Saravanan666/pypi-org huggingface dataset:
Package: datools
datoolsIntroductiondatoolsis a collection of Python-based tools for working with data in relational databases. While it contains several utilities for smoothing the rough edges of SQL, its most baked component isdatools.diff, an algorithm that's best explained in ablog postandJupyter Notebook.To learn more,read the docsorreach out.Database supportWhiledatoolsgenerates SQL for its operations, different databases
have their nuances.datoolsmay run on your database today, but in
an attempt to give you some certainty as to databases we know it has
successfully run on, we run all tests in the test suite against the
following databases:DatabaseEvaluated by test suiteSQLiteSince v0.1.2DuckDBSince v0.1.4PostgreSQLSince v0.1.5Redshift, SnowflakeYou provide an instance, I'll make the tests passHistory0.1.5 (2022-04-13)Support for PostgreSQL! The test suite now runs against PostgreSQL, anddatools.explanations.diffnow allows you to ask "why" about data stored in Postgres. Get excited!datools.sqlalchemy_utils.grouping_sets_querywill now generate a GROUPING SETs query for databases that support grouping sets (e.g., Postgres, DuckDB) or the equivalent UNION ALL version for databases without grouping sets support (e.g., SQLite). For more, check out theexample in the docs.0.1.4 (2022-02-27)Python 3.10 support.Updated test suite to run tests against multiple databases, in particular expanding from SQLite only to DuckDB and SQLite.As a result of the last bullet, ensured code runs against DuckDB in addition to SQLite.First stab at documentation (https://datools.readthedocs.io/en/latest/).0.1.3 (2021-12-31)Introduced mypy to linting and CI to ensure code that makes it tomainhas proper types.Created first working example ofDIFF working on a real-world dataset as a Jupyter notebook. This example partially replicates the Scorpion paper when only moteid/sensorids are considered.Separated theon_columnsargument ofdiffintoon_column_values(columns for which you want to generate equality predicates as explanations) and andon_column_ranges(columns for which you want to generate range predicates as explanations after bucketing the ranges into 15 equi-sized buckets).0.1.2 (2021-11-07)First release of DIFF algorithm implementation.0.1.0 (2021-05-09)First release on PyPI.
Package: datoso-plugin-internetarchive
Datoso Plugin InternetArchiveDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.Datoso Plugin InternetArchive is a helper plugin for other seed plugins for datoso.InstallationDatoso requires python 3.10+.datoso_plugin_internetarchive will be installed automatically when a seed need it.UsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-fbneo
Datoso Seed FinalBurn NeoDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.FinalBurn Neo is an emulator for Arcade Games & Select Consoles.InstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_fbneoUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-md-enhanced
Datoso Seed MegaDrive EnhancedDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.MegaDrive - Genesis Enhanced RomsInstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_md_enhancedUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-nointro
Datoso Seed No-IntroDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.No-Intro is a collection of DATs aimed for video game preservation especialized in cartridge-based and digital systems.InstallationDatoso requires python 3.10+.Install firefox and geckodriver, put geckodriver in your path.TutorialUse pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_nointroUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-pleasuredome
Datoso Seed PleasuredomeDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.PleasureDome is a group that gathers dats for MAME, HBMAME and fruit machines.InstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_pleasuredomeUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-redump
Datoso Seed RedumpDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.Redump.org is a disc preservation database and internet community dedicated to collecting precise and accurate information about every video game ever released on optical media of any system.InstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_redumpUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-sfc-enhancedcolors
Datoso Seed Super Famicom - Enhanced ColorsDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.Super Famicom - Enhanced Colors.InstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_sfc_enhancedcolorsUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-sfc-msu1
Datoso Seed Super Famicom MSU1Datoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.Super Famicom MSU1 - Enhancing audio of SNES gamesInstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_sfc_msu1UsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-sfc-speedhacks
Datoso Seed Super Famicom Speed HacksDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.Super Famicom Speed Hacks - Adding support for additional chips to prevent slowdowns.InstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_sfc_speedhacksUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-translatedenglish
Datoso Seed Translated EnglishDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.Translated-English is a collection of DATs aimed for video game translations.InstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_translatedenglishUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datoso-seed-vpinmame
Datoso Seed Visual PinMAMEDatoso is a WIP Python command line tool to download and organize your Dat Roms.
As today the tool supports dat-omatic, redump, and translated-english dats.
It merges all the dats in a tree folder structure thought to use with Emulators rather than dats.
The dat file format must be compatible withROMVault.Visual PinMAME it's a Dot Matrix Display emulator, does not include any pinball tables, but can be used with pinball emulators such as Visual Pinball X.InstallationDatoso requires python 3.10+.Use pip (recommended to use a virtual environment):
If you only need this plugin you can install it directlypipinstalldatoso_seed_vpinmameUsageCheckDatosofor usage.TODOTestsContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.LicenseMIT
Package: datpack-update
datpack-updateUpdate No-Intro DAT filesDescriptionThis script updates your current No-Intro dat files with new versions that you download. The benefit over doing this manually is that this program automatically finds which dat files you have in your existing folder, and only updates those dat files. This is helpful if you download the Daily datfile pack fromDAT-o-MATIC.Your dat filesmustbe named exactly as DAT-o-MATIC names them. This program relies on the specific timestamp format being used. Some examples names are:Atari - 2600 (20200514-091155).dat
Bandai - WonderSwan (20210609-224257).dat
Sega - Master System - Mark III (20210527-151920).datUsageusage: datpack-update [-h] src dest

Update No-Intro DAT files

positional arguments:
  src         Source directory of new DAT files
  dest        Destination directory of updated DAT files

optional arguments:
  -h, --help  show this help message and exitLicenseCopyright (C) 2023 Andrew SimmonsThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this program. If not, seehttps://www.gnu.org/licenses/.
Package: datrafilcha
datrafilchaData transformed/filtred charted.Give CSV, choose column, choose function, get plot.Data filters/transformersUniq values: count most repeated linesFrequency of occurrences: Suppose you want to know how often you will meet the value when reading list of values line by line expressed as histogram.
For example in the following set:
a,b,a,b,a,b,a,b
We have 8 items and we want to know the frequency of occurrences of letter b.
X axe value = number of items - sequence
Y axe value = for each x - y is either 0 b is not present or 1 b is presentOutputs x,y coordinates 0:0,1:1,2:0,3:1,4:0:5:1... each 1 is bar on plot, 0 is empty spaceSample codefrom datrafilcha import facade


configuration = {
    'id': 'UniqValues',
    'column_name': <COLUMN NAME>,
    'desc': True,
    'limit': 5,
}

data_filters_facade = facade.DataFiltersFacade()
html_plot = data_filters_facade.csv_column(configuration,
                                            data_source_file)

with open('new_plot.html', 'w') as f:
    f.write(new_chart.html)
Package: datreant
In many fields of science, especially those analyzing experimental or
simulation data, there is often an existing ecosystem of specialized tools and
file formats which new tools must work around, for better or worse.
Furthermore, centralized database solutions may be suboptimal for data
storage for a number of reasons, including insufficient hardware
infrastructure, variety and heterogeneity of raw data, the need for data
portability, etc. This is particularly the case for fields centered around
simulation: simulation systems can vary widely in size, composition, rules,
paramaters, and starting conditions. And with increases in computational power,
it is often necessary to store intermediate results obtained from large amounts
of simulation data so it can be accessed and explored interactively.These problems make data management difficult, and serve as a barrier to
answering scientific questions. To make things easier,datreantis a Python
package that addresses the tedious and time-consuming logistics of intermediate
data storage and retrieval. It solves a boring problem, so we can focus on
interesting ones.For more information on whatdatreantis and what it does, check out theofficial documentation.Getting datreantSee theinstallation instructionsfor installation details.
The package itself is pure Python.If you want to work on the code, either for yourself or to contribute back to
the project, clone the repository to your local machine with:git clone https://github.com/datreant/datreant.gitContributingThis project is still under heavy development, and there are certainly rough
edges and bugs. Issues and pull requests welcome!Check out ourcontributor’s guideto learn how to get started with
contributing back.
Package: datreant.core
In many fields of science, especially those analyzing experimental or
simulation data, there is often an existing ecosystem of specialized tools and
file formats which new tools must work around, for better or worse.
Furthermore, centralized database solutions may be suboptimal for data
storage for a number of reasons, including insufficient hardware
infrastructure, variety and heterogeneity of raw data, the need for data
portability, etc. This is particularly the case for fields centered around
simulation: simulation systems can vary widely in size, composition, rules,
paramaters, and starting conditions. And with increases in computational power,
it is often necessary to store intermediate results obtained from large amounts
of simulation data so it can be accessed and explored interactively.These problems make data management difficult, and serve as a barrier to
answering scientific questions. To make things easier,datreantis a Python
package that addresses the tedious and time-consuming logistics of intermediate
data storage and retrieval. It solves a boring problem, so we can focus on
interesting ones.For more information on whatdatreantis and what it does, check out theofficial documentation.Getting datreantSee theinstallation instructionsfor installation details.
The package itself is pure Python.If you want to work on the code, either for yourself or to contribute back to
the project, clone the repository to your local machine with:git clone https://github.com/datreant/datreant.core.gitContributingThis project is still under heavy development, and there are certainly rough
edges and bugs. Issues and pull requests welcome!Check out ourcontributor’s guideto learn how to get started with
contributing back.
Package: datreant.data
Thisdatreantsubmodule adds a convenience interface fornumpyandpandasdata storage and retrieval usingHDF5within a Treant’s directory
structure. It provides thedatalimb for Treants, Trees, Bundles, and Views
fromdatreant.core.See thedatreant.datadocumentationfor information on how to use this
submodule.For more information on whatdatreantis and what it does, check out theofficial documentation.Getting datreant.dataSee theinstallation instructionsfor installation details.If you want to work on the code, either for yourself or to contribute back to
the project, clone the repository to your local machine with:git clone https://github.com/datreant/datreant.data.gitContributingThis project is still under heavy development, and there are certainly rough
edges and bugs. Issues and pull requests welcome!Check out ourcontributor’s guideto learn how to get started with
contributing back.
Package: datrics-json
DescriptonOpen source library for theDatricsmodels deserializationInitial sourceThe solution is based onhttps://github.com/mlrequest/sklearn-jsonlibraryGetting Starteddatrics-json makes importing the models imlemented in theDatrics AI platformfrom their JSON representationInstallpip install datrics-jsonExample Usageimportdatrics_jsonasdatjsonmodel_dict=datjson.from_json(file_name)deserialized_model=list(model_dict.get('trained_models').values())[0]['model']sample_data=model_dict.get('sample_data')['input']deserialized_model.predict(sample_data)Featuressklearn-json requires scikit-learn >= 0.22.2.
LightGBM >= 2.3.1Supported scikit-learn Modelssklearn.linear_model.LogisticRegressionsklearn.ensemble.IsolationForestsklearn.clustering.KMeanssklearn.clustering.DBSCANsklearn.linear_model.LinearRegressionsklearn.linear_model.Ridgesklearn.linear_model.Lassosklearn.linear_model.ElasticNetSupported lightGBM Modelslightgbm.LGBMClassifier - binary - Gradient Boosting Treeslightgbm.LGBMClassifier - multiclass - Gradient Boosting Treeslightgbm.LGBMClassifier - binary - Random Forestlightgbm.LGBMClassifier - multiclass - Random Forestlightgbm.LGBMRegressor - Gradient Boosting Treeslightgbm.LGBMRegressor - Random ForestTest dataExamples of JSON Datrics models represendation
Package: dat-struct-py
# dat-struct-pyBasic Data Structures and Operations Implemented in Python## Installation Instructions- Python 3.5+ is currently supported- Install from PyPI using- pip install dat-struct-py## Supported Data Structures- Singly Linked List- Doubly Linked List- Circularly Singly Linked List- Stack using Linked List- Queue using Linked List- Tree (Binary Search Tree)### Operations Supported for Linked Lists- Create a linked list through input sequence or inserts- At the beginning- At the end- At any Position- Delete a node carrying some value- Size- Print- Quick check whether the list has even length- Return nth element from the end- Quick check whether a cycle exists- Return cycle length(if one exists)- Reverse in Place- Swap Pairs - Works only for Even length linked list### Operations Supported for Stacks- Create a stack by pushing elements one by one or through an input sequence- Check whether the stack is empty- Check whether the stack is full- Push an element- Pop an element- Peek the top element- Check balanced symbols- Filter out all adjacent elements from the input- Print the elements of the Queue### Operations Supported for Queues- Create a queue by queuing elements one by one or through an input sequence- Check whether the Queue is empty- Enqueue an element- Dequeue an element### Operations Supported for Binary Search Trees- Create a binary search tree by- Inserting values one by one- Passing in an input list- Traversals- Preorder- Inorder- Postorder- Spiral- Clockwise- Anticlockwise- Boundary- Projections/Views- LHS- RHS- Nodes at K distance away from root- Connect Nodes at the Same Level- Singly Linked List- Circularly Singly Linked List## Developer Tools- Full Fledged Vagrant Box in tools/DevelopDatStructPy- Prerequisites- [VirtualBox Installation](https://www.virtualbox.org/wiki/Downloads)- [Vagrant Installation](https://www.vagrantup.com/downloads.html)- Navigate to tools/DevelopDatStructPy- Modify bootstrap.sh to contain your git username and email (**MANDATORY STEP**)- Open Terminal/Command prompt- Execute `vagrant up` to bring up the VM- Execute `vagrant ssh` to login to the VM- Master Code will be present at `home/ubuntu/Development/Repos/`- Execute `source /home/ubuntu/Development/developEnv/bin/activate` to activate Python Virtual Environment
Package: datta
OverviewImmutable data structures based onestruttura.Examples>>>fromdattaimportData,attribute>>>classPoint(Data):...x=attribute(types=int)...y=attribute(types=int)...>>>point_a=Point(3,4)>>>point_aPoint(3,4)>>>point_b=point_a.update(x=30,y=40)>>>point_bPoint(30,40)>>>fromdattaimportData,attribute,list_attribute>>>fromtippoimportLiteral>>>classVehicle(Data):...kind=attribute(types=str)...>>>classGarage(Data):...vehicles=list_attribute(types=Vehicle)...gate=attribute(default="automatic",types=str)# type: Literal["automatic", "manual"]...>>>garage=Garage([Vehicle("bicycle"),Vehicle("car")],gate="manual")>>>garageGarage([Vehicle('bicycle'),Vehicle('car')],gate='manual')>>>garage.serialize()=={"vehicles":[{"kind":"bicycle"},{"kind":"car"}],"gate":"manual"}True>>>Garage.deserialize({"vehicles":[{"kind":"bicycle"},{"kind":"car"}],"gate":"manual"})==garageTrue>>>fromdattaimportlist_cls>>>MyStrList=list_cls(converter=str,qualified_name="MyStrList")>>>my_str_list=MyStrList([1,2.2,None,True])>>>my_str_listMyStrList(['1','2.2','None','True'])