Saravanan666/pypi-org huggingface dataset:
Package: pyMFDR
pyMFDRA python package for MF (matrix factorization) based DR (dimensionality reduction) algorithms.This repo contains the source and research materials for the article "Matrix Factorization Based Dimensionality Reduction Algorithms - A Comparative Study on Spectroscopic Profiling Data" by Zhang, et al. (Analytical Chemistry, 2022, DOI: 10.1021/acs.analchem.2c01922,https://pubs.acs.org/action/showCitFormats?doi=10.1021/acs.analchem.2c01922)Content of repo
  ├── src : source code
  ├── data : contains the dataset (.csv) used for the research
  └── notebooks : contains the jupyter notebook for the researchInstallationpip install pyMFDRHow to useDownload the sample dataset from the /data folder
Use the following sample code to use the package:# import the library
  from pyMFDR import mfdr

  # load the dataset or generate a toy dataset by X,y = mvg(md = 2)
  df = pd.read_csv('7047_C02.csv')
  X = df.iloc[:,2:cols-1].values # -1 for removing the last column that contains NAN
  y = df.iloc[:,1].values.ravel() # first col is index and not used in this study

  # get a list of available MFDR algorithms
  mfdr.get_algorithms() # it will ouptut ['PCA', 'NMF', 'LAE', 'RP', 'SRP', 'VQ', 'AA', 'ICA']

  # Run PCA on X. It will return W, H, Xr and the inner algorithm object.
  W,H,Xr,o = mfdr.mf(X, 3, alg = 'PCA', display = False) 

  # evaluate the dimensionality reduction quality by various metrics
  mfdr.evaluate_dr(X,W,Xr)

  # visualize H
  mfdr.visualize_dictionary(H)
Package: pymfl
Python wrapper for the MyFantasyLeague API.MyFantasyLeague WebsiteMyFantasyLeague API DocumentationInstallationUse the package managerpipto install.pipinstallpymflUsageFor examples on how to utilize the different features of this library, see
theexamplefolder.Running TestsTo run tests, run the following command:pytestContributingPull 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: pymgclient
pymgclient - Memgraph database adapter for Python languagepymgclient is aMemgraphdatabase adapter for Python
programming language compliant with the DB-API 2.0 specification described by
PEP 249.mgclient module is the current implementation of the adapter. It is implemented
in C as a wrapper aroundmgclient, the
official Memgraph client library. As a C extension, it is only compatible with
the CPython implementation of the Python programming language.pymgclient only works with Python 3.Check out the documentation if you need help withinstallationor if you want tobuildpymgclient for yourself!DocumentationOnline documentation can be found onGitHub
pages.You can also build a local version of the documentation by runningmakefrom
thedocsdirectory. You will needSphinxinstalled in order to do that.Code sampleHere is an example of an interactive session showing some of the basic
commands:>>>importmgclient# Make a connection to the database>>>conn=mgclient.connect(host='127.0.0.1',port=7687)# Create a cursor for query execution>>>cursor=conn.cursor()# Execute a query>>>cursor.execute("""CREATE (n:Person {name: 'John'})-[e:KNOWS]->(m:Person {name: 'Steve'})RETURN n, e, m""")# Fetch one row of query results>>>row=cursor.fetchone()>>>print(row[0])(:Person{'name':'John'})>>>print(row[1])[:KNOWS]>>>print(row[2])(:Person{'name':'Steve'})# Make database changes persistent>>>conn.commit()
Package: pymget
==================================================PyMGet :: Utility for parallel downloadingfiles from multiple mirrors==================================================Homepage: http://pymget.sourceforge.net/Author: Taras Gaidukov================================Installation Instructions:================================Dependencies:Python 3.xBuild and install by running:$ python setup.py build$ sudo python setup.py install================Usage notes:================The program is designed for parallel download files from multiple mirrors.Supported protocols: HTTP, HTTPS, FTP.Usage:pymget [ARGUMENTS...] LINKS...Arguments:-h                             Show this help.--help-v                             Show version.--version-b block_size                  Specify the size of data block received frommirrors in each task. Default value is 4MB.--block-size=block_size        Value could be in bytes, kilobytes or megabytes.To specify units add symbol K or M.-T timeout                     Specify timeout for mirror response in seconds.--timeout=timeout              Default value is 10 seconds.-o filename                    Specify a name of the file data will be saved--out-file=filename            to. By default the filename on the server isused. If it's impossible to detect the filename,'out' will be used.-u filename                    Specify the file with links on each line.--urls-file=filename           Links from this file will be added to links fromcommand line.Links should start with protocol http://, https:// or ftp:// and should besplitted with space. If there is argument specifing a file with links in commandline, then you may omit links in the command line.
Package: pyMHMuser
pyMHMuser LibraryCore library ofThe MHMuser, a python based telegram userbot.Installationpip3install-UpyMHMuserDocumentationUsageCreate folders namedplugins,addons,assistantandresources.Add your plugins in thepluginsfolder and others accordingly.Create a.envfile with following mandatory Environment VariablesAPI_ID
API_HASH
SESSION
REDIS_URI
REDIS_PASSWORDCheck.env.samplefor more details.Runpython3 -m pyMHMuserto start the bot.Creating pluginsTo work everywhere@MHMup_cmd(pattern="start")asyncdef_(e):awaite.eor("MHMuser Started!")To work only in groups@MHMup_cmd(pattern="start",groups_only=True,)asyncdef_(e):awaiteor(e,"MHMuser Started.")Assistant Plugins 👇@asst_cmd("start")asyncdef_(e):awaite.reply("MHMuser Started.")See more working plugins onthe offical repository!Made with 💕 by@MHMuser.LicenseMHMuser is licensed underGNU Affero General Public Licensev3 or later.CreditsLonamiforTelethon
Package: pymhopt
# pymhopt: Python Wrapper for Optimization AlgorithmsThis Python 3 code provides wrapper for symbolic regression providing two implementations a) Genetic Programming with symbolic regression b) multi-objective genetic programming using NSGA-II (https://ieeexplore.ieee.org/document/996017).## Dependencies
pandas; numpy; scikit-learn; gplearn; graphviz.## Installation
Runpip install pymhopt## Example
Seetest.pyfor an example.## Acknowledgements & CreditsThe gplearn module is used as base for symbolic expression and genetic programming evaluations. (https://gplearn.readthedocs.io/en/stable/)The nsga-II algorithm is adapted from [marcovirgolin repo](https://github.com/marcovirgolin/pyNSGP/).A fast and elitist multiobjective genetic algorithm: NSGA-II (https://ieeexplore.ieee.org/document/996017)Note:- This package is still under development & WORK IN PROGRESS, I will aim to cover a bunch of different meta-heuristics optimization algorithms.
Package: pymhz19-async
mhz19_asyncPython library for communicating with the MH-Z19x sensors for CO2 with asyncio.This sensor is very badly documented and there are conflicting reports on its working. To make things worse, several
hardware versions exist (including knockoffs), and there are also different firmware versions.Testing was done on an
MH-Z19B sensor (green PCB) that reports firmware version 0502.Only the commands that I could test and verify personally are
implemented. Several other commands were tried (including the unlimited/raw CO2 readings, "background" and calibration
data readings), but results are inconsistent with docs or other implementations, and they are left out for now.Here is a list of references used to implement the protocol:some datasheets, include operational limitsreverse engineering of theMH-Z19andMH-Z19Bsensors, quite thoroughhttps://github.com/WifWaf/MH-Z19: reference Arduino library, several inconsistencies with the above reverse engineeringraw braindumpof the author of the Arduino libraryanother blog postin Russian that sheds some light on CO2 readings and the MODBUS protocol.Please note that it is unclear whether you can "brick" the sensor, or whether you can fix calibration values once they
are messed up.FeaturesThis library providesaclass MHZ19Protocol(asyncio.Protocol)to speak the protocol of the sensoran executable that reads commands and prints results in json format.Implemented commandsdocumented
in source, as well asresponse parsing.__main__.pyis your reference for
using the library, it is very simple.Contrary to most implementations, this library makes no attempt to match input commands to responses from the sensor.
This is because the input and output dataframes do not have identifiers that allow a reliable match between requests and
responses. The intended use case is to send periodically CO2 read requests, in order to get readings at a user-defined
time interval.Command line operation with jsonThe module can be run as an executable. It reads commands from stdin and prints data from the sensor, one json per line.exportDEVICE=/dev/ttyUSB0
python-mmhz19_async"${DEVICE}"# pipe commands to stdin, receive outputs in stdoutInput json fields:command(required): enum name of the command, or raw intargs: scalar or list of scalars (depends on command)raw_args: list of 5 byte arguments to be appended as-is to the command (for debugging and hacking).Output json fields:command: enum name of the received command, or raw int if not recognizedraw: raw hex string of incoming arguments (6 bytes)if parsing is implemented, fields are extracted from the raw data and made available in additional fieldserror: error message generated during parsing.Protocol examples (input json followed by output json):{"command":"GET_CO2"}{"command":"GET_CO2","raw":"06DC01A905E6","CO2":425}{"command":"GET_CLAMPED_CO2_TEMPERATURE"}{"command":"GET_CLAMPED_CO2_TEMPERATURE","raw":"03553B000000","CO2":853,"temperature":19}{"command":"SET_ABC","args":false}{"command":"SET_ABC","raw":"010000000000","ack":true}{"command":"GET_FIRMWARE_VERSION"}{"command":"GET_FIRMWARE_VERSION","raw":"303530320000","version":"0502"}
Package: pymh-z19b-serial
This package is ment to be used to read data from MH_Z19B CO2 Sensor via serial. Sensor can be bought on AliExpress for around 18€ (Jan 2019)It is recommended to disable Auto calibration and set measuring range to 5000 to get adequate readings after first day of use.Requirements:You need a USB to TTL adapter to read data from the sensor. I use Prolific Technology PL2303 adapter plugged into my router.
Router runs a python script with this library that reads metrics periodically and sends it via MQTT.Python 3.5+Installation:pip3installpymh-z19b-serialLibrary Usage examples:It is possible to use sync and async way to read from the sensorSyncUsespyserialfrommh_z19b.syncimportSensorsensor=Sensor(port='/dev/cu.usbserial',baudrate=9600)print(sensor.read_metric())AsyncUsesasyncioandpyserial-asyncioimportasynciofrommh_z19b.asyncimportSensorasyncdefrun(loop):sensor=Sensor(loop)awaitsensor.open_serial(url='/dev/cu.usbserial',baudrate=9600)data=awaitsensor.read_metric()print(data)loop=asyncio.get_event_loop()loop.set_debug(True)loop.run_until_complete(run(loop))loop.close()
Package: pymia
pymiapymia is an open-source Python (py) package for deep learning-based medical image analysis (mia).
The package addresses two main parts of deep learning pipelines: data handling and evaluation.
The package itself is independent of the deep learning framework used but can easily be integrated into TensorFlow and PyTorch pipelines.
Therefore, pymia is highly flexible, allows for fast prototyping, and reduces the burden of implementing data handling and evaluation.Main FeaturesThe main features of pymia are data handling (pymia.datapackage) and evaluation (pymia.evaluationpackage).
The data package is used to extract data (images, labels, demography, etc.) from a dataset in the desired format (2-D, 3-D; full- or patch-wise) for feeding to a neural network.
The output of the neural network is then assembled back to the original format before extraction, if necessary.
The evaluation package provides both evaluation routines as well as metrics to assess predictions against references.
Evaluation can be used both for stand-alone result calculation and reporting, and for monitoring of the training progress.
Further, pymia provides some basic image filtering and manipulation functionality (pymia.filteringpackage).
We recommend following ourexamples.The following figure depicts pymia in the deep learning environment. The data package allows to create a dataset from raw data.
Extraction of the data from this dataset is possible in nearly every desired format (2-D, 3-D; full- or patch-wise) for feeding to a neural network.
The prediction of the neural network can, if necessary, be assembled back to the format before extraction.
The evaluation package allows to evaluate predictions against references using a vast amount of metrics. It can be used stand-alone (solid) or for performance monitoring during training (dashed).Getting StartedIf you are new to pymia, here are a few guides to get you up to speed right away:Installationfor installation instructions - or simply runpip install pymiaExamplesgive you an overview of pymia's intended use. Jupyter notebooks and Python scripts are available in the directory./examples.Do you want to contribute?Change historyAcknowledgmentsCitationIf you use pymia for your research, please acknowledge it accordingly by citing our paper:Jungo, A., Scheidegger, O., Reyes, M., & Balsiger, F. (2021). pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis. Computer Methods and Programs in Biomedicine, 198, 105796.BibTeX entry:@article{Jungo2021a,
author = {Jungo, Alain and Scheidegger, Olivier and Reyes, Mauricio and Balsiger, Fabian},
doi = {10.1016/j.cmpb.2020.105796},
issn = {01692607},
journal = {Computer Methods and Programs in Biomedicine},
pages = {105796},
title = {{pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis}},
volume = {198},
year = {2021},
}
Package: pymiau
PyMiauUse a short explanatory subtitle like, Make a Miau and a GuauAlways start the README explaining clearly what your package do.  If
you can include here some beautiful image to call the attention of the
potential user do it!This is an example:Download and installDescribe here how the package can be downloaded and install it in
different arquitectures.If you are usingPyPIinstallation it's as simple as:pip install pymiauYou can also test the unstable version of the package with:pip install -i https://test.pypi.org/simple/ pymiauQuick startIn this section you should provide the most simple instructions to use
your package.For instance:import pymiau
print(pymiau.miau())Code examplesProvide some detailed examples for more advanced users.For instance:import pymiau
print(pymiau.miau(n=2))What's newIf your package will be frequently updated with new features include a
section describing the new features added to it:Version 0.2:Functionguauwas implemented.New parameters added to themiauroutine.Version 0.1:First version of the package.This package has been designed and written by Fulanit@ de Tal (C) 2023
Package: pymica
(py)Meteorological variable Interpolation based on Clustered data AnalysispyMICA is an interpolation system that combines multiple linear regressions and clustering. The philosophy of MICA is based on an iterative process to reduce the final error of the interpolated field.The iterativeness of MICA is that there is not a defined number of clusters in which stations are grouped or divided but several number of clusters are considered and the one that performs the best, in terms of RMSE, is selected.What is MICA and how it worksMICA spatially interpolates meteorological surface observations through multiple linear regressions (MLR) and residual corrections. Two options are available: using a simple MLR for all the surface observations or divide them in clusters.The first option implies the calculation of a MLR considering all the observations at once and using explanatory variables, such as altitude, distance to coast, latitude and longitude. The second option uses clusters to classify stations in different groups. This allows to separate stations that may behave differently due to the presence of dissimilar weather conditions. For example, weather stations placed at a similar altitude, ones under the effects of fog and others with clear sky conditions.A complete explanation of how it works and the idea behind MICA can be found inpyMICA documentationIf you use pyMICA, please cite us as:Casellas, E., Bech, J., Veciana, R., Miró, J. R., Sairouni, A., & Pineda, N. (2020). A meteorological analysis interpolation scheme for high spatial-temporal resolution in complex terrain. Atmospheric Research, 246, 105103.InstallationTo install pyMICA you can have a look atpyMICA installationdocumentation.UseA set of examples were designed to explore the different possibilities of MICA.Another repository was created to store sample data and the examples in jupyter notebookspymica-examplesTry the demo|Read the docs
Package: pymicra
Pymicra is a Python package designed to make working with micrometeorological
datasets more efficient. It is aimed at improving productivity (by allowing us
to focus more on micrometeorology) while still being flexible enough to let us
program project-specific things.Please check out theGithub pageand thedocumentation.Here’s a quick (incomplete!) list of what Pymicra does:Reading, separating and understanding micrometeorological data in
virtually any column-separated ASCII format (thanks to pandas).Quality control methods (max and min values check, spikes,
reverse-arrangement test and etc..Rotation of coordinates (2D).Detrending of data in the most common ways (block averages, moving
averages and polynomial detrending).Correction of sensor drift.Automatic calculation of most auxiliary variables based on
measurements (air density, dry air density, etc.).Calculation of spectra and cross-spectra.Calculation fluxes and characteristic scales with or without WPL correction.Provide common constants generally used in atmospheric sciences.Plus all native features of Pandas (interpolation, resampling,
grouping, statistical tests, slicing, handling of missing data and
etc.).The package is extensively (almost entirely) based on Pandas, mostly thepandas.DataFrameclass. We use (generally) Numpy or Scipy for some
numerical functions not contained in Pandas, Matplotlib for plotting and Pint
for units control.This code is distributed under the GNU General Public License v3.0.
Package: pyMicroCode
pyMicroCode 7.0.0It's a module to generate microcode(these are sort of like qrcode but have someother uses)FeaturesEasy to use.Have a lot of customisation freedom.Secure(AES).Data Compression.ExamplesimportpyMicroCodemc=pyMicroCode.MicroCode()Write :
write()args :file_name = > name of the file to save. 
   data_ = > data which will be saved.
   password = > password to read the file.
   mode="normal" = > mode (safe/normal).
   size=(200,200) = > size of the MicroCode.
   start_pos=(0,0) = > where to start the data writing.
   dim_brightness=0 = > to decrease the color brightness.
   brightness=0 = > to increase the color brightness.
   vertical_spacing=0 = > to add spacing vertically.
   spacing=0 = > horizontal spacing.mc.write("Hello_world.png","HI","my_pass")Read :args :file_name = > file to read.
   password = > password of the MicroCode.
   mode="normal" = > mode in which the MicroCode was created.
   start_pos=(0,0) = > at which pos the msg starts.
   dimed_brightness=0 = > decreased brightness.
   brightness=0 = > increased brightness.Read()mc.Read("Hello_world.png","my_pass")LicenseMITAuthor@Merwin
Package: pymicroconnectors
Py Micro ConnectorsThis is a simple example package. You can useGithub-flavored Markdownto write your content.ConfigLoggerAdding colored logs to the application in an easy and configurable wayimport pymicroconnectors.logger as logger

logger.init()Ebayimport pymicroconnectors.ebay as ebay

logger.init()Dev Notespython3 setup.py sdist bdist_wheel
twine upload dist/*
Package: py-microhtml
Minimalistic HTML generator for Python 3.6+ with compact syntaxSafely construct valid HTML with Python code. Example:frommicrohtmlimport*print(ᑉhtml(ᑉhead(ᑉtitle('Test page')),ᑉbody(ᑉspan('Simple example.',class_='example'))).pretty())This prints out a valid, formatted XHTML document:<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN""http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><htmlxmlns="http://www.w3.org/1999/xhtml"><head><title>Testpage</title></head><body><spanclass="example">Simpleexample.</span></body></html>Yes, you'll probably need to copy-paste the unicode symbol and yes, you'll want to use a modern editor, but the resulting syntax is very compact and will not collide with your identifiers even with theimport *.Minimalism also extends to implementation – it'sveryshort (<80 lines in v0.3). Check outmicrohtml/__init__.py.Longer example with more features showcased:frommicrohtmlimport*# Rendering a non-indented string (result: <p>Third <em>and last</em> paragraph</p>)raw_html=str(ᑉp("Third ",ᑉem("and last"),' paragraph'))# Writing a nicely formatted / tidied XHTML document to a file descriptorprint(ᑉhtml(lang='en_US')(ᑉhead(ᑉtitle("Test page")),ᑉbody(ᑉp("Hi!",width=123),# 123 becomes "123"ᑉhr(class_='someclass'),# Reserved words like "class" can be written with a trailing underscoreᑉp('Literal strings are safely <em>escaped</em> by default.'),ᑉrawstr(raw_html),# Use ᑉrawstr() if you don't want escapingᑉtag('applet',code='Bubbles.class',width=350,height=350),# Tag with custom nameᑉdiv("custom",data__custom="abc"),# '__' in attribute names is replaced with '-'ᑉdiv(style='float: right')(# This is how you can type attributes on left and content on rightᑉdiv(style='border: 1px solid black')(ᑉa("Nested",href='#anchortest'),'|','link')))).pretty())This outputs:<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN""http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><htmllang="en_US"xmlns="http://www.w3.org/1999/xhtml"xml:lang="en_US"><head><title>Testpage</title></head><body><pwidth="123">Hi!</p><hrclass="someclass"/><p>Literalstringsaresafely&lt;em&gt;escaped&lt;/em&gt;bydefault.</p><p>Third<em>andlast</em>paragraph</p><appletcode="Bubbles.class"width="350"height="350"></applet><divdata-custom="abc">custom</div><divstyle="float: right"><divstyle="border: 1px solid black"><ahref="#anchortest">Nested</a>|link</div></div></body></html>BuildingTo build a pypi package, simply issuemake. It will install a venv, run tests, generate a .pyi (type/syntax completion stub) and build a source package (sdist). Usemake cleanto clean up.Alternatives and acknowledgementsIf you find unicode characters in source code a horrendous abomination, and don't mind endless nestedwithexpressions, you might preferYattag.Usestidylibfor pretty printing. Inspiration drawn frompyhtmlby Cenk Altı.CopyrightCopyright 2019 Jarno Elonen.
Released under the MIT license. See LICENSE for details.
Package: pymicropel
pymicropelPython library for communication with Micropel PLC.
The library solves everything for the smooth communication of your program with the Micropel PLC. Communication is over TCP/IP, the library handles everything from packet dropping to encryption.You can install the library using the commandpipinstallpymicropelDeploy to PIPPIP repository:https://pypi.org/project/pymicropel/pipinstallsetuptools
pipinstallwheel
pipinstalltwine
pythonsetup.pysdistbdist_wheel
twineuploaddist/*
Package: pyMicVoiceDetection
# Author:KuoYuan Li[![N|Solid](https://images2.imgbox.com/8f/03/gv0QnOdH_o.png)](https://sites.google.com/ms2.ccsh.tn.edu.tw/pclearn0915)
本程式提供一個以能量為基準的 VAD(Voice Activitive Dectection)演算法
由 mic 動態擷取有聲段
背景能量門檻值可由 TH 值做設定