Saravanan666/pypi-org huggingface dataset:
Package: tpu-tf2
TPU usage in colab with single line of codeBefore installation Make sure your TPU is onInstallationTo install the package from the PyPi repository you can execute the following
command:pipinstalltpu_tf2If you prefer, you can clone it and run the setup.py file. Use the following commands to get a copy from GitHub and install all dependencies:gitclonehttps://github.com/ashishpatel26/regressionmetrics.gitcdtpu_tf2
pipinstall.Usageusage in colab :fromtpu_tf2.tpuimportstrategystrategy.num_replicas_in_sync>output:8💁 Thanks for reading and forking.
Package: tpu-util
# TPU_utility
Utility for TPU with PyTorch XLA.One more examples please check [this kaggle notebook](https://www.kaggle.com/wabinab/test-tpu-training).We have move the packages to foldertpu_util. For installation, call!python -m pip install git+https://github.com/Wabinab/TPU_utility.git@mainon colab for nightly releases.Update: currently we have published the packages on PyPI for stable versions. You could install withpip install tpu-util.Currently, if by any chance you are running TPU on Google Cloud Platform, the dependenciesare not installed automatically. One doesn’t have any access to GCP so one isn’t sure how it works, and hence you are responsible for installing requirements.txt and all other requirements by yourself, or clone colab/kaggle environment to your machine and setup from there.Sample contains data taken from [this](https://www.kaggle.com/wabinab/gnet-cqt-shannon-alt) dataset. This image is a spectrogram, it’s not easy to understand; however it is small enough to be use as test cases.
Package: tpy
tpyInstallationpipinstalltpyUsageSeetpy --help:usage: tpy [--socket-name SOCKET_NAME] [--socket-path SOCKET_PATH]
           [--session-name SESSION_NAME] [--window-name WINDOW_NAME]
           [--hotkey HOTKEY] [--reset-window] [--reset-pane] [--dry] [--debug]
           [-h]
           {cmd,again} ...

Runs commands in tmux.

positional arguments:
  {cmd,again}

optional arguments:
  --socket-name SOCKET_NAME
                        Socket name
  --socket-path SOCKET_PATH
                        Socket path
  --session-name SESSION_NAME
                        Session name
  --window-name WINDOW_NAME
                        Window name
  --hotkey HOTKEY       Register and wait for hotkey. Requires `sudo`
                        invocation.
  --reset-window        Resets window before execution
  --reset-pane          Resets pane before execution
  --dry                 Will send but not execute commands
  --debug               Debugging info
  -h, --help            Usage info


usage: tpy cmd command

Runs arbitrary command.

positional arguments:
  command  Command to execute



usage: tpy again [-tu TIMES_UP]

Runs previous command again.

optional arguments:
  -tu TIMES_UP, --times-up TIMES_UP
                        Number of times to press cursor up
Package: tpyfilestructure
tpyfilestructurePython package to read and display segregated file names present in a directory based on type of the file.InstallationYou can install tpyfilestructure fromPyPIpipinstalltpyfilestructureHow to useNavigate to the directory from where the file structure should be displayed. Open Command Prompt(cmd)/powershell in the same path. Once the Command Prompt/powershell pops up just type 'tpyfilestructure' and hit enter.$ tpyfilestructure
--displays categorized file names based on type--
Package: t-python-markdown
t-python-markdownA simple to use markdown generator for Python.Installationpip install t-python-markdownExamplefromt_python_markdownimportDocument,Header,Paragraph,Sentence,Bold,Table,UnorderedListimporttimeimportrequestsj=requests.get("https://api.coindesk.com/v1/bpi/currentprice.json").json()bpi=j["bpi"]front_matter={"title":j["chartName"],"authors":["A.U.Thor"],"date":time.strftime('%Y-%m-%dT%H:%M:%SZ',time.gmtime()),}doc=Document(front_matter)doc>>Header(j["chartName"],1)doc>>Paragraph([j["disclaimer"]])al=[("--:"ifisinstance(_,(int,float))else":-:"if_.startswith("&")else":--")for_inbpi[list(bpi.keys())[0]].values()]t=Table([_.replace("_"," ").title()for_inbpi[list(bpi.keys())[0]].keys()],alignment=al)doc>>tul=UnorderedList()doc>>Paragraph("Bitcoin Price Index")doc>>ulfork,vinbpi.items():t>>[_for_inbpi[k].values()]ul>>Sentence([Bold(k),bpi[k]["description"]])# Write markdown to filedoc.write("example.md")Saved asexample.pythen runningpython example.pyresults in:---
title: Bitcoin
authors:-A.U.Thor
date: '2023-02-25T14:17:02Z'
...# BitcoinThis data was produced from the CoinDesk Bitcoin Price Index (USD). Non-USD currency data converted using hourly conversion rate from openexchangerates.org

|**Code**|**Symbol**|**Rate**|**Description**|**Rate Float**|
| :-- | :-: | :-- | :-- | --: |
| USD | &#36; | 23,007.6135 | United States Dollar | 23007.6135 |
| GBP | &pound; | 19,224.9778 | British Pound Sterling | 19224.9778 |
| EUR | &euro; | 22,412.7746 | Euro | 22412.7746 |

Bitcoin Price Index-**USD**United States Dollar.-**GBP**British Pound Sterling.-**EUR**Euro.UsageFor usage, seehttps://www.cix.co.uk/~toyne/t-python-markdown/usage/Recent Changelog1.4.1Resolve issue where embedding tables in lists in tables in lists in tables (etc...) would not render correctly1.4.0Improve support for embedded lists and tables. Now able to wrap child items in other markdown elements1.3.2Add support for attribute lists when processing embedded lists1.3.1Add support for attribute lists when processing embedded tables1.3.0Add support for tables and lists embedded in other tables and lists1.2.1Bring into line with docs site1.2.0Add support for Strikethrough
Package: tq010or-distributions
Distribution: A package to demonstrate a few statistical distributionsThistq010or-distributionpackage is a demo for a few statistical distributions.FeaturesCansamplefrom the distributionCanestimatedistribution parameters withsamplesEasyplotintegrationSupport Gaussian and Binomial (as of July 2020)Extensible to include other distributionsInstallationOperating system: Linux · Windows (Cygwin, MinGW, Visual Studio)Python version: Python 3.6+pippipinstalltq010or-distributionOr you can install them in your virtual environment:python-mvenv.envsource.env/bin/activate
pipinstalltq010or-distributionUsage>>>fromdistributions.gaussianimportGaussian
>>>g=Gaussian(0,1)>>>print(g)Gaussian(0,1)>>>samples=g.sample(10000)>>>ge=Gaussian.estimate(samples)>>>print(ge)Gaussian(0.016869274639641572,0.9910041611791893)>>>g1=Gaussian(0,3)>>>g2=Gaussian(1,4)>>>g3=g1+g2
>>>assertg3==g1+g2
>>>Run testspython-c"import os; import distributions; print(os.path.dirname(distributions.__file__))"pipinstall-rpath/to/requirements.txt
python-mpytest<distributions-directory>
Package: tqatest
backend-moduleMain backend module, which is used for developing web-app logic and deploying AI model.InstallationRun the following to install:pipinstalltqatestUsagefromtqatest.app.services.predictimportMachineLearningModel# Generate modelmodel=MachineLearningModel()# Using Exampledata_input=[1.0,2.0,3.0,4.0]result=model.predict(data_input)print(result)Developing HelloGTo install helloG, along with the tools you need to develop and run tests, run the following in your virtualenv:$pipinstalltqatest[dev]
Package: tqc
tqc-pyCommand line interface written in Python for the TinyQueries™ CompilerInstallationMake sure you have an API key for the TinyQueries™ Compiler - you can get one here:https://tinyqueries.com/signup.Add the API key asTINYQUERIES_API_KEYto your ENV variables.Make sure you have Python and pip installedrunpip install tqcSetup TinyQueries™ for your projectIt is assumed you have a folder for your project. It can be an empty folder or a folder which contains other code as well.Create a folder inside your project folder (for exampletinyqueries) in which you put your TinyQueries source queriesCreate a folder inside your project folder (for examplesql) in which you want the compiler to put your compiled queriesCreate a config filetinyqueries.jsonortinyqueries.yamlin the root of your project folder. For example:project:label:my-project-labelcompiler:dialect:mysqlinput:./tinyqueriesoutput:./sqlFor a more detailed description of config files please checkhttps://compile.tinyqueries.comCompile your queriesOnce you have setup your project you just have to executetqcfrom your project folder each time you want to compile your source files
Package: tqcli
# TQCLI - TranQuant ClientTQCLI is the client application for using [TranQuant services](http://tranquant.com)[TranQuant](http://tranquant.com) is a data marketplace that delivers real-time or batch data at a large scale from suppliers to end-users.This client will allow you to upload and download data to and from TranQuant platform efficiently## How to install TQCLI?1) Install [Python3](https://www.python.org/downloads/) and the Python package manager, [`pip`](https://pip.pypa.io/en/stable/installing/).2) Install the `tqcli` command line app.If you have `pip` installed:pip3 install tqcli --no-cache-dirIf you want to install the latest version from GitHub:git clone https://github.com/Tranquant/tqcli.gitcd tqclipython3 setup.py install## How to use TQCLI?# 1- Create a Data Source on http://tranquant.com website# 2- You will see a command generated with datasource id that looks like:tqcli --datasource-id <a-datasource-id> --input <path-to-dataset-file># 3- Go back to http://tranquant.com and view your published datasource# 4- You will find the dataset available under that datasource now!If you have any questions please contact us `info@tranquant.com`You can also join our channel: [![Gitter](https://badges.gitter.im/tqcli/Lobby.svg)](https://gitter.im/tqcli/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=body_badge)## Troubleshooting install### Windows#### I can't install TQCLI from source because of a permission denied error.If you get an error like the following, then you probably have an old version of `pip` installed where this error has since been fixed.```Cleaning up...Exception:Traceback (most recent call last):File "C:\Python34\lib\shutil.py", line 370, in _rmtree_unsafeos.unlink(fullname)PermissionError: [WinError 5] Access is denied: '<LONG_USER_TEMP_PATH>'```To install the latest version of `pip` on Windows, run the following command:`python -m pip install -U pip`
Package: tqdj
tfdj :musical_keyboard::coffee:Set the mood for your lengthy data processing or model training tasks using tfdj, a detailedprogress barthat is also aLOFI RADIO!!! Run your process, get some coffee, V I B E to the tunes and you'll know you're done when the music stops.tqdj borrows a bunch of code fromtqdm.Installation$pipinstalltqdjUsageWrap any iterable with tqdj(iterable), and you're done!Note: Only works on local machine.LicenseMIT
Package: tqdl
requests-based file downloads withtqdmprogress bars.fromtqdlimportdownloaddownload(file_url,target_file_path)19%|█▊|20.5M/110M[00:06<00:29,2.98MiB/s]Contents1Installation2Features3Use4Contributing4.1Installing for development4.2Running the tests4.3Adding documentation5Credits1Installationpipinstalltqdl2FeaturesSimple and straightforward API.Installable from PyPI withpip install tqdl.Tested on TravisCIagainst Linux, Windows and macOS, and Python versions 3.5 through 3.7.The name - تقدّل - can be traslated as “get ahead”, which is awesome.3Usetqdlcontains a single funcion, aptly named download, which you can use to download files with a nice progress bar by providing it with a URL and a file path. Obviously, this is only useful for downloads of large files, and where you cae about showing the user (of your code/script/whatever) the progress of the ongoing download.Here is a very straightforward example:fromtqdlimportdownloaddownload(file_url,target_file_path)19%|█▊|20.5M/110M[00:06<00:29,2.98MiB/s]4ContributingPackage author and current maintainer isShay Palachy(shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.4.1Installing for developmentClone:gitclonegit@github.com:shaypal5/tqdl.gitInstall in development mode, including test dependencies:cdtqdlpipinstall-e'.[test]'4.2Running the testsTo run the tests use:cdtqdlpytest4.3Adding documentationThe project is documented using thenumpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, followthese conventions.Additionally, if you update thisREADME.rstfile,  usepython setup.py checkdocsto validate it compiles.5CreditsCreated byShay Palachy(shay.palachy@gmail.com).Based on astackoverflow answer by user leovp.
Package: tqdm-batch
tqdm_batchBatch processing using joblib including tqdm progress barsAdd batch processing to joblib, including tqdm progress bars.Installpipinstalltqdm_batchUsageProcess a list ofitemsusing afunction.fromtqdm_batchimportbatch_processimportrandomimporttimedefbatch_process_function(row,some_var):time.sleep(0.01)returnrow+random.randint(0,some_var)N=1_000items=range(N)result=batch_process(items,batch_process_function,some_var=42,n_workers=6,sep_progress=True,)
Package: tqdm-logger
TQDM LoggerA utility to log tqdm progress bars into a file instead of stdout.DescriptionWell we all use tqdm progress bars to track the progress of a running code. Sometimes we would like to log those progress bars in a log file instead of stdout. Using tqdm as is has a problem. It write each update in a newline. This utility is to solve this problem and be able to update progress bars inplace.General UsageUse the TqdmLogger class to declare a log stream handler with a log file, and pass that to tqdm's file argument. Thats it!!!Example Usageimporttimefromtqdm_loggerimportTqdmLoggerlog_file='temp_log.log'tqdm_stream=TqdmLogger(log_file)tqdm_stream.reset()forxintqdm(range(100),file=tqdm_stream):time.sleep(.5)
Package: tqdm-logging-wrapper
TQDM logging wrapperWrap standard-library logging to support TQDM progress-bars.Installationpipinstalltqdm-logging-wrapperUsageimportloggingimporttqdmimporttqdm_logging_wrapperlogger=logging.getLogger(__name__)logging.basicConfig(level=logging.INFO)items=[1,2,3]items_iter=tqdm.tqdm(items)logger.info(f"Items:{items}")withtqdm_logging_wrapper.wrap_logging_for_tqdm(items_iter),items_iter:foriteminitems_iter:logger.info(f"Item:{item}")logger.info(f"Items:{items}")
Package: tqdm-multi-thread
A tqdm multi-thread helperexampleimportthreadingfromconcurrent.futuresimportThreadPoolExecutorimporttimefromtqdm_multi_threadimportTqdmMultiThreadFactorydefdemo(factory,position,total):withfactory.create(position,total)asprogress:for_inrange(0,total,5):progress.update(5)time.sleep(0.001*(position%5+1))withThreadPoolExecutor(max_workers=20)asexecutor:tasks=range(100)multi_thread_factory=TqdmMultiThreadFactory()fori,urlinenumerate(tasks,1):executor.submit(demo,multi_thread_factory,i,100)
Package: tqdm-pathos
tqdm_pathosProgress bars for multiprocessing with pathosWrappers based onparmapfor multiprocessing withpathosand progress bar completion withtqdm. Following parmap, multiprocessing is extended to functions of multiple iterables, arguments, and keyword arguments.While parmap includes these extensions and a progress bar, it is built on the defaultmultiprocessinglibrary. The multiprocessing/pools module in pathos includes enhanced serialization to allow multiprocessing of, e.g., lambda functions, class methods, etc.InstallationYou canpip install tqdm-pathos.UsageA pool with an automatically detected number of cores is set up by default. To choose the number of cores, use then_cpuskwarg.
Alternatively, an existing pool can be used by passing it to thepoolkwarg.
Extrakwargscan be passed to thetqdmprogress bar using thetqdm_kwargsdictionary argument, e.g.,tqdm_kwargs = {'desc': 'pbar description'}.Function of a single iterable:f = lambda x: x**2
iterable = [1, 2, 3]

# Serial
y = [f(x) for x in iterable]

# Parallel
y = tqdm_pathos.map(f, iterable)Function of a single iterable, with non-iterable args and kwargs:def f(x, a, b=0):
    return x**2 * a + b
iterable = [1, 2, 3]
a = 1
b = 0
    
# Serial
y = [f(x, a, b=b) for x in iterable]

# Parallel
y = tqdm_pathos.map(f, iterable, a, b=b)Function of multiple iterables:f = lambda x, y: x * y
iterable1 = [1, 2, 3]
iterable2 = [4, 5, 6]

# Serial
z = [f(x, y) for x, y in zip(iterable1, iterable2)]

# Parallel
z = tqdm_pathos.starmap(f, zip(iterable1, iterable2))Function of multiple iterables, with non-iterable args and kwargs:def f(x, y, a, b=0):
    return x * y * a + b
iterable1 = [1, 2, 3]
iterable2 = [4, 5, 6]
a = 1
b = 0

# Serial
z = [f(x, y, a, b=b) for x, y in zip(iterable1, iterable2)]

# Parallel
z = tqdm_pathos.starmap(f, zip(iterable1, iterable2), a, b=b)
Package: tqdm-thread
tqdm-threadWhen you want to show that something is working, but don't have an iterable. For example, if you're loading a large
Pickle file and want to show progress. All kwargs work as expected withtqdmwith a couple exceptions:step_sec-- this is new. how many seconds to sleep between steps. default:1.0total-- since we don't actually know the total, this create a new progress bar everytotalsteps. default:Nonedefaultbar_formatis different; iftotalthen '{desc} {bar} {elapsed}' else '{desc} {elapsed}'importtimefromtqdm_threadimporttqdm_threadwithtqdm_thread(desc="doing serious work"):# no total; no pogress bar; just a countertime.sleep(10)withtqdm_thread(desc="doing serious work",total=10):# adding 'total' means that you will get a bartime.sleep(10)withtqdm_thread(desc="doing serious work",total=5):# here, two progress bars will be createdtime.sleep(10)Tests# pip install pytestpytest-vs.
Package: tqdmX
tqdmXThis is a tqdm wrapper for multi-line logging.DemoInstallationpip install tqdmXUsageThe following is a simple example.importtimefromtqdmXimportTqdmWrapper,format_strtw=TqdmWrapper(range(10))foriintw:tw.add(f'Iter{i}')tw.add('line1')tw.add(format_str('blue','line2'))tw.update()time.sleep(0.5)For the demo gif shown above, you may refer totests/test.py.
Package: tq-dockie-db
DockieDbA simple in-memory document database.InstallationInstall usingpip install tq-dockie-db.Document Database PrimerLooking at the tests in thetestfolder will give you a good idea on how to use DockieDb. Nevertheless,
this section offers some guidance.In general, document database objects have the following hierarchy:Database ---> Container (1 or more) --->Document (1 or more)At the root, there is a Database. A database consists of one or more Containers. A
Container holds one or more Documents.If you're coming from a relational background then you can
think of the Database as the schema, a Container as the table, and a Document as a row in the table.Whereas the rows in the table conform to the same schema, Documents in a Container
are schemaless. In other words you can store Documents of different shapes in the same container.This tutorialis a great read for those new to document databases. It does
a better job explaining it than I ever could in this readme.Creating Database ObjectsCreate a Databasefromdockie.core.databaseimportDatabasedb=Database()Create a Containercontainer=db.add_container("items")Add a Document to the Containerfromdockie.core.documentimportDocumentdocument=Document("item1",{"name":"basketball","price":29.99})container.add_document(document)A document id must be a string or integer.Retrieving DocumentsThere are two ways to retrieve a document:By its id.By querying against one or more non-id attributes.Retrieving a Document by IDfromdockie.query.queryimportDocumentIdQueryquery=DocumentIdQuery()document=query.execute(container,document_id="item1")Retrieving a Document by a Non-ID AttributeQuerying by non-ID attributes is accomplished with thedictquery library.fromdockie.query.queryimportDocumentAttributeQueryquery=DocumentAttributeQuery()document=query.execute(container,query='name=="basketball"')Refer to the tests in this repository for more query examples. Refer to the dictquery
homepage for details on dictquery syntax.Persisting the DatabaseAlthough DockieDb is an in-memory database, it can be saved and loaded to/from a file.Save the Database to Filefromdockie.core.persistenceimportpersist_to_filepersist_to_file(db,"db.bak")Load the Database from Filefromdockie.core.persistenceimportload_from_filedb=load_from_file("db.bak)MiscellaniaRunning TestsFrom the project root folder, runpytestwithout any arguments.
Package: tqenergymanager300
TQ-Automation Energy Manager 300Python library to request data from TQ-Automation Energy Manager EM300 (aka B-control Energy Manager EM-300 aka Kostal EM300LR).Usage:importtqenergymanager300client=TqEnergyManagerJsonClient("192.168.251.22","12345678","Password")logged_in=client.login()iflogged_in:print(client.fetch_data())Special thanks to Kilian Knoll for anexample projecthow to access the API.This project is not affiliated with TQ-Systems GmbH or any of its affiliates.
Package: tq-lahman-datasets
Lahman Baseball DatasetsThis package consists of theLahman Baseball Databasewith the intent
of consuming it from Python code via thePandaslibrary.
The package was inspired from the bookAnalyzing Baseball Data with R. Obviously R is not
Python so this package allows one to do the analyses covered in the book (as well as you own) using Python.InstallationInstall using pip:pip install tq-lahman-datasetsUsageDownload and load the PandasDataFramesinto memory:fromteqniqly.lahman_datasetsimportLahmanDatasetsld=LahmanDatasets()ld.load()Get the dataframe names. Each dataframe corresponds to a CSV file in the Lahman database:df_names=ld.dataframe_namesLoad datasets by providing the dataset name as an indexer to theLahmanDatasetsinstance:batting_df=ld["Batting"]The datasets are PandasDataFramesso work with them as you would with other DataFrames.
Package: tql-ANN
:rocket: ANN :facepunch:Installpip install fast-annUsagefromannimportANNimportnumpyasnpdata=np.random.random((1000,128)).astype('float32')ann=ANN()ann.train(data,index_factory='IVF4000, Flat',noramlize=True)dis,idx=ann.search(data[:10])print(dis)print(idx)milvus镜像yum -y install python3
# ln -sf /usr/bin/python3 /usr/bin/python
# ln -sf /usr/bin/pip3 /usr/bin/pip
pip3 install -U --no-cache-dir -i https://mirror.baidu.com/pypi/simple pip meutils pymilvus

rm -rf /tmp/*
rm -rf /root/.cache/pip*faiss不同量级对应的训练时间及内存测试压缩方式测试四种组合：默认是查向量返回 distance与indexid => id/vectorvector => id/vectorpush场景需要 docid => title_vector => docid线上服务id2wordid2vector
Package: tql-Python
:rocket: Yuan :facepunch:数据不是创造规律,而是展示那些原本的规律https://www.kaggle.com/c/elo-merchant-category-recommendation/discussion/82055#479196CompetitionsInstallpip install YuanUsagefrom yuan.pipe import *@Xdefxfunc1(x):_=x.split()print(_)return_@Xdefxfunc2(x):_='>>'.join(x)print(_)return_'I am very like a linux pipe'|xfunc1|xfunc2xtqdmxfilter / xmap / xsort / xreduceiterable|xfilter(lambdax:len(x)>1)|xmap(str.upper)|xsort|xreduce(lambdax,y:x+'-'+y)'AM-LIKE-LINUX-PIPE-VERY'xsummaryiterable|xsummarycounts7uniques7missing0missing_perc0%typesuniqueName:iterable,dtype:object...
Package: tqma
A poor man's tqdm - no progress bar, only numbers, no configuration optionspip install tqmaTested against Windows 10 / Python 3.10 / AnacondaNo progress bar, only numbers (printed to stderr)No configuration optionsOnly 10 lines of codeOnly one import: sysNo dependenciesfromtqmaimporttqmafromtimeimportsleepf=0forrintqma(range(10)):f=f+rsleep(2)print(f)