Saravanan666/pypi-org huggingface dataset:
Package: ptc
Pulse Tool Calibration LibraryUtility library (ptc) and command line interface (ptc-cli) to calculate tool
offset for Pulse Robotic Arm.InstallationStandalone library:pip3 install -U ptcCommand line interface and library:pip3 install -U ptc-cliUsageBasic usage, assuming that you have followed installation step and installed theptc-clipackage.In order to determine the tool offset, you MUST provide at least 4 different
positions, that represent the same point in space with tool installed.
The more points you give the better calibration precision could be achived.The positions could be obtained using theget_position()method frompulse-apilibrary and should be provided in a text file in the following
order:x,y,z,roll,pitch,yawEach position MUST be on a separate line and the values MUST be separated with
comas.Calculation is performed using following command in terminal:ptc-cli --file /absolute/path/to/your/positions/fileThe output will be the calculated tool offset:[x_offset,y_offset,z_offset]This output could be used in thechange_tool_infomethod of thepulse-apilibrary.ExampleSample file with robot positions located at/home/user/sample.csv:0.4000000016853294, -0.39999999880983755, 0.3000000025723091, -3.1415926381331523, 2.188741009988131e-08, 2.6794896299132232e-08
0.4000000029422969, -0.2999999962803114, 0.19999999804274032, 1.5707963642922351, 3.757424773546488e-15, 7.285142337505987e-08
0.3292893264445241, -0.4707106768261343, 0.2000000019309496, -1.5707963309948643, 0.7853981633974412, -5.0320115533448106e-08
0.47071067682613615, -0.32928932644452064, 0.19999999806904972, 1.570796322594932, -0.7853981633974498, 5.363765550429646e-08Terminal command:ptc-cli --file /home/user/sample.csvOutput:Calculated TCP offset: [0.00027102 0.00027103 0.10055737]To get more detailed output:ptc-cli --file /home/user/sample.csv --verboseOutput:Calculated TCP offset: [0.00027102 0.00027103 0.10055737]
More detailed calibration info:
 active_mask: array([0., 0., 0.])
        cost: 5.770939282001564e-13
         fun: array([5.31035219e-07, 7.68248630e-07, 5.31035183e-07])
        grad: array([1.27999602e-09, 1.28000956e-09, 2.89674021e-09])
         jac: array([[1.62617755e-03, 3.03639571e-12, 1.11474501e-03],
       [5.42062087e-04, 5.42079840e-04, 2.22948991e-03],
       [1.68880868e-11, 1.62617750e-03, 1.11474494e-03]])
     message: '`gtol` termination condition is satisfied.'
        nfev: 11
        njev: 10
  optimality: 2.8967402130836482e-09
      status: 1
     success: True
           x: array([0.00027102, 0.00027103, 0.10055737])
Package: ptcache
Simple cache based on the redis and cPickle for Python.# Installation:pip install ptcache# Usage:## as a decorator for function### use redis```pythonfrom ptcache import RedisCache, Cachecache = Cache(RedisCache(password='***'))@cache.cached(60*10)def test(a, b, name='age'):return {"sum": a+b, "name": name}@cache.uncache(test, kwarg_names=['name'])def update_test(a, b, name="age"):return {"sum": a*b, "name": name}# computed and stored on cachetest(1, 2)# update and remove cache for function testupdate_test(1, 2)# remove cache manuallycache.remove_cache(test, 1, 2)cache.remove_cache(test, 1, 2, name="age")```### use python object```pythonfrom ptcache import SimpleCache, Cacheimport jsoncache = Cache(SimpleCache(pickle=json))     # use json, default is cPickle@cache.cached(60*10)def test(a, b, name='age'):return {"sum": a+b, "name": name}@cache.uncache(test, kwarg_names=['name'])def update_test(a, b, name="age"):return {"sum": a*b, "name": name}print(test(1, 2))print(test(1, 2))```## as object```pythonfrom ptcache import RedisCache, SimpleCache# use rediscache = RedisCache(password='***')cache.set('aaa', {'name':'fang', 'age':10, 'money': 12.03}, 60)print(cache.exists('aaa'))print(cache.get('aaa'))print(cache.ttl('aaa'))cache.delete('aaa')print('*' * 10)# use SimpleCachecache = SimpleCache(timeout=20, threshold=5)for x in range(6):cache.set(x, x)print(cache.count())for x in range(6):print(cache.exists(x))print(cache.ttl(x))print(cache.get(x))cache.delete(x)print('\n')```Changelog=========1.0.0-----Highlights:* Init
Package: pt-cardano-cli-pooloverflow
ptcardanocliA simple auto-complete prompt for cardano-cliInstallpip install pt-cardano-cli-pooloverflowAfter Installation, ptccli will be added to PATH.UsageRequired an environment variable for then path of cardano-cli.CARDANO_CLI_PATH="cardano-cli" ptccli
CARDANO_CLI_PATH="podman run -it --rm pooloverflow/cardano-image:v1.18.0 cardano-cli" ptccliDockerpodman run -it --rm -e CARDANO_CLI_PATH="<cardano_cli_path>" -v /path/to/ccli:/path/to/ccli pooloverflow/pt-cardano-cli:v0.0.11
Package: pt-cli
It uses an pivotal tracker api and does lot of cool stuffs like creating tickets.Installation$ pip install -r requirements.txt

$ pip install setup.pyDevelopmentThis project includes a number of helpers in theMakefileto streamline common development tasks.Environment SetupThe following demonstrates setting up and working with a development environment:### create a virtualenv for development

$ make virtualenv

$ source env/bin/activate


### run pt cli application

$ pt --help


### run pytest / coverage

$ make testReleasing to PyPiBefore releasing to PyPi, you must configure your login credentials:~/.pypirc:[pypi]
username = YOUR_USERNAME
password = YOUR_PASSWORDThen use the included helper function via theMakefile:$ make dist

$ make dist-uploadDeploymentsDockerIncluded is a basicDockerfilefor building and distributingPivotal Tracker,
and can be built with the includedmakehelper:$ make docker

$ docker run -it pt --help
Package: ptcn
ptcnTensorflow (2.x) implementation of a Temporal Convolutional Network architecture, with a probabilistic twist.This project indulges a couple of curiosities:Working with convolutional sequence-to-sequence models a laAn Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingAdding a bayesian twist to the network a laBayesian Segnet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene UnderstandingThis implementation has been inspired by other projects, including:https://github.com/locuslab/TCNhttps://github.com/Baichenjia/Tensorflow-TCNhttps://github.com/philipperemy/keras-tcn
Package: ptcookie
PTCOOKIECookie Analyserptcookie is a tool for analysing cookies.Installationpip install ptcookieAdd to PATHIf you cannot invoke the script in your terminal, its probably because its not in your PATH. Fix it by running commands below.Add to path for BASHecho"export PATH=\"`python3 -m site --user-base`/bin:\$PATH\"">>~/.bashrcsource~/.bashrcAdd to path for ZSHecho"export PATH=\"`python3 -m site --user-base`/bin:\$PATH\"">>~/.zshhrcsource~/.zshhrcUsage examplesptcookie -s ASP.NET_SessionId
ptcookie -s PHPSESSIDOptions-s   --search-for  <cookie>        Search for cookie
-p   --proxy       <proxy>         Set proxy (e.g. http://127.0.0.1:8080)
-ua  --user-agent  <ua>            Set User-Agent header
-H   --headers     <header:value>  Set custom header(s)
-j   --json                        Output in JSON format
-v   --version                     Show script version and exit
-h   --help                        Show this help message and exitDependenciescloudscraperptlibsVersion History- 0.0.1
    - Initial releaseLicenceCopyright (c) 2020 HACKER Consulting s.r.o.This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program. If not, seehttps://www.gnu.org/licenses/.WarningYou are only allowed to run the tool against the websites which
you have been given permission to pentest. We do not accept any
responsibility for any damage/harm that this application causes to your
computer, or your network. Penterep is not responsible for any illegal
or malicious use of this code. Be Ethical!
Package: ptcrossd
PTCROSSDcrossdomain.xml misconfigurations testing toolInstallationpip install ptcrossdAdd to PATHIf you cannot invoke the script in your terminal, its probably because its not in your PATH. Fix it by running commands below.Add to PATH for Bashecho"export PATH=\"`python3 -m site --user-base`/bin:\$PATH\"">>~/.bashrcsource~/.bashrcAdd to PATH for ZSHecho"export PATH=\"`python3 -m site --user-base`/bin:\$PATH\"">>~/.zshhrcsource~/.zshhrcUsage examplesptcrossd -u https://www.example.com/crossdomain.xml
ptcrossd -u https://www.example.com/Options-u   --url         <url>           Connect to URL
-p   --proxy       <proxy>         Set proxy (e.g. http://127.0.0.1:8080)
-T   --timeout     <timeout>       Set timeout (default to 10)
-c   --cookie      <cookie>        Set cookie
-ua  --user-agent  <user-agent>    Set User-Agent header
-H   --headers     <header:value>  Set custom header(s)
-C   --cache                       Cache requests (load from tmp in future)
-v   --version                     Show script version and exit
-h   --help                        Show this help message and exit
-j   --json                        Output in JSON formatDependenciesdefusedxml
requests
ptlibsVersion History1.0.2
    - Full script rework
1.0.1
    - Fix urllib3 error
1.0.0
    - updated for ptlibs 1.0.0
    - code refactorization
0.0.1 - 0.0.2
    - Alpha releasesLicenseCopyright (c) 2023 Penterep Security s.r.o.ptcrossd is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.ptcrossd is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with ptcrossd. If not, seehttps://www.gnu.org/licenses/.WarningYou are only allowed to run the tool against the websites which
you have been given permission to pentest. We do not accept any
responsibility for any damage/harm that this application causes to your
computer, or your network. Penterep is not responsible for any illegal
or malicious use of this code. Be Ethical!
Package: ptct
Python Temperature Conversion TestPython program to convert temperature between Kelvin, Celsius, Fahrenheit, and Rankine to check students calcuationsFree software: Apache Software License 2.0Documentation:https://ptct.readthedocs.io.FeaturesTakes the following inputs:an input temperaturean input unit of measurea target unit of measurean optional numeric responseThe student’s response must match an authoritative answer after both the student’s response and authoritative answer are rounded to the ones place. The system indicates that the response is correct, incorrect, or invalid.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-03-21)First release on PyPI.
Package: ptdb
ptbMotivationpdb sucks, the other packages do weird stuff and type completion does not work in interact mode (looking at you ipdb).Installationpip install ptbUsageSimple do:fromptdbimportset_trace# some where in your code do:set_trace()# Then just type the following to be thrown into a ptpython shellinteractLicenseThis project is licensed under the GPL-3 license.
Package: ptdc
[][]Python Twitter data collector built onTweepythat allow users to
dynamically collect accounts and statuses from Twitter during streaming,
and automatically generate Datasets from collected data that you can as
CSV.This library provides a framework that you can use to build your own
data collector, specifying which are your features that have to be
extracted from Twitter accounts/statuses.Creating your Twitter dataset: 1. Instantiate anAccountCollectorand/orStatusCollectorclass in according to what you want collect,
accounts, statuses or both. At this step you can re-defined your own
features that have to be extracted from twitter data, you have to pass
dict-like parameters in the following form: <feature_name, function>
where the function has to be applied to the user or status object.
Please refer todocumentationfor more details about Twitter objects 2. Instantiate theOnlineStreamerpassing the collector as parameter 3. Start streaming
on some topics 4. Save the created dataset at specified locationNB: It is not mandatory to use both collectors and streamer but you can
also use Collectors alone, for instance if you already have the users
and/or statuses to collect and you don’t need to stream anything.NEW FEATURES:*Offline collection by name, allow user to make a
query by name and collect some name-similar users extracting features
defined in the collector constructorINSTALLATIONThe package is available on PyPihere$pipinstallptdcEXAMPLE USAGEImport modulesfrom ptdc import authenticate, AccountCollector, OnlineStreamer, StatusCollectorDefine tokensReplace the following tokens with yours, see Twitter developersauthenticationfor more details about how obtain them.consumer_key = "xxxxxxxxxxx"
consumer_key_secret = "xxxxxxxxxxxxx"
access_token = "xxxxxxxxxxxxxxxxxxxxxx"
access_token_secret = "xxxxxxxxxxxxxxxxxx"Create the default Tweepy API object of tweepyapi = authenticate(consumer_key=consumer_key, consumer_key_secret=consumer_key_secret, access_token=access_token, access_token_secret=access_token_secret)Create your own Collectors for collecting dataCreate your own StatusCollector objects_collector = StatusCollector(api=api)Create your own AccountCollector object, which will collect also
statusescollector = AccountCollector(api=api, statuses_collector=s_collector)Create the StreamerCreate Online Streamer that will collect data (in this case will collect
only 5 accounts)streamer = OnlineStreamer(api=api, collector=collector, data_limit=5, n_statuses=400)Start streamingYou can start streaming in all ways defined by Tweepy, see its doc for
more detailsstreamer.stream(track=['famous', 'web', 'vip', 'holiday', 'pic', 'photo'], is_async=False)Save dataset/sAfter streaming ended (in according to your defined limits), save
DataFrame/s generated into csv file/s. You just need to access the
collector object and call the save_dataset method providing the path.streamer.collector.save_dataset(path="../data/accounts.csv")Questions and ContributingFeel free to post questions and problems on the issue tracker. Pull
requests are welcome!Feel free to fork and modify or add new features and functionality to
the library
Package: ptdeco
ptdecoptdecois a library for model optimization by decomposition built on top of PyTorch.Table of contentsIntroductionSample resultsconvnext_femto.d1_in1kconvnextv2_nano.fcmae_ft_in22k_in1krexnetr_200.sw_in12k_ft_in1kefficientformerv2_s2.snap_dist_in1kmobilevitv2_200.cvnets_in22k_ft_in1kswinv2_cr_tiny_ns_224.sw_in1kdeit3_small_patch16_224.fb_in1kresnet18.a2_in1kresnet50d.a1_in1kIntroductionCurrently,ptdecoimplements two methods:lockd- our custom method based on local knowledge distillation
(lockd =LOCalKnowledgeDistillation)falor- described inCompressing Transformers: Features Are Low-Rank, but Weights Are Not! by Yu Hao, Wu Jianxin (2023)(falor =FeaturesAreLOwRank)lockdmethod requires short (~ 10 ImageNet epochs) knowledge distillation
pretraining before decomposition is made. It can decompose linear layers and
convolutions.falormethod does not require pretraining. Model decomposition lasts < 1
GPU hour (depending on model size and parameters). It can decompose linear
layers and 1x1 convolutions.Sample resultsconvnext_femto.d1_in1kResolution: 224 x 224nameparamskmappsaccepochs_ftmethodsettingsbaseline5.2230.5777.56lockd_nt0104.1322.8276.48200lockdnsr_threshold=0.10On device timing results:convnext_femto.d1_in1k - t1_pro_gpunamelatencylatency_stdspeedup [%]baseline81.61.5lockd_nt01067.31.617.5convnext_femto.d1_in1k - t1_pro_cpu1namelatencylatency_stdspeedup [%]baseline123.00.3lockd_nt010112.00.58.9convnext_femto.d1_in1k - t1_pro_cpu4namelatencylatency_stdspeedup [%]baseline68.64.1decomposed_nt01063.64.47.4convnextv2_nano.fcmae_ft_in22k_in1kResolution: 224 x 224nameparamskmappsaccepochs_ftmethodsettingsbaseline15.6295.5981.97lockd_nt00513.3378.8682.09200lockdnsr_threshold=0.05lockd_nt01010.7863.6181.60200lockdnsr_threshold=0.10rexnetr_200.sw_in12k_ft_in1kResolution: 224 x 224nameparamskmappsaccepochs_ftmehtodsettingsbaseline16.5261.8882.41lockd_nt01013.5945.8181.78200lockdnsr_threshold=0.10efficientformerv2_s2.snap_dist_in1kResolution: 224 x 224nameparamskmappsaccepochs_ftmethodsettingsbaseline12.7149.5082.20lockd_nt01011.6442.8480.54200lockdnsr_threshold=0.10lockd_nt0159.7435.4575.37200lockdnsr_threshold=0.15mobilevitv2_200.cvnets_in22k_ft_in1kResolution: 256 x 256Remarks:normalization zero to onenameparamskmappsaccepochs_ftmethodsettingsbaseline18.45215.1582.28lockd_nt01011.1499.2881.71200lockdnsr_threshold=0.10swinv2_cr_tiny_ns_224.sw_in1kResolution: 224 x 224nameparamskmappsaccepochs_ftmethodsettingsbaseline28.33181.4481.53lockd_nt01015.62102.8281.26200lockdnsr_threshold=0.10falor_nft00517.22103.4380.99200falornsr_final_threshold=0.0455deit3_small_patch16_224.fb_in1kResolution: 224 x 224nameparamskmappsaccepochs_ftmethodsettingsbaseline22.06165.4681.30lockd_nt01014.53107.7581.30200lockdnsr_threshold=0.10falor_nft00514.51107.5681.31200falornsr_final_threshold=0.045resnet18.a2_in1kMethod: lockdResolution: 224 x 224namensr_thrparamskmappsaccoriginal11.697170.87decomposed_10.039.235662.88decomposed_20.057.774662.80decomposed_30.105.843462.58resnet50d.a1_in1kMethod: lockdResolution: 224 x 224namensr_thrparamskmappsaccoriginal25.5816980.52decomposed_10.0517.7810578.24
Package: pte
PTE - Python tools for electrophysiologyPTE is an open-source software package for working with electrophysiological
dataPTE builds upon the packagesMNEandMNE-BIDS.Installing PTEStable releaseTo install the latest stable release, simply type:$pipinstallpteDevelopment versionTo install the latest development version, first clone this repository usinggit:$gitclonehttps://github.com/richardkoehler/pteUse the package managercondato set up a
new working environment. To do so navigate to the PTE root directory in your
terminal and type:$condaenvcreate-fenv.ymlThis will set up a new conda environment calledpte.To activate the environment then type:$condaactivatepteIf you want to install pte-stats into another existing environment, type:$pipinstall-e.UsageimportpteContributingPlease feel free to contribute yourselves or to open anissuewhen you
encounter a bug or would like to add a new feature.For any minor additions or bugfixes, you may simply create apull request.For any major changes, make sure to open anissuefirst. When you then
create a pull request, be sure tolink the pull requestto the open issue in
order to close the issue automatically after merging.To contribute yourselves, consider installing the full conda development
environment to include such tools as black, pylint and isort:$condaenvcreate-fenv_dev.yml
$condaactivatepte-devContinuous Integration (CI) including automated testing are set up.LicensePTE is licensed under theMIT license.
Package: ptemcee
ptemcee /’tɛmsiː/ (noun):Adaptive parallel temperingmeetsemcee.ptemcee, pronounced “tem-cee”, is fork of Daniel Foreman-Mackey’s wonderfulemceeto implement
parallel tempering more robustly.If you’re trying to characterise awkward, multi-model probability distributions, thenptemceeis
your friend.Documentation… will be here soon.AttributionPlease citeVousden, Farr, Mandel (2015)andForeman-Mackey,
Hogg, Lang & Goodman (2012)if you find this code useful in your
research.Licenseptemceeis free software distributed under the MIT License; see theLICENSEfile for details.
Package: ptemcee-for-orbitize
This is the version of ptemcee used for orbitize! This is a medium-term
solution for ptemcee not being actively maintained. This version branches
from ptemcee at the pypi v1 release on Aug 28, 2015.ptemcee: (noun):Adaptive parallel temperingmeetsemcee.ptemcee, pronounced “tem-cee”, is fork of Daniel Foreman-Mackey’s wonderfulemceeto implement
parallel tempering more robustly.  As far as possible, it is designed as a drop-in replacement foremcee.If you’re trying to characterise awkward, multi-modal probability distributions, thenptemceeis
your friend.AttributionPlease citeVousden, Farr, and Mandel (2015)andForeman-Mackey,
Hogg, Lang, and Goodman (2012)if you find this code useful in your
research.Licenseptemceeis free software distributed under the MIT License; see theLICENSEfile for details.
Package: pter
pterYour console and graphical UI to manage your todo.txt file(s).There’s a presentation of pter in motion onasciinema.Also in this package, the QT5 based graphical user interface, qpter.pter has a bunch of features that help you managing your todo.txt file:Fully compatible to the todo.txt standardSupport fordue:,h:,t:,rec:,id:Save search queries and sorting for quick accessConvenient entering of datesTime tracking, notes, task delegationConnect tasks in sequencesTemplate system and auto-completion for ease of creating tasksSupport for archive and trashCan work with multiple todo.txt files at the same timeConfigurable behaviour, shortcuts, and colorsThere is also a graphical user interface called qpter, but some features
are missing.InstallationInstall from PIPTo install pter you can either clone the repository (see at the end) or, much
simpler, use pip to install it:pip install pterIf you want to use the Qt GUI qpter, you have to install PyQt5::pip install PyQt5Using pterTo launch pter you have to tell it where your todo.txt file is:pter ~/todo.txtFor the graphical user interface (requires QT), you have to start it like
this:qpter ~/todo.txtBoth will give you a listing of all your tasks order by how soon they will be
due and what priority you have given them.You can navigate the tasks with your cursor keys and edit selected tasks by
pressinge.More default shortcuts are:e, edit the selected taskn, create a new taskd, mark the selected task as done (or toggle back to not done)?, show all keyboard shortcutsq, quit the programThere is a complex search available (have a look at the manual for details), but the short version is:press/to enter your search termssearch fordone:nto only show incomplete taskssearch for a context with@contextsearch for a project with+projectsearch for tasks that do not belong to a context with-@contextornot:@contextpressReturnto return the focus to the task listUsing qpterTo launch the Qt GUI, you may (but don’t have to) provide the location of your
todo.txt file::qpter ~/todo.txtContributingJust like any other open source project, you’re invited to participate in
pter’s development. Any contribution is welcome, from bug reports to pull
requests/sending of patches!You can find the project atcodebergor just email any of the authors with your input.There is also a general XMPP discussion channelpterover atspacepanda.se.
Package: pter-analysis
apteris a complementary analysis tool to the todotxt handlerpter. If certain tags are consequently used in tasks several statistics and distributions can be calculated usingaptersuch as:Estimated time left per projectEstimated workload for completing all tasks inptersearch(es)Task estimation accuracyTask delay (usage of thet:tag) or task completion time (completedbefore or afterdue:tag) distributionsAnd much more…See the github README file for more information.