Saravanan666/pypi-org huggingface dataset:
Package: quadratic-sieve
quadratic-sieveAn implementation of quadratic sieve algorithm for factorization.TechnologiesTechnologies used:Python version: 3.9.1numpy version: 1.20.1Development:py.test version: 6.2.2wheel version: 0.36.2Install$ pip install quadratic_sieveRun$ quadratic_sieve 12666334082118686111Running optionsusage: quadratic_sieve [-h] [-b SMOOTHNESS] [-s BASE_SIZE] [-l] n

positional arguments:
  n                     Number to factorize.

optional arguments:
  -h, --help            show this help message and exit
  -b SMOOTHNESS, --smoothness SMOOTHNESS
                        Set smoothness bound.
  -s BASE_SIZE, --base_size BASE_SIZE
                        Set the size of generated QS base.
  -l, --loud            Display messages while computing.DevelopmentTestingRunning the tests:$ pip install pytest
$ pytestDistribution building$ pip install wheel
$ python setup.py bdist_wheelInstalling the built distribution$ pip install dist/quadratic_sieve-0.1.1-py3-none-any.whlMore information about the quadratic sieve algorithm:Smooth numbers and the quadratic sieveA Fast Algorithm for Gaussian Elimination over GF(2) and Its Implementation on the GAPP*
Package: quadrille
quadrilleSquare grid simulationsConway's Game of LifeCreditsThanks tobethlenkeatHSBPfor describing
his C algorithm for grid advancement in a Conway implementation
which was the starting point of my own approachLicenseMozilla Public License 2.0This 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 at http://mozilla.org/MPL/2.0/.
Package: quadruped
QuadrupedMy robot software.YouTubeVimeoDocumentationNote:This re-write is still very early and not fully running yet, just
parts.InstalllinuxYou will also need:pip install ds4drvpipThe recommended way to install this library is:pip install quadrupedDevelopmentIf you wish to develop and submit git-pulls, you can do:git clone https://github.com/MomsFriendlyRobotCompany/quadruped
cd quadruped
pip install -e .TestingSince I have both python2 and python3 installed, I need to test with both:python2 -m nose *.py
python3 -m nose *.pyLayoutHere issortof the layout of the code:Quadruped (see examples folders):Engine(serial)AHRS() - tilt compensated compassIMU() - NXP IMU from AdafruitMCP3208() - ADC for IR and whatever elseGait:command() - plans all feet through 1 gait cycle (12 steps)eachLeg(x,y,z)Engine({serial}): - handles movement hardwarelegs[4]servos[3]anglesetServoLimits()bulkWrite() - change to synccoxa, femur, tibiafk()ik()moveFoot(x,y,z)moveFootAngle(a,b,c)The example quadruped (in the examples folder), takes a dictionary. Currently
it takes:data = {
        'serialPort': '/dev/tty.usbserial-AL034G2K',
        'write': 'bulk'
}If you don’t pass it a serial port, then it falls back to a simulated serial
port (which does nothing) which is useful for testing.ToolsThis directory contains several tools for the robot:Change Log2017-Jul-070.4.1broke out into package and published to PyPi2016-Aug-100.0.1initMIT LicenseCopyright (c) 2016 Kevin J. WalchkoPermission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the “Software”), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
of the Software, and to permit persons to whom the Software is furnished to do
so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: quads
quadsA pure Python Quadtree implementation.Quadtreesare a useful data
structure for sparse datasets where the location/position of the data is
important. They're especially good for spatial indexing & image processing.An actual visualization of aquads.QuadTree:UsageFull documentation athttps://quads.readthedocs.io/en/latest/>>>importquads>>>tree=quads.QuadTree(...(0,0),# The center point...10,# The width...10,# The height...)# You can choose to simply represent points that exist.>>>tree.insert((1,2))True# ...or include extra data at those points.>>>tree.insert(quads.Point(4,-3,data="Samus"))True# You can search for a given point. It returns the point if found...>>>tree.find((1,2))Point(1,2)# Or `None` if there's no match.>>>tree.find((4,-4))None# You can also find all the points within a given region.>>>bb=quads.BoundingBox(min_x=-1,min_y=-2,max_x=2,max_y=2)>>>tree.within_bb(bb)[Point(1,2)]# You can also search to find the nearest neighbors of a point, even# if that point doesn't have data within the quadtree.>>>tree.nearest_neighbors((0,1),count=2)[Point(1,2),Point(4,-4),]# And if you have `matplotlib` installed (not required!), you can visualize# the tree.>>>quads.visualize(tree)Installation$ pip install quadsRequirementsPython 3.7+ (untested on older versions but may work)Running Tests$ git clone https://github.com/toastdriven/quads.git
$ cd quads
$ poetry install
$ poetry shell

# Just the tests.
$ pytest .

# With coverage.
$ pytest -s --cov=quads .
# And with pretty reports.
$ pytest -s --cov=quads . && coverage htmlLicenseNew BSD
Package: quad-sim-python
quad_sim_pythonSimple quadcopter simulator in PythonInstallation:Method #0$ pip3 install quad_sim_python --upgradeMethod #1$ git clone https://github.com/ricardodeazambuja/quad_sim_python.git
$ cd quad_sim_python
$ pip3 install . --upgradeMethod #2$ pip3 install git+git://github.com/ricardodeazambuja/quad_sim_python --upgradeHow to use it:Check the files inside the directoryexamples:run_online.pyandrun_trajectory.py.AcknowledgmentsAdapted fromhttps://github.com/bobzwik/Quad_Exploration
Package: quadx88
QUADx88Configurable dynamical model of quadcopterAPI documentationTheQuadcopterClassTo construct a quadcopter object we can call the constructorimportquadx88asqxcopter=qx.Quadcopter(mass=1.15,ts=0.0083,prop_mass=0.01)The following parameters can be provided to the above constructorParameterDescriptionDefaulttsSampling time (s)1/125massTotal mass of the copter (kg)1arm_lengthArm length (m)0.225moi_xxx-x moment of inertia0.01788moi_yyx-x moment of inertia0.03014moi_zzz-z moment of inertia0.04614gravity_accgravitational acceleration (m/s2)9.81air_densityair density (kg/m3)1.225K_vKv constant of motors (rpm/V)1000motor_time_constantmotor time constant (s)0.05rotor_massrotor mass (kg)0.04rotor_radiusrotor radius (m)0.019motor_massmotor mass (kg)0.112voltage_maxmaximum voltage (V)16.8voltage_minminimum voltage (V)15.0thrust_coeffthrust coefficient of propellers0.112power_coeffpower coefficient of propellers0.044prop_masspropeller mass (kg)0.009prop_diameter_inpropeller diameter (inches)10GettersMethod/PropertyDescriptionstateNine dimensional state of the system, $x=(q, \omega, n)$quaternionCurrent quaternionhover_rpmHovering spin in RPMeuler_angles()Current Euler angles in degreesSettersTo initialise the state of the system the following methods are availableset_initial_quaternion(q), whereqis the quaternionset_initial_angular_velocity(w)set_initial_motor_spin(spin)set_initial_euler_angles(yaw, pitch, roll, angle_unit=ANGLE_UNITS.RADIANS), whereyaw,pitchandrolland the three Euler angles (in this order) andangle_unitis the units of measurement. For example to construct a quadcopter object with an initial attitude of $\phi=0^\circ$, $\theta=1^\circ$ and $\psi=-5^\circ$ we can docopter=qx.Quadcopter()copter.set_initial_euler_angles(-5,0,1,ANGLE_UNITS.DEGREES)System matricesThe methodscontinuous_linearised_matricesanddiscrete_linearised_matricesreturn a dictionary with the continuous-time and discrete-time matrices of the linearised system respectively. The discrete-time linearised system has the form
$$\begin{aligned}
x_{t+1} =& Ax_t + Bu_t,
\
y_t =& Cx_t.
\end{aligned}$$Nonlinear dynamical systemTo simulate the nonlinear model you can usemove(u)which updates the system state following the application of a given control action for the duration of the sampling time.
Package: quaerere-base-client
Aboutquaerere-base-clientcontains the common elements shared between components of the Quaerere Platform.LinksDocumentation:http://quaerere-base-client.readthedocs.io/PyPI:https://pypi.org/project/quaerere-base-client/Source:https://github.com/QuaererePlatform/quaerere-base-client.gitInstallationUsing pip:pipinstallquaerere-base-clientOr using setup.py:pythonsetup.pyinstallTests can be ran using:pythonsetup.pytestDocumentation can be generated using:pythonsetup.pybuild_sphinxUsage
Package: quaerere-base-common
Aboutquaerere-base-commoncontains the common elements shared between components of the Quaerere Platform.LinksDocumentation:http://quaerere-base-common.readthedocs.io/PyPI:https://pypi.org/project/quaerere-base-common/Source:https://github.com/QuaererePlatform/quaerere-base-common.gitInstallationUsing pip:pipinstallquaerere-base-commonOr using setup.py:pythonsetup.pyinstallTests can be ran using:pythonsetup.pytestDocumentation can be generated using:pythonsetup.pybuild_sphinxUsage
Package: quaerere-base-flask
Aboutquaerere-base-flaskcontains the common elements shared between components of the Quaerere Platform.LinksDocumentation:http://quaerere-base-flask.readthedocs.io/PyPI:https://pypi.org/project/quaerere-base-flask/Source:https://github.com/QuaererePlatform/quaerere-base-flask.gitInstallationUsing pip:pipinstallquaerere-base-flaskOr using setup.py:pythonsetup.pyinstallTests can be ran using:pythonsetup.pytestDocumentation can be generated using:pythonsetup.pybuild_sphinxUsage
Package: quaerere-columbia-common
Aboutquaerere-columbia-commoncontains the common elements shared between the columbia micro-service
and client.LinksDocumentation:http://quaerere-columbia-common.readthedocs.io/PyPI:https://pypi.org/project/quaerere-columbia-common/Source:https://github.com/QuaererePlatform/columbia-common.gitInstallationUsing pip:pipinstallquaerere-columbia-commonOr using setup.py:pythonsetup.pyinstallTests can be ran using:pythonsetup.pytestDocumentation can be generated using:pythonsetup.pybuild_sphinxUsage
Package: quaerere-willamette-client
Aboutquaerere-willamette-clientis the client library used to interact with the willamette service RESTful APILinksDocumentation:http://willamette-client.readthedocs.io/PyPI:https://pypi.org/project/quaerere-willamette-client/Source:https://github.com/QuaererePlatform/willamette-client.gitInstallationUsing pip:pipinstallquaerere-willamette-clientOr using setup.py:pythonsetup.pyinstallTests can be ran using:pythonsetup.pytestDocumentation can be generated using:pythonsetup.pybuild_sphinxUsage
Package: quaerere-willamette-common
Aboutquaerere-willamette-commoncontains the common elements shared between the willamette micro-service
and client.LinksDocumentation:http://quaerere-willamette-common.readthedocs.io/PyPI:https://pypi.org/project/quaerere-willamette-common/Source:https://github.com/QuaererePlatform/willamette-common.gitInstallationUsing pip:pipinstallquaerere-willamette-commonOr using setup.py:pythonsetup.pyinstallTests can be ran using:pythonsetup.pytestDocumentation can be generated using:pythonsetup.pybuild_sphinxUsage
Package: quaesit
QuAESiTA framework for easy development of agent-based modelsQuaesit stands forQuickandEasySimulationTools. This package is based on the concepts and architecture of platforms such as Netlogo, as well as other Python packages for agent-based modelling, namely mesa. It is also based on the architecture of ExPaND, a model developed by me for simulating human migrations in South America.Documentation and Tutorialjgregoriods.github.io/quaesit
Package: quail
Quail is a Python package that facilitates analyses of behavioral data from memory experiments. (The current focus is on free recall experiments.) Key features include:Serial position curves (probability of recalling items presented at each presentation position)Probability of Nth recall curves (probability of recalling items at each presentation position as the Nth recall in the recall sequence)Lag-Conditional Response Probability curves (probability of transitioning between items in the recall sequence, as a function of their relative presentation positions)Clustering metrics (e.g. single-number summaries of how often participants transition from recalling a word to another related word, where “related” can be user-defined.)Many nice plotting functionsConvenience functions for loading in dataAutomatically parse speech data (audio files) using wrappers for the Google Cloud Speech to Text APIThe intended user of this toolbox is a memory researcher who seeks an easy way to analyze and visualize data from free recall psychology experiments.
Package: quake3rcon
quake3rconA tiny library for using Quake 3's RCON protocol feature for some game servers likeFiveM.
The RCON protocols are used to remotely control game servers, i.e. execute commands on a game server and receive the respective results.LicenseMIT LicenseCopyright (c) 2022 hellyetPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.class Rconfunctionargsreturnsdescription__init__ip (ip), port (int), password (str), timeout (float, optional)NoneInitialize rcon connection__del__NoneNoneClose rcon connection on object destructionrecvNoneNoneGets server responsesendcommand (str)strSend rcon commandHow i can use this?To install runpip install quake3rconThis is an example using q3rcon-py in your codefromquake3rcon.rconimportRconrcon=Rcon('192.168.0.1',30120,'yourstrongpassword')# Connecting to RCONresponce=rcon.send('command and some args for it')# Send RCON commandprint(responce)# Show server response# Do some stuffdelrcon# Delete object and disconnect from RCON
Package: quakeanalysis
This is prototype to analyse earthquake data fromhttps://earthquake.usgs.gov/https://earthquake.usgs.gov/earthquakes/feed/v1.0/geojson.phpenter a place name to get distance from nearest earthquakeearthquake_distance(‘place’)enter a place name to get time of nearest earthquakeearthquake_time(‘place’)enter a place name to get place of nearest earthquakeearthquake_place(‘place’)enter a place name to get longitude of nearest earthquakeearthquake_longitude(‘place’)enter a place name to get latitude of nearest earthquakeearthquake_latitude(‘place’)get the magnitude of nearest earthquakeearthquake_magnitude(‘place’)To get dataframe list of all recent earthquakes and their distance from placeearthquake_df(‘place’)To get dataframe list of all recent earthquakes and their distance from place, where distance less than 5000
earthquake_less_than(‘Tokyo’, 5000)Check out how to use this libraryhttps://betterprogramming.pub/python-library-for-finding-nearest-earthquake-8c96f97c9ddbhttps://github.com/g00387822/quakeanalysisChange Log0.0.1 (03/06/2022)First Release0.0.2 (03/06/2022)Second Release0.0.3 (03/06/2022)Third Release0.0.4 (03/06/2022)Fourth Release0.0.5 (03/06/2022)Fifth Release0.0.6 (04/06/2022)Sixth Release0.0.7 (04/06/2022)Seventh Release0.0.8 (14/06/2022)Eigth Release0.0.9 (14/06/2022)Ninth Release0.1.0 (15/06/2022)Tenth Release0.1.1 (18/06/2022)Eleventh Release0.1.2 (18/06/2022)Twelfth Release0.1.3 (18/06/2022)Thirteenth Release0.1.4 (18/06/2022)Fourteenth Release0.1.5 (18/06/2022)Fifteenth Release0.1.6 (18/06/2022)Sixteenth Release0.1.7 (19/06/2022)Seventeenth Release
Package: quake-cli-tools
quake-cli-toolsquake-cli-tools is a set of command line tools for creating Quake content.Installation$pipinstallquake-cli-toolsToolspak: Add files to a PAK file.unpak: Extract files from a PAK file.wad: Add file to a WAD file.unwad: Extract files from a WAD file.bsp2wad: Create a WAD file from a BSP file.qmount: Mount a PAK file as a drive.image2spr: Create an SPR from image files.spr2image: Extract frames from an SPR.bsp2svg: Create an SVG file from a BSP file.ContributingHave a bug fix or a new feature you'd like to see in quake-cli-tools? Send it our way! Please make sure you create an issue that addresses your fix/feature so we can discuss the contribution.Fork this repo!Create your feature branch:git checkout -b features/add-cool-new-toolCommit your changes:git commit -m 'Adding must have new tool!'Push the branch:git push origin features/add-cool-new-toolSubmit a pull request.Create anissue.LicenseMITSee thelicensedocument for the full text.
Package: quakefeeds
Python 3 tools for handling USGS earthquake data feeds.Thequakefeedspackage provides a classQuakeFeedthat captures data
from a GeoJSON feed, given a valid severity level and time period.
The class provides some shortcuts for accessing data of interest within
the feed and provides other useful methods - e.g. one to generate a simple
Google map plotting quake locations and magnitudes.The data feeds and a description of their GeoJSON format are available athttp://earthquake.usgs.gov/earthquakes/feed/v1.0/geojson.php.Examples of Use>>>fromquakefeedsimportQuakeFeed>>>feed=QuakeFeed("4.5","day")>>>feed.title'USGS Magnitude 4.5+ Earthquakes, Past Day'>>>feed.timedatetime.datetime(2015,4,16,19,18,39,tzinfo=datetime.timezone.utc)>>>len(feed)6>>>feed[0]{'properties':{'cdi':1,'tsunami':0,'alert':None,...}# full JSON data for first event in feed>>>feed.location(0)[26.8608,35.135]>>>feed.magnitude(0)6.1>>>feed.event_title(0)'M 6.1 - 47km SW of Karpathos, Greece'>>>feed.depth(0)20.86>>>feed.depths<generatorobjectdepthsat0x7fef054b5fc0>>>>list(feed.depths)[20.86,46.35,76.54,48.69,10,28.64]>>>map1=feed.create_google_map()>>>map2=feed.create_google_map(style="titled")>>>feed.write_google_map("map.html",style="titled")ScriptsThe installation process will install some scripts in addition to thequakefeedspackage:quakemap, which plots earthquakes on a Google mapquakestats, which computes basic statistics for a feedDependenciesPython 3RequestsJinja2template engine (for map generation)docopt(for the scripts)InstallationUsepipto install the package, its scripts
and their dependencies:pip install quakefeedsAlternatively, you can install from within the unpacked source distribution:python setup.py install(Note that this requires prior installation ofsetuptools.)If you don’t want the scripts, copying thequakefeedsdirectory from
the source distribution to somewhere in your PYTHONPATH will suffice.
Package: quake-inverse-squareroot
quake 3's fastest inverse square rootThis module is a port from Quake 3's inverse square root algorithm.InstallationGet yourself a C compiler (like gcc, clang or msvc)Runpython setup.py installEnjoy!BuildGet yourself a C compiler (like gcc, clang or msvc)Runpython setup.py build bdist_wheel sdistCheck thedistfolder for the wheel and source distributionDocumentationquake_inverse_sq.coarse_inv_sqrt(number: float) -> floatThis is fastest inverse square root algorithm. It is not as accurate as thequake_inverse_sq.fined_inv_sqrtfunction, but it is much faster. It is implemented from thiswikipediaquake_inverse_sq.fined_inv_sqrt(number: float) -> floatThis is the original inverse square root algorithm. It is more accurate than thequake_inverse_sq.coarse_inv_sqrtfunction, but it is slower. It is implemented frommath.h
Package: quakeio
quake-ioQuakeIO is a library of utilities for parsing ground motion files. Interfaces are provided for Python, Matlab, and the command line.InstallFormatsPython APIFormatsFormatReadWriteReference[quakeio.]json☑☑schemacsmip☐☐csmip.v2☑☐CSMIPeqsig☑☑eqsigPEER.NGA☑☐plain.tsv☐☐opensees☐☑plain.csv☐☐mdof☐☐SimCenter.Event☐☐InstallRun the following command:pipinstallquakeio
Package: quaker
QuakerDocsQuakerDocs is a modern and reliable static documentation generator. It
was designed from the ground up to replace older documentation
generators. It generates a fully static web page with your
documentation, so it can work without a server. Some of the features
that QuakerDocs has are:Fast generation of a static websiteWrite documentation using reStructuredTextSuper-fast live searchHandy bookmarking systemEasily configurableOnly takes one command!Getting Started with QuakerDocsQuakerDocs is very easy to use, and you do not need a lot to get
started!InstallationFirst of all, make sure you have Clang and the LLVM wasm-compiler installed.For example, on Ubuntu:apt install clang lldThen, to install the QuakerDocs application use the following command:pip install quakerAfter running this command all the requirements are installed and you can
immediately use the quaker command.QuickstartTo create an example quickstart project in a directory, you can use the
following command:quaker --init <your_directory_name>This command creates a directory with some of the necessary files to get
you started such asconf.py, andindex.rst.UsageTo use QuakerDocs to turn your RST or Markdown files into static
webpages you need the follow these steps:Open the directory containing yourconf.pyin the terminal.cd path/to/my/project/To convert your documentation files into static webpages, run the
following command.quaker .Change into thebuild/directory, and start a webserver.cd build/
python3 -m http.serverTo visit the generated documentation page visitlocalhost:8000in
your web browser.
Package: quakerheritage
quakerHeritageProject to support the collation of PDF data on the Quaker Meeting House Heritage Project into a databaseDependenciesRequired Python Libariesbs4numpypandaspdfplumberrequestsDisclaimerThis project has been specifically coded for the Quaker Meeting House Heritage Project, both in hard-coded variables, and hard-coded parameters for extracting text. It is a tool to suit a very specific use-case and may not work if used otherwise. The project further depends on the files required being listed online at the URLs provided. If Britain Yearly Meeting takes down the website and associated pdfs, back-ups are available on the Internet Archive's Wayback Machine. The code can also be adapted to work with locally downloaded pdfs. go to Appendix: Hosting Errors to note the required changes.Installationpip install quakerheritageHow-To UseSimply run the following command:python -m quakerheritage.buildYou will be prompted to select a location for the csv output to be placed. Once chosen, the code will run quietly in the background until complete, and the csv available at your chosen directory as 'quakerHeritageDB.csv'ContributingFeedback is both welcome and encouraged. If you use the code, or just find issues while browsing, please report them byclicking here.LicenceDistributed underAGPL version 3.0ContactFor queries please reach out via GitHub by either raising an issue or contacting me directly.https://github.com/aclayden