Saravanan666/pypi-org huggingface dataset:
Package: udi-interface
UDI Python Interface Module for Polyglot version 3This is the PG3 interface API module that is portable to be imported into your Python 3.4+ based NodeServers.InstallationPip > 9 should be installed. This typically isn't the case, so you will have to upgrade Pip first.# Check your pip version
pip -V
pip 9.0.1 from /home/e42/.local/lib/python2.7/site-packages (python 2.7)
pip3 -V
pip 9.0.1 from /usr/lib/python3/dist-packages (python 3.5)

# If Pip is < Version 9
sudo pip install -U pipThe module is updated in Pypi (Python's package interface Pip) on a regular basis. So simply install the module like you would any Python module:# Install the UDI interface
pip install udi_interface --userStarting your NodeServer buildWhen you start building a NodeServer you are helping build the free and open Internet of Things. Thank you! If you run in to any issues please ask your questions on theUDI Polyglot Forums.To get started,use the python template.From there just read the code itself, it is fully explained step by step.How to Enable your NodeServer in the CloudLink to PGC InterfaceControlling loggingBy default when the Polyglot Python Interface is started up the logging is in WARNING mode. If you want to change the level set logLevel:  in your server.json file.  Valid levels are:CRITICALERRORWARNINGINFODEBUG
Package: udiph
Uniform Distributed Persistent Homology (UDiPH)UDiPH is a package that creates a new metric space where the points are uniformly sampled with respect to a global metric. It is oriented for creating Vietoris-Rips filtrations that are independent of the metric of the initial space. This work is very influced onUMAP.UDiPH excells when comparing homology of spaces with different metrics, for example consider a continuous deformation acting on a set of points:Standard Vietoris-Rips filtration:Vietoris-Rips filtration using metric space created by UDiPH is based on local density:Since we are in the presence of a continuous deformation it is excpeted that the homology remains invariant. However due to the difference in metric in the image space, the standard Vietoris-Rips filtration does not accuratly capture this invariance. Notice how a filtration using UDiPH captures manages to capture both homology generators.InstallationDependencies:NumpyScipySklearnUDIPH is available on PyPI,pipinstalludiphUsageimportudiphM=udiph.UDIPH(X=X,n_neighbors=15,distance_matrix=False,return_complex=False)Parameters:X: numpy array of shape=(samples,features) or shape=(samples,samples) containing the data. If data is a pairwise distance matrix thendistance_matrixmust be set to True.n_neighbors: This determines the number of points sampled from the decision boundary. More points equates for a denser sample but slows the algorithm. Default is 1000.distance_matrix: Boolean value indicating if input data is or not a distance_matrixreturn_complex: Boolean value indicating whether to return the weighted 1-d simplicial complex instead of a pairwise distance matrix.Returns:M: numpy array (samples, samples) pairwise distance matrix with respect to new Riemannian metric.A: (optional) numpy array (samples, samples) adjacency matrix of the weighted 1-d simplicial complex.How does it work?The basic assumption on UDiPh relies on the idea that data comes uniformly sampled from an unknown Riemmannian manifold. As a consequence, "bigger" holes in the governing manifold are respresented by having a higher number of points sampled from their boundary, and "small" holes will have less points sampled.For an in-depth explanation look at thispost.CitationIf you use UDiPH in your work or parts of the algorithm please consider citing:Paper coming soon
Package: udk_configparser
udk_configparserLike CPython standard configparser but adjusted for UDK config files.Installationpipinstalludk_configparserExample usageSetting a single valuefromudk_configparserimportUDKConfigParsercg=UDKConfigParser()cg.read("Engine.ini")cg["Section"]["Key"]="NewValue"Setting a multi-value field and writing the file# Multi-value example.pkg_name="MyPackage"edit_packages=cg["UnrealEd.EditorEngine"].getlist("+EditPackages")ifpkg_namenotinedit_packages:edit_packages.append(pkg_name)# Currently, setting multi-value data requires manually# joining the data to ensure it is written correctly.cg["UnrealEd.EditorEngine"]["+EditPackages"]="\n".join(edit_packages)withopen("Engine.ini","w")asconfig_file:cg.write(config_file,space_around_delimiters=False)# Engine.ini before writing:#  [UnrealEd.EditorEngine]#  +EditPackages=UTGame#  +EditPackages=UTGameContent# After:#  [UnrealEd.EditorEngine]#  +EditPackages=UTGame#  +EditPackages=UTGameContent#  +EditPackages=MyPackageKnown issuesComments get removed during writes.
Package: udkm1Dsim
Welcome to udkm1DsimTheudkm1Dsimtoolbox is a collection of Python classes and routines to
simulate the thermal, structural, and magnetic dynamics after laser excitation
as well as the according X-ray scattering response in one-dimensional sample
structures after ultrafast excitation.The toolbox provides the capabilities to define arbitrary layered structures
on the atomic level including a rich database of element-specific physical
properties.
The excitation of ultrafast dynamics is represented by anN-temperature-model
which is commonly applied for ultrafast optical excitations.
Structural dynamics due to thermal stresses are calculated by a linear-chain
model of masses and springs.
The implementation of magnetic dynamics can be easily accomplished by the user
for the individual problem.The resulting X-ray diffraction response is computed by kinematical or
dynamical X-ray theory which can also include magnetic scattering.Theudkm1Dsimtoolbox is highly modular and allows to introduce user-defined
results at any step in the simulation procedure.Theudkm1Dsimtoolbox was initially developed for MATLAB® in theUltrafast Dynamics in Condensed Mattergroup of Prof. Matias Bargheer at theUniversity of Potsdam, Germany.
The MATLAB® source code is still available atgithub.com/dschick/udkm1DsimML.The current toolbox, written in Python, is maintained by Daniel Schick at theMax-Born-Institute, Berlin, Germany.DocumentationThe documentation can be found atudkm1Dsim.readthedocs.io.CitationPlease cite the current preprint if you use the toolbox in your own work:D. Schick,udkm1Dsim - A Python toolbox for simulating 1D ultrafast dynamics in condensed matter,Comput. Phys. Commun. 266, 108031 (2021)(preprint).You can also cite the original publication if appropriate:D. Schick, A. Bojahr, M. Herzog, R. Shayduk, C. von Korff Schmising & M. Bargheer,udkm1Dsim - A Simulation Toolkit for 1D Ultrafast Dynamics in Condensed Matter,Comput. Phys. Commun. 185, 651 (2014)(preprint).InstallationYou can either install directly from pypi.org using the commandpip install udkm1Dsimor if you want to work on the latest develop release you can clone
udkm1Dsim from the main git repository:git clone https://github.com/dschick/udkm1Dsim.git udkm1DsimTo work in editable mode (source is only linked
but not copied to the python site-packages), just do:pip install -e ./udkm1DsimOr to do a normal install withpip install ./udkm1DsimOptionally, you can also let pip install directly from the repository:pip install git+https://github.com/dschick/udkm1Dsim.gitYou can have the following optional installation to enable parallel
computations, unit tests, as well as building the documentation:pip install udkm1Dsim[parallel]
pip install udkm1Dsim[testing]
pip install udkm1Dsim[documentation]Contribute & SupportIf you are having issues please let us know via theissue tracker.You can also ask questions, share ideas, or engage with community members via thediscussions.You can contribute to the project via pull-requests following theGitHub flow concept.LicenseThe project is licensed under the MIT license.
Package: udkundoku
UD-KundokuClassical Chinese to Modern Japanese Translator, working onUniversal Dependencies.Basic usage>>>importudkundoku>>>lzh=udkundoku.load()>>>s=lzh("不入虎穴不得虎子")>>>t=udkundoku.translate(s)>>>print(t)# text = 虎の穴に入らずして虎の子を得ず1虎虎NOUNn,名詞,主体,動物_3nmod_Gloss=tiger|SpaceAfter=No2の_ADP__1case_SpaceAfter=No3穴穴NOUNn,名詞,固定物,地形Case=Loc5obj_Gloss=cave|SpaceAfter=No4に_ADP__3case_SpaceAfter=No5入ら入VERBv,動詞,行為,移動_0root_Gloss=enter|SpaceAfter=No6ずして不AUXv,副詞,否定,無界Polarity=Neg5advmod_Gloss=not|SpaceAfter=No7虎虎NOUNn,名詞,主体,動物_9nmod_Gloss=tiger|SpaceAfter=No8の_ADP__7case_SpaceAfter=No9子子NOUNn,名詞,人,関係_11obj_Gloss=child|SpaceAfter=No10を_ADP__9case_SpaceAfter=No11得得VERBv,動詞,行為,得失_5parataxis_Gloss=get|SpaceAfter=No12ず不AUXv,副詞,否定,無界Polarity=Neg11advmod_Gloss=not|SpaceAfter=No>>>print(t.sentence())虎の穴に入らずして虎の子を得ず>>>print(s.to_tree())不<════╗advmod入═══╗═╝═╗root虎<╗║║nmod穴═╝<╝║obj不<════╗║advmod得═══╗═╝<╝parataxis虎<╗║nmod子═╝<╝obj>>>print(t.to_tree())虎═╗<╗nmod(体言による連体修飾語)の<╝║case(格表示)穴═╗═╝<╗obj(目的語)に<╝║case(格表示)入═╗═══╝═╗root(親)ら║║ず<╝║advmod(連用修飾語)し║て║虎═╗<╗║nmod(体言による連体修飾語)の<╝║║case(格表示)子═╗═╝<╗║obj(目的語)を<╝║║case(格表示)得═╗═══╝<╝parataxis(隣接表現)ず<╝advmod(連用修飾語)udkundoku.load()is an alias forudkanbun.load()ofUD-Kanbun.udkundoku.translate()is a transcriptive converter from Classical Chinese (under Universal Dependencies of UD-Kanbun) into Modern Japanese (under Universal Dependencies ofUniDic2UD).udkundoku.reorder()is called to rearrange Classical Chinese into Japanese word-order insideudkundoku.translate().to_tree()andto_svg()are borrowed from those of UD-Kanbun.You can simply useudkundokuon the command line:echo不入虎穴不得虎子|udkundoku-jHTTP-server usagepython-mudkundoku.server5000Try to connecthttp://127.0.0.1:5000with your local browser. Input a Classical Chinese sentence there and push 解析-button (at least) three times.Installation for LinuxTar-ball is available for Linux, and is installed by default when you usepip:pipinstalludkundoku旧仮名口語UniDicis automatically downloaded for UniDic2UD.Installation for CygwinMake sure to getgcc-g++python37-pippython37-develpackages, and then:pip3.7installudkundokuUsepython3.7command inCygwininstead ofpython.Installation for Jupyter Notebook (Google Colaboratory)!pipinstalludkundokuTrynotebookfor Google Colaboratory.AuthorKoichi Yasuoka (安岡孝一)References安岡孝一:漢文の依存文法解析にもとづく自動訓読システム, 日本漢字学会第3回研究大会予稿集(2020年11月), pp.60-73.安岡孝一:漢文の依存文法解析と返り点の関係について, 日本漢字学会第1回研究大会予稿集(2018年12月), pp.33-48.
Package: udlai
UDL.AI Python APIudlaiis a way of interacting with the UDL.AI platform for location intelligence using a convenient Python API.Note, that to use theudlai, you will need to obtain an access token. Seeudl.aifor details.Installationudlaican be installed either fromPyPIorconda-forge.pipinstalludlaiOrcondainstall-cconda-forgeudlai# or alternativelymambainstall-cconda-forgeudlaiFeaturesIn the near future,udlaiwill offer a convenient interface to all parts of the UDL.AI platform. At the moment, only the feature API module is available, allowing you to retrieve data from the UDL.AI data warehouse.Feature APIUsing the token received from UDL you will be able to perform the following tasks:Fetch the list of attributesGet a list of all of the attributes the user has access to, completed with their metadata and description.>>>udlai.attributes(token)idname...yearvalue_formatter01Buildingsfloorspace...2013None12Buildingsfootprint...2013None23Height...2013None34Stories...2013None49box_area...2013None.................234283usefit_ind_old...0None235288Distancesecondaryschool...NoneNone236294Price2bedroom...NoneNone237295Price3bedroom...NoneNone238296Price4bedrooms...NoneNone[239rowsx22columns]Fetch specific feature detailsGet the properties, description and summary statistics of a specific attribute.>>>udlai.attribute_detail(token,22)id22nameobj_compactdescriptionCompactnessofobject:C=obj_peri²/(4*box_area*π)short_descriptionNoneunit-tags.id4tags.nameMorphologymain_tag.id4main_tag.nameMorphologydata_versionNonedata_last_updateNonedata_processorUDLsource_providerSwissTopoTLMsource_provider_linkhttps://www.swisstopo.admin.ch/de/wissen-fakte...coverage_generalSwitzerlandepsg_code2056min_value0.960614max_value24.141694standard_deviation0.893447mean1.647733year2013value_formatterNonedtype:objectFetch feature values for a given coordinate(s)Get the feature values for a provided coordinate location or locations.>>>udlai.features(token,47.37,8.54,[10,11,22],index_by="name")box_length104box_perim335obj_compact2.2064113123322Name:(47.37,8.54),dtype:objectFetch a summary over an area of interestGet the summary statistics of an attribute within a specific area.>>>udlai.aggregates(token,shapely_geom,[10,12],index_by='name')summeanmedianminmaxstdbox_length12921.094.31386994.019.0135.030.600546box_width13118.095.751825100.029.0142.030.870646Contact personMartin Fleischmann(@martinfleis)m.fleischmann@urbandatalab.net
Package: udlr
Up-Down-Left-Right: A C++ Sparse Matrix LibraryUp-down-left-right (UDLR) is a templated sparse matrix library designed the facilitate the development of algorithms that run on sparse graphs. The core datastructure is a quadrupally linked list representing the non-zero entries of the matrix. Every entry in this structure is connected with its immediate neighbours in theup, down, left, and rightdirections. The quad-directional linked layout enables rapid matrix traversal both horizontally and vertically, making it highly efficient for algorithms such as Gaussian elimination. UDLR is fully templated, allowing for arbitrary meta data to be appended to each list entry. This is particularly useful for graph algorithms such as belief propagation in which messages are passed between nodes.CPP InstallationUDLR is a header only libary. To use, include the fileudlr.hppand enjoy the library!Python InstallationInstall using pip. Navigate to the repository root and run:pip install -Ue .CPP FeaturesUDLR has initially been developed to provide sparse matrix operations over a GF2 field for applications in classical and quantum coding theory. As such, the sparse GF2 matrix is currently the most developed feature of this package. However, more functiionality will be added soon. The current fuctionality is listed below:A sparse GF2 matrix class. This class can be initialised with a custom node type that can contain user defined meta data.Function for performing linear algebra on GF2 matrices. Current funcitons include rank calculation, nullspace computation, LU decomposition and matrix inversion.Python bindings (via cython) for linear algebra operations on sparse GF2 matrices.
Package: udl-vis
UDL (Make Available onPyPI:tada:)UDL is a unified Pytorch framework for vision research:UDL has a faster library loading speed and a more convenient reflection mechanism to call different methods.UDL is based on MMCV which provides the following functionalities.UDL is based on NNI to perform automatic machine learning.English|简体中文See therepofor more detailed descriptions.NoteFor the implementation of DCFNet as described in the ICCV paper "Dynamic Cross Feature Fusion for Remote Sensing Pansharpening," please refer to thebranchin the this repository.RequirementsPython3.7+, Pytorch>=1.9.0NVIDIA GPU + CUDARunpython setup.py developNote: Our project is based on MMCV, but you needn't to install it currently.Quick Startpip install udl-vis -ihttps://pypi.org/simpleAlso, you can quick start from PanCollection, which is remote sensing pansharpening and is one of our applications.pip install pancollection -ihttps://pypi.org/simpleQuick Start for developerStep0.We use UDL in PanCollection, first please set your Python environment.git clonehttps://github.com/XiaoXiao-Woo/UDLgit clonehttps://github.com/XiaoXiao-Woo/PanCollectionThen,python setup.py developPlanningsPlease expect more tasks and modelspansharpeningmodelsderain(not available on PyPI)modelsHISRmodelsImprove MMCV repo to simplify expensive hooks.ContributionWe appreciate all contributions to improving PanCollection. Looking forward to your contribution to PanCollection.CitationPlease cite this project if you use datasets or the toolbox in your research.@misc{PanCollection,
    author = {Xiao Wu, Liang-Jian Deng and Ran Ran},
    title = {"PanCollection" for Remote Sensing Pansharpening},
    url = {https://github.com/XiaoXiao-Woo/PanCollection/},
    year = {2022},
}

@InProceedings{Wu_2021_ICCV,
    author    = {Wu, Xiao and Huang, Ting-Zhu and Deng, Liang-Jian and Zhang, Tian-Jing},
    title     = {Dynamic Cross Feature Fusion for Remote Sensing Pansharpening},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {14687-14696}
}AcknowledgementMMCV: OpenMMLab foundational library for computer vision.License & CopyrightThis project is open sourced under GNU General Public License v3.0.
Package: udmurttagger
This module contains a utility for part-of-speech tagging of Udmurt text.
The tool based on LSTM neural network and takes a word order into account.InstallationThe tool could be installed with pippip3 install udmurttaggerNote:the model for the utility must be downloaded separately.
Due to limitations on the size of the project, I could not place it
on a github or PiPy. After launching the program it will download
and unpack the model. No action is necessary on your part. But you
will need an Internet connection and about 150 megabytes of incoming
traffic.Usage exampleTagging one sentence at a time>>> from udmurttagger import Tagger
>>> t = Tagger()
>>> sentence = "Сьӧд зарезе."
>>> tagged_sentence = t.predict_pos(sentence)
>>> print(tagged_sentence)
[('сьӧд', 'N'), ('зарезе', 'N')]TagsetTagset based onUDMURT CORPORAADJ — adjectiveADJPRO — adjectival pronounADV — adverbADVPRO — adverbial pronounCNJ — conjunctionIMIT — ideophoneINTRJ — interjectionN — nounNUM — numeralPARENTH — parenthetic wordPART — particlePN — proper noun (subtype of nouns)POST — postpositionPREDIC — predicativePRO — pronounV — verbSee thepagefor the details.ModelThis tool can be used for disambiguation of rule-based markup.You can make your own wrap of the trained model.Model’s evaluation: loss: 0.2281 - acc: 0.9845 - val_loss: 0.2643 - val_acc: 0.9782.ContactsYou can contact the contriutor of the project via email:Boris Orekhov(nevmenandr)@ gmail
Package: udnn
UDNNA Convolutional Neural Network for fast upscaling of low-resolution sinograms in x-ray CT time-seriesTo install visit first the Pytorch website (https://pytorch.org/get-started/locally/) to manually install the correct version depending on the OS, Python and CUDA version you are using.Then install either via pip:pip install udnnOr using conda:conda install -c dimitriosbellos udnn
Package: udns
udns is an authoritative, caching DNS server for development and small
deployments written inPythonusing thecircuitsApplication Framework
and thednslibDNS library. udns can be run standalone, viaDockeror using theDocker Composetool. udns is designed to be small,
lightweight, fast and flexible. udns fully supports forwarding, caching
as well as honoring TTL(s). udns will also read your/etc/hostsfile
at startup and use this to populate an internal hosts cache so that entries
in your local/etc/hostsfile are fully resolvable with tools such ashost,digand resolver client libraries.Installation and UsageFrom Source:$ git clone https://github.com/prologic/udns
$ cd udns
$ python setup.py develop
$ sudo udnsd --debug  # Server
$ udnsc --help        # ClientFrom Source usingDockerandDocker Compose:$ git clone https://github.com/prologic/udns
$ cd udns
$ docker-compose up -d                  # Server
$ docker-compose run --rm udns udnsc    # ClientUsingDocker:$ docker run -d -p 53:53/udp prologic/udnsFrom PyPi (coming soon):$ pip install udns
$ udnsd     # Server
$ udnsc     # ClientRunning as a Daemon:$ sudo udnsd -d --logfile=$(pwd)/udnsd.log --pidfile=$(pwd)/udnsd.pidManaging Zones and Recordsudns is a full authoritative, caching DNS server and ships with a client to
help manage zones and records. Here are some quick examples:Create new Zone:$ udnsc create abc.com.Create new Zone from a file:$ udnsc create abc.com. abc.comor::$ udnsc create abc.com. - < abc.comList Zones:$ udnsc listShow Zone Records:$ udnsc show abc.com.Export a Zone::$ udnsc export abc.com.NoteThis exports the Zone to stdout which you can pipe into an output
file for storage using your shell.
e.g:udnsc export abc.com. > abc.comAdd Zone Records:$ udnsc add abc.com. www 127.0.0.1Delete a Zone Record:$ udnsc delete abc.com. wwwDelete a Zone:$ udnsc delete abc.com.NoteYou __must__ specify zones as fully qualified domain names with a
trailing period. e.g:abc.com.Changesudns 0.0.2 (2016-01-26)Added support for CNAME(s)Added support for @ ($ORIGIN)Various small bugfixesudns 0.0.1 (2014-08-26)Initial Release
Package: udon2
Prepare your UD trees to be served!UDon2 is a library for manipulating dependency trees in the Universal Dependencies format. Written in C++ with Python bindings through Boost.Python. Pre-compiled for Python 3.6-3.9 on Linux (both i386 and x86_64), Python 3.7-3.9 on Windows (win32 and win_amd64) and Python 3.7-3.9 for macOS (x86_64 only).DocumentationOur online documentation is available athttps://udon2.github.io/ContributingPlease check ourcontribution guidelines.CitingThe associated paper is accepted to the Universal Dependencies Workshop 2020 @ COLING 2020. If UDon2 is of any use in your research, please cite the paper.@inproceedings{kalpakchi2020udon2,
  title={UDon2: a library for manipulating Universal Dependencies trees},
  author={Kalpakchi, Dmytro and Boye, Johan},
  booktitle={Proceedings of the Fourth Workshop on Universal Dependencies (UDW 2020)},
  pages={120--125},
  year={2020}
}
Package: udos
udosUniversal Dataset Open StandardA place to develop an open standard for dataset
search, acquisition, reusable preparation, and sharing.Target user storiesSearch outside data (use several sources such as kaggle, github, named urls, etc?)Get local copy of dataSet rules for automatic updates of dataSearch locally using:General query expansion (using word2vec?)Custom aliasesFuzzy matchingConstruct data feeds by combining multiple data feedsVisualize relationships (to understand the data, spot errors, etc.)Operable quick models (for quick-and-dirty inference)
Package: udp-chat
Asynchronous UDP-based chat applicationHosted atudpchat://209.97.187.173How to set up and run the application.1. Create a python virtual enviromentRunpython3 -m venv venvto create the virtual enviroment.Runsource venv/bin/activateto activate the virtual enviroment.Runpip install -e .to install dependencies to the virtual environment2. Starting the serverOn localhostRunpython3 -m udp_chat.serverto start running the server.On specified IP addressRunpython3 -m udp_chat.server (IP address) (port number)to start running the server3. Starting a client GUIIn a new terminal:Connect to a server on localhostRunpython3 -m udp_chat.gui_clientto start the client GUIConnect to a server IP addressRunpython3 -m udp_chat.gui_client (IP address) (port number)to start the client GUI4. Starting a command line clientNote:Must already have created an accountIn a new terminal:Connect to a server on localhostRunpython3 -m udp_chat.clientto start the clientConnect to a server IP addressRunpython3 -m udp_chat.client (IP address) (port number)to start the client GUI5. Delete the enviromentRunrm -rf venvto delete the virtual enviroment
Package: udp-filetransfer
UDP FiletransferFast file transfer over UDP Broadcast.Install (CLI)From PyPIpipinstalludp-filetransferFrom Gitgitclonehttps://gitlab.com/Trickster-Animations/udp-filetransfer.gitcdudp-filetransfer
poetry||pipinstallpoetry
poetryinstallNow, you can use it through poetry:poetry run python -m udp_filetransferTo use it from system python, do:poetrybuildcddist
pip3install*.whlNow, you can use it by running:python3 -m udp_filetransferUsage (CLI)To send a file:python3 -m udp_filetransfer send [filepath]To receive a file:python3 -m udp_filetransfer receiveNote: The receiver has to be started first.Install (Dependency)Just add theudp-filetransferpackage, like with any other dependency.Usage (Dependency)# receive.pyimportudp_filetransferoutput=udp_filetransfer.receive()print(output)# send.pyfromsysimportargvimportudp_filetransferudp_filetransfer.send(argv[1])Note: Just like with CLI, the receiver needs to be started first.CreditsPackage maintained by Trickster Animations