Saravanan666/pypi-org huggingface dataset:
Package: tcsv2png
NAMEtcsv2png - data visualization of csv data with a time column.SYNPSIStcsv2png [Options] CSV_FILE [COL0 [COLi ...]]DESCRIPTIONtcsv2png uses gnuplot to convert a csv file with a time column
format %H:%m:%S into a chart in png format.It scales the data to show all of the data sets in the same
png chart.You can select the columns of interest. The tool uses gnuplot
to generate the chart, so you can customize the script to your
need.CSV_FILE: a csv file with TAB or semicolon fields separator
COL0:     the column indice that contains the time formated H:M:S
COLi:     column indice to plotNote that column indice starts with 0 and that the first line
should contains columns headers.REQUIREStcsv2png requiresgnuplot.INSTALLATIONsudo easy_install tcsv2pngEXAMPLESYou can view some screenshots in thetcsv2png wikitcsv2png data.csvCreates data.png file with all the columns, assuming
column 0 is the time column.tcsv2png -v -c -t “Foo title” -o foo.png  data.csv 0 3 5Creates foo.png chart with “Foo title” title, column 0 is
the time column, plotting column 3 and 5 using smooth
csplines rendering.tcsv2png -hGives you the available options.
Package: tctc-envelopes
Mailing for human beings.AboutEnvelopes is a wrapper for Python’semailandsmtplibmodules. It aims to
make working with outgoing e-mail in Python simple and fun.Short examplefromenvelopesimportEnvelope,GMailSMTPenvelope=Envelope(from_addr=(u'from@example.com',u'From Example'),to_addr=(u'to@example.com',u'To Example'),subject=u'Envelopes demo',text_body=u"I'm a helicopter!")envelope.add_attachment('/Users/bilbo/Pictures/helicopter.jpg')# Send the envelope using an ad-hoc connection...envelope.send('smtp.googlemail.com',login='from@example.com',password='password',tls=True)# Or send the envelope using a shared GMail connection...gmail=GMailSMTP('from@example.com','password')gmail.send(envelope)FeaturesEnvelopes allows you to easily:Provide e-mail addresses with or without name part.Set text, HTML or both bodies according to your needs.Provide any number of CC and BCC addresses.Set standard (e.g.Reply-To) and custom (e.g.X-Mailer) headers.Attach files of any kind without hassle.Use any charset natively supported by Python’sunicodetype in addresses,
bodies, headers and attachment file names.Project statusThis project should be consideredbeta. Proceed with caution if you decide
to use Envelopes in production.Envelopes has been developed and tested with Python 2.7. Currently, Envelopes
supports Python 2.6, 2.7 and 3.3+.AuthorEnvelopes is developed byTomasz Wójcik.LicenseEnvelopes is licensed under the MIT License.Source code and issuesSource code is available on GitHub at:tomekwojcik/envelopes.To file issue reports and feature requests use the project’s issue tracker on
GitHub.
Package: tctl
Tradologics CLItctl(Tradologics Controller) is Tradologics’ command-line utility.It simplifies your development workflow by allowing you to communicate with Tradologics directly from the command-line, thus removing even more friction.→Full documentation and tutorialAbout Tradologics:Tradologics is the fastest way to go from idea to trading – without worrying about infrastructure.Tradologics helps traders, investors, and firms across the world develop, test, run, and scale their programmatic trading strategies in the cloud – without worrying about broker connectivity, data management, infrastructure, or any of that other boring stuff.Our cloud platform is tailored to the specific needs of programmatic traders and solves every part of the trading business cycle - allowing them to focus on the trading logic, while we take care of everything else.To learn more about Tradologics, visit:https://tradologics.com
Package: tc-transform
Matrix and Signal Processing FunctionsThis repository contains Python functions for matrix and signal processing operations, including matrix transposition, 1D signal windowing, and 2D convolution.Functionstranspose2ddeftranspose2d(input_matrix:List[List[float]])->List[List[float]]:This function takes a 2D matrix (input_matrix) as input and returns its transpose. It initializes an empty matrix with transposed dimensions and populates it with the transposed values of the input matrix.window1ddefwindow1d(input_array:List[float]|np.ndarray,size:int,shift:int=1,stride:int=1)->List[List[float]|np.ndarray]:Given a 1D array (input_array), this function creates overlapping windows of a specified size, shift, and stride. The result is a list of windows, where each window is a subarray of the input.convolution2ddefconvolution2d(input_matrix:np.ndarray,kernel:np.ndarray,stride:int=1)->np.ndarray:This function performs 2D convolution between an input matrix and a kernel. The stride parameter determines the step size while sliding the kernel over the input matrix. The result is a new matrix representing the convolved output.UsageTo use these functions, simply import the desired function from the module and provide the required parameters. Ensure that the input data types and sizes align with the function specifications.fromtc_transformimporttranspose2d,window1d,convolution2d# Example usageinput_matrix=[[1,2,3],[4,5,6],[7,8,9]]transposed_matrix=transpose2d(input_matrix)input_array=[1,2,3,4,5,6,7,8,9]windows=window1d(input_array,size=3,shift=1,stride=1)input_matrix=np.array([[1,2,3],[4,5,6],[7,8,9]])kernel=np.array([[1,0],[0,1]])convolved_output=convolution2d(input_matrix,kernel,stride=1)DependenciesPython >= 3.10NumPy
Package: tcutils
TropiCoders Utility LibraryOverviewtcutilsis a Python>=3.6 library that defines code for
some common operations like path manipulation, file system operations,
packaging, configuration, schema definitions, function utilities,
YAML extensions, etc.DevelopmentGitHub:https://github.com/ktomala/tcutils.gitLicenseMozilla Public License v2 (see LICENSE file).
Package: tcut-to-qastle
IntroductionTCutselection for ROOT TTree toQastlewrapper for ServiceX uproot backends.Supported expressionsArithmetic operators:+, -, *, /Logical operators:!, &&, ||Relational and comparison operators:==, !=, >, <, >=, <=Ternary operator:(A?B:C)- has to be enclosed in parenthesesMathematical function:sqrtUsage<tree_name>: Tree name of input flat ROOT ntuple (required only for uproot)<selected_columns>: List of selected branches (or columns) to deliver. Branches are separated by comma. Deliver all branches if nothing specified.<tcut_selection>: Selection expression with a combination of the branches. No selection is applied if nothing specified.import tcut_to_qastle

# Get Qastle query
query = tcut_to_qastle.translate(<tree_name>, <selected_columns>, <tcut_selection>)

# Get the list of columns in the TCut selection
columns_in_selection = tcut_to_qastle.get_list_of_columns_in_selection(<TCut selection>)Example>>> import tcut_to_qastle
>>> query = tcut_to_qastle.translate("tree", "A,B", "A && B * C>0")
>>> query
"(Select (Where (call EventDataset 'ServiceXDatasetSource' 'tree') (lambda (list event) (and (> (attr event 'A') 0) (> (* (attr event 'B') (attr event 'C')) 0)))) (lambda (list event) (dict (list 'A' 'B') (list (attr event 'A') (attr event 'B')))))"
>>> columns_in_selection = tcut_to_qastle.get_list_of_columns_in_selection("A && B * C>0")
>>> columns_in_selection
['A', 'B', 'C']CompatibilityCurrent version is compatible with the following docker image tag of the uproot transformersslhep/servicex_func_adl_uproot_transformer:v1.0.0-rc.3sslhep/servicex_func_adl_uproot_transformer:developDevelopmentVerbose mode shows intermediate steps from TCut syntax to qastle. The last arguement of the functiontranslate()is a flag to enable the verbose mode. It prints the input TCut selection expression, translated func-adl selection expression, full func-adl query which includes the name of tree and selected branches, and full qastle query.>>> query = tcut_to_qastle.translate("tree", "A,B", "A && B * C>0", verbose=True)
TCut selection syntax:
A && B * C>0


Translated func-adl selection syntax:
 event.A > 0 and event.B * event.C > 0


Full func-adl query:
EventDataset("ServiceXDatasetSource", "tree").Where("lambda event: event.A > 0 and event.B * event.C > 0 ").Select("lambda event: {'A': event.A, 'B': event.B} ")


Full qastle query:
(Select (Where (call EventDataset 'ServiceXDatasetSource' 'tree') (lambda (list event) (and (> (attr event 'A') 0) (> (* (attr event 'B') (attr event 'C')) 0)))) (lambda (list event) (dict (list 'A' 'B') (list (attr event 'A') (attr event 'B')))))
Package: tcw
TCW: Tiny Contest WinnersA simple web app to create ephemeral raffle contests.DescriptionAllows someone to create a contest. When the contest has expired, the winners
will be randomly selected and emailed to the original contest creator.Contest ExpirationA contest is finished once its expiration time has passed, or once the max
number of entrants is reached.Contest EntrantsThe contest creator is responsible for managing the signup link to the contest,
and distributing it to potential entrants. There is no search or recovery
option for a lost contest link.NotificationsThis app does not notify the winners. The contest creator will be notified by
email of the winners once the contest expires. It is the responsibility of the
contest creator to notify entrants if they won.Storage of Contest dataOnce a Contest has expired, and the creator of the contest has been notified
of the winners, all information about the contest is removed from the database.
This information cannot be recovered.
Package: tcx2gpx
tcx2gpxThis module converts the GarmintcxGPS file format
to the more commonly usedgpxfile format.
Both formats are a form ofXMLbut there are some fields in the former that are not
present in the later.
It uses two packages to do the grunt worktcxparserandgpxpy.InstallationInstall fromPyPi.orgusing...pipinstalltcx2gpxFor more information on installing please refer to thedocumentation(see alsoReadTheDocs).UsageThe easiest way to usetcx2gpxis the command line version included. It will by default search the current directory
path for files with the.tcxextension and convert them to.gpx. There are various options available that change
where output is written or the directory that is searched. For details on usage see...tcx2gpx--helpFor more information on usage please refer to thedocumentation(see alsoReadTheDocs).Licensetcx2gpxis licensed under GNU GPL-3.0-only, please refer to theCOPYINGfile for further details.
Package: tc_xml_python
===============================Typecraft XML Python===============================.. image:: https://img.shields.io/pypi/v/tc_xml_python.svg:target: https://pypi.python.org/pypi/tc_xml_python.. image:: https://img.shields.io/travis/Typecraft/tc_xml_python.svg:target: https://travis-ci.org/Typecraft/tc_xml_python.. image:: https://readthedocs.org/projects/tc-xml-python/badge/?version=latest:target: https://tc-xml-python.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image:: https://pyup.io/repos/github/Typecraft/tc_xml_python/shield.svg:target: https://pyup.io/repos/github/Typecraft/tc_xml_python/:alt: UpdatesLightweight implementation of the Typecraft XML format in python.* Free software: MIT license* Documentation: https://tc-xml-python.readthedocs.io.Installation--------pip install tc_xml_pythonFeatures--------This repository contains a lightweight python implementation of the Typecraft IGT-model, bundled with a simple parser.Usage-------Currently, the project can only be used as a library, and does nothing as a standalone.Credits---------This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template... _Cookiecutter: https://github.com/audreyr/cookiecutter.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage=======History=======0.1.1 (2016-08-15)-----------------* Fixed some small bugs.0.1.0 (2016-08-14)------------------* First release. Added main bulk of initial code:* Parser works in its most basic inception and parses TC-XML documents into its object-tree
Package: td3a_cpp
documentationSimple template to implement an algorithm withcythonandopenmp.
It implements simple examples to demonstrate the speed up
obtained by usingcython. The module must be compiled
to be used inplace:python setup.py build_ext --inplaceGenerate the setup in subfolderdist:python setup.py sdistGenerate the documentation in folderdist/html:python -m sphinx -T -b html doc dist/htmlRun the unit tests:python -m unittest discover testsOr:python -m pytestTo check style:python -m flake8 td3a_cpp tests examplesThe functioncheckor the command linepython-mtd3a_cpp checkchecks the module is properly installed and returns processing
time for a couple of functions or simply:import td3a_cpp
td3a_cpp.check()
Package: td3a-cpp-deep
documentationSimple template to implement an algorithm withcythonandopenmp.
It implements simple examples to demonstrate the speed up
obtained by usingcython. The module must be compiled
to be used inplace:python setup.py build_ext --inplaceGenerate the setup in subfolderdist:python setup.py sdistGenerate the documentation in folderdist/html:python -m sphinx -T -b html doc dist/htmlRun the unit tests:python -m unittest discover testsOr:python -m pytestTo check style:python -m flake8 td3a_cpp_deep tests examplesThe functioncheckor the command linepython-mtd3a_cpp_deep checkchecks the module is properly installed and returns processing
time for a couple of functions or simply:import td3a_cpp_deep
td3a_cpp_deep.check()
Package: td4
td4-pyThis repository is a 4bit CPU emulator written by Python.The original isCPUの創りかた.featuresRead from plain text files, td4 format filesVariable clock, manual clock selectableBeep supportRuns in CLIInstallhttps://pypi.org/project/td4pip3installtd4Usageargstd4[-h][-iINPUT][-cCLOCK][-b]filefileFile to read.Checkhttps://github.com/yashikota/td4-py#support-file-formatfor the supported format.exampletd4 program.txttd4 Knight2K.td4helpShow help message and exit.exampletd4 -hinputAny binary can be specified.Default is0000.exampletd4 -i 0000td4 --input 1111clockAny number or manual can be specified.Default is10.exampletd4 -c 1td4 --clock 10td4 -c manualbeepSpecifies whether a beep is sounded.Operates when the MSB of out is set to 1.Default isFalse.exampletd4 -btd4 --beepSupport file formatInput from plain text files supports several patterns.Pattern 1Opcode(Assembly LowerCase) + Space + Immediate dataout 0011
out 0110
out 1100
out 1000
out 1000
out 1100
out 0110
out 0011
out 0001
jmp 0000Pattern2Opcode(Assembly UpperCase) + Space + Immediate dataOUT 0111
ADD A,0001
JNC 0111
ADD A,0001
JNC 0011
OUT 0110
ADD A,0001
JNC 0110
ADD A,0001
JNC 1000
OUT 0000
OUT 0100
ADD 0001
JNC 1010
OUT 1000
JMP 1111Pattern3Opcode(binary) + Immediate data10110011
10110110
10111100
10111000
10111000
10111100
10110110
10110011
10110001
10010000Pattern4Opcode(binary) + Space + Immediate data1011 0011
1011 0110
1011 1100
1011 1000
1011 1000
1011 1100
1011 0110
1011 0011
1011 0001
1001 0000
Package: tda
Topological Data Analysis (TDA)ContentsInstallationCompiling from sourceTDA is a python package for topological data analysis written in Rust.InstallationPre-built packages currently only for MacOS, Windos and most Linux distributions inPyPIand can be installed with:pipinstalltdaOn uncommon architectures, you may need to firstinstall Cargobefore runningpip install tda.Compilation from sourceIn order to compile from source you will need toinstall Rust/Cargoandmaturinfor the python bindings.
Maturin is best used within a Python virtual environment:# activate your desired virtual environment first, then:pipinstallmaturin
gitclonehttps://github.com/antonio-leitao/topological-data-analysis.gitcdtopological-data-analysis# build and install the package:maturindevelop--release
Package: tdam-api
docstestspackageA Python Client for the TDAmeritrade APIFree software: MIT licenseInstallationpip install tdam_apiDocumentationhttps://tdam_api.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxDocumentationThe full documentation is athttp://tdam_api.rtfd.org.
Package: tda-mapper
tda-mapperThis Python package provides a simple and efficient implementation of Mapper algorithm.Installation:pip install tda-mapperDocumentation:https://tda-mapper.readthedocs.io/en/main/About MapperThe Mapper algorithm is a well-known technique in the field of topological data analysis that allows data to be represented as a graph.
Mapper is used in various fields such as machine learning, data mining, and social sciences, due to its ability to preserve topological features of the underlying space, providing a visual representation that facilitates exploration and interpretation.
For an in-depth coverage of Mapper you can readthe original paper.Step 1Step 2Step 3Step 4Chose a lensCover the image of the lensPerform clustering on the pullback coverBuild the Mapper graphUsageHereyou can find an example to use to kickstart your analysis.
In this toy-example we use a two-dimensional dataset of two concentric circles.
The Mapper graph is a topological summary of the whole point cloud.importnumpyasnpfromsklearn.datasetsimportmake_circlesfromsklearn.decompositionimportPCAfromsklearn.clusterimportDBSCANfromtdamapper.coreimportMapperAlgorithmfromtdamapper.coverimportCubicalCoverfromtdamapper.plotimportMapperPlotX,y=make_circles(# load a labelled datasetn_samples=5000,noise=0.05,factor=0.3,random_state=42)lens=PCA(2).fit_transform(X)mapper_algo=MapperAlgorithm(cover=CubicalCover(n_intervals=10,overlap_frac=0.3),clustering=DBSCAN())mapper_graph=mapper_algo.fit_transform(X,lens)mapper_plot=MapperPlot(X,mapper_graph,colors=y,# color according to categorical valuescmap='jet',# Jet colormap, for classesagg=np.nanmean,# aggregate on nodes according to meandim=2,iterations=60,seed=42)fig_mean=mapper_plot.plot(width=600,height=600)fig_mean.show(config={'scrollZoom':True})fig_std=mapper_plot.with_colors(# reuse the plot with the same positionscolors=y,cmap='viridis',# viridis colormap, for rangesagg=np.nanstd,# aggregate on nodes according to std).plot(width=600,height=600)fig_std.show(config={'scrollZoom':True})DatasetMapper graph (average)Mapper graph (deviation)
Package: tdameritrade
tdameritradePython interface to TD Ameritrade ApiGetting StartedInstallInstall from pippip install tdameritradeor from sourcepython setup.py installDocsMajor changes in the v0.1.0 update to the way tokens are handled.You will still need the original authentication instructions, but the TDClient now takes the refresh token and client
id, not the access token. A new session class handles token expiration and will automatically call a new token as
needed.It is recommended that you store these as environmental variables.client_id = os.getenv('TDAMERITRADE_CLIENT_ID')
account_id = os.getenv('TDAMERITRADE_ACCOUNT_ID')
refresh_token = os.getenv('TDAMERITRADE_REFRESH_TOKEN')

tdclient = tdameritrade.TDClient(client_id=client_id, refresh_token=refresh_token, account_ids=[account_id])See the tests\test_client.py file for examples on current usage.All functionality is available as methods on theTDClientobject. For most methods, there is a convenience method to return the result as a pandas DataFrame.Most data fetching methods accept the symbol as argument. For equities, this is just the ticker.For different assets, utilize thesearchandinstrumentmethods to lookup symbols. For options, you can utilize the options method.
Package: td-ameritrade-api
TD Ameritrade APISetup instructionsMake sure you are using python 3.6 or greater, preferably python 3.7
I also highly recommend usingpython-dotenvto keep your api information safe.
To do this, create a .env file with theREFRESH_TOKEN,CONSUMER_KEY, andACCOUNT_IDvariables and installpython-dotenv.How to usefromdotenvimportload_dotenvimporttd_ameritrade_apiastdfromosimportgetenvload_dotenv()client=td.client(getenv("REFRESH_TOKEN"),getenv("CONSUMER_KEY"),getenv("ACCOUNT_ID"))print(client.quote("AAPL"))You can find all of the other functions in thetd_client.pyfile.
There is no documentation yet, but it will be coming soon.
Package: tdameritrade-client
TDAmeritrade ClientA tool that links to the TDA API to perform requests.Read thedocs.Installation:Runpip install tdameritrade-clientwithin a virtual environmentBasic Usage:To get started, use the following code snippet:fromtdameritrade_client.clientimportTDClienttd_client=TDClient(acct_number=<youraccountnumber>,oauth_user_id=<theidregisteredtotheTDappyouwouldliketoauthenticatewith>,redirect_uri=<theredirectURIregisteredtotheTDapp>,token_path=<optionalpathtoanexistingaccesstoken>)td_client.run_auth()acct_info=td_client.get_positions()TestsThis package uses poetry, which can be installed by running:curl -sSL https://raw.githubusercontent.com/sdispater/poetry/master/get-poetry.py | pythonTo run the tests, first install the package by cloning the repo and runningpoetry installfrom the root of the
repository. Then, runpoetry run pytest tests/from the root directory of this repository.
Package: tdasampling
tdasamplingCopyright (C) 2019Parker
EdwardsExternal requirementsBertiniLibspatialindex(A
requirement of the Python packageRtree)An mpiexec executable, like the one you can compile from sourcehereDescriptionPython package for sampling real algebraic varieties from their
polynomial systems. See the articlehttps://arxiv.org/abs/1802.07716for
theoretical details. It has been tested on Linux.The package installs two command line scripts:tdasampling- Entry point to the main sampling algorithm.sampling-setup- Script for setting up a directory for sampling
computation from just a list of polynomials in the system.See the included tutorial for detailed information about all the
different options.Version 1.1.4Basic usage for tdasampling$tdasampling[options]boundsdensitynumber_of_functions_in_systemexecution_directoryBounds is a list of a form like -1.0,1.0,-1.0,1.0, which indicates
the region in which to sample the polynomial system is box [-1.0,1.0]
x [-1.0,1.0] in Euclidean spaceexecution_directory is a directory containing, at minimum:Aminimizerdirectory which contains parameter homotopy files for
Bertini. Unless you have experience with Bertini, set these up withsampling-setup(Recommended, not required) A parameters fileparameters.json.
See examples for format. If you include aparameters.jsonfile and
use the option flag--parameterswithtdasampling, theparameters.jsonfile should include all the information exceptexecution_directory, which can then be omitted from the command
line call.Basic usage for sampling-setup$sampling-setup[options]path_to_directory_to_setupThe directory indicated atpath_to_directory_to_setupshould
contain a file namedpolynomial_system. The general format of that
file is text:list of variable names separated by commas
polynomial 1
polynomial 2
...
polynomial nFor example, if we were sampling from a circle of radius 1:x1,x2
x1^2 + x2^2 - 1--mpiexecutable/a/path/to/mpiexecoption to indicate a path tompiexec. Unnecesssary if yourmpiexeccan be called asmpiexec--bertini/a/path/to/bertini: a path to yourbertiniexecutable if it cannot be called asbertini--processorsk: the number of processes you would like to use for
thebertinisolving run associated with setup--hostsname1,name2,...,namek: list of ssh names for nodes to use
for thebertinicomputation. By default, thebertinirun will
run only on your local machineLicensetdasampling is licensed under an MIT license.