Saravanan666/pypi-org huggingface dataset:
Package: view-logger
View loggerView Logger is a decorators for Django views. It allows you to collect basic information about the user who made the request and about errors.Quick startCreate empty dirlogsin the root of your projectCreateloggingconfiguration in your setting. By default,view-loggeruseserror_loggerandinfo_logger. These two loggers should be in the logging settings.LOGGING = {
    'version': 1,
    'formatters': {
        ...
    },
    'handlers': {
        ...
    },
    'loggers': {
        'info_logger': {
            ...
        },
        'error_logger': {
            ...
        },
    },
}Import fromview_loggerdecorators:log_wrapperorhttp_loggerlog_wrapper- Decorator for logging unhandled errors.http_logger- Decorator for logging user`s information and unhandled errors.Extra tuningYou can specify additional keys in the settings for additional configuration.HTTP_ERROR_LOGGER- you can set your custom logger name for using. Default iserror_logger.HTTP_INFO_LOGGER- you can set your custom logger name for using. Default isinfo_logger.USER_OBJECT- for use other then django user object in request. Default isuser.
If you want to change the object where the user data comes from, you must set user-like object inrequestobject.
Package: viewmask
viewmaskA Python package and CLI to view XML annotations and NumPy masks.viewmask is meant to be a quick-and-easy tool for visualizing masks and a quick-and-easy library for working with annotations. For a more powerful library, considerHistomicsTK(seeannotations and masks).Installationpippython3 -m pip install --upgrade pip
python3 -m pip install viewmaskor:python3 -m pip install --upgrade pip
python3 -m pip install git+git://github.com/sumanthratna/viewmask.git#egg=viewmaskPoetrypoetry run python -m pip install --upgrade pip
poetry add viewmaskor:poetry run python -m pip install --upgrade pip
poetry add git+https://github.com/sumanthratna/viewmask.git
Package: viewmydb
Viewmydb is a program for working with MySQL or PostgreSQL databases. Executed from a command-line terminal or shell, viewmydb provides a graphical user interface implemented using Urwid, a framework library based on ncurses. General functions like viewing tables in a database and viewing data in tables is supported, as well as more specific functionality such as creating tables, populating tables with data, and running direct SQL queries against a database from within the program. The application was developed by David Adams and Adam Pedigo as part of their curriculum at Oregon State University while pursuing degrees in Computer Science.Documentation can be found in the GitHub repository at:https://github.com/OSU-CS419/viewmydb
Package: view.py
The Batteries-Detachable Web Framework[!Warning]
view.py is in very early stages and not yet considered to be ready for production.
If you would like to follow development progress, jointhe discord.
For contributing to view.py, please see ourCONTRIBUTING.mdview.py is affiliated withSpace HostingDocsSourcePyPIDiscordExamplefromviewimportnew_app,h1app=new_app()@app.get("/")asyncdefindex():returnh1("Hello, view.py!")app.run()InstallationPython 3.8+ is required.Development$ pip install git+https://github.com/ZeroIntensity/view.pyLinux/macOS$ python3 -m pip install -U view.pyWindows> py -3 -m pip install -U view.py
Package: views
views– efficient tools for generators and sequencesTheviewsmodule provides additions to the existing python comprehensions and generator expressions. (Chained) sequence views can be made usingseqand generators can be chained withgen, as shown in the examples below.Any feedback or suggestions are very welcome.Email:koos.zevenhoven@aalto.fiTwitter:@k7hovenGetting StartedRequirementsPython 3.6+InstallationThe package can be installed withpip(make sure you have it installed):pip3installgit+http://github.com/k7hoven/viewsOr if your default python is Python 3:pipinstallgit+http://github.com/k7hoven/viewsBasic UsageBasic introductory examples here, but they should get you started.Sequence view comprehension syntaxYou can change chain single objects and sequences into one sequence view. Use::just like you would use*in tuple (un)packing. The resulting object supports slicing and indexing.Example:>>>fromviewsimportseq>>>seq[::range(3),None,::"abc","Hi!"]<sequenceview8:[0,1,2,None,'a','b','c','Hi!']>>>>seq[::range(100)]<sequenceview100:[0,1,2,3,4,...,96,97,98,99]>Generator comprehension syntaxUse likeseq. The resulting object is a generator.Example:>>>fromviewsimportgen>>>list(gen[::range(3),3,4,::range(5,7),7])[0,1,2,3,4,5,6,7]Chaining sequences and generators/iterablesYou can chain an arbitrary number of sequences withseq.chain(*sequences)and of generators withgen.chain(*iterables). The latter is equivalent toitertools.chain(*iterables).Example:>>>fromviewsimportseq,gen>>>seq.chain([1,2,3],[4,5,6])<sequenceview6:[1,2,3,4,5,6]>>>>list(gen.chain([1,2,3],[4,5,6]))[1,2,3,4,5,6]Have fun!Changelog0.3.0 (2017-10-10)Addseq.chain(*sequences)andgen.chain(*iterables)Some speed optimizations, especially toseq.0.2.0 (2017-09-11)LengthChangedError is now a subclass ofRuntimeError.Minor optimizations toseqandgen.0.1.0 (2017-06-06)First version
Package: viewscad
ViewSCAD: CAD in JupyterViewSCAD is a module that renders within a Jupyter notebook 3D objects described in theOpenSCADlanguage, or described using theSolidPythonmodule.  It provides a workflow for CAD design completely within the Jupyter notebook.InstallationViewSCAD utilizes theopenscadexecutable to render objects, and as such, OpenSCAD must be installed on your system.ViewSCAD installs simply with pip:pip install viewscadViewSCAD uses thepythreejsnotebook extension to render the 3D graphics.
If you intend to usejupyter notebook, you should be all set.  However, if you intend to usejupyter lab,  additional steps are necessary to getpythreejsworking in the lab.  Firstly, you must installnode.js and npm.  Then you must install the lab extensions:jupyter labextension install @jupyter-widgets/jupyterlab-managerSetup and UsageSee theOpenSCADandSolidPythonpages for how to use these languages to represent geometric objects.The following shows how to use ViewSCAD to render a SolidPython object in Jupyter:ViewSCAD attempts to locate your openscad executable, based on your platform, but failing that, you can set theOPENSCAD_EXECenvironment variable to point at youropenscadcommand-line executable, or pass it in the constructor of the renderer:renderer = viewscad.Renderer(openscad_exec='path_of_my_openscad')You can also render OpenSCAD code:The renderer can also create an.STLfile directly by simply adding anoutfilekeyword:r.render(c, outfile='my_thing.stl')IntropectionBy double-clicking on a rendered object, you can select faces, edges, and vertices, and learn their indices.  This allows you
to use several useful utilities, likeRenderer.rotate_face_down()put orient the object with a particular face downward,
orRenderer.place_on(), which lets you place one object upon another:AcknowledgementsThanks to Evan Jones for help creating the PyPI package.
Package: view-spark-timeline
Command line application to visualize the timeline of Apache Spark executions, reading Spark’s log files.A fundamental assumption is that all the executors are added before the Spark application submits any job.
That is, this tool does not support dynamic scaling.Can you spot the bottleneck from the following visualization?Image explanationOn the vertical axis we have the executor cores (grouped by executor).
On the horizontal axis we have the time, going from left to right.
Each task is a horizontal bar that starts at a certain time on a core of an executor and ends after some time.
The color normally ranges from green, used for shorter tasks, to red, used for longer tasks. Failed tasks are black.
All the white space corresponds to some unused core.Usually, the greener the image is, the better. If there is a bottleneck in the execution it is easy to spot the guilty task(s).
By opening the SVG in a browser and by moving the mouse over a task there should appear a tooltip with the task ID.
It is then useful to inspect the task using the standard Spark UI.Installationpipinstallview-spark-timelineExampleview-spark-timeline-iexamples/application_1472176676028_555248_1-odocs/timeline.svg-u1000Output:Read events from 'examples/application_1472176676028_555248_1'...
Total cores: 32
Total duration: 312.5s
Number of tasks: 2990
Min task duration: 0.0s
Max task duration: 25.9s
Cluster utilization: 57.70%
Drawing events...
Read events from 'examples/application_1472176676028_555248_1'...
SVG size: 1500 160
Saving SVG...Usageview-spark-timeline--helpOutput:usage: view-spark-timeline [-h] -i INPUT_LOG -o OUTPUT_IMAGE
                       [-t TIME_UNCERTAINTY] [-v]

Visualize the timeline of a Spark execution from its log file. (v0.2.0)

optional arguments:
-h, --help            show this help message and exit
-i INPUT_LOG, --input-log INPUT_LOG
                        path to the spark's application log
-o OUTPUT_IMAGE, --output-image OUTPUT_IMAGE
                        path of the output image
-u TIME_UNCERTAINTY, --time-uncertainty TIME_UNCERTAINTY
                        maximum allowed time uncertainty (in ms) of the
                        timestamps in the log file. An high uncertainty
                        determines a slower, but more robust, execution.
                        (Default: 0)
-v, --version         print version and exitLicenseCopyright (c) 2017-2020, Federico Poli <federpoli@gmail.com>This project, except for files in thelibandexamplesfolders, is released under the MIT license.
Package: views-py
Views-PyViews and related utilities for generic sequence types.Getting StartedThis project is available through pip (requires Python 3.9 or higher):pip install views-pyExternal documentation (i.e., documentation that exists beyond in-code) is currently under development, and will be linked here when finalized.ContributingThis project is currently maintained byBraedyn L. Feel free to report bugs or make a pull request through this repository.LicenseDistributed under the MIT license. See theLICENSEfile for more details.
Package: views-query-planning
Views Query PlanningThis package exposes a classviews_query_planning.QueryComposerthat makes it
possible to generate queries against a relational database using a network
representation of the database. Such networks can be inferred using theviews_query_planning.join_networkfunction that takes a dictionary ofsqlalchemytables and returns anetworkx.DiGraphthat can be passed to the
composer.For an example service that uses the QueryComposer class to expose columns in a
relational DB RESTfully, seebase_data_retriever.
Package: views-runs
views-runsThis package is meant to help views researchers with training models, by
providing a common interface for data partitioning and stepshift model
training. It also functions as a central hub package for other classes and
functions used by views researchers,
includingstepshift(StepshiftedModels)
andviews_partitioning(DataPartitioner).InstallationTo installviews-runs, use pip:pip install views-runsThis also installs the vendored librariesstepshiftandviews_partitioning.UsageThe library offers a class imported atviews_runs.ViewsRun, that wraps the to
central components of a ViEWS 3 run: A partitioning scheme expressed via aviews_partitioning.DataPartitionerinstance, and a stepshifted modelling
process expressed via astepshift.views.StepshiftedModelsinstance.
For documentation on the data partitioner, seeviews_partitioning. For documentation on stepshifted modelling, seeviews.StepshiftedModels.The wrapper takes care of applying these two classes to your data, in order to
produce predictions in a familiar and predictable format, as well as ensuring
that there is no overlap between training and testing partitions.
Instantiating a run requires instances of both of these classes, like so:run = ViewsRun(
   DataPartitioner({"A":{"train":(1,100),"test":(101,200)}}),
   StepshiftedModels(LogisticRegression,[1,2,3,4,5,6],"my_dependent_variable"),
)This instance can then be applied to atime-unit indexed
dataframeto train the models, and produce predictions for the timespans defined in the data partitioner:run.fit("A","train",dataframe)
predictions = run.predict("A","test",dataframe)ExamplesThere are notebooks that show various workflows withviews_runsand the
vendored libraries:BasicExample.ipynbFundingThe contents of this repository is the outcome of projects that have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 694640,ViEWS) and Horizon Europe (Grant agreement No. 101055176,ANTICIPATE; and No. 101069312,ViEWS(ERC-2022-POC1)), Riksbankens Jubileumsfond (Grant agreement No. M21-0002,Societies at Risk), Uppsala University, Peace Research Institute Oslo, the United Nations Economic and Social Commission for Western Asia (ViEWS-ESCWA), the United Kingdom Foreign, Commonwealth & Development Office (GSRA –Forecasting Fatalities in Armed Conflict), the Swedish Research Council (DEMSCORE), the Swedish Foundation for Strategic Environmental Research (MISTRA Geopolitics), the Norwegian MFA (Conflict TrendsQZA-18/0227), and the United Nations High Commissioner for Refugees (the Sahel Predictive Analytics project).
Package: views-storage
Views StorageThis package contains various classes used for storage and retrieval of data
within views. The main class exposed by the package isviews_storage.key_value_store.KeyValueStore. This class is composed withviews_storage.serializers.serializer.Serializerandviews_storage.backends.storage_backend.StorageBackendsubclasses to provide
storage in various formats using various backends.Examplefrom views_storage.key_value_store import KeyValueStore
from views_storage.backends.azure import AzureBlobStorageBackend
from views_storage.serializers.pickle import Pickle

my_storage = KeyValueStore(
      backend = AzureBlobStorageBackend(
         connection_string = "...",
         container_name = "..."),
      serializer = Pickle()
   )

my_object = ...

my_storage.store("key", my_object)
Package: viewstate
A small Python 3.5+ library for decoding ASP.NET viewstate.Viewstate is a method used in the ASP.NET framework to persist changes to a web form across postbacks. It is usually saved on a hidden form field:<inputtype="hidden"name="__VIEWSTATE"id="__VIEWSTATE"value="/wEP...">Decoding the view state can be useful in penetration testing on ASP.NET applications, as well as revealing more information that can be used to efficiently scrape web pages.Install$pipinstallviewstateUsageThe Viewstate decoder accepts Base64 encoded .NET viewstate data and returns the decoded output in the form of plain Python objects.There are two main ways to use this package. First, it can be used as an imported library with the following typical use case:>>>fromviewstateimportViewState>>>base64_encoded_viewstate='/wEPBQVhYmNkZQ9nAgE='>>>vs=ViewState(base64_encoded_viewstate)>>>vs.decode()('abcde',(True,1))It is also possible to feed the raw bytes directly:>>>vs=ViewState(raw=b'\xff\x01....')Alternatively, the library can be used via command line by directly executing the module:$catdata.base64|python-mviewstateWhich will pretty-print the decoded data structure.The command line usage can also accept raw bytes with the-rflag:$catdata.base64|base64-d|python-mviewstate-rViewstate HMAC signatures are also supported. In case there are any remaining bytes after parsing, they are assumed to be HMAC signatures, with the types estimated according to signature length.>>>vs=ViewState(signed_view_state)>>>vs.decode()>>>vs.mac'hmac_sha256'>>>vs.signatureb'....'DevelopmentDevelopment packages can be installed withpipenv. Unit tests and code formatting tasks can be run with the builtin scripts:$pipenvsync-d$pipenvruntest$pipenvrunformatFor PyPI releases, follow the build, check and upload scripts.$pipenvrunbuild$pipenvruncheck$pipenvrunuploadNote that for uploading a new package version, a valid PyPI auth token should be defined in~/.pypirc.ReferencesSince there is no publically available specification of how .NET viewstate is encoded, reverse engineering was based on prior work:https://github.com/mutantzombie/JavaScript-ViewState-Parserhttp://viewstatedecoder.azurewebsites.net/https://referencesource.microsoft.com/#System.Web/UI/ObjectStateFormatter.cs,45https://msdn.microsoft.com/en-us/library/ms972976.aspxAny official documents would be gladly accepted to help improve the parsing logic.LicenseMIT
Package: views-transformation-library
ViEWS Transformation LibraryThis package contains transforms made available in ViEWS cloud.  The transforms
are all locally available, and do not depend on remote resources or
credentials.Contributing:There is an urgent need to fill this library with transforms.
Transforms we need, that are not implemented, are listed under issues.
Please follow the guidelines for contributing outlined in theCONTRIBUTING.mddocument.Usage:pip install views-transformation-libraryfrom views_transformation_library.timelag import timelag
import viewser

ged_best_ns = viewser.get_variable("priogrid_month","priogrid_month.ged_best_ns",2010,2020)
lagged_data = timelag(ged_best_ns,10)

lagged_from_api = viewser.get_variable("priogrid_month","ged_best_ns",2010,2020,transforms:[
   {"type":"tlag","args":[10]}
])

assert lagged_from_api == lagged_dataAPI:Each transform function operates on a pandas dataframe.FundingThe contents of this repository is the outcome of projects that have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 694640,ViEWS) and Horizon Europe (Grant agreement No. 101055176,ANTICIPATE; and No. 101069312,ViEWS(ERC-2022-POC1)), Riksbankens Jubileumsfond (Grant agreement No. M21-0002,Societies at Risk), Uppsala University, Peace Research Institute Oslo, the United Nations Economic and Social Commission for Western Asia (ViEWS-ESCWA), the United Kingdom Foreign, Commonwealth & Development Office (GSRA –Forecasting Fatalities in Armed Conflict), the Swedish Research Council (DEMSCORE), the Swedish Foundation for Strategic Environmental Research (MISTRA Geopolitics), the Norwegian MFA (Conflict TrendsQZA-18/0227), and the United Nations High Commissioner for Refugees (the Sahel Predictive Analytics project).
Package: view-utube
utubeSimple TUI app to watch suscribed youtube channel videos.Table of contentsInfoTechnologiesUsageControlsInfoutubeis a basic TUI application to watch youtube videos. Just add the channel links you want to watch, and you are ready to go.TechnologiesBuilt in python3 withcurses.Dependencies:- scrape and parse youtube pagesUsage# installpipinstallview-utube# runutubeControlsjMove UPkMove DOWNgMove to TOPGMove to BOTTOMaAdd channellEnter sub-menuhExit sub-menuqQuit
Package: viff
The Virtual Ideal Functionality Framework is a framework for doing
secure multi-party computations (SMPC). Features include:secret sharing based on standard Shamir and pseudo-random secret
sharing (PRSS).arithmetic with shares from Zp or GF(2^8): addition, multiplication,
exclusive-or.two comparison protocols which compare secret shared Zp inputs, with
secret GF(2^8) or Zp output.reliable Bracha broadcast secure against active adversaries.computations with any number of players for which an honest majority
can be found.All operations are automatically scheduled to run in parallel meaning
that an operation starts as soon as the operands are ready.
Package: viffbench
ViffBench is a set of scripts that makes it easy to write, execute, and
visulize benchmarks of VIFF protocols. Features includeBenchmarking using arbitrary VIFF revisions.Based on ssh and thus benefits from key agents like ssh-agent.Benchmarking of distributed VIFF protocols as well as local VIFF code.Automatically stores benchmark results in central database.Stores benchmark results for every single run, e.g. no info is lost.
Package: viffIO
viff-xvA viff or xv file is from Khoros/VisiQuest packages which has a header of 1024 bytes and is BSQ formated raw raster file. The file is parsed based upon the C struct definition provided in the source section.RequirementsPython 3.7numpyFeatures and I/OReaddata = viff.read(filename: str)Writeviff.write(filename: str, data: np.array)Variable Descriptionfilenames (str) - Path to the input file to readdata (np.array) - data to save formated as np.array with dimensions [NumberOfImages,NumberOfBands,NumberOfColumns,NumberOfRows]Code ExampleRead and Display FileRead and display the first image and band of the read in file.import viff
import matplotlib.pyplot as plt
import argparse

parser = argparse.ArgumentParser(description='viff reader example')
parser.add_argument('--file', type=str, help='filename (default=None)')
parser.add_argument('--save', action='store_true', help='save a copy as testing.xv')
args = parser.parse_args()

""" Read a file """
data = viff.read(args.file)

plt.imshow(data[0,0,:,:])
plt.show()Write a new fileif args.save:
  """ Write a new file """
  viff.write('testing.xv',data)Command line usage$ python viff.py --file <filename> --saveThis generates a plot and a new file 'testing.xv'.Sources:File Formathttp://www.fileformat.info/format/viff/egff.htm
Package: vificov
VisualFieldCoverage (ViFiCov) visualization in python.This package takes as input parameters that describe a 2D Gaussian model
for a set of voxels and returns projections of the voxels’ visual field
coverage.InstallationFor installation, follow these steps:Option A: install via pippipinstallvificovOption B: install from github repository(Optional) Create conda environmentcondacreate-nenv_vificovpython=3.6sourceactivateenv_vificovcondainstallpipClone repositorygitclonehttps://github.com/MSchnei/vificov.gitInstall vificov with pippipinstall/path/to/cloned/vificovHow to use1. Adjust the csv fileAdjust the information in the config_default.csv file, such that the
provided information is correct. It is recommended to make a specific
copy of the csv file for every subject and project.2. Run pyprf_motionOpen a terminal and runvificov -config path/to/custom_config.csvReferencesThis application is based on the following work:Le, R., Witthoft, N., Ben-Shachar, M., & Wandell, B. (2016). The
field of view available to the cortical reading circuitry. BioRxiv,
17, 1–19.https://doi.org/https://doi.org/10.1101/069369Kok, P., Bains, L. J., Van Mourik, T., Norris, D. G., & De Lange, F.
P. (2016). Selective activation of the deep layers of the human
primary visual cortex by top-down feedback. Current Biology, 26(3),
371–376.https://doi.org/10.1016/j.cub.2015.12.038LicenseThe project is licensed underGNU General Public License Version
3.
Package: vif-plug-hw-veb
Anos-vifVIF plugin for plugging and unplugging virtual interfaces that use
a hardware virtual ethernet bridge.FeaturesAvif_plug_hw_veb.hw_veb.HardwareVebPluginVIF plugin for hardware virtual
ethernet bridges.InstallationInstall the Hardware VEB VIF plugins usingpip:sudo pip install vif_plug_hw_vebAfter doing so, theos-viflibrary’sinitialize()method will automatically
load the Hardware VEB VIF plugins in this library and allow Nova and any other
system to plug VIFs that use hardware virtual ethernet bridges.ConfigurationThe following configuration options are used by thevif_plug_hw_veb.hw_veb.HardwareVebPluginVIF plugin and are passed from theos_vif.initialize(**config)function:disable_rootwrap– Defaults toFalse. Override to entirely disable any
use of rootwrap and instead rely solely on sudoers files.use_rootwrap_daemon– Defaults toFalse. Override to enable the rootwrap
daemon mode which can increase the performance of root-run commands.rootwrap_config– Defaults to‘/etc/nova/rootwrap.conf’. Path to theoslo.rootwapconfig file.