Saravanan666/pypi-org huggingface dataset:
Package: dftd-labeler
DFT Dispersion Correction LabelerThis Python-based project is designed to process a database of structures and
label them with DFT dispersion corrections. It reads an existing database,
applies the desired DFT dispersion scheme ontop of the present energy and force
labels, and writes the corrected structures to a new database.InstallationWe recommend working in a conda environment:condacreate-ndftd-envpython=3.9-y
condaactivatedftd-envThen install conda-specific dependencies:condainstall-cconda-forgeasesimple-dftd3dftd3-pythondftd4dftd4-python-yFinally, install the tools:pipinstalldftd-labelerUsageAfter installation, you can use the tool as a command-line program:dftd_labeler-iINPUT_DATABASE_PATH-oOUTPUT_DATABASE_PATH-dDISPERSION_SCHEMEOptions-m or --dft_method: Specifies the DFT Method to be used. Default is 'SCAN'.-i or --input_database: Specifies the path to the database to be read. This is a required argument.-o or --output_database: Specifies the path to write the labeled database. This is a required argument.-d or --dispersion: Specifies which dispersion scheme to use. Only values 3 or 4 are allowed, indicating DFT-D3 and DFT-D4 respectively. This is a required argument.ExampleTo process a database using the default DFT method (SCAN) and the DFT-D4 dispersion scheme:dftd_labeler-imy_database.extxyz-ooutput_database.extxyz-d4DependenciesThis project depends on several Python libraries, including ase, dftd4.ase,
dftd3.ase. Ensure you have all required dependencies installed.ContributingPull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.
Package: df-telegram-connector
Dialog Flow Telegram ConnectorDialog Flow Telegram Connectoris python module add-on forDialog Flow Framework, a free and open-source software stack for creating chatbots, released under the terms of Apache License 2.0.Dialog Flow Telegram Connectorallows you to ...Quick StartInstallationpipinstalldf_telegram_connectorBasic exampleTo get more advanced examples, take a look atexampleson GitHub.Contributing to the Dialog Flow Telegram ConnectorPlease refer toCONTRIBUTING.md.
Package: df-to-azure
DF to AzurePython module for fast upload of pandas DataFrame to Azure SQL Database using automatic created pipelines in Azure Data Factory.IntroductionThe purpose of this project is to upload large datasets using Azure Data Factory combined with an Azure SQL Server.
In steps the following process kicks off:1. The data will be uploaded as a .csv file to Azure Blob storage.2. A SQL table is prepared based onpandas DataFrame types,
which will be converted to the correspondingSQLAlchemy types.3. A pipeline is created in datafactory for uploading the .csv from the Blob storage into the SQL table.4. The pipeline is triggered, so that the .csv file is bulk inserted into the SQL table.How it worksBased on the following attributes, it is possible to bulk insert your dataframe into the SQL Database:fromdf_to_azureimportdf_to_azuredf_to_azure(df=df,tablename="table_name",schema="schema",method="create")df: dataframe you wish to exporttablename: desired name of the tableschema: desired sql schemamethod: option for "create" "append" or "upsert"id_field: id field of the table. Necessary ifmethodis set to "upsert"Important: the csv's are uploaded to a container calleddftoazure, so create this in your storage account before using this module.Upsert / create or appendIt is possible to upsert the SQL table with (new) records, if present in the dataframe you want to upload.
Based on the id_field, the SQL table is being checked on overlapping values.
If there are new records, the "old" records will be updated in the SQL table.
The new records will be uploaded and appended to the current SQL table.SettingsTo use this module, you need to add theazure subscriptions settingsandazure data factory settingsto your environment variables.
We recommend to work with.envfiles (or even better, automatically load them withAzure Keyvault) and load them in during runtime. But this is optional and they can be set as system variables as well.
Use the following template when using.envParquetSince version 0.6.0, functionality for uploading dataframe to parquet is supported. simply add argumentparquet=Trueto upload the dataframe to the Azure storage container parquet.
The arguments tablename and schema will be used to create a folder structure. if parquet is set to True, the dataset will not be uploaded to a SQL database.# --- ADF SETTINGS ---

# data factory settings
rg_name : ""
rg_location: "westeurope"
df_name : ""

# blob settings
ls_blob_account_name : ""
ls_blob_container_name : ""
ls_blob_account_key : ""

# SQL settings
SQL_SERVER: ""
SQL_DB: ""
SQL_USER: ""
SQL_PW: ""

# --- AZURE SETTINGS ---
# azure credentials for connecting to azure subscription.
client_id : ""
secret : ""
tenant : ""
subscription_id : ""Maintained byZypp:Melvin FolkersErfan NarimanSupport:For support on using this module, you can reach us athello@zypp.ioTestingTo run the test suite, use:pytest df_to_azureTo run pytest for a single test:pytest df_to_azure/tests/test_df_to_azure.py::test_duplicate_keys_upsert
Package: df-to-dictionary
Author : Nilesh Shinde

% This module is for convert dataframe to dictionary in river dataset format %

River is the famous online machine learning dictionary in python but it can only support dictionary as a input. For that reason we have to convert dataframe to dictionary.

WHY DataFrame
In this situation you might be thinking whay first we convert other formate to dataframe and then dictionary ,because pandas gives us some amazing feature for data visualization.
For more info
https://pandas.pydata.org/

Function in this module

xdata,ydata = df_to_dictionary(x_data,y_data)

here x_data means the fetures and y_data means the labels of the dataset.
It will returns transformed xdata and ydata


Installation :
    Requirements:
pandas
numpy
Installation :
    pip install pandas==1.5.2
    pip install numpy==1.23.5


Github link
    https://github.com/Nileshshinde09/df_to_dictionary
Package: dftogsheet
dftogsheetA Python module for writing pandas DataFrame objects directly to Google Spreadsheetsdftogsheet is maintained onGitHubandPyPI.Install dftogsheet$pipinstalldftogsheetSetupEnable theGoogle Sheets API.Enter a name for your project. This is the name that will be displayed when your
app asks for permission to edit your spreadsheets in Google Drive later.Downloadcredentials.jsonintoproject-root-folder/.dftogsheet/credentials/folder.Simple usageimportpandasaspdimportdftogsheetdata_frame=pd.DataFrame()dftogsheet.write_to_sheet(data_frame,spreadsheet_id,sheet_name)ParametersThere are three mandatory parameters for the above function:data_frameis any pandas DataFrame object.spreadsheet_idis the part of the Google Spreadsheet URL
that is between/d/and/edit.sheet_nameis the name of the sheet within the Google spreadsheet.
E.g.Sheet1.Advanced usageWith only the above function, this library pretty much only does one thing.
But there is actually a lot more flexibility provided to you if you know what's going on.
Please look into the two functions indftogsheets/__init__.pyto understand what is going on
under the hood when you run the above code and to understand how you can use theSheetclass
and its methods more powerfully. If you have any suggestions, you are always more than welcome
tocontribute;)Changing scopes, valueInputOption, and location of credentials fileWhat is the valueInputOption?
Readthis pageThese settings are actually controlled by theConfigclass. A new instance of theConfigclass
with default values is created for you indftogsheet.write_section_to_sheetso you don't have to
think about it for the sample code above. However, it is possible to fully customize
these configurations by creating your ownconfigobject for your project.
Please look intodftogheets/config.pyto understand how you can do that.What's new in version 0.0.7AddSheet.overwrite()method.Include this method by default.How can I contribute?Thanks for asking!I appreciate everyone who contributes, no matter how you choose to do it.And if you feel like conforming to my workflow (which I try to stick to), that's great ;)My workflowUseGitflow Workflow.Include GitHub issue number infeatureandhotfixbranch names.If an issue doesn't exist on GitHub, create one.Include branch name in every commit message, even ondevelop.Only small "one commit" fixes done directly ondevelop.Commit style: imperitive sentences in all lower case except proper nouns.Merge intodevelopusing pull requests and keeping the entire commmit history.Merge intoproductionsqushing commits and keeping only version number in commit message.At some point I would like to automatically publish every release to pushed toproductionon PyPy.If you add new dependencies, don't usepip freeze > requirements.txt. Add new dependencies manually.
Packages we install can install their own dependencies.
Package: dftools-core
DF Tools - Core libraryPython data engineering tool enabling the standardThis library provides standard methods for data engineering recurrent tasks, linked to structure and data flow managementMain FeaturesData Structure CatalogingData Structure Templating / Checking / ComparisonInstallationYou can install with pippipinstall--upgradedftools-coreDependenciesstrenumregexpandasjinja2ordered_setProject statusOn going development
Package: dftopo
DFTOPOHow to Installpip install dftopoImport into your codefrom dftopo import dfpsExamplesConnection.dbname='your_dbname'
user='your_user'
password='your_password'
host='your_host'
port='your_port'
schema='your_schema1,your_schema2'

engine = dfps.connection(dbname,user,password,host,port,schema)Pure SQL.query = f"""
    SELECT * FROM your_schema1.your_table
    """

list(dfps.query_execution(query, engine))Read data from postgres in python.query = f"""
    SELECT * FROM your_schema2.your_table
    """

df = dfps.read(query, engine)Load data from python to Postgres.dfps.load(df,"test2", engine,'your_schema1')
dfps.load(df,"test2", engine,'your_schema2')
Package: df-to-rs
df_to_rsdf_to_rsis a Python package that provides convenient methods to upload and upsert Pandas DataFrames to Amazon Redshift.InstallationInstall the package using pip:pipinstalldf_to_rsUsageInitializationfromdf_to_rsimportdf_to_rsimportpsycopg2uploader=df_to_rs(region_name='ap-south-1',s3_bucket='your-s3-bucket',aws_access_key_id='your-access-key-id',aws_secret_access_key='your-secret-access-key',redshift_c=psycopg2.connect(dbname='more',host="hostname.ap-south-1.redshift.amazonaws.com",port=1433,user='username',password='password'))Upload DataFrame to Redshiftuploader.upload_to_redshift(df,dest='analytics.ship_pen')Upsert DataFrame to RedshiftUpsert (insert or update) the DataFrame into a specified destination table in Redshift. Matching rows are identified by the specified columns, and existing rows are deleted before new rows are inserted.uploader.upsert_to_redshift(df,dest_table='analytics.ship_pen',upsert_columns=['id','name'])LicenseThis project is licensed under the MIT License - see theLICENSE.txtfile for details.
Package: df-to-sql
df_to_sqlDataFrame to SQL facilitates importing Pandas DataFrames into SQL databases.  It automates table and type migrations and deduplication of records.Usagefromdf_to_sqlimportDFToSQLfromdf_to_sqlimportdate_regexesfromdf_to_sqlimportnumber_regexesdate_regexes.append(r'^[0-9]{4}-[0-9]{2}-[0-9]{2}$')dts=DFToSQL(date_regexes=date_regexes)if'sqlite.db'inos.listdir('.'):os.remove('./sqlite.db')sqlite3.connect('sqlite.db')sl_engine=sa.create_engine('sqlite:///sqlite.db')for_inrange(0,10):df=pd.util.testing.makeMixedDataFrame()dts.integrate(df=df,table_name='table_name',schema='main',engine=sl_engine)sl_engine=sa.create_engine('sqlite:///sqlite.db')df=pd.read_sql_table('table_name',con=sl_engine)print(df.dtypes)print(df.shape)The imported table has these properties:print(df.dtypes)
a             int64
b             int64
c            object
d    datetime64[ns]

print(df.shape)
dtype: object
(5, 4)Installpipinstalldf_to_sql
Package: dftp
Don't Forget the PythonDon't Forget the Python is a command-line interface forRemember the Milk, an online task-management system.Remember the Milk is very good at what it does - and is very easy to use in the browser and various apps - so Don't Forget the Python does not focus on duplicating its abilities, but instead adds a couple features that RTM doesn't currently provide, like exporting your tasks to pdf and printing your lists and tasks to the terminal. Currently, only a simplified version of tasks is supported (name and due or completed date).InstallationUsepipx:pipx install dftpUsageFrom a terminal, rundftpand the list of available commands will be shown, including full help documentation.New in Version 0.2.0You can now filter tasks by various due and/or completed dates - on, before, or after a date (and between two dates). Rundftp tasks --helpin a terminal to see the options.DisclaimerThis CLI uses the Remember The Milk API but is not endorsed or certified by Remember The Milk.
Package: dfttools
[![build](https://github.com/pulkin/dfttools/actions/workflows/test.yml/badge.svg)](https://github.com/pulkin/dfttools/actions)
[![pypi](https://img.shields.io/pypi/v/dfttools)](https://pypi.org/project/dfttools/)# DFT ToolsDFT Tools is a helper python package to parse, operate and present text data related to density functional theory (DFT) codes.
More information and docs:http://dfttools.readthedocs.io/en/latest/index.html
Package: df-turn-cacher
Dialog Flow Turn CacherDialog Flow Turn Cacheris python module add-on forDialog Flow Framework, a free and open-source software stack for creating chatbots, released under the terms of Apache License 2.0.Dialog Flow Turn Cacherallows you to ...Quick StartInstallationpipinstalldf_turn_cacherBasic exampleTo get more advanced examples, take a look atexampleson GitHub.Contributing to the Dialog Flow Turn CacherPlease refer toCONTRIBUTING.md.
Package: dfupsert
dfupsertOverviewdfupsertis an efficient Python package designed for synchronizing pandas DataFrames with databases using upsert operations (insert or update). It works seamlessly with SQLAlchemy's well-defined table mappings, facilitating a smooth integration between pandas and various databases.FeaturesStreamlined Upsert Operations: Facilitates upserts directly from pandas DataFrames to database tables using SQLAlchemy mappings.Wide Database Compatibility: Built on top of SQLAlchemy, it supports a diverse range of database systems.Efficient Handling of Large DataSets: Implements chunk-based data processing for optimized performance with large data volumes.Customizable and User-Friendly: Offers flexibility in connection types, table mappings, and chunk sizes to accommodate different use cases.InstallationInstalldfupsertusing pip:pipinstalldfupsertUsagefromdfupsertimportupsertfrompandasimportDataFramefromsqlalchemyimportcreate_enginefromyour_application.modelimportYourTableClass# Import your SQLAlchemy table class# Example DataFramedata={'column1':[1,2],'column2':[3,4]}df=DataFrame(data)# Establish a database connection using SQLAlchemyengine=create_engine('your-database-connection-string')# Upsert DataFrame into the database tableupsert(df=df,con=engine,table=YourTableClass,chunksize=1000)RequirementsPython 3.xpandasSQLAlchemyContributingWe welcome contributions to thedfupsertgithub project.Licensedfupsertis available under the MIT License. See theMITorLICENSEfile for more details.SupportFor questions and support, please open an issue in the project's GitHub issue tracker.
Package: dfuzz
dfuzz - automatic daemon configuration fuzzer. Rather than
being a fuzzer itself, dfuzz is a wrapper for other simple fuzzers
solving few common flaws which makes automated testing a bit difficult.Although its primary targets are configuration files, it is possible
to use dfuzz to fuzz any input files.The main problems dfuzz solves are:alpha versions of fuzzers - most of the fuzzers are just alpha versions which are no longer developed or maintainedcommon format - no need to understand how to use every single underlying fuzzercustomizable monitoring and automatic error analysisstraightforward specification of what to test and which file to supply to the targetcombination of mutation and generation of fuzzed filesRequirementspython >= 2.6fuzzer (zzuf, autodafe, …)gdb, valgrind (both are optional)Featuresindependent of underlying fuzzerhighly configurablebuilt to be extendibleautomation friendlySupported fuzzerszzuf (mutation)autodafe (generation)plain (debugging purposes)Usageinstall requirementsinstall dfuzz (for example easy_install dfuzz)create a working directorysupply fuzz.conf file (sample follows)according to the modules you want to use, create mut or gen directory in your working directory and supply a file to fuzz or a template to userun dfuzz -d -oname_of_the_working_directoryobserve outputif everything is fine remove the -d and -o options and run the command againcheck the samples directory created in your working directoryuse included incident_viewer to browser incidents if there are anyComplete documentation in docs directory.Sample fuzz.conf file[global]
binary=libvirtd
args=-f FUZZED_FILE --verbose
threads = 1
timeout = 2

generation  = 0
mutation    = 1
combination = 0

[generation]
modules  = dfuzz.gen.autodafe
priority = high

[mutation]
modules = dfuzz.mut.zzuf; dfuzz.mut.plain
priority = high

[combination]
modules = dfuzz.comb.simple
priority = lowIn it’s simplest form, dfuzz can be used as
a zzuf wrapper with enhanced detection and reporting
capabilities.
Package: dfviewer
dfviewerdfviewer is PyQt5 based a data view tool for pandas data frames working on Jupyter Notebook or IPython.Installationpip install dfviewerUsagefromdfviewerimportvimportpandasaspddf=pd.read_csv(r'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')v(df)You can set number of rows displayed per page. Default value is 100. For example:v(dataFrame,500)SortingYou can easily sort all the data by double-clicking the column headers. When you double-click on the header again, ascending sorting option will be active.SearchingRegular SearchUsing the search bar at the top, you can search within the columns of the dataframe. To trigger the search, you need to press the enter key after typing the word to be searched for. You can change the column to be searched with the combo box to the right of the search bar.Pandas QueryYou can use pandas queries to perform more complex (for example, conditional search) searches. To enable this option, click on the "Search Query" box next to the search bar. Then type the query in the search box and press the enter button.Known bugsDataframe indexes should not be multi index.All column names must be string.
Package: dfwsgroup
###快速启动-android稳定性测试前置条件：需安装adb环境、需安装adb环境、需安装adb环境，重要事情说三遍简介：
结合字节跳动提供的开源工具Fastbot_android, 进行封装了基础版的稳定性测试，
只需要数据线连接电脑，并确定在cmd中输入adb devices，看到了手机设备号，即可运行命令。命令参数必选参数:
  startproject              创建工程目录
  
  run                       运行
  
    package 包名，必须输入，不然会抛出异常
    
    -m 10 是设置分钟，置默认为5分钟
    
    -t 500 是设置点击事件，默认设置为500
    
    -log 是捕获异常日志，布尔值，默认是true
    
    -module 是获取自定义文件中的模块名称基础教程新建一个工程目录testProject，在Terminal中输入命令创建脚手架：PS E:\testProject> py -m dfwsgroup startproject demo          
2022-02-23 11:27:43.369 | INFO     | dfwsgroup.scaffold:create_folder:29 - 创建文件夹：demo
2022-02-23 11:27:43.371 | INFO     | dfwsgroup.scaffold:create_file:38 - 创建文件：demo\__init__.py
2022-02-23 11:27:43.372 | INFO     | dfwsgroup.scaffold:create_folder:29 - 创建文件夹：demo\modules
2022-02-23 11:27:43.373 | INFO     | dfwsgroup.scaffold:create_file:38 - 创建文件：demo\modules\dfwsgroup.json
2022-02-23 11:27:43.374 | INFO     | dfwsgroup.scaffold:create_file:38 - 创建文件：demo\debugfeng.py在Terminal中输入运行命令默认是运行 5 分钟点击 500 下PS E:\testProject> py -m dfwsgroup run "包名" -module "login"(login是dfwsgroup.json文件中的模块名)默认是运行 10 分钟点击 1000 下PS E:\testProject> py -m dfwsgroup run "包名" -m 10 -t 100 -module "login"(login是dfwsgroup.json文件中的模块名)默认是运行 5 分钟点击 500 下，并不要记录日志PS E:\testProject> py -m dfwsgroup run "包名" -module login -l flase设置自定义文件dfwsgroup.json文件json文件固定格式：{"login":[{"prob":1,"activity":"xz.act.app_v4.account.LoginActivityV4","times":1,"actions":[{"xpath":"//*[@resource-id='xz.act:id/tv_account']","action":"CLICK","throttle":2000}]},{"prob":1,"activity":"xz.act.app_v4.account.AccountLoginActivityV4","times":1,"actions":[{"xpath":"//*[@resource-id='xz.act:id/et_select']","action":"CLICK","throttle":2000},{"xpath":"//*[@resource-id='xz.act:id/et_search']","action":"CLICK","text":"*测试酒店","clearText":false,"throttle":2000},{"xpath":"//*[@resource-id='xz.act:id/group_name']","action":"CLICK","throttle":2000}]}]}|后续会在增加黑白名单操作，敬请期待。。。###严正声明作者只提供工具使用，当用户使用其他用途时，对用户或者他人造成任何形式的损失和伤害，该工具不承担任何责任。本协议的一切解释权与修改权归本工具所有。
Package: dfwsgroup-ios
###快速启动-ios稳定性测试前置条件：需安装tidevice环境，tidevice安装完成后需要安装iTunes软件，不然会运行失败。手机上要安装提供的包（需要开发运行起来后帮忙安装）官方中文文档简介：
结合字节跳动提供的开源工具Fastbot_ios, 进行封装了基础版的稳定性测试，结合alibaba开源的tidevice工具，可以在windows平台读取ios设备，并执行稳定性测试命令命令参数必选参数:

  run                       运行
  
    bundle_id 包名，必须输入，不然会抛出异常
    
    -d 10 是设置时长，置默认为5分钟
    
    -t 500 是设置点击事件，默认设置为500基础教程启动CMD控制台# 默认是运行 5 分钟点击 500 下
PS E:\testProject> py -m dfwsgroup_ios run "包名-BUNDLEID"# 默认是运行 10 分钟点击 1000 下
PS E:\testProject> py -m dfwsgroup_ios run "包名-BUNDLEID" -d 10 -t 100###严正声明作者只提供工具使用，当用户使用其他用途时，对用户或者他人造成任何形式的损失和伤害，该工具不承担任何责任。本协议的一切解释权与修改权归本工具所有。
Package: dfx-apiv2-client
DeepAffex APIv2 Python Client Librarydfx-apiv2-clientis a Python 3 asyncio client library for the NuraLogix
DeepAffex API.Installationpipinstall--upgradedfx-apiv2-clientUsageFor usage examples, please seehttps://github.com/nuralogix/dfx-demo-pyor the apiexample.py file in this repo.For more information, please visithttps://deepaffex.ai/developers-api
Package: dg645-ioc
EPICS-IOC for the Stanford DG645 Delay GeneratorQuick 'n dirty:Download via PyPI:pip install dg645-iocor via GitLab:git clone https://gitlab.com/kmc3-xpp/kronos-iocConfigure at least the IP address and the EPICS prefix and start via shell:$ export DG645_EPICS_PREFIX="BEAMLINE:DG645:"
$ export DG645_HOST="10.0.0.17"
$ DG645-ioc
INFO:kronos.ioc:Stanford Research Systems,DG645,s/n001776,ver1.14.10E
INFO:root:Starting IOC, PV list following
[...]
INFO:caproto.ctx:Server startup complete....or create container and start via Docker or Podman:$ podman build -t dg645-ioc -f kronos-ioc/Dockerfile -v $PWD/kronos-ioc:/kronos_src:z
$ podman run -ti --rm \
    -e DG645_EPICS_PREFIX="BEAMLINE:DG645:" \
    -e DG645_HOST=10.0.0.17\
    --name fridge dg645-ioc:latest
[...]Here's a list of environment variables that might help:DG645_HOST: host name or IP of the DG645 controllerDG645_PORT: this is better left blank (the default). In that
case, the IOC will create a "TCPIP::::INSTR" PyVISA device
name. If the port is specified, it will create a
"TCPIP::::::SOCKET" device name instead.DG645_VISA_DEV: PyVISA device to connecto to, instead of the
TCP/IP device. If set, overrides host/port.DG645_VISA_RMAN: PyVISA resource manager. Defaults to "@py".
If you've got this far, you know what this is good for ;-)DG645_EPICS_PREFIX: EPICS PV prefix to use. Include trailing column
(:) if you need one. Defaults toKMC3:XPP:DG645:.DG645_LOGGING: one of "error", "warn", "info" or "debug". Defaults
to "info".DG645_LOG_STATUS: if set to "yes", the IOC will periodically
(about once per second) log the
current status of all variables it observes to the "info" logging
facility. The default is not to do that.Enjoy.