Saravanan666/pypi-org huggingface dataset:
Package: lazydog-merge-sqlite
lazydog_merge_sqliteMerge SQLite DB files by copying the tables.
If the tables do not overlap they can be copied straight, if they do overlap there are some checks which see if the tables have the same schema. This isn't bulletproof and is more done on a best effort basis.TODOSupport Nuitka - single binaryCheck files if they are sqlite and give better errors based on that
Package: lazyearth
Lazyearth: Python earth science packageWhat is it?Lazyearth is a Python package that offers ease and speed in analyzing geospatial data. Lazyearth was designed to support the functions of the Open Data Cube, which primarily aggregates aerial satellite imagery, but it can also operate on personal computers. The purpose of creating Lazyearth is for it to become a widely used tool in the field of geoscience.Website :https://lazyearth.org/PyPI :https://pypi.org/project/lazyearth/Mailing :Tun.k@ku.thBug reports :https://github.com/Tun555/lazyearth/issueslearn :https://lazyearth.org/install/learnInstallationIf you want to work on a personal computer, you need to install the GDAL package first
Open command promptcondainstall-cconda-forgegdalHowever, if you want to work on Open Data Cube or Google Colab, you can get started immediately.
The latest released version are available at thePython
Package Index (PyPI)# PyPIpipinstalllazyearthMain FeaturesOpening and Saving : It can open various types of images and save them easily in multiple formats after processing.Image plotting : This feature supports the display of a diverse range of images for single or comparative purposes. It can accommodate various formats, such as 1 or 3-dimensional numpy arrays, as well as xarray.Band combination : It can easily blend different color bands of satellite imagesRemote Sensing Calculation: There are multiple calculation indices such as NDVI, EVI, BSI etc.Water: This features water analysis, including water detection and water quality.
Package: lazyeda
lazyedaLazy EDA is a python library to help shorten the coding time for basic understanding  of data, EDA techniques and repetitive tasks with less lines of codeProject Organization├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.orgProject based on thecookiecutter data science project template. #cookiecutterdatascience
Package: lazyEEG
# OverviewAdvantanges:Disadvantages:—
## Documentation
Seehttp://lazyEEG.readthedocs.io/en/latest/for introduction, tutorials, and reference manual.—
# Installation instructionslazyEEG has been tested on Python3.5 64bit.
(Current it might be unfit for older versions like Python2.7. But since Python2 will become obsolete in the years to come, why not drop it anyaway and take Python3 instead?)The simplest way to install lazyEEG is through the Python Package Index (PyPI), which ensures that all required dependencies are established. This can be achieved by executing the following command:` pip install lazyEEG `or:` sudo pip install lazyEEG `###*Required Dependencies*numpypandasscipymatplotlibstatsmodelsseabornmnepermutetqdm—
# Simple example
Package: lazyemployee
(LazyEmployee) Employee OOP for Testing PyPI by Tun Kedsaroโปรแกรมแชทนี้เป็นโปรแกรมที่เขียนขึ้นมาโดยใช้ Python โดยการเขียนแบบ OOP
สำหรับใช้งานในการสร้าง class employee อย่างรวดเร็ว เพียงแค่มี Python
อยู่ในคอมพิวเตอร์ก็สามารถใช้งานได้เลย เบื้องหลังการทำงานจะประกอบไปด้วย
Engineer,Accounting,Saleปล. เวอร์ชั่นนี้ยังไม่สมบูรณ์ (เขียนแบบรีบๆ) เขียนเพื่อนำ concept OOP
มาสร้าง library แล้วก็ upload ขึ้น Github กับ PyPI ต่อไป
สำหรับนักพัฒนาสามารถนำ Source Code ไปพัฒนาต่อได้เต็มที่
ซึ่งก็คืออีกโปรเจคนึ่งนั้นแหละ เพราะว่า jupyter มันไม่สามารถ import def
ได้เหมือนกับตัว คอมก็เลยใช้วิธีนี้เอา และก็ยังมีอีกหลาย library
ที่เขียนดองเอาไว้ตั้งแต่สมัยเรียน ปี3, 4 แต่ว่ายังไม่ได้ upload
(เขียนไปงั้นแหละต้องใช้ความยาวระดับหนึ่งไม่งั้นไม่สามารถอัพได้)วิธีติดตั้งเปิด CMD / Terminalpipinstalllazyemployeeวิธีใช้[STEP 1] - เปิด IDLE ขึ้นมาแล้วพิมพ์…fromlazyemployeeimportEmployeefromlazyemployeeimportAccounting,Programmer,Saleหรือเปิด cmd / terminal แล้วพิมพ์python-mlazyemployee[STEP 2] - ประกาศ class employee กรอก ชื่อ ตำแหน่งงาน และ เงินเดือน -
สามารถเรียกการแสดงผลได้โดยการใช้คำสั่ง detail() -
สามารถเปลี่ยนข้อมูลข้างในได้ด้วยการใช้ฟังก์ชัน setname()
,setsalary(),setjob() - สามารถรับค่าข้อมูลข้างในได้ด้วยการใช้ฟังก์ชัน
getname() ,getsalary(),getjob()[STEP 3] - ใช้งานได้เลยจ้าาาDevelop by : Tun Kedsaro
FB: I dont tell you.
YouTube: Tun Kedsaro
Package: lazy-fields
Lazylazyfieldshas utilities to allow for lazily defined attributes so that you can avoid some patterns like the one below or avoid calculating a property twice.classMyClass:def__init__(self):self._val=Nonedefget_val(self):ifvalisNone:self._do_something()returnself._vallazyfieldThelazyfields.lazyfielddescriptor streamlines the process of implementing lazy attributes. Here's how you can use it:fromlazyfieldsimportlazyfieldclassMyClass:@lazyfielddefexpensive_value(self):returndo_expensive_operation()instance=MyClass()instance.expensive_value# Does the expensive operation, saves the result and returns the valueinstance.expensive_value# Returns directly the cached valuedelinstance.expensive_value# Cleans the cacheinstance.expensive_value# redo the expensive operationinstance.expensive_value="Other"# Overwrites the cached value with the value assignedlazyfieldsaves the value directly in the class as a hidden attribute so you don't have to worry about garbage collection or weakrefsasynclazyfieldThe lazyfields.asynclazyfield descriptor tackles the same issue as lazyfield, but while preserving the asynchronous APIfromlazyfieldsimportasynclazyfieldclassMyClass:@asynclazyfieldasyncdefexpensive_value(self):returnawaitdo_expensive_operation()instance=MyClass()awaitinstance.expensive_value()# you still call it as a function, but it will do the same thing, call the function, store the result and return the valueawaitinstance.expensive_value()# now it only returns the valuedellazy(instance,"expensive_value")# clear the stored valueawaitinstance.expensive_value()# here the calculation is done againsetlazy(instance,"expensive_value","Other")# overwrite the stored value with "Other"Helperslazyfieldsprovides helpers to work with frozen classes or when using the asynclazyfield and need to set or reset the field manuallysetlazy(instance: Any, attribute: str, value: Any, bypass_setattr: bool = False)setlazy allows you to directly change the hidden attribute behind any lazy field, and the bypass_setattr parameter will instead of using thesetattrfunction, useobject.__setattr__.dellazy(instance: Any, attribute: str, bypass_delattr: bool = False)dellazy allows you to clear the value stored in the hidden attribute to allow for recalculation. Similar to setlazy the bypass_delattr parameter will useobject.__delattr__instead ofdelattrforce_set(instance: Any, attribute: str, value: Any)force_set is just a shortcut for setlazy(..., bypass_setattr=True)force_del(instance: Any, attribute: str)force_del is just a shortcut for dellazy(..., bypass_delattr=True)is_initialized(instance: Any, attribute: str)Returns whether the lazyfield has stored a value yet, without triggering the routine inadvertently.
Package: lazyforecast
LazyForecastLazyForecast is a Python library for performing univariate time series analysis using a lazy forecasting approach. This approach is designed to provide quick and simple forecasting models without requiring extensive configuration or parameter tuning.Table of ContentsInstallationFeaturesUsageOutputInstallationYou can install LazyForecast using pip:pipinstalllazyforecastFeaturesLazyForecasting automatically selects the best model based on the characteristics of the input time series.It supports univariate time series analysis.LazyForecasting provides functions for data preprocessing, model training, forecasting, and evaluation.It includes various popular forecasting models such as Auto ARIMA, Vanilla LSTM, and RNN.UsageHere's an example of how to use the LazyForecast library to forecast stock prices using historical data:importpandasaspdimportmatplotlib.pyplotaspltimportnumpyasnpimportyfinanceasyfimportLazyForecastaslf# Set the number of periods and steps for forecastingn_periods=50n_steps=5# Create an instance of LazyForecastts=lf.LazyForecast(n_periods=n_periods,n_steps=n_steps,n_members=5)# Specify the start and end dates for the datastart_date='2021-01-01'end_date='2022-12-31'# Fetch the historical stock data for Googledf=yf.download('GOOGL',start=start_date,end=end_date)# Reset the index of the DataFramedf.reset_index(level=0,inplace=True)# Fit the data to the LazyForecast model and obtain evaluation metrics, forecasts, and confidence intervalseval_df,fc,confint=ts.fit(df,x_axis='Date',y_axis='Close')# Print the evaluation metrics for each modelprint(eval_df)OutputAuto-Regressive Integrated Moving Average (ARIMA)Multi-Layer Perceptron (MLP)Vanilla Long Short-Term Memory (LSTM)Stacked LSTMBi-directional LSTMRecurrent Neural Network (RNN)Gated Recurrent Unit (GRU)Evaluation TablemodelmdarmsemapeR2maecorrARIMA0.551022.628520.02083770.724561.95320.863465GRU0.551022.69930.02161350.6679122.038980.85146BIDIRECTIONAL LSTM0.551022.725040.02204560.6398952.086010.851686VANILLA LSTM0.551022.796710.02253550.6300662.136590.848691STACKED LSTM0.5102042.907710.02338170.5817362.222430.851591MLP0.5102047.578710.0664394-4.66696.667320.698219RNN0.4693882.969330.02403810.6090762.273330.821808
Package: lazy_format
Simple way to have partially applied formatted strings.This python package provides a wrapper around the ‘str.format’ functionality in order to skip unresolved formatting parts. All arguments are applied lazily.InstallEnter the following command:$ pip install lazy_formatExamplesMore examples you can find in the tests.Common use case:>>> from lazy_format import LazyFormat
>>>
>>> lazy = LazyFormat('{0} {1} {0} {msg} {var1} {var2} {obj!r} {x:.2f}', 'fist arg', msg='Hello')
>>> lazy
<lazy_format.format.LazyFormat at 0x22a2590>
>>> print lazy
fist arg {1} fist arg Hello {var1} {var2} {obj!r} {x:.2f}
>>>
>>> lazy.format('second arg', obj=LazyFormat, x=22/7.)
"fist arg second arg fist arg Hello {var1} {var2} <class 'lazy_format.format.LazyFormat'> 3.14"
>>>
>>> lazy.format('second arg', obj=LazyFormat, x=22/7., var1='1', var2=2)
"fist arg second arg fist arg Hello 1 2 <class 'lazy_format.format.LazyFormat'> 3.14"
>>>
>>> lazy.format('second arg', obj=LazyFormat, x=22/7., var1='1', var2=2, msg='Bye')
"fist arg second arg fist arg Bye 1 2 <class 'lazy_format.format.LazyFormat'> 3.14"Global context:>>> LazyFormat.global_context.update_context_with({'global_var1': 'GV1', 'global_var2': 'GV2'})
>>> lazy2 = LazyFormat('{global_var1}, {global_var2}')
>>> print lazy2
GV1, GV2
>>> print lazy2.format(global_var1='my var1')
my var1, GV2Extra features:>>> from lazy_format import Aliases
>>> from lazy_format import LazyAliasFormat
>>>
>>> Aliases.add_alias('project_name', 'LazyFormat')
>>> lazy3 = LazyAliasFormat('{alias.project_name}')
>>> print lazy3
LazyFormat
>>>
>>>
>>> def func(alias, context):
>>>     return 'dyn value for ' + alias
>>>
>>> Aliases.add_alias('val', func)
>>> lazy4 = LazyAliasFormat('{alias.val}')
>>> print lazy4
dyn value for val
>>>
>>>
>>> LazyAliasFormat('{project_dir}', alias='dir')
<lazy_format.format.LazyAliasFormat at 0x22a2a50>
>>> templates = LazyAliasFormat('{alias.dir}/{template_dir_name}',
>>>                             template_dir_name='templates',
>>>                             alias='template_dir')
>>>
>>> my_prj_templates = LazyAliasFormat('{alias.template_dir}',
>>>                                    project_dir='/code/lazy_format',
>>>                                    template_dir_name='tmps')
>>> print templates
{project_dir}/templates
>>> print my_prj_templates
/code/lazy_format/tmps
>>> print my_prj_templates.format(project_dir='/code2')
/code2/tmps
Package: lazy-ghost-hunters
Lazy Ghost HuntersGhost Hunters is a little game edited by Smart Games.
The goal of the game is to light six ghosts arranged on a 4x4 grid with 6 transparent covers of different sizes on which are drawn 0, 1 or 2 torches.The goal of this program is to determine the solution. When the program is launched, indicate 4 rows of 4 numbers 0 (no ghost) or 1 (ghost) to indicate the arrangement of the ghosts. The solution will appear with "o" for the ghosts and colored arrows for the torches.InstallationThe program is available onPypi.
You need at least python 3.6 and you can use pipx to install it.pipx install lazy-ghost-huntersRunTo use it interactivelylghTo see all possibilitylgh -hRemovepipx uninstall lazy-ghost-hunters
Package: lazy_git
A streamlined REST API for wrapping access to Git repositories.RunningRunningpython manage.py runserver –host 0.0.0.0 –port 65004This will run the process locally on port 65004, the–host 0.0.0.0is there
so that hosts other thanlocalhostcan access the service, omit it if this is
not required.DockerBuildingdocker build -t lazy_git:latest .This will build an image based on the official
[Python 3 Docker image](https://hub.docker.com/_/python/) and defaults to
setting theLAZY_GIT_MODEenvironment variable toProduction.Runningdocker run -d -p <local_port>:65004 –name lazy_git -v <local_repo_dir>:/var/lazy_git lazy_git:latestThis command will run a lazy_git process in the background on the port specified
in<local_port>.  If you want to override theLAZY_GIT_MODEenvironment
variable (or any other, such asLAZY_GIT_CONFIG) then use the
[-eargument](https://docs.docker.com/engine/reference/run/#/env-environment-variables).
Package: lazygrid
LazyGrid is a python package providing an automatic, efficient and flexible
implementation of complex machine learning pipeline generation and cross-validation.Before fitting a model or a pipeline step, LazyGrid checks inside an internal
SQLite database if the model has already been fitted. If the model is found,
it won’t be fitted again.Documentation for thelatest stable versionis available on ReadTheDocs.Table Of ContentsGetting StartedDocumentationRunning testsContributingAuthorsLicenceGetting StartedYou can install LazyGrid along with all its dependencies fromPyPI:$pipinstall-rrequirements.txtlazygridor from source code:$gitclonehttps://github.com/glubbdubdrib/lazygrid.git$cd./lazygrid$pipinstall-rrequirements.txt.LazyGrid is compatible with Python 3.5 and above.DocumentationDocumentation for thelatest stable versionis available on ReadTheDocs.Running testsYou can run all unittests from command line by using python:$python-munittestdiscoveror coverage:$coveragerun-munittestdiscoverContributingPlease readContributing.mdfor details on our code of conduct, and the process for submitting pull requests to us.AuthorsPietro Barbiero - Mathematical engineer -GitHubGiovanni Squillero - Professor of computer science at Politecnico di Torino -GitHubLicenceCopyright 2019 Pietro Barbiero and Giovanni Squillero.Licensed under the Apache License, Version 2.0 (the “License”); you may
not use this file except in compliance with the License. You may obtain
a copy of the License at:http://www.apache.org/licenses/LICENSE-2.0.Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions and
limitations under the License.
Package: lazyhttp
# lazyhttpDrop in replacement for `http.server` with convenience methods added.```pythonimport jsonimport http.serverimport lazyhttpclass Handler(lazyhttp.Handler):def req(self, task):if '/create' in self.path or '/update' in self.path and \self.has(task, 'id', 'text'):TASKS[task['id']] = taskelif '/delete' in self.path and self.has(task, 'id'):del TASKS[task['id']]elif '/get' in self.path and self.has(task, 'id'):self.json(TASKS[task['id']])elif '/all' in self.path:self.json(TASKS)else:self.err('Nothing to do')if __name__ == '__main__':lazyhttp.run(Handler)```
Package: lazyimport
lazyimportlets you import python modules lazily.fromlazyimportimportlazyimportlazyimport(globals(),"""
  from mypackage import submodule
  import another_package
  """)Contents1Installation2Use3Credits1InstallationInstalllazyimportwith:pipinstalllazyimport2UseThelazyimportfunction taks two parameters:scope- The scope into which the given imports are imported. A common use case is to provide this parameter with the global scope by assigningscope=globals().text- The string giving the import code to perform lazily. For example,"from pandas import DataFrame".Example use:fromlazyimportimportlazyimportlazyimport(globals(),"""
  from mypackage import submodule
  import another_package
  """)3CreditsWritten by John Arbash Meinel, Martin von Gagern, Martin Packman and others
as part of the the GNU Bazaar revision control system.As such, lazyimport is distributed under the GNU General Public License v3,
like GNU Bazzar itself.Slightly adapted to package form byShay Palachy.
Package: lazy-imports-lite
lazy-imports-litechanges the semantics of python imports and defers the import until it is used the first time like inPEP 690.I liked the idea of lazy imports and wanted to use them for my projects without having to change my code.
I hope this project allows more people to use lazy imports in their projects.[!NOTE]
It is important to note that this project is not affiliated in any way with the original PEP.
I hope this project allows a wider adoption of lazy imports, but there is currently no plan to standardize it.[!IMPORTANT]lazy-imports-liteis still in early development and may contain bugs.
Make sure to test your code carefully before you use it.Key Featureslazy imports almost likePEP 690no code changes requiredcan be enabled per module (by keyword in the package)How is it different to PEP 690?It has not the same performance as the implementation from the pep. Every access to parts of imported modules is transformed to an attribute accessx->x._lazy_value.Exceptions during deferred import are converted toLazyImportError.modules which useexecorevalcan not be transformed.Usageaddlazy-imports-liteto your project dependencies.add lazy-imports-lite-enabled to the keywords of yourpyproject.toml.[project]
  keywords=["lazy-imports-lite-enabled"]This enables lazy imports for all top-level imports in your modules in your project.
One way to verify if it is enabled is to check which loader is used.>>>importyour_project>>>print(type(your_project.__spec__.loader))<class 'lazy_imports_lite._loader.LazyLoader'>Implementationlazy-imports-liteworks by rewriting the AST at runtime before the code is compiled.The following code:fromfooimportbardeff():print(bar())is internally transformed to:importlazy_imports_lite._hooksas__lazy_imports_lite__globals=__lazy_imports_lite__.make_globals(lambdag=globals:g())bar=__lazy_imports_lite__.ImportFrom(__package__,"foo","bar")deff():print(bar._lazy_value())This transformation should be never visible to you (the source location is preserved) but it is good to know if something does not work as expected.You can view a preview of this transformation withlazy-imports-lite preview <filename>if you want to know how your code would be changed.TODOmutableglobals()cache generated bytecodeIssuesIf you encounter any problems, pleasereport an issuealong with a detailed description.LicenseDistributed under the terms of theMITlicense, "lazy-imports-lite" is free and open source software.
Package: lazyins
lazyinsA stupid MySQL insert helperThis tool makes MySQL table with the values, then insert the values.Installpython -m pip install lazyinsUsageSet environment variables to connect your database.export LAZYINS_HOST="localhost"
export LAZYINS_PORT=3306
export LAZYINS_USER="root"
export LAZYINS_PASSWD="password"Then make cursor.from lazyins import Cursor
cur = Cursor()ThisCursor()function also recieveshost,port,user,passwd,db_name, andtable_nameas arguments.Case 1If you have two list to insert MySQL database.names = ['value1', 'value2', 'value3']
values = [1, 2, 3.3]You can make table and insert withcur.register(values, names).
The column types will be assumed.Case 2You have a list of tuple like that.data = [('value1', 1), ('value2', 2), ('value3', 3.3)]This simplecur.register(data)works.Case 3If you want to let it assume almost all values, but you want set explicit types for some values.
For example, you need to settinyintinstead ofint(assumed) forvalue2,explicit_types = [('values', 'tinyint')]
cur.register(values, names, explicit_types=explicit_types)Detail optionFor string, default length is 40.
If you need more,cur.default_char_length = 100.