Saravanan666/pypi-org huggingface dataset:
Package: jailconf
Parse and edit your FreeBSD jail.conf file with python.InstallationTo install jailconf, simply:pip3 install jailconfjailconf requires Python 3.ExamplesimportjailconfLoad the configuration from a pathconf=jailconf.load('/etc/jail.conf')Load the configuration from a stringconf=jailconf.loads(open('/etc/jail.conf').read())Create an empty configurationconf=jailconf.JailConf()The configuration is represented as a dictionnary (actually a subclass of OrderedDict).Let’s modify some settings.The quoted strings in the configuration should be passed with the quotes.
For example, to obtain the setting:path="/var/jail/$name";you write:conf['path']='"/var/jail/$name"'The string should be exactly what you want to appear on the right side of the
parameter name in the configuration file.
If you want the value of a parameter to be a quoted string, you pass
a string containing a quoted string.
This allows you to specify what kind of quotes you want to see in the output configuration
file (single quotes, double quotes, or no quote at all).conf['exec.start']='"/bin/sh /etc/rc"'conf['exec.stop']='"/bin/sh /etc/rc.shutdown"'Boolean parameters. To obtain:exec.clean;mount.devfs;you write:conf['exec.clean']=Trueconf['mount.devfs']=TrueAdd a jail:conf['myjail']=jailconf.JailBlock([('host.hostname','"example.com"'),('ip4.addr',['10.1.1.1','10.1.1.2','10.1.1.3'])])Modify a jailconf['myjail']['ip4.addr']='192.168.1.2'# this will be rendered as the line: ip4.addr = 192.168.1.2# To set multiple ips, use a list:conf['myjail']['ip4.addr']=['192.168.1.2','192.168.1.3']Delete a jaildelconf['uselessjail']Iterate over jailsforname,jail_blockinconf.jails():jail_block['host.hostname']='"%s"'%nameOutput the configuration as a string>>>print(conf.dumps())path = "/var/jail/$name";
exec.start = "/bin/sh /etc/rc";
exec.stop = "/bin/sh /etc/rc.shutdown";
exec.clean;
mount.devfs;
myjail {
        host.hostname = "myjail";
        ip4.addr = 192.168.1.2, 192.168.1.3;
}Write the configuration to a fileconf.write('/etc/jail.conf')GitHub repo:https://github.com/leforestier/jailconf
Package: jailconf_tools
jailconf_toolsis a python package which includes a lot of tools to use with the new jail format on a freebsd host.HowtoThis is an example of howto use jailconf parser :#!/usr/bin/env python
import sys
from jailconf_tools.parser import get_jails_config


def main():
    """Main function"""

    try:
        print(get_jails_config())
    except:
        return 1
    else:
        return 0

if __name__ == "__main__":
    sys.exit(main())This peace of code returns a python dict which contains all jails definition.
Package: jailrootdetector
#+TITLE: jailrootDetectorAttempted automation to detect root and jailbreak detection in mobile applications.* Example UsageDetect common detection strings like "jailbroken" and "rooted" as well as a few others. The script leverages [[https://www.radare.org/r/][radare2]] and [[https://sourceware.org/binutils/docs/binutils/strings.html][GNU strings]] to attempt to quickly identify if that application is going to give you a hard time.** Demo#+begin_src shell :results output :dir ./jailrootdetector/ :exports bothjrd --help#+end_srcThe script *does not* extract the IPA or APK, It assums you know how to do that ;) .#+RESULTS:: usage: jrd [-h] (--dex DEX | --ios IOS):: [+] root & jailbreak detection:: optional arguments::   -h, --help  show this help message and exit:   --dex DEX   path to android dex file:   --ios IOS   path to extracted payload binaryOnce you have an extracted app, then run the script with the relevant option, for example;#+begin_src shell :results output :dir ./jailrootdetector/ :exports bothjrd --ios ./Documents/IPAs/Discord/Payload/Discord.app/Discord#+end_srcThen the script will atempt to find hard-coded well known detection strings, frist with [[https://www.radare.org/r/][radare2]] and then falls back to [[https://sourceware.org/binutils/docs/binutils/strings.html][GNU strings]].#+RESULTS:#+begin_example[+] searching[+] detection strings found:/Applications/Cydia.app/bin/sh/bin/bashjailbroken0x100d1be97 11 10 jailbroken0x100d1c0dc 13 12 isJailbroken0x100d1c0e9 22 21 TB,R,N,V_isJailbroken0x100ddcc6b 20 19 computeIsJailbroken0x100ddcd0a 13 12 isJailbroken0x100ddcd57 14 13 _isJailbroken#+end_example* InstallationYou can install with =pip3=.#+begin_src shell :results outputpip3 install jailrootdetector#+end_srcOr you can install with [[https://python-poetry.org/][poetry]]#+begin_src shell :results outputgit clone https://gitlab.com/JxTx/jailrootdetector && \cd jailrootdetector && \poetry install && \poetry shelljrd --help#+end_srcif all else fails, here is a checklist of dependencies.- [[https://www.radare.org/r/][radare2]]- Use the [[https://www.radare.org/r/down.html][installation documentation]] for this.- [[https://www.radare.org/n/r2pipe.html][r2pipe]]- This can be installed with =pip3 install r2pipe=- [[https://pypi.org/project/sh/][sh]]- This can be installed with =pip3 install sh=- [[https://sourceware.org/binutils/docs/binutils/strings.html][GNU Strings]]- This should already be installed on your system, if not you should be able to install it with your package manager.
Package: jaime
NOTE: THIS IS AT THE MOMENT (FEB 2014) ALPHA SOFTWARE AND IT’S STILL FAR FROM
BEING ABLE TO DO WHAT’S DESCRIBED BELOW. SOON, THOUGH.Jaime is a programmer’s “code butler”. It is designed to help you get up to
speed on a new project, whatever that means. For instance, let’s say you want
to start a new WordPress-based Website:$ jaime wp new –domain example.com –codename sputnik –webserver nginx–db mariadb –codehost bitbucket –branching simpleAssuming Jaime has your API keys and/or logins for all the services involved,
the above command would register example.com on Namecheap, create a new code
project with the codename “sputnik”, generate nginx config files, setup
WordPress on a git repository using the “simple” branching strategy, create a
private project on BitBucket, and setup your git remotes. Let’s say now you
want to start working on your project, no problem:# Setup MariaDB user and database for WordPress, configure nginx for the
# project, and point example.com to localhost via the /etc/hosts file.
$ jaime wp setup dev# Install a couple WP plugins
$ jaime wp plugin install NAME1 NAME2# Make sure WP is up to date and upgrade it if it’s not
$ jaime wp upgrade –version latest# Let’s setup Varnish…
$ jaime wp varnish# And let’s activate the Varnish config
$ jaime wp setup dev# Work on WP on the browser…# All done. Let’s deploy to a new server using our local DB.
$ jaime wp deploy –host digitalocean –db copy
Package: jaims
jaimsA jax based affine-invariant MCMC sampler that can leverage GPUs to speed up sampling for computationally intensive likelihoods. It implements theGoodman-Wearealgorithm as described indfm++and is inspired by the popularemceelibrary. Thejust-in-timecompilation together withvectorizedlikelihood evaluation for the walkers gives significant speed-up even on CPUs when compared to emceeInstallationTo installjaims, please clone this repository and then runpython setup.py installinside itYou can also install this viapipusingpip install jaimsTo run it on a GPU, you must have an installation ofjaxlibcompatible with your CUDA version. For more information, please refer to the officialguidelinesThe API forjaimsis slightly different fromemcee. This might change in the future.
Package: jaims-py
jAImsMy name is Bot, jAIMs Bot.🕶️jAIms is a lightweight Python framework built on top of the OpenAI library that lets you create powerful LLM agents.
It is designed with simplicity and ease of use in mind and only depends onopenai,tiktokenandPillow.Installationpipinstalljaims-py👨‍💻 UsageBuilding an agent is as simple as this:fromjaimsimportJAImsAgentagent=JAImsAgent()response=agent.run([{"role":"user","content":"Hi!"}])print(response)⚙️ FunctionsOf course, an agent is just a chatbot if it doesn't support functions. jAIms uses the built-in OpenAI tools feature to call the functions you pass to it. Here's an example where we create a simple sum function and make a simple agent that lets you sum two numbers:importjaimsdefsum(a:int,b:int):returna+b# this is a class that wraps your function, it will# receive the actual function plus all the info required# by the llm to invoke it.func_wrapper=JAImsFuncWrapper(function=sum,name="sum",description="use this function when the user wants to sum two numbers",params_descriptors=[JAImsParamDescriptor(name="a",description="first operand",json_type=JAImsJsonSchemaType.NUMBER,),JAImsParamDescriptor(name="b",description="second operand",json_type=JAImsJsonSchemaType.NUMBER,),],)# instantiate the agent passing the functionsagent=JAImsAgent(openai_kwargs=JAImsOpenaiKWArgs(model=JAImsGPTModel.GPT_4,tools=[func_wrapper,],),)# a simple loop that simulates a chatbotwhileTrue:user_input=input("> ")ifuser_input=="exit":breakresponse=agent.run([{"role":"user","content":user_input}],)forchunkinresponse:print(chunk,end="",flush=True)print("\n")✨ Other featuresComplete control over openai call parameters.Automatic chat history managementConfiguration of the OpenAI model to useInjectable prompt to shape agent behaviorSafety checks to prevent the agent from endlessly looping over function callsI will routinely update the examples to demonstrate more advanced features.
Also, I've made sure to document the code as best as I can; everything should be self-explanatory; I plan to add a proper documentation in the future if this project gets enough traction.🤖 Supported modelsCurrently, jAIms supports the new OpenAI models with functions enabled, specifically:gpt-3.5-turbo-0613gpt-3.5-turbo-16k-0613gpt-4-0613gpt-4-32k-0613gpt-4-1106-previewgpt-4-vision-previewI'm not planning to add support for non-OpenAI models at the moment, but contributions are always appreciated.⚠️ Project statusThis is a work in progress. I still need to write some tests and add many features, but the core functionality is there.
I'm creating this framework because I need a lightweight and easy-to-use framework to create LLM agents. This project may not be as advanced as tools likelangchainand others, but if you need a simple tool to create agents based on the OpenAI API, you might find jAIms useful.TODOS:Add testsAdd more examplesAdd more chat history optimization strategiesAdd function calling callbacksAdd history persistance📝 LicenseThe license will be MIT, but I need to add this properly.
Package: jaina
jainaRelease log1.0.0 - 第一个正式版本，支持了最重要的zkCli的原生命令以及一些常见的类似Linux系统命令，如:cd,ls,alias,history等OverviewJaina是一个轻量级的zookeeper命令行交互客户端工具，特点是：安装方便，使用符合Linux的命令行习惯，zk原生自带的zkCli太难用，极其不友好，
所以萌生了自己写一个类似的工具的想法。Installation / UsageTo install use pip:$ pip install jainaOr clone the repo:$ git clone https://github.com/kaixinbaba/jaina.git
$ python setup.py installUpgrade$ pip install -U jainaExample$jaina-h127.0.0.1:2181You will see this if zk connectedZookeeperconnected_____________________|__|/\|__||_\|__|/\||/_\|||\||/_\_||/___\||||\ \||/___\||__' | _/ /   \ \_  _| |_  _| |_\   |_  _/ /   \ \_`.____.'|____||____||_____||_____|\____||____||____|WelcometouseJAINA
Ifyouhaveproblem,justtryhelp!
Ifyouwanttoquit,pleaseinputCtrl+DorCtrl+C(jaina)[/]Then you can usehelpcommand for help!Please review the online docsjainaget more help.
Package: jai-sdk
Jai SDK - Trust your dataInstallationThe source code is currently hosted on GitHub at:https://github.com/jquant/jai-sdkThe latest version of JAI-SDK can be installed frompip:pipinstalljai-sdk--userNowadays, JAI supports python 3.7+. For more information, here is ourdocumentation.Getting your auth keyJAI requires an auth key to organize and secure collections.
You can quickly generate your free-forever auth-key by running the command below:fromjaiimportget_auth_keyget_auth_key(email='email@mail.com',firstName='Jai',lastName='Z')ATTENTION:Your auth key will be sent to your e-mail, so please make sure to use a valid address and check your spam folder.How does it work?With JAI, you can train models in the cloud and run inference on your trained models. Besides, you can achieve all your models through a REST API endpoint.First, you can set your auth key into an environment variable or use a :file:.envfile or :file:.inifile.
Please check the sectionHow to configure your auth keyfor more information.Bellow an example of the content of the :file:.envfile:JAI_AUTH="xXxxxXXxXXxXXxXXxXXxXXxXXxxx"In the below example, we'll show how to train a simple supervised model (regression) using the California housing dataset, run a prediction from this model, and call this prediction directly from the REST API.importpandasaspdfromjaiimportJaifromsklearn.datasetsimportfetch_california_housing# Load datasetdata,labels=fetch_california_housing(as_frame=True,return_X_y=True)model_data=pd.concat([data,labels],axis=1)# Instanciating JAI classj=Jai()# Send data to JAI for feature extractionj.fit(name='california_supervised',# JAI collection namedata=model_data,# Data to be processeddb_type='Supervised',# Your training type ('Supervised', 'SelfSupervised' etc)verbose=2,hyperparams={'learning_rate':3e-4,'pretraining_ratio':0.8},label={'task':'regression','label_name':'MedHouseVal'},overwrite=True)# Run predictionj.predict(name='california_supervised',data=data)In this example, you could train a supervised model with the California housing dataset and run a prediction with some data.JAI supports many other training models, like self-supervised model training.
Besides, it also can train on different data types, like text and images.
You can find a complete list of the model types supported by JAI onThe Fit Method.Read our documentationFor more information, here is ourdocumentation.
Package: jaison
jaison is like Legos for Machine Learning.A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with one objective:Make it so simple that you can build predictive models with as little as one line of code.DocumentationLearn more from thejaison's docs.Try it outInstallationYou can install jaison from pip:pip3installjaisonNote: depending on your environment, you might have to use pip instead of pip3 in the above command.UsageGiven the simple sensor_data.csv let's predict sensor3 values.sensor1sensor2sensor31-1-1010-1- 11ImportPredictorfrom jaisonfromjaisonimportPredictorTrain the model.importpandassensor3_predictor=Predictor(output=['sensor3']).learn(from_data=pandas.read_csv('sensor_data.csv'))You can now predict whatsensor3value will be.prediction=sensor3_predictor.predict(when={'sensor1':1,'sensor2':-1})You can also try jaison in Google Colab:ContributingThanks for your interest. There are many ways to contribute to this project. Please, check out ourContribution guide.Current contributorsMade withcontributors-img.Licensejaison License
Package: jais-search
Simple cli client for ja.isPersonal tool that is probably not useful for anyone elseInstallpipinstalljais-searchPlay aroundStart by exporting your api tokenexportJAIS_API_KEY=your-tokenAlternatively you can always pass it as an argument to each command, but that's just boringjaissearch--tokenyour-token-n"Code"The token will be inferred from your environmentjaissearch-n"Code"Only one command is available, that issearch, but comes with multiple search filter options.jaissearch--helpSearch examplesjaissearch-n"Banana"jaissearch-p105# postal codejaissearch-sSuðurlandsbraut# streetjaissearch-mReykjavík# municipalityjaissearch-bE1# business type, I have no idea what codes are available.Or just pass them all in if you wantjaissearch-n"Banana"-p105-sSuðurlandsbraut-mReykjavíkmunicipality-bE1It will store results intest.csv, until I make it configurable and fix the dict serialization.Thehttps://ja.isapi paginates the data so you have to pass in--start [default: 1]and--count [default: 1000], but it comes with defaults.# Get me 50 records and start at record 1000jaissearch-n"Banana"--start1000--count50
Package: jajapy
Introductionjajapyis a python library implementing theBaum-Welchalgorithm on various kinds of Markov models.jajapygenerates models which are compatible with the Stormpy model checker. Thus,jajapycan be use as a learning extension to the Storm model checker.Main featuresjajapyprovides:Markov ModelLearning Algorithm(s)MCBaum-Welch for MCsAlergia (ref)MDPBaum-Welch for MDPs (ref)Active Baum-Welch (ref)IOAlergia (ref)CTMCBaum-Welch for CTMCsBaum-Welch for synchronous compositions of CTMCsPCTMCBaum-Welch for PCTMCs (ref)HMMBaum-Welch for HMMs  (ref)GoHMMBaum-Welch for GoHMMs (ref)jajapyis compatible withPrismandStorm.Installationpip install jajapyRequirementsnumpyscipyalive-progresssympystormpy(recommended: if stormpy is not installed,jajapywill generate models in jajapy format).DocumentationAvailable onreadthedocAbout the authorMy website
Package: jakarto-datasets
Jakarto datasets for 3d detection challenge of urban assetsWe built that python API to share some real-world 3d lidar datasets of urban assets. We hope to help some of you to develop and test algorithms about 3d lidar processing.Those datasets have been gathered with theJakartotruck.InstallationThis API requirespython 3.6+.pipinstalljakarto-datasetsUsagefromjakarto_datasets.datasets.storm_drainsimportStormDrainsDataset# Load datasetstorm_drains_2019=StormDrainsDataset()fordatainstorm_drains_2019.training_set:coordinates=data.get_coordinates_data()lidar_data=data.get_lidar_data()label=data.get_label_lidar_data()print(data)print(coordinates.shape)print(lidar_data.shape)print(label.shape)print(lidar_data['intensity'])Datasetsdatasetsyear3d lidarlabelrastermasklen(training_set)len(testing_set)examplesdescriptionstorm drains2019:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:223150see examplessee detailsBenchmarksWe will be more than happy to share your experiments.datasetstitleauthorslinksdescriptionstorm drains 2019balanced random forestJakarto teamlinkUse a deadly simple balanced random forest to classify each point from lidar data. Although it doesn't use spatial information, it allowed Jakarto to detect ~25% of storm drains. Those storm drains will be added to the Jakarto storm drains 2020 dataset.............CitationIf you find this work useful and wish to refer to, please consider the following BibTeX entry:@MISC{jakarto_datasets,
    author = {Loic Messal and Cedric Pelletier and {Jakarto Cartographie 3d team}},
    title = {Jakarto datasets},
    year = {2019},
    howpublished={\url{https://github.com/jakarto3d/jakarto_datasets}}
}A github star may also help.ContactIf you want to email us, please send an email tocontact@jakarto.com.LicenseThis project is licensed under the terms of the MIT license. (see LICENSE.txt file for details).
Package: jaklas
Note:This library was originally created at Jakarto because we needed a single library to easily create las files
from pandas arrays. We found using the old laspy api a bit unintuitive, so we created this wrapper.
Since laspy 2.0.0, this has been greatly improved. Someone looking for the simplest way to read and write las files
could find jaklas useful, but I would encourage reading laspy's documentation also.jaklasjaklas is a thin wrapper aroundlaspyto make reading and writing las files as
simple as possible.The main use case is to write a pandas array to a las file in a single function call.
The las file attributes (point offset, point scaling, file version, point format, etc.)
are inferred depending on column names, datatype and point values.The las writer supports any object implementing__getitem__that has the
correct field names.InstallationpipinstalljaklasTestinggitclonegit@github.com:jakarto3d/jaklas.gitcdjaklas
pipinstall-rrequirements-dev.txt
python-mpipinstall.
pytestUsagejaklas.writewrites a pandas dataframe (or a dict) to a las file.The dataframemusthave either (case insensitive):'x', 'y' and 'z' columnsor an 'xyz' columnand it can have other las attributes (case sensitive names taken fromlaspy):gps_timeintensityclassificationredgreenblueedge_of_flight_linekey_pointnirnumber_of_returnsoverlappoint_source_idraw_classificationreturn_numberreturn_point_wave_locationscan_anglescan_angle_rankscan_direction_flagscanner_channelsyntheticuser_datawavepacket_indexwavepacket_offsetwavepacket_sizewithheldx_ty_tz_tother column names will be written as extra dimensions.Exampleimportjaklasimportpandasdata={'gps_time':[0,1.232,2.543,3.741],'intensity':[14578,54236,1425,12543],'X':[456,234,567,432],'Y':[10234,10256,10789,10275],'Z':[10,11,12,13],}dataframe=pandas.DataFrame(data)filename='example.las'jaklas.write(dataframe,filename)Note the upper case 'X', 'Y' and 'Z' point data are the real coordinates,
not the scaled int32 ones like in the las file.Seejaklas.writedocstring for more options like controlling offset and scaling.
Package: jakopicevca
Programs for Astro Pi - Mission Space Lab - Team JakopičevcaAll programsAll Astro Pi programs are available onGitHubandPyPI. You can access them
withjakopicevcaYEAR(for examplejakopicevca2017).Astro Pi 2017/18Program for Astro Pi 2017/18 is available onGitHubandPyPI.LicenseThis project is licensed under the GNU GPL License. See the LICENSE file
for more details.
Package: jakt
JAKTJakt is just another (k)ommand-line timetracker tool.⚠️ Jakt is under heavy development: Breaking changes can occur at any moment. ⚠️If you find an issue please submit ithere.But what is it?Jakt helps you keep track of how you spend your time. Whether you want to keep better track of how much time you spend on each project or want to keep yourself accountable while working, jakt is the perfect tool.The nameThe name comes from the norwegian word for hunt. Timetracking is one of the best tools in your arsenal in your hunt for productivity.And if you are wondering, yes, I started with the acronym.FeaturesJakt is a tool for timetracking. It has support for:ProjectsTagsLive tracking from commandlineManually adding time that is not tracked liveEditing tags and projects in previously tracked timeA python library that can be used in other applicationsFor feature requests please open anissue.InstallationJakt can be installed from PyPI using:pip install jaktYou can then start using jakt by simply typingjakt start <PROJECT> <[TAGS]>in any terminal.From SourceClone the repocd jaktpip install -e .Make sure to use the correct version of pip, some users will need to usepip3instead.If you intend to develop with Jakt as well as using it for timetracking there is a step 4:jakt debug trueThis way you can create a testing environment without risking your tracked timeslots. The only file that is accessed by both environments is the configfile.LicenseThis project is licensed under the MIT License. SeeLICENSE.