Saravanan666/pypi-org huggingface dataset:
Package: visip
Visip - VIsual SImulation ProgrammingRationaleSimulation workflows are usually composed from several dependent steps forming a directid acyclic graph (DAG).
A single step of this DAG could be a simple task as copying of a file or extracting a few numbers from a file via. a Python script,
ot it may be a complex task as several hours long parallel finite element calculation.
Developing such a DAG is often painful for following reasons:Slow FeedbackSlow error feedback is related to the complex tasks. Thier runtimes are huge and usage of anHPCsystem is necessary.
Further latency is caused by waiting to the free resources as the task is processed by the job scheduling system (JSS), e.g.PBS).
One solution is to test the task DAG with simplified complex tasks (e.g. using a coarse computational grid) idealy on the local workstation without dealing with JSS.
However, this leads to other issues ..Scaling and Portability IssuesPorting the workflow from the workstation to the HPC system is laborous due to complex software dependencies.Essential featuressimulation workflow DAG is described in a form of simple pure functional language based on Pythonautomatic type deduction and checking is performed to catch some errors before executiontask results are stored to a permanent cache allowing to skip repeated calculations for the same inputsautomatic parallelization (grouping of small tasks)graphical languageconteinersPure Functional Languagevisip docker imagetypy: time map (time -> snapshot), bilance, VTK,zobrazovani dat z vypoctu: hodnoty, pole, BREP, MSH, VTKImportant features (TODO)Test casesBGEM - GMSH - Flow123dModel editor - Flow123dSimple calibration (While + foreach) - fracture position - Flow123dFlow123d mikro model - Flow123d makro modelFlow123d HM - Flow123d TransportPython based syntaxSources are stored in individual module files. There are two kinds of the file:
Files containing actions implemented in Python using the@action_defdecorator.
Files containing workflows (functional definitions of actions) using the@worflowdecorator.The first kind can only be modified through in the source form not through the GUI.
The second kind can be fully modified through the gUI allowing graphical composition
of the actions of the first kind.Further one can define classes (@Class) and enums (@Enum) data types.
All names must be valid Python names and must not begin with underscore.
All invalid names are ignored in the module and result in error when used in a workflow.
Package: visitoolkit-connector
visitoolkit_connectorclient-side implementation of 'DMS JSON Data Exchange'(most parts of specification v1.4 is already implemented,but my code currently contains many rough edges...)Installation via piphttps://pypi.org/project/visitoolkit-connector(runs on Python 3)descriptionAn INOFFICIAL OpenSource client library written in Python toProMoS NT (c)AKASaia Visi.Plus (c)version 1.7 or highersearch for datapointsmanipulations on datapoints (get, set, delete)retrieving of trend dataretrieving of alarms and protocolsevent-based monitoring of datapointsvisitoolkit_connectorcommunicates over websockets to DMS and implements the official 'DMS JSON Data Exchange' protocol.All JSON objects (requests and responses) are mapped to instances of Python classes.All JSON values are mapped to attributes.Events from datapoint subscriptions are handled byvisitoolkit_eventsystemfor executing your Python callbacks in a background thread.documentation for this protocol is without any warranty temporary available underhttps://github.com/stefanbraun-private/stefanbraun-private.github.io/tree/master/ProMoS_dev_docsAll rights reserved by the respective owner 'MST Systemtechnik AG'usagefromvisitoolkit_connectorimportconnectorTODO: add examples...=>at the moment you find minimal usage examples at the end of the main Python script:https://github.com/stefanbraun-private/visitoolkit_connector/blob/master/visitoolkit_connector/connector.pyIncreasing logging level for bughunting:importlogginglogging.getLogger('visitoolkit_connector').setLevel(logging.DEBUG)background informationvisitoolkit_connectoris a core part ofvisitoolkit.visitoolkitis written for the proprietary Building and Process Management System
'ProMoS NT' (c) MST Systemtechnik AG'(also known as 'Saia Visi.Plus' (c) Saia-Burgess Controls AG)Intention:Support creator of visualisation projects...Add efficiency...Reduce manual error-prone processes...Add missing features...Disclaimer:'visitoolkit' is an open source homegrown hobby projectwithout any warranty and liability.=>Use 'visitoolkit' at your own risk!
Package: visitoolkit-eventsystem
visitoolkit_eventsystemminimalistic event systemInstallation via piphttps://pypi.org/project/visitoolkit-eventsystem(runs on Python 3)descriptionRegistered handlers (a bag of handlers) getting called when event gets firedusing ideas from "axel events"https://github.com/axel-events/axeland "event system" fromhttp://www.valuedlessons.com/2008/04/events-in-python.htmlusagefromvisitoolkit_eventsystemimporteventsystem# handlers are callback functions in your code,# when firing an event visitoolkit_eventsystem will call them with the given argument(s)defcb1(event_id,*arg,**args):ifevent_id>0:# handle event...returnTrueelse:returnFalse# Default is synchronous execution of handlers (blocking main thread, collecting all results)# sync_mode=False means asynchronous execution of handlers (one background thread calls all handlers)# =>Details about flag "exc_info"(default is True): https://docs.python.org/3/library/sys.html#sys.exc_info# =>Flag "traceback" (default is False) controls verbosity of error_info when an exception occurredes=eventsystem.EventSystem(sync_mode=True)# adding or removing handlers in list-like syntaxes+=cb1#The execution result is returned as a list containing all results per handler having this structure:#  exec_result = [#      (True, result, handler),        # on success#      (False, error_info, handler),   # on error#      (None, None, handler), ...      # asynchronous execution#  ]# firing eventresult=es(42)Increasing logging level for bughunting:importlogginglogging.getLogger('visitoolkit_eventsystem').setLevel(logging.DEBUG)background informationvisitoolkit_eventsystemis used invisitoolkit_connectoras core part ofvisitoolkit.visitoolkitis written for the proprietary Building and Process Management System
'ProMoS NT' (c) MST Systemtechnik AG'(also known as 'Saia Visi.Plus' (c) Saia-Burgess Controls AG)Intention:Support creator of visualisation projects...Add efficiency...Reduce manual error-prone processes...Add missing features...Disclaimer: Use 'visitoolkit' at your own risk!
Package: visitor
A tiny library to facilitatevisitorimplementation in Python
(which are slightly peculiar due to dynamic typing). In fact, it is so small,
you may just be better off copy & pasting the source straight into your
project…Example useA simple JSON-encoder:fromvisitorimportVisitorclassJSONEncoder(Visitor):def__init__(self):self.indent=0defescape_str(self,s):# note: this is not a good escape function, do not use this in# production!s=s.replace('\\','\\\\')s=s.replace('"','\\"')return'"'+s+'"'defvisit_list(self,node):self.indent+=1s='[\n'+'  '*self.indents+=(',\n'+'  '*self.indent).join(self.visit(item)foriteminnode)self.indent-=1s+='\n'+'  '*self.indent+']'returnsdefvisit_str(self,node):returnself.escape_str(node)defvisit_int(self,node):returnstr(node)defvisit_bool(self,node):return'true'ifnodeelse'false'defvisit_dict(self,node):self.indent+=1s='{\n'+'  '*self.indents+=(',\n'+'  '*self.indent).join('{}:{}'.format(self.escape_str(key),self.visit(value))forkey,valueinsorted(node.items()))self.indent-=1s+='\n'+'  '*self.indent+'}'returnsdata=['List','of',42,'items',True,{'sub1':'some string','sub2':{'sub2sub1':False,'sub2sub2':123,}}]print(JSONEncoder().visit(data))Output:[
  "List",
  "of",
  42,
  "items",
  true,
  {
    "sub1": "some string",
    "sub2": {
      "sub2sub1": false,
      "sub2sub2": 123
    }
  }
]
Package: visitor-design-pattern
Visitor Design PatternFlexible and complete pytonic implementation of the visitor design patternFeaturesImplement a visitor design pattern for any data structureConvenient decorator APIReady to use accept method for tree-like data structuresCompatible with AttrsLinksAccess the sphinxDocumentationandUsageSee thePypipackageCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-04-14)First version.
Package: visitsimv2
===visitsimv2 README===Introduction===visitsimv2 wraps VisIt's simv2 insitu library based on the included SWIG wrapper for Python 2 and 3.It can be installed using Pythons distutils/setuptools.Installation===In order to install this package the path to VisIt's "visit" executable needs to be set, otherwise an OSError is thrown.Usage===In your limsim(V2) instrumented simulation:Change the import statement to use visitsimv2 instead of simV2
Package: visium_stitcher
Visium stitcherThe repository houses a Visium stitcher, allowing for a simple joining of adjacent tissue slices into a joint AnnData object for analysis. This is a two-step process, with the first step requiring aligning the images and extracting the transformation matrices in Fiji (TrakEM2). The resulting.xmlfile is subsequently used to transform the images and coordinates in Python.InstallationThe package is available on PyPipipinstallvisium_stitcherUsagePlease refer to theexample notebookfor exact Fiji instructions and a subsequent Python demonstration. The demo data is available atftp://ftp.sanger.ac.uk/pub/users/kp9/demo_data.tar.gz.The function docstrings are provided onReadTheDocs.CitationPlease cite ourpreprint@article{zhang2022human,
  title={A human embryonic limb cell atlas resolved in space and time},
  author={Zhang, Bao and He, Peng and Lawrence, John E and Wang, Shuaiyu and Tuck, Elizabeth and Williams, Brian A and Roberts, Kenny and Kleshchevnikov, Vitalii and Mamanova, Lira and Bolt, Liam and others},
  journal={bioRxiv},
  pages={2022--04},
  year={2022},
  publisher={Cold Spring Harbor Laboratory}
}
Package: visjobs
visjobsUsing visjobs getting the Latest Atmospheric Model, Reanalysis, Observation data and plotting them is very easy.1.Installation1.1. Dependenciesxarraypydaptwinesiphonpandasnumpymatplotlibcartopydatetime1.2. Pypi Installationpip install visjobs1.3. Github Clonegit clonehttps://github.com/donmezkutay/visjobs2. UsageUsage can be found atVisjobs Documentation Page
Package: visjs-component
Streamlit visjs componentStreamlit component that wrapsvis.js.Installation instructionspipinstallvisjs-componentUsage instructionsimportstreamlitasstfromvisjs_componentimportvisjsdata={"nodes":[{"id":1,"label":"Node 1"},{"id":2,"label":"Node 2"},{"id":3,"label":"Node 3"},{"id":4,"label":"Node 4"},{"id":5,"label":"Node 5"},],"edges":[{"from":1,"to":3},{"from":1,"to":2},{"from":2,"to":4},{"from":2,"to":5},],"options":{"nodes":{"shape":"dot","size":16,},"edges":{"color":"#000000",},"physics":{"enabled":True,},"interaction":{"hover":True,},"height":"500px",},}eventHandlers=[{"event":"click","callback":lambdaeventData:st.json(eventData['data']),}]visjs(title="# Network Vis",type="network",data=data,eventHandlers=eventHandlers)Supports all vis.js types:networktimelinegraph3dDonateIf you like this project and want to support it, please consider donating.
Package: vislib
vislibA library of web-based visualizations for python and ipythonSet Up / Usagepip install vislibvislibis set up to optionally be used within an ipython interactive environment.
If used outside of ipython, it will generate html files and open
the default browser with the created visualizations.Visualizationsvis.js timelineA library for rich interactive timelines.
Renders a vis.js timeline component. Seehttp://visjs.org/docs/timeline/fromvislib.visjsimporttimelinetimeline([{'text':'first item','start':'2018-05-21 17:30:08.202'},{'text':'second item','start':"2018-05-21 17:31:53.712"},{'text':'third item','start':"2018-05-21 18:14:05.843"}],content_field='text',title='example1')TauchartsA d3 based charting library that elegantly mixes power and simplicity.
Renders a basic taucharts chart with the given data. Seehttps://www.taucharts.com/fromvislib.tauchartsimportchartchart([{'item':'car','amount':1},{'item':'book','amount':10},{'item':'bed','amount':3}],x='item',y='amount',title='Taucharts example')TreantRenders a Treant.js tree. Seehttp://fperucic.github.io/treant-js/fromvislib.treantimporttreetree({'text':{'name':"Parent node"},'children':[{'text':{'name':"First child"}},{'text':{'name':"Second child"}}]})DevelopmentGenerate Documentationcd docs
 rm -rf _build
 make htmlPublishrm -rf dist
python setup.py sdist bdist_wheel
twine upload dist/*
Package: vislparser
Python VISL ParserPython module for automating calls to the morphosyntactic parser online for portugueseVISL.Getting StartedThe following instructions will get you a copy of the project up and running on your local machine for development and testing purposes.PrerequisitesClone this repoInstallvirtualenvwrapperfollowing the steps listedhereCreate your venv using the command:mkvirtualenv --python=<your_python3.6_installation_dir> <your_env_name>Install the needed modules using the command:pip install -r requirements.pipAfter that you shold be able to work just fine.UsageTo use the module you have just to import and call theparsemethod.Example:importvislparservislparser.parse('Bom dia')# [#     {'word': 'bom', 'stem': 'bom', 'semantic_tags': [], 'word_class_tag': 'ADJ', 'other_tags': 'M S', 'syntatic_tag': '@>N'},#     {'word': 'dia', 'stem': 'dia', 'semantic_tags': ['temp', 'dur', 'per', 'unit'], 'word_class_tag': 'N', 'other_tags': 'M S', 'syntatic_tag': '@NPHR'}# ]Now GO TO WORK!
Package: vismap
VismapProvides a ViewBox for creating maps withVispy.  The view automatically gets its tiles
from aTileProviderbased on the zoom level.
Several tile providers are bundled with this project; adding a tile provider
simply requires knowing the URL at which to grab a tile, and providing the
attribution text for the tiles.  You can add your own data to the map by
creating SceneVisuals and setting the parent to the createdview.scene.Run the providedexampleto check out how interacting with
the map works.  The script is installed asvismap-exampleif you install
vismap; so from your command line just runvismap-exampleUse the left and right arrow keys to change the tile provider.Vismap provides you with a map on which to plot your own data.  An example map:What's the Point?The idea is to be able to display real-time geographical data on top of a
map–for example, radar data.  This project provides the view which
automatically fills the map based on the current zoom level, and also provides
transforms, so you can actually view data on top of the map.  The transforms
are located invismap/transforms.py.Currently, two transforms are provided:MercatorTransformandRelativeMercatorTransform.  Use theMercatorTransformwhen your data is
naturally represented in longitude/latitude pairs. For example, the following
will draw a line from Norman, OK to Oklahoma City, OK:from vismap import Canvas, MercatorTransform
import vispy.scene.visuals as visuals
from vispy import app
import numpy as np

c = Canvas()
c.show()
line = visuals.Line(np.array([[-97.4395, 35.2226], [-97.5164, 35.4676]]),
                    parent=c.view.scene)
line.transform = MercatorTransform()  # the magic line!
app.run()TheRelativeMercatorTransformis for plotting data that is naturally
expressed in units of length, but would be nice if it were centered somewhere
geographically.  The motivating use case is radar data: it makes sense to think
of the data in meters, relative to a specific latitude and longitude.When you are using these transforms, make sure youalways remember to add the
visual to the correct scene, otherwise the data will not show up and it will
probably be confusing.InstallingVismap is available via pip:pip install vismap
Package: vismatrix
Visual MatrixAbout:Visual Matrix - module for python, allowing you to display matrices or tables in a beautiful form.Functional:Create and view tables and matrix;Screenshots:Table visualTable visual (with border)Matrix visualHow to use:fromvismatriximportVisualMatrixheader=["Name","Age","Sex"]m=[["Alexandra","18","Female"],["Igor","21","Male"],["Vladimir","18","No"]]matrix=VisualMatrix()# initializationmatrix.setMatrix(m,header,True)# method in which you can immediately initialize a matrix with header# and without bordersmatrix.myMatrix()matrix.setMatrix(m)# method in which you can immediately initialize a matrix without headermatrix.myMatrix()First wayfromvismatriximportVisualMatrixmatrix=VisualMatrix()# initialization# second way (in this way you cant change no_border parameter)matrix.addHeader(["Name","Age","Sex"])matrix.addRow(["Alexandra","18","Female"])matrix.addRow(["Igor","21","Male"])matrix.addRow(["Vladimir","18","No"])matrix.myMatrix()matrix.addRow(["Alexandra","18","Female"])matrix.addRow(["Igor","21","Male"])matrix.addRow(["Vladimir","18","No"])matrix.myMatrix()Second way-The output from both methods will be the same. You can immediately assign lists to methods (as in the first method with header)addHeader(header)Adds a table header.ParameterTypeDescriptionheaderlistIs the header for the tableaddRow(row)Adds a row table/matrix.ParameterTypeDescriptionrowlistMatrix or table rowsetMatrix(user_matrix, header, no_border)Creates a matrix or table (Depending on whether a header is specified or not).ParameterTypeDescriptionuser_matrixlistMatrix or table`s dataheaderlistTable`s headerno_borderboolUse borders (True - No, False - Yes)myMatrix()Output table or matrix.Must be written at the end of creating a matrix or tableInstall:pipinstallvismatrix
Package: visoptslider
VisOpt SliderQt-based implementation of VisOpt Slider widget [UIST 2014]If your applications are based on Qt (PySide2), it is quite easy to integrate a VisOpt Slider widget into your applications.FeaturesVisOpt Slider is a GUI widget consisting of multiple sliders. It is specifically designed for interactive exploration of a high-dimensional scalar-valued function. It has the following special features.Visualization: VisOpt Slider visualizes the values of the target function along with the sliders in the interface using a colormap.Optimization:Not available yet. Please refer to the original paper(Koyama et al. 2014)and its extended version(Koyama et al. 2016).InstallThis package can be install viapip:pip install visoptsliderBy this, the dependencies (matplotlib,numpy,PySide2, and their dependencies) will be automatically installed together.ExamplefromPySide2.QtWidgetsimportQApplicationimportnumpyasnpimportvisoptsliderif__name__=="__main__":app=QApplication()# Define a target functionnum_dimensions=3deftarget_function(x):return1.0-np.linalg.norm(x)# Define a target boundupper_bound=np.array([+1.0,+1.0,+1.0])lower_bound=np.array([-1.0,-1.0,-1.0])maximum_value=1.0minimum_value=0.0# Instantiate and initialize VisOpt Slidersliders_widget=visoptslider.SlidersWidget()sliders_widget.initialize(num_dimensions=num_dimensions,target_function=target_function,upper_bound=upper_bound,lower_bound=lower_bound,maximum_value=maximum_value,minimum_value=minimum_value)# Show VisOpt Sliderssliders_widget.show()app.exec_()Seehttps://github.com/yuki-koyama/visoptslider/tree/master/python_testsfor more detailed examples.ReferencesYuki Koyama, Daisuke Sakamoto, and Takeo Igarashi. 2014.Crowd-Powered Parameter Analysis for Visual Design Exploration. InProceedings of the 27th annual ACM symposium on User interface software and technology(UIST '14), pp.65-74. DOI:https://doi.org/10.1145/2642918.2647386Project page:https://koyama.xyz/project/CrowdPoweredAnalysis/Yuki Koyama, Daisuke Sakamoto, and Takeo Igarashi. 2016.SelPh: Progressive Learning and Support of Manual Photo Color Enhancement. InProceedings of the 2016 CHI Conference on Human Factors in Computing Systems(CHI '16), pp.2520--2532. DOI:https://doi.org/10.1145/2858036.2858111Project page:https://koyama.xyz/project/SelPh/
Package: visor
VisorMakes background process execution easy. Tested for unix-like operation systems.Features:No external dependenciesMinimal, if you don't want to install you can just copy/paste thecode.Supports loggingInstallationpip3install-UvisorUsagefromvisorimportVisorvisor=Visor()visor.add("sleep 20")visor.add("sleep 20 && echo done")visor.show()# print all added processesvisor.kill_all()# send SIGKILL to all running processedvisor.terminate_all()# send SIGTERM to all running processedvisor.active# List of all `added` processes# Get log files for first added process (sleep 20)stdout_path,stderr_path=visor.get_log_files(visor.active[0])# Wait for all processes to exitvisor.wait()
Package: viso-sdk-python
viso-sdk-pythonviso-sdk-pythonis a utility forviso.aicontainers.InstallationUsepipto install the latest stable version ofviso-sdk-python:pipinstallviso-sdk-pythonBuildpython3-mpipinstall-e.
python3setup.pysdistbdist_wheel# pip3 install setuptools-cythonize# pip3 install setuptools# pip3 install --upgrade pip-removebuildfilesbeforepushingcdviso_sdk
find.-typef-name"*.c"-delete
find.-typef-name"*.so"-deleteBuild sphinx document# setup dependencies for sphinx autodoc extensionspip3install-rrequirements-docs.txt#mkdirdocscddocs# run script for generating autodocsphinx-apidoc-e-P-f-osource/../viso_sdk
makehtml# execute html pagecddocs/_build/index.html
Package: visplotlib-test
visplotlibStandardized plot styling for Visium SAInstallationTo plot withvisplotlib, you need to install the library into your environment:pipinstallvisplotlibYou can then use the following import statements:importvisplotlibasvplfromvisplotlib.pyplotimportmpl,plt# <-- Wrapped Matplotlib and Pyplotfromvisplotlib.seabornimportsns# <-- Wrapped Seabornfromvisplotlib_test.plotlyimportgo,px# <-- Wrapped PlotlyExampleWe want to make your plots beautiful. We don't want to change your coding practices with long and annoying procedures.All you need to do is callplt.format()(for matplotlib and seaborn) when making a plot. That's it.fig,ax=plt.subplots()iris=sns.load_dataset('iris')sns.scatterplot(data=iris,x='sepal_length',y='petal_length',ax=ax)ax.set_title('Sepal vs. Petal length')plt.format()# All we need to do is call plt.format()!plt.show()orplotly.format()(for plotly)df=px.data.iris()fig=px.scatter(df,x="sepal_width",y="sepal_length",color="species",title="A Plotly Express Figure")plotly.format(fig)# All we need to do is call plt.format()!fig.show()Loom demonstrationhttps://user-images.githubusercontent.com/32436482/124758979-6c65cd00-df2f-11eb-8b3e-fc156d441913.mp4
Package: visread
visreadA set of tools to help read and process interferometric visibilities.For more information, see theDOCUMENTATIONIf you find this piece of code useful, please cite@software{ian_czekala_2021_4432520,
author       = {Ian Czekala and
                Loomis, Ryan and
                Andrews, Sean and
                Huang, Jane and
                Rosenfeld, Katherine},
title        = {MPoL-dev/visread},
month        = jan,
year         = 2021,
publisher    = {Zenodo},
version      = {v0.0.1},
doi          = {10.5281/zenodo.4432501},
url          = {https://doi.org/10.5281/zenodo.4432501}
}
Package: visrl
VisrlVisrl (pronounced "visceral") is a simple wrapper to analyse and visualise reinforcement learning agents' behaviour in the environment.Reinforcement learning requires a lot of overhead code to inspect an agent's behaviour visually, typically throughenv.render(). Visrl allows users to easily intervene and switch between agent control and human control, and allows inserting a breakpoint in the game state to pause only at a relevant state of interest.FeaturesSet action hotkeysHuman intervention: Take actions 1 step at a timeAgent control: Return control to the agentSpeed up/ slow down frame rateVisualise relevant values across historyBreakpoint: Run until a condition involving values is fulfilledPlayback: Show past frames and ationsRecord: Record a .mp4, .gif or download a .csv of the history.InstallpipinstallvisrlUsageimportgymfromstable_baselines3importDQNfromvisrlimportVisrlenv=gym.make('LunarLander-v2')agent=DQN('MlpPolicy',env,verbose=1)agent.learn(total_timesteps=int(2e5))Visrl(env,agent).run()