Saravanan666/pypi-org huggingface dataset:
Package: pypdm-db
pypdm-dbmysql/sqlite 的 PDM 生成器运行环境安装说明执行脚本：python -m pip install --upgrade pip
python -m pip install pypdm-db使用指引示例代码可参考单元测试：sqlite:test_pypdm_sqlite.pymysql:test_pypdm_mysql.py通过以下函数可生成对应数据库的连接对象：from pypdm.dbc._sqlite import SqliteDBCfrom pypdm.dbc._mysql import MysqlDBC通过函数from pypdm.builder import build可生成指定数据表的 PDM 文件。例如数据库中已有表t_teachers，会在指定的 package 目录生成两个代码文件：Bean 文件：*/bean/t_teachers.pyDAO 文件：*/dao/t_teachers.py其中 Bean 文件与表t_teachers的表结构一一对应， DAO 文件则封装了针对表t_teachers的增删改查函数。利用这两个文件，就可以方便地对表t_teachers进行操作。
Package: pypdns
Client API for PDNSClient API to query any Passive DNS implementation following the Passive DNS - Common Output Format.https://datatracker.ietf.org/doc/draft-dulaunoy-dnsop-passive-dns-cof/InstallationpipinstallpypdnsUsageCommand lineYou can use thepdnscommand to trigger a request.usage:pdns[-h]--usernameUSERNAME--passwordPASSWORD--queryQUERY[--rrtypeRRTYPE]TriggersarequestagainseCIRCLPassiveDNS.

options:-h,--helpshowthishelpmessageandexit--usernameUSERNAMETheusernameofyouaccount.--passwordPASSWORDThepasswordofyouaccount.--queryQUERYThequery,canbeanIP.domain,hostname,TLD.--rrtypeRRTYPEFiltertherequestbasedontheRRType.LibrarySeeAPI ReferenceExampleimport pypdns
import json
x = pypdns.PyPDNS(basic_auth=('username','yourpassword'))

for record in x.iter_query(q='circl.lu', filter_rrtype='A'):
    print(json.dumps(record.record, indent=2))Passive DNS Services(default)CIRCL Passive DNS
Package: pypdsh
pypdshPdsh in Python version.How to Use It?Open your console, typepypdsh -h, you will see some parameters:-h, --help--
show this help message and exit-i IP, --ip=IP--
single ip or ip in format '192.168.1.[1-10,16,19,30-40]'-I IP, --IP=IP--
ip(s) in CSV file: ip | username | password-c COMMAND, --command=COMMAND--
single command or command in TXT file-f FILE, --file=FILE--
file to transmit to each host-g LOCALPATH, --get=LOCALPATH--
get file from remote host-d DESTINATION, --destination=DESTINATION--
destination path of each host, and this path must be existing in host(s)-p PASSWORD, --password=PASSWORD--
password of host(s)-u USERNAME, --username=USERNAME--
username of host(s)--log-level=LOG_LEVEL--
log level: INFO or ERROR-v, -V, --version--
show version number of pypdsh and exit
Package: pypdtools
PypdtoolsPypdtools is a Python package that extends the functionality of Pandas DataFrames by introducing thePtDataFrameclass.PtDataFrameprovides additional features and enhancements to work with DataFrames in a more convenient and flexible way.FeaturesAsynchronous iteration support:PtDataFrameimplements both synchronous and asynchronous iterators, allowing for efficient and convenient data processing in asynchronous contexts.Context manager support:PtDataFramecan be used as a context manager to easily access the encapsulated Pandas DataFrame.Concatenation and reduction:PtDataFramesupports concatenation of multiple instances and reduction of a list ofPtDataFrameobjects into a single instance.Column extraction: Easily extract a column as a list from aPtDataFrame.Docs:https://msbar.github.io/pypdtoolsSource code:https://github.com/msbar/pypdtoolsInstallationYou can install pypdtools using pip:pip install pypdtoolsUsageHere's an example of how to use thePtDataFrameclass:importpandasaspdfrompypdtools.core.dataframeimportPtDataFrame# Create a Pandas DataFramedata={"a":[1,2,3],"b":[4,5,6],"c":[7,8,9]}df=pd.DataFrame(data)# Create a PtDataFrame from the Pandas DataFramept_df=PtDataFrame(df)# Async Iterate over rowsasyncforrowinpt_df:print(row)# Concatenate PtDataFramespt_df2=PtDataFrame(df)concatenated=pt_df+pt_df2# Reduce a list of PtDataFramespt_df3=PtDataFrame.reduce([pt_df,pt_df2])# Extract a column as a listcol_values=pt_df.col_to_list("a")print(col_values)
Package: pypdxapi
Paradox® HD77 Camera APIIntroductionThis is a python module that provides an interface for interacting with a Paradox® HD77 camera
(and possibly other models).This is licensed under the MIT license.Getting startedThis API is not documented by Paradox®.I used reverse engineering on the HD77 model (v1.25.7) to discover it and create the python functions to access it.It does not work with the IP150 network module and has not yet been tested with other camera models.I also discovered how to discover Paradox® devices on the network and created the code in the helpers folder
(see the documentation below).InstallpipinstallpypdxapiQuick StartAPI usage exampleimportasynciofrompypdxapi.cameraimportParadoxHD77asyncdefmain():"""Show example of connecting to your Paradox HD77 camera."""asyncwithParadoxHD77(host='192.168.1.3',port=80,module_password='paradox')ashd77:awaithd77.login(usercode='111111',username='user1')ifhd77.is_authenticated():print(awaithd77.vod(channel_type='normal'))if__name__=="__main__":loop=asyncio.get_event_loop()loop.run_until_complete(main())Discovery usage examplefrompypdxapi.helpersimportdiscover_modulesmodules=discover_modules()For more information see docs.DisclaimerThe code was developed as a way of integrating personally owned Paradox® HD77 cameras and other modules, and it cannot
be used for other purposes. It is not affiliated with any company and it doesn't have have commercial intent.The code is provided AS IS and the developers will not be held responsible for failures in the camera, or any other
malfunction.Paradox® is a registered mark. Other brands are owned by their respective owners.
Package: pype32
What is pype32?Yet another python library to parse PE/PE+ binary files.Where can I find pype32?https://github.com/crackinglandia/pype32Project homepage:https://github.com/crackinglandia/pype32Download links:https://github.com/crackinglandia/pype32/releasesDocumentation:https://github.com/crackinglandia/pype32
Package: pype9
PYthon PipelinEs for 9ML (Pype9) is a collection of Python pipelines
for simulating networks of neuron models described inNineMLwith various
simulator backends.LinksDocumentation:http://pype9.readthedocs.orgMailing list:NeuralEnsemble Google GroupIssue tracker:https://github.com/NeuralEnsemble/pype9/issuesSupported SimulatorsPype9 works with either or both of the following simulator backendsNeuron>= 7.3NEST>= 2.12.0Detailed instructions on how to install these simulators on different platforms
can be found in theInstallation documentation.Unsupported NineMLNineMLaims to be a comprehensive description language for neural simulation. This
means that it allows the expression of some uncommon configurations that are
difficult to implement inNeuronandNEST. Work is planned to make the NEURON
and NEST pipelines in Pype9 supportNineMLfully, however until then the
following restrictions apply to models that can be used with Pype9.synapses must be linearsynapses can only have one variable that varies over a projection (e.g.
weight)no recurrent analog connections between populations (e.g. gap junctions)only one event send port per cellnames given toNineMLelements are not escaped and therefore can clash with
built-in keywords and some PyPe9 method names (e.g. ‘lambda’ is a reserved
keyword in Python). Please avoid using names that clash with C++ or Python
keywords (NB: This will be fixed in future versions).ExamplesGiven a cell model described inNineMLsaved inmy_hodgkin_huxley.xml, the simulator pipeline can run from the command line:$pype9simulatemy_hodgkin_huxley.xml#hh_propsneuron100.00.01\--playisynisyn.neo.pkl--recordvv.neo.pkl--init_valuev-65.0mVor in a Python scriptfrompype9.simulator.neuronimportcell,SimulationfromninemlimportunitsasunHodgkinHuxley=cell.MetaClass('my_hodgkin_huxley.xml#hh_class')withSimulation(dt=0.01*un.ms,seed=1234)assim:hh=HodgkinHuxley('my_hodgkin_huxley.xml#hh_props',v=-65.0*un.mV)hh.record('v')sim.run(100.0*un.ms)v=hh.recording('v')Pype9 also supports network models described inNineMLvia integration withPyNN$pype9simulatebrunel.xmlnest1000.00.01\--recordExc.spike_outputExc-nest.neo.pkl\--recordInh.spike_outputInh-nest.neo.pkl\--seed12345orfrompype9.simulator.neuronimportNetwork,SimulationfromninemlimportunitsasunwithSimulation(dt=0.01*un.ms,seed=1234)assim:brunel_ai=Network('brunel.xml#AI')brunel_ai.component_array('Exc').record('spike_output')brunel_ai.component_array('Inh').record('spike_output')sim.run(1000.0*un.ms)exc_spikes=brunel_ai.component_array('Exc').recording('spike_output')inh_spikes=brunel_ai.component_array('Inh').recording('spike_output')SeeCreating Simulations in Pythonin the Pype9 docs for more examples and pipelines.In addition to thesimulatecommand there is also aplotcommand for
conveniently plotting the results of the simulation withMatplotlib,
and aconvertcommand to convertNineMLfiles between different serialization
formats (XML, YAML, JSON and HDF5) andNineMLversions (1.0 and 2.0dev). See the
documentation for details.copyright:Copyright 20012-2016 by the Pype9 team, see AUTHORS.license:MIT, see LICENSE for details.
Package: pyPEAKO
pyPEAKOPEAKOis a supervised radar Doppler spectrum peak finding algorithm. It finds the optimal
parameters for detecting peaks in cloud radar Doppler spectra using user-generated training data.PEAKOis used to:create labeled data (peaks marked by a user in cloud radar Doppler spectra), which can be used for training and testing the learned functiontrain the algorithm using the labeled data to obtain the optimal parameter combination for peak detection. Optimization is done using a similarity measure based on the area below the peaks.test the performance of the learned function [TBD]detect peaks in cloud radar Doppler spectra using the learned functionReference for PEAKO:Kalesse et al. (2019), AMTDocumentation is available at:https://pypeako.readthedocs.io/en/latest/TBD : InstallationI want this package to be available via pip so that one can simply do :$ pip install pyPEAKOIn the meantime, you will have to clone the repository, e.g. by$ git clone https://github.com/ti-vo/pyPEAKOThen navigate to the main folder (pyPEAKO):$ pip install -e .How PEAKO worksThe current release is tailored to use cloud radar Doppler spectra netcdf files. The files are in a format which is
currently under discussion in the Cloudnet community. Changes are likely to be made in the future, and Peako will have
to be adjusted to work with the most current spectra file format.
The cloudnet community will hopefully share their routines for bringing spectra files from different cloud radars into
the desired format. Ongoing discussion is happening in theCloudnet forum.ContributingIf you want to help develop peako, feel free to contact me, or open an issue on GitHub. If you want to become an active
developer, that would be awesome! You will first have to install the "dev" dependencies specified in setup.py.
To install PEAKO  along with the tools you need for developing and running tests, run:$pipinstall-e.[dev]in the directory containing the setup.py file. Like this, you install pyPEAKO with the dev extras.
Package: pypeaks
Identifying peaks from data is one of the most common tasks in many
research and development tasks.pypeaksis a python module to detect
peaks from any data like histograms and time-series.Following are the available methods implemented in this module for peak
detection: * Slope based method, where peaks are located based on how
the data varies. * Intervals based method, where a set of intervals can
be passed to provide apriori information that there will be at most one
peak in each interval, and we just pick the maximum in each interval,
filtering out irrelevant peaks at the end. * A hybrid method which
combines these two methods.Installation$ sudo pip install --upgrade pypeaksUsageThere is an example case included along with the code. If you don’t have
this folder, please load your data instead. Or get it fromhttps://github.com/gopalkoduri/pypeaks.Important noteThe peak finding function expects a normalized smoothed histogram. It
does smoothing by default. If you want to change the smoothness,
customize the corresponding argument. If the data is not normalized (so
that the area under the curve comes to 1), there is a function provided
to do that. If you don’t get any peaks, then you probably overlooked
this!import pickle
from pypeaks import Data, Intervals

[x, y] = pickle.load(file('examples/sample-histogram.pickle'))
data_obj = Data(x, y, smoothness=11)

#Peaks by slope method
data_obj.get_peaks(method='slope')
#print data_obj.peaks
data_obj.plot()

#Peaks by interval method
ji_intervals = pickle.load('examples/ji_intervals.pickle')
ji_intervals = Intervals(ji_intervals)
data_obj.get_peaks(method='interval', intervals=ji_intervals)
#print data_obj.peaks
data_obj.plot(intervals=ji_intervals)

#Read the help on Data object, and everything else is explained there.
help(Data)In case you face some issue, report it ongithub, or write to me atgopala [dot] koduri [at] gmail [dot] com!
Package: pypear
:pie::pear:pypear: Terminal based python development.Motivation:  How does a python developer streamline test driven development with only an ssh connection?Installation: Caution this is a work in progress. The following command will install the vim plugins under the ~/.pypear/plugins/vim/ folder.pip install pypear
pypear #installs plugins in the ~/.pypear folderThere are many great tools available for python development.  This set of tools was collected to streamline test driven development through just a terminal and a simple pip install. (Future: Would like to have this available from conda-forge)Visual Studio Code Remote Development plugins are excellent and are likely to be a better choice in most cases.  However, the simplicity of workflow possible with this collection of tools is impressive and should not be overlooked.  As with any tool it comes at the cost of learning to use the tools properly.Terminal Multiplexing / Session Management / Pair Programming Supporttmux: Terminal multiplexer running on server size, supports pair programming and session suspension.Text Editingvim (or Neovim): Vim is everywhere and is very usable over connections of any speed. Vim8 is recommended. Neovim because I like it. :)Vundle: vim plugin manager (just because it works)Version Controlgit: the only way to configuration manage code.vim-fugitive: vim git plugin so good it should be illegalvim-gitgutter: vim git gutter pluginnerdtree: vim enhanced file explorer pluginnerdtree-git-plugin: vim git enhancement for file explorer pluginpre-commit: framework for managing hooks for pre-commit actionspre-commit-hooks: commonly used pre-commit hooksLintingale: vim code linting for many languages pluginflake8: python lintingautopep8: automatic pep8 formating (pre-commit linting)Searchingctags: enables simple navigation of large or small code bases within vim.ctrlp: fuzzy file search (interested in better alternatives)Testingpytest: the "best" python testing framework.pytest-vim-compiler: pytest compilervim-dispatch: enables asyc test and command running with tmuxThemes (Because it should be attractive)vim-airline: Vim themevim-airline-themes: airline themestumxline: Coordinates tmux theme with vim themepromptline: Coordinates prompt theme with vim themeCheat Sheetstmux:vim:TODOAdd good cheatsheet linksBuild autodocs based on plugin documentationconda-forage installationneovim conda-forge installationpypear vim documentationoffline pre-commit configurationContributionI don't intend to put a lot of effort into this packages, but completely willing to collaborate with others. If anyone knows of a better meta package that I could support rather than rolling my own let me know.LicenseMITAll plugins retain existing licences:  Vim, MIT, Apache 2.0, Public Domain (WTFPL)
Package: pypec
Descriptionpypecaims at fitting PEC data with an graphical interface.
The program was initially developped in Python 2.7 during my PhD from 2012 to 2015.In 2016, the code was rewritten in Python 3 at the end of my PhD with slight modifications of the original. The code
remained untouched for almost 3 years and is now again under development in this repository.The first release after migrating to github will serve as a backup for the version developed during my
PhD with completed documentation.InstallationSeeINSTALL.txt.DependenciesSeerequirements.txt.License informationSeeLICENSE.txtfor information on the history of this
software, terms & conditions for usage, and a DISCLAIMER OF ALL
WARRANTIES.
Package: pypedrive
Installing-------------Clone this repo or use pip command:$ pip install pypedriveUsing-------------Python Code:from pypedrive import Pypedrivepipedrive = Pypedrive('AUTH_TOKEN')# Creating organizations, deals, person ...pipedrive.organizations({'method': 'POST','name': 'Organization inc.','phone': '(12) 1231-1412',...})# You can use lists to update or exclude a bunch of informationpipedrive.persons({'method': 'DELETE','id': [1,2,3]})For more information about how to use Pipedrive's API, consult https://developers.pipedrive.com/v1.
Package: pypeec
PyPEEC - 3D Quasi-Magnetostatic SolverWebsite:pypeec.otvam.chRepository:github.com/otvam/pypeecPackage:pypi.org/project/pypeecSummaryPyPEECis a3D quasi-magnetostatic PEEC solverdeveloped atDartmouth Collegewithin the Power Management Integration Center (PMIC).
PyPEEC is afast solver(FFT and GPU accelerated) that can simulate a large variety ofmagnetic components(inductors, transformers, chokes, IPT coils, busbars, etc.).
The tool contains amesher(STL, PNG, and GERBER formats), asolver(static and frequency domain), andadvanced plottingcapabilities.
The code is written inPythonand is fullyopen source!CapabilitiesPyPEECfeatures the followingcharacteristics:PEEC methodwithFFT accelerationRepresentation of thegeometrywith3D voxelsMultithreadingandGPU accelerationare availableFastwithmoderate memoryrequirementsImport thegeometryfromSTL,PNG, andGERBERfilesDraw thegeometrywith stacked 2Dvector shapesorvoxel indicesPure Pythonandopen sourceimplementationCan be used from thecommand lineCan be used withJupyter notebooksAdvancedplottingcapabilitiesPyPEECsolves the following3D quasi-magnetostatic problems:Frequency domain solution (DC and AC)Conductive and magnetic domains (ideal or lossy)Isotropic, anisotropic, lumped, and distributed materialsConnection of current and voltage sourcesExtraction of the loss and energy densitiesExtraction of the current density, flux density, and potentialExtraction of the terminal voltage, current, and powerComputation of the free-space magnetic fieldPyPEEChas the followinglimitations:No capacitive effectsNo dielectric domainsNo advanced boundaries conditionsNo model order reduction techniquesLimited to voxel geometriesThePyPEECpackage contains the followingtools:mesher: create a 3D voxel structure from STL or PNG filesviewer: visualization of the 3D voxel structuresolver: solver for the magnetic field problemplotter: visualization of the problem solutionProject LinksWebsite:https://pypeec.otvam.chRepository:https://github.com/otvam/pypeecReleases:https://github.com/otvam/pypeec/releasesTags:https://github.com/otvam/pypeec/tagsIssues:https://github.com/otvam/pypeec/issuesPyPi:https://pypi.org/project/pypeecAuthorThomas GuillodEmail:guillod@otvam.chWebsite:https://otvam.chCreditsPyPEEC was created atDartmouth Collegeby the research group ofProf. Sullivan:Dartmouth College, NH, USA:https://dartmouth.eduDartmouth Engineering:https://engineering.dartmouth.eduPMIC:https://pmic.engineering.dartmouth.eduThe FFT-accelerated PEEC method with voxels has been first described and implemented in:Torchio, R., IEEE TPEL, 10.1109/TPEL.2021.3092431, 2022Torchio, R.,https://github.com/UniPD-DII-ETCOMP/FFT-PEECCopyright(c) 2023-2024 / Thomas Guillod / Dartmouth CollegeThis Source Code Form is subject to the terms of the Mozilla Public
License, v. 2.0. If a copy of the MPL was not distributed with this
file, You can obtain one athttp://mozilla.org/MPL/2.0/.
Package: pypeek
Peek - Screen Recorder and Screenshot with AnnotationCross platform screen recorder with an easy to use interface and annotation features written in Python and PySide6.Inspired byPeek.A developer, designer, or a student, you may need to record or capture your screen to create tutorials, report bugs, provide technical support or simply reflect on your own work and progress. And if you have different operating systems on your different machines, it would be great if you could use the same tool on all of them. That's why I created Peek.Peek is a simple screen recorder that allows you to record your screen as gif or mp4, capture a screenshot as jpg, and annotate your recordings with drawing, text, arrows, and highlights.Annotation tool is very useful when you want to highlight a specific area or draw attention to a specific part of your recording. You can add text, arrows or shapes to your recording. Range slider allows you to trim your recording to a desired length. You can create a complete recording with annotation without leaving the app.FeaturesRecord your screen as gif or mp4Capture a screenshot as jpg or pngRecord a selected area or the whole screenAnnotation features like drawing, text, arrows, and highlightsDelay recording start with a countdownLimit recording to a fixed timeBlog postDownload The App:Apple StoreMicrosoft StoreInstall in Python:Requirements:Python 3.9 or laterFfmpeg [Optional], it will be downloaded if not found in system path.Windows, MacOS, Linux: Debian based distros on Xorg, Classic or X11. No Wayland support yet. (seeHow to switch to Xorg)Need help for LinuxWaylandsupport. If you are interested, please create a pull request or open an issue.Known Issues:Linux:"qt.qpa.plugin: Could not load the Qt platform plugin "xcb"...", install the following packages:sudo apt install libxcb-*Linux:"ImportError: libdouble-conversion.so.3: cannot open shared object file: No such file or directory"error, install the following package:sudo apt install libdouble-conversion3Intallation:pip install pypeekUsage:pypeekCreate a desktop shortcut:Make sure you run the pypeek command at least once before creating a shortcut. On Linux it will create an app inside applications, on Windows and MacOS it will create a desktop shortcut.pypeek --shortcutImport as a module:importpypeekpypeek.show()Run as a module:python -m pypeekUpdate:pip install --upgrade pypeekUninstall:pip uninstall pypeekLicensePeek Copyright © 2023Peek 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, either version 3 of the License, or
(at your option) any later version.Peek 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 Peek. If not, seehttps://www.gnu.org/licenses/.
Package: pypeeper
Py-PeeperA very simple, minimalist, single-threaded and broadcast-routed approach to the observable pattern in Python.InstallationpipinstallpypeeperUsageImplementing an Observable classEvery class can be converted to observable just by subclassing it from
Observer. If class attribute "pattern" is overwritten with a valid regex
expression, only matching attributes defined within the class constructor will
be observed. Otherwise, all attributes will be observed.frompypeeperimportObservable,ObserverclassAnyClass(Observable):def__init__(self):self.attribute_a=Falseself.whatever_attr=0defset_attribute(self,value):self.attribute_a=valueImplementing an Observer classAny class can be Observer of all Observable classes defined as shown above. To
define how the class should react on Observable's changes, just override
"notify" method from Observer class.classObserverClass(Observer):defnotify(self,class_name,object_id,attribute_name,old,new):print(class_name,object_id,attribute_name,old,new)Instantiate and playmy_observable=AnyClass()my_observer=ObserverClass()my_observable.set_attribute(True)my_observable.attribute_a=10my_observable.whatever_attr=NoneConsole output>>> AnyClass 2448221506696 attribute_a False True
>>> AnyClass 2448221506696 attribute_a True 10
>>> AnyClass 2448221506696 whatever_attr 0 None