Saravanan666/pypi-org huggingface dataset:
Package: demcompare
Demcompare, a DEM comparison toolOverview•Install•Usage•Documentation•OverviewDemcompare is a python software that aims atcomparing two DEMstogether.A DEM is a 3D computer graphics representation of elevation data to represent terrain.Demcomparehas several characteristics:Works whether or not the two DEMs share common format projection system, planimetric resolution, and altimetric unit.Performs the coregistration based on the Nuth & Kääb universal coregistration method.Offers two coregistration modes to choose which of both DEMs is to be adapted during coregistration.Provides a wide variety of standard metrics which can be classified.Classifies the stats by slope ranges by default, but one can provide any other data to classify the stats.InstallOnlyLinux Plaformsare supported (virtualenv or bare machine) withPython >= 3.8installed.Demcompare is available on Pypi and can be typically installed through avirtualenv:python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install demcompareUsageDownload the data samples and run the python scriptdemcomparewith sample configuration:# download data sampleswgethttps://raw.githubusercontent.com/CNES/demcompare/master/data_samples/srtm_blurred_and_shifted.tif
wgethttps://raw.githubusercontent.com/CNES/demcompare/master/data_samples/srtm_ref.tif# download demcompare predefined configurationwgethttps://raw.githubusercontent.com/CNES/demcompare/master/data_samples/sample_config.json# run demcomparedemcomparesample_config.jsonA report can be observed with:firefox test_output/report/published_report/html/index.htmlDocumentationPlease consultour online documentation.LicensingDemcompare software is distributed under the Apache Software License (ASL) v2.0.SeeLICENSEfile orhttp://www.apache.org/licenses/LICENSE-2.0for details.Copyrights and authoring can be found inNOTICEfile.Related toolsCARS- CNES 3D reconstruction softwarePandora- CNES Stereo Matching framework
Package: demes
Tools for describing and manipulating demographic models.A demographic model describes one or more demes (or populations),
how they change over time, and their relationships to one another.demesprovides a self-contained and unambiguous in-memory description
of a demographic model.Please see thedocumentationfor more details.GoalsA simple declarative high-level format for specifying demographic models. This format is
intended to be human-readable and to make it easy to correctly specify models.A corresponding low-level format that is an entirely unambiguous and portable description
of a model, which can be used by many different simulation frameworks as input. Thus,
we can see part of the role of this package ascompilingthe high-level description of
a model into the corresponding low-level description.Robust validation of models and reporting of errors for invalid models.
Package: demesdraw
DemesDrawdemesdrawis a Python package that contains drawing functions forDemesdemographic models, usingmatplotlibto create the figures.
DemesDraw offers both a command line interface, and a Python API.
Feedback is very welcome.InstallationInstall with pip:$ python3 -m pip install demesdrawOr with conda:$ conda install -c conda-forge demesdrawExample usageCommand line interface (CLI)The CLI can be used to quickly plot a Demes YAML file.
Any file format supported by matplotlib can be specified,
but a vector format such as svg or pdf is recommended.$ demesdraw tubes --log-time \
	examples/stdpopsim/HomSap__AmericanAdmixture_4B11.yaml \
	AmericanAdmixture_4B11_tubes.svgPython APICompared with the CLI, the Python API provides additional control.
In the following example, the horizontal positions of the demes
are chosen manually and the names of extinct demes are moved to
a legend.importdemesimportdemesdrawgraph=demes.load("examples/stdpopsim/HomSap__AmericanAdmixture_4B11.yaml")w=demesdraw.utils.separation_heuristic(graph)positions=dict(ancestral=0,AMH=0,AFR=0,OOA=1.5*w,EAS=1*w,EUR=2*w,ADMIX=-w)ax=demesdraw.tubes(graph,log_time=True,positions=positions,labels="xticks-legend")ax.figure.savefig("AmericanAdmixture_4B11_tubes_custom.svg")DocumentationComplete API and CLI details are available in theDemesDraw documentation.
Package: demesstify
Demystify your messages.demesstifyis a Python library that demystifies your messages and allows for easy analysis and visualization of conversations.Table of contentsMain featuresInstallationDependenciesDocumentationExample usageAnalyzing the messagesCreating a word cloudFuture improvementsAuthorsMain featuresHere are just a few things thatdemesstifycan do:Read message data from various sources, including your local iMessages database, aTanseetext file, or some randomly generated dummy textPerform text analysis on your messages so you can see things like the average number of texts received per day or the most number of messages that were sent in a rowAnalyze which emojis or reactions (if you're using iMessage) were most frequently used, among other thingPerform sentiment analysis on your messages to see the polarity of your conversationsCalculate statistics about the attachments you exchanged (if you're using iMessage)Generate tailored visualizations such as word clouds or a radial heatmap that plots hour of the day against day of the weekInstallationdemesstifycan be installed via pip:pip install demesstifyThe source code can be viewed on GitHubhere.Dependenciesdemesstifydepends on the following packages:PackageDescriptionpandasFor easy manipulation of message datamatplotlibFor visualizationswordcloudFor creating wordcloudsvaderSentimentFor sentiment analysiscalmapFor creating calendar heatmapsemojiFor working with emojisloremFor creating dummy textDocumentationFor information on how to usedemesstify, please see thedocumentation.Example usageAnalyzing the messagesimportdemesstifyasdmfromdemesstify.analysisimportemojis,sentiment,text# Create the messages object and dataframes from dummy textmessages=dm.Messages.from_random(total_messages=1000)all_df=messages.get_all()sent_df=messages.get_sent()received_df=messages.get_received()# Determine the 3 most frequent emojismost_frequent_emojis=emojis.Emojis(all_df).get_most_frequent(3)# Determine the total number of messages senttotal_messages_sent=text.Text(sent_df).get_total()# Determine the average number of messages received per dayaverage_received_daily=text.Text(received_df).get_average_per_day()# Determine the number of times "velit" appear as a substringvelit_count=text.Text(all_df).get_count_of_substring('velit')# Determine the average polarity of the messagesaverage_polarity=sentiment.Sentiment(all_df).get_average_sentiment()Creating a word cloudimportdemesstifyasdmfromdemesstify.visualizeimportcloud# Create the messages object and dataframes from dummy textmessages=dm.Messages.from_random(total_messages=1000)# Create and save a Cloud objectwordcloud=cloud.Cloud(messages.as_string('all'))wordcloud.min_word_length=3wordcloud.repeat=False# will not repeat any wordswordcloud.collocations=False# will not include pairs of wordswordcloud.include_numbers=False# will not include numberswordcloud.generate()wordcloud.save('wordcloud.png')Future improvementsAdd support for other message sources, e.g. Android or social media platformsAdd unit testsAuthorsJake Brehm-Email|Github|LinkedIn
Package: demeuk
DemeukDemeuk is a simple tool to clean up corpora (like dictionaries) or any dataset
containing plain text strings. Example use cases are: cleaning up language dictionaries,
password sets (like for example RockYou) or any file / stdin containing plain text strings.In those corpora you'll find encoding mistakes that have been made, or you want to remove some parts
of a line. Instead of creating a huge bash oneliner you can use demeuk to do all your cleaning.Example usages:CuttingLength checkingEncoding fixingDemeuk is written in Python3, this means of course that it is slower than for example cut.
However, Demeuk is written multithreaded and thus can use all your cores. Besides this Demeuk
can easily be extended to match your needs.This application is part of the CERBERUS project that has received
funding from the European Union's Internal Security Fund - Police under
grant agreement No. 82201Please read the docs for more information.Quick startThe recommended way to install demeuk is to install it in a virtual
environment.# Create virtual environment
virtualenv <virtual environment name>
# Activate the virtual environment
source <virtual environment name>/bin/activate
pip3 install -r requirements.txtNow you can run bin/demeuk.py:Examples:demeuk -i inputfile.tmp -o outputfile.dict -l droppedfile.txt
    demeuk -i inputfile -o outputfile -j 24 -l logfile.log
    demeuk -i inputfile.tmp -o outputfile.dict -l droppedfile.txt --leak
    demeuk -i inputfile -o outputfile -j 24 -l logfile.log --leak-full
    demeuk -i inputdir/*.txt -o outputfile.dict -l logfile.log
    demeuk -o outputfile.dict -l logfile.logDocsThe docs are available at:http://demeuk.rtfd.io/
Package: demi
demi - de-multiple-inheritance; squash class inheritanceDid you make a mistake in designing a mess of classes with multiple inheritance
everywhere?Do you want to understand deeply nested class hierarchies without navigating
many different source code files?Will you avoid making such mistakes in the future?... well, my answers to the above questions are "yes :(", "yes!", and "probably
not", respectively.In any case -This repo is a proof-of-concept of a tool that may have the potential to help
with the above.  It probably won't help you just yet.RequirementsPython 3.9+ (forast.unparse)UsageInstall$pipinstalldemiTry with a provided test class:$demiimportdemi.tests.cls_ab.COr preferrably, reformat with black at the same time:$demiimportdemi.tests.cls_ab.C|black-classC:"""A DocstringB DocstringC Docstring"""a_value="A"defafunc(self)->str:return"A"defsuperfunc(self)->int:def_super_A()->int:b=ord(self.afunc())a=b+2returnareturn_super_A()+1b_value="B"c_value="C"defbfunc(self)->str:return"B"defcfunc(self)->str:return"C"Or maybe try something from the standard library:demi import argparse.BooleanOptionalAction
demi import tkinter.Widget
...Maybe TODOProperties have the same name twice, so only the setter gets shownSwitch to redbaron to retain comments?Automatically invoke black to reformat the output?Test suite?For returns at the end of the method, simplify code?Consider astor for Python below 3.9?
Package: demicestimator
DEMICESTIMATORIntroductionThis package contains standalone code for computing data of any epidemic through various Epidemic Models (SIR, SEIR etc) . The data can be further used for studying, graphing, stimulating and predicting the spread of any epidemic.SIR Model:A simple mathematical description of the spread of a disease in a population is the so-called SIR model, which divides the (fixed) population of N individuals into three "compartments" which may vary as a function of time.SEIR Model:For many important infections, there is a significant incubation period during which individuals have been infected but are not yet infectious themselves. During this period the individual is in compartment E (for exposed). This is where SEIR model is used.For The UsersThe package can be installed via:pipinstallepidemic_estimatorThe package can be used to call the functions of available Epidemic ModelsExample for SIR Modelimportdemicestimatorasdede.sir_model(0.5,1/4,1000,0,1,160)print(de.sir_df)Here you can call the function for SIR model usingee.sir_model()Parameters:Effective contact RateRecovery RateTotal populationRecoveredInfectedNo. Of DaysProbable Epidemic Data is computed from the code above which can be used to plot graph, create stimulation and so on.UpdatesFurther updates with support for other models like SEIR and SIS is coming soon.LicenseMIT
Package: demin
DeminDemin presents helpful functions for data analysis in the field of Mining Engineering. It leverages the resources of widely recognized data science libraries, such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn.Instalation (Google Colaboratory):To integrate Demin into your Google Colaboratory environment, use the following pip command:!pip install deminTo ensure you have the latest version:!pip install --upgrade deminHow to import (Google Colaboratory):Once installed, importing Demin is straightforward. Use the following Python code in your Google Colaboratory notebook:import demin as dmHistoryDemin emerged from an idea during my internship in Data Science at the Department of Mining Engineering (DEMIN) at the Federal University of Minas Gerais (UFMG). As data science is still an underexplored resource in this field, Demin proposes to bring functions that are easy to understand and can assist in common tasks related to geometallurgical data analysis. In this early stage of development, many functions are yet to be added and tested. Consequently, comprehensive documentation will not be available until version 1.0.0.About the DeveloperFor feedback, questions, or collaboration, feel free to reach out:LinkedIn:Gustavo Ortiz
Package: demiurge
PyQuery-based scraping micro-framework.
Supports Python 2.x and 3.x.Documentation:http://demiurge.readthedocs.orgInstalling demiurge$ pip install demiurgeQuick startDefine items to be scraped using a declarative (Django-inspired) syntax:>>> import demiurge
>>> class TorrentDetails(demiurge.Item):
...     label = demiurge.TextField(selector='strong')
...     value = demiurge.TextField()
...     def clean_value(self, value):
...         unlabel = value[value.find(':') + 1:]
...         return unlabel.strip()
...     class Meta:
...         selector = 'div#specifications p'
...
>>> class Torrent(demiurge.Item):
...     url = demiurge.AttributeValueField(
...         selector='td:eq(2) a:eq(1)', attr='href')
...     name = demiurge.TextField(selector='td:eq(2) a:eq(2)')
...     size = demiurge.TextField(selector='td:eq(3)')
...     details = demiurge.RelatedItem(
...         TorrentDetails, selector='td:eq(2) a:eq(2)', attr='href')
...     class Meta:
...         selector = 'table.maintable:gt(0) tr:gt(0)'
...         base_url = 'http://www.mininova.org'
...
>>>
>>> t = Torrent.one('/search/ubuntu/seeds')
>>> t.name
'Ubuntu 7.10 Desktop Live CD'
>>> t.size
u'695.81\xa0MB'
>>> t.url
'/get/1053846'
>>> t.html
u'<td>19\xa0Dec\xa007</td><td><a href="/cat/7">Software</a></td><td>...'
>>> results = Torrent.all('/search/ubuntu/seeds')
>>> len(results)
116
>>> for t in results[:3]:
...     print t.name, t.size
...
Ubuntu 7.10 Desktop Live CD 695.81 MB
Super Ubuntu 2008.09 - VMware image 871.95 MB
Portable Ubuntu 9.10 for Windows 559.78 MB
...
>>> t = Torrent.one('/search/ubuntu/seeds')
>>> for detail in t.details:
...     print detail.label, detail.value
...
Category: Software > GNU/Linux
Total size: 695.81 megabyte
Added: 2467 days ago by Distribution
Share ratio: 17 seeds, 2 leechers
Last updated: 35 minutes ago
Downloads: 29,085See documentation for details:http://demiurge.readthedocs.orgWhydemiurge?Plato, as the speaker Timaeus, refers to the Demiurge frequently in the Socratic
dialogue Timaeus, c. 360 BC. The main character refers to the Demiurge as the
entity who “fashioned and shaped” the material world. Timaeus describes the
Demiurge as unreservedly benevolent, and hence desirous of a world as good as
possible. The world remains imperfect, however, because the Demiurge created
the world out of a chaotic, indeterminate non-being.http://en.wikipedia.org/wiki/Demiurge
Package: demjson
The “demjson” module, and the included “jsonlint” script, provide methods
for encoding and decoding JSON formatted data, as well as checking JSON
data for errors and/or portability issues.  The jsonlint command/script
can be used from the command line without needing any programming.Although the standard Python library now includes basic JSON support
(which it did not when demjson was first written), this module
provides a much more comprehensive implementation with many features
not found elsewhere.  It is especially useful for error checking or
for parsing JavaScript data which may not strictly be valid JSON data.
Package: demoazure
IntroductionTODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project.Getting StartedTODO: Guide users through getting your code up and running on their own system. In this section you can talk about:Installation processSoftware dependenciesLatest releasesAPI referencesBuild and TestTODO: Describe and show how to build your code and run the tests.ContributeTODO: Explain how other users and developers can contribute to make your code better.If you want to learn more about creating good readme files then refer the followingguidelines. You can also seek inspiration from the below readme files:ASP.NET CoreVisual Studio CodeChakra Core
Package: democlassi
DEmoClassiDEmoClassi stands forDemographic (age, gender, race) andEmotions
(happy, sad, angry, ...)Classification.This repository is an attempt to tackle the task of predicting the facial expression,
age gender and race prediction from  facial images.As I was planning to train the models on Google Colab platform, I decided to gather
all my utility functions/classes in a package I named democlassi which is pip
installable viapip install --upgrade democlassi.The Training is done using Pytorch and pytorch-ignite and more
information about the data used, the training process and live prediction using
pretrained models and opencv can be found onthis page.Some sample predictions :
Package: democrite
DemocriteThis library permit to createVGrainfor using enDemocriteenvironment.InstallationpipinstalldemocriteHow to useYour grain could be used in two mode :OneShot: Script mode, the script is execute at each requestDeamon: The script is start and stay alive to perform requestCreate VGrainTo start you need to create avgrainobject with command line argument pass in parametersimportdemocriteapp=democrite.vgrain(sys.argv)Create functionYou need to pass a function to execute each requestdefexecFunc(command:democrite.vgrainCommand,tools:democrite.vgrainTools)->any:Choose ModeOneShotThe command data are pass in command line argument in format (Json -> Base64)The command result is returned by the strandard output in format 'ExecutionId:(json -> Base64)'app.execute(execFunc);DeamonA connection is made in local using the port pass in argument '--port:4242'app.run(execFunc);testDuring you developement you need to test your code.To do so use thetestmethod with data you need as input.app.test("5+6",execFunc);vgrainCommandThe command is pass on an object exposing :# uid unique of the flow executing the grainvgrainCommand.get_flow_uid()->uuid# uid grain executionvgrainCommand.get_execution_uid()->uuid# get command inputvgrainCommand.get_content()->uuidvgrainToolsThis object provide tools dedicated to request# provide access to a logger that will send back the logs to democritevgrainTools.get_logger.logInformation("LOG")# provide the remain argument not consumed by the libraryvgrainTools.get_args()->list[str]
Package: democritus
Copyright (c) 2020, Floyd HightowerPermission 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.Description: # Democritus![Pipeline status](https://gitlab.com/fhightower/democritus/badges/master/build.svg)Future home of democritus project.## UsageComing soon…## CreditsThis package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and Floyd Hightower’s [Python project template](https://gitlab.com/fhightower-templates/python-project-template).Keywords: democritus
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Package: demod
DemodDomestic Energy Demand Modelling LibraryDemod is a Python library for assembling bottom-up domestic
energy demand models.DocumentationAvailable athttps://demod.readthedocs.io/InstallationpipinstalldemodSource code:GitHub RepositoryDependenciesnumpypandasscipyopenpyxlmatplotlibLicenceGPLv3AboutThis repository is managed byHERUS labfromEPFL : École Polytechnique Fédérale de Lausanne.Please report bugs and ideas for improvement to
our team:demod@groupes.epfl.ch.
Package: demo-energy-consumption
demo energy consumptiondemo energy consumption shows how to seamlessly leverage Teradata Vantage in your Python projects.Free software: MIT licenseDocumentation:https://demo-energy-consumption.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-09-01)First release on PyPI.
Package: demo-fa
Torch Compatible Augmentation Engine For Solar Filaments v0.0.2An ML-Ready Filament Augmentation Toolkit with Labeled Magnetic Helicity SignABSTRACTA halo Coronal Mass Ejection can have a devastating impact on Earth by causing damage to satellites and electrical transmission line facilities and disrupting radio transmissions. To predict the orientation of the magnetic field (and therefore the occurrence of a geomagnetic storm) associated with an occurring CME, filaments' sign of magnetic helicity can be used. This would allow us to predict a geomagnetic storm.With the deluge of image data produced by ground-based and space-borne observatories and the unprecedented success of computer vision algorithms in detecting and classifying objects (events) on images, identification of filaments' chirality appears to be a well-fitted problem in this domain. To be more specific, Deep Learning algorithms with a Convolutional Neural Network (CNN) backbone are made to attack this very type of problem. The only challenge is that these supervised algorithms are data-hungry; their large number of model parameters demand millions of labeled instances to learn. Datasets of filaments with manually identified chirality, however, are costly to be built. This scarcity exists primarily because of the tedious task of data annotation, especially that identification of filaments' chirality requires domain expertise. In response, we created a pipeline for the augmentation of filaments based on the existing and labeled instances. This Python toolkit provides a resource of unlimited augmented (new) filaments with labeled magnetic helicity signs. Using an existing dataset of H-alpha based manually-labeled filaments as input seeds, collected from August 2000 to 2016 from the big bear solar observatory (BBSO) full-disk solar images, we augment new filament instances by passing labeled filaments through a pipeline of chirality-preserving transformation functions. This augmentation engine is fully compatible with PyTorch, a popular library for deep learning and generates the data based on users requirement.
Package: demo-game-guess-number
猜 0-100 中的一个数字：$ 猜数字
我想了个 100 之内的数，猜猜是几？
请猜吧: 50
太大了!
请猜吧: 20
太大了!
请猜吧: 5
太大了!
请猜吧: 2
中了!介绍文章在 PyPI 发布 Python 纯文字游戏试水——猜数字