Saravanan666/pypi-org huggingface dataset:
Package: overboard-logger
OverBoardOverBoard is a lightweight yet powerful dashboard to monitor your experiments.FeaturesA table ofhyper-parameterswith Python-syntax filtering.Multiple views of the same data (i.e.custom X/Y axes).Hyper-parameter visualisation (i.e.bubble plots).Percentile intervals for multiple runs (i.e.shaded plots).Custom visualisations (tensors and any custom plot with familiarMatPlotLibsyntax).Fastclient-side rendering (the training code is kept lightweight).InstallationYou can install the dependencies with:With Conda:conda install pyqt=5.12 pyqtgraph=0.12 -c conda-forgeWith pip:pip install pyqt5==5.12 pyqtgraph==0.12If you intend to use custom 3D plots withPyQtGraph, add PyOpenGL 3.1 to the lists above.Finally, OverBoard itself can be installed with:pip install overboardInstallation - logger onlyYour scripts can log data without installing the full GUI and its dependencies (so your remote GPU cluster does not need PyQt at all).Just use:pip install overboard_loggerAnd remember to importoverboard_loggerinstead ofoverboardin your scripts.UsageMain interface:python3 -m overboard <logs-directory>Logging experiments is simple:fromoverboardimportLoggerwithLogger('./logs')aslogger:foriterationinrange(100):logger.append({'loss':0,'error':0})You can also pass in ametakeyword argument, which can be adictwith hyper-parameters names and values (or other meta-data), to help organize your experiments. These will be displayed in a handy table, which supports sorting and filtering. Themetadata can also be anargparse.Namespace, which is useful if your hyper-parameters are command-line arguments parsed withargparse.By default a unique folder (using the current timestamp) is created for the logs. For full documentation on initialization arguments and other methods, typepydoc overboardon the command-line (Python built-in doc viewer).You can also check theexamplesdirectory:examples/synthetic.py: A minimal example. Generates some test logs.examples/mnist.py: The mandatory MNIST example. Also includes custom MatPlotLib plots.A note about importing: You can either import theLoggerclass fromoverboardor fromoverboard_logger. If you installed the "logger only" version as described above (no dependencies), then you can only import fromoverboard_logger.Remote experimentsThe easiest way to monitor remote experiments is to mount their directory over SFTP, and point OverBoard to it.Tested with:SSHFS(Linux, available in most distros),FUSE(Mac),SFTP NetDrive(Windows).Since most of these don't allow OverBoard to monitor log files with the default light-weight method, the plots may not update automatically; in that case use the command-line argument--force-reopen-files.Depending on the remote server's configuration (e.g. firewall settings), you might need to use a VPN to tunnel to the server's network, to ensure that the right ports are not blocked to you (i.e. having SSH access does not guarantee SFTP access from an external network).AuthorJoão Henriques,Visual Geometry Group (VGG), University of Oxford
Package: overcast-parser
Overcast URL to podcast informationPython library to obtain RSS feed URL from Overcast link, built especially for PythonistaInstallation / UpgradeThis can be installed one of two ways:First, it can be installed directly in Pythonista withStaShpip install overcast-parserAlternately, you can copy this entire repo to Pythonista and start immediately editing demo.py. This can be done i.e. with a git tool in Pythonista or, on your Mac, copying this to the Pythonista3 iCloud directory.UsageSee demo.py for full pythonista scriptfrom overcast_parser.OvercastParser import OvercastParser


parser = OvercastParser()

data = requests.get(overcast_url).text

(itunes_id, stream_url, overcast_id, title, feed_url) = parser.parse_overcast(data)Shortcutdemo.py is meant to be used from either the share extension or from an iOS Shortcut. (Note that due to Pythonista limitations share extension cannot call a script in iCloud.)You candownload a shortcut hereor create one like:Where the final URL block is like (adjusted for your path or removing root=icloud)pythonista3://overcast_parser/demo.py?action=run&root=icloud&args=URL Encoded TextBuilt Withpythonista-stubs- for desktop developmentitunes_podcast_rss- obtain RSS feed URL from iTunes link (built-in)podcastparser- for parsing podcast feed (dependency)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.AuthorsHarold Martin- harold.martin at gmailLicenseMIT
Package: overcooked-ai
Overcooked-AI5 of the available layouts. New layouts are easy to hardcode or generate programmatically.IntroductionOvercooked-AI is a benchmark environment for fully cooperative human-AI task performance, based on the wildly popular video gameOvercooked.The goal of the game is to deliver soups as fast as possible. Each soup requires placing up to 3 ingredients in a pot, waiting for the soup to cook, and then having an agent pick up the soup and delivering it. The agents should split up tasks on the fly and coordinate effectively in order to achieve high reward.You cantry out the gamehere(playing with some previously trained DRL agents). To play with your own trained agents using this interface, you can usethis repo. To run human-AI experiments, check outthis repo. You can find some human-human gameplay data already collectedhere.Check outthis repofor the DRL implementations compatible with the environment and reproducible results to our paper:On the Utility of Learning about Humans for Human-AI Coordination(also see ourblog post).InstallationInstalling from PyPIYou can install the pre-compiled wheel file using pip.pip install overcooked-aiNote that PyPI releases are stable but infrequent. For the most up-to-date development features, build from sourceBuilding from sourceIt is useful to setup a conda environment with Python 3.7 (virtualenv works too):conda create -n overcooked_ai python=3.7
conda activate overcooked_aiClone the repogit clone https://github.com/HumanCompatibleAI/overcooked_ai.gitFinally, use python setup-tools to locally installpip install -e overcooked_ai/Verifying InstallationWhen building from source, you can verify the installation by running the Overcooked unit test suite. The following commands should all be run from theovercooked_aiproject root directory:python testing/overcooked_test.pyIf you're thinking of using the planning code extensively, you should run the full testing suite that verifies all of the Overcooked accessory tools (this can take 5-10 mins):python -m unittest discover -s testing/ -p "*_test.py"Code Structure Overviewovercooked_ai_pycontains:mdp/:overcooked_mdp.py: main Overcooked game logicovercooked_env.py: environment classes built on top of the Overcooked mdplayout_generator.py: functions to generate random layouts programmaticallyagents/:agent.py: location of agent classesbenchmarking.py: sample trajectories of agents (both trained and planners) and load various modelsplanning/:planners.py: near-optimal agent planning logicsearch.py: A* search and shortest path logicPython VisualizationsOne can adapt a version ofthis filein order to be able to play games in terminal graphics with custom-defined agents.Further Issues and questionsIf you have issues or questions, don't hesitate to contact eitherMicah Carrollatmdc@berkeley.eduorNathan Milleratnathan_miller23@berkeley.edu
Package: overdub
Load and merge nested configurations.UsageInstallation:python -m pip install overdubWith these structures:from overdub import MutableOverdub

a = {'foo': 1, 'bar': {'baz': 2}, 'qux': {'one': 1}}
b = {'foo': 3, 'bar': {'baz': 4}, 'qux': {'two': 2}}Update:overdubbed = MutableOverdub(a)
overdubbed.update(b)
assert overdubbed.foo == 3
assert overdubbed.bar.baz == 4
assert overdubbed.qux == {'two': 2}Merge:overdubbed = MutableOverdub(a)
overdubbed.merge(b)
assert overdubbed.foo == 3
assert overdubbed.bar.baz == 4
assert overdubbed.qux == {'one': 1, 'two': 2}Rebase:overdubbed = MutableOverdub(a)
overdubbed.rebase(b)
assert overdubbed.foo == 1
assert overdubbed.bar.baz == 2
assert overdubbed.qux == {'one': 1, 'two': 2}Unbound the data:overdubbed = MutableOverdub(a)
assert isinstance(overdubbed, Overdub)
naked = MutableOverdub(a)
assert not isinstance(naked, Overdub)Freeze configuration:overdubbed = overdubbed.frozen()YAML filesIt can also read configuration from yaml files, for this install:python -m pip install overdub[yaml]And then, merge all files:from overdub import yaml

overdubbed = yaml.load_from_file('a.yml', b.yml')
assert overdubbed.foo == 3
assert overdubbed.bar.baz == 4
assert overdubbed.qux == {'one': 1, 'two': 2}
Package: overhang
The Edinburgh Genome Foundry’s compendium of overhangs.
This Python package generates a description of an input set of DNA overhangs and outputs a PDF file.
Additionally, it evaluates suitability of an input set of overhangs for Golden Gate DNA assembly.Install:pipinstalloverhangWeb documentation:https://edinburgh-genome-foundry.github.io/Overhang/Github page:https://github.com/Edinburgh-Genome-Foundry/OverhangLicense:MIT, Copyright 2021 Edinburgh Genome Foundry, University of EdinburghMore biology softwareOverhang is part of theEGF Codonssynthetic biology software suite for DNA design, manufacturing and validation.
Package: overlab
overlabThis is a function to prevent overlapping when annotating a plot in matplotlib. The algorithm works by starting from the coordinate to annotate and slowly moving the label in a spiral centered in the starting point progressively distancing from it until find a suitable place that is not overlapping any of the other labels.The function is built on top ofax.annotateand takes the same **kwargs. The difference is that differently from ax.annotate that takes as argument  single desired annotation and coordinates, overlab takes as arguments 3 iterables (x, y, and the list of labels) and the target ax).InstallationThe package can be installed withpip install overlabExampleNo overlabimport numpy as np
import matplotlib.pyplot as plt

# Fixing random state for reproducibility
np.random.seed(19680801)


N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2  # 0 to 15 point radii
annotations=[f'label {str(n)}' for n in range(N)]

fig, ax = plt.subplots()
ax.scatter(x, y, s=area, c=colors, alpha=0.5)
for cordx, cordy, label in zip(x, y, annotations):
	ax.annotate(label,(cordx,cordy)) 

plt.show()overlabimport numpy as np 
import matplotlib.pyplot as plt
import overlab as ol

# Fixing random state for reproducibility
np.random.seed(19680801)


N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2  # 0 to 15 point radii
annotations=[f'label {str(n)}' for n in range(N)]

fig, ax = plt.subplots()
ax.scatter(x, y, s=area, c=colors, alpha=0.5)
ol.annoatate(x,y,annotations,ax=ax)

plt.show()
Package: overlay
OverlayA package that creates and manipulates screen overlays based on tkinter.PlatformsMac OS (tested and works)Linux (not tested)Windows (tested and does not work)InstallationpipinstalloverlayUsageA basic overlay is created as such:fromoverlayimportWindowwin=Window()Window.launch()The constructor of theWindowclass takes the following (optional) parameters:size:tuple, the dimension (width, height) of the overlay window.position:tuple, the position of the overlay (on screen).transparent:bool, whether to set the overlay background transparent.alpha:float [0, 1], the alpha (transparency) of the overlay.draggable:bool, whether the window can be draggedNote that the parameters mentioned above can be edited any time as attributes of an instance ofWindow.In order to edit the content of a overlay, one needs to obtain the root of the overlay, upon which all else shall be build.importtkinterastkfromoverlayimportWindowwin=Window()label=tk.Label(win.root,text="Window_0")label.pack()Window.launch()Multiple overlays can be created just as easily:importtkinterastkfromoverlayimportWindowwin_0=Window()label_0=tk.Label(win_0.root,text="Window_0")label_0.pack()win_1=Window()label_1=tk.Label(win_1.root,text="Window_1")label_1.pack()Window.launch()The following program covers a brief explaination on other methods of theWindowclass.importtkinterastkfromtimeimportsleepfromoverlayimportWindowdefother_stuff(text):'''A simple demonstration. The usage of sleep is to emphasize the effects of each action.'''print(text)sleep(2)win_0.hide()# Hides the overlay.sleep(1)win_0.show()# Shows the overlay.sleep(1)win_0.focus()# Sets focus to overlay.win_1.center()# Moves the overlay to the center of the screen.sleep(1)Window.hide_all()# Hides all overlays.sleep(1)Window.show_all()# Shows all overlays.sleep(1)win_0.destroy()# Kills the overlay.sleep(1)Window.destroy_all()# Kills all overlays and ends the mainloop.'''Creates two windows.'''win_0=Window()label_0=tk.Label(win_0.root,text="Window_0")label_0.pack()win_1=Window()label_1=tk.Label(win_1.root,text="Window_1")label_1.pack()Window.after(2000,other_stuff,'Hello World')# Identical to the after method of tkinter.Tk.Window.launch()
Package: overlay4u
Simple way to create an overlayfs in ubuntu within Python.Warnings!overlay4u requires root. Be sure you’re aware of that. It’s still very much a
work in progress so please use with caution and hopefully on VM that you do not
care about.Caveatsoverlay4u is not currently being designed for anything but Ubuntu 12.04. At
this time that’s the first priority.Currently WorkingMounting works! (Tested and works)Creating a table of mounted filesystemsUnmountingUsing overlay4uCreate an overlay at dest:import overlay4u

overlay = overlay4u.mount('dest', 'lower', 'upper')

overlay.unmount()If the destination already has something mounted it won’t mount again:import overlay4u

overlay1 = overlay4u.mount('dest', 'lower', 'upper')

# This will throw an error.
overlay2 = overlay4u.mount('dest', 'lower', 'upper')List all overlays:import overlay4u

overlays = overlay4u.list()
# Overlays is now a list of all the currently mounted overlays on your
# systemGrab a previously mounted overlayfs:import overlay4u

overlay = overlay4u.get('/some_mount_point')That’s all. It’s a relatively simple tool.
Package: overlay-arrows-and-more
Overlay arrays and moreWARNING:This library is still at a very early stage of development.DescriptionThe ‘Overlay arrays and more’ library displays a transparent window on top of all other windows. The transparent window serves as a support for adding graphical elements such as arrows, rectangles, circles, text, etc…Installationpip install overlay_arrows_and_moreUsageThis is a simple example:importoverlay_arrays_and_moreasoaamimporttimemain_overlay=oaam.Overlay()transparent_overlay=oaam.Overlay(transparency=128)transparent_overlay.add(geometry=oaam.Shape.rectangle,x=300,y=300,width=100,height=100,thickness=10,color=(0,255,0))transparent_overlay.refresh()main_overlay.add(geometry=oaam.Shape.ellipse,x=10,y=10,width=40,height=40)main_overlay.add(geometry=oaam.Shape.rectangle,x=100,y=100,width=100,height=100,color=(0,0,255))main_overlay.add(geometry=oaam.Shape.rectangle,x=300,y=100,width=100,height=100,thickness=10,color=(0,255,0))main_overlay.refresh()time.sleep(9.0)main_overlay.clear_all()main_overlay.refresh()FunctionsTo be completed
Package: overlaymodule
OverlayModuleThis module finder makes it possible to load modules with overlays. This
can be useful in cases you try to customize some kind of application in places
at moment that is out of your control.For example, it is useful when you want to customize an application that make
extensive uses of global. This module will allow you to load an overlay over
a module that is being loaded right after the module has been loaded.Let say you have this modulea.b. You'd want to customize the classa.b:Application.
To make sure that something that inherits later froma.b:Applicationwill in fact
inherit fromo.overlay.a.b:Application.When the module is being loaded it will first loada.bthen it will execute the moduleo.overlay.a.bon top of the modulea.b.In short, this is more or less monkey patching on steroid.How to use:For the script using it:import importlib
from overlaymodule import OverlayFinder

module_spec = importlib.util.find_spec('a')
module_path = odoo_spec.submodule_search_locations[0]

sys.meta_path.insert(
    0,
    OverlayFinder(
        'a',
        module_path,
        overlays=[
            "o.overlay",
        ]
    )
)

from a.b import Application, OldApplicationA module overlayo.overlay.a.b# since a.b is already loaded, it's possible to import it within itself!
from a.b import Application as OldApplication

class Application(OldApplication):
    pass
Package: overlincs-git
OverLINCS Git\OverLincs Gitis a git-bridge for theLINCSOverleaf server.It allows to clone, push, and pull between a local repository and theOverleaf server.Under the hood,OverLincs Gitis an unofficial fork of the greatpython-sharelatexmodule that has been patched forOverleaf CE v4.Free software: GNU General Public License v3Documentation:https://balouf.github.io/overlincs-git/.FeaturesProvides aolincsCLI for interaction with theLINCSOverleaf server.olincs clone remote_url local_repo_nameolincs pullolincs pushCreditsThis package was created withCookiecutterand thePackage Helper 3project template.It is based onpython-sharelatex.
Package: overload
Simple overloading of methods and functions through an @overload decorator.This module allows one to provide multiple interfaces for a functions,
methods, classmethods, staticmethods or classes. See below for some notes
about overloading classes, you strange person you.The appropriate implementation is chosen based on the calling argument
pattern.For example:>>> class A(object):
...   @overload
...   def method(self, a):
...     return 'a'
...   @method.add
...   def method(self, a, b):
...     return 'a, b'
...
>>> a = A()
>>> a.method(1)
'a'
>>> a.method(1, 2)
'a, b'The overloading handles fixed, keyword, variable (*args) and arbitrary
keyword (**keywords) arguments.It also handles annotations if those annotations are types:>>> @overload
... def func(a:int):
...   return 'int'
...
>>> @func.add
... def func(a:str):
...   return 'str'
...
>>> func(1)
'int'
>>> func('s')
'str'
>>> func(1.0)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "overload.py", line 94, in f
    raise TypeError('invalid call argument(s)')
TypeError: invalid call argument(s)This feature (and currently the module in general) requires Python 3.The docstring and name (ie. documentation) of the resultant callable will
match that of thefirstcallable overloaded.Overloading ClassesOverloading classes allows you to select a class type based on the
construction arguments of each alternative type’s __new__ method.There’s a catch though: the __new__ method mustexplicitlyinvoke the
base class __new__ method, rather than use super() like usual. This is
because after being @overloaded the class is a function, and super()
doesn’t like being passed functions. So instead of:@overload
class A(object):
    def __new__(cls):
        # this will fail because "A" is a function now
        return super(A, cls).__new__(cls)you must:@overload
class A(object):
    def __new__(cls):
        # must explicitly reference the base class
        return object.__new__(cls)I’ll leave it up to the reader to justify their use of @overloading
classes.Version History (in Brief)1.1 altered the text of the invalid call TypeError. Removed debug prints.1.0 the initial releaseSee the end of the source file for the license of use.I would be interested to know whether this module is useful - if you use it
please indicate so athttps://www.ohloh.net/p/pyoverload
Package: overloader
OverloadAboutUsageAbout"Overload" is a Python 3 package for functions overloading.
"Overload" use pure python3 typing and hasn't requirements.
Support python version >= 3.7.UsageInstall packagepip install overloaderOverloading functions example.To overload function you need importing overload decorator from package
and using it with your overloading target function. Now this function becomes
a default implementation and will be called in the last place or when call
arguments will success compare with it.
For registering new implementation of it, use it "register" method.
Untyped variables be ignored.fromoverloadimportoverload@overloaddefmy_function(var1:int,var2:str):# function logic.print('I am a default function.')@my_function.registerdef_(var1:str,var2:int):# implementation logic.print('I am an implementation.')When you call "overload object", will called implementation with success
compare variables types or default implementation.my_function(1,'string')# stdout: I am a default function.my_function('string',1)# stdout: I am an implementation.Overloading function specific parameters typing.For typing specific parameters, like *args or **kwargs, must use special type
Args and Kwargs.fromtypingimportUnionfromoverloadimportArgs,Kwargs,overload@overloaddefmy_function(*args:Args[int],**kwargs:Kwargs[Union[str,int]]):passIf you ignored special types and set type for it like regular parameter
*args: int, this will interpret not as *args parameter, but args.Overloading function parameters.Overload decorator has two settings:strict (bool, default=True)overlapping (bool, default=False)Strict- activate validation of implementation annotations count
compared overload object annotations.@overload(strict=True)defmy_function(var1:str):...@my_function.registerdef_(var1:str,var2:int):...# Will raised AnnotationCountError.Overlapping- activate registration of implementation with
same annotations as the default overload object.@overload(overlapping=False)defmy_function(var1:str):...@my_function.registerdef_(var1:str):...# Will raised OverlappingError.
Package: overloading
overloadingis a module that provides function and method dispatching
based on the types and number of runtime arguments.When an overloaded function is invoked, the dispatcher compares the supplied
arguments to available signatures and calls the implementation providing the
most accurate match:@overloaddefbiggest(items:Iterable[int]):returnmax(items)@overloaddefbiggest(items:Iterable[str]):returnmax(items,key=len)>>>biggest([2,0,15,8,7])15>>>biggest(['a','abc','bc'])'abc'FeaturesFunction validation during registration and comprehensive resolution rules
guarantee a well-defined outcome at invocation time.Supports thetypingmodule introduced in Python 3.5.Supports optional parameters.Supports variadic signatures (*argsand**kwargs).Supports class-/staticmethods.Evaluates both positional and keyword arguments.No dependencies beyond the standard libraryDocumentationThe full documentation is available athttps://overloading.readthedocs.org/.
Package: overlock
TheOverlockpython library allows logging and instrumentation of
program code as a drop in replacement for python logging, but with some
extensions. Only for use with the Overlock Agent for linux.Seehttps://docs.overlock.io/libraries/python/for instructions.Python versions supported:2.73.43.53.6pypypypy3Using the libraryThe overlock library needs to be ‘installed’ in your code to configure
and provide some details to the Overlock Agent:fromoverlockimportinstallinstall(# The name of the application program, defaults to "unknown"process_name="sensor-reader",# Any custom metadata for the app can be added heremetadata={"version":"2.0.1","hardware_revision":get_hardware_version()},)When installed, it can be used to log information from all parts of your
systemfromoverlockimportOverlockLoggerol=OverlockLogger()# 1 - Import the loggerdefread_sensor():try:value=read_sensor()ol.set_state({"sensor_value":value})# 2 - Use set_state to add state to the storeol.info("got a new reading for the sensor")# 3a - Add a log messagereturnvalueexceptSensorReadExceptionase:ol.set_state({"sensor_value":"unknown"})ol.exception("Error trying to get sensor value!")# 3b - Log an exceptionRunning testsInstall tox:pip install toxRun teststoxNote: you may want to comment out some of the python versions if your
system does not have them installed.