Saravanan666/pypi-org huggingface dataset:
Package: dsci-prediction
predictiona package for predicting if cancer is treatable or notInstallation$pipinstalldsci-predictionUsagepredictioncan be used to load, clean, build a test model and plot results
as follows:fromdsci_prediction.dsci_predictionimport*file_path="data.txt"# path to your fileload=load_data(input_path,output_path)clean=clean_data(input_path,output_path_train,output_path_test)model=build_test_model(train_df,test_df,cross_val_output,tuned_para_output,classification_output,confusion_matrix_output)plt.show()ContributingInterested in contributing? Check out the contributing guidelines.
Please note that this project is released with a Code of Conduct.
By contributing to this project, you agree to abide by its terms.Licensepredictionwas created by Clichy Bazenga. It is licensed under the terms
of the MIT license.Creditspredictionwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: dsc-labs
DSC Labs common libraries.A python3 library for commonly used utilitiesInstallation and Usage:Install with pippip3 install dsc_labs -U --trusted-host 192.168.195.200 --no-cache-dir --extra-index-url http://192.168.195.200:8151/simple/note:-Uto upgrade to the latest versionUsage:Import libraries# example: using logger from dsc_labsfromdsc_labs.libs.loggerimportDSCLabsLoggerMixinfromdsc_labs.commonimportcurrent_utc_timestampclassTimer(DSCLabsLoggerMixin):def__init__(self):super().__init__()defget_current_time(self):utc_time=current_utc_timestamp()# self.log is a method that inherits from `DSCLabsLoggerMixin` classself.log.info('event=get-current-time message="Current time:%s"',str(utc_time))Command line:dsc_labs-config -h

dsc_labs-* ...Package developmentInstall required librariespip install -r requirements.txtBuildpython setup.py sdistReleasepython -m twine upload --repository-url http://192.168.195.200:8151 -u USERNAME -p PASSWORD FILEPACKAGE
Package: dscleaner
DS Cleaner allows for easy cleanup of energy disagregation datasets and allows
for easy wave convertion.This library is based in librosa and PySoundFile library.Visit the Jupyter notebook for examples, available here:https://www.osf.io/vraqzView the documentation in ReadTheDocs:https://dscleaner.readthedocs.io/en/latest/View the paper “dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Instrusive Load Monitoring Datasets”, available in:https://doi.org/10.3390/data4030123
Package: dsclient
A Python client for interacting withdebugserver-jsDocumentation can be found athttps://dsclient-py.readthedocs.io/InstallFrom PyPipip install dsclientFrom Sourcecd dsclient-py/
pip install .DevelopmentTo setup your development environment, you’ll need at least one device to run
tests on. Below are the devices supported out of the box that include resources
for testing (if you want to run tests on a device not listed below, you’ll need
to provide similar resources for that device. Seetests/resources/cc13x0for an
example)cc13x0StepsEdit the filetests/env.cfgEnter the full path to your ccs installation underccsEnter the required device information (seetests/resources/cc13x0/README.rstfor what’s required)Configure the test setupmake configureRun testsmake test
Package: dsc-mailer
dsc-mailerA simple mailing library for python powered applications using Sendgrid.InstallationTo install the library using pip, run;pip install dsc-mailerRequired parametersThe library has one module for sending emailsmailerthe mailer module contains a send_mail function that expects five parametersSenderReceiverEmail SubjectHtml ContentSendgrid Api_keyExample Usagefromdscmailerimportmailermy_email=mailer.send_mail(sender,receiver,subject,html_content,api_key)
Package: dsconfig-wrapper
dsconfig-wrapperThis is a small Python library to complementMAX IV'sexcellentdsconfiglibrary, which can make the database for theTangocontrols systemdeclarativeby updating it from a JSON file.Usually, it looks like this:flowchart TD
    DatabaseDs[(Tango Database)]
    DatabaseDs -->|dsconfig.dump| JsonFile[JSON file]
    Human -->|vim/emacs| JsonFile
    JsonFile -->|json2tango| DatabaseDsWith this tool, it looks like this:flowchart TD
    DatabaseDs[(Tango Database)]
    Human -->|vim/emacs| PythonFile[Python file]
    DsconfigWrapper[dsconfig-wrapper] -->|import| PythonFile
    PythonFile -->|dsconfig-wrapper| JsonFile
    JsonFile -->|json2tango| DatabaseDsThis way, we can have our configuration not in JSON, but in Python! And in Python, we can have, for instance,argparse, to create a little program that can generate a configuration with parameters. You can also use Python DRY elements in your configuration (say, you have the same motor, but with various different properties set).UsageJust dopip install dsconfig-wrapperand then use it to create a config JSON. Example:test_servers=[Server(instance_name="my_instance_name",class_name="my_class_name",devices=[Device(identifier=Identifier(domain="my_domain",family="my_family",member="my_member",host="my_host:10000",),properties={"prop_str":"strvalue","prop_list_str":["value1","value2"],"prop_list_int":[2000,3000],"prop_int":2000,},polled_commands={"update":1000},attribute_properties={"State":AttributeProperties(archive_period_ms=1001),"velocity_mm_per_s":AttributeProperties(archive_abs_change=(-0.5,0.5),archive_period_ms=5000,rel_change=(-0.05,0.05),),},)],)]test_config=Config(servers=test_servers)print(config_to_json(test_config))Here, we create a config for a device server with classmy_class_nameand one instance running attango://myhost:10000/my_domain/my_family/my_member. Theupdatecommand is polled every second, and we also set some properties and attribute properties.We now run this program (say it's stored inmain.py) and pipe it into dsconfig:python main.py | json2tangoVoila!
Package: dscore
D-scoredscoreis a meta-server tool for the prediction of disordered regions in protein sequences. It works by querying several webservers and gathering the results in an easy-to-use format. All the work is automated using simple web requests where possible, falling back to webscraping withseleniumfor servers without a public API.The servers currently used are the following (follow the links for more information and references to papers):disembl,disopred,disprot,espritz,globplot,iupred,jpred,pondr,prdos,foldindex,metapredict(used for comparison, but not included in the final dscore),seg(for complexity prediction)Multiple algorithms are used from all servers, but not all the available ones. Check the output for details.Featuresdscorethen aggregates the data in a single table in a text file for further processing. It will also automatically generate a few plots for quickly visualising the data.Server summary plot: disorder predictions from each algorithm available and consensus D-score.D-score plot: line plot of the raw averaged value used for D-score prediction.Consensus plot: an indication of how close to the consensus each server is, useful to find outliers.Text outputs: can easily be parsed for more advanced analysis.# 0. resn
# 1. residue
# 2. disprot_VSL2
# 3. disprot_VSL3
# 4. disprot_VLXT
# 5. disprot_PONDR-FIT
# 6. dscore_raw
# 7. dscore
  0  1    2    3    4    5       6  7
---  ---  ---  ---  ---  ---  ----  ---
  1  M    D    D    D    D    0     D
  2  T    D    D    D    D    0     D
  3  K    D    D    D    D    0     D
  4  K    D    D    D    D    0     D
  [...]Installationdscoreis available on PyPi and easilyinstallable withpip:pip install dscoreFirefox webdriverTo run,dscorealso requires theFirefoxwebdriver to be installed. Download the latest version of the driverfrom the official github release: scroll down and choose the appropriate one (for example, the...linux64.tar.gzfor a 64bit linux installation). Make sure to unzip/untar the driver and make it accessible on thePATHenviroment variable.Here's a detailed guide on how to do this.UsageNote that this program works by opening a ton of firefox windows and issuing automated commands. Let it do its thing and don't get scared! :)DISCLAIMER:dscorerelies on other webservers for its results. Some of these servers have limitations on the amount of requests per user, so you may get temporarily blocked if you submit too many sequences. Spread them out!dscorecan be used either as a python library or from the command line. The latter has a simple interface:dscore <sequence_or_fasta_file>Use the--helpoption for more information.You can also generate a new dscore and new plots from a subset of columns of an existing.dscorefile:dscore_plot <dscore_file> <column> <other_column>ReferencesThis tool implements a simple automated version of the D-score calculation and analysis performed in the paperModular organization of rabies virus phosphoproteinby F. Gerard et Al.
Package: ds-core-sanpier
Data Science Core FunctionalitiesModules included in this package:regressor_utils.pyclassifier_utils.pyexploratory_data_analyzer.py1) regressor_utils.pyContaining Regressor class which has certain type of functions that make life easier for regression problems.
These functions are quite various for different type of problems. The following functionalities are included in this module:Data splittingOversamplingExperimenting different regression algorithmsTraining given modelCalculating residual difference between the target feature and predicted or calculated feature with visualizationRegression plotsRegression scoring metricsQuantile regression2) classifier_utils.pyHaving Classifier class which contains a set of functions for modeling ML classification problems in the shortest time.
The functions included in the class are quite various, these can be seen as follows:Data splittingExperimenting different regression algorithmsTraining given modelCross validation score of the given modelConfusion matrix visualizationCreating a stack modelEvaluating model in the test dataset with classification metrics3) exploratory_data_analyzer.pyThis module has EDA_Preprocessor class in it where the class functions serve as a baseline for all kinds of EDA.
The functions in this module are including the following analysis tasks:filling missing values in the datashowing distributions / counts of the columnsdummification of the categorical data columnsPCA decomposition of the given datastandardization of the dataapplying transformation function for handling data skewnessshowing heatmap correlation of the features before modelingchecking the correlation of the categorical features compare to target featurefeature importances of a default model in the given problem domain
Package: ds-create
ds-createcreated by Austin PoorA simple CLI program (inspired bycreate-react-app) for creating program templates.To Installpipinstallds-createTo RunInstructions are formatted as follows:ds-create[command]-n[template-name]List of commands:snap: save a new templatecreate: extract template contentsupdate: update templatedelete: delete templatelist: list templates or list template contentsList TemplatesList all templates:ds-createlistList info for templateapp-template:ds-createlist-napp-templateSave a TemplateTo save the current directory as a new template calledapp-template, run the following:ds-createsnap-napp-templateCreate from TemplateExtract the contents ofapp-templatein the current directory:ds-createcreate-napp-templateUpdate from TemplateUpdate the contents ofapp-templatein the current directory:ds-createupdate-napp-templateDelete a TemplateDelete templateapp-template:ds-createdelete-napp-templateDelete all templates (you'll be prompted for confirmation):ds-createdelete
Package: dscsrf
# DSCsrfVery simple and (almost) plug-and-play global CSRF filtering for Flask using the Double Submit technique outlined on OWASP athttps://www.owasp.org/index.php/Cross-Site_Request_Forgery_(CSRF)_Prevention_Cheat_Sheet#Double_Submit_Cookies.How to use:from dscsrf import Csrf
from flask import Flaskapp = Flask(__name__)csrf = Csrf(app)and that’s it!For rendering the CSRF token in your application, under your form HTML use:{{ csrf_token() }}
Package: dsd
dsd_pipThis package contains three algorithms that solve dense subgraph problem exactly or approximately, including Goldberg's max flow based algorithm [1], charikar's greedy peeling algorithm [2], and state-of-the-art flowless (greedy++) algorithm [3].We also leverage all algorithms to find k-clique dense subgraph, given list of k-cliques in a certain graph. Remind this can be done with algorithms like KClist [5].
We follow [4] when contructing clique based max flow graph.To install the package, runpip install dsdCheckhttps://github.com/c752334430/dsd_pip/blob/main/dsd_package_demo.ipynbto see how to use the package.[1] A. V. Goldberg. Finding a maximum density subgraph. University of California Berkeley, CA, 1984.[2] M. Charikar. Greedy approximation algorithms for finding dense components in a graph. In
International Workshop on Approximation Algorithms for Combinatorial Optimization, pages
84–95. Springer, 2000[3] Boob D., Gao Y., Peng R., Sawlani S., Tsourakakis C.~E., Wang D., Wang J., Flowless: Extracting Densest Subgraphs Without Flow Computations,arXiv e-prints, 2019.[4] Shuguang Hu, Xiaowei Wu, and T-H. Hubert Chan. 2017. Maintaining Densest Subsets Efficiently in Evolving Hypergraphs. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, 929–938. DOI:https://doi.org/10.1145/3132847.3132907[5] Maximilien Danisch, Oana Balalau, and Mauro Sozio. 2018. Listing k-cliques in Sparse Real-World Graphs*. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 589–598. DOI:https://doi.org/10.1145/3178876.3186125
Package: dsdata
sdk-pydsdata 使用样例：from dsdata import DsClient

endpoint = 'https://xxxx.com'
AppKey = 'xxx'
AppSecret = 'xxxx'
ds = DsClient(endpoint=endpoint, app_key=AppKey, app_secret=AppSecret)

# 测试获取主体数据和定义
data = ds.load_objects('/api-service/220sc/default/12334/objects-definitions',
                       'select * from object_lhc_product', columns=['subject_id'], chunksize=2)
# 测试获取指标主题和定义
data = ds.load_metrics('/api-service/220sc/default/12323/definitions',
                       'SELECT * FROM h5', columns=None, chunksize=2)
# 测试获取数据集
data = ds.load_dataset('/api-service/220sc/default/dataset-ms-test1', 20)
# 测试上传标签
tags = {"objects":[{'object_id':"3", 'tag_code':'tag_wbbq2', 'tag_values': 123}]}
msg, valid_nums = ds.upload_object_tags('/api-service/220sc/default/dsdata0830/tags-upload', tags)
Package: dsdatabase
DS Databasedsdatabaseis a library that simplifies accessing databases onDesignSafeviaJupyter Notebooks.FeaturesConnects to SQL databases on DesignSafe:Databasedbnameenv_prefixNGLnglNGL_Earthake RecoveryeqEQ_VpvpVP_Define the following environment variables:{env_prefix}DB_USER
{env_prefix}DB_PASSWORD
{env_prefix}DB_HOST
{env_prefix}DB_PORTFor e.g., to add the environment variableNGL_DB_USERedit~/.bashrc,~/.zshrc, or a similar shell-specific configuration file for the current user and addexport NGL_DB_USER="dspublic".InstallationInstalldsdatabasevia pippip3installdsdatabaseTo install the current development version of the library use:pipinstallgit+https://github.com/DesignSafe-CI/dsdatabase.git--quietExample usage:fromdsdatabase.dbimportDSDatabasedb=DSDatabase("ngl")sql='SELECT * FROM SITE'df=db.read_sql(sql)print(df)# Optionally, close the database connection when donedb.close()
Package: dsdb
Copyright (c) 2018-2020 Giacomo Barone, Buildnn Team. All Rights Reserved.Permission 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: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Provides-Extra: dev
Provides-Extra: postgres
Provides-Extra: dynamodb
Provides-Extra: mongo
Provides-Extra: redis
Provides-Extra: nosql
Provides-Extra: all
Package: dsdiagram
data structure diagram generatorinstallationdownloadgraphvizpip install dsdiagramquick startsave imagefromdsdiagramimportsave_imgclassNode:def__init__(self,id,next=None):self.id=idself.next=nextn=5nodes=[Node(id)foridinrange(n)]head=nodes[0]foridinrange(n):nodes[id].next=nodes[(id+1)%n]save_img(head,'linked-list')linked-list.pngrender diagram in jupyter
Package: dsdk
OverviewAn opinionated library to help deploy data science projectsFree software: MIT licenseInstallpip install "."Develop, Lint & TestSetup virtual environment:python3.10 -m venv .venvOr setup homebrew virtual environment:brew install python@3.10
/opt/homebrew/Cellar/python@3.10/.../Frameworks/Python.framework/Versions/Current/bin/python@3.10 -m venv .venvOnce virtual environment is setup:. .venv/bin/activate
pip install -U pip setuptools wheel
pip install -e ".[dev]"
pre-commit installSession:. .venv/bin/activate
pytest
...
pre-commit run --all-files
...
git commit -m 'Message'
...
deactivateCI/CD Lint & Test:docker compose up test
docker compose up pre-commit
docker compose up build-wheel && docker compose up install-wheel
...
docker compose down
Package: dsdn-distrib-20230330-gk
# dsdn-distrib-20230330-gk packageThis is the first package I created as part of the Data Science Nanodegree course I am currently studying at UdacityThere is a parent class called Distibution from which another two classes (Gaussian and Binomial) inherit a few attributes and methods.For Binomial and Gaussian classes there are some methods for calculating mean and st. dev. as well as plotting some bar charts#FilesGeneraldistribution.py: Contains the parent class (Distribution)
Gaussiandistribution.py: Contains the class for gaussian distribution. Methods for mean and st.dev available
Binomialdistribution.py: Contains the class for binomial distribution. Methods for mean, st.dev and bar charts available#InstallationThe package is available in pypi so you can just use pip install dsdn-distrib-20230330-gk#LicensingYou can find details of the licensing in file license.txt
Package: ds-down
ds-down is a Python 3 program to send URLs and local files to Synology
DownloadStation using aforementioned DownloadStations Web API (API pdf).The main intended use-case is from console and a desktop file for xdg-open or
similar tools is provided.Exampleconfigfile:# Example config file, by default it should be located at:# ~/.config/ds-down.confusername=adminhost=https://192.168.1.10:5001passwordeval=gpg --no-tty -d ~/.passwords/synology-admin.gpgExamplesSend a magnet link to the DownloadStation:$ds-down'magnet:?xt=urn:btih:dbd7d976a5bf566504357bf9f543a37d3513e4df&dn=archlinux-2014.07.03-dual.iso&tr=udp://tracker.archlinux.org:6969&tr=http://tracker.archlinux.org:6969/announce'Send a locally stored torrent file to the DownloadStation:$ds-down'archlinux-2014.07.03-dual.iso.torrent'InstallExample of a simple user install with from source:python3setup.pyinstall--exec_name="ds_down"--userThe executable console script name can be controlled with the ´–exec_name=´
argument. The default is ´ds_down´. The executable script in this example is
placed in ´$HOME/.local/bin´ which can be added to the PATH variable if needed.
This also installs needed dependencies.PyPI InstallAlternatively last published version can be installed from PyPI with pip:pip3install--userds-down
Package: dse-driver
DSE and OSS drivers unified under single DataStax driverAs of the 3.21.0 release of the Datastax Python Driver for Apache Cassandra® (OSS Driver), the Datastax Enterprise Python Driver (DSE driver) functionality has been merged into this single, DataStax driver. Please refer to theblog postfor more information on this change.DataStax Enterprise Python DriverA modern,feature-richand highly-tunable Python client library for DataStax Enterprise (4.7+) using exclusively Cassandra’s binary protocol and Cassandra Query Language v3.The driver supports Python 2.7, 3.4, 3.5, 3.6 and 3.7.Note:DataStax products do not support big-endian systems.Feedback RequestedHelp us focus our efforts!Provide your input on thePlatform and Runtime Survey(we kept it short).FeaturesSynchronousandAsynchronousAPIsSimple, Prepared, and Batch statementsAsynchronous IO, parallel execution, request pipeliningAutomatic node discoveryAutomatic reconnectionConfigurableload balancingandretry policiesConcurrent execution utilitiesObject mapperConnecting to DataStax Apollo database (cloud)DSE Graph execution APIDSE Geometric type serializationDSE PlainText and GSSAPI authenticationA fluent API extension for DSE Graph is available in thedse-graphpackage. For more information, seethe documentation here.InstallationInstallation through pip is recommended:$ pip install dse-driverFor more complete installation instructions, see theinstallation guide.DocumentationThe documentation can be found onlinehere.A couple of links for getting up to speed:InstallationGetting started guideAPI docsReporting ProblemsPlease report any bugs and make any feature requests on theJIRAissue tracker.If you would like to contribute, please feel free to open a pull request.Getting HelpYour best options for getting help with the driver are themailing listand the#datastax-driverschannel in theDataStax Academy Slack.LicenseCopyright DataStax, Inc.The full license terms are available athttp://www.datastax.com/terms/datastax-dse-driver-license-terms