assertlen(devices)>0deftest_deleteMachinesWithNameContainingAuto():pertinoSdk=PertinoSdk(Env().get("Pertino","login"),Env().get("Pertino","password"))organizations=pertinoSdk.listOrgs()devices=pertinoSdk.listDevicesIn(organizations[0],where("hostName").contains("auto"))pertinoSdk.deleteFrom(organizations[0],devices)assertnotpertinoSdk.listDevicesIn(organizations[0],where("hostName").contains("auto"))To BuildantenvantinitantpackageTo run unit testsanttestTo run acceptance testsCreate a file with your Pertino credentials (see acceptance/conftest.py for format/name)antacceptance
Package: perturbation-classifiers
Perturbation ClassifiersPerturbation Classifiers is an easy-to-use library focused on the implementation of the Perturbation-based Classifier (PerC) [1]_ and subconcept Perturbation-based Classifier (sPerC). The library is is based on scikit-learn_, using the same method signatures:fit,predict,predict_probaandscore.Installation:The package can be installed using the following command:.. code-block:: bash# Clone repository
git clone https://github.com/rjos/perturbation-classifiers.git
cd perturbation-classifiers/

# Installation lib
python setup.py installDependencies:perturbation_classifiers is tested to work with Python 3.7. The dependencies requirements are:scikit-learn(>=0.24.2)numpy(>=1.21.2)scipy(>=1.7.1)matplotlib(>=3.4.3)pandas(>=1.3.2)gap-stat(>=2.0.1)gapstat-rs(>=2.0.1)These dependencies are automatically installed using the command above.Examples:Here we show an example using the PerC method:.. code-block:: pythonfrom perturbation_classifiers import PerC

# Train a PerC model
perc = PerC()
perc.fit(X_train, y_train)

# Predict new examples
perc.predict(X_test)and here we show an example using the sPerC method:.. code-block:: pythonfrom perturbation_classifiers.subconcept import sPerC

# Train a sPerC model
sperc = sPerC()
sperc.fit(X_train, y_train)

# Predict new examples
sperc.predict(X_test)References:.. [1] : Araújo, E.L., Cavalcanti, G.D.C. & Ren, T.I. Perturbation-based classifier. Soft Comput (2020)... _scikit-learn:http://scikit-learn.org/stable/
Package: perturbationx
PerturbationXPerturbationX is a package for analyzing causal networks in combination with gene expression data. It is based on theTopoNPAalgorithm. It was developed as part of a Master's thesis at the University of Ljubljana, Faculty of Computer and Information Science and in collaboration with the National Institute of Biology.InstallationThe package can be installed from PyPI or directly from GitHub. It requires a Python version of 3.10 or newer. It is based on NetworkX and pandas and requires Cytoscape for visualization. The latter can be downloaded fromhere.python-mpipinstallperturbationx# PyPIpython-mpipinstallgit+https://github.com/mikethenut/perturbationx# GitHubUsageAn example Jupyter notebookexample.ipynbis available for step-by-step instructions on how to use the package. For advanced usage, refer to thedocumentationandMaster's thesis.
Package: perturbopy
perturbopy - Python suite for Perturbo postprocessing and testingThis package provides utilities to postprocess and test the Perturbo code, which is an open-source software to compute from first principles the scattering processes between charge carriers (electrons and holes) and phonons, defects, and photons in solid state materials, including metals, semiconductors, oxides, and insulators. Perturbo is written in modern Fortran, including efficient MPI and OpenMP parallelization, allowing to perform calculations efficient on supercomputers using up to ~100,000 cores. Read more about the main code Perturbohere.Perturbopy provides a user-friendly object-oriented environment to postprocess Perturbo calculations, such as band structure interpolation, electron transport, ultrafast carrier dynamics, and many more. Using Perturbopy, a user can easily load the Perturbo output files (HDF5 or YAML) into Python objects, perform calculations, and visualize data.The second purpose of the Perturbopy package is to test the main Perturbo code. After a compilation of the Perturbo code on a new machine or after a new implementation, it is always a good practice to run the testsuite, verifying that the existing functionality was not affected, and add new test cases to ensure that the newly implemented features will be compatible with future implementations. Perturbopy, using thepytestpackage, provides extensive testing of Perturbo, allowing for both serial and parallel runs.Find more about Perturbopy on itsdocumentation website. We are open to collaboration! Please create a fork and submit a pull request with detailed description of your work, and we will be happy to discuss and include your contribution.
Package: perturb-tools
perturb-toolsis ananalysis framework for pooled CRISPR genome-editing screens. Thus far, development has focused on local (i.e., not genome-wide) tiling screens with specific phenotypic readouts though expansion of this scope is of interest.Data Structure and Analysis Frameworkimportperturb_toolsasptscreen=pt.Screen(X)Genome Editing Screen composed of: n_guides x n_conditions = 946 x 12

   guides:    'barcode', 'barcode_id', 'experiment', 'sequence', 'target_id', 'pred_ABE_edit', 'pred_CBE_edit'
   samples:    'condition', 'replicate'
   samples_m:  'barcode_counts', 'unexpected_sequences'
   samples_p:  'correlation'
   layers:    'X_lognorm'
   uns:       'run_info', 'poolq3', 'metadata', 'SampleBarcodeReadCounts', 'CommonSampleBarcodeReadCounts'This format and organization of metadata surrounding a multidimensional experiment is inspired byAnnData, a useful solution for the analogous organization of single-cell data.The three main components of this data strcuture:screen.X(Numpy array)screen.samples(pandas DataFrame) of shape:[n_samples x sample_annotation]screen.guides(pandas DataFrame) of shape:[n_guides x guide_annotation]See thetutorialfor more information.InstallationInstall the development package:# (1) clone this repositorygitclonehttps://github.com/pinellolab/perturb-tools.git# (2) install the local project in editable modecd./perturb-tools;pipinstall-e.General analysis StepsSeetutorialwhich includes:API tutorialNormalizationArithmeticCalculating the mean, standard deviation, and log-fold change between/across replicatesCorrelation calculationHit discovery(under development)Visualization(under development)Items under consideration:Sequence prediction of targeted base-editTF motif annotationa. Occupancy of Cas9 for CRISPRi (and how this may disrupt a TF motif)b. Putative creation / destruction of TF motifs upon predicted base-editing outcome
Package: perun.connector
perun.connectorLibrary for high volume machine-to-machine communication with Perun IAM system.InstallationUse pip:pipinstallperun.connectorConfigurationTo use AdaptersManager, filladapters_manager_cfg.yaml.To use RPC connector or LDAP connector only, fillopenapi_cfg.yamlorldap_connector_cfg.yamlrespectivelly.
Package: perusatproc
Process PeruSat-1 primary scenes into calibrated, orthorectified and
pansharpened images.DescriptionThis package allows you to process PeruSat-1 primary scenes. You can perform
the following processing steps:Radiometric calibration (top-of-atmosphere)OrthorectificationPansharpeningIt depends on Orfeo Toolbox and some Python packages:rasterioxmltodictModesScriptsperusat_calibrate: Calibrates image to top-of-atmosphere (ToA).perusat_orthorectify: Adds projection and RPC tags to an image from its
metadata files, and then orthorectifies it using the RPC data and a DEM image.
If no DEM is provided, a DEM from SRTM is used (1-arc second / 30m aprox GSD).perusat_pansharpen: Bundles a pancromatic (P) and multispectral (MS) image to
form a pansharpened image.perusat_process: Given a path to a PeruSat-1 product, it peforms all required
steps to form a single calibrated, orthorectified and pansharpened image. Final
output can be a virtual raster or a tiff file.NoteThis project has been set up using PyScaffold 4.0a1. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: peruse
peruseExplore a waveform with slangTo install:pip install peruseExamplefromnumpyimport*fromhumimportdisp_wf,plot_wfimportsoundfileassffromperuse.single_wf_snip_analysisimportTaggedWaveformAnalysisExtendedimportosimportpickletry:fromslang.util_dataimportdisplayable_unichrunichr_code_of_snip=array(displayable_unichr+list(unique(list(set(range(33,20000)).difference(displayable_unichr)))))snip_of_unichr_code=(nan*empty(unichr_code_of_snip.max()+1)).astype(int)snip_of_unichr_code[unichr_code_of_snip]=arange(len(unichr_code_of_snip))defsnip_to_str(snip):returnchr(unichr_code_of_snip[snip])exceptImportErrorase:defsnip_to_str(snip):returnchr(33+snip)defstring_of_snips(snips):return"".join(map(snip_to_str,snips))filepath="Enter audio filepath here"wf,sr=sf.read(filepath)disp_wf(wf,sr)UnsupervisedPerhaps you just want to get a perspective on your sound, without specifying annotations.Perhaps you don't know what to annotate and you want snips to help you find patterns to annotate.Fit the snipperfromperuse.single_wf_snip_analysisimportTaggedWaveformAnalysisExtendedtw=TaggedWaveformAnalysisExtended(sr=sr,tile_size_frm=2048,chk_size_frm=43008,prior_count=1)tw.fit(wf)Get the snips of a waveform (here the same as fit with, but could be another)snips=tw.snips_of_wf(wf)len(snips),len(unique(snips))View them as charactersprint(string_of_snips(snips))annkncckkobbjhihjacjcjeneijaeofgibiecikjjkdnhajekachbobchoadkjjjjknkkkjofglmijaiinieajdccnkjnollil
hjhkokejkeacdkcofgaldikljnekdijekkjieoboocinchjadannadnnjofgiiielaekheiccnkkeejlbbllichckkinojaeoa
aofgllllnadkdkcneianPlot (inverse of) snip probabilities (says how rare they are (outliers) from the perspective of the wf that was used to fit, and gives SOME view of the sound)tw.plot_tiles(1/array(list(map(tw.prob_of_snip.get,snips))));tw.plot_tiles(log(1/array(list(map(tw.prob_of_snip.get,snips)))));
Package: pervect
PerVectVectorization of persistence diagrams and approximate Wasserstein distance. This is
managed by approximating persistence diagrams with Gaussian mixture models and then
measuring the Wasserstein distance between the Gaussian mixtures. As the number of
components in mixture model increases the accuracy of the approximation increases
accordingly until, with equivalence in the limit.The library is implemented as aScikit-learntransformer – taking a list of
persistence diagrams (preferably in birth-lifetime format) as input, and transforming
it into a vector representation (specifically the component weights for a Gaussian
mixture model fit to the union of all the diagrams). Distances can then be computed
as Wassterstein distance over a ground-distance matrix provided as an attribute of the
transformer. Alternatively UMAP can be used to convert toa lower dimensional
Euclidean distance representation.How to use PerVectThe pervect library inheritis from sklearn classes and can be used as an sklearn
transformer.importpervectvects=pervect.PersistenceVectorizer().fit_transform(diagrams)It can also be used in standard sklearn pipelines along with other machine learning
tools including clustering and classifiers.InstallationRequirements:Python >= 3.6scikit-learnumap-learnnumbajoblibpotYou can install pervect from PyPI with pip:pipinstallpervectFor a manual install get this package:wgethttps://github.com/scikit-tda/pervect/archive/master.zipunzipmaster.ziprmmaster.zipcdpervect-masterInstall the requirementssudopipinstall-rrequirements.txtInstall the packagepipinstall.LicenseThe pervect package is 3-clause BSD licensed.We would like to note that the pervect package makes heavy use of
NumFOCUS sponsored projects, and would not be possible without
their support of those projects, so pleaseconsider contributing to NumFOCUS.ContributingContributions are more than welcome! There are lots of opportunities
for potential projects, so please get in touch if you would like to
help out. Everything from code to notebooks to
examples and documentation are allequally valuableso please don’t feel
you can’t contribute. To contribute pleasefork the projectmake your changes and
submit a pull request. We will do our best to work through any issues with
you and get your code merged into the main branch.
Package: pesapal-py
pesapal-pyA minimalist python library that integrates with PesaPal's API 3.0 - JSON APIs (https://developer.pesapal.com/how-to-integrate/e-commerce/api-30-json/api-reference).This library abstracts PesaPals API 3.0 into four main methods,authenticate,register_ipn,transactandget_transaction_statusGetting startedRegister as a merchantRegister as a PesaPal merchant athttps://www.pesapal.com/On successfull registration, you will receive a consumer key and consumer secret via email - keep these safe as they are your API credentialsInstall pesapal-pypip install pesapal-pyAuthenticatingfrom pesapal_py.payments import PesaPal

pesapal = PesaPal("test_consumer_key", "test_consumer_secret")
auth = pesapal.authenticate()
print(auth)This step returns a bearer token that is used with all the other methods below. The token expires after every 5 minutes. To prevent calling this method every time you need to transact, I recommend that you store it in an in-memory data store (e.g. redis) and expire it before 5 minutes.Register IPNregister_ipn = pesapal.register_ipn("sample_token", "https://www.sample-url.com/ipn")
print(register_ipn)Transacttransact = pesapal.transact(
    token="sample_token",
    description="sample_description",
    transaction_id="sample_transaction_id",
    amount=1000,
    callback_url="https://www.callback-url.com/status",
    ipn_id="sample_ipn_id",
    email_address="sample@email-address.com",
    phone_number="254722001122",
    country_code="KE",
    first_name="first",
    last_name="last",
    currency="KES",
)
print(transact)Validate Transactiontransaction_status = pesapal.get_transaction_status(
    token="sample_token", order_tracking_id="sample_order_tracking_id"
)
print(transaction_status)
Package: pesapal-v3
pesapalUnofficial SDK wrapper for the pesapal v3 payments API.Requirementspython version >= 3.8Installationpip install pesapal-v3Usagefrompesapal_v3importPesapalclient=Pesapal(consumer_key,consumer_secret)Supported endpointsRegister IPNipn_url="https://example.com/ipn/notifications"ipn=client.register_ipn_url(ipn_url=ipn_url)ifipn.status!="200":# error occured registering your IPNprint(ipn.error)else:# IPN successfully registeredprint(ipn)List registered IPNsipns=client.get_registered_ipns()print(ipns)Submit order requestorder_request={"id":"s945e4af-80a5-4ec1-8706-e03f8332fb04","currency":"KES","amount":350.00,"description":"Thank you for this SDK","callback_url":"https://example.com/cancellation","notification_id":"fe078e53-78da-4a83-aa89-e7ded5c456e6","billing_address":{"email_address":"john.doe@example.com","phone_number":"0723xxxxxx","country_code":"KE","first_name":"John","middle_name":"","last_name":"Doe","line_1":"Pesapal Limited","line_2":"","city":"","state":"","postal_code":"","zip_code":""}}request=client.submit_order_request(order_request=order_request)Transaction statusstatus=client.get_transaction_status(order_tracking_id="b945e4af-80a5-4ec1-8706-e03f8332fb04")Subscriptionsorder_request={"id":"s945e4af-80a5-4ec1-8706-e03f8332fb04","currency":"KES","amount":350.00,"description":"Thank you for this SDK","callback_url":"https://example.com/cancellation","notification_id":"fe078e53-78da-4a83-aa89-e7ded5c456e6","billing_address":{"email_address":"john.doe@example.com","phone_number":"0723xxxxxx","country_code":"KE","first_name":"John","middle_name":"","last_name":"Doe","line_1":"Pesapal Limited","line_2":"","city":"","state":"","postal_code":"","zip_code":""},"account_number":"555-678","subscription_details":{"start_date":"24-01-2023","end_date":"31-12-2023","frequency":"DAILY"}}request=client.submit_order_request(order_request=order_request,is_subscription=True)print(request)Refundrefund =
{
    "confirmation_code": "AA11BB22",
    "amount": "100.00",
    "username": "John Doe",
    "remarks": "Service not offered"
}

client.refund(refund)SupportFor any issues, bugs you can raise an issue on the project issues board.For compliments andgigsyou can reach out to the developer via emailmartinmshale@gmail.com.For service level issues, reach out to Pesapal.Lastly, I need the developer juice to produce more of these open source solutions. Be among the few who have supported these effort bybuying me a coffee.
Package: pescea
pescea - python interface to Escea fireplacesThis is a python interface to theEscea rangeof fireplaces.The specifications for the interface are contained in theincluded pdf.Further information on the Escea controller's network communications can be found inthis pdfNote: The above documents are copyright of EsceaUsageThis package is not a standalone application for end user implementation.The aim of this package is to be used by software developers and integrators to implement
the Escea interface on home automation platforms (principally Home Assistant )CreditThepizone interfacewas used as a template and
for inspiration in developing this interface (which saved a lot of time, so thanks!)
Package: pesepay
InstallationpipinstallpesepayGetting StartedImport the library into your project/applicationfrompesepayimportPesepayCreate an instance of thePesepayclass using your integration key and encryption key as supplied by Pesepay.pesepay=Pesepay("INTEGRATION KEY","ENCRYPTION KEY");Set return and result urlspesepay.result_url='https://example.com/result'pesepay.return_url='https://example.com/return'Make seamless paymentCreate the paymentNB: Customer email or number should be providedpayment=pesepay.create_payment('CURRECNCY_CODE','PAYMENT_METHOD_CODE','CUSTOMER_EMAIL','CUSTOMER_PHONE_NUMBER(OPTIONAL)','CUSTOMER_NAME(OPTIONAL)')Create adictof the required fields (if any)required_fields={'requiredFieldName':'requiredFieldValue'}Send of the paymentresponse=pesepay.make_seamless_payment(payment,'PAYMENT_REASON',1.00,required_fields)ifresponse.success:# Save the reference number and/or poll url (used to check the status of a transaction)poll_url=response.pollUrlreference_number=response.referenceNumberelse:# Get Error Messagemessage=response.messageMake redirect paymentCreate a transactiontransaction=pesepay.create_transaction(amount,'CURRENCY_CODE','PAYMENT_REASON')Initiate the transactionresponse=pesepay.initiate_transaction(transaction)ifresponse.success:# Save the reference number and/or poll url (used to check the status of a transaction)poll_url=response.pollUrlreference_number=response.referenceNumber# Get the redirect url and redirect user to complete transactionredirect_url=response.redirectUrlelse:# Get Error Messagemessage=response.messageCheck Payment StatusMethod 1: Check using reference numberresponse=pesepay.check_payment(reference_number)ifresponse.success:ifresponse.paid:# Payment was successfulelse:# Get Error Messagemessage=response.messageMethod 2: Check using poll urlresponse=pesepay.poll_transaction(poll_url)ifresponse.success:ifresponse.paid:# Payment was successfulelse:# Get Error Messagemessage=response.message