Saravanan666/pypi-org huggingface dataset:
Package: intel-tensorflow-avx512
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google's AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains. TensorFlow is licensed underApache
2.0.
Package: inteltime
This is a simple module for managing checkpoint and cycle information
for Niantic Labs Ingress game.It includes a command-line front end and a flask based daemon for
responding to Slack slash commands.Niantic Labs is not responsible for this program, and neither endorses
nor supports it.  The author is not associated with Niantic Labs and
makes no representations.Installationpip install inteltimeAfter installing, you will end up with a set of applications (mostly used for
testing) and Slack:inteltime- command line utility for testinginteltime-flask- stantalone (for debugging only) flask daemon for SlackUseUse the source, or try theinteltimetest-jig command.  This is mainly an
API, the slack plugin and test-jig are just fun examples.Slack UseThere is rudimentary natural language time/date processing available, and
timezone support.  Timezones are separated by commas.By default, checkpoint will give you the next checkpoint, and cycle will give
you the current cycle./checkpoint
/checkpoint on today, America/Los_Angeles
/checkpoint ,UTC
/checkpoint 2016-05-04,Asia/Bejing

/cycle
/cycle next Monday,UTCApache with mod_wsgi for SlackConfigure Apache with HTTPS and valid (not self-signed) SSL certificates.
Slack requires that your certificates are not self-signed.Configure Apache to serve WSGI files out of of a particular directory,
in this example, we’re using/srv/prod/wsgiWSGIScriptAlias /inteltime /srv/prod/wsgi/inteltime.wsgi
<Directory "/srv/prod/wsgi">
    Require all granted
    SSLOptions +StdEnvVars
    Order allow,deny
    Allow from all
</Directory>Install mod_wsgi and copy the .wsgi file in this module to the directory
where you decided to serve .wsgi files (as above).sudo pip install mod_wsgi
sudo a2enmod mod_wsgi
cp inteltime/examples/inteltime.wsgi /srv/prod/wsgi/inteltime.wsgiRestart apache with the new configuration and modules.systemctl apache2 restartSlackCreate a two slack custom integration slash commands.
(Seehttps://yourteam.slack.com/apps/manage/custom-integrations)command: /checkpoint
url: https://address.of.your.server/inteltime/checkpoint
method: POST

command: /cycle
url: https://address.of.your.server/inteltime/cycle
method: POSTWhen someone uses the/checkpointslash-command, a HTTPS post will be issued
to the URL you specified, which, in turn, will cause Apache to execute
the shim script, which will, in turn, execute the wsgi module.Changelog1.0.4 - Fri May  6 09:54:39 PDT 2016Checkpoint number was off by one.Add home page url to packaging.Improve documentation.Pylint cleanup.
Package: intelurls
This module started as a simple parser for Intel URLs for Niantic Lab’s
Ingress game.  It then grew, like an ugly monster, to support most flavors
of Google Maps and Apple Maps URLs.When a URL is parsed, we return a structure that contains the latitude and
longitude of the place, along with a host of other values, such as a name
for the map, potential zoom levels for the map, and human captions if we
can decipher them from the map service provider.See the source code for the exact return values.Niantic Labs is not responsible for this program, and neither endorses
nor supports it.  The author is not associated with Niantic Labs and
makes no representations.Use>>> from intelurls import check_mapurl, parse_mapurl, parse_natural
>>> from pprint import pprintAn Ingress Intel URL:>>> check_mapurl("this is a string with a url in it https://intel.ingress.com/intel?ll=37.821523,-122.377385&z=17 and more test")
'https://intel.ingress.com/intel?ll=37.821523,-122.377385&z=17'A Google Maps URL with a caption:>>> pprint(parse_mapurl("https://maps.google.com/maps?ll=37.765727,-122.431961&cid=10889150637731333995&q=Beck's%20Motor%20Lodge"))
{'caption': "Beck's Motor Lodge",
 'latlng': [37.765727, -122.431961],
 'name': 'map_@37.765727,-122.431961'}A Google Maps ShortURL:>>> pprint(parse_mapurl("http://goo.gl/maps/r6T6a"))
{'caption': '',
 'latlng': [45.0215057, 14.6293757],
 'name': 'map_@45.0215057,14.6293757'}A natural language location:>>> pprint(parse_natural("San Francisco, CA"))
{'caption': 'San Francisco, CA, USA',
 'latlng': [37.7749295, -122.4194155],
 'name': 'map_@37.7749295,-122.4194155_z13',
 'zoom': 13}For a full list of everything we can parse, look attests/gentest.py,
this list is fairly extensive.TestingThere are so many combinations of URLs, that a regression test jig
was created.  Most of the code is self-contained, however the natural
language location and URLs that need expansion or page scraping need
to interact with a third party service (typically the Google Maps API)
and that data has been known to change over time.A failed test isn’t a reason to not install the module.valid.outis correct at the time of this publication, but may get
out of date if the third party services change or move map items. If
unit test fails, it’s best to examine -why- it failed to see if it is
an actual bug or just a change by a third party.  If you need to, you
may re-createvalid.outwithgentest.pyChangeLog0.0.2:  README.rst formatting
0.0.1:  Original publication
Package: intensio-obfuscator
Intensio-ObfuscatorProject not maintained anymoreThanks to all contributorsWhat is this ?Intensio-Obfsucator tool takes a python source code and transform it into an obfuscated python codeReplaceall names ofvariables/classes/functions/files-nameto random strings with length defined then allcharsto their hexadecimal valueDeleteallcomments, allspaces linesPaddingrandomsnippets code/functions/classeswith an always differents valuesWhat purpose ?Provides a high level obfuscation layer to prevent or delay the reading and understanding of your python programLevel of obfuscationWeak obfuscation if used alone, can be used with other types of obfuscationRequirementsPython >= 3.5requirements.txtFiles supportedFiles written in python 2.x and 3.xDocumentation(https://github.com/Hnfull/Intensio-Obfuscator/blob/master/README.md)LicenseMITContactHnfullgitland@protonmail.comDisclamerIntensio-Obfuscator is for education/research purposes only. The author takes NO responsibility ay for how you choose to use any of the tools provided
Package: intensipy
intensipyNormalize intensity values in 3D image stacks.Current Methods1.Intensify3DPython implementation of the Intensify3D algorithm originally developed byYoyan et al. There are some minor adjustments:Semi-quantile normalization is the only Z-normalization method currently implemented.Pixels that are quantile normalized are optionally smoothed using they Savitzky-Galoy method outlined in the original paper. In practice this was necessary to reduce artefact noise.Tissue detection is not currently supported.By default, contrast stretching is performed byskimage.exposure.rescale_intensity(). To perform contrast stretching as implemented by the originalIntensify3D, setstretch_method='intensify3d'If no maximum background intensity thresholdtis provided,twill be estimated for each slice using Otsu's method.Original Paper ResultsIntensipy ResultsZ-normalization ExampleAverage Intensity ComparisonInstallationClone the repository and from the terminal run:pip install .Exampleimportnumpyasnpimportmatplotlib.pyplotaspltfromintensipyimportIntensify# decreasing average intensity as z increases.img_stack=1/np.arange(1,6)[:,np.newaxis,np.newaxis]\*np.random.randint(0,255,(5,512,512))foreachinimg_stack:plt.imshow(each,vmin=img_stack.min(),vmax=img_stack.max(),cmap='gray')plt.show()model=Intensify()out=model.normalize(img_stack)foreachinout:plt.imshow(each,vmin=out.min(),vmax=out.max(),cmap='gray')plt.show()References1.Yayon, N. et al. Intensify3D: Normalizing signal intensity in large heterogenic image stacks. Scientific Reports 8, 4311 (2018).
Package: intentify
IntentifyA python library for training intent classification models.Setting Up A Basic ModelfromintentifyimportIntentifyfile_path='intents.json'name='test_model'Model=Intentify(file_path,model_name=name)Model.train_model()Model.save_model()done=Falsewhilenotdone:message=input("You: ")ifmessage=="STOP":done=Trueelse:response=Model.request(message)print(response)Binding Functions To RequestsfromintentifyimportIntentifydeffunction_for_greetings():print("Greetings Intent Trigger!")# Some action you want to takedeffunction_for_stocks():print("Stocks Intent Trigger!")# Some action you want to takemappings={'greeting':function_for_greetings,'stocks':function_for_stocks}file_path='intents.json'name='test_model'Model=Intentify(file_path,intent_methods=mappings,model_name=name)Model.train_model()Model.save_model()done=Falsewhilenotdone:message=input("Enter a message: ")ifmessage=="STOP":done=Trueelse:Model.request(message)Once you have saved the model you can simply call "Model.load_model()" instead of ".train_model()" and ".save_model()"Sample intents.json File{"intents":[{"tag":"greeting","patterns":["Hi","How are you","Is anyone there?","Hello","Good day","Whats up","Hey","greetings"],"responses":["Hello!","Good to see you again!","Hi there, how can I help?"],"context_set":""},{"tag":"goodbye","patterns":["cya","See you later","Goodbye","I am Leaving","Have a Good day","bye","cao","see ya"],"responses":["Sad to see you go :(","Talk to you later","Goodbye!"],"context_set":""},{"tag":"stocks","patterns":["what stocks do I own?","how are my shares?","what companies am I investing in?","what am I doing in the markets?"],"responses":["You own the following shares: ABBV, AAPL, FB, NVDA and an ETF of the S&P 500 Index!"],"context_set":""}]}
Package: intent-pilot
⧐ Intent PilotWhat can be said can be solved.Get early access to the PTA model‎ ‎ |‎ ‎Scale on our shouldersIntent PilotIntent-Pilot is an orchestration of two tools: AskUI's object detector with OpenAI's GPT-4v to achieve automation. It is designed to automate repetitive tasks, and to assist users in performing complex tasks with ease. This repository is our attempt to understand the GPT-4v's potential in automation and building an end-to-end automation tool.We are inspired by and improve uponSelf-Operating-Computerby a more accurate object detection model and an improved prompting strategy. We also provide a more user-friendly interface, and a more intuitive way to interact with the tool. For example, the notification feature to let users know what is happening and what to do next. Also, our toolworks across all keyboard layouts - US, German, etc, which was one of the limitations of similar tools.Demohttps://github.com/askui/intent-pilot/assets/106730702/582d7dec-e3ff-43fd-9ab7-0cad9103b366Quick StartSetupPython 3.9 or laterOpenAI KeyAskUI tokenFor staters, we provide a global AskUI key with 4000 free credits.ASKUI_WORKSPACE_ID: 'e26b43ea-a18f-4cd8-a6f1-e1e41dddec18' andASKUI_TOKEN: 'gbwdeDjlWBrsr8nLjOVB'After that, you can get your own AskUI key by signing up atAskUIYou can also copy the .env.example file to .env and fill in the required details OR You can enter the credentials in the terminal when you start the app.LinuxIn case of linux, you may need to install the following packages:sudoapt-getinstallxselxclippython3-tkpython3-devMacOSIn case of MacOS, you will have to grant permissions to the terminal to access the clipboard. You can do this by going toSystem Preferences->Security & Privacy->Privacy->Accessibilityand then adding the terminal to the list of apps that can control your computer.WindowsWe are currently working on the Windows version of the tool. It will be availablesoon.Quick Fix: The package also works on Windows but the Windows Defender is deleting thesrc/intent_pilot/utils/screenshot.pyfile. You have to restore the file from the quarantine and add it to the exclusion list.Installationpipinstallintent-pilotTerminalAfter installation, simply runintentin your terminal:intentIn case, you are unable to run the command, try running the following command:python-mintent_pilotJoin Our Discord CommunityFor real-time discussions and community support, join our Discord server:Join ourDiscord Serverand then navigate to the #intent-pilot channel.ContributingThank you for your interest in contributing! We welcome involvement from the community.We are still deciding on the contribution guidelines. Please stay tuned for updates.RoadmapWe are currently in the process of building PTA (Prompt-to-Automation) model, a Multi-Modal Model that can understand and execute commands in natural language, in real-time and faster than any VPA (Virtual Personal Assistant) in the market.
Package: intents
Intents ⛺Intentsis a Python framework to define and operate
Conversational Agents with a simple, code-first approach.Intentscomes with
built-in support for Dialogflow ES and experimental Alexa and Snips connectors. Its main benefits are:Agents are Python projects. You will develop with autocomplete, static type checking
and everything you are already used to.Versioning and CI. Agents can be versioned on Git, and programmatically
deployed just like software.Human-friendly Connectors. Intents are classes, predictions are their
instances. Support can be extended beyond Dialogflow by implementing custom connectors.A detailed view of the available features can be found inSTATUS.md. Also, check out theProjectspage to keep track of
recent developments.InstallpipinstallintentsUsageIntents are defined like standard Pythondataclasses:@dataclassclassHelloIntent(Intent):"""A little docstring for my Intent class"""user_name:Sys.Person="Guido"MyAgent.register(HelloIntent)Theirlanguageresources are stored in separate YAML files:utterances:-Hi! My name is $user_name{Guido}-Hello there, I'm $user_name{Mario}responses:default:-text:-Hi $user_name-Hello $user_name, this is Bot!Agents can beuploadedas Dialogflow ES projects directly from code:df=DialogflowEsConnector('/path/to/service-account.json',MyAgent)df.upload()# You will find it in your Dialogflow ConsoleIntentswill act transparently as apredictionclient:predicted=df.predict("Hi there, my name is Mario")predicted.intent# HelloIntent(user_name="Mario")predicted.intent.user_name# "Mario"predicted.fulfillment_text# "Hello Mario, this is Bot!"For a complete working example, check out the includedExample Agent. Also,Intentsdocumentationis published athttps://intents.readthedocs.io/📚DisclaimerThis project is not affiliated, associated, authorized, endorsed by, or in any way officially connected with Dialogflow. The names Dialogflow, Google, as well as related names, marks, emblems and images are registered trademarks of their respective owners.
Package: intent-suggestions
Intent suggestionsEarly user intent detection using n-gram modelsThe idea behind intent suggestions is similar to autofill when we use words that were typed to make predictions. But instead of predicting the next word, we try to detect the user's intent.The proposed approach usesnrecursively initialised models. Each next model uses a smallern. I.e. a model initialised withn=3will include three models (withn=3,n=2andn=1)
This recursive approach allows to also take into account frequency counts from smaller n-grams in case there is no match for the parent model.Usagefrom model import IntentSuggester

model = IntentSuggester()

items = ["one two three four", "five six seven eight"]
labels = ["intent_1", "intent_2"]

model.fit(items, labels)

print(model.predict("zero two three four"))Output:{'intent_1': 0.9902, 'intent_2': 0.0098}NotationAccording to the common notation, an n-gram language model usesn-1words to predict the next word.
Given that we are trying to predict a user's intent rather the next word, we'll use a slightly different notation.nin our case will represent the number of words used to predict intent probabilities. So a 3-gram (or trigram) model will use three words to make predictions.ReferencesThe approach was insipred bythiswork
Package: inteq
Solve Volterra and Fredholm integral equationsThis Python package estimates Volterra and Fredholm integral equations using known techniques.VolterraThis package provides the functionSolveVolterrawhich approximates the solution, g(x), to the Volterra Integral Equation of the first kind:using the method inBetto and Thomas (2021).See the "Trapezoid and Midpoint Rules" section for a discussion of these two rules.Parametersk : function
    The kernel function that takes two arguments.
f : function 
    The left hand side (free) function with f(a) = 0.
a : float
    Lower bound of the integral, defaults to 0.
b : float
    Upper bound of the estimate, defaults to 1.
num : int
    Number of estimation points between zero and `b`.
method : string
    Use either the 'midpoint' (default) or 'trapezoid' rule.Returnsgrid : 2-D array
    Input values are in the first row and output values are in the second row.FredholmThis package provides the functionSolveFredholmwhich approximates the solution, g(x), to the Fredholm Integral Equation of the first kind:using the method described inTwomey (1963). It will return a smooth curve that is an approximate solution. However, it may not be a good approximate to the true solution.Parametersk : function
    The kernel function that takes two arguments.
f : function 
    The left hand side (free) function that takes one argument.
a : float
    Lower bound of the of the Fredholm definite integral, defaults to -1.
b : float
    Upper bound of the of the Fredholm definite integral, defaults to 1.
num : int
    Number of estimation points between zero and `b`.
smin : float
    Optional. Lower bound of enforcement values for s.
smax : float
    Optional. Upper bound of enforcement values for s.
snum : int
    Optional. Number of enforcement points for s.Returnsgrid : 2-D array
    Input values are in the first row and output values are in the second row.Trapezoid and Midpoint RulesVolterra integral equations are typically solved using the midpoint rule. However, the trapezoid rule often converges faster. See below an example of the trapezoid rule performing well with just six grid points.Thus, the trapezoid rule typically performs better. However, the trapezoid rule is less stable than the midpoint rule. An example where this this instability is an issue is provided below.This can be remedied by smoothing the function. For example, withinteq.helpers.smooth().
Package: inter
Toolset for interactive command-line applicationsinteris a utility for providing an interactive command line interface.
You can use it to ask question and coerce values into ints, floats, boolean values,
and you have a number of options like specifying integer and floating point constraints,
setting the color of the question text, and specifying default values.Installationinter is installable through pip. To install to your system, just run:$ sudo pip install interor to install from source, run:$ python setup.py installUsageinter is easy to use in your python scripts. Check the following example:from inter import Interact

cli = Interact()

# Takes a string from the user and strips the whitespace, and lowercase it.
first_name = cli.ask_str('enter your first name', strip=True, lower=True)

# title=True will take input like "hey there" and return "Hey There"
full_name = cli.ask_str('enter your full name', strip=True, title=True)

# Prints "whats your age: " and gives an error message regarding min and max
# if the user fails to enter a value within that range.
age = cli.ask_int('whats your age', min=1, max=120)

# Prints "are you in the US [yn]: "
in_the_us = cli.ask_bool('are you in the US')

# Prints "are you a developer [Yn]: "
# If user provides no input, default True is returned. Otherwise it checks for
# "yes", "y", "true", or "no", "n", "false" and if it receives none of those,
# it will print an error message in red.
developer = cli.ask_bool('are you a developer', default=True)

# Asks for a path to a file that doesn't exist and is creatable
new_path = cli.ask_path('path to file ill create', exists=False, creatable=True)

# Asks for path to a directory that it can add files inside
inside_dir = cli.ask_path('path to dir i will write in', is_dir=True, writeable=True)

# Asks for path to a file it can read but cant write to and exists as a file
readable = cli.ask_path('i can read but not write this file', readable=True,
                        writeable=False, is_file=True)Release Notes0.1.4:bugfixes for ask_str default and more python 2.7 compatibility0.1.3:Make python 2.7 compatible with print as function0.1.2:AddInteract.printandInteract.exit0.1.1:Fix path stuff0.1.0:Alpha release with usable int/float/bool/str tooling.0.0.1:Project created
Package: interact-cli
Interactive command line tool.Github repo:https://github.com/caizhengxin/interact-cliDocumentation:https://interact-cli.readthedocs.ioFree software: BSDFeaturesInteract inputSupports multiple data typesSupport input checkSupport regex, seenetwork.jsonSupport typebooleanstringintfloatlistchoicemacipv4cidr, eg:192.168.1.1/24hexInstallationTo install interact-cli, run this command in your terminal:$pip3installinteract-clior:$gitclonehttps://github.com/caizhengxin/interact-cli.git$cdinteract-cli$pip3install-e.UsageSeeinteract.jsonstring:frominteractimportinteractsconfig={"name":{"type":"string","default":"jankinca","max_length":10,"min_length":1,"description":"Your name"}}if__name__=="__main__":"""
    Your name [jankinca]: sssssssssssss
    Error: Invalided `sssssssssssss`
    Your name [jankinca]: jankincai
    """print(interacts(config).name)regex:frominteractimportinteractsconfig={"ipv4":{"type":"string","regex":r"^\d{1,3}.\d{1,3}.\d{1,3}.\d{1,3}$","default":"192.168.166.12","description":"IPv4 address"}}if__name__=="__main__":"""
    IPv4 address [192.168.166.12]: 22
    Error: Invalided `22`
    IPv4 address [192.168.166.12]: 192.168.166.2
    """print(interacts(config).ipv4)when:frominteractimportinteractsconfig={"use_code_hosting":{"type":"boolean","default":True,"description":"Use code hosting platform"},"code_hosting":{"type":"choice","default":1,"choice":["github","gitee","gitlab"],"description":"Code hosting","when":"use_code_hosting == true"},"code_hosting_username":{"type":"string","default":"jankincai","description":"Your code hosting username","when":"use_code_hosting == true"}}if__name__=="__main__":"""
    Use code hosting platform [y]: y
    Select code hosting:
    1 - github
    2 - gitee
    3 - gitlab
    Choose from [1]:
    Your code hosting username [jankincai]: jankincai

    {'use_code_hosting': True, 'code_hosting': 'github', 'code_hosting_username': 'jankincai'}
    """"""
    Use code hosting platform [y]: n
    {'use_code_hosting': False, 'code_hosting': None, 'code_hosting_username': None}
    """print(interacts(config).get_interact_data())SeedemoCreditsThis package was created withCookiecutterand thecaizhengxin/cookiecutter-packageproject template.