Saravanan666/pypi-org huggingface dataset:
Package: timit-utils
TIMITThe TIMIT database, in brief, contains audio recordings of sentences spoken
by a set of people. It also includes word and phoneme transcriptions, along with
their exact positions, as ranges, within the audio files.As such, it is an interesting target for ML: we are given high-grade audio recordings as well as real-time phoneme and word transcriptions (or guesses at them, anyway).The actual TIMIT database is NOT included, and is not free. Get it here:https://catalog.ldc.upenn.edu/LDC93S1. This library merely adds
convenience, parsing, sampling, drawing, etc.timit_utilsThe code herein can lazily load, parse, and expose the TIMIT database
of spoken audio, word and phoneme transcriptions. The layout of the TIMIT file system looks like this:This library models the data with several classes:Corpus (such as '../TIMIT', contains two SubCorpuses: train and test)SubCorpus (such as 'train'|'test', contains several Regions)Region (such as 'DR1', contains several Persons)Person (such as 'Name:CJF0,Female')Sentence (such as 'SA1', contains audio, word, and phoneme transcriptions as numpy arrays)All the above give many ways to index, iterate, parse, search, and expose the data as pandas Dataframes.various audio sampling, padding routines, mel filterbank frequency extractions, and a quick display systemInstallationpip install timit_utilstimit_utils requires numpy, pandas, matplotlib, scipy, python_speech_features, and SoundFile.Example usage (i.e. in jupyter)%matplotlib inline
import timit_utils as tu
import timit_utils.audio_utils as au
import timit_utils.drawing_utils as du

corpus = tu.Corpus('../TIMIT')
sentence = corpus.train.sentences_by_phone_df('aa').sentence[0]
du.DrawVerticalPanels([du.AudioPanel(sentence.raw_audio, show_x_axis=True),
                       du.WordsPanel(sentence.words_df, sentence.raw_audio.shape[0], show_x_axis=True),
                       du.PhonesPanel(sentence.phones_df, sentence.raw_audio.shape[0])
                      ])Full usage here:https://github.com/colinator/timit_utils/blob/master/timit_utils_demonst.ipynb
Package: timm-info
timm-infoInstallationpipinstalltimm-infoUsagetimm-info provides the CLI tooltimminfowith commands:searchandinfoUsage: timminfo [OPTIONS] COMMAND [ARGS]...

  Timm model utility

Options:
  --help  Show this message and exit.

Commands:
  info    Get information about a particular timm model.
  search  Search for timm models.searchtimminfo search ...Usage: timminfo search [OPTIONS] [NAME_PATTERN]...

  Search for timm models. Multiple patterns can be passed.

Options:
  -p, --pretrained  Only show pretrained models
  -s, --simple      Display results in simple format (useful for chaining I/O)
  --help            Show this message and exit.ExamplesCommandtimminfosearch'resnet50*''xception*'OutputResults for 'resnet50*':
------------------------
0. resnet50
1. resnet50_gn
2. resnet50c
3. resnet50d
4. resnet50s
5. resnet50t

Results for 'xception*':
------------------------
0. xception41
1. xception41p
2. xception65
3. xception65p
4. xception71Commandtimminfosearch-s'resnet50*''xception*'Outputresnet50
resnet50_gn
resnet50c
resnet50d
resnet50s
resnet50t

xception41
xception41p
xception65
xception65p
xception71Commandtimminfosearch-p'resnet50*''xception*'OutputResults for 'resnet50*':
------------------------
 0. resnet50.a1_in1k
 1. resnet50.a1h_in1k
 2. resnet50.a2_in1k
 3. resnet50.a3_in1k
 4. resnet50.am_in1k
 5. resnet50.b1k_in1k
 6. resnet50.b2k_in1k
 7. resnet50.bt_in1k
 8. resnet50.c1_in1k
 9. resnet50.c2_in1k
10. resnet50.d_in1k
11. resnet50.fb_ssl_yfcc100m_ft_in1k
12. resnet50.fb_swsl_ig1b_ft_in1k
13. resnet50.gluon_in1k
14. resnet50.ra_in1k
15. resnet50.ram_in1k
16. resnet50.tv2_in1k
17. resnet50.tv_in1k
18. resnet50_gn.a1h_in1k
19. resnet50c.gluon_in1k
20. resnet50d.a1_in1k
21. resnet50d.a2_in1k
22. resnet50d.a3_in1k
23. resnet50d.gluon_in1k
24. resnet50d.ra2_in1k
25. resnet50s.gluon_in1k

Results for 'xception*':
------------------------
0. xception41.tf_in1k
1. xception41p.ra3_in1k
2. xception65.ra3_in1k
3. xception65.tf_in1k
4. xception65p.ra3_in1k
5. xception71.tf_in1kinfotimminfo info ...Usage: timminfo info [OPTIONS] [NAME]...

  Get information about a particular timm model. Multiple names can be passed.

Options:
  --help  Show this message and exit.ExampleCommandtimminfoinfoconvnextv2_attoefficientnet_b0OutputModel name:                            convnextv2_atto
Number of params:                      3,387,400
Estimated model size:                  13.550 MB
Number of in features for classifier:  320
Has extractable feature layers:        True
Number of feature layers:              4
Number of channels per feature:        [40, 80, 160, 320]
Pretrained Input Size:                 (3, 224, 224)

Model name:                            efficientnet_b0
Number of params:                      4,007,548
Estimated model size:                  16.030 MB
Number of in features for classifier:  1280
Has extractable feature layers:        True
Number of feature layers:              5
Number of channels per feature:        [16, 24, 40, 112, 320]
Pretrained Input Size:                 (3, 224, 224)
Package: timmins-sort
Sorting Algorithm VisualizerPurposeThis has been a fun project of mine to help visualize the inner workings of sorting algorithms. It is a fun mental exercise to write these algorithms in an iterable fashion version a 'method' implementation.DescriptionThe BubbleSort, HeapSort, and QuickSort have been added to show the number of compare operations as well as time-to-completion relative to each algorithm.InstallationUse a virtual environment if you like. Just need to enter the folling items in the command line:> git clone https://github.com/ctimmins96/Sorting-Algorithm-Visualizer.git
> cd ./Sorting-Algorithm-Visualizer
> pip install -e .Package is now available on PyPi! In can be installed doing the following:> pip install timmins_sortUseOnce installed, enter the following in the command line:> timmins-sorterAlternatively, the project and be imported and run via the following:importtimmins_sorttimmins_sort.run()
Package: timml
TimML, A Multi-Layer, Analytic Element ModelIntroductionTimML is a computer program for the modeling of steady-state multi-layer flow with analytic elements
and consists of a library of Python scripts and FORTRAN extensions.
TimML may be applied to an arbitrary number of aquifers and leaky layers.
The head, flow, and leakage between aquifers may be computed analytically at any point in the aquifer system.
The design of TimML is object-oriented and has been kept simple and flexible.
New analytic elements may be added to the code without making any changes in the existing part of the code.
TimML is coded in Python and uses numba to speed up evaluation of the bessel line elements.InstallationPython versions:TimML requires Python >= 3.8 and can be installed from PyPI.Dependencies:TimML requires:numpyscipymatplotlibnumbaInstallation:To install TimML, open a command prompt and type:pip install timmlTo update TimML type:pip install timml --upgradeTo uninstall TimML type:pip uninstall timmlDocumentationThe documentation is hosted onreadthedocs.Example Notebooks are available from the notebooks directory on github, of fromhere.Latest releaseTimML 0.6.5Improved documentation: new look, better organization, tutorials, how-to guides etc. Check it outhere!New elementsBuilding pit elements for 3D (multi-layer single aquifer) models.Large diamater wells (only for radial flow).EnhancementsBuilding pit leaky wall resistance can be set per layer and per side (for modeling leaks or gaps).Return integrated normal flux per layer and per line segment.TimML VersionsTimML version 0.6 has the same functionality as version 5, but doesn't depend on a fortran extension anymore, so installation is easy on all platforms.TimML version 0.5is a total rewrite and is not backwards compatible with previous TimML versions.is intended to be compatible with TTim.has many new features and elements, the code base is Python 3, and the object oriented design is much simpler.TimML version 0.4 remains available through the timml4 branch.CitationSome of the papers that you may want to cite when using TimML areBakker, M., and O.D.L. Strack. 2003. Analytic Elements for Multiaquifer Flow. Journal of Hydrology, 271(1-4), 119-129.Bakker, M. 2006. An analytic element approach for modeling polygonal inhomogeneities in multi-aquifer systems. Advances in Water Resources, 29(10), 1546-1555.
Package: timm-vis
PyTorch Image Models VisualizerImplementation of various visualization techniques for pytorch image classifiers. This library can be used to visualize and understand any PyTorch image classifier. This is NOT an official PyTorch library, nor is it affiliated with Ross Wightman'spytorch-image-modelslibrary.details.ipynbhas visual examples of all methods implemented.Currently, the following methods are implemented:Filter visualizationActivations visualizationMaximally activated patchesSaliency maps [1]Synthetic image generation [1]Adversarial attacks to fool modelsFeature inversion [2]Grad-CAM [3]Deep Dream [4]Specific examples and details about the implementation and parameters of the above methods are described in details.ipynb. All of the above visualization techniques are discussed inthislecture.Installation$pipinstalltimm-visUsagefromtimm_vis.methodsimport*# available methods - visualize_filters, visualize_activations,#   maximally_activated_patches, saliency_map,#   generate_image, fool_model, feature_inversion, deep_dreamPaper References[1] Karen Simonyan, Andrea Vedaldi, Andrew Zisserman. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.https://arxiv.org/abs/1312.6034.[2] Aravindh Mahendran, Andrea Vedaldi. Understanding Deep Image Representations by Inverting Themhttps://arxiv.org/abs/1412.0035[3] Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization [https://arxiv.org/abs/1610.02391 (https://arxiv.org/abs/1610.02391)][4] Alexander Mordvintsev, Christopher Olah, Mike Tyka. Inceptionism: Going Deeper into Neural Networkshttps://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.htmlCode References[5] Ross Wightmanpytorch-image-models[6] Irfan Alghani KhalidSaliency Map for Visualizing Deep Learning Model Using PyTorch[7] Utku Ozbulak.pytorch-cnn-adversarial-attacks[8] Duc Ngodeep-dream-in-pytorch
Package: timmy
OpenStack Ansible-like tool for parallel node operations: two-way data transfer, log collection, remote command executionThe tool is based onhttps://etherpad.openstack.org/p/openstack-diagnosticsShould work fine in environments deployed by Fuel versions: 4.x, 5.x, 6.x, 7.0, 8.0, 9.0, 9.1, 9.2Operates non-destructively.Can be launched on any host within admin network, provided the fuel node IP is specified and access to Fuel and other nodes is possible via ssh from the local system.Parallel launch - only on the nodes that are ‘online’. Some filters for nodes are also available.Commands (from ./cmds directory) are separated according to roles (detected automatically) by the symlinks. Thus, the command list may depend on release, roles and OS. In addition, there can be some commands that run everywhere. There are also commands that are executed only on one node according to its role, using the first node of this type they encounter.Modular: possible to create a special package that contains only certain required commands.Collects log files from the nodes using filename and timestamp filtersPacks collected dataChecks are implemented to prevent filesystem overfilling due to log collection, appropriate error shown.Can be imported into other python scripts (ex.https://github.com/f3flight/timmy-customtest) and used as a transport and structure to access node parameters known to Fuel, run commands on nodes, collect outputs, etc. with ease.
Package: timmytimer
# TimmytimerNamed after the childrens TV-show Timmy TimeA simple wrapper to log/print functionname, \*args and \**kwargs and how long it took to execute## INSTALLpip install timmytimer## Example Usage```pythonfrom timmytimer import timmy, timmyp, timmyprintimport loggingfrom time import sleepfrom random import random# *if* you have installed colorama you get colored printtry:from colorama import Fore, Back, Style, initinit(autoreset=True)except ImportError:  # fallback so that the imported classes always existclass ColorFallback():__getattr__ = lambda self, name: ''Fore = Back = Style = ColorFallback()@timmydef test_timmy(greeting, name='Henrik'):sleep(random())print('{} {}'.format(greeting, name))@timmyprintdef test_timmyprint(greeting, name='Henrik'):sleep(random())print('{} {}'.format(greeting, name))@timmypdef test_timmyp(greeting, name='Henrik'):sleep(random())print('{} {}'.format(greeting, name))if __name__ == '__main__':log = logging.getLogger()log.setLevel(logging.DEBUG)test_timmy('Hello', name='Gustav')test_timmyprint('Goodday', name='Ellinor')test_timmyp('P-man')```output:```console(wenv) C:\Users\henrik\Desktop\git_temp\timmytimer>py tests\test.pyHello GustavDEBUG:root:[timmytimer]test_timmy(('Hello',){'name': 'Gustav'})370.00msGoodday Ellinor[timmytimer] test_timmyprint(('Goodday',){'name': 'Ellinor'}) 536.00msP-man Henrik[timmytimer] test_timmyp(('P-man',){}) 230.00ms```
Package: timone
timoneCurrent version: 0.4.0timoneis a lightweight object router to store Git LFS objects on different storage backend, including AWS S3 compatible storage services.Install$ pip install timoneConfigurationtimonecan be configured by passing settings through environment variables, following the 12 factor principles.Basic settingsTIMONE_LOG_LEVEL: Python logging level (default: INFO).TIMONE_ENDPOINT_URL: URL to reach service.TIMONE_TOKEN_SECRET: secret for token based authentication.TIMONE_STORAGE: storage driver to use (default: DumbStorageDriver).Currently supportingS3StorageDriver, for AWS S3 services, orDumbStorageDriver, for testing purposes.TIMONE_OBJECT_EXPIRESIN: object availability in seconds (default: 3600).S3StorageDriver settingsTIMONE_STORAGE_S3_URL: S3 endpoint URL.TIMONE_STORAGE_S3_REGION: S3 region.TIMONE_STORAGE_S3_BUCKET: S3 bucket name.TIMONE_STORAGE_S3_KEY: S3 access key.TIMONE_STORAGE_S3_SECRET: S3 access secret.Authentication and authorizationtimoneimplements a simple token-based authentication system based on JWT, which streamlines authentication and authorization.You can create tokens to accesstimoneusingpyjwt. For example, you can run the following command to create a token for userbob:$ pyjwt --key=mykey encode user=bobwherekeyis theTIMONE_TOKEN_SECRET.Deploymenttimoneis a WSGI application, thus it requires a WSGI HTTP Server, likegunicorn, to run.You can run atimoneinstance usinggunicornas follows:$ gunicorn 'timone.wsgi:run()'Configuring GIT LFSYou can usetimoneto track files for the repositoryfoo/baras follows:git config -f .lfsconfig remote.origin.lfsurl https://<timone_url>/foo/bar/info/lfswhere<timone_url>is thetimoneendpoint URL.When pushing for the first time, Git will ask LFS credentials; here, you should use your username and token provided by the service administrator.IssuesPlease post an issue to report a bug or request new features.AuthorsGiovanni Stracquadanio,dr.stracquadanio@gmail.com
Package: timor-locations
Timor GISTimor Leste GIS data as Django models
This initial release uses administrative boundariew from Estrada, tweaked to include Atauro as a separate entity, with pcode which are intended to match existing data from PNDS.Data inputs are stored asgpkgfiles in Git LFS.EnvironmentThis is intended to be compatible with:Django 4.1+Python 3.10+ghrepoclonecatalpainternational/timor_locationscdtimor_locations
poetryinstallDevelopmentWhen developing please install prerequisites withpoetry install --with dev --no-rootPre CommitIfpre-commitis installed your code will be checked before commit.
This includesblackflake8isortmypyThe same checks are run on push. Seepytest.yamlfor details on the checks being run.New ReleaseFor a new release, change theversionproperty in pyproject.toml and push a git tag with the version numberSeebuild.yamlfor details on release taggingChangelog...Manually Uploading a new version to PyPiBumppyproject.tomlThen runpoetry buildandpoetry publishpoetrybuild
poetrypublishSee the filebuild.ymlfor the workflow
Package: timple
TimpleTimple offers extended functionality for plotting timedelta values with
Matplotlib.InstallationInstallation via pip is recommended:pip install timpleOverviewMatplotlib generally supports plotting of timedelta values but only as
numeric values and only for limited data types. It does not natively
have locators and formatters to create fancy plot ticks.This package provides the necessary locators and formatters to create
axis ticks in intervals of minutes, hours, ...
Ticks can be formatted into a more readable time format. For example,
something like185seconds can be turned into3:05minute:seconds
representation.Example plot:The full documentation can be found here:https://theoehrly.github.io/Timple/FeaturesFormatters and Locators for timedeltaMatplotlib patching, so that everything can happen with minimal effortSupportsnumpy.timedelta64,datetime.timedelta,pandas.TimedeltaAdditionallySupport forpandas.NaTwhen plotting timedelta and optionally when
plotting date values too.UsageMinimal example: import and enable Timple. This will patch Matplotlib and
register Timple's timedelta converter. By default, Timple's automatic
locators and formatters will be used to determine the tick locations and
format best suited for the plotted data. Further customization is possible.import matplotlib.pyplot as plt
import timple

tmpl = timple.Timple()
tmpl.enable()

# you can now just use matplotlib as always

plt.plot(...some timedelta related data...)
plt.show()
Package: timrr
Time to submit while mobile working ヽ(´▽`)/Setup (currently)docker tryout one-linerdockerrun-it--rmpython:3.9-slim-busterpython3-mpipinstalltimrr&&python3-mtimrrcalclocallypython3-mpipinstallpoetry
poetryinstall
poetryshell
poetryrunpython3timrr.pyUse the tool❯python3-mtimrrcalc--start-time8:15--end-time18:00--break-time1.0|Description|Result||-----------------------+----------||Starttime|08:15:00||Endtime|18:00:00||Durationatwork|9:45:00||Includedbreaktime|1.0||Hoursworked|8.75||1dayofmobilework|7.4||Addextramobilework|1.35|❯python3-mtimrrcalc|Description|Result||-----------------------+----------||Starttime|09:00:00||Endtime|17:30:00||Durationatwork|8:30:00||Includedbreaktime|1.0||Hoursworked|7.5||1dayofmobilework|7.4||Addextramobilework|0.1|❯python3-mtimrrcalc--help
Usage:timrr.pycalc[OPTIONS]Options:-s,--start-timeTEXT-e,--end-timeTEXT-b,--break-timeTEXT-ch,--contract-hours-per-dayTEXT--local-config/--no-local-config--helpShowthismessageandexit.configure defaults❯python3-mtimrrconfigure
Provideadefaultstarttimeofwork:8:00
Provideadefaultendtimeofwork:17:30
Provideadefaultbreaktime:1,25
Provideadefaultforworkhoursperday:7.4
Configurationcompleted!(configfilelocation:/home/tempusr/.timrr)Usage:timrr.pyconfigure[OPTIONS]Options:--start-timeTEXT[required]--end-timeTEXT[required]--break-timeTEXT[required]--contract-hours-per-dayTEXT[required]--helpShowthismessageandexit.
Package: timshive
Tim's Hive (timshive)By TimC July 2019
Version 1.0.0Use:
create class 'hive' with inputs:points: a two dimensional list where the first dimension represents each axis you wish to draw and the second represents the points on each axes. eg. to plot a hive with three axes each containing points 2 and 3, you would use [[2,3],[2,3],[2,3]] as the input value for pointscolors: one dimensional list of all colors that corresponds to the first, second third, point in each list. Using the last example, if ['green','red'] was entered in the color field given the same points input, all of the twos would show up as green and the threes as red. If the length of any axes exceed the length of colors, all points will be filled in as blackkwargs:range: this gives the value to which the axes should be drawn. Assumed to round up to the nearest 10 unless another value is givenlabels: used to give the text associated with each color if a key is desired. Entering labels = ['Set 1','Set 2'] for the previous example would produce a key in which green is labeled as 'Set 1' and red is labeled as 'Set 2'axlab: the label given to each axis. If string, will be applied in order as string 1 , string etc. if it is a list, each item in the list will be used in order. if not enough conditions are given, axes will be unlabeledfunction 'hive.show(title,name)' takes two string inputs:title: what words should appear at the top of the graphname: what the file should be called. Do not include extensionsOutput examples at PyPI.
Package: timst
DescriptionTimstis a python package based onpyTorchthat extracts the features of an image and tranfers them into another; such a technique is known asimage style transfer. The following implementation is a re-implementation of thiscodethat is based on the followingscientific paper. The architecture is based onConvolutional Neural Network(CNN) which is one of the applications ofDeep Learning.How to installThere are different ways the package can be installed:By clonning this repository and running the following command in the terminal (you might requiresudoprivilege)gitclonehttps://github.com/Radonirinaunimi/Style-TransfercdStyle-Transfer/
pythonsetup.pyinstall--userBy installing it through the Python Package Index (PyPI)pipinstalltimst--upgradeHow to useTo usetimst, just run the following:timst-i[IMAGE_TO_BE_STYLED]-s[STYLE_TO_BE_APPLIED][-nNUMBER_OF_ITERATIONS]For bugs and feature requestOpen anissueor apull request.
Package: timus.scoreboard
AboutThis Python package provides support for custom contests on theTimus Online Judge.Define the players and problems to get an automatically-updating score
board which is generated according to the ACM ICPC rules.Thesample outputis available for preview.Configurationtimus.scoreboarduses INI-style files for configuration. Here is a
sample configuration:[contest]
title = My sample contest
start = 12:00:00 1 Oct 2012
end = 17:00:00 1 Oct 2012

[users]
1000 = Team 1 (John, Jack, Suzan)
1001 = Team 2 (Mike, Roger, Dan)

[problems]
1000 = A
1001 = B
1002 = C
1003 = D

[config]
output_dir = ./outputThe values forcontestsection do not require an explanation. Just
be sure to stick to the date format used and remember that the Timus
Online Judge is operating in UTC+6 time zone.Next, list the user IDs and their names in theuserssection. The
ID is the numerical part of your Judge ID. The remaining letters
should be kept secret and not entered in the configuration. This
means that if you’re organizing a contest, the IDs must be collected
beforehand. It should be noted that while contestants may have specified
their names in the Timus system, you will have to specify official
names (e.g. containing the last names of team members) in the configuration
file.Problems must be entered in theproblemssection. In the Timus system,
problems have names, but for an ACM-style contest you will want to have
short names (A/B/C/… is a standard).Running timus-scoreboardAfter installingtimus.scoreboardegg, you should be able to execute
thetimus-scoreboardscript. The location of this script depends on
the way you installed the egg. For instance, if zc.buildout was used,
the script will reside inbindirectory as generated by buildout.The script requires the name of the configuration file as a parameter:wormhole:timus-scoreboard admp$ bin/timus-scoreboard -n sample.conf
Retrieving http://acm.timus.ru/status.aspx?count=100...
Writing ./output/index.html...
Writing ./output/top.html...
Writing ./output/table.html...If you, however, omit the-noption, the script will keep running
until the contest ends. This is useful if you want to update the score
table every few minutes.