Saravanan666/pypi-org huggingface dataset:
Package: tdk-sozluk
TDK-SözlükEn iyi TDK sözlük arama kütüphanesi.Best TDK dict. search library.Yükleme / Install:$pipinstalltdk-sozlukGereksinimler / Requirements:Python 3.3+pipTemel Kullanım / Basic Usageimporttdksonuclar=tdk.ara("yüzmek")forsinsonuclar:foriins.anlamlar():print(i.anlam())Output / Çıktı:Kol, bacak, yüzgeç vb. organların özel hareketleriyle su yüzeyinde veya su içinde ilerlemek, durmak
Yüzme sporu yapmak
Bir sıvının yüzeyinde batmadan durmak
Herhangi bir durumun en aşırı derecesinde olmak
Dalgalanmak
Herhangi bir şeyle üzeri kaplanmak, bir şeye bulanmak
Derisini çıkarmak, derisini soymak
Çok para istemekDokümantasyon / DocumentationDokümanlar henüz hazır değil. Hazır olunca burada olacak.Docs are not ready yet. They will be here when they're completed.Ama kullanımla ilgili bilgi için kaynak kodlarındaki yorum satırlarını okuyabilirsiniz.But you can read the explanations in the source code.Katkı Yap / ContributeKatkı yapmak için bu projeyi forklayın, geliştirin ve bir pull request ekleyin.To contribute this project: Fork, develop and submit a pull request.LICENSEThis project is distributed under MIT License.
Package: tdl-client-python
[![Python Version](http://img.shields.io/badge/Python-3.10-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![PyPi Version](http://img.shields.io/pypi/v/tdl-client-python.svg)](https://pypi.python.org/pypi/tdl-client-python)# tdl-client-python Development### SubmodulesProject contains submodules as mentioned in the.gitmodulesfile:brokertdl/client (gets cloned into test/features)wiremock### Getting startedRequirements:
-Python 3.10(support forPython 2.xhas been dropped)
-pip(ensure it supportsPython 3.10)Python client to connect to the central kata server.Update submodules` git submodule update--init`Setting up a development environment:` python3-mvenv venv . venv/bin/activate pip install-rrequirements.txt `Your virtualenv will be created in./venv/# Testing#### ManualAll test require the ActiveMQ broker to be started.
The following commands are available for the broker.` python./broker/activemq-wrapper.pystart pythonwiremock/wiremock-wrapper.pystart 41375 pythonwiremock/wiremock-wrapper.pystart 8222 `Stopping the above services would be the same, using thestopcommand instead of thestartcommand.#### Automatic (via script)Start and stop the wiremocks and broker services with the below:`bash ./startExternalDependencies.sh ``bash ./stopExternalDependencies.sh `# CleanupStop external dependencies` python./broker/activemq-wrapper.pystop pythonwiremock/wiremock-wrapper.pystop 41375 pythonwiremock/wiremock-wrapper.pystop 8222 `or`bash ./stopExternalDependencies.sh `# TestsRunning all the tests,` $ behave `Pass arguments to behave, e.g. to run a specific scenario,` $ behave test/features/queue/QueueRunner.feature:154 `or` $ behave-n"Process message then publish" `Seebehave[docs](https://python-behave.readthedocs.io/en/latest/behave.html) for more details.## DistributableRun the below to generate a distributable archive:`bash python3-mbuild `Thetdl-client-python-x.xx.x.tar.gzarchive can be found in thedistfolder.# To releaseRun` ./release.sh `
Package: tdlink
tdlinkPython Library for TDAmeritrade APIAll the dates and times are in UTC.AuthenticationNOTE:Please make sure you obtain a code or refresh token from TDAmeritrade before using this library.Follow these steps to get a code:Go to TDAmeritrade developer:https://developer.tdameritrade.com/Login and go to "My Apps"Create an app. Usehttp://localhostas the callback url.Go to this url:https://auth.tdameritrade.com/auth?response_type=code&redirect_uri=http://localhost&client_id=[YourAppName]@AMER.OAUTHAPEnter your TD credentials and give premissionOnce you login, you will be redirected to a url of this formhttps://localhost/?code=[code]Copy the [code] part. Add it as a parameter to the TDAmeritrade class.The TDAmeritrade class should now work!NOTE:The code you obtain is only valid for a limited period of time. If expired, follow steps again to get a new code.InstallationFrom PyPIpip install tdlinkFrom Sourcepip install --upgrade git+git://github.com/ysriram1/tdlinkExamplesConnect to the APIfromtdlinkimportTDlinktd=TDlink(app_key='TRADING123',redirect_uri='http://localhost',code=code,# follow instructions to get the codereturn_raw_response=False# will return formatted df if False, else returns HTTP response)Get the refresh and access tokenstd.refresh_token# returns refresh tokentd.access_token# returns the access tokenNote:You can save the refresh token for creating a future session instead of using a code.Get Data from APICurrent Quote```python
td.get_current_quote(symbol='QQQ',
          to_return=['askPrice', 'bidPrice', 'totalVolume'])
```NOTE:Use this also for prices of options.Historical Datatd.get_historical_prices(symbol='QQQ',period_type='day',# 'day'period=2,# day: 1, 2, 3, 4, 5, 10* month: 1*, 2, 3, 6 year: 1*, 2, 3, 5, 10, 15, 20 ytd: 1*frequency_type='minute',# day: minute* month: daily, weekly* year: daily, weekly, monthly* ytd: daily, weekly*frequency=5,# minute: 1*, 5, 10, 15, 30 daily: 1* weekly: 1* monthly: 1*start_date='06/03/2019',# mm/dd/yyyystart_time=None,# hh:mm:ssend_date='06/04/2019',end_time=None,extended_hours=True,return_df=True# if False, returns a dictionary instead)Options Chaintd.get_options_chain(symbol='QQQ',# symbol of underlying securitystrike=None,# mention a specific strikefrom_date='01/01/2020',# only expirations after this date (mm/dd/yyyy)from_time=None,to_date='01/30/2020',# only expirations before this dateto_time=None,expiry_month='ALL',# option expiry month ALL or JANkind='OTM',# ITM, NTM, OTM, SAK (Strikes Above Market), ALL etc.include_quotes=False,# if FALSE only returns the option names (use get_current_quote() to get value)contract_type='CALL',# CALL, PUTstrikes=5,# number of strikes above or below at-the-money pricestrategy='SINGLE',return_df=True# if False, returns a dictionary instead)Top Movers of an Indextd.get_movers_for_index(symbol='DJI',# has to be an index DJI, SPX.X  etcdirection=None,# 'up' or 'down'. if None, returns bothchange=None# 'value' or 'percent'. if None, defaults to percentage)SupportPlease email me atysriram@umich.eduif you have any questions, suggestions, or ideas for improvement.
Package: td-logger
td-loger: logging library for Treasure Data Cloud=================================================Setup-----Before using this logging handler, td-agent must be properly configured at thelocalhost. Please confirm these settings are in your/etc/td-agent/td-agent.conf.## built-in TCP input<source>type tcp</source># Treasure Data output# match events whose tag is td.DATABASE.TABLE<match td.*.*>type tdlogapikey YOUR_API_KEY</match>For more information, please look at the documentation.* http://docs.treasure-data.com/Install-------$ easy_install td-loggeror$ pip install td-loggerUsage-----Check out the tests folder for more samples.import loggingfrom tdlog import loggerlogging.basicConfig(level=logging.INFO)l = logging.getLogger('td_logger.test')l.addHandler(logger.TreasureDataHandler())l.info('Some message')js = { "semicolon" : ";", "at" : "@" }l.info(js)This will throw the log entries to the local td-agent. And there's an importantpoint. This logger accepts only 'dict' and 'json string'.Also, these parameters are logged by default.* sys_host* sys_name* sys_module* sys_lineno* sys_levelno* sys_levelname* sys_filename* sys_funcname* sys_exc_info* msgThese parameters can be specified at TreasureDataHander constructor.* host: td-agent host (default: 127.0.0.1)* port: td-agent port (default: 24224)* db: td database name (default: log)* table: td table name (default: default)* bufmax: buffer size max when td-agent is unavailable (default: 1*1024*1024)* timeout: network timeout (default: 3.0)Have fun!How to Develop--------------Folloings are the way to setup development envionment on MacOSX.$ sudo easy_install virtualenv$ virtualenv --no-site-packages .New python executable in ./bin/pythonInstalling setuptools............done.Installing pip...............done.$ source bin/activate$ bin/pip install msgpack-python$ python run_tests.pySpecial Thanks--------------- [Joshua Kuntz](https://github.com/j3kuntz) for the original work
Package: tdmagsus
This is a small library for working with temperature-dependent magnetic
susceptibility data measured on AGICO kappabridges from the KLY and MFK
series (e.g. Agico, 2003; Agico, 2009).Overviewtdmadsus provides three classes:Furnacerepresents the temperature-susceptibility behaviour of the empty
furnace (i.e. the measurement apparatus without a sample). It allows a “raw”
set of sample measurements to be corrected to remove the effects of the
changes in the susceptibility of the equipment itself. AFurnaceobject is
created from a.CURfile produced from a measurement run with no sample.
Since measuremed data is frequently noisy,Furnaceprovides methods for
smoothing the data with a spline before it is used for corrections.MeasurementCyclerepresents the temperature-susceptibility behaviour of a
sample during a single heating-cooling sample. It is initialized from a.CURfile and, optionally, aFurnaceobject. If a furnace is supplied, it is
used to correct the measured sample data.MeasurementCycleprovides methods
to calculate a disordering (Curie or Néel) temperature using different
techniques (Petrovský  & Kapička, 2006), calculate the alteration index, and
write the data to a CSV file.MeasurementSetrepresents the data from a progressive sequence of
heating-cooling cycles, which it stores as a dictionary ofMeasurementCycleobjects indexed by peak temperature. It is initialized from a directory
containing multiple.CURfiles.LicenseCopyright 2019 Pontus Lurcock; released under theGNU General Public License,
version 3.0. See the fileCOPYINGfor details.ReferencesAgico, 2003.KLY-3 / KLY-3S / CS-3 / CS-L / CS-23 user’s guide, Brno,
Czech Republic: Advanced Geoscience Instruments Co.https://www.agico.com/downloads/documents/manuals/kly3-man.pdfAgico, 2009.MFK1-FA / CS4 / CSL, MFK1-A / CS4 / CSL, MFK1-FB, MFK1-B user’s
guide4th ed., Brno, Czech Republic: Advanced Geoscience Instruments Co.https://www.agico.com/downloads/documents/manuals/mfk1-man.pdfPetrovský, E. & Kapička, A., 2006. On determination of the Curie point from
thermomagnetic curves.Journal of Geophysical Research, 111, p.B12S27.
Package: tdmat
tdmatWhat does "tdmat" stand for?"tdmat" stands for Thermal Demand Model Adapted from Tabula.What is it?tdmatis a non-GUI tool that implements a simple thermal demand prediction model for european residential buildings.tdmatfollows an approach similar to the one of the Tabula-Episcope research project^1.
It covers the space heating and space cooling demands and presents additional tools regarding domestic hot water demand. Load profiles follow an hourly time step.How does it work?The typical procedure is as follow:tdmatreads buildings properties from the Tabula-Episcope database^2. Data is stored locally, no internet connection needed.tdmatcomputes solar, transmission and ventilation contributions based on indoor setpoint temperature and weather datatdmatput contributions together to create hourly profile of thermal demandsInstallation notesThe packaged version oftamosis available on PyPi. Please run:pip install tamosWhere is the project hosted?Sources are managed on GitHub:https://github.com/BNerot/tdmatIs it difficult to use?Please follow the example inexamplesas a quick start guide.
You can also find a web version of the documentation indocs/build/html. Once this directory is downloaded, please open 'index.html'.CopyrightThe code is distributed under an Apache-2.0 license.
Most of the development work was done in the context of a PhD thesis.
This thesis was funded by two French institutions:Commissariat à l'Energie Atomique:https://www.cea.fr/englishAgence de la transition écologique:https://www.ademe.fr/en/frontpage/
Package: tdmctl
tdmctl Cli for management of TouDoum-FrameworkSample of config filecurrent_context:cluster1context:cluster1:host:localhostuser:adminpass:admin.tdmctl folder structure.tdmctl:config.yml:modules:<context-name1>:<module-name1>:main.py:<context-name2>:<module-name1>:main.py:<module-name2>:main.py:
Package: tdmelodic
Tokyo Dialect MELOdic accent DICtionary (tdmelodic) generatorThis module generates a large scale accent dictionary of
Japanese (Tokyo dialect) using a neural network based technique.For academic use, please cite the following paper.[IEEE Xplore][arXiv]@inproceedings{tachibana2020icassp,author="H. Tachibana and Y. Katayama",title="Accent Estimation of {Japanese} Words from Their Surfaces and Romanizationsfor Building Large Vocabulary Accent Dictionaries",booktitle={2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},pages="8059--8063",year="2020",doi="10.1109/ICASSP40776.2020.9054081"}Installation and UsageEnglish:tdmelodic Documentation日本語:tdmelodic 利用マニュアルAcknowledgementSome part of this work is based on the results obtained from a project subsidized by the New Energy and Industrial Technology Development Organization (NEDO).
Package: tdml
Transform Dataframe for Machine LearningA lightweight and easy-to-use Python package that transforming dataframes into machine learning friendly data format.Current dataframes, including Pandas and PySpark, are widely used to manipulate tabular data. These packages provide rich functionalities and optimizations for data processing. But after the processing, data is usually input to machine learning or deep learning models, which are constructed by other ML packages. In this step, the user needs to spend time in transforming dataframes into arrays or tensors, splitting data into several sets, mapping categorical data to integers, and even representing text data by vectors. To make the whole process more efficient, TDML bridges the dataframes and ML frameworks by addressing the aforementioned painful issues. Currently, TDML provides functions including:Automatically transform dataframe (Pandas or PySpark) into ML framework (NumPy, PyTorch or TensorFlow) arrays or tensors.Map categorical data to integers, represent text data by bag-of-word and support UDF on text transformation.Split transformed data into several sets (train-test or train-validation-test) by one line of code.Support reshuffling the train set after the splitting.Examplescdexamples/numpy
pythonsimple_sklearn_regression.pyFor more examples, please go to theexamplesfolder.TestsPlease refer to thetests.Contactzecheng@cs.stanford.edu
Package: td-ml-auto-build-segments
ml-auto-build-segmentsIntroductionThis Python Library allows you to automatically add attributes to Parent Segment in Treasure Data and Auto-build Folders and Segments in Audience Studio.Inputsconfig/auto_build.yml: This is the.yamlconfig file that defines the params for the Python functions that add new tables to Parent Segment via API, and use the column_name wheremain_attr: yesparam flags which attribute will be used for the segment creation rules. Exampleconfig/auto_build.ymlfile with notes on each param below:#####################################################################
########################## GLOBAL PARAMS ############################
#####################################################################
sink_database: ml_db                        #database where all model output tables will be saved
unique_user_id: td_canonical_id             #the main join key that will be used to join behaviors tables to customers table      
api_endpoint: 'https://api.treasuredata.com'
ps_name: 'ENTER PS NAME'                 #name of Parent Segment where ML Folders & Audiences will be built
v5_flag: 1                                  #indicate if you want to build Segments in V5 or V4
folder_depth: 10                            #determines how deeply into nested folders in Audience Studio the code will scan to find the Segments you want 
ps_stats_table: 'ps_stats'                  #stores list of all PS, Folders, and Segments in given 

######### Params for new attributes to add to PS #####
src_table:                                  #params for ML Model output table to be added to PS and used for Audience Creation
  name: next_best_product_final
  join_key: td_canonical_id
  cols:                                     #list of columns from src_table to be added to PS as attributes
    - name: nbp_helmets
      type: number
      build_segments: yes                   #if = 'yes', this column is used as the attribute value for building auto segments               
    - name: next_best_product
      type: string
      build_segments: yes                   #if = 'yes', this column is used as the attribute value for building auto segments

########## Auto Build Segment Params ############
rerun_ps: yes                             #if = 'yes', ps will be re-run after the ML model table is added to PS
ml_folder: '[ML] Base Audiences'          #name of Base Folder where all ML Model Audiences will be created
sub_folder: 'Auto-Segmentation'           #name of Sub-Folder where specific Audiences to current WF ML Model will be added
attr_group: 'ML Models'                  #name of Attribute Group where attributes added to Parent Segment will be grouped in Audience StudioCopyright © 2022 Treasure Data, Inc. (or its affiliates). All rights reserved
Package: td-ml-datamodel-create
td-ml-datamodel-createIntroductionThis Python Library allows you to define the main JSON params of a Treasure Insights Datamodel in aconfig.jsonfile inside aTreasure Workflow Projectand build datamodel automatically via API.Inputsconfig.json: the file that contains the needed params for Python code to read from and build the TI Datamodel. See below:{
## -- (name of datamodel)
"model_name":  "datamodel_automated" 
,
## -- (list of tables to be added to datamodel)
"model_tables": [
  {"db":"reporting","name":"table_1"},
  {"db":"reporting","name":"table_2"}
                ] 
,
## -- (list of users to share datamodel with)
"shared_user_list": ["ENTER EMAIL HERE","ENTER EMAIL HERE"] 
,
## -- (list of columns you want to change datatype from raw table to datamodel. Ex. in "date" you provide column names that will be converted to `datetime`)
"change_schema_cols": {"date": ["ENTER_NAME"], "text": ["ENTER_NAME"], "float": ["ENTER NAME"], "bigint": ["ENTER NAME"]}
, 
## -- (if any joins were required you can add a list of table_name:join_key pairs)
"join_relations": {"pairs":
[ 
  {"db1": "reporting", "tb1":"table_1","join_key1":"user_id","db2": "reporting","tb2":"table_2","join_key2":"user_id"},
  {"db1": "reporting", "tb1":"table_1","join_key1":"date","db2": "reporting","tb2":"table_2","join_key2":"date"}
]
                  }
}Copyright © 2022 Treasure Data, Inc. (or its affiliates). All rights reserved
Package: td-ml-map-segment-profiles
td-ml-map-segment-profilesIntroductionThis Python Library allows you to extract the query syntax for each Segment built in Audience Studio and write syntax to a table in TD with Folder/Segment names and canonical_ids that belong to each Audience. This mapping table can then be joined to attribute/behavior tables, so you can calcualte KPIs across different Audiences and track performance and auience changes over time.Input Paramsconfig.yml: The workflowYAMLfile contains the params that are required by this library to extract the neded information. See below:apikey= TD APIKEYtdserver= TD ENDPOINTsink_database= Database where outout tables will be writtenoutput_table= name of output tableunique_id= distinct user_id ingldndatabase (ex.canoncial_id)secondary_id_list= any secondary IDs needed for joins in Parent Segment if one main identifier is not used across all tables in PS
Package: td-ml-mta
td-ml-mtaTreasure Data Multi-Touch Attribution (TD-ML-MTA) PackageWelcome to the TD-ML-MTA package, designed for Multi-Touch Attribution (MTA) within the Treasure Data environment. This Python package uses Shapley and Markov models to help businesses understand the impact of different marketing touchpoints on conversions or other key goals.OverviewMulti-Touch Attribution (MTA) is a critical aspect of marketing analytics, enabling companies to analyze how various marketing touchpoints contribute to customer conversions or other desired outcomes. The TD-ML-MTA package integrates Shapley values and the Markov model to provide a comprehensive solution for MTA within Treasure Data.With TD-ML-MTA, you can determine the individual and collective contributions of different touchpoints, considering the temporal aspect of customer journeys and leveraging the power of Treasure Data for data storage and analysis.InstallationYou can install the TD-ML-MTA package using pip:pipinstalltd-ml-mta




ThankyouforchoosingTD-ML-MTAforyourTreasureDataMulti-TouchAttributionneeds!📊🚀`Copyright©2022TreasureData,Inc.(oritsaffiliates).Allrightsreserved`
Package: tdm-loader
This module allows National Instruments TDM/TDX files to be accessed like
NumPy structured arrays.To install the newest version use::pip install tdm-loaderSample usage::import tdm_loader
data_file = tdm_loader.OpenFile('filename.tdm')Access a channel by channel group and channel index combination::data_file.channel(channel_group, channel)Get a dict of all channels in a channel group:data_file.channel_dict(channel_group)Search for a column name.  A list of all column names that containsearch_termand their indices will be returned::data_file.channel_search(search_term)
Package: td-ml-probabilistic-unification
td-ml-probabilistic-unificationIntroductionThetd-ml-probabilistic-unificationis a Python package designed for Scalable Probabilistic Unification within the Treasure Data environment. It provides functionality to unify and cluster records probabilistically based on various attributes, making it useful for a wide range of data integration and analysis tasks.In order to perform probabilistic unification using this package, you should have an input table containing the data you want to unify. The package will use the specified configuration parameters to perform probabilistic unification and generate an output table with clustered records.ConfigurationBefore using this package, you need to set the following environment variables:# Configuration variablesTD_SINK_DATABASE=os.environ.get('TD_SINK_DATABASE')TD_API_KEY=os.environ.get('TD_API_KEY')TD_API_SERVER=os.environ.get('TD_API_SERVER')## Profiles id column nameid_col=os.environ.get('id_col')## cluster id column name by default : cluster_idcluster_col_name=os.environ.get('cluster_col_name')## convergence threshold for SoftImpute in case of missing valuesconvergence_threshold=float(os.environ.get('convergence_threshold'))## The cluster threshold is a parameter that determines the similarity level required for two entities to be considered part of the same cluster. When performing hierarchical clustering, entities are merged into clusters based on their similarity. The cluster threshold sets a limit on how similar two entities must be to belong to the same cluster.## Example: If set to 0.9, entities with a similarity level of 0.9 or higher will be grouped into the same cluster.cluster_threshold=float(os.environ.get('cluster_threshold'))## Type of string matching technique used.string_type=os.environ.get('string_type')## Binary variables to fill missing values or not in adjacency matrixfill_missing=os.environ.get('fill_missing')## it is fetched column dictionary with weightage which are being used in Unificationfeature_dict=json.loads(os.environ.get('feature_dict'))## blocking and output table nameblocking_table=os.environ.get('blocking_table')output_table=os.environ.get('output_table')## number of records to be used for a single docker image , below parms are being used for wf optimisationrecord_limit=int(os.environ.get('record_limit'))lower_limit=int(os.environ.get('lower_limit'))upper_limit=int(os.environ.get('upper_limit'))range_index=os.environ.get('range_index')paralelism=os.environ.get('paralelism')input_table=blocking_table'''pythonThank you for choosing td-ml-probabilistic-unification for your probabilistic unification needs! 📊🚀`Copyright © 2022 Treasure Data, Inc. (or its affiliates). All rights reserved`