Saravanan666/pypi-org huggingface dataset:
Package: tsfmeta
tsf-meta -- A Python package for large scale time series forecasting with meta-learningtsfmetais a Python package for time series forecasting with meta-learning. Its goal is to make meta-learning available for researchers and time series forecasting practitioners in a unified, easy-to-use framework.InstallationpipinstalltsfmetaResourcesExamples to use the packageDescriptiontsfmetawas created with foundations on existing libraries in the PyTorch eco-system, stream-lined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. Specifically, the library provides a metadata class that abstracts handling data initialization, data iterating, train-test splitting, and feature extracting, etc., a meta-learner class that provides multiple predefined neural network architectures for meta-learning, and a utility class that provides functions that help users to create meta-data with their datasets.CitingIf you usetsfmeta(including ideas proposed in the documentation, examples and tests) in your research pleasemake sure to cite it.Ma, S., & Fildes, R. (2021). Retail sales forecasting with meta-learning. European Journal of Operational Research, 288(1), 111-128. doi:https://doi.org/10.1016/j.ejor.2020.05.038
Package: tsforecast
ForecastingInstallFirst, clone the repo like and cd to its root path, like so:gitclonegit@github.com:JaumeAmoresDS/forecasting.gitcdforecastingThen install dependencies using one of the following options:Option 1: installing from forecasting.yml filecondaenvcreate-nforecasting--fileforecasting.yml
condaactivateforecastingOption 2: installing from requirements filecondacreate-nforecastingpython=3.10pip
condaactivateforecasting
pipinstallrequirements.txtOption 3: installing from setup.pycondacreate-nforecastingpython=3.10pip
condaactivateforecasting
pipinstall-e.[dev]How to useRun:pythonscripts/run_pipeline.pyThis will save the predictions into a file calledpredictions.parquet.
To display it, you can do something like:importpandasaspdpredictions=pd.read_parquet('predictions.parquet')predictions.plot();
Package: tsformatter
TSFormatterHelper package to format TeamSpeak BBCode📦 Installationpipinstalltsformatter✏️ Usageimporting the formatterfromtsformatterimportformatterColors# Using HTML color name>>>formatter.color("red","This text is red")'[COLOR=red]This text is red[/COLOR]'>>>formatter.color("PaleTurquoise","This text is turquoise")'[COLOR=PaleTurquoise]This text is turquoise[/COLOR]'# Using Hex Triplet>>>formatter.color("#f00","This text is red")'[COLOR=f00]This text is red[/COLOR]'>>>formatter.color("#AFEEEE","This text is turquoise")'[COLOR=#AFEEEE]This text is turquoise[/COLOR]'Horizontal line>>>formatter.hr'[HR]'Images>>>formatter.img("https://i.imgur.com/ml09ccU.png")'[IMG]https://i.imgur.com/ml09ccU.png[/IMG]'Links# Without specifying link text>>>formatter.link("https://www.teamspeak.com/")'[URL]https://www.teamspeak.com/[/URL]'# With a link textformatter.link("https://www.teamspeak.com/","TeamSpeak Website")'[URL=https://www.teamspeak.com/]TeamSpeak Website[/URL]'Lists# Default style of the list is bullet list>>>formatter.list_(f"List item #{x}"forxinrange(1,6))'[LIST]\n[*]List item #1\n[*]List item #2\n[*]List item #3\n[*]List item #4\n[*]List item #5\n[/LIST]'# You can specify the style of the list>>>formatter.list_((f"List item #{x}"forxinrange(1,6)),style="1")'[LIST=1]\n[*]List item #1\n[*]List item #2\n[*]List item #3\n[*]List item #4\n[*]List item #5\n[/LIST]'List styles that work:Style"1""a""i""A""I"RendersPlacement>>>formatter.left("Formatted to the left")'[LEFT]Formatted to the left[/LEFT]'>>>formatter.right("Formatted to the right")'[RIGHT]Formatted to the right[/RIGHT]'>>>formatter.center("Center of the space")'[CENTER]Center of the space[/CENTER]'Size# Using absolute sizes>>>formatter.size(24,"I am huge!")'[SIZE=24]I am huge![/SIZE]'# Relative sizes can be either positive or negative>>>formatter.size('-4',"I am 4 units smaller than the rest")'[SIZE=-2]I am 2 units smaller than the rest[/SIZE]'>>>formatter.size('+2',"I am 2 units bigger than the rest")'[SIZE=+2]I am 2 units bigger than the rest[/SIZE]'Styles>>>formatter.bold("Example text")'[B]Example text[/B]'>>>formatter.italic("Example text")'[I]Example text[/I]'>>>formatter.underline("Example text")'[U]Example text[/U]'>>>formatter.strike("Example text")'[S]Example text[/S]'Tables>>>header=("Place","City","Date")>>>data1=("Statue of Liberty","New York City","October 28, 1886")>>>data2=("Eiffel Tower","Paris","31 March, 1889")>>>data3=("Big Ben","London","31 May, 1859")>>>formatter.table(formatter.table_header_row(header),formatter.table_row(data1),formatter.table_row(data2),formatter.table_row(data3))'[TABLE]\n[TR][TH]Place[/TH][TH]City[/TH][TH]Date[/TH][/TR]\n[TR][TD]Statue of Liberty[/TD][TD]New York City[/TD][TD]October 28, 1886[/TD][/TR]\n[TR][TD]Eiffel Tower[/TD][TD]Paris[/TD][TD]31 March, 1889[/TD][/TR]\n[TR][TD]Big Ben[/TD][TD]London[/TD][TD]31 May, 1859[/TD][/TR]\n[/TABLE]'Results when rendered by TeamSpeak client:
Package: tsfpga
tsfpga is a flexible and scalable development platform for modern FPGA projects.
With its Python-based build/simulation flow it is perfect for CI/CD and test-driven development.
The API is simple and easy to use
(a completesimulation projectis set up in less than
15 lines).See documentation on the website:https://tsfpga.comCheck out the source code on GitHub:https://github.com/tsfpga/tsfpgaKey featuresSource code-centricproject structurefor scalability.
Build projects, test configurations, constraints, IP cores, etc. are handled close to the
source code, not in a central monolithic script.Automatically adds build/simulation sources if a recognized folder structure is used.Enableslocal VUnit test configurationwithout
multiplerun.py.Handling ofIP coresandsimlibfor your simulation project, with automatic re-compile when needed.Python-basedVivado build systemwhere many builds can
be run in parallel.Tightly integrated withhdl-registers.
Register code generation is performed before each simulation and each build.Released under the very permissive BSD 3-Clause License.The maintainers place high focus on quality, with everything having good unit test coverage and a
thought-out structure.
The project is mature and used in many production environments.
Package: tsfracdiff
Efficient and easy to use fractional differentiation transformations for
stationarizing time series data in Python.tsfracdiffData with high persistence, serial correlation, and non-stationarity
pose significant challenges when used directly as predictive signals in
many machine learning and statistical models. A common approach is to
take the first difference as a stationarity transformation, but this
wipes out much of the information available in the data. For datasets
where there is a low signal-to-noise ratio such as financial market
data, this effect can be particularly severe. Hosking (1981) introduces
fractional (non-integer) differentiation for its flexibility in modeling
short-term and long-term time series dynamics, and López de Prado (2018)
proposes the use of fractional differentiation as a feature
transformation for financial machine learning applications. This library
is an extension of their ideas, with some modifications for efficiency
and robustness.DocumentationGetting StartedInstallationpip install tsfracdiffDependencies:# Required
python3 # Python 3.7+
numpy
pandas
arch

# Suggested
joblibUsage# A pandas.DataFrame/np.array with potentially non-stationary time seriesdf# Automatic stationary transformation with minimal information lossfromtsfracdiffimportFractionalDifferentiatorfracDiff=FractionalDifferentiator()df=fracDiff.FitTransform(df)For a more in-depth example, see thisnotebook.ReferencesHosking, J. R. M. (1981). Fractional Differencing. Biometrika, 68(1),
165--176.https://doi.org/10.2307/2335817López de Prado, Marcos (2018). Advances in Financial Machine Learning.
John Wiley & Sons, Inc.
Package: tsfresh-ppi
tsfresh_ppiThis package provides some peak-to-peak interval (PPI) variability features to augmenttsfresh[1].
Currently,tsfreshwill find and count peaks using a couple of different methods.
However, it does not measure the variability in timing between those peaks.InstallationOption 1: Clone this repo.  Install prereqs fromrequirements.txt.  Then install the cloned directory, e.g. withpip3 install -e [repo path].Option 2: Install directly from PyPI (tsfresh-ppi).Usagefrom tsfresh import extract_features
from tsfresh_ppi import get_fc_parameters

my_signal = ...  # some pandas DataFrame

# The default way to extract features with tsfresh looks something like this:
features = extract_features(
    my_signal,
    ...
)

# This is how to get all of the default tsfresh features, plus default PPI features:
fc_params_with_ppi = get_fc_parameters()
features = extract_features(
    my_signal,
    default_fc_parameters = fc_params_with_ppi,
    ...
)References[1]https://github.com/blue-yonder/tsfresh
Package: tsgd
TAdamThe Pytorch implementation of TSGD algorithm in：'Scaling transition from SGDM to plain SGD'https://arxiv.org/abs/2106.06749UsagefromtsgdimportTSGD...optimizer=TSGD(model.parameters(),iters=required,momentum=0.9,lr=1e-3,moment=3/8,up_lr=0.1,low_lr=0.005)#iters(int, required): iterations#	iters = (testSampleSize / batchSize) * epoch##moment(float, optional): transition moment#       moment = transition_iters / iters#set default value: momentum=0.9, moment=3/8, up_lr=0.1, low_lr=0.005The code will be uploaded as soon as possible
Package: tsg-export-cli
TSG Export CLIProvides access to thetsg-clicommand that exports tsg dataInstallationDue to a direct dependency, the installation requires two steps. Replacepipwithpip3ifpipis unrecognised or bound to python2.pipinstallgit+https://github.com/CSIRO-GeoscienceAnalytics/tsg-xr@8aea5c890daa4663e5d7251f2f0ca584b61e2237
pipinstalltsg-export-cliUsageProducing Scalar Dumpstsg-cliexport-csvs--helpProducing Borehole ImageryA downsampling factor ofqresults in a performance boost and quality reduction ofq².If images are meant for human viewing use the depth delta (-d) to get smaller and wider images. 0.5m of core per image is a good start.tsg-cliexport-images--helpFor DevelopmentPackage Build & Uploadpython-mbuild&&python-mtwineupload--repositorypypidist/*
Package: tsgm
Time Series Generative Modeling (TSGM)Documentation|TutorialsAbout TSGMTSGM is an open-source framework for synthetic time series generation and augmentation.The framework can be used for:creating synthetic data, using historical data, black-box models, or a combined approach,augmenting time series data,evaluating synthetic data with respect to consistency, privacy, downstream performance, and more.Install TSGMpip install tsgmTrain your generative modelIntroductory Tutorial "Getting started with TSGM"Tutorial on usingTime Series AugmentationsTutorial onEvaluation of Synthetic Time Series DataTutorial on usingMultiple GPUs or TPU with TSGMFor more examples, seeour tutorials.importtsgm# ... Define hyperparameters ...# dataset is a tensor of shape n_samples x seq_len x feature_dim# Zoo contains several prebuilt architectures: we choose a conditional GAN architecturearchitecture=tsgm.models.architectures.zoo["cgan_base_c4_l1"](seq_len=seq_len,feat_dim=feature_dim,latent_dim=latent_dim,output_dim=0)discriminator,generator=architecture.discriminator,architecture.generator# Initialize GAN object with selected discriminator and generatorgan=tsgm.models.cgan.GAN(discriminator=discriminator,generator=generator,latent_dim=latent_dim)gan.compile(d_optimizer=keras.optimizers.Adam(learning_rate=0.0003),g_optimizer=keras.optimizers.Adam(learning_rate=0.0003),loss_fn=keras.losses.BinaryCrossentropy(from_logits=True),)gan.fit(dataset,epochs=N_EPOCHS)# Generate 100 synthetic samplesresult=gan.generate(100)Getting startedWe provide:Documentationwith a complete overview of the implemented methods,Tutorialsthat describe practical use-cases of the framework.For contributorsgitclonegithub.com/AlexanderVNikitin/tsgmcdtsgm
pipinstall-e.Run tests:python-mpytestTo check static typing:mypyCLIWe provide two CLIs for convenient synthetic data generation:tsgm-gdgenerates data by a stored sample,tsgm-evalevaluates the generated time series.Usetsgm-gd --helportsgm-eval --helpfor documentation.DatasetsTSGM provides API for convenient use of many time-series datasets (currently more than 15 datasets). The comprehensive list of the datasets in thedocumentationAugmentationsTSGM provides a number of time series augmentations.AugmentationClass in TSGMReferenceGaussian Noise / Jitteringtsgm.augmentations.GaussianNoise-Slice-And-Shuffletsgm.augmentations.SliceAndShuffle-Shuffle featurestsgm.augmentations.Shuffle-Magnitude warpingtsgm.augmentations.MagnitudeWarpingData Augmentation of Wearable Sensor Data for Parkinson’s Disease Monitoring using Convolutional Neural NetworksWindow warpingtsgm.augmentations.WindowWarpingData Augmentation for Time Series Classification using Convolutional Neural NetworksContributingWe appreciate all contributions. To learn more, please checkCONTRIBUTING.md.CitingIf you find this repo useful, please consider citing our paper:@article{
  nikitin2023tsgm,
  title={TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series},
  author={Nikitin, Alexander and Iannucci, Letizia and Kaski, Samuel},
  journal={arXiv preprint arXiv:2305.11567},
  year={2023}
}LicenseApache License 2.0
Package: tsh
tee-shellTee shell is a set of utilities for running other applications.It enables you to run shell commands in various modes and has an api for fine
tuning your running processes.Usage>>>importtsh>>>ls=tsh.Command("ls")>>>ls=ls.bake("-1")>>>process=ls("tsh")>>>process.wait()True>>>print(process.read())__init__.py__pycache__command.pyconsts.pycontexts.pydecorators.pyerrors.pyprocess.pyshell.pytypes.py
Package: tshelve
DescriptionA near dupicate of theshelvemodule,
but withthreadingused under the
hood to speed up execution time.PurposeRegularshelveoperations can be
notoriously slow. Thetshelvemodule
aims to provide a more efficient program,
that is still similar to the built-in.InstallationInstallingtshelveshould be done withPIP:$pipinstalltshelveBenefitsA more meaningfulrepr:The defaultrepr:repr(shelve.Shelf({})=='<shelve.Shelf object at 0x00572CB8>'tshelve’srepr:repr(tshelve.TShelf({}))==TShelf(dict={},protocol=5,writeback=False,keyencoding='utf-8')Similar to the shelve module in the standard library.The amount of speed up thattshelvebrings will depend on the code. However,
there will usually be a speed-up, especially
in code that interacts heavily with the module.The following block of code was run withshelve.openandtshelve.open.
Bothtshelveandshelvewere tested in
the same environment to ensure realistic results.importshelveimporttshelvesync_open=shelve.open('shell',writeback=True)thread_open=tshelve.open('shell',writeback=True)defspeedup(opened):opened['testvalue']=Falseopened['tester']=5324opened['tester']opened.get('testvalue')opened['testit']='Hello'opened.items()opened.keys()opened.values()delopened['testit']'testvalue'inopenedfor_inopened:passopened.popitem()opened.sync()opened.close()speedup(sync_open)speedup(thread_open)The difference in execution time between
the two is drastic.tshelveoperations
were, on average, nearly 44% faster than those
found in theshelvemodule.Exampleimporttshelvewithtshelve.open('shell')asf:f['fruit']=['apple','banana']f['language']='Python'delf['fruit']print(f['language'])LicenseThis module is currently licensed under theMITlicense.
Package: tshirt
tshirtREADMEThe Time Series Helper & Integration Reduction Tooltshirtis a general-purpose tool for time series science.
Its main application is to process raw data on exoplanet systems.tshirtcan:Reduce raw data: flat field, bias subtract, gain correct, etc. This has been demonstrated to work with merged CCD images from Mont4K imager on the Kuiper-61 inch on Mt Bigelow, AZ.Extract PhotometryExtract SpectroscopySee the read-the-docs page for information on how to install and usetshirt:https://tshirt.readthedocs.io/en/latest/
Package: tshistory-client
TSHISTORY CLIENTThis library provides easy access totshistoryinstances exposed with
the help oftshistory_rest(http end points).All the basetshistoryapi is available from there.ExamplesDirect invocationfromtshistory_client.apiimportClientc=Client('http://my.tshistory.instance/api')series=c.get('banana_spot_price')Using the tshistory api (preferred way)fromtshistory.apiimporttimeseriesc=timeseries('https://my.tshistory.instance/api')series=c.get('banana_spot_price')
Package: tshockapi
Quick access to TShock REST API using python.EndpointsServer/status/tokentest/v2/token/create/v2/server/broadcast/v3/server/rawcmd/v3/server/motd/v2/server/off/v3/server/reload/v3/server/rules/v2/server/statusUser/v2/users/create/v2/users/destroy/v2/users/read/v2/users/list/v2/users/update/v2/users/activelistWorld/v3/world/bloodmoon/v2/world/butcher/world/meteor/world/read/v2/world/saveBan/v3/bans/create/v3/bans/destroy/v3/bans/read/v3/bans/listPlayer/v2/players/kick/v2/players/kill/v2/players/list/v2/players/mute/v4/players/read/v2/players/unmuteExampleimporttshockapi# Import the tshockapi moduleserver=tshockapi.Server(host="127.0.0.1",port=7878,token="1234567890")# Create a server objectprint(server.v3_server_rawcmd(cmd="/who"))# Print the result of the /who command# {'status': '200', 'response': ['There are currently no players online.']}InstallationWindowspipinstalltshockapiLinuxpip3installtshockapi
Package: tshoot
TshootA simple CLI command for troubleshooting errors with LLMsDocumentation:https://di3go-sona.github.io/tshoot/Source Code:https://github.com/di3go-sona/tshootIntroductionThe idea of tshoot is quite simple, by running the commandtshootyou'll open a chat session with one of OpenAI's LLMs, you can dialogue with it and at the same time run commands by prefixing the line with!, the command will be executed on your local machine and the output will be displayed in the chat and added to the conversation history.The main idea is to have a quick way to have your troubleshooting workflow overseen by an AI, potentially retrieving the information you need to solve your problem without any context switch, directly in your terminal.The philosophy behind this project is to create a tool that should have minimal dependencies and should be easy to install and ready to use.InstallationYou can install tshoot with pip:pip install tshootDevelopment SetupThe project is packaged and shipped withHatch.
The following scripts are installed in your virtual environment:hatch run docs-buildhatch run docs-servehatch run linthatch run lint-checkhatch run testhatch run test-cov-xmlRun unit testsYou can run all the tests with:hatchruntestFormat the codeExecute the following command to apply linting and check typing:hatchrunlintServe the documentationYou can serve the Mkdocs documentation with:hatchrundocs-serveIt'll automatically watch for changes in your code.ContributingEvery contribution that is in synch with the package philosofy is more than welcome, feel free to open an issue or submit a pull request.LicenseThis project is licensed under the terms of the MIT license.
Package: ts-hyperparam-opt
Time Series Hyperparameter Optimization (CV + Parallel)Convenience package for optimizing hyperparameters for Time Series forecasting
using methods likeExponentialSmoothingorSARIMAX. Especially useful
for Jupyter Notebooks where parallelization (with e.g.ProcessPoolExecutor)
only works when importing the function used in parallel.Install it from PyPIpipinstallts-hyperparam-optUsagefromts_hyperparam_optimportparallel_hyperparameter_optimizationasphoparams_sarima=[[(1,1,1),(1,1,1,7)],[(1,1,0),(1,1,1,7)]]if__name__=='__main__':freeze_support()results=process_map(functools.partial(pho.optimize_hyperparams,data=df_data,func="sarima",n_steps=15),params_sarima)results_sorted=pho.sort_results(results)DevelopmentAlpha VersionCurrently supported methods:(Triple) Exponential Smoothing (Holt-Winters)SARIMA(X)
Package: tsia
tsia: a Python package for time series analysis through imagestsia (time series images analysis) is a package to perform time series
analysis and diagnostics thanks to imaging techniques.InstallationDependenciestsia requires:python (>= 3.7)matplotlib (>= 3.3.0)networkx (>= 2.5)numba (>= 0.48.0)numpy (>= 1.17.5)python-louvain (>= 0.14)pyts (>= 0.11.0)User installationIf you already have a working installation of the aforementioned modules,
you can easily install tsia usingpip:pip install tsiaImplemented featurestsia consists of the following modules:markov: this module provides some methods to compute statistics from Markov
transition fields as implemented in thepytspackage. It also implements
functions to map back MTF output to the original time series.network_graph: this module provides some methods to compute statistics and
several encoding techniques to interpret network graphs built upon MTF.plot: this module implements several useful visualization techniques to
provide insights into univariate timeseries. This module implement MTF, network
graph visualization and several line graphs (vanilla time series, colored
time series with different color encodings, quantile discretization overlay...).utils: this modules includes some utilities leveraged by the other ones.
Package: tsic
python-tsicReceive temperature readings from TSic sensor chips connected to Raspberry Pi GPIO.Supported:TSic 206/306TSic 506TSic 716Don't forget the bypass capacitor as near as possible to the sensor's power supply pins to get stable behavior.DependenciesThe packagetsicrequires Python 3 and the great librarypigpiofor GPIO access with precise timing. Note that Python 2 will not work.InstallationInstall from Python package indexPyPI:pip3 install tsicUsageThe moduletsicprovides the following classes:TsicInputChannelreceive temperature measurementsMeasurementa temperature measurementTsicTypeTSic type definition with instancesTSIC206,TSIC306,TSIC506,TSIC716(206 and 306 are currently equivalent)ZacWireInputChannelreceive byte packets over ZACWire protocol (low-level handler forTsicInputChannel)Command Line Test ToolRuntsic <gpio-bcm> [--type {206,506,716,306}] [--loop]to read temperatures from a GPIO pin (Broadcom numbering). Seetsic --helpfor command line usage.pi@raspi3:~ $ sudo tsic 19 --type 306
Receiving data from TSic 206/306...
Measurement 17.90°C at 2018-11-10 16:16:11.419573ExamplesFrom fileexample.py:importtimeimportpigpiofromtsicimportTsicInputChannel,Measurement,TSIC306# TsicInputChannel and ZacWireInputChannel require pigpio# for GPIO access with precise timing:pi=pigpio.pi()tsic=TsicInputChannel(pigpio_pi=pi,gpio=17,tsic_type=TSIC306)print('\nA. Single measurement:')print(tsic.measure_once(timeout=1.0))print('\nB. All measurements for 1 second:')tsic.start(lambdameasurement:print(measurement))time.sleep(1)tsic.stop()print('\nC. One measurement per second for 3 seconds:')# start receiving in a context:withtsic:foriinrange(3):time.sleep(1)measurement=tsic.measurementifmeasurement==Measurement.UNDEF:print(measurement)else:print('{:d}{:.1f}°C'.format(i+1,measurement.degree_celsius))pi.stop()Source CodeHosted ongithub.com/grillbaer/python-tsicWith greetings from Bavaria,
Holger
Package: tsico2
tsico2This is under review.tsico2 is a tool available on PyPI that allows users to visualize monthly values of Total Solar Irradiance (TSI) and global CO2 levels. Users can specify a start and end date for the period they wish to visualize, and the tool will display a graph with four lines representing the raw and linear regression values for both TSI and CO2.Additionally, the calculated R-squared and p-value for the linear regression lines are also displayed on the graph. This provides users with an easy and convenient way to analyze trends in TSI and CO2 levels over time.How to run tsico2Before running tsico2, you must install it by pip command.$ pip install tsico2$ tsico2Enter start-date for March 2007 in this exampleEnter the beginning year-month (e.g. 1978-11): 2007-3Enter end-date for March 2014Enter the end year-month (e.g. 2023-3): 2014-3The result will be popped on the screen.
The solid lines are the original and regression lines for CO2 and the dotted lines are the original and regression lines for TSI.