Saravanan666/pypi-org huggingface dataset:
Package: registryspy
Registry Spy: Cross-Platform Windows Registry BrowserRegistry Spy is a free, open-source cross-platform Windows Registry viewer. It is a fast, modern, and versatile explorer for raw registry files.Features include:Fast, on-the-fly parsing means no upfront overheadOpen multiple hives at a timeSearchingHex viewerModification timestampsRequirementsPython 3.8+InstallationDownload the latest version from thereleases page. Alternatively, use one of the following methods.pip (recommended)pip install registryspyregistryspyManualpip install -r requirements.txtpython setup.py installregistryspyStandalonepip install -r requirements.txtpython registryspy.pyScreenshotsMain WindowFind DialogBuildingDependencies:PyInstaller 5.10+Delete any existing venv, dist, and build directories.Create and activate a new venv and install the requirements.txt and pyinstaller.Regular building:pyinstaller registryspy_install.specCreating a single file:pyinstaller registryspy_onefile.specCreate the EXE installer with Inno Setup.LicenseRegistry SpyCopyright (C) 2023 Andy SmithThis 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/.
Package: registtro
OverviewWeak entry, strong value immutable registry data structure.
Think of it as an immutableWeakKeyDictionarythat efficiently evolves into a new
copy everytime you want to make a change to it.MotivationImmutable data structures are great for when you need to implement some kind of
“snapshot” of states for easy undo/redo, time-travelling functionality.
The librarypyrsistentis great for that, but
it lacks a map-like structure in which the keys are stored as weak references.Registtrois an implementation of that structure, which allows for proper garbage
collection of the keys/entries, while still allowing to store their states in a
centralized, immutable structure.ExampleSimple implementation of an undoable store that keeps track of states for entries.>>>fromregisttroimportRegistry>>>classStore(object):..."""Keeps track of the history of states for entries."""...def__init__(self):...self._done=[Registry()]...self._undone=[]...definit(self,entry,state):...self._done.append(self._done[-1].update({entry:state}))...delself._done[:-1]...delself._undone[:]...defget_state(self,entry):...returnself._done[-1].query(entry)...defset_state(self,entry,state):...delself._undone[:]...self._done.append(self._done[-1].update({entry:state}))...defundo(self):...assertlen(self._done)>1,"can't undo"...self._undone.append(self._done.pop())...defredo(self):...assertself._undone,"can't redo"...self._done.append(self._undone.pop())...>>>classEntry(object):..."""Reads/sets state in a store."""...def__init__(self,store,state):...self._store=store...store.init(self,state)...defget_state(self):...returnself._store.get_state(self)...defset_state(self,state):...self._store.set_state(self,state)...>>># Initialize entries.>>>global_store=Store()>>>entry_a=Entry(global_store,"foo")>>>entry_b=Entry(global_store,"bar")>>>(entry_a.get_state(),entry_b.get_state())('foo','bar')>>># Modify entries.>>>entry_a.set_state("FOO")>>>entry_b.set_state("BAR")>>>(entry_a.get_state(),entry_b.get_state())('FOO','BAR')>>># Undo modifications.>>>global_store.undo()>>>(entry_a.get_state(),entry_b.get_state())('FOO','bar')>>>global_store.undo()>>>(entry_a.get_state(),entry_b.get_state())('foo','bar')>>># Redo modifications.>>>global_store.redo()>>>(entry_a.get_state(),entry_b.get_state())('FOO','bar')>>>global_store.redo()>>>(entry_a.get_state(),entry_b.get_state())('FOO','BAR')
Package: regit
regit: keep those branches rebasedregit is a git add-on for managing branch dependencies.WhyMany github projects are using a pull request based workflow. Developers
who believe their feature branch is ready for merging open a pull request,
and after some time, someone might or might not press the merged button, and
the branch gets merged.If you now have your branch (let's call it A) PR'ed, and you'd like to do some
work based on that branch in another branch (let's call that B), and also PR
that, the new PR will contain all the commits from branch A, possibly
cluttering the commit list.  Now if a reviewer finds a flaw in A that needs
fixing, in order to reflect those changes in B, that branch needs to be rebased
on A.  Not a big deal with just two branches. But if you're trying to keep your
PR's small and to the point, you might end up with a lot of interdependend
branches, like "driver D needs generic_driver C, which itself depends on
core_fix A and B". Now any change to "core_fix A" implies a cascade of manually
updating C and D.regit helps with that.How to use
Package: regius-dl
RegiusRegius是一个简单的深度学习工具包，用于构建图像、文本以及结构化数据模型简介Regius目前对于结构化数据采用Google的Wide&Deep模型。
模型的参考文章参见这里安装Regius基于PyTorch进行开发，安装之前需要先安装PyTorch之后通过pip进行安装pipinstallregius-dl
Package: regli
regliREgular Grid Linear Interpolator, capable to deal with spectral library or similar model data.This package implements the basicbilinear interpolationin multi-dimension.
However, as commented inNumerical Recipes:Bilinear interpolation is frequently “close enough for government work.”
As the interpolating point wanders from grid square to grid square,
the interpolated func- tion value changes continuously.
However, the gradient of the interpolated function changes
discontinuously at the boundaries of each grid square.As a consequence, the interpolation model can not, in general,
be used as a forward model and incorporated with
Gradient-based optimization algorithms to estimate parameters.authorBo Zhang,bozhang@nao.cas.cnhome pagehttps://github.com/hypergravity/reglihttps://pypi.org/project/regli/installfor the lateststableversion:pip install -U reglifor the latestgithubversion:pip install -U git+git://github.com/hypergravity/reglifor the Zenodo version:https://zenodo.org/record/3461514testfromregliimporttesttest()output:regli.interp3 x 10000: 0.5675415992736816 sec
regli.interpn x 10000: 2.5326197147369385 sec
rgi x 10000: 5.4028871059417725 secdoc# import ReglifromregliimportRegliimportnumpyasnp# construct grid coordinatesx1=np.linspace(-1,1,30)x2=np.linspace(-1,1,30)x3=np.linspace(-1,1,30)# initiate regli using coordinatesregli=Regli(x1,x2,x3)# an arbitrary function of coordinates (for demo)f=lambda_x1,_x2,_x3:_x1+_x2+_x3# regli.flats stores flattened coordinates of ND gridflats=regli.flats# evaluate your function on flatsvalues=np.array([f(*_)for_inflats]).reshape(-1,1)# set values for regliregli.set_values(values)regli(pos)# use any of the 3 ways to interpolateregli.interpn(pos)# method 1 is equivalent to 2regli.interp3(pos)# this is accelerated for 3DHow to cite the codeBibTex:@misc{https://doi.org/10.5281/zenodo.3461514,
  doi = {10.5281/zenodo.3461514},
  url = {https://zenodo.org/record/3461514},
  author = {Zhang, Bo},
  title = {hypergravity/regli: The Zenodo version},
  publisher = {Zenodo},
  year = {2019}
}Please go tohttps://search.datacite.org/works/10.5281/zenodo.3461514for other formats.
Package: regme
Registration for MongoEngine============================User registration and management library using MongoEngineFeatures--------* Create inactive user* Generate activation token* Try to activate user via activation token* Set user password* Check is activation token expired* Integrate with django.contrib.auth* Registration form* Activation form* Sample/default registration templatesTODO----* Django command to prune users that were not activated.Installation------------`pip install regme`Configuration-------------In addition to [MongoEngine Django support settings](http://docs.mongoengine.org/django.html) Regme requires following settings:# The number of days activation token will be validACCOUNT_ACTIVATION_DAYS = 7# Regme custom user document for MongoEngine# You should not change it unless you know what are you doingMONGOENGINE_USER_DOCUMENT = 'regme.documents.User'# Include regme into installed apps listINSTALLED_APPS = (# …'django.contrib.auth',# …'mongoengine.django.mongo_auth',# …'regme',# …)Usage-----### Simple* Include `regme.urls` into your `urlconf`.* Use tag `{% url 'register' %}` to point to the regme registration view.* (Optional) Override default templates placed in `regme/templates` folder.### Advanced* Use or subclass `regme.forms.UserCreationForm` and `regme.forms.UserActivationForm` manually.* Subclass `regme.documents.User` and perform whatever magic you want.Contribute----------Feel free to report any issue or fork this project on [Regme Github page](https://github.com/lig/regme).Authors-------[Serge Matveenko](https://github.com/lig)License-------Apache License. See `LICENSE` file.
Package: regmean-imputer
Regularized Mean ImputerIntroductionHandling missing data is a common challenge in data analysis and machine learning. Regularized mean imputation offers a technique to fill missing values using a regularized mean based on specific grouping columns.This package provides one main utility for this purpose: a standalone functionimpute_columnwhich can be used for imputation on a pandas DataFrame. This function is meant to be used in a machine learning preprocessing pipeline to impute missing values in both the training and testing datasets with averages only from the train set, preventing information leakage.How it WorksThe imputation process works by grouping the data based on the specified columns and computing a regularized mean for each group. This regularized mean is a weighted average of the group mean and the global mean, adjusted by a regularization parameter. The regularization parameter is tuned using cross-validation.Installationpipinstallregmean-imputerUsageImputation usingimpute_columnwith separate train and test data:fromregmean_imputerimportimpute_column# Sample train datatrain_data={'Age':[25,None,30,35,40],'Title':['Mr','Mrs','Mr','Miss','Miss'],'Pclass':[1,2,1,3,3]}# Sample test datatest_data={'Age':[None,45],'Title':['Mrs','Mr'],'Pclass':[2,1]}train_df=pd.DataFrame(data=train_data)test_df=pd.DataFrame(data=test_data)# Impute the 'Age' column using 'Title' and 'Pclass' as group by columnsimputed_train_data,imputed_test_data=impute_column(train_data=train_df,test_data=test_df,impute_col='Age',group_by_cols=['Title','Pclass'])print(imputed_train_data)print(imputed_test_data)This approach of separating the train and test data before imputation is crucial in a machine learning preprocessing pipeline to prevent information leakage. Information leakage happens when information that would not be available at prediction time is used when building the model. This can lead to overly optimistic performance estimates. For example, if we impute missing values in the entire dataset using the mean of a column, the mean is influenced by the test set values, which wouldn't be available at prediction time in a real-world scenario.Parameterstrain_data(pd.DataFrame): The training dataset.test_data(pd.DataFrame): The testing dataset.impute_col(str): Column to be imputed.group_by_cols(list): Columns used for grouping to compute the regularized mean.m_values(list, optional): List of regularization parameters to be tested for optimal performance. Default is [1,2,3,4,5,6,7,8,9,10].n_splits(int, optional): Number of splits for cross-validation during regularization evaluation. Default is 5.verbose(bool, optional): Whether to print the best regularization parameter. Default is False.random_state(int, optional): Random state for cross-validation. Default is None.ConclusionRegularized mean imputation provides an efficient way to handle missing data, especially when certain columns can provide context on how the imputation should be done. The provided utilities in this package make it easier to apply this method.
Package: regmitter
regmitterDescriptionThis utility is designed for transferring docker registries data.Installationpython setup.py installorpip install-e.orpip install regmitterHow to useRunregmitter-tpull images.ymlExample of yml file entry:"sample-registry.com:5000/app":["1.0.0-124"]Also checkout list ofargumentsargumentsimages- Path to the file with images to transfer.-t,--type- Type of the operation. Choose from:pull,push,remove-l,--log- Redirect logging to file
Package: regml
Regularization for Machine LearningThese contents were taugh in summer schoolRegML 2016byLorenzo Rosascoand this GUI in python was submitted as part of final exam.All the coded and tested functions are inRegML.pyand GUIs code structure is inRegML_GUIv2.1.pyGithub PagePyPi -projectInstallationpip install regmlOpening GUI:import regml
regml.GUI()MethodsRegularized Least Squares -RLSReferanceNu-MethodReferanceIterative Landweber MethodReferanceSingular Value DecompositionReferenceTrunctated SVDReferance 1Referance 2Spectral cut-offKernal Learning(Linear, Polynomial, Gaussian)LinearPolynomialGaussian (RBF)K-Fold Cross ValidationGUIUsing local filesUse these filesRegML.pyRegML_GUIv2.1.pyGetting_Started_Demo.ipynbRequirmentsFollowing libraries are required to use all the functions in RegML libraryPython(=2.7)Numpy(>=1.10.4)NumpyMatplotlib(>=0.98)MatplotlibScipy(>=0.12)       Optional -(If you need to import .mat data files)ScipyTested with following versionGUI is tested on followwing version of librariesPython     2.7, 3.7Numpy      1.10.4Matplotlib 1.15.1Scipy      0.17.0Getting starting with GUIWindows------------------------After lauching python, go to directory containing RegML.py and RegML_GUIv2.1.py files and run following command on
python shell>> run RegML_GUIv2.1.pyIf you are using Spyder or ipython qt, browes to directory, open RegML_GUIv2.1.py file and run itUbuntu/Linux-------------------Open terminal, cd to directory contaning all the files and execute following command$ python RegML_GUIv2.1.pyif you have both python 2 and python 3$ python2 RegML_GUIv2.1.pyIf you are using Spyder or ipython qt, browes to directory, open RegML_GUIv2.1.py file and run itGetting Started with DEMOGetting_Started_Demo is a IPython -Notebook, which can be open in Ipython-Notebook or JupyterNotebookRegML LibraryNikesh Bajajn.bajaj@qmul.ac.uknikesh.bajaj@elios.unige.ithttp://nikeshbajaj.in
Package: regmod
Regression ModelsThis package is design for general regression models including
generalized linear models and others.It featuresBayesian framework, allows user to include priors into the mdoel.Easy spline interface and spline shape priors and constraint.Current model pool contains:Linear modelPoisson modelBinomial modelInstallPackage requires Python version above>=3.10.To install the package, the simplest way is throughpip,pipinstallregmodOr you could clone this repository and do,pythonsetup.pyinstallAcknowledgementDesign idea of this package comes from“Animal” package.
It is a general tool building template, for more detail please check the link above.
Package: regnorm
Install ViNorm packagepip install vinormUsing in python scriptfromvinormimportTTSnormS=TTSnorm("Hàm này được phát triển từ 8/2019. Có phải tháng 12/2020 đã có vaccine phòng ngừa Covid-19 xmz ?")Some optionTTSnorm(text,punc=False,unknown=True,lower=True,rule=False)lower: If true, get normalization with lowercaserule: If true, just get normalization wit Regex, not using Dictionary Checking (this flag is not used with another flag)punc: If true, do not replace punctuation with dot and comaunknown: If true, replace unknown word, discard word undefine and do not contain vowel, do not spell word with vowelFrom version 2.0, do not replace unknown words, skip them for espeak handle in phonetization stepThis version does not parse case: "Tổ chức WTO"
WTO do not in dictionary -> unknow -> keep origin, do not spell as in version 1.0, this aim to use with espeak, let espeak handle, but the drawback is the output of espeak for this case is "ve1kɛɜpte1ɔ7", it does not split each syllable.For new entity, need to update in the dictionaryFor update lastest version access:https://github.com/NoahDrisort/vinormFor version 1.0: spell words that is unknow by each character, check previous commitFor C++ version:https://github.com/NoahDrisort/vinorm_cpp_versionUpdate pypipythonsetup.pysdistbdist_wheel
twineuploaddist/*
Package: rego
regoAutomatic Time Series Forecasting and Missing Value Imputationregois a machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping.regois fast because all the code is C++.PyPi installationNote! Only Python3 is supported!pipinstall--upgradesetuptools
pipinstallwheel
pipinstallCython
pipinstallpandas
pipinstallregoCompile from sourcecd/python

python-mvenvenvsourceenv/bin/activate

pipinstall-rrequirements.txt
pythonsetup.pybuild_ext--inplace
Package: regobslib
regobslib — A Regobs client libraryThis client library aim to simplify interaction with the Regobs API v5.PrerequisitesA client ID for the test or prod environment, contactregobs@nve.noto acquire this.A user account for the test or prod environment. Register here:Test environment:https://test-konto.nve.no/Prod environment:https://konto.nve.no/Basic knowledge about snow science nomenclature. For more info, see the following resources:NVE Field BookObservation Guidelines and Recording Standards for Weather, Snowpack and AvalanchesSnow, Weather and AvalanchesInstallationTo install usingpip:pip install regobslibExample programsSearching for dataHere is an example program searching for weather observations in Ofoten.fromregobslibimport*connection=Connection(prod=True)results=connection.search(SnowRegistration,regions=[SnowRegion.OFOTEN],observation_types=[Weather])print(f"Length of results:{len(results)}\n")print("First result")print(results[0])print("\nIterates over results")forresultinresults:print(f"Id:{result.id}, Observer:{result.observer.nickname}, Date:{result.obs_time}")Submitting dataBelow is a simple program demonstrating how to register a whumpf sound
at a given location and time.fromregobslibimport*importdatetimeasdt# Contact regobs@nve.no to get a client ID.CLIENT_ID="00000000-0000-0000-0000-000000000000"# Create a user at https://test-konto.nve.no/ or https://konto.nve.no/USERNAME="ola.nordmann@example.com"PASSWORD="P4ssw0rd"# First we create an empty SnowRegistration objectreg=SnowRegistration(REGOBS_TZ.localize(dt.datetime(2021,8,17,9,48)),Position(lat=68.4293,lon=18.2572))# Then we add a DangerSign observation to itreg.add_danger_sign(DangerSign(DangerSign.Sign.WHUMPF_SOUND))# Authenticate to Regobs to be able to submit observationsconnection=Connection(prod=False).authenticate(USERNAME,PASSWORD,CLIENT_ID)# Send our SnowRegistration to Regobsstored_reg=connection.submit(reg)print(stored_reg)For a more extensive demonstration, refer todemo.py.DocumentationWe have yet to write documentation, but the function declarations
are written with type hints and most parameters are enums, so the code insubmit.pymay help you on your way. A properly set
up IDE will also use the type hints and enums to inform you about your
alternatives.You can also use GitHubs built-in code navigation to quickly find the
definition of a class or method in theexample code.
Package: rego-imager-readfile
Redline All-Sky Imager Raw PGM Data Readfile (REGO)Python library for reading REGO All-Sky Imager (ASI) stream0 raw PGM-file data. The data can be found athttps://data.phys.ucalgary.ca.Supported DatasetsREGO raw:stream0PGM filesInstallationThe rego-imager-readfile library is available on PyPI:$python3-mpipinstallrego-imager-readfileSupported Python Versionsrego-imager-readfile officially supports Python 3.8+.ExamplesExample Python notebooks can be found in the "examples" directory. Further, some examples can be found in the "Usage" section below.UsageImport the library usingimport rego_imager_readfileWarning: On Windows, be sure to put anyreadcalls into amain()method. This is because we utilize the multiprocessing library and the method of forking processes in Windows requires it. Note that if you're using Jupyter or other IPython-based interfaces, this is not required.Read a single file>>>importrego_imager_readfile>>>filename="path/to/data/2020/01/01/fsmi_rego-654/ut06/20200101_0600_fsmi_rego-654_6300.pgm.gz">>>img,meta,problematic_files=rego_imager_readfile.read(filename)Read multiple files>>>importrego_imager_readfile,glob>>>file_list=glob.glob("path/to/files/2020/01/01/fsmi_rego-654/ut06/*6300.pgm*")>>>img,meta,problematic_files=rego_imager_readfile.read(file_list)Read using multiple worker processes>>>importrego_imager_readfile,glob>>>file_list=glob.glob("path/to/files/2020/01/01/fsmi_rego-654/ut06/*6300.pgm*")>>>img,meta,problematic_files=rego_imager_readfile.read(file_list,workers=4)Read with no output>>>importrego_imager_readfile,glob>>>file_list=glob.glob("path/to/files/2020/01/01/fsmi_rego-654/ut06/*6300.pgm*")>>>img,meta,problematic_files=rego_imager_readfile.read(file_list,workers=4,quiet=True)Read only the first frame of each file>>>importrego_imager_readfile,glob>>>file_list=glob.glob("path/to/files/2020/01/01/fsmi_rego-654/ut06/*6300.pgm*")>>>img,meta,problematic_files=rego_imager_readfile.read(file_list,first_frame=True)Exclude reading the metadata>>>importrego_imager_readfile,glob>>>file_list=glob.glob("path/to/files/2020/01/01/fsmi_rego-654/ut06/*6300.pgm*")>>>img,meta,problematic_files=rego_imager_readfile.read(file_list,no_metadata=True)DevelopmentClone the repository and install dependencies using Poetry.$gitclonehttps://github.com/ucalgary-aurora/rego-imager-readfile.git$cdrego-imager-readfile/python$makeinstallTesting$maketest[ or do each test separately ]$maketest-flake8$maketest-pylint$maketest-pytest
Package: regolith-json-template
A Python package for easier reuseability of the JSON Template Regolith filter for Minecraft Bedrock EditionDocumentationThis module is just a component used by the JSON Template Regolith filter and its intended to be used by that filter only. You can find the documentation of the filter on the page of the filter:https://github.com/Nusiq/regolith-filters/tree/master/json_templateInstallationpipinstallregolith-json-templateMIT LicenseCopyright (c) 2023 NusiqPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.