Saravanan666/pypi-org huggingface dataset:
Package: screentime
ScreentimeScreenTime™ for non iOS devicesWhy?Now adays most modern mobile phones are equiped with software to keep track of its usage while many desktop devices do not so I decided to make one.  Also it is made entirely from the Python standard library.Installationpip3 install --user screentimeUsage:usage: screentime [-h] [--begin] [--end] [--summary]

Keep track of Daniel's unhealthy screen usage.

optional arguments:
  -h, --help     show this help message and exit
  --begin, -b    begin a new session
  --end, -e      end a session
  --summary, -s  get infromation on previous sessionsThe program works based on sessions.  A session is a period of time that you spend on your device.  This means that you have tomanuallysay tell it when you start and stop using your device.Begin a session$ screentime -bEnding a session$ screentime -eView a summary of your screentime$ screentime -sSome issues you should know aboutYou have tomanuallytell the programm when you are using your deviceThe screen time information is presented in the most plain and boring way possible
Package: screenton-maker-rs
libimportcv2importnumpyasnpfromscreenton_makerimportScreentonimg=cv2.cvtColor(cv2.imread("1708268835.555137.png"),cv2.COLOR_RGB2GRAY).astype(np.float32)/255s=Screenton(7)#Screenton(7,lx_plus=3,ly_plus=3))  automatic values lx_plus = dot_size/2, ly_plus = dot_size/2img=s.run(img)cv2.imwrite("2.png",img*255)
Package: screenviz
screenvizA CRISPR screen visualization toolkit.InstallationpipinstallscreenvizUsageThis program is meant to be run from the commandline and will generate interactive visualizations for your CRISPR screen results.Gene EnrichmentTo explore the gene-level enrichment of your analysis - specifically the classic volcano plot (log-fold-change on the x-axis and negative log p-value on the y-axis) -
you can use thescreenviz genesubcommand:screenvizgene-iresults.gene_results.tsvIf your screen result is an output ofcrispr_screen, the defaults of the program will immediately generate a visualization which will be written to an*.htmlfile
on your computer. You can open this file in your favorite browser and explore the results interactively there.For a better visualization, pass in the.yamlconfiguration file produced from yourcrispr_screenrun.
This will automatically determine ifRRAorINCwas used and show appropriate thresholds.screenvizgene-iresults.gene_results.tsv-cresults.screenviz.yamlIf your screen result is an output of a different program, you will need to tellscreenvizwhat the names of the columns in your data represent.If your result is fromMAGeCKyou can do the following:# Left side of the volcanoscreenvizgene\-isample1.gene_summary.txt\-g"id"\-f"neg|lfc"\-p"neg|fdr"\-t"neg|fdr"# Right side of the volcanoscreenvizgene\-isample1.gene_summary.txt\-g"id"\-f"pos|lfc"\-p"pos|fdr"\-t"pos|fdr"Check out what the arguments are with the help command:screenvizgene--helpsgRNA EnrichmentTo explore the sgrna-level enrichment of your analysis - specifically the classic volcano plot (log-fold-change on the x-axis and negative log p-value on the y-axis) -
you can use thescreenviz sgrnasubcommand:screenvizsgrna-iresults.sgrna_results.tsvIf your screen result is an output ofcrispr_screen, the defaults of the program will immediately generate a visualization which will be written to an*.htmlfile
on your computer. You can open this file in your favorite browser and explore the results interactively there.If your screen result is an output of a different program, you will need to tellscreenvizwhat the names of the columns in your data represent.If your result is fromMAGeCKyou can do the following:screenvizsgrna\-isample1.sgrna_summary.txt\-g"Gene"\-f"LFC"\-p"FDR"\-t"FDR"ComparisonTo compare the results of two different analysis methods you can use thescreenviz comparesubcommandscreenvizcompare\-iresults_a.gene_results.tab\-Iresults_b.gene_results.tabThis will generate a scatter plot to compare the calculated FDR values of two different results.To compare betweencrispr_screenandMAGeCKone can do:# compare depleted genesscreenvizcompare\-iresults.gene_results.tsv\-Isample1.gene_summary.txt\-xfdr_low\-X"neg|fdr"\-mgene\-Mid\-tfdr_low\-T"neg|fdr"# compare enriched genesscreenvizcompare\-iresults.gene_results.tsv\-Isample1.gene_summary.txt\-xfdr_high\-X"pos|fdr"\-mgene\-Mid\-tfdr_high\-T"pos|fdr"
Package: screenwriter
screenwriterPython Library for writing progress texts, echo messages, warnings and errors to standard outputExamples:1 - Default prefixfromscreenwriterimportScreenwritersw=Screenwriter()sw.echo('my output')Output:
2019-07-26-11:16:04 my output2 - Date Time parts in prefixfromscreenwriterimportScreenwritersw=Screenwriter('%Y-%m-%d%H:%M:%S.%f')sw.echo('my output')Output:
2019-07-26 11:16:04 my output3 - Error, Warning & Info standard prefixesfromscreenwriterimportScreenwritersw=Screenwriter()sw.error('an error message')sw.warn('a warming message')sw.info('an informational message')Output:
2019-07-29-11:39:00 ERROR: an error message
2019-07-29-11:39:00 WARN:  a warming message
2019-07-29-11:39:00 INFO:  an informational message4 - Trimming content lengthBy default, log strings are trimmed to 120 chars.
You can change this setting:fromscreenwriterimportScreenwritersw=Screenwriter()sw.set_maxlen(80)#Set maximum length to 805 - Dynamically toggele outputYou can toggle the output between ON and OFF.fromscreenwriterimportScreenwritersw=Screenwriter()sw.echo('print this line')sw.set_verbose(False)sw.echo('do not print this line')sw.set_verbose(True)sw.echo('print this line')Output:
2019-10-05-20:09:10 print this line
2019-10-05-20:09:10 print this lineFor format options, seehttp://strftime.org/
Package: screeny
ScreenyA simple python library for working with screens and images.InstallationpipinstallscreenyUsagefromscreenyimportScreenysc=Screeny()img=sc.take_screenshot()API-ReferenceScreeny.get_mouse_posScreeny.locate_imageScreeny.read_textScreeny.take_screenshotImage.initImage.binarizeImage.compute_descriptorsImage.denoiseImage.detect_featuresImage.detect_keypointsImage.get_dataImage.invertImage.locate_imageImage.match_featuresImage.read_textImage.resizeImage.saveImage.showImage.show_matchesImage.to_grayscaleImage.to_hsvScreeny-classScreeny.get_mouse_pos()Returns the current position of the mouse as a tuple of xy-coordinates.Screeny.locate_image(image: str | np.ndarray | Image [, rect: Rect, confidence: float])Search for an image on the screen. Returns the location of the found image in pixel or False, if no image was found.Screeny.read_text(rect: Rect)Reads text from a given area on the screen. Returns the founded text as string.Screeny.take_screenshot([rect: Rect])Takes a screenshot of the complete monitor or a given area as "rect". Returns an Image-Instance.Image-ClassImage.init(image: str | np.ndarray | Image)Creates a new instance of the Image-Class.Image.binarize(method: str[, threshold: int])Binarize the image.Image.compute_descriptors()Computes the descriptors of the image.Image.denoise()Denoise the image.Image.detect_features()Finds features (keypoints and descriptors) in the image.Image.detect_keypoints()Finds the keypoints of the image.Image.get_data()Returns the pixels as numpy-array.Image.invert()Inverts the image.Image.locate_image(image_to_find: str | np.ndarray | Image[, confidence: float])Locates an second image in the image and returns the position of the founded image or False if no image was found.Image.match_features(image_to_find: str | np.ndarray | Image, cross_check: bool)Matches the features of the image with another image.Image.read_text([resize_factor: int, whitelist: str])Reads the text from the image.Image.resize(factor: int)Resizes the image with a given factor.Image.save(title: str[, path: str])Saves the image on the disk in a given path with a given title.
If no path is specified, the file will be saved in the current active directory.Image.show([title: str])Displays the image.Image.show_matches(image_to_find: str | np.ndarray | Image, matches: list)Show the two images with matches as lines connect points.Image.to_grayscale()Converts the image to grayscale.Image.to_hsv()Converts the image to HSV.
Package: screeplot
Scree PlotIn multivariate statistics, a scree plot is a line plot of the eigenvalues of factors or principal components in an analysis. The scree plot is used to determine the number of factors to retain in an exploratory factor analysis (FA) or principal components to keep in a principal component analysis (PCA)installationcommand Prompt - pip install screeplotJp notebook  - ! pip install screeplotUsefrom screeplot import scree as spsp.scree_plot(x)Authors@ranjit MaityDemo
Package: screeps
# Screeps libraryLibrary to connect to the screeps API either via REST or websocket.Example for using the library can be seen athttps://github.com/TooAngel/screeps-cli.## Rest- `get_me()` Returns the user object for the logged in user- `console(command)` Sends the command to the console## WebsocketWebsocket connection for fetching the console log```from screeps.screeps import Connectiondef sysout(message):print(message)conn = Connection(email, password, arguments['--ptr'])conn.startWebSocket(sysout)```## Deploy`python setup.py sdist upload`
Package: screepsapi
This is an unofficial client for theScreepsUnoffical API.Since the API is unofficial it could in theory change at any time. In
practice though breaking changes are rare.Setup:Simply install the library usingpip.UsageAuthenticationTo authenticate to the primary servers just supply a username and
password.importscreepsapiUSER="MyUsername"PASSWORD="TimeTravelingSecretAgentForHire"api=screepsapi.API(USER,PASSWORD)It is also possible to access private servers with thehostandsecureparameters.importscreepsapiUSER="MyUsername"PASSWORD="TimeTravelingSecretAgentForHire"api=screepsapi.API(USER,PASSWORD,host="server1.screepspl.us:443",secure=True)Note that by default private servers do not use SSL and all traffic is
unencrypted.APIThe API itself is a simple REST-based API. Each method in the api
library corresponds to a different endpoint for the API.The best way to discover functionality is to read through the library
itself.Console ExampleimportscreepsapiUSER="MyUsername"PASSWORD="TimeTravelingSecretAgentForHire"api=screepsapi.API(USER,PASSWORD,host="server1.screepspl.us:443",secure=True)# Run "Game.time" on shard1 via the consoleapi.console('Game.time',shard='shard1')User Information ExampleimportscreepsapiUSER="MyUsername"PASSWORD="TimeTravelingSecretAgentForHire"api=screepsapi.API(USER,PASSWORD,host="server1.screepspl.us:443",secure=True)# Find the GCL for `tedivm`user=api.user_find("tedivm")printuser["user"]["gcl"]SocketScreeps provides a sizable amount of data over a websocket. This
includes console data and room details.The best way to utilize the socket is to extendscreepsapi.Socketand override the various abstract functions.
Package: scresonators-fit
scresonatorsWelcome to the scresonators repository of the Boulder Cryogenic Quantum Testbed! This is a library for measuring the loss in superconducting resonators.Importing the LibraryInstall the library and its relevant dependencies withpip install scresonators-fitLead your python code withimport fit_resonator.resonator as resonatorContributing/Modifyingclone the repository into a folder of your choice withgit clone https://github.com/Boulder-Cryogenic-Quantum-Testbed/scresonators.gitInstall the dependencies, westronglyrecommend usingvirtual environmentsfor managing your dependences. To install dependencies run:pip install -r requirements.txtIf you are running on Windows, install Microsoft Visual Studio before using the libraryUsing the libraryFitting resonator data will revolve around the resonator class.Here's an example using some of the data hosted on this repository. Hosted
datasets from groups around the world can be foundhere.importnumpyasnpimportfit_resonator.resonatorasscres# The object all following code will be called frommy_resonator=scres.Resonator()# Load the raw dataurl='https://raw.githubusercontent.com/Boulder-Cryogenic-Quantum-Testbed/scresonators/master/cryores/test_data/AWR/AWR_Data.csv'raw=np.loadtxt(url,delimiter=',')# Can also use our file input system of my_resonator.from_file(url)# Test with file load into classmy_resonator.from_columns(raw)# Assign your desired fit method variablesfit_type='DCM'MC_iteration=10MC_rounds=1e3MC_fix=['w1']manual_init=None# Pass these to your resonator objectmy_resonator.fit_method(fit_type,MC_iteration,MC_rounds=MC_rounds,MC_fix=MC_fix,manual_init=manual_init,MC_step_const=0.3)# Fit!my_resonator.fit()Ane in depth description is given in the fit_resonators folder.Code OrganizationFor fitting code collaboration, all code should live in thefit_resonatornamespace. This ensures easy integration
with other Python packages, and avoids name collisions; everything is referred
to as e.g.fit_resonator.experimentsrather than justexperiments.
Package: scresonatorsTEST
scresonatorsWelcome to the scresonators repository of the Boulder Cryogenic Quantum Testbed! This is a library for measuring the loss in superconducting resonators.Installationclone the repository into a folder of your choice withgit clone https://github.com/Boulder-Cryogenic-Quantum-Testbed/scresonators.gitInstall the dependencies, westronglyrecommend usingvirtual environmentsfor managing your dependences. To install dependencies run:pip install -r requirements.txtIf you are running on Windows, install Microsoft Visual Studio before using the libraryUsing the libraryHere's an example using some of the data hosted on this repository. Hosted
datasets from groups around the world can be foundhere.This particular example code is meant to be run in the root directory.importnumpyasnpimportfit_resonator.functionsasffimportfit_resonator.Sdataasfsdimportfit_resonator.resonatorasresurl='https://raw.githubusercontent.com/Boulder-Cryogenic-Quantum-Testbed/scresonators/master/cryores/test_data/AWR/AWR_Data.csv'# Load the raw data:raw=np.loadtxt(url,delimiter=',')# Choose a fitting method:fit_type='DCM'MC_iteration=10MC_rounds=1e3MC_fix=['w1']manual_init=Nonemethod=res.FitMethod(fit_type,MC_iteration,MC_rounds=MC_rounds,MC_fix=MC_fix,manual_init=manual_init,MC_step_const=0.3)# Fit the data:fsd.fit("output test",method,normalize=10,data_array=raw)Ane in depth description is given in the fit_resonators folder.Code OrganizationUntil the module is officially distributed, all code should live in thefit_resonatornamespace. This ensures easy integration
with other Python packages, and avoids name collisions; everything is referred
to as e.g.fit_resonator.experimentsrather than justexperiments.
Package: sc-retail-analysis
A simple Python projectThis project provides a simple utility to analysis retail reports.InstallationIt is possible to install the tool withpip:pip install sc-retail-analysisConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: sc-retail-client-analysis
A simple Python project sample structureThis project provides a sample structure for creating python project.InstallationIt is possible to install the tool withpip:pip install sc-retail-client-analysisConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: sc-retail-email-sender
A simple Python project sample structureThis project provides a sample structure for creating python project.InstallationIt is possible to install the tool withpip:pip install sc-retail-email-senderConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: sc-retail-post-rpa
A simple Python projectThis project provides a simple process after retail analysis.InstallationIt is possible to install the tool withpip:pip install sc-retail-post-rpaConfigurationConfiguration files reading in this order, the first is the top most priority:production.xml in current directory,production.xml in <project_name> directory under User directory,production.xml in <project_name> directory under /var/opt/sc/ directory,default.xml in <project_name> directory under /var/opt/sc/ directory.The default configuration file looks like this:dev:
  # whether this program is running is development mode
  dev_mode: FalseLicenseThe script is released under the MIT License.  The MIT License is registered
with and approved by the Open Source Initiative[1].[1]https://opensource.org/licenses/MIT
Package: screwdriver
Another tool for your python toolset.  General odds and ends that kept coming
up in projects.Installation$pipinstallscrewdriverSupportsscrewdriver has been tested with Python 2.7, 3.6 and 3.7Docs & SourceDocs:http://screwdriver.readthedocs.io/en/latest/Source:https://github.com/cltrudeau/screwdriver
Package: screwdrivercd
screwdrivercdPython helper utilities for CI/CDImplementing CI Pipeline templates for screwdriver?  This package provides a number of useful utility scripts that can
be called from CI jobs to automate common operations.These utilities are used by the screwdrivercd python templates but can be used with other CI/CD
systems as well.Table of ContentsBackgroundInstallUsageContributeLicenseBackgroundScrewdriver templates that perform complicated operations can be tricky to write and test.  This package contains a number of useful and tested scripts used by Yahoo/Oath/Verizon to perform steps within Screwdriver CI/CD templates.InstallThis package can be installed using the Python pip package manager that has been configured to use the Oath/Yahoo
internal package repository.In order to install this package the python environment must have:Python version 3.6 or newerpip version 8.1.1 or highersetuptools 31.0.0 or higherInstall this package using the Python package installer$pipinstallscrewdrivercdUsageThis package contains a number of scripts which are documented in theproject documentationContributePlease refer tothe contributing.md filefor information about how to get involved. We welcome issues, questions, and pull requests. Pull Requests are welcome.MaintainersDwight Hubbard:dhubbard@verizonmedia.comLicenseThis project is licensed under the terms of theBSDopen source license. Please refer toLICENSEfor the full terms.
Package: screwhashcounter
provides a convenient and efficient way to count the occurrences of items (hashable and unhashable)pip install screwhashcounterTested against Windows 10 / Python 3.10 / AnacondaCounttheoccurrencesofitemsinalist,tupleandreturnalistoftupleswiththeitemanditscount.Args:arg(list|tuple):Thelist|tuplecontainingitemstobecounted.Returns:list[tuple]:Alistoftupleswhereeachtuplecontainsanitemanditscount.Example:fromscrewhashcounterimportcountitemscounted_items=countitems(["mississippi",[1,2],[1,2],{34,4,3},{1:2},{1:2},None,3])print(counted_items)# Output: [('mississippi', 1), ([1, 2], 2), ({34, 3, 4}, 1), ({1: 2}, 2), (None, 1), (3, 1)]
Package: screwmpcpy
screwmpcpyGenerate smooth motions for a n-DOF robot arm end effector using Dual
Quaternions while enforcing velocity-, acceleration- and jerk constraints. The
package includes a base optimization scheme and a base motion generator, which
can be easily extended for custom robot arms. A motion generator for the Franka
Emika Panda robot is included.InstallThe package is available in PyPI, you can install it via pip:pip install screwmpcpyGetting startedThe motion generators yield velocity commands for an n-DoF robot arm. For the
basic usage have a look atexamples/motiongenerator.py. You can also use the
dual quaternions to generate waypoints. Check it out the example for the panda
robot atexamples/panda_screwmotion_ik.py. To install the required package
dependencies executepip install screwmpcpy[examples]. You can find advanced
usage exampleshere. We
include two generators for the panda robot:The basic screw motion generator and a modified variant, which includes manipulability
maximization from[1].Extending the available robotsYou can easily extend the base class by adding your custom robot arm. Ensure the
forward kinematics of your robot are available.Create a new fileyour_robot_mg.pyin foldersrc/screwmpc.py.Custom robot examplefrom dqrobotics import DQ
from .basemg import BaseMotionGenerator
from .screwmpc import BOUND


class YourRobotMotionGenerator(BaseMotionGenerator):

    def __init__(
                self,
                n_p: int,
                n_c: int,
                q_mpc: float,
                r_mpc: float,
                lu_bound_vel: BOUND,
                lu_bound_acc: BOUND,
                lu_bound_jerk: BOUND,
                ) -> None:
        super().__init__(
            n_p, n_c, q_mpc, r_mpc, lu_bound_vel, lu_bound_acc, lu_bound_jerk
        )
        # custom attributes here
        self._kin = YourRobotKinematics()

    def step(self, q_robot: np.ndarray, goal: DQ) -> np.ndarray:

        # Initialize Dual Quaternion with 6D pose
        x_current = DQ(self._kin.your_forward_kinematics(q_robot))
        error, smooth_traj = super()._step(x_current, goal)
        # calculate commanded dq here
        ...
        return dqReferences[1]Haviland, J., & Corke, P. (2020). A Purely-Reactive
Manipulability-Maximising Motion Controller. ArXiv. /abs/2002.11901
Package: scrfd
SCRFDEfficient face detection using SCRFD onnxruntime inference.Requires python >= 3.10pipinstallscrfdfromscrfdimportSCRFD,ThresholdfromPILimportImageface_detector=SCRFD.from_path("./models/scrfd.onnx")threshold=Threshold(probability=0.4)image=Image.open("./images/solvay_conference_1927.jpg")faces=face_detector.detect(image,threshold=threshold)forfaceinfaces:bbox=face.bboxkps=face.keypointsscore=face.probabilityprint(f"{bbox=},{kps=},{score=}")