Saravanan666/pypi-org huggingface dataset:
Package: deiman
Deiman is a Python utility class for daemonizing a process.
It has start and stop methods, as well as a method for retrieving running status information.Linux/Unix-only.Uses the Unix double fork method to fork your process to the background.InstallationFrom PyPIpip install deimanOreasy_install deimanFrom GitHubpip install -e git+git://github.com/kura/deiman.git#egg=deimanFrom sourceDownload the latest tarball from PyPI or GitHub. Unpack and run:python setup.py installUsageTo use Deiman, you simply need to import the main Deiman class,
passing a path to where you want the pid to be stored and call
the start and stop methods as required:from deiman import Deiman


d = Deiman("/tmp/a.pid")
d.start()

while True:
    print "This print will be hidden because I am daemonized"ExamplesSee theexamplesdirectory for usage examples on GitHub.
Package: deims
deimsPyThis repository represents the latest version of a collection of python functions with the intention of easing access to DEIMS-SDR data (https://www.deims.org).
This python package is openly available and free to use. Should you use DEIMS-SDR data for any studies or analyses, please cite the service accordingly.
For further information about DEIMS-SDR, please refer to its About page (https://www.deims.org/about).InstallationVia pip (see the package onPyPI):pip install deims.
Manually: downloaddeims.pyto working directory.You can thenimport deimsas normal.UsageGet the list of all verified sites of a network, e.g. ECNdeims.getListOfSites(network="a197664f-569e-4df6-933a-86de676dbfc5",verified_only=True)Get the list of all network sites (verified and not verified), e.g. LTER Austriadeims.getListOfSites("d45c2690-dbef-4dbc-a742-26ea846edf28")Get the list of all sitesdeims.getListOfSites()Get a particular site record by its DEIMS.ID (multiple formats of the DEIMS.ID are accepted)deims.getSiteById(site_id="deims.org/8eda49e9-1f4e-4f3e-b58e-e0bb25dc32a6")deims.getSiteById("1b94503d-285c-4028-a3db-bc78e31dea07")Get all sites on DEIMS within 30km of latitude 47.84, longitude 14.44deims.getSitesWithinRadius(lat=47.84,lon=14.44,distance=30000)Get all sites on DEIMS within 1500m of latitude 57.08, longitude -3.667deims.getSitesWithinRadius(57.08,-3.667,1500)Get the coordinates or boundaries of a particular site as a GeoDataFramedeims.getSiteCoordinates("8eda49e9-1f4e-4f3e-b58e-e0bb25dc32a6")deims.getSiteBoundaries("8eda49e9-1f4e-4f3e-b58e-e0bb25dc32a6")Get the coordinates boundaries of multiple sites and save them as a shapefile called "lter-austria.shp"deims.getSiteCoordinates(deims.getListOfSites("d45c2690-dbef-4dbc-a742-26ea846edf28"),"lter-austria")deims.getSiteBoundaries(deims.getListOfSites("d45c2690-dbef-4dbc-a742-26ea846edf28"),"lter-austria")
Package: deinterf
deinterfThe project aims to compensate for magnetic interference related to the magnetic field of the measurement platform itself within the magnetic total field data. Utilizing the Tolles-Lawson model and Ridge Regression, it models the relationship between flight attitude and magnetic interference, forming a magnetic compensation model. Herein, the flight attitude is obtained from magnetic tri-component measurements, while the magnetic interference is derived from the FOM flight measurement results after being processed through a high-pass filter.Getting starteddeinterf can be installed using pip:pipinstalldeinterfA usage case can be found in theexamplefolderManual installationgitclonehttps://github.com/dorian-li/deinterf.gitcddeinterf
pipinstall-e.# If editable mode is not required:# pip install .# This means that modifications made within the cloned code cannot be directly fed back to the running code and need to be reinstalled.LicensingThe code in this project is licensed under MIT license.
Package: deirokay
DeirokayDeirokay (dejɾo'kaj) is a tool for data profiling and data validation.Deirokay separates document parsing from validation logic,
so that you can create your statements about your data
without worrying whether or not your file has been properly
parsed.You can use Deirokay for:Data parsing from files (CSV, parquet, excel, or any other
pandas-compatible format);Data validation, viaDeirokay Statements;Data profiling, which generates Deirokay Statements automatically
based on an existing file. You may use these statements later against
new documents to make sure the validation still holds for new data.InstallationTo start using Deirokay, install its package by following the instructions below.By default, only core dependencies are installed when supplying the pip
command:pipinstallDeirokayDepending on your use cases, you need to include extra dependencies.
Use any of the commands below:pipinstallDeirokay[s3]# Optional dependencies for use with AWS S3pipinstallDeirokay[pandas]# For Pandas backendpipinstallDeirokay[dask]# For Dask or Dask Distributed backendsYou may also install any combination of the extras above by separating
them with commas:pipinstallDeirokay[dask,s3]# Dask + S3 depsTo check if Deirokay is properly installed and to know its version,
enter the following command in the Python prompt:>>> import deirokay
>>> deirokay.__version__
'X.Y.Z'If you wish to contribute for Deirokay development, you need to install
a more restrict set of packages, in order to guarantee that you are
always creating and testing code to work with least recent supported
versions:pipinstallDeirokay[dev]DocumentationPlease,read the docs.ContributingCheck ourcontributingguidelines.
Package: deize-method
Deize メソッド説明Deize が使いそうなメソッドをまとめたよdirectory_listing指定したディレクトリ内のファイルの一覧を出力するよすべて、ディレクトリのみ、ファイルのみの三つがあるよversion_checkgitに登録しているtagを取得してバージョンを返すよ
Package: dejacode
DejaCode is a complete enterprise-level application to automate open source license
compliance and ensure software supply chain integrity, powered byScanCode,
the industry-leading code scanner.Run scans and track all the open source and third-party products and components used
in your software.Apply usage policies at the license or component level, and integrate into
ScanCode to ensure compliance.Capture software inventories (SBOMs), generate compliance artifacts, and keep
historical data.Ensure FOSS compliance with enterprise-grade features and integrations for DevOps and
software systems.Scan a software package, simply by providing its Download URL, to get comprehensive
details of its composition and create an SBOM.Load software package data into DejaCode with the integration for the open source
ScanCode.io and ScanCode Toolkit projects to create a product’s SBOM.Track and report vulnerability tracking and reporting by integrating with the open
source VulnerableCode project.Create, publish and share SBOM documents in DejaCode, including detailed attribution
documentation and custom reports in multiple file formats and standards, such as
CycloneDX and SPDX.Getting startedThe DejaCode documentation is available here:https://dejacode.readthedocs.io/If you have questions please ask them inDiscussions.If you want to contribute to DejaCode, start with ourContributingpage.Build and tests statusTestsDocumentationDejaCode License NoticeDejaCode is an enterprise-level application to automate open source license
compliance and ensure software supply chain integrity, powered by ScanCode,
the industry-leading code scanner.SPDX-License-Identifier: AGPL-3.0-onlyCopyright (c) nexB Inc.This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, version 3 of the License.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 Affero General Public License for more details.You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.Commercial License optionDejaCode is offered under a nexB commercial license as an alternative.
You can learn more about this option by contacting us athttps://www.nexb.com/contact-us/
Package: dejankify
DEJANKIFY -- remove the jank from your codeThis is a tool that helps you maintain your python environment by removing unnecessary dependencies.
By default, it will remove all packages that are not explicitly imported in your code.
It will also remove any import statements that are for packages that are not referenced in your code.
It also creates a requirements.txt file that contains all the packages that are explicitly imported in your code
and are not part of the standard library and are pip installable.This is meant to enhance workflows that are using virtual environments and pip to manage dependencies.Dejankify works best with pyenv, virtualenv, black, isort, and pylint.Usage:
dejankify [options]To install run:pipinstalldejankifyA sample script before dejankify:importosimportsysimportreimportjsonimportrequestsimportnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltfromsklearnimportlinear_modelfromsklearn.metricsimportmean_squared_error,r2_scorename=input("What is your name? ")first,last=re.match(r"(\w+)\s(\w+)",name).groups()print(f"Hello{first}{last}!")The above script would imply the following external dependencies:numpypandasmatplotlibsklearnBefore running dejankify, you would need to install the above packages in your virtual environment and when your code
runs it would import the above packages, which would be unnecessary if you are not using them and would slow down your
code in addition to increasing the size of your virtual environment.Now, let's run dejankify:dejankifyAfter running dejankify, the script would look like this:importrename=input("What is your name? ")first,last=re.match(r"(\w+)\s(\w+)",name).groups()print(f"Hello{first}{last}!")The above script would have no external dependencies and would run faster.Dejankify will also create several files after it runs that you may delete or version control:requirements.txt - contains all the packages that are explicitly imported in your code and are not part of the standard library and are pip installable.temp_requirements.txt - Your original requirements.txt file will be renamed to this file.invalid_requirements.json - contains a list of packages that are explicitly imported in your code but are not pip installable.valid_requirements.json - contains a list of packages that are explicitly imported in your code and are pip installable.builtins.json - contains a list of all the built-in modules in your version of python.
Package: dejavu-gi
Bindings to graph isomorphism solver dejavuThis package provides a basic ctypes wrapper to the graph isomorphism solverdejavu(https://www.mathematik.tu-darmstadt.de/dejavu). The goal of the package is to provide easy-to-use, quick access to the main functionality ofdejavu. For performance critical software, consider using the C++ version ofdejavudirectly.FeaturesThe package exposes methods of the probabilistic graph isomorphism solverdejavu. The main features include easy access to a probabilistic graph isomorphism test, probabilistic computation of graph automorphisms (AKA symmetries), color refinement (AKA 1-WL) and random walks of IR trees.QuickstartOnce installed using pip, the package can simply be imported usingimport dejavu_gi.Lets assume we want to compute the symmetries of the 5-cycle. Graphs in the package are represented using the number of verticesn(in this case 5) and an (undirected) edgelist, in this case[[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]]. We can then simply compute the symmetries as follows.import dejavu_gi

group = dejavu_gi.get_automorphisms(5, [[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]])
print(group)The above code outputs the following representation of the automorphism group:{'generators': [[4, 3, 2, 1, 0], [2, 3, 4, 0, 1]], 'base': [2, 4], 'size': 10.0}generatorsis a generating set of the automorphism group, whereasbaseis a base andsizeis the order of the automorphism group. Note that this computation is probabilistic. More precisely, the solver is only guaranteed to return all the symmetries with some bounded error probability (which can be set usingerr). For more precise information on this, see the code documentation.If we want to test two graphs for isomorphism, we can do so using:import dejavu_gi

is_iso1 = dejavu_gi.are_isomorphic(5, [[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]], 5, [[1, 0], [1, 2], [3, 2], [3, 4], [4, 0]])
is_iso2 = dejavu_gi.are_isomorphic(5, [[0, 1], [1, 2], [2, 3], [3, 4], [4, 0]], 5, [[1, 0], [1, 2], [3, 2], [3, 4], [4, 1]])
print(is_iso1)
print(is_iso2)The output for the above would be:0
0We can also use the color refinement ofdejavu, using the following code:colors = dejavu_gi.color_refinement(3, [[0, 1],[0, 2]])
print(colors)This returns a mapping from vertices to colors, in the above example this is:[2, 0, 0]
Package: dejax
dejaxAn implementation of replay buffer data structure in JAX. Operations involving dejax replay buffers can be jitted and run on both CPU and GPU.Package contentsdejax.circular_buffer— an implementation of a circular buffer data structure that can store pytrees of arbitrary structure (with the restriction that the corresponding tensor shapes in different pytrees match).dejax.uniform— a FIFO replay buffer of fixed size that samples replayed items uniformly.dejax.clustered— a replay buffer that assigns every trajectory to a cluster and maintains a separate replay buffer per cluster. Sampling is performed uniformly over all clusters. This kind of replay buffer is helpful when, for instance, one needs to replay low and high reward trajectories at the same rate.How to use dejax replay buffersimportdejaxFirst, instantiate a buffer object. Buffer objects don't have state but rather provide methods to initialize and manipulate state.buffer=uniform_replay(max_size=10000)Having a buffer object, we can initialize the state of the replay buffer. For that we would need a prototype item that will be used to determine the structure of the storage. The prototype item must have the same structure and tensor shapes as the items that will be stored in the buffer.buffer_state=buffer.init_fn(item_prototype)Now we can fill the buffer:foriteminitems:buffer_state=buffer.add_fn(buffer_state,make_item(item))And sample from it:batch=buffer.sample_fn(buffer_state,rng,batch_size)Or apply an update op to the items in the buffer:defitem_update_fn(item):# Possibly update an itemreturnitembuffer_state=buffer.update_fn(buffer_state,item_update_fn)
Package: dejima
Dejima - A flexible content importer for AnkiLearning a foreign language?  It takes a lot of effort to make flash cards every time you find a new word you'd like to learn -- Dejima makes it possible for you to use your time more efficiently by automatically creating Anki flash cards from various sources you might be using to learn a new language including "Language Learning with Netflix".Free Software: MIT LicenseRequirementsAnkiConnectmust be installed into Anki as an add-on.Sources SupportedLanguage Learning with NetflixReading annotations on Onyx Boox devices.A source importer you write yourself -- see "Adding your own Sources" below.You can read more about the built-in sources below.Language Learning with Netflix (lln-json)In "Language Learning with Netflix" (LLN), you can"save"particular words or phrases.  Those phrases can be exported from LLN using your browser, and imported into Anki as flash cards.ExamplesQuestion:Answer:Reverse Question (Optional):Reverse Answer (Optional):How to useDownload your saved annotations in "Language Learning with Netflix" using the "JSON" export type:Rundejimawith the following command, replacingMy Deck Namewith the name of the deck you'd like the cards generated in, and/path/to/export.jsonwith the path to the file you exported above:dejima import "My Deck Name" lln-json -i /path/to/export.jsonThat's it!Onyx Boox Reading Annotation Exports (boox)In Onyx Boox e-readers and tablets, there's an annotation mode in which you can highlight particular words or sentences, see a translation into your native language, or write notes to yourself about:If you, too, use these for making note of unfamiliar words, you can use Dejima to convert those highlighted annotations into flash cards.How to useExport your saved annotations from your device and save them to the device that you are running Anki and Dejima on:Rundejimawith the following command, replacingMy Deck Namewith the name of the deck you'd like the cards generated in, and/path/to/export.txtwith the path to the file you exported above:dejima import "My Deck Name" boox -i /path/to/export.txtInstallationYou can install dejima from pypi by running:pip install dejimaAdding your own sourcesDejima was built to make it easy formeto easily add sources I need so hopefully that effort makes it easy for you, too!A sample project exists athttps://github.com/coddingtonbear/dejima-importer-exampleshowing you how you might create your own importer class.Why is this named "Dejima"Anki is the Japanese word for "memorization".During one particular part of Japanese history, one of the few ways you could import goods into Japan was via the port ofDejimain Nagasaki.
Package: dejunk
dejunkblock any junk (including ads, spams websites etc..) and improve your browsing experienceFree software: GNU General Public License v3Documentation:https://dejunk.readthedocs.io.Installationusing pip:pipinstall-Udejunkfrom source:gitclonehttps://github.com/nidhaloff/dejunk.gitcddejunkpythonsetup.pyinstall# or python3 setup.py installUsageAfter installation, just open the terminal and run the dejunk command:dejunkThat’s it. Now all junk websites have been blocked in your systemHistory0.1.0 (2021-05-08)Added more junk hostsOS independent0.1.0 (2021-01-25)First release on PyPI.
Package: deker
DEKER™DEKER™ is pure Python implementation of petabyte-scale highly parallel data storage engine for
multidimensional arrays.DEKER™ name comes from termdekeract, the10-cube.DEKER™ was made with the following major goals in mind:provide intuitive interface for storing and accessinghuge data arrayssupportarbitrary number of data dimensionsbethread and process safeand aslean on RAMuse as possibleDEKER™ empowers users to store and access a wide range of data types, virtually anything that can be
represented as arrays, likegeospacial data,satellite images,machine learning models,sensors data, graphs, key-value pairs, tabular data, and more.DEKER™ does not limit your data complexity and size: it supports virtually unlimited number of data
dimensions and provides under the hood mechanisms topartitionhuge amounts of data forscalability.FeaturesOpen sourceunder GPL 3.0Scalable storage of huge virtual arrays viatilingParallel processingof virtual array tilesOwnlockingmechanism enabling virtual arrays parallel read and writeArray levelmetadata attributesFancy data slicingusing timestamps and named labelsSupport for industry standardNumPy,XarrayStorage level datacompression and chunking(via HDF5)Code and DocumentationOpen source implementation of DEKER™ storage engine is published athttps://github.com/openweathermap/dekerAPI documentation and tutorials for the current release could be found athttps://docs.deker.ioQuick StartDependenciesMinimal Python version for DEKER™ is 3.9.DEKER™ depends on the following third-party packages:numpy>= 1.18attrs>= 23.1.0tqdm>= 4.64.1psutil>= 5.9.5h5py>= 3.8.0hdf5plugin>= 4.0.1Also please not that for flexibility few internal DEKER™ components are published as separate
packages:deker-local-adaptersdeker-server-adaptersdeker-toolsInstallTo install DEKER™ run:pipinstalldekerPlease refer to documentation for advanced topics such as running on Apple silicone or using Xarray
with DEKER™ API.First StepsNow you can write simple script to jump into DEKER™ development:fromdekerimportClient,ArraySchema,DimensionSchema,TimeDimensionSchemafromdatetimeimportdatetime,timedelta,timezoneimportnumpyasnp# Where all data will be keptDEKER_URI="file:///tmp/deker"# Define 3-dimensional schema with to numeric and one time dimensiondimensions=[DimensionSchema(name="y",size=128),DimensionSchema(name="x",size=128),TimeDimensionSchema(name="forecast_dt",size=128,start_value=datetime.now(timezone.utc),step=timedelta(3),)]# Define array schema with float dtype and dimensionsarray_schema=ArraySchema(dtype=float,dimensions=dimensions)# Instantiate client using context managerwithClient(DEKER_URI)asclient:# Create collectioncollection=client.create_collection("my_collection",array_schema)# Create arrayarray=collection.create()# Write some dataarray[:].update(np.ones(shape=array.shape))# And read the data backdata=array[:].read()
Package: deker-shell
Deker ShellDeker Shell is an interactive REPL based management interface forDekerstorage engine.FeaturesAutocompletionSyntax highlightingclientandcollectionsvariables initialized at startShortcutusefunction to change currentcollectionImported at start:numpyasnp,datetimeand alldekerpublic classesRunningasyncioloop (thus, enabling you to useasyncandawait)All theptpythonfeaturesQuick StartYou need Deker and Python 3.9 or later installed:pipinstalldekerdeker-shellThen you may run Deker Shell with storage location as a command line parameter:dekerfile:///tmp/dekerPlease refer to Dekerdocumentationfor more details.Special Thanksptpython- a better Python REPL
Package: dekespo-ai-sdk
Dekespo's AI SDK in python3My pet project where it uses standard python modules to cover different AI algorithms, data structure and SDK that can be used for example in an application or online bot competition.One can download viaPyPi.Currently it is used inIts own applications (seehttps://github.com/dekespo/dekespo_apps_py)https://www.codingame.com/multiplayer/bot-programming
Package: dekeyrej-pages
pages latest {version='0.5.12'}Matrix - serverpage and displaypage classesIncorporates dekeyrej-kube to interact with Kubernetes Secrets and ConfigMapsPages have been split (ininit.py)now requires:from pages.displaypage import DisplayPagefrom pages.serverpage import ServerPagesecuredict has been relegated to 'other method'reads encoded secrets from inside kubernetes cluster, orreads from JSON file
Package: deki
:sparkles: DekiDeki is a library is to create beautiful ✨interative✨ visuals to understand your optimizer better, because seeing is believing!Just a few easy steps to get your own visuals:from deki import Rosenbrock

optimiser = my_special_optimiser
optimiser_state = {"lr":0.01}
steps = 1000

Rosenbrock.plot((optimiser, optimiser_state), steps)

#output: get a interative Rosenbrock plot