Saravanan666/pypi-org huggingface dataset:
Package: qanda
BackgroundInteractive command-line programs need to query users for information, be it
text, choices from a list, or simple yes-or-no answers.qandais a Python
module of simple functions to prompt users for such information, allowing
validation and cleanup of answers, default responses, consistent formatting and
presentation of help text, hints and choices. It is not a replacement for
textual interfaces like curses and urwid, but intended solely for simple console
scripts with user input is required.InstallationThe simplest way to installqandais viaeasy_installor an equivalent
program:% easy_install qandaAlternatively the tarball can be downloaded, unpacked andsetup.pyrun:% tar zxvf qanda.tgz
% cd qanda
% python set.py installqandahas no requisites and should work with just about any version of Python.Using qandaExamples>>> from qanda import prompt
>>> prompt.string ("What is your name")
What is your name: Foo
>>> fname = prompt.string ("Your friends name is",
                help="I need to know your friends name as well before I talk to you.",
                hints="first name",
                default='Bar',
        )

I need to know your friends name as well before I talk to you.
Your friends name is (first name) [Bar]:
>>> print fname
Bar
>>> years = prompt.integer ("And what is your age", min=1, max=100)
And what is your age: 101
A problem: 101 is higher than 100. Try again ...
And what is your age: 28Central conceptsqandapackages all question-asking methods in a Session class. This allows
the appearance and functioning of all these methods to be handled consistently
and modified centrally. However, you don’t necessarily have to create a Session
to use it - there’s pre-existing Session in the variable calledprompt:>>> from qanda import Session
>>> s = Session()
>>> from qanda import prompt
>>> type (prompt)
<class 'qanda.session.Session'>The question methods are named after the type of data they elicit:>>> print type(prompt.integer ("Pick a number"))
Pick a number: 2
<type 'int'>
>>> print type(prompt.string ("Pick a name"))
Pick a name: Bob
<type 'string'>Many of the question methods with accept a list of “converters”, each of which
is used to sucessively transform or validate user input. If input fails
validation, the question is posed again.qandasupplies a number of basic
validators:ToInt, ToFloatConvert inputs to other typesRegexnly allow values that match a certain patternRangeCheck that input falls within given boundsLengthCheck that input length falls within given boundsSynonymsMap values to other valuesVocabEnsure values fall within a fixed setReferences[qanda-home]qanda home pagehttp://www.agapow.net/software/py-qanda[qanda-pypi]qanda on PyPiHistoryv0.1dev (20110624)Initial release, sure to be buggy and incomplete
Package: qandaomr
OMRThis package can create a form which can be read in with OMR and can read those forms.UsageCreating an OMR formTo create an OMR enabled form you must make a form (with Word, LaTeX or similar software) and mark the first bullet points with aredbackground and the last with agreenbackground. At last the corners should be marked: the bottom right corner must be marked with a black square and the three other corners with a QR code like corner. At this moment the user must do this themselfes (this may change in the future). The imput to OMR must be an image file.Running the following will generate a printable output sheet with barcodes at the bottom which OMR uses to detect the settingsqandaomr -c input_file output_file columns rows points_per_question questions_per_blockReading an OMR formTo get the answers back from the form a scanned image can be the input to OMR. OMR will print the answers to the terminal in a CSV format.qandaomr -r input_fileGenerating intermediate filesFor debug purposes the-dflag can be used. This will generate multiple images which are used in the steps within OMR. This can be useful if OMR does not react the way it shouldInstallationTo install this package clone this repository and install via pip:pip install qandaomrUninstalling can be done via pip:pip uninstall qandaomr
Package: qandaxfmrartifact
qandaxfmrartifactBentoML artifact framework for Q&A Transformers.Installation:pip install qandaxfmrartifact==0.0.1Usage example (decorate service):from qandaxfmrartifact.QandaTransformersModelArtifact import QandaTransformersModelArtifact

@artifacts([QandaTransformersModelArtifact('albert')])
class MyBentoService(BentoService):Usage example (package model):svc = MyBentoService()

opts = {
    'embedder_model_path': my_embedder_model_path,
}

svc.pack('albert', my_transformer_model_path, opts)Alternatively, during training:svc.pack('albert', {'model': my_trained_model, 'tokenizer': my_tokenizer, 'embedder': my_embedder})
Package: qaner
QaNERUnofficial implementation ofQaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition.You can adopt this pipeline for arbitraryBIO-markupdata.Installationpip install qanerCoNLL-2003Pipeline results on CoNLL-2003 dataset:MetricsTrained Hugging Face modelHow to useTrainingScript for training QaNER model:qaner-train \
--bert_model_name 'bert-base-uncased' \
--path_to_prompt_mapper 'data/conll2003/prompt_mapper.json' \
--path_to_train_data 'data/conll2003/train.bio' \
--path_to_test_data 'data/conll2003/test.bio' \
--path_to_save_model 'dayyass/qaner-conll-bert-base-uncased' \
--n_epochs 2 \
--batch_size 128 \
--learning_rate 1e-5 \
--seed 42 \
--log_dir 'runs/qaner'Required arguments:--bert_model_name- base bert model for QaNER fine-tuning--path_to_prompt_mapper- path to prompt mapper json file--path_to_train_data- path to train data (BIO-markup)--path_to_test_data- path to test data (BIO-markup)--path_to_save_model- path to save trained QaNER model--n_epochs- number of epochs to fine-tune--batch_size- batch size--learning_rate- learning rateOptional arguments:--seed- random seed for reproducibility (default: 42)--log_dir- tensorboard log_dir (default: 'runs/qaner')InfrerenceScript for inference trained QaNER model:qaner-inference \
--context 'EU rejects German call to boycott British lamb .' \
--question 'What is the organization?' \
--path_to_prompt_mapper 'data/conll2003/prompt_mapper.json' \
--path_to_trained_model 'dayyass/qaner-conll-bert-base-uncased' \
--n_best_size 1 \
--max_answer_length 100 \
--seed 42Result:question: What is the organization?

context: EU rejects German call to boycott British lamb .

answer: [Span(token='EU', label='ORG', start_context_char_pos=0, end_context_char_pos=2)]Required arguments:--context- sentence to extract entities from--question- question prompt with entity name to extract (examples below)--path_to_prompt_mapper- path to prompt mapper json file--path_to_trained_model- path to trained QaNER model--n_best_size- number of best QA answers to considerOptional arguments:--max_answer_length- entity max length to eliminate very long entities (default: 100)--seed- random seed for reproducibility (default: 42)Possible inference questions for CoNLL-2003:What is the location? (LOC)What is the person? (PER)What is the organization? (ORG)What is the miscellaneous entity? (MISC)RequirementsPython >= 3.7Citation@misc{liu2022qaner,title={QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition},author={Andy T. Liu and Wei Xiao and Henghui Zhu and Dejiao Zhang and Shang-Wen Li and Andrew Arnold},year={2022},eprint={2203.01543},archivePrefix={arXiv},primaryClass={cs.LG}}
Package: qante
MotivationExtracting the highly-valuable data from unstructured text often
results in hard-to-read, brittle, difficult-to-maintain code.
The problem is that using regular expressions directly embedded
in the program control flow does not provide the best level of
abstraction. We propose a query language (based on the tuple
relational calculus) that facilitates data extraction.
Developers can explicitly express their intent declaratively,
making their code much easier to write, read, and maintain.SolutionThis package allows programmers to express what they are searching
for by using higher-level concepts to express their query as tags,
locations, and expressions on location relations.Thelocationof a string of characters within the document is
the interval defining its starting and ending position.Locations are grouped into sets named bytags.  Tags can be
used in conjunctions and disjunctions of interval relations to
query for tuples of locations.DocumentationWe invite you to view our YouTubevideoof ourpresentationfrom thePlaylistforPyData Global 2022.We presented this material from ourGitHubrepo:pydataG22.pdfslides of our talk.ipynb/pydata.ipynbajupyter notebookwith examples.RELEASE_NOTES.rstdescribes updates for each release.Use one of these pip or python commands (rev 3 or above) to install fromPyPI:pip install qante
python -m pip install qanteUse python docstrings for API Documentation:python    # rev 3 or above
  from quante.tagger import Tagger
  from quante.query import Query
  from quante import loctuple as lt
  from quant.table import get_table

  help(Tagger)    # annotate text with tags using tagRE('tagname', regexp)
  help(Query)     # Syntax for querying annotated text
  help(lt)        # Predicates used by queries
  help(get_table) # extract table (as dictionaries) from textSee also: “API Documentation” at the end of our jupyter notebook.We welcome your questions by electronic mail at: qante{at}asgard.com
Package: qanty
qantyPython package for integration of Qanty in other applicationsSupported Python VersionsThis library supports the following Python implementations:Python 3.8Python 3.9Python 3.10Python 3.11InstallationInstall from PyPi usingpip, a
package manager for Python.pip3installqantyTest your installationTry listing your company branches. Save the following code sample to your computer with a text editor. Be sure to update theauth_token, andcompany_idvariables.fromqantyimportQanty# Your Auth Tokenclient=Qanty(auth_token="your_auth_token",company_id="your_company_id")branches=client.get_branches()forbranchinbranches:print(f"Branch ID{branch.id}, name{branch.name}")
Package: qany
introsuppose you want to make a comand line tool called qany.
it accept integer as numbers
ouput carrots we need to feedex:
	cmd> qany 2 
	ouput: 4we can get the output by two ways:command lineqany2call python moudlepython-mqany2best practice schemaenvGenerated bymake envThis is first step you need to do in any python projectit will create a virtual envauto source itinstall requirements for the start uptestrunmake testrunmake runwill run in module modemake mainwill run in normal modeYou can see thatmain.pyandqany/__main__.pyare the same  content file.why do we needmain.py?
It is eay to make an  entry in IDE , like intellij.Could I just use__main__.pyto run  like this?pythonqany/__main__.pyYou cound not.That is because the top-level package problem.Ex:python main.py 
top-level package is the same as main.py

python src/func/main.py  
top-level package is the same as main.py, aka func 
top-level package is the folder where you run this command fromSo , whyqany/__main__.pydoes not work?because top-level package is qany now.loggingAlways use logging for log print,dont useprintlogging config is controlled bylogging.yml, which can control every module level callsetup_logging` in main file (ra)make moduleupdate readmeput moudle in the folder parallel with logx folder.logxyour_modulepacking module or cmdlocalfor quick test purposeinstall:pip install .uninstall:pip uninstall <module_name>make install
make uninstallupload to test or prod PYPI serverupload to test server:make upload-to-testupload to prod server:make upload-to-prodtest and coveragepure testmake testtest with coveragemake coverage
Package: qaoalib
qaoalibA package for QAOA Max-cut Calculations.Packages required:numpynetworkxmatplotlibqiskitHow to installYou can install from the PyPI:pip install --upgrade qaoalibUsageCalculate Max-cut expectation withQmcorQmcFastKron(faster version):import networkx as nx
from qaoalib.qaoa import Qmc, QmcFastKron

G = nx.fast_gnp_random_graph(10, 0.5)  # Random graph with 10 nodes
params = [0.2, 0.4, 0.3, 0.5]          # 4 params, depth = 2

qmc = Qmc(G, params) # or QmcFastKron(G, params)
qmc.run()
print(qmc.expectation)Plot landscapes of the QAOA Max-cut expectation function:import networkx as nx
from qaoalib.qaoa.landscape import HybridFast

G = nx.fast_gnp_random_graph(10, 0.5)
prev_params = [0.1, 0.2] # previous parameters (gamma1, beta1)

ins = HybridFast(G, prev_params) # plots the landscape wrt gamma2 and beta2 with previous parameters given.
ins.create_grid()
ins.show_landscape()
Package: qaoa-quimb
QAOA-QUIMBThis librairy implements an optimized version of the Quantum Approximate Optimization Algorithm (QAOA) and various extensions with Quimb.InstallationTo install with pip:pip install qaoa_quimbReferencesGray, J. (2018). quimb: a python library for quantum information and many-body calculations. Journal of Open Source Software, 3(29), 819. doi:10.21105/joss.00819.
Package: qaquora
Quora-ScraperThis package will allow you to scrap all Questions-Answers related to any topic e.g. mathematics-and-physics,coronavirus etc from Quora.It includes two functions for now-quora_scrap(self,keyword,PATH)arguments-keyword- keyword is an argument which describes your topic for e.g coronavirus,coronavirusinindia etc.PATH- to define path of chromedriver.exe filereturn-questions- questions scrapped for keywordanswers- answers scrapped for each questionfinal_qa_dict- dictionary of scrapped questions-answerssave_to_csv(self,filename,questions,final_question_answer_pairs_quora_dict)arguments-filename- specify name of your csv filequestions- questions returned by function 'quora_scrap'final_question_answer_pairs_quora_dict- specifies question-answer dictionary returned by function 'quora_scrap'If anyone wants to contribute in this project,feel free to do.
Package: qarg
quick argument parsingA simple concise way of defining arguments for an argparse.ArgumentParser-- Syntax --comma seperated list of argsargs:<short name>(<long name>{<dest>[<type>=<default>@<action>#<nargs>$<const>all but short name are optionalif long name is missing, short name will be usedif long name is missing, and short name has len > 1long name will be short name and short name will be thefirst character of short nameexamples:f(foo)[int]=10f[int]=10supported add_argument optionsname or flags (short name & long name)action = '@'nargs = '#'const = '$'default = '='type = '['choices = not supportedrequired = '!'help = not supportedmetavar = not supporteddest = '{'-- Example --import qargns = qarg.get('f(foo[int=1,r(bar,baz[str,p(pop@store_true')
Package: qarnot
Qarnot computing Python SDKThis package allows you to use Qarnot cloud computing service.You can launch, manage and monitor payloads running on distributed computing nodes deployed in Qarnot’sdigital boilers.Basic usageCreate an account, retrieve your token and get free computation time onaccount.qarnot.comLaunch a docker container in 7 lines:importqarnotconn=qarnot.connection.Connection(client_token="xxxx_mytoken_xxxx")task=conn.create_task('hello world','docker-batch',4)task.constants['DOCKER_CMD']='echo hello world from node #${FRAME_ID}!'task.run()print(task.stdout())Samples and documentationsYou can find samples and detailed information onqarnot.com.SDK documentation is availablehereGenerating documentationTo generate the SDK documentation you can use the following commandmake-CdochtmlThe index of the doc is then generated indoc/_build/html/index.html
Package: qarrayrun
A helper tool for running array jobs on an HPC computational node.The qarrayrun package was developed by the United States Food
and Drug Administration, Center for Food Safety and Applied Nutrition.This script executes a single slot of an array job on an HPC compute node.
It is intended to be used with Sun Grid Engine, SLURM, or Torque job schedulers.
It assumes every instance of the job array runs the same command but with
different arguments.  This script performs the work of looking-up the
arguments in a text file and substituting those arguments into the command
to be executed.Free softwareDocumentation:https://qarrayrun.readthedocs.ioSource Code:https://github.com/CFSAN-Biostatistics/qarrayrunPyPI Distribution:https://pypi.python.org/pypi/qarrayrunFeaturesExecutes a single slot of an array job on an HPC compute nodeSimple parameter lookup languageSupports execution in a subshell when neededCiting qarrayrunTo cite qarrayrun, please reference the qarrayrun GitHub repository:https://github.com/CFSAN-Biostatistics/qarrayrunLicenseSee the LICENSE file included in the qarrayrun distribution.History1.1.0 (2020-04-28)Update command line help to mention SLURM support.1.0.1 (2018-12-11)Fix the text in usage example.1.0.0 (2018-11-29)First public release.
Package: qarray-rust-core
qarray-rust-coreQuantum Dot Constant Capacitance Simulatoris a high-performance Python package that leverages the power of Rust and Rayon to provide a fully parallelised and optimised simulation environment for quantum dots with constant capacitance.This package provides core functionality; it is not intended that the user will interact with it directly.FeaturesUltra-fast Simulation:Harnesses the speed of Rust and the parallelism of Rayon to deliver lightning-fast simulations.Constant Capacitance:Specialized for simulating quantum dots with constant capacitance, allowing precise modelling of charge dynamics.User-Friendly:Designed with ease of use in mind, making it accessible to both experts and newcomers in quantum dot simulations.Extensive Documentation:Comprehensive documentation and examples to help you get started quickly.InstallationInstall Quantum Dot Constant Capacitance Simulator using pip:pipinstallqarray-rust-coreUsageThis package exposes two functions to be called from python:ground_state_open- computes the lowest energy state of a quantum dot array with constant capacitance and which is open, such that the total number of changes is not fixed.ground_state_closed- computes the lowest energy state of a quantum dot array with constant capacitance and which is closed, such that the total number of changes is fixed.The python code to call these functions is as follows:fromqarray-rust-coreimport(ground_state_open,ground_state_closed)importnumpyasnp# the dot-dot capacitance matrixcdd=np.array([[1,-0.1],[-0.1,1]])cdd_inv=np.linalg.inv(cdd)# the dot-gate capacitance matrixcgd=np.array([[1,0.3],[0.3,1]])# define a matrix of gate voltages to sweep over the first gatevg=np.stack([np.linspace(-1,1,100),np.zeros(100)],axis=-1)n_charge=3# the number of changes to confine in the quantum dot array for the closed casethreshold=1# threshold to avoid having to consider all possible charge states, setting it 1 is always correct, however has a computatinal cost.n_open=ground_state_open(vg,cgd,cdd_inv,threshold)n_closed=ground_state_closed(vg,n_charge,cgd,cdd,cdd_inv,threshold)It is not intended the user ever call these functions directly.There is a pure Python wrapper that provides a more user-friendly interface to this core functionality.
SeeQuantum Dot Constant Capacitance Simulator. This package provides:A user-friendly interfaceto the core functionality.Plotting, charge sensing, virtual gateand gate voltage sweeping (1d and 2d) functionality.Advanced type checkingusing pydantic.Automated testingincluding for the functionality in this package.More examples.
Package: qasdad
QASDAD, the quick and simple data analysis and documentation program.QASDAD is intended to speed up and simplify the process of analysing the data of
physical experiments and documenting it and make that process less error prone.
You can find examples that are intended to show the capabilities of QASDAD in the “examples” directory.
You can find a german tutorial in the “german tutorial” directory.
You can find the documentation in the “docu” directory.
“docu/latex/refman.pdf” is the documentation in pdf-format.
You should note that QASDAD is still under development, super buggy and not
feature complete, but feel free to contact me for bug reports and feature
requests atvolker.weissmann@gmx.de.
The public API is not stable
yet, so feel free to propose API changes.QASDAD is written by Volker Weißmann, a German physics student at the University
of Stuttgart.
For licensing issues, bug reports, feature requests etc.
contact me atvolker.weissmann@gmx.de.
This program is free software (free as in freedom)
and you can find the full license of this program in LICENSE.TXT
The direct dependencies of QASDAD are
* Python 3 (https://www.python.org, not included, needs to be installed separately)
* Scipy (https://www.scipy.org, not included, needs to be installed separately)
* Numpy (http://www.numpy.org, not included, needs to be installed separately)
* Matplotlib (https://matplotlib.org/, not included, needs to be installed separately)
* Sympy (https://www.sympy.org, not included, needs to be installed separately)
* latex2sympy (https://gitlab.com/volkerweissmann/latex2sympy, not included, needs to be installed separately)You can also install QASDAD using python3 -m pip install qasdadpGood Luck.QASDAD, the quick and simple data analysis and documentation program
Copyright (C) 2018 Volker Weißmann . Contact:volker.weissmann@gmx.deThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.