Saravanan666/pypi-org huggingface dataset:
Package: uncertain
Python module to keep track of uncertainties in mathematical calculations.Usage exampleDefine uncertain valuesimportuncertainasuc# Uncertain value between 3 and 8 and a normal distribution with# mean=5 and standard deviation=1.a=uc.UncertainValue(5,3,8,'normal',[5,1])# Uncertain value with a uniform distribution between 0.1 and 4b=uc.UncertainValue(1,0.1,4)# Uncertain value from measured data pointsc=uc.from_data([1,2,3,4,5])Perform mathematical calculations: addition, substraction, multiplication, power, sine, cosine, tangent. In order for the trigonometric functions to work, they need to be called fromnumpy(mathdoes not work).d=-b+2*a**(b/3)importnumpyasnpe=np.pi/180*uc.UncertainValue(40,35,45)f=np.sin(e)+np.cos(e)+np.cos(e)Display properties and plot results in density plots or cumulative density plotsprint(c.describe(),"\n\nThe standard deviation of b is "+str(b.std),"\n\nThe probability of /c/ being between 2 and 6 is "+str(probability_in_interval(c,[2,6])))a.plot_distribution(title="Uncertain numbers",label="a")b.plot_distribution(label="b",alpha=0.5)d.plot_distribution(label="d",alpha=0.5)d.plot_distribution(plot_type='cdf',new_figure=True)Output:This variable is an uncertain value. It has the following properties:

    - Nominal value: 2.4199518933533937

    - Mean: 5.1973349566661415
    - Median: 3.8063419262133795
    - Variance: 13.086116036143682
    - Standard deviation: 3.6174737091157527
    - Skewness: 1.5519941650511524

    - Lower bound: -1.9254016053940988
    - Percentile 5: 2.0248565203431506
    - Q1: 2.432100693608657
    - Q3: 6.832833238201248
    - Percentile 95: 12.808458201483177
    - Upper bound: 31.899999999999995

    - Probability distribution type: custom
    - Number of samples: 100000


The standard deviation of b is 1.1245368594834484

The probability of /c/ being between 2 and 6 is 0.67164
Package: uncertainpy
Uncertainpy is a python toolbox for uncertainty quantification and sensitivity
analysis tailored towards computational neuroscience.Uncertainpy is model independent and treats the model as a black box where the
model can be left unchanged. Uncertainpy implements both quasi-Monte Carlo
methods and polynomial chaos expansions using either point collocation or the
pseudo-spectral method. Both of the polynomial chaos expansion methods have
support for the rosenblatt transformation to handle dependent input parameters.Uncertainpy is feature based, i.e., if applicable, it recognizes and calculates
the uncertainty in features of the model, as well as the model itself.
Examples of features in neuroscience can be spike timing and the action
potential shape.Uncertainpy is tailored towards neuroscience models, and comes with several
common neuroscience models and features built in, but new models and features can
easily be implemented. It should be noted that while Uncertainpy is tailored
towards neuroscience, the implemented methods are general, and Uncertainpy can
be used for many other types of models and features within other fields.
Package: uncertainty-estimation-models
IntroductionTODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project.Getting StartedTODO: Guide users through getting your code up and running on their own system. In this section you can talk about:Installation processSoftware dependenciesLatest releasesAPI referencesBuild and TestTODO: Describe and show how to build your code and run the tests.ContributeTODO: Explain how other users and developers can contribute to make your code better.If you want to learn more about creating good readme files then refer the followingguidelines. You can also seek inspiration from the below readme files:ASP.NET CoreVisual Studio CodeChakra Core
Package: uncertainty-framework
uncertainty-frameworkFramework for propagating uncertainties through V-FOR-WaTerInstallpip install uncertainty-frameworkExamplesThe examples are implemeted as a python module. The main function importsfireto
have a nice command line interface. Currently there is only one example.Variogram estimationThis example illustrates, how the MonteCarlo simulation can be used to simulate
measurement uncertainties on a variogram estimation. It replaces the observations by
a gaussian distribution around the observation using a scale of5. The observation
value range is[0, 256[.
Theuncertainty_framework.examples.variogramsexample has additional dependencies that
need to be installed separatly:pip install scikit-gstat plotlyThen, you can run it through the command line. It is recommended to decrease the default
number of iterations for this example.python -m uncertainty_framework.examples.variograms --num-iter=500 --verboseIt is possbile to increase the used scale for generating new observations and also some
of the variogram parameters are exposed:python -m uncertainty_framework.examples.variograms --num-iter=500 --estimator=cressie --ons-scale=15 --verbose
Package: uncertainty-rejection
uncertainty-rejectionAnalysis of uncertainty estimates for classification with rejection.Installation$pipinstalluncertainty-rejectionDocumentationThe documentation is deployed touncertainty-rejection.readthedocs.io.ExamplesAn example notebook is provided, which can be found in the"Example usage" sectionof the documentation.ContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licenseuncertainty-rejectionwas created by Arthur Thuy. It is licensed under the terms of the Apache License 2.0 license.Creditsuncertainty-rejectionwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: uncertainty-wrapper
UncertaintyWrapperUse@unc_wrapperdecorator to wrap any Python callable to append the
covariance and Jacobian matrices to the return values. See documentation and
tests for usage and examples.InstallationUsepip install UncertaintyWrapperto install fromPyPIor download a source
distribution, extract and usepython setup.py install.RequirementsNumPyOptional RequirementsNosefor testing.Sphinxto build documentation.NREL SOLPOSfor testingAlgoPyfor testingUsageExample:from uncertainty_wrapper import unc_wraper
import numpy as np

@unc_wrapper
def f(x):
    return np.exp(x)

x, cov = np.array([[1.0]]), np.array([[0.1]])
f(x, __covariance__=cov)Returns:(array([[ 2.71828183]]),      # exp(1.0)
 array([[[ 0.73890561]]]),    # (delta-f)^2 = (df/dx)^2 * (delta-x)^2
 array([[[ 2.71828183]]]))    # df/dx = exp(x)HistoryReleases are named aftergeological eons, periods and epochs.v0.4.2 Terreneuvian Seriesfuturizeremove pintv0.4.1Paleozoic EraJagged arrays of covariance keys work now.simplifyv0.4Phanerozoic EraFixes #5,ValueErrorif covariance keys have multiple observationsfix covariance cross terms not scaled correctlyv0.3.3Neoproterozoic EraFixes #4,ValueErrorif just one observationv0.3.2Mesoproterozoic EraFixes #2, don’t need to tile scalar x for multiple observationsFixes #3, use sparse matrices for dot product instead of denseuses pvlib example instead of proprietary solar_utilsv0.3.1Paleoproterozoic EraFixes #1 works with Pint’s @ureg.wraps()Use indices for positional arguments. Don’t use inspect.argspec since not
guaranteed to be the same for wrapped or decorated functionsTest Jacobian estimate for IV withAlgoPyShow Jacobian errors plot in getting started docs.v0.3Proterozoic Eonnewunc_wrapper_args()allows selection of independent variables that the
partial derivatives are with respect to and also grouping those arguments
together so that in the original function they can stay unpacked.return values are grouped correctly so that they can remain unpacked in
original function. These allow Uncertainty Wrapper to be used withPint’s wrappercovariance now specified as dimensionaless fraction of square of argumentsmore complex tests: IV curve and solar position (requiresNREL’s solpos)v0.2.1Eoarchean Eraupdate documentationv0.2Archean EonFix nargs and nf order mixup in Jacobianadd more complex testfix tile cov by nobsmove partial derivative to subfunctiontry threading, but same speed, and would only work with NumPy anywayv0.1Hadean Eonadds covariance to outputallows __covariance__ to be passed as inputuses estimate Jacobian based on central finite difference method
Package: unchanged
UnchangedA simple approvals/snapshot/golden master library that lets you configure your testing tools any way you want.FeaturesSensible defaults: Just want to do some basic snapshot on most Python objects?  Go for it.  Snapshot testing should be easy.Highly Modular: Want to do fuzzy comparison, pre-check file scrubbing, have the diff display in a non-Python program, and use the latest test runner?  No problem, plug your tools and functions intounchangedbased on your own preferences and project needs.No External Dependencies:unchanged's internals are stupidly simple, using only the Python standard library--you decide what packages your tests will rely on, not us.InstallationpipinstallunchangedExample UsagefromunchangedimportverifyHello world is still "Hello world"fromunchangedimportverifyverify("Hello, world",path="greeting.txt")Use Git Diff to Compare filesWithsubprocess:fromunchangedimportVerifierfromsubprocessimportPopenverify=Verifier(show_diffs=lambdaf1,f2:Popen(['git','diff','--no-index',f1,f2]))verify("Hello, world",path='greeting.txt')With theunchanged.Programutility:fromunchangedimportVerifier,Programverify=Verifier(show_diffs=Program(['git','diff','--no-index']))verity("Hello world")Use NBDime to Compare Jupyter NotebooksfromunchangedimportVerifierfromsubprocessimportPopennotebooks_match=lambdaf1,f2:Popen(['nbdime','diff','--ignore-metadata',f1,f2]).stdout.read()==''show_notebook_diff=lambdaf1,f2:Popen(['nbdime','diff-web'])verify_notebook=Verifier(show_diffs=Program(['nbdime','diff-web']),files_match=notebooks_match)Use BeyondCompare 3 to Display DiffsfromunchangedimportVerifierfromsubprocessimportPopenpath_to_bc3="{ProgramFiles}/Beyond Compare 4/BCompare.exe"show_diff_in_BC3=lambdaf1,f2:Popen([path_to_bc3])verify=Verifier(show_diffs=show_diff_in_BC3)Check Matplotlib Plots, Displaying Diffs in VSCodefrom unchanged import Verifier
from subprocess import Popen
Package: unclechat
โปรแกรมแชทนี้เป็นโปรแกรมที่ลุงเขียนขึ้นมาโดยใช้ Python และไลบรารี่
Socket สำหรับใช้งานในการแชทอย่างรวดเร็ว เพียงแค่มี Python
อยู่ในคอมพิวเตอร์ก็สามารถใช้งานได้เลย เบื้องหลังการทำงานลุงมีโปรแกรมฝั่ง
Server รันอยู่บน Server ลุง
สำหรับคอยรับส่งข้อมูลต่อไปยังผู้ใช้งานตามห้องต่างๆที่สร้างขึ้นหากใครต้องการเรียนพื้นฐานเรื่อง Python Socket
ลุงได้สอนไปอย่างละเอียดระดับหนึ่ง ดูวิดีโอสอนได้ตามลิ้งค์ยูทูปช่องลุง:https://youtu.be/MEaEVF3ZWfE(ยาว 8 ชั่วโมง 38 นาที) —> ฝากกด
Subscribe ด้วยจ่ะ!ปล. เวอร์ชั่นนี้ยังไม่สมบูรณ์ (เขียนแบบรีบๆ 55)
ลุงเขียนขึ้นมาเพื่อประยุกต์นำ Python + Socket Library
สร้างเป็นโปรแกรมแชทตัวอย่าง สำหรับนักพัฒนาสามารถนำ Source Code
ไปพัฒนาต่อได้เต็มที่วิธีติดตั้งเปิด CMD / Terminalpipinstallunclechatวิธีใช้[STEP 1] - เปิด IDLE ขึ้นมาแล้วพิมพ์…importunclechatหรือเปิด cmd / terminal แล้วพิมพ์python-munclechat[STEP 2] - กรอกชื่อผู้ใช้งานในช่อง Name - กด Enter Chatroom
เพื่อเข้าห้องแชทรวม (เลขห้องแชทรวมคือ 10001)หากต้องการสร้างห้องใหม่ - กด New Room - โปรแกรมจะสร้างห้องให้อัตโนมัติ
(จำเลขห้องไว้ส่งให้เพื่อน) - กด Enter Chatroom เพื่อเริ่มแชท[STEP 3] - ใช้งานได้เลยจ้าาา
หากมีการกดปิดโปรแกรมระบบจะแจ้งให้สมาชิกท่านอื่นทราบว่าผู้ใช้ได้ออกจากกลุ่มแล้วพัฒนาโดย: ลุงวิศวกร สอนคำนวณ FB:https://www.facebook.com/UncleEngineerYouTube:https://www.youtube.com/UncleEngineer
Package: unclenews
PyPI:https://pypi.org/project/unclenews/วิธีติดตั้งเปิด CMD / Terminalpipinstallunclenewsวิธีใช้เปิด IDLE ขึ้นมาแล้วพิมพ์…importunclenewsหรือเปิด cmd / terminal แล้วพิมพ์python-munclenews[ปุ่มกดต่างๆ]
-  กด F1 รัน/เพิ่มความเร็ว
-  กด P เพื่อหยุดชั่วคราว
-  กด R เพื่อรันต่อ
-  กด F2 หยุด+รีเซ็ต/ลดความเร็ว
-  กด F3 แก้ไขข้อความ
-  กด F5 เต็มจอ/ย่อ
-  กด F10 วิธีใช้งาน
-  กด F12 ปิดโปรแกรม
-  กดลูกศรซ้าย/ขวา ปรับตำแหน่ง
-  กดลูกศรขึ้น/ลงปรับขนาดฟอนต์พัฒนาโดย: ลุงวิศวกร สอนคำนวณ FB:https://www.facebook.com/UncleEngineerYouTube:https://www.youtube.com/UncleEngineer
Package: uncleprofile
PyPi:https://pypi.org/project/uncleprofile/สวัสดีจ้าาาา แพ็คนี้คือแพ็คเกจที่อธิบายโปรไฟล์ของนาย Loong
และสามารถนำไปใช้กับผู้อื่นได้วิธีติดตั้งเปิด CMD / Terminalpipinstalluncleprofileวิธีใช้งานแพ็คเพจนี้เปิด IDLE ขึ้นมาแล้วพิมพ์…fromuncleprofileimportProfilemy=Profile('Loong')my.company='uncle-engineer'my.hobby=['Youtuber','Reading','Sleeping']print(my.name)my.show_email()my.show_myart()my.show_hobby()my.show_cat()พัฒนาโดย: ลุงวิศวกร สอนคำนวณ FB:https://www.facebook.com/UncleEngineerYouTube:https://www.youtube.com/UncleEngineer
Package: uncleprofilek
PyPi:https://pypi.org/project/uncleprofilek/ตัวอย่างวิธีติดตั้งเปิด CMD / Terminalpipinstalluncleprofilekวิธีใช้งานแพ็คเพจนี้เปิด IDLE ขึ้นมาแล้วพิมพ์…my=Profile('Loong')my.company='uncle-engineer'my.hobby=['Youtuber','Reading','Sleeping']print(my.name)my.show_email()my.show_myart()my.show_hobby()my.show_cat()my.show_loong()FB:https://www.facebook.com/kanapoj.sirisantYouTube:https://www.youtube.com/UncleEngineer
Package: uncleschool
วิธีติดตั้งเปิด CMD / Terminalpipinstalluncleschoolวิธีเล่นเปิด IDLE ขึ้นมาแล้วพิมพ์…fromuncleschoolimportStudent,SpecialStudentprint('========1 Jan========')student0=SpecialStudent('Mark Zuckerberg','Bill Gates')student0.AskEXP()student0.ShowEXP()student1=Student('Albert')print(student1.name)student1.Hello()print('------------')student2=Student('Steve')print(student2.name)student2.Hello()print('========2 Jan========')print('------uncle: ใครอยากเรียนโค้ดดิ้ง?---(ให้ 10 exp)----')student1.AddEXP(10)print('========3 Jan========')student1.name='Albert Einstein'print('ตอนนี้ exp ของแต่ละคนได้เท่าไรกันแล้ว')print(student1.name,student1.exp)print(student2.name,student2.exp)print('========4 Jan========')foriinrange(5):student2.Coding()student1.ShowEXP()student2.ShowEXP()พัฒนาโดย: ลุงวิศวกร สอนคำนวณ FB:https://www.facebook.com/UncleEngineerYouTube:https://www.youtube.com/UncleEngineer
Package: uncommon
uncommonUncommon is a Python package to generate uncommon phrases that can be used for passphrases or other use casesGetting StartedTo useuncommonyou can clone this repository or install frompypi.InstallingYou can installuncommonusingpip.pip3 install uncommonYou can clone this repository and install the package directly using the followinggit clone https://github.com/MSAdministrator/uncommon.git
cd uncommon
python3 setup.py installUsingOnce you haveuncommoninstalled, you can use it by importing the package and utilizing the built-in methods:fromuncommonimportUncommonprint(Uncommon().get())You can provide additional parameters like the desired separator and the number of words to include in your phrase:fromuncommonimportUncommonprint(Uncommon(separator=';',count=10).get())Built Withcarcass- Python packaging templateContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.VersioningWe useSemVerfor versioning.AuthorsJosh Rickard -Initial work-MSAdministratorSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the MIT License - see theLICENSEfile for details
Package: unconcealed
UNCONCEALEDA series of tools and Jupyter widgets for evaluating images,
to aid in forensic investigation or reporting.
Unconcealed allows you to apply a series of transformations to
a directory of images, allowing you to visualize evidence of
image splicing or duplication.How it worksThe primary feature of Unconcealed is a Jupyter widget that
lets you select images, apply a mask, then access various
transformation and visualization tools via tabs.The widget also displays a record of all parameters used
during an evaluation, allowing for reproducibility of the
work.A specific feature captures and compares contours of image
elements (eg Western Blot bands), revealing similarities
that may be the result of image duplication.InstallationGetting startedThe basic installation will need you to open your terminal and
run:pip install unconcealedIf you are using Python environments along with Jupyter,
make sure that you install unconcealed into an environment
accessible as a Jupyter kernel. You can read more about
this from theIPython documentation.DependenciesTo use the full functionality of unconcealed, you will need to
separately install the following dependencies.pdfimages (only if you need to use the image extraction feature)Jupyter, Ipywidgets (if you want to use the interactive widget module)UsageStart up your Jupyter notebookNote the directory/s where the images are locatedIf you need to, you can use unconcealed.extract_from_pdf() to create a directory containing all images from a PDF, with the ability to filter based on image size. This requires separate installation of pdfimages.Import the widget module:fromunconcealedimportwidgetsPrepare and load the widget:load_evaluation_widget(IMAGE_DIRECTORY)Navigate through the tabs to perform various transformations of the selected image.Use the first tab to change directory, and select which image to analyze.How to contributeWe welcome contributions! Even if it is justletting us know about an issue.If you would like to contribute changes to the code:Clone repo and create a new branch:$ git checkout https://github.com/oxpeter/unconcealed -b name_for_new_branchMake changes and testSubmit Pull Request with comprehensive description of changesLicenseThis software has been developed under theMITlicense
Package: uncover
uncoverUncover runtime objects. Enlighten your mind. Sleep soundly.InstallationIf you want to……use this tool in your project 💻You might simply install it with pip:pip install uncoverIf you usePoetry, then run:poetry add uncover…contribute touncover🚀Happy to accept contributions![!Note]
If you use Windows, it is highly recommended to complete the installation in the way presented below throughWSL2.First,install Poetry.Poetry is an amazing tool for managing dependencies & virtual environments, building packages and publishing them.pipx install poetryIf you encounter any problems, refer tothe official documentationfor the most up-to-date installation instructions.Be sure to have Python 3.8 installed—if you usepyenv, simply run:pyenv install 3.8Then, run:git clone https://github.com/bswck/uncover path/to/uncover
cd path/to/uncover
poetry env use $(cat .python-version)
poetry install
poetry shell
pre-commit install --hook-type pre-commit --hook-type pre-pushLegal info© Copyright by Bartosz Sławecki (@bswck).This software is licensed under theMIT License.
Package: uncover-ml
Machine learning tools for the Geoscience Australia uncover project.Installation and UsageUncoverML can be run on a local Linux machine or a HPC platform such as NCI’s Raijin. For
detailed installation and usage instructions, see thedocumentation.Useful LinksHome Pagehttp://github.com/GeoscienceAustralia/uncover-mlDocumentationhttp://GeoscienceAustralia.github.io/uncover-mlChangeloghttp://GeoscienceAustralia.github.io/uncover-ml/history.htmlIssue trackinghttps://github.com/GeoscienceAustralia/uncover-ml/issuesBugs & FeedbackFor bugs, questions and discussions, please useGithub Issues.
Package: uncovr
This package aims to uncover the hidden fallacies in the AI/ML models. It is under constant development at both the philosophical level as well as the algorithmic level. While this package is open source, the mathematical concepts underpinning the core algorithm are not. To be more precise, this package calls an API managed by FOYI and thereforerequires a free API subscription. Please visit our official documentation athttps://www.foyi.co.nz/posts/documentation/documentationuncovr/. For updates, please follow us athttps://www.linkedin.com/company/foyi/
Package: uncrumpled_async_gui
async_guiasync_guiis a library aimed to make use of threads in GUI applications simpler.
It’s inspired by PyCon talkUsing futures for async GUI programming in Python 3.3andtornado@gen.engineimplementation.Most of GUI toolkits don’t allow you to access graphical elements from non-GUI thread.
Python 3.2+ has nice new featureconcurrent.futures, but we can’t just
wait for result from future and callbacks are not very handy.Combination ofCoroutines via Enhanced Generators (PEP-342)andfuturescreates a rich and easy to use asynchronous programming model
which can be used for creating highly responsive GUI applications.ExampleDemo of button click handler:@engine.asyncdefon_button_click(self,*args):self.status_label.setText("Downloading image...")# Run single task in separate threadimage_data=yieldTask(self.load_url,"http://www.google.com/images/srpr/logo4w.png")pixmap=QtGui.QPixmap.fromImage(QtGui.QImage.fromData(image_data))self.image_label.setPixmap(pixmap)self.status_label.setText("Downloading pages...")urls=['http://www.google.com','http://www.yandex.ru','http://www.python.org']# Run multiple task simultaneously in thread poolpages=yield[Task(self.load_url,url)forurlinurls]self.status_label.setText("Done")avg_size=sum(map(len,pages))/len(pages)self.result_label.setText("Average page size:%s"%avg_size)Tasks yielded fromon_button_click()executed in thread pool, but
GUI updates done in the GUI thread.
For CPU-bound applications there is also ability to run tasks in pool of
processes.Seefull exampleinexamplesdirectory.FeaturesPython 2.7+ (futuresrequired),
Python 3+ supportPyQt4/PySide, Tk, Wx, Gtk GUI toolkits support. Easy to add anotherCan execute tasks in Thread, Process, Greenlet (geventrequired)Possibility to run multiple tasks at the same timeStraightforward exception handlingFull test coverageInstallationUsingpip:$ pip install async_guiOr download, unpack and:$ python setup.py installTo run tests use:$ python setup.py testLinksDocumentationatreadthedocs.orgSource code and issue trackingatGitHub.History0.2.0 (2013-0x-xx)MultiTask can return generator of ready tasks instead waiting for all done0.1.0 (2013-04-06)initial PyPI release
Package: uncrustify-objc
uncrustify-objc –helpUsage: uncrustify-objc [OPTIONS] PROJECT_PATHuncrustify-obj is used to format the objective-c files under the specified
project path.For the uncrustify.cfg config file, it will search it recursively from the
project directory to its top parent directory. If none, the
~/uncrustify.cfg or ~/.uncrustify/uncrustify.cfg will be assumed to use.Configure the uncrustify_ignore in the project path to make a path
blacklist which matched items will not be formatted. It uses the gitignore
file template, more documentation could be found fromhttps://git-scm.com/docs/gitignore.By default, it will search the ignore file from the project path, if not
specify with –ignore-file. Besides, there is a global ignore file located
~/.uncrustify/uncrustify_ignore_global which will be combined to the
target ignore spec.If the project path is under the git version control, passing the –git-
only option will make the changed source files to be used as source file
list, the ignore files will be  omitted because the git-scm already used
the gitignore spec.Highly recommend you to make a shell alias likealias uncrustify-objc
=’uncrustify-objc –git-only’.Options:-c,--cfg-fileCFG_FILEThe CFG config file used to format.-i,--ignore-fileIGNORE_FILEThe ignore file used to ignore the matching
files for formatting.-g,--git-onlyFormat the current git staged and unstaged
files only.-n,--dry-runDo not format any files, but show a list of
files to be formatted.-v,--verboseEnables verbose mode.--helpShow this message and exit.