Saravanan666/pypi-org huggingface dataset:
Package: py4lab
Welcome to Python For The Lab's documentation!Python for the Lab (PFTL) is a simple program to acquire data from a DAQ device. It is designed following the MVC design pattern, splitting the code into Controllers for defining drivers, Models for specifying the logic on how to use devices and perform an experiment. The View is where all the GUI is developed.PFTL was developed byUetketo explain to researchers, through simple examples, what can be achieved quickly with little programming knowledge. The ultimate goal of this project is to serve as a reference place for people interested in instrumentation written in Python.You can find the code of this package atGithub, the documentation is hosted atRead The Docs. If you are interested in learning more about Python For The Lab, you can checkUetke's courses.The GUIIf you follow the Python for the Lab course, the GUI is going to be the last step. You perform an analog output scan while acquiring the voltage on a different port. This will allow the users to acquire an I-V scan or any other voltage-dependent measurement.The DeviceThe objective of PFTL is to control a device to measure the IV curve of an LED. The device is built on an Arduino DUE which has two Digital-to-Analog channels. The program monitors the voltage across a resistance while increasing the voltage applied to an LED. We can change all the parameters of the scan, including the input and output channels, the range, time delay, etc.DocumentationAditional documentation can be found atRead the Docs
Package: py4linear-regression
py4linear-regressionLinear Regression Python LibraryGetting StartedThis project is simply implementation of linear regression algorithm in python programming language.PrerequisitesNumpyInstallingThe easiest way to install py4linear-regression is using pippip install py4linear-regressionUsageThere is 2 public method of Linear Regression class. It is learn and predict method, learn method takes 5 argument namely x_train, t_train, alpha, and epoch. It is the training data, it's label, learning rate, and number of iteration respectively. predict method takes 1 argument namely x_test. It is the data to be predictedfrom py4linear_regression.regression import linear_regression
x_train = [[0,0],[0,1],[1,0],[1,1]]
t_train = [0,1,2,3]
classifier = linear_regression()
classifier.learn(x_train,t_train,0.01,250)
x_test = [[0.01,0.99],[0.99,0.01]]
y = classifier.predict(x_test)
Package: py4logistic-regression
py4logistic-regressionLogistic Regression Python LibraryGetting StartedThis project is simply implementation of logistic regression algorithm in python programming language.PrerequisitesNumpyInstallingThe easiest way to install py4logistic-regression is using pippip install py4logistic-regressionUsageThere is 2 public method of Logistic Regression class. It is learn and predict method, learn method takes 5 argument namely x_train, t_train, alpha, and epoch. It is the training data, it's label, learning rate, and number of iteration respectively. predict method takes 1 argument namely x_test. It is the data to be predictedfrom py4logistic_regression.regression import logistic_regression
x_train = [[0,0],[0,1],[1,0],[1,1]]
t_train = [0,0,0,1]
classifier = logistic_regression()
classifier.learn(x_train,t_train,0.1,50)
x_test = [[0.02,0.25],[0.97,0.89]]
y = classifier.predict(x_test)
Package: py4math
This module is extensively designed for non-math coders and beginners. The user need not know math to solve complex math problems. This module does it all for you.The most special feature of this module is its search() function. Now Just search your query after importing the module and it would be resolved. This module consists over 50+ math formulas and is still developing.Syntax:>>>import py4math>>>py4math.search(<Type your problem here>)Example: >>>py4math.search(‘area of triangle’)Example: >>>py4math.search(‘convert binary to hexadecimal’)Example: >>>py4math.search(‘is 3557 a prime number’)Type >>>py4math.help() for further help.
Package: py4N-gram
py4N-gramN-gram Python LibraryGetting StartedThis project is simply implementation of N-gram algorithm in python programming language.PrerequisitesThis Project Has No PrerequisitesInstallingThe easiest way to install py4N-gram is using pippip install py4N-gramUsageIt's only a function named Ngram. It takes 2 argument, the first argument is the text and the second argument is the number of N.from py4N_gram.tokenize import Ngram
x = "i love python programming language"
unigram = Ngram(x,1)
bigram = Ngram(x,2)
trigram = Ngram(x,3)
Package: py4openag
PY4OPENAGThis open-source package aims to establish the ML4Ops pipeline from public databases to integrated analytics for agriculture. It provides  a variety of functions  to study climate trends and simplify the calculation of commonly used metrics in agriculture, such as growing degree days, extreme heat degree days, the base temperature for different crop types, as well as basic climate metrics like average temperature and total precipitation for user specified time periods. The package also includes unsupervised and supervised learning based on these metrics. .The development of this package is supported by the SMARTFARM project. The SMARTFARM program of DOE’s Advanced Research Projects Agency-Energy (ARPA-E) aims to innovate technologies that can help to cost-effectively and efficiently quantify feedstock emissions at the field level. The project aspires to facilitate advanced biofuels that can potentially be a carbon-negative source of energy and aims to promote environmental sustainability while simultaneously increasing farmer profitability and productivity.Functions In The Package:The following functions are currently included in the package. More information and a demonstration on each function can be found in the links provided.Average Temperature: average_temperatureTotal Precipitation: total_precipitationExtreme Degree Days: extreme_degree_daysGrowing degree days: growing_degree_daysHeavy Precipitation Days: heavy_precipitation_daysBase Temperature For Growing Degree Days: growingdays_basetempTemperature Trend: temptrendPrecipitation Trend: preciptrendPlot Map: plot_mapOutput Diagrams:Ipyleaflet Map Demonstrating Temperature Variablility Across U.S. Farm Sites:PCA Biplot:Demonstration Notebooks:Download data from GEERunning functionsDemonstration Of ClusteringDemonstration Of PCADemonstration Of Supervised Learning for NPPDemonstration Of Supervised Learning for GPPDemonstration Of Importance Ranking for NPPDemonstration Of Importance Ranking for GPP
Package: py4pa
Python for People AnalyticsDocumentationhttps://py4pa.readthedocs.io/en/latest/py4pa.htmlInstallationDownload and install the package on your machine:pip install py4paIf you need to bypass SSL verification on your machine (if you are behind a proxy for example), you can use the following install command:pip install py4pa --trusted-host pypi.org --trusted-host files.pythonhosted.orgImport the package into your Python script:import py4pa
# or
from py4pa import [arm]
Package: py4pm
Utilities to handle PMF dataContentsUsageAPIDescriptionpy4pmis a set of utilities to handle the xlsx output of EPA PMF5 software in python.This project started because I needed to run several PMF for my PhD  and also needed to run some computation on these results.
The raw output of the EPA PMF5 software is a bit messy and hard to understand at a first
glance, and copy/pasting xlsx file is not my taste... So I ended developping this tools
for handling the tasks of maping the xlsx output to nice python objects, on which I can easily run
some computation.Since I needed to plot the results afterward, I also added some plot utilities in this
package. It then has built in support for ploting :chemical profile (both absolute and normalized)species repartition among factortimeserie contribution (for all speciesand profiles)uncertainties plots (Bootstrap and DISP)seasonal contributionMoreover, this package include some of the DeltaTool utilities developed by the JRC, notably for profile chemical similarities between several PMF results.
Package: py4pnn
py4pnnPseudo Nearest Neighbors Python LibraryGetting StartedThis project is simply implementation of Pseudo Nearest Neighbors algorithm in python programming language.PrerequisitesThis Project Has No PrerequisitesInstallingThe easiest way to install py4pnn is using pippip install py4pnnUsageThere is only 1 public method of pnn class. It is predict method, it takes 5 argument namely x_train, t_train, x_test, k, and distance calculation method. We provide 6 distance namely euclidean, squared_euclidean, manhattan, canberra, chebyshev, and bray_curtis.from py4pnn.p_nearest_neighbors import pnn
classifier = pnn()
x_train = [[0.23,0.54],[0.45,1.23],[1.54,0.23],[0.93,0.535]]
t_train = [0,1,0,1]
x_test = [[0.34,0.65],[0.90,0.50]]
y = classifier.predict(x_train,t_train,x_test,1,'euclidean')
Package: py4research
Py4research: A step-by-step tutorial on how to build a Python package for researchPy4research provides a thorough tutorial on how to build and upload a Python package for research, which is going to be presented at the "X Workshop do Programa de Pós-Graduação em Ciência da Computação". Apart from the tutorial itself, we also implemented a straightforward package to illustrate how to build its package.GuidelinesEvery needed piece of information is elucidated in thisREADME;Installationis also straightforward and well-explained;If there is a problem, please do nothesitateand call us.InstallationThe installation process is straightforward as the package is hosted at PyPI, and the dependencies are listed on therequirements.txtfile. One can install the package with either command, as follows:pipinstallpy4researchpipinstall-rrequirements.txtAdditionally, the source files for the lecture are presented in LaTeX. Thus, one might need an additional compiler or even Overleaf to build the files into a PDF file.Getting StartedThis section provides an overview of the "build your python package" tutorial and the main package description.TutorialThe tutorial is written in Portuguese in a slide-based format. The contents are available in theslidesfolder and compiled to PDF using a LaTeX compiler.Py4researchPy4research is a standard package composed of a singlemathmodule composed of two sub-modules:clusteringandrandom. Essentially, the idea is to provide a simple framework that can foster research and help the community deploy their official implementations to PyPI.SupportIt is inevitable to make mistakes or create some bugs. If there is any problem or concern, we will be available at this repository orgustavo.rosa@unesp.br.
Package: py4Soundex
py4SoundexSoundex Python LibraryGetting StartedThis project is simply implementation of Soundex algorithm in python programming language.PrerequisitesThis Project Has No PrerequisitesInstallingThe easiest way to install py4Soundex is using pippip install py4SoundexUsageIt's only a function named Soundex. It takes string as argument and return soundex codefrom py4Soundex.code import Soundex
print(Soundex('Smith'))
print(Soundex('Smyth'))
# Both Yield Same Code
Package: py4spice
Python for SpiceLightweight Python interface to NgspiceMovtivationNgspiceis an open source circuit simulator with a command line interface (CLI). It's natural to use Python to interact with the CLI, making it easier to use Ngspice for advanced design analysis.There exists a simulator calledPySpicewhich embeds Ngspice through its API.Python for Spice, on the other hand, is a lightweight module that facilitates Ngspice interaction. Simple classes and functions transparently create Ngspice netlists and commands. You can launch multiple analyses and convert the results toPandasandNumpy, enabling downstream computation or plotting withMatplotlib.A modest knowledge of Ngspice and Python is assumed.UsagePython for Spicerequires an Ngspice design, either in the form of a netlist or aKiCad(version 7.0 or higher) schematic.Ngspice can be used in many different ways. However,Python for Spicewill interface to Ngspice only through the CLI in non-interactive mode. It creates acontrol sectionthat will execute simulations in batch mode. Results from the simulation will be converted toPandasDataFrames.Typical Program FlowA main Python program controls the flow. An example can be foundhere.import py4spiceSpecify paths to KiCad and Ngspice executables, and project directoryDefine signals of interest (node voltage, currents, etc.) withVectorsobjectsUse aKicadCmdobject to extract and condition a netlist from a schematicDefine one or more analyses (op, dc, tr, ac) withAnalysesobjectsAControlobject defines the control section. The analyses and other control functions are part of it.ASimulationObject is created and executed.The tabular results from the simulation are converted to Pandas DataFramesAPlotobject can be used to encapsulate Matplotlib to view the results.Installationinstall package:python -m pip install --upgrade py4spiceDownload thisexample(a bipolar amplifier) to try out thepy4spicepackage.
Package: py4structure
**************************************************************************                                                                       **                         Py4Structure 0.1                             **        Structural engeenering design python scripts for SeePy         **                                                                       **                        (c) 2017 Lukasz Laba                           **                                                                       **************************************************************************Py4Structure is a tool to create interactive report for scientific calculationyou made with python script.The mission of the project is to provide a simple and practical tool thatwill interest engineers to use Python language in their daily work.Py4Structure is free software; you can redistribute it and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation; either version 2 of the License, or(at your option) any later version.Py4Structure is distributed in the hope that it will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See theGNU General Public License for more details.You should have received a copy of the GNU General Public Licensealong with Foobar; if not, write to the Free SoftwareFoundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA  02110-1301  USA-------------------------------------------------------------------------* Visible changes:-------------------------------------------------------------------------SeePy 0.0.1 (pre-alpha)- first PyPI versionSeePy 0.1 (alpha)- alpha version-------------------------------------------------------------------------* Prerequisites:-------------------------------------------------------------------------Python 2.7.Non-standard Python library needed:- SeePy- Strupy-------------------------------------------------------------------------* To install Py4Structure:-------------------------------------------------------------------------After the Python and needed library  was installed:On Windows use "pip install Py4Structure" in Python shell.To run Py4Structure GUI execute Py4Structure.py from installed Py4Structurepackage folder - probabiliit is "C:\Python27\Lib\site-packages\py4structure"For easy run make shortcut on your system pulpit to this file.There is install instruction on project website.https://seepy.orghttps://bitbucket.org/lukaszlaba/seepy/wiki/installingLinux compatible.-------------------------------------------------------------------------* Other information :-------------------------------------------------------------------------- Project website:http://seepy.orghttp://struthon.orghttps://bitbucket.org/lukaszlaba/py4structure/wiki/Home- E-mail : lukaszlab@o2.pl=========================================================================
Package: py4svd-regressor
py4svd-regressorSVD Based Linear Regression Python LibraryGetting StartedThis project is simply an implementation of a Linear Regression algorithm based on SVD in python programming languagePrerequisitesNumpyInstallationThe easiest way to install py4svd-regressor is by using pippip install py4svd-regressorUsage2 public methods are provided namelylearnandpredict. Thelearnmethod used to train the model. It takes 2 arguments, the data, and its target. Thepredictmethod used to predict the given data, it takes 1 argument, it is the  data user wanted to predict. It returns the resulting prediction. The weight and intercept are stored on attributes namelywandbrespectivelyfrom py4svd_regressor.regression import Svd_Regressor
from sklearn.datasets import make_regression
from matplotlib import pyplot

x, y = make_regression(n_samples=200, n_features=1, noise=5)
model = Svd_Regressor()
model.train(x, y)
pyplot.scatter(x, y)
pyplot.plot(x, model.w*x + model.b, color='red')
pyplot.show()
y_predict = model.predict(x)
Package: py4tfidf
py4tfidfTerm Frequency â€“ Inverse Document Frequency (TF-IDF) Python LibraryGetting StartedThis project is simply an implementation of TF-IDF algorithm in python programming language.PrerequisitesNumpyInstallingThe easiest way to install py4tfidf is by using pippip install py4tfidfUsageThere are 2 public methods ofTfidfclass. It isvectorize_trainandvectorize_test.vectorize_trainused to build the corpus, calculate idf based on training text, and transform it into a usable vector by multiplying its tf and its idf, whilevectorize_testis just simply transforming the test text into a usable vector by multiplying its tf with previously obtained if. Bothvectorize_trainandvectorize_testtake 1 argument namely x_train and x_text respectively. Because tokenizing is usually done in the text preprocessing phase, we assume you tokenize your text on your own, so the argument forvectorize_trainandvectorize_testshould be a list of tokenized text.from py4tfidf.vectorizer import Tfidf
vec = Tfidf()
x_train = [['i','love', 'python'],['natrual','language','processing','is','fun'],['python','is','fun']]
x_test = [['python','language','is','fun'],['im','learning','natrual','language','processing']]
x_train = vec.vectorize_train(x_train)
x_test = vec.vectorize_test(x_test)
Package: py4uview
Py4Uview: Tools for Uview imagesIntroductionPy4Uviewprovides a set of tools for images saved by Uivew, which is used by SPEELEEM in MAXPEEM to save the user's data in format of .dat file. Py4Uview can read the image and metadata from the file saved by Uview software.Quick startInstallpython -m pip install py4uviewimport>>>importpy4uviewHow to use?useread_uv_dat()function to read the data and metadata.In [1]: import py4uview as pu

In [2]: data = pu.read_uv_dat("./testfiles/LEEM.dat")

In [3]: data.metadata
Out[3]:
{'id': b'UKSOFT2001',
 'size': 104,
 'version': 8,
 'bitsperpix': 16,
 'width': 1024,
 'height': 1024,
 'noimg': 1,
 'attachedRecipeSize': 0,
 'isize': 288,
 ......

In [4]: data.data
Out[4]:
array([[3047, 2880, 2652, ..., 2716, 2630, 2562],
       [2789, 2848, 2862, ..., 2549, 2485, 2655],
       [2808, 2840, 2739, ..., 2640, 2602, 2747],
       ...,
       [3054, 2675, 2656, ..., 1926, 2017, 2021],
       [2885, 2949, 2705, ..., 1907, 1886, 1945],
       [2810, 2688, 2807, ..., 2009, 2025, 2641]], dtype=uint16)UseUviewclass methodread_dat()to read the dat fileIn [1]: import py4uview as pu

In [2]: data = pu.Uview.read_dat("./testfiles/LEEM.dat")

In [3]: data.attrs
Out[3]:
{'id': b'UKSOFT2001',
 'size': 104,
 'version': 8,
 'bitsperpix': 16,
 'width': 1024,
 'height': 1024,
 'noimg': 1,
 'attachedRecipeSize': 0,
 'isize': 288,
 ......

In [4]: data.data
Out[4]:
array([[3047, 2880, 2652, ..., 2716, 2630, 2562],
       [2789, 2848, 2862, ..., 2549, 2485, 2655],
       [2808, 2840, 2739, ..., 2640, 2602, 2747],
       ...,
       [3054, 2675, 2656, ..., 1926, 2017, 2021],
       [2885, 2949, 2705, ..., 1907, 1886, 1945],
       [2810, 2688, 2807, ..., 2009, 2025, 2641]], dtype=uint16)
Package: py4vasp
py4vaspPlease note that this document is intended mostly for developers that want to use
the version of py4vasp provided on Github. If you just want to install py4vasp to
use it, please follow theofficial documentation.InstallationWe use thepoetry dependency managerwhich takes care of
all dependencies and maintains a virtual environment to check the code. If you want to
test something in the virtual environment, just use e.g.poetry run jupyter-notebook.Using poetry installing and the code requires the following steps. The last step will
test whether everything workedgitclonegit@github.com:vasp-dev/py4vasp.git
pipinstallpoetry
poetryinstall
poetryrunpytestNote that this will install py4vasp into a virtual environment managed by poetry. This
isolates the code from all other packages you have installed and makes sure that when
you modify the code all the relevant dependencies are tracked.Occasionally, we encountered errors when installing themdtrajdependency in this
fashion, in particular on MacOS and Windows. If you notice the same behavior, we
recommend to manage your environment withcondaand installpy4vaspin the
following mannergitclonegit@github.com:vasp-dev/py4vasp.git
condacreate--namepy4vasp-envpython=3.8
condaactivatepy4vasp-env
condainstall-cconda-forgepoetry
condainstall-cconda-forgemdtraj
poetryconfigvirtualenvs.createfalse--local
poetryinstall
poetryrunpytestCode styleCode style is enforced, but is not something the developer should spend time on, so we
decided on using the black formatter. Please runblack .before committing the code.Contributing to py4vaspWe welcome contributions to py4vasp. To improve the code please follow this workflowCreate an issue for the bugfix or feature you plan to work on, this gives the option
to provide some input before work is invested.Implement your work in a fork of the repository and create a pull request for it.
Please make sure to test your code thoroughly and commit the tests in the pull
request in the tests directory.In the message to your merge request mention the issue the code attempts to solve.We will try to include your merge request rapidly when all the tests pass and your
code is covered by tests.Please limit the size of a pull request to approximately 200 lines of code
otherwise reviewing the changes gets unwieldy. Prefer splitting the work into
multiple smaller chunks if necessary.