Saravanan666/pypi-org huggingface dataset:
Package: adium-sh
Adium Shell (adium-sh) is a command-line tool and Python wrapper for Adium.Descriptionadium-sh provides shell utilities and Python wrapper based onAppleScript supportof Adium.FeatureThe current features are:Set default service and accountSend messages using exact account name or aliasReceive and reply to messages using patterns or external API (SimSimi currently supported)React to eventsInstallation$ pip install adium-shUsageYou must specify the account and service to associate with the current use, either as command-line arguments or in the config file. When specifying them as arguments, you must put them before the sub-commands.Send messagesSend a message using account name:$ adiumsh -s GTalk -t yourname@gmail send -b buddy@gmail.com
Hello, there
<<EOFSend a message using alias:$ adiumsh -s GTalk -t yourname@gmail.com send -a 'John Smith'
Hello, there
<<EOFSet default configuration file at~/.adiumsh:[default]
service = GTalk
account = yourname@gmail.comThen you can send messages without specifying-s/--serviceand-t/--account:$ adiumsh send -a 'John Smith'You can also pass as argument your message:$ adiumsh send -a 'John Smith' -m 'Hello, there'Receive messagesYou must specify a chat method to receive messages. By default, adium-sh uses “Simple Chat”, which basically replies to received messages according to the patterns you set. You must set the patterns in the config file, possibly like the following settings:[default]
service = GTalk
account = yourname@gmail.com

[chat-default]
type = wildcard
patterns =
    *hello*: hi
    *what*: sorry
    *: I'm not available nowThen, you can invoke the “receive” sub-command with the-c/--chatarguments:$ adiumsh receive -c defaultThe patterns is a list of string pairs where each pair is separated by a colon. The string to the left of the colon is the pattern against which the received text will be matched, and the right one is the corresponding reply text. There is also a “type” option in the chat section, which defaults to “wildcard” that uses globbing pattern matching; another value to it is “regex”, which uses regular expression.You can also use “SimSimi Chat” which hits the SimSimi API with the messages received. You have to set the API key in the config file and the key type (“trial”, which is default, or “paid”):[chat-simi]
simi-key = some-really-long-key
simi-key-type = trialThen, invoke “receive” with this chat from command line:$ adiumsh receive -c simiSet the default chat in the default settings:[default]
service = GTalk
account = yourname@gmail.com
chat = default

[chat-default]
patterns =
    *hello*: hi
    *what*: sorry
    *: I'm not available now

[chat-another]
patterns =
    *: not hereNow you can also switch between chats from the command line other than the default:$ adiumsh receive -c anotherTODOComplete Python wrapper API to AppleScript supportExhaustive commands based on the wrapper
Package: adi.workingcopyflag
This package extends all Archetypes of a Plonesite with a boolean field, that will be set to true if a workingcopy was created for an item via plone.app.iterate.This way, it is possible to have this information availiable as a criterion in a collection (old-style collections, a.k.a. topics).It’s friendly name is ‘Has workingcopy’.The field is set to hidden, we don’t want it in the UI of an item.Changelog1.3 (2012-11-19)Corrected namespace in MANIFEST.in [ida]1.3 (2012-11-19)Added MAINIFEST.in [ida]1.2 (2012-11-19)try again with different pypirc-config [ida]1.1 (2012-11-19)configure.zcml and profiles are missing in pypi-release for unknown reason,
let’s try to make it work with this release [ida]Better description [ida]1.0 (2012-11-01)Initial release [ida]
Package: adj-dataparrots-5
Copyright (c) 2021 The Python Packaging AuthorityPermission 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: adjectiveanimalnumber
AdjectiveAnimalNumberThis is a port in Python of theadjectiveadjectiveanimalInstallationpipinstalladjectiveanimalnumberUsageCLIusage: adjectiveanimalnumber [-h] [-a ADJS] [-s SEP] [-n NUM] [-i INF] [-u SUP]                                                                                                                                                                                     

Generate a random adjective with an animal name and a random integer.

optional arguments:
  -h, --help            show this help message and exit
  -a ADJS, --adjs ADJS  Number of adjectives to generate
  -s SEP, --sep SEP     Separator between adjectives and animal name
  -n NUM, --num NUM     Add a random integer to the end of the string
  -i INF, --inf INF     Inferior limit for random integer
  -u SUP, --sup SUP     Superior limit for random integerExamples:adjectiveanimalnumber-a2-n1-i0-u100adjectiveanimalnumber-a3-s"_"-i0-u1024Pythonfromadjectiveanimalnumberimportgenerategenerate()
Package: adjsim
Designed and developed by Sever TopanNavigationFeature AbstractInstallation InstructionsTutorialClass DocumentationFeaturesEngineAt its core, AdjSim is an agent-based modelling engine. It allows users
to define simulation environments through which agents interact through
ability casting and timestep iteration. The framework is targeted
towards agents that behave intelligently, for example a bacterium
chasing down food. However, the framework is extremely flexible - from
enabling physics simulation to defining an environment in whichConway’s Game of
Lifeplays
out! AdjSim aims to be a foundational architecture on top of which
reinforcement learning can be built.Graphical Simulation RepresentationThe simulation can be viewed in real time as it unfolds, with graphics
are rendered and animated using PyQt5. Below are four of the distinct
examples packadged with AdjSim, ranging from bacteria to moon system
simulation.Post Simulation Analysis ToolsAgent properties can be marked for tracking during simulation, allowing
for viewing the results of these values once the simulation completes.
For example, we can track the population of each different type of
agent, or the efficacy of the agent’s ability to meet its intelligence
module-defined goals.|||
|:————-:|:————-:|Intelligence ModulePerhaps the most computationally interesting aspect of AdjSim lies in
its intelligence module. It allows agents to set goals (for example, the
goal of a bacterium may be to maximize its calories), and assess its
actions in terms of its ability to meet its goals. This allows the
agents to learn which actions are best used in a given situation.
Currently the intelligence module implementsQ-Learning, but more
advanced reinforcement learning techniques are coming soon!
Package: adjsoned
AdjsonedThis Python library can help you easily load your program's configuration/properties
from a json file to a Python runtime object making key-value data accessible via
python's "." syntax—as these key-value pairs were fields of this object.Installationpip install adjsonedGet startedInstantiate a FileJsonProperties object giving it your config file's path and it will just work:fromadjsonedimportFileJsonPropertiesproperties=FileJsonProperties(filepath="examples/some_properties.json")# We can now access key-value/arrays data stored in our JSON file the following way:# (NB: the ROOT element of your JSON file MUST be a DICT!)ifproperties.debug_mode:print("We're in debug mode!")print("P.S. Properties told me that :)")# 'properties' is an instance of FileJsonProperties (as well as JsonProperties — a parent class)# 'properties.app_version' is also a JsonProperties instance, so we can access its fields like that:print(properties.app_version.code)# prints '4.1.2'# arrays are interpreted as regular Python lists, you can just normally iterate through them:forsectioninproperties.project_settings.ignored_sections:print("Ignoring section",section)# if an array element is a dictionary, it gets interpreted as a JsonProperties object as well:print(properties.messages[1].title)# prints: 'Hello again.'The JSON file used in this example:{"debugMode":true,"appVersion":{"code":"4.1.2","build":2},"projectSettings":{"frameRate":60,"ignoredSections":[2,6,9]},"messages":[{"title":"Hello!","description":"This is the first message."},{"title":"Hello again.","description":"This is the second one. Nice to see you again!"}]}
Package: adjspecies
TheadjspeciesPython module generates random names formed from
an animal and a descriptor.Installation$pipinstall-UadjspeciesUsageFrom the command line$adjspecies--helpusage:adjspecies.py[-h][--maxlenMAXLEN][--sepSEPARATOR][--countCOUNT][--prevent-stutter]Printthenameofarandomadjective/species,moreorless…optionalarguments:-h,--helpshowthishelpmessageandexit--maxlenMAXLENMaximumlengthforthename,excludinganyseparator.(default=8)--sepSEPARATORSeparatorbetweentheadjectiveandspecieswords.(default='')--countCOUNTNumberofadjective/speciescombinationstoprint.--prevent-stutterPreventthesameletterfromappearingonanadjective/speciesboundary.(default=True)$pythonadjspecies.py--count4sillyfoxredpigpinkdogelynxpawIn Python code>>>importadjspecies>>>help(adjspecies.random_adjspecies)Helponfunctionrandom_adjspeciesinmoduleadjspecies:random_adjspecies(sep='',maxlen=8,prevent_stutter=True)Returnarandomadjective/species,separatedby`sep`.Thekeywordarguments`maxlen`and`prevent_stutter`arethesameasfor`random_adjspecies_pair`,butnotethatthemaximumlengthargumentisnotaffectedbytheseparator.>>>adjspecies.random_adjspecies('.',7)'wolf.toy'AboutWhile writing a deployment system targettingDigitalOceanDroplets,
the author found the largest bottleneck was finding names for the transient
test servers.The adjective/species contrivance comes from the furry culture in general
and more directly from the site[adjective][species]. It provides a
wide namespace of easy-to-remember randomness.Everything up until the initial commit was an exercise in yak shaving and
procrastinating getting out of bed.CreditsTheadjspeciesmodule is written and maintained byAdam Wright,
who plays a cheetah on Twitter under the guise of@chipikat, a Python
developer called@pypikatand a human being named@hipikat.
Package: adjspecies3
Print the name of a random adjective/species, more or less…Home-page:http://github.com/tavallaie/adjspecies/Author: Ali Tavallaie
Author-email:a.tavallaie@gmail.comLicense: BSD 2-Clause
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 5 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Package: adjugate
adjugateA package for calculating submatricies, minors, adjugate- and cofactor matricies.importnumpyasnpfromadjugateimportadjM=np.random.rand(4,4)adjM=adj(M)M,adjM(array([[0.46445212, 0.90776864, 0.5617845 , 0.46365445],
        [0.23096052, 0.37817574, 0.19684724, 0.26416446],
        [0.8157284 , 0.35109541, 0.29357478, 0.63978973],
        [0.97616731, 0.71221494, 0.18073805, 0.7949908 ]]),
 array([[ 0.04081465, -0.13855441,  0.00220198,  0.0204637 ],
        [ 0.01427584, -0.02328107, -0.02248499,  0.01750541],
        [ 0.01310092, -0.00671938,  0.02476545, -0.02533861],
        [-0.06588407,  0.19251526,  0.01180968, -0.02407058]]))InstallationpipinstalladjugateUsageThis package provides four functions:submatrixsubmatrix(M, i, j)Removes thei-th row andj-th column ofM. Multiple indices or slices can be provided.fromadjugateimportsubmatrixM=np.arange(16).reshape(4,4)M,submatrix(M,1,2)(array([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11],
        [12, 13, 14, 15]]),
 array([[ 0,  1,  3],
        [ 8,  9, 11],
        [12, 13, 15]]))minorminor(M, i, j)Calculates the (i,j) minor ofM.fromadjugateimportminorM=np.random.rand(4,4)minor(M,1,2)-0.2140487789380897adjadj(M)Calculates the adjugate ofM.M,adj(M)(array([[0.08665864, 0.99394655, 0.12535963, 0.26405097],
        [0.5579125 , 0.86322768, 0.89414941, 0.568058  ],
        [0.67013036, 0.84454395, 0.47131153, 0.19339756],
        [0.66467323, 0.85295762, 0.13306573, 0.5569822 ]]),
 array([[-0.23323852, -0.12057808,  0.24929258,  0.14698786],
        [ 0.21400119, -0.06933021,  0.09234215, -0.06280701],
        [-0.03500821,  0.21404878,  0.04025053, -0.21568468],
        [-0.04102134,  0.19892593, -0.44852065,  0.29057721]]))cofcof(M)Calculates the cofactor matrix ofM.fromadjugateimportcofM,cof(M)(array([[0.08665864, 0.99394655, 0.12535963, 0.26405097],
        [0.5579125 , 0.86322768, 0.89414941, 0.568058  ],
        [0.67013036, 0.84454395, 0.47131153, 0.19339756],
        [0.66467323, 0.85295762, 0.13306573, 0.5569822 ]]),
 array([[-0.23323852,  0.21400119, -0.03500821, -0.04102134],
        [-0.12057808, -0.06933021,  0.21404878,  0.19892593],
        [ 0.24929258,  0.09234215,  0.04025053, -0.44852065],
        [ 0.14698786, -0.06280701, -0.21568468,  0.29057721]]))SpeedIf you know that you matrix is invertible, thennp.linalg.det(M) * np.linalg.inv(M)might be a faster choice (O(3) instad of O(5)). But this is in general, especially as the determinant approaches zero, not possible or precise:importmatplotlib.pyplotaspltN=20dets,errs=[],[]for_inrange(10000):M=np.random.rand(N,N)dets+=[np.linalg.det(M)]errs+=[np.linalg.norm(adj(M)-np.linalg.det(M)*np.linalg.inv(M))]fig,ax=plt.subplots()ax.scatter(dets,errs,marker='o',s=(72./fig.dpi)**2)ax.set_yscale('log')ax.set_xlim(-2,+2)ax.set_ylim(1e-16,1e-12)ax.set_xlabel('det')ax.set_ylabel('err')plt.show()
Package: adjustable-random-package
More adjustable random functionsGenerating adjustable random values,
from min to max with numbers to adjust.For example, we have values from 1 to 10.If number 2 and 3 will be in numbers to adjust with k=5,
final array will look like this:[1,2,2,2,2,2,3,3,3,3,3,4,5,6,7,8,9,10]and chance of random.choice for those values
will be increasedExamplesRun basic functionsfrom adjustable_random import (
    get_adjustable_random_list,
    get_adjustable_random_value
)

if __name__ == '__main__':

    adjustable_list: list[int] = get_adjustable_random_list(
        min_value=0,
        max_value=100,
        numbers_to_adjust=[
            10,15
        ]
    )
    adjustable_value: int = get_adjustable_random_value(
        min_value=0,
        max_value=100,
        numbers_to_adjust=[
            10,15
        ]
    )

    print(f"{adjustable_list=}")
    print(f"{adjustable_value=}")Run tests to see how it worksfrom adjustable_random import (
    init_random_values,
    run_graph_test
)

if __name__ == '__main__':
    values: list[int] = init_random_values()
    run_graph_test(values)
Package: adjustor
AdjustorHome of the Adjustor TDP plugin for Handheld Daemon.
Adjustor currently allows for TDP control of all AMD Handhelds past generation
6### (support is added manually).
Since it integrates with Handheld Daemon, it is available throughDecky,
and throughhhd-ui.Adjustor implements a reversed engineered version of AMD's vendor function for
setting TDP on demand in Ryzen processors, through ACPI.
This means that it can be used regardless of the current memory policy
or secure-boot/lockdown status (provided the moduleacpi_callis installed.).In addition, it fully implements Lenovo's WMI protocol for the Legion Go, allowing
setting the TDP, including boosting behavior, without interference from
the Embedded Computer.
As part of the latest Lenovo bios, it also allows for setting a custom fan curve
for the Legion Go.AMD TDP ControlAdjustor controls TDP through the Dynamic Power and Thermal Configuration Interface
of AMD, which exposes a superset of the parameters that can be currently found inRyzenAdj, through ACPI.
This vendor interface is part of the ACPI ASL library, and provided through the
ALIB method 0x0C.
The underlying implementation of the interface is SMU calls.
This means that as long as the kernel moduleacpi_callis loaded, Adjustor
can control TDP in an equivalent way toRyzenAdj.Right now, Adjustor only implements a subset of useful ALIB parameters that are
well documented.
In addition, ALIB does not provide a way for reading the performance metrics table,
so Adjustor can only write (not read) TDP values.
From reverse engineering the Legion Go (seehere), and seeing how it
interacts with ALIB, it was found that there are at least 10 parameters which control
the method STTv2 and are not part of RyzenAdj or have been documented elsewhere.InstallationAdjustor is available onAURand provided Handheld Daemon has been installed throughAURtoo, it will load it automatically
on restart.
COPR coming soon.Alternatively, on a local install of Handheld Daemon you may:~/.local/share/hhd/venv/bin/pipinstall--upgradeadjustorHowever, the autoupdater in Handheld Daemon does not support updating yet.DevelopmentInstall to the same virtual environment as hhd to have Adjustor picked up
as a plugin upon restart, or to its own venv to use independently.pipinstall-e.
Package: adjustpy
adjustpya simple utility python module to calculate adjusted p-values.IntroductionI was tired to copying over the same function between python scripts so
I decided to write this into a simple utilty you can install via pip.The computation is done with myadjustprust crate which I created to replicate the same p-value corrections done in R.InstallationYou can install this python module using pip.pipinstalladjustpyUsageThis is a single-function library which expects any dimension numpy arrays.1 Dimensional Inputimportnumpyasnpfromadjustpyimportadjustp_values=np.array([0.02,0.05,0.08,0.11,0.04])q_values=adjust(p_values,method="bh")# array([0.08333333, 0.08333333, 0.1       , 0.11      , 0.08333333])print(q_values)2 Dimensional InputThis works with multidimensional input as well, but will return a 1D output.
This is easy to work around though as you can just reshape to your original
input shape.importnumpyasnpfromadjustpyimportadjustp_values=np.random.random((10,10))q_values_flat=adjust(p_values,method="bh")q_values=adjust(p_values,method="bh").reshape(p_values.shape)# (100,)print(q_values_flat.shape)# (10, 10)print(q_values.shape)
Package: adjustText
adjustText - automatic label placement formatplotlibInspired byggrepelpackage for R/ggplot2 (https://github.com/slowkow/ggrepel)Alternative:textallochttps://github.com/ckjellson/textallocBrief descriptionThe idea is that often when we want to label multiple points on a graph the text will start heavily overlapping with both other labels and data points. This can be a major problem requiring manual solution. However this can be largely automatized by smart placing of the labels (difficult) or iterative adjustment of their positions to minimize overlaps (relatively easy). This library (well... script) implements the latter option to help with matplotlib graphs. Usage is very straightforward with usually pretty good results with no tweaking (most important is to just make text slightly smaller than default and maybe the figure a little larger). However the algorithm itself is highly configurable for complicated plots.Getting startedInstallationShould be installable from pypi:pip install adjustTextOr withconda:conda install -c conda-forge adjusttextFor the latest version from github:pip install https://github.com/Phlya/adjustText/archive/master.zipDocumentationWikihas some basic introduction, and more advanced usage examples can be foundhere.Thanks to Christophe Van Neste @beukueb,adjustTexthas a simple documentation:http://adjusttext.readthedocs.io/en/latest/CitingadjustTextTo cite the library if you use it in scientific publications (or anywhere else, if you wish), please use the link to the GitHub repository (https://github.com/Phlya/adjustText) and a zenodo doi (see top of this page). Thank you!
Package: adjutant-odoo
Adjutant-Odoo is a plugin for Adjutant which adds a few actions and views
specific to the Odoo ERP system. These views can then be setup as active for
your users, and the actions can be used with your existing taskviews. Or just
as easily extend these views and actions for your own development.InstallingTo install:python setup.py installorpip install adjutant-odooAfter installation is complete addodoo_actionsandodoo_viewsto your
ADDITIONAL_APPS in the Adjutant conf.You can then use the Odoo actions as part of your Adjutant workflows, and
setup the Odoo views from this package in your ACTIVE_TASKVIEWS. For example
to introduce signups backed to Odoo you’d replace your other signup view in
ACTIVE_TASKVIEWS withOpenStackSignUp.You will also need to add some taskview settings for the new signups view:signup:
    additional_actions:
        - NewProjectDefaultNetworkAction
    notifications:
        standard:
            EmailNotification:
                emails:
                    - signups@example.com
            RTNotification:
                queue: signups
        error:
            EmailNotification:
                emails:
                    - signups@example.com
            RTNotification:
                queue: signups
    default_region: RegionOne
    # If 'None' (null in yaml), will default to domain as parent.
    # If domain isn't set explicity, will use Adjutant's admin user domain.
    default_domain_id: default
    default_parent_id: null
    setup_network: TrueOnce active, and if debug is turned on, you can see the endpoint and test it
with the browsable django-rest-framework api.You will also need to add ‘adjutant-odoo’ plugin settings:PLUGIN_SETTINGS:
    adjutant-odoo:
        odoorpc:
            odoo:
                hostname: <odoo_hostname>
                protocol: jsonrpc+ssl
                port: 443
                version: <odoo_version>
                database: <odoo_db_name>
                user: <odoo_username
                password: <odoo_password>
Package: adk
ADKA Develop Kit, for easy coding.Develop Tools:RPCConsole Commands: adk/adk.d config/start, details see "adk -h" for helpRPC config: adk.set_rpc_config, adk.get_rpc_config, adk.rpc_connect_argsadk.TaskManager, implemented methods ['set', 'get', 'clear', 'get_work_que', 'get_res_que', 'run']adk.start_rpc_server, base class is multiprocessing.managers.BaseManageradk.start_rpc_worker, get task from queue(get_work_que()), put result to queue(get_res_que())adk.rpc_client, get rpc client. ['set', 'get', 'clear'] for cache data, 'run' for submit task to serveradk.clt, a rpc client instance like above, for convenienceUtilsadk.TaskQue, Task Queue with key-value mapping, priority, speed limit, timeout and callbacks.adk.PriQue, Priority Queue with mutex lock and speed limit.adk.PriDict, Priority Dict with mutex lock and speed limit.Data types:Priority containersextension of heap, main methods are: push, pop, peekadk.PriorityQueue, Priority Queue, push(data, priority)adk.SimplePriorityQueue, Priority Queue, push(priority)adk.PriorityDict, Priority Dict, push(key, data, priority)adk.SimplePriorityDict, Priority Dict, push(key, priority)