Saravanan666/pypi-org huggingface dataset:
Package: doubleblind
Useful links:Documentation|Source code|Bug reports||||What is DoubleBlind?DoubleBlind is a software tool that automatically and reversibly replaces file names with random strings to help experimenters quantify microscopy experiments blindly and maintain experimental integrity.DoubleBlind aims to solve a common problem in scientific image quantification - keeping oneself blind to the experimental conditions.
When experimenters are able to see the experimental conditions of their data during quantification, it can bias their observations and interpretations of the results.
DoubleBlind solves this problem by automatically replacing file names with random strings, which can then be unblinded later when the experiment is complete. This allows experimenters to analyze their experiments without being influenced by knowledge of the experimental conditions, while still being able to access their original files later.DoubleBlind file name replacement process is completely reversible, so users can easily unblind their image files when the experiment is complete.
You don’t need to keep any special mapping of the original filenames - a blinded file can be unblinded just based on its encoded name.Files can be blinded and unblinded at any time, individually or in groups.
This allows experimenters to maintain the blinding of their data until the end of the experiment, while still being able to access the original filenames when needed.DoubleBlind supports most accepted file formats, as well as specialized microscopy image formats, such as Olympus .vsi files.Once the experiment is complete, DoubleBlind makes it easy to unblind the files and to replace the coded filenames with the original filenames in data files such as Excel sheets and text files.
This ensures that the final data analysis is accurate and reliable.DoubleBlind is also designed to be easy to use, with a simple and intuitive interface that works on all operating systems.
This ease of use is particularly important for experimenters who may not have extensive experience with software tools.
Package: double-buffer-shared-memory-manager
DoubleBufferedSharedMemoryManageris a Python class designed to
manage double-buffered shared memory for concurrent read/write
operations, particularly suited for handling video frames stored
as NumPy ndarrays.FeaturesDouble-Buffered Memory: Allows one buffer to be read while the
other is being written to, minimizing read-write conflicts.Thread-Safe Access: Utilizes reader-writer locks to allow
multiple readers and a single writer.Dynamic Memory Management: Dynamically creates or connects to
shared memory segments based on the provided base name.Customizable Data Handling: Supports specifying the shape and
data type of the NumPy ndarray.RequirementsPython 3.xNumPymultiprocessingreaderwriterlockcolorlog (for enhanced logging with color)InstallationEnsure you have the required libraries:pipinstallnumpyreaderwriterlockcolorlogUsageInitializationImport and initialize the manager:fromdouble_buffered_shared_memoryimportDoubleBufferedSharedMemoryManagerimportnumpyasnp# Initialize with default parametersmanager=DoubleBufferedSharedMemoryManager('image_buffer')# Initialize with custom shape and dtypemanager=DoubleBufferedSharedMemoryManager('image_buffer',shape=(576,720,3),dtype=np.uint16)Writing to Shared Memory# Create a random HD imageimg=np.random.randint(255,size=(1080,1920,3),dtype=np.uint8)# Write to the active buffermanager[0]=img0 is a convention, you can use any number, but for clarity use 0Reading from Shared Memory# Switch the active buffermanager.__switch()# Read from the non-active bufferimg_copy=manager[0]0 is a convention, you can use any number, but for clarity use 0CleanupIt’s important to clean up shared memory resources when they’re no
longer needed:manager.cleanup()Using with Context ManagerThe class supports context management protocol for automatic resource
management:withDoubleBufferedSharedMemoryManager('image_buffer')asmanager:# Your code to use the managerpassLoggingThe class utilizescolorlogfor enhanced logging. Log messages will
vary in color based on the log level to provide a clearer and more
intuitive understanding of the operations and events.ContributingContributions, issues, and feature requests are welcome. Feel free to
checkissues pageif you want
to contribute.AuthorMassimo Ghianim.ghiani@gmail.comLicenseDistributed under the MIT License. SeeLICENSEfor more information.AcknowledgementsNumPyreaderwriterlockcolorlog
Package: double-cartpole-custom-gym-env
This package contains OpenAI Gym environment designed for training RL agents to balance double CartPole. The environment is automatically registered under id: double-cartpole-custom-v0, so it can be easily used by RL agent training libraries, such as StableBaselines3.At thehttps://github.com/marek-robak/Double-cartpole-custom-gym-env-for-reinforcement-learning.gityou can find a detailed description of the environment, along with a description of the package installation and sample code made to train and evaluate agents in this environment.This environment was created for the needs of my bachelor's thesis, available athttps://www.ap.uj.edu.pl/diplomas/151837/site.
Package: doublecheck
A library for property-based testing in two different ways:“QuickCheck” - randomly generated inputs“SmallCheck” - exhaustively enumerated inputsThese testing approaches are named for the pioneering Haskell libraries.  The two
approaches truly shine when used together - once a property is found to be
refutable by some probably-huge-and-crazy random input, it is often worth investing
the CPU time to find the _smallest_ counterexample.This library is a brand new work-in-progress (see commit log for dates) with known
issues:Does not have randomly generated strings just yet.No existential quantification yet, but smallcheck does allow it!Needs more thorough testing.Needs to be able to test that a property always throws an exception.Needs lots more combinators.Needs one-liner interface rather than checking status of test cases.Installation & Usage(TODO: Describe in more detail. For now, read doublecheck/gen.py and doublecheck/prop.py)$ pip install doublecheck
$ python
>>> from doublecheck import *
>>> ForAll(lambda i: i > 0, PosInts()).quickcheck(100000).status
‘Certain’Further Reading & Related ProjectsThe original libraries.[QuickCheck]() ([mirrored on github](https://github.com/darcs-mirrors/QuickCheck) if you’d like to fork it)[SmallCheck and Lazy SmallCheck](http://www.cs.york.ac.uk/fp/smallcheck/) ([also on github](https://github.com/feuerbach/smallcheck))There are also already a few python libraries inspired by QuickCheck. None seem to take
the approach of a full port, and certainly none include SmallCheck-style exhaustive testing.[PayCheck](https://github.com/markchadwick/paycheck)[qc.py](https://github.com/dbravender/qc)Essentially every language has multiple implementations.
Here are some that I think are worth checking out.[ScalaCheck](https://github.com/rickynils/scalacheck) (Scala)[jsqc](https://github.com/sakari/jsqc) (JavaScript)[QueenCheck](https://github.com/rosylilly/QueenCheck) (Ruby)[Rantly](https://github.com/hayeah/rantly) (Ruby)[Triq](https://github.com/krestenkrab/triq) (Erlang)Copyright & LicenseCopyright 2012- Kenneth KnowlesLicensed under the Apache License, Version 2.0 (the “License”); you may not use
this file except in compliance with the License. You may obtain a copy of the
License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Package: doubleclickcrypto
DoubleClickCryptoPython module for decrypting doubleclick price confirmations.Installationpip install doubleclickcryptoFeaturesThe module handles decryption of price confirmations and advertising identifiers with signature checking and, for price confirmations, stale response handling.Example usageLook at the test cases in the source for more examples.Price decryptionfromdoubleclickcryptoimportDoubleClickCryptoe_key="skU7Ax_NL5pPAFyKdkfZjZz2-VhIN8bjj1rVFOaJ_5o="i_key="arO23ykdNqUQ5LEoQ0FVmPkBd7xB5CO89PDZlSjpFxo="cipher="YWJjMTIzZGVmNDU2Z2hpN7fhCuPemCce_6msaw"DoubleClickCrypto.decrypt_price(cipher,e_key,i_key)#  -> 100# Check for stale responsesDoubleClickCrypto.decrypt_price(cipher,e_key,i_key,max_timedelta_seconds=0)#  -> throws StaleResponseExceptionAdvertising id decryptionfromdoubleclickcryptoimportDoubleClickCryptoe_key="sIxwz7yw62yrfoLGt12lIHKuYrK_S5kLuApI2BQe7Ac="i_key="v3fsVcMBMMHYzRhi7SpM0sdqwzvAxM6KPTu9OtVod5I="cipher="5nmwvgAM0UABI0VniavN72_tyXf-QJOmQdL0tmh_fduB2go_"DoubleClickCrypto.decrypt_ad_id(cipher,e_key,i_key)# -> bytes([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])Repositoryhttps://github.com/danielhedren/doubleclickcrypto
Package: double-debias
double_debiasThis package implements the double debiased estimator from"Double/Debiased Machine Learning for Treatment and Structural Parameters"by Chernozhukov et. al.installationpip install double_debias_joe5saiaUsageThis package estimates models of the form y = theta D + g(z) + e where z is a high dimensional object.fromsklearn.ensembleimportGradientBoostingRegressorfromsklearn.linear_modelimportLinearRegressiondd=double_debias(y=np.array([iforiinrange(0,10)]),D=np.array([i//2foriinrange(0,10)]),p.array([[i**2foriinrange(0,10)],[i**3foriinrange(0,10)]]).transpose(),y_method=GradientBoostingRegressor(n_estimators=1000),D_method=LinearRegression(),n_folds=3)dd.est_theta()The user initializes the estimator object with the data for y, D, and z along with the method for estimating y ~ g(z) + e and D ~ f(z) + e.
They_methodandD_methodcan be any model from the sklearn library that implements the fit and predict methods. The user may also supply their
own class that implements these methods. This class does no parameter tuning or cross validation. Parameter tuning is left up to the user.Custom Estimator MethodsThe user may supply their own estimators if these are not available in sklearn. This module assumes that the class passed has the fit and predict methods, i.e. the following
code must workz=np.array([[i**2foriinrange(0,10)],[i**3foriinrange(0,10)]]).transpose()y=np.array([iforiinrange(0,10)])m=my_estimator()m.fit(z,y)m.predict(z)
Package: doubledouble
doubledouble.py - Double-double aritmetic for Pythondoubledouble.py is a library for computing with unevaluated sums of two double
precision floating-point numbers. The so-called “double-double arithmetic”
enables operations with at least 106-bit significand (2x53-bit) and 11-bit
exponent, compared to 113-bit and 15-bit, respectively, of the IEEE 754-2008
quadruple precision floating-point format.Install withpip install doubledouble.
Package: double_down
Double downA silly example of a python decorator.Simply calls the decorated function twice.InstallationDouble down has been published to python package index and is available via pip:pip install double_downOtherwise, if you want to install it manually:git clone git@github.com:smelnicki/double_down.git
sudo python setup.py installUsageThis is about as simple as it gets when it comes to decorators. It literally makes
your function code execute twice.For example, see below:# example.pyfromdouble_downimportdouble_down@double_downdefhello():print"Hello, Python"if__name__=="__main__":hello()Then in your terminal:> python example.py
Hello, Python
Hello, PythonQuestionsThis project was just a fun little foray into learning how decorators work. In all
honesty, it’d be a big stretch of the imagination to justify its use in some real
life application.If you’re curious how decorators work or want to get some hands on experience, I’d
strongly encourage making one of your own. There’s plenty of documentation out there
to use.
Package: double-indent
double-indentA code formatter to add double indentation to function and method definitions.Installationpip install double-indentusageusage: double-indent [-h] [-i INDENT] [filenames ...]positional arguments:filenamesoptional arguments:-h, --help            show this help message and exit-i INDENT, --indent INDENTnumber of spaces for indentationpre-commit hookSeepre-commitfor instructionsSample.pre-commit-config.yaml:-repo:https://github.com/theendlessriver13/double-indentrev:0.1.4hooks:-id:double-indentindent function and method definitions twicedef func(-    arg,-    arg2,+        arg,+        arg2,):...class C:def __init__(-        self,-        arg,+            self,+            arg,):...
Package: double-indent-rotki
double-indent-rotkiA code formatter to add double indentation to function and method definitions.
Original repository:https://github.com/theendlessriver13/double-indent/We forked it to add additional functionality that the original author did not want to include upstream:https://github.com/theendlessriver13/double-indent/pull/19Installationpip install double-indent-rotkiusageusage: double-indent [-h] [-i INDENT] [filenames ...]positional arguments:filenamesoptional arguments:-h, --help            show this help message and exit-i INDENT, --indent INDENTnumber of spaces for indentation--dry-run             do not modify anything, just print if any file would have changesindent function and method definitions twicedef func(-    arg,-    arg2,+        arg,+        arg2,):...class C:def __init__(-        self,-        arg,+            self,+            arg,):...
Package: double-pendula
Double pendula"It simply deals with unpredictability in complex systems. The shorthand is the Butterfly Effect. A butterfly can flap its wings in Peking and in Central Park you get rain instead of sunshine." - Dr. Ian Malcolm, Jurassic Park300 double pendulums with nearly identical (but not quite) start conditionsTable of ContentsInstallationBlog PostsContributingDependenciesLicenseSupport:computer: InstallationClone this repository locally to your machine:gitclonehttps://github.com/chris-greening/double-pendula.gitand install required dependencies with:pip3install-rrequirements.txtMinimum Python versionThis code requiresPython 3.5or higher.:newspaper: Blog PostsCheck out blog post(s) onDEVfor ideas and tutorials!Visualizing the Butterfly Effect with Python:pray: ContributingAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome!Feel free toopen an Issue, check out existingIssues, orstart a discussion.Beginners to open source are highly encouraged to participate and ask questions if you're unsure what to do/where to start :heart::spider_web: Dependenciespandasnumpymatplotlibscipy:credit_card: LicenseThis code operates under theMITlicense.:grey_question: SupportReach out to me if you want to connect or have any questions and I will do my best to get back to youEmail:chris@christophergreening.comTwitter:@ChrisGreeningLinkedInChris GreeningPersonal contact form:www.christophergreening.com
Package: double-pendulum
Double Pendulum Using Curses in Python.A nice relaxing double pendulum simulation using ASCII, able to simulate multiple pendulums at once, and provide tracing of pendulums as well as providing some system information.
  If you spot any bugs or features that need adding (especially with the specs), just open an issue :)FeaturesAs mentioned, able to simulate multiple pendulums at once.Able to specify the weight and mass of pendulums to create different butterfly effects.Uses equations fromhere.If -s is used, it grabs system specs and displays them.(hi r/unixporn)Obviously a lot could be done to it (see below the Args), Any contributions are appreciated)Quickstart:Dependencies:Python 3.2+,curses (standard on UNIX) or relevent windows port, psutils if you want to use -sGithubClone usinghttps://github.com/Nekurone/double-pendulum-ascii.gitor download the zip.Extract if necessary and head inside the folder.python3 pendulum.py [args]Pippython3 -m pip install double-pendulum(orpip install double-pendulum)double-pendulum [args]ArgsVisualshelp (-h)trace (-t)tracedrop (-tD)specs (-s)Maths and Pendulum settingspendulum (-p)speed (-sP)gravity (-g)mass (-m)length (-l)Window settingsHEIGHT (-H)WIDTH (-W)dHEIGHT (-dH)dWIDTH (-dW)Visuals--help (-h)Spits out a fairly standard argparse help message. Note this is printed out when the program is run anyway.type: None, default: None
 setting in example: -h--trace (-t)Enables 'tracing', a faint line behind the pendulums that fades.type: bool, default: off,
 setting in example: -t--traceDrop (-tD)Controls the rate at which the trace from -t fades. Higher is faster fading.type: float, default: 1.0,
 setting in example: -t -tD 0.5--specs (-t)Enables showing system info, note this feature is still a WIP, any bugs or issues just let me know.type: bool, default: off,
 setting in example: -sMaths and Pendulum Settings--pendulum (-p)Number of pendulums to simulate at once. Combine with the gravity settings and some speed settings for some really pretty visuals.type: int, default: 1,
 setting in example: -p 300--speed (-sP)Multiplier for speed of simulation. Around 0.5-1.5 is a good rangetype: float, default: 1.0,
 setting in example: -sP 3.0--gravity(-g)Controls the strength of gravity, note, this directly affects the speed of the simulation. So slowing down is recommended.type: float, default: 9.81,
 setting in example: -g 1--mass (-m)Controls the mass of the pendulums, useful mostly for butterfly effectstype: float, default: 100.0,
 setting in example: -m 300 -sP 0.2--length (-l)Controls the length of the arms of the pendulums, useful mostly for butterfly effectstype: float, default: 250.0, setting in example: -l 150 -sP 0.3todo: Window settingsIf you have any suggestions, or anything you'd like to add (or, more likely you found a bug) just open a PR :)
Package: doubles
Doublesis a Python package that provides test doubles for use in automated tests.It provides functionality for stubbing, mocking, and verification of test doubles against the real objects they double.
In contrast to the Mock package, it provides a clear, expressive syntax and better safety guarantees to prevent API
drift and to improve confidence in tests using doubles. It comes with drop-in support for test suites run by Pytest,
Nose, or standard unittest.DocumentationDocumentation is available athttp://doubles.readthedocs.org/en/latest/.DevelopmentSource code is available athttps://github.com/uber/doubles.To install the dependencies on a fresh clone of the repository, runmake bootstrap.To run the test suite, runmake test.To build the documentation locally, runmake docs.LicenseMIT:http://opensource.org/licenses/MIT
Package: doublespike
The double spike toolboxError propagation and data reduction for the double spike technique in mass spectrometry.The workings of this package are described in:Rudge J.F., Reynolds B.C., Bourdon B. The double spike toolbox (2009) Chem. Geol. 265:420-431https://dx.doi.org/10.1016/j.chemgeo.2009.05.010and at:https://johnrudge.com/doublespikeInstallationThis is a standard python 3 package, which can be installed using pip:pip install doublespikeUsageA series of Jupyter python notebooks describing usage can be found in thesrc/folder. New
users should begin by looking atsrc/example.ipynb
Package: double-stream-handler
DoubleStreamHandlerA StreamHandler wrapper that outputs messages to different streams based on logLevelLogging to the console is a nice feature to have, and Python'sStreamHandleris of great help. However, it can only output to one stream at a time, and by default it'sstderr.This goes well with the default settings (level=WARNING), but it isn't good when you decide to outputINFOmessages as well.This package prevents you from outputting non-error messages tostderras well as from having to juggle with your output streams.Usageimportloggingfromdouble_stream_handlerimportDoubleStreamHandler# create loggerlogger=logging.getLogger()logger.setLevel(logging.DEBUG)# create the handlerch=DoubleStreamHandler()# you can customize the logger by providing different streams and level, from which# the stderr output will startfromioimportStringIOcustom_out,custom_err=(StringIO(),StringIO())ch=DoubleStreamHandler(err_level=25,streams=(custom_out,custom_err))# set handler's level and add it to the loggerch.setLevel(logging.INFO)logger.addHandler(ch)logger.debug("This is not printed")logger.info("This is printed to stdout")logger.error("This is printed to stderr")InstallRequirements:Python 3.6 or higherThis package is hosted on PyPI, so you can install it with your package manager of choice# for Pippipinstalldouble-stream-handler# for Pipenvpipenvinstalldouble-stream-handler# for Poetrypoetryadddouble-stream-handler# for Poetrycondainstalldouble-stream-handlerLicenceBSD-3-Clause© 2021, Nikita Karamov
Package: doubletdetection
DoubletDetectionDoubletDetection is a Python3 package to detect doublets (technical errors) in single-cell RNA-seq count matrices.Installing DoubletDetectionInstall from PyPIpipinstalldoubletdetectionInstall from sourcegitclonehttps://github.com/JonathanShor/DoubletDetection.gitcdDoubletDetection
pip3install.If you are usingpipenvas your virtual environment, it may struggle installing from the setup.py due to our custom Phenograph requirement.
If so, try the following in the cloned repo:pipenvrunpip3install.Running DoubletDetectionTo run basic doublet classification:importdoubletdetectionclf=doubletdetection.BoostClassifier()# raw_counts is a cells by genes count matrixlabels=clf.fit(raw_counts).predict()# higher means more likely to be doubletscores=clf.doublet_score()raw_countsis a scRNA-seq count matrix (cells by genes), and is array-likelabelsis a 1-dimensional numpy ndarray with the value 1 representing a detected doublet, 0 a singlet, andnp.nanan ambiguous cell.scoresis a 1-dimensional numpy ndarray representing a score for how likely a cell is to be a doublet. The score is used to create the labels.The classifier works best whenThere are several cell types present in the dataIt is applied individually to each run in an aggregated count matrixInv2.5we have added a new experimental clustering method (scanpy's Louvain clustering) that is much faster than phenograph. We are still validating results from this new clustering. Please see the notebook below for an example of using this new feature.TutorialSee ourtutorialfor an example on 10k PBMCs from 10x Genomics.Obtaining dataData can be downloaded from the10x website.Credits and citationsGayoso, Adam, Shor, Jonathan, Carr, Ambrose J., Sharma, Roshan, Pe'er, Dana (2020, December 18). DoubletDetection (Version v3.0). Zenodo.http://doi.org/10.5281/zenodo.2678041We also thank the participants of the 1st Human Cell Atlas Jamboree, Chun J. Ye for providing data useful in developing this method, and Itsik Pe'er for providing guidance in early development as part of the Computational genomics class at Columbia University.This project is licensed under the terms of the MIT license.