Saravanan666/pypi-org huggingface dataset:
Package: llamazure.msgraph
llamazure.msgraph : Microsoft Graph clientThellamazure.azgraphpackage provides a usable client for the Microsoft Graph.Benefits:no boilerplateeasily navigate paginated queriesmsgraphUsageCreate aGraphwith thefrom_credentialand any of the standard Azure credentials.fromazure.identityimportDefaultAzureCredentialfromllamazure.msgraph.msgraphimportGraphg=Graph.from_credential(DefaultAzureCredential())QueryingMake a simple query with theqmethod, which will return your data directly:>>>g.q("me")Res(req=Req(query='me',options=QueryOpts(count=None,expand=set(),filter=None,format=None,orderby=None,search=None,select=None,skip=None,top=None)),odata={'@odata.context':'https://graph.microsoft.com/v1.0/$metadata#users/$entity'},value={...},nextLink=None)Or specify options with thequerymethod, which will return the full result object:>>>fromllamazure.msgraph.modelsimportReq>>>g.query(Req("me",options=QueryOpts(expand={"memberOf"})))Res(req=Req(query='me',options=QueryOpts(count=None,expand={'memberOf'},filter=None,format=None,orderby=None,search=None,select=None,skip=None,top=None)),odata={'@odata.context':'https://graph.microsoft.com/v1.0/$metadata#users(memberOf())/$entity'},value={...},nextLink=None)RetriesEvery query can be automatically retried by the retry policy. You can modify this by setting theGraph.retry_policyattribute:g.retry_policy=RetryPolicy(retries=10)PaginationPagination is handled automatically. If you want to manually paginate, you can manually walk the pages:req=Req(query="users")res0=g.query_single(req)res1=g.query_next(req,res0)res2=g.query_next(req,res1)
Package: llamazure.rbac
llamazure.rbac : Azure roles, users, and assignmentsThellamazure.rbacpackage provides a helpful interface to Azure RBAC.Benefits:a coherent view of roles and assignmentsautomatically handles internals of the Azure RBAC model. For example, deleting a role will first delete all role assignmentsautomatic retries and waits for the cloud to workrbacUsageFor roles definitions and role assignments, createRoleOpswith anAzRestcreated by itsfrom_credentialmethod:fromazure.identityimportDefaultAzureCredentialfromllamazure.azrest.azrestimportAzRestfromllamazure.rbac.rolesimportRoleOpsrole_ops=RoleOps(AzRest.from_credential(DefaultAzureCredential()))For users and groups, create aUsersor aGroupswith a MSGraph created by theirfrom_credentialsmethod:fromazure.identityimportDefaultAzureCredentialfromllamazure.msgraph.msgraphimportGraphfromllamazure.rbac.resourcesimportGroups,Usersusers=Users(Graph.from_credential(DefaultAzureCredential()))groups=Groups(Graph.from_credential(DefaultAzureCredential()))Create a rolefromllamazure.rbac.rolesimport*role=role_ops.rds.put(RoleDefinition.Properties(roleName="llamazure-rbac-asn",description="test finding assignments",permissions=[Permission(actions=["Microsoft.Authorization/*/read"])],),scope="/subscriptions/00000000-0000-0000-0000-000000000000",)Assign a roleThis will also automatically add the scope of assignment to the role's assignable scopes if necessary:me=users.current()role_ops.ras.assign(principalId=me["id"],principalType="User",role_name=role.properties.name,scope="/subscriptions/00000000-0000-0000-0000-000000000000")Delete a roleThis will also delete all role assignments before deleting a role:role_ops.delete_role(role)You can also delete a role by name:role_ops.delete_by_name("llamazure-rbac-asn")Get users with their groupsusers.list_with_memberOf()Get groups with their membersgroups.list_with_memberships()
Package: llamazure.tresource
llamazure.rid : Resource IDs you can useThellamazure.tresourcepackage provides a way to group Azure resources into their hierarchy.UsageThere are several variants of Tresources.PlainTresource: This tresource does not store any information about the resources except for their parsed resource ID. This is best for exploration and visualisation. For example, if you wanted to display all the VMs in a tenancy, this tresource would help you show them by subscription and resource groupTresourceData: This tresource includes a space to put data. An obvious choice for the data would be the serialised JSON of the resource itself, which you could get from the graphapi or from the cli or through change events. You can also use the data for other information, like whether an object exists in IAC or whether someone knows what a resource is for.ExamplesDesign notes
Package: llambda
LLambdaLLambda is a library that makes it easy to integrate Large Language Models (LLM) and Python. It enables calling multiple pre-defined functions using natural language and executing them with appropriate arguments.InstallationYou can easily install LLambda using pip:pip install llambdaUsageHere is a basic usage example. The following code defines two functions and calls them using natural language.Registering Functions with Decoratorsfromllambdaimportregister@registerdefmake_coffee(n_cups:int)->str:"""Make coffee of the amount you want.Can't make tea."""return"☕"*n_cups@registerdefsend_email(recipient_name:str,subject:str,content:str,your_name:str)->str:"""Send an email to someone."""returnf"Sent email to{recipient_name}with subject '{subject}'\nand content '{content}' from{your_name}"Now we have two functions. Let's call them using natural language:fromllambdaimportcreate_llambda,set_openai_api_keyset_openai_api_key("Your OpenAI API Key")llambda=create_llambda()print(llambda("I want to drink 3 cups of coffee"))# ☕☕☕print(llambda("Write an email in which GPT shares the method for training neural networks with Adam."))# Sent email to Adam with subject 'Training Neural Networks'# and content'Hey there! I wanted to share my method for training neural networks. Let's discuss soon!' from GPTLLambda automatically recognizes the functions and their arguments, and calls the appropriate function with the arguments inferred from the instruction.print(llambda("I want to drink 3 cups of tea"))# NotImplementedError: Can't make tea.LLambda raisesNotImplementedErrorwhen it cannot find a function that matches the instruction.Directly Passing FunctionsIn addition to using the@registerdecorator, you can also directly pass the functions tocreate_llambda.llambda=create_llambda([make_coffee,send_email])print(llambda("I want to drink 3 cups of coffee"))# ☕☕☕Custom ContextUsing ContextVars ClassFunctionscan access context variables by defining a subclass ofContextVars. The class variables of this subclass will be automatically recognized as context variables:classContext(ContextVars):your_name:str="GPT"your_friends:list[str]=["Adam","Bert"]llambda=create_llambda(context=Context)print(llambda("Share with your friend about your method for training neural networks"))# Sent email to Adam with subject 'Training Neural Networks'# and content'Hey there! I wanted to share my method for training neural networks. Let's discuss soon!' from GPTDirectly Passing ContextVar InstancesYou can also pass the context directly as a list ofContextVarinstances:context_vars=[ContextVar("your_name","str","GPT"),ContextVar("your_friends","list[str]",["Adam","Bert"])]llambda=create_llambda(context=context_vars)
Package: llamp
# LLamp - Large Languge Models for PlanningThis is a package that uses LLMs (closed and open-source) for planning.**WARNING PACKAGE IS STILL UNDER DEVELOPMENT and requirements needs cleaning up.**## Installation:1. Textworld Game (pip install textworld)2. Textworld Visualisation (pip install -r requirements_textworld_visualisation.txt)3. (install chromedriver or firefox driver)## Generate New Textworld games:1. `tw-make custom --world-size 2 --nb-objects 10 --quest-length 5 --seed 1234 --output tw_games/w2_o10_l5_game.z8`## Playgame:1. (In terminal with browser visualiser) `tw-play tw_games/first_game.z8 --viewer`2. (as Gym environement in terminal) `python3 playground_tw_gym.py`### Available Commands to agent:```bashAvailable commands:look:                describe the current roomgoal:                print the goal of this gameinventory:           print player's inventorygo <dir>:            move the player north, east, south or westexamine ...:         examine something more closelyeat ...:             eat edible foodopen ...:            open a door or a containerclose ...:           close a door or a containerdrop ...:            drop an object on the floortake ...:            take an object that is on the floorput ... on ...:      place an object on a supportertake ... from ...:   take an object from a container or a supporterinsert ... into ...: place an object into a containerlock ... with ...:   lock a door or a container with a keyunlock ... with ...: unlock a door or a container with a key```
Package: llck
Lingorithm Language Core KitThis package is the core function for any NLP operation or pacakge used by lingorithm.InstallationUse the package managerpipto install llck.pipinstallllckUsageimportllcknlp=llck('ar',{'tokenize':'tokenize'})processed=nlp.process('منشار أرضيتم بالحيوة الدنيا من الأخرة')print(processed.sentences[0].tokens)# [منشار, أرضيتم, ب, الحيوة, الدنيا, من, الأخرة]ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: llckbdm
Line List Clustering Krylov Basis Diagonalization MethodThis library implements a noise-robust version of Krylov Basis Diagonalization Method (KBDM) [1, 2] for solving the Harmonic Inversion Problem (HIP) by exploiting the method instability in the presence of noise and making use clustering techniques.An ensemble of solutions for the same input signal is generated by varying the number of input points to be considered or adding a very small quantity of noise (pseudo-noise).
Estimations of each component are grouped by non-supervised machine learning algorithms in this ensemble and average values of each cluster are used as final estimations.The idea of using average values in the parameter domain was firstly shown in [3] and a first naive implementation of LLC-KBDM was proposed in [4].Instalationconda-forge (recommended)conda install -c conda-forge llckbdmPyPIpip install llckbdm[1] Mandelshtam, V. a., & Taylor, H. S. (1997). Harmonic inversion of time signals and its applications. The Journal of Chemical Physics, 107(17), 6756.https://doi.org/10.1063/1.475324[2] Mandelshtam, V. A. (2001). FDM: The filter diagonalization method for data processing in NMR experiments. Progress in Nuclear Magnetic Resonance Spectroscopy, 38(2), 159–196.https://doi.org/10.1016/S0079-6565(00)00032-7[3] Silva, D. M. D. D., Lima, T. S., Tannús, A., Magon, C. J., & Paiva, F. F. (2015). MRS data quantification through the KBDM: reducing the effect of noise by using multiple signal truncations. Proceedings of the ISMRM 23rd Annual Meeting & Exhibition, 1967–1967. Toronto.[4] da Silva, D. M. D. D., Vaz, Y., & Paiva, F. F. (2015). MRS data deconvolution through KBDM with multiple signal truncation and clustering: Circumventing noise effects. In IFMBE Proceedings (Vol. 51, pp. 1022–1025).https://doi.org/10.1007/978-3-319-19387-8_249
Package: ll-core
This package is no longer maintained separately. It is now part of ll.xist
(http://pypi.python.org/pypi/ll-xist).ll-core is a collection of the following modules:ansistylecan be used for colored terminal output (via ANSI
escape sequences).colorprovides classes and functions for handling RGB color values.
This includes the ability to convert between different color models
(RGB, HSV, HLS) as well as to and from CSS format, and several functions
for modifying and mixing colors.makeis an object oriented make replacement. Like make it allows you
to specify dependencies between files and actions to be executed
when files don’t exist or are out of date with respect to one
of their sources. But unlike make you can do this in a object oriented
way and targets are not only limited to files, but you can implement
e.g. dependencies on database records.miscprovides several small utility functions and classes.sisyphusprovides classes for running Python scripts as cron jobs.daemoncan be used on UNIX to fork a daemon process.urlcontains an RFC2396 compliant implementation of URLs and classes for
accessing resource metadata (like modification dates or permission bits) as
well as file like classes for reading data from URLs and writing data to URLs.xpitis a module that makes it possible to embed Python expressions
in text (as XML style processing instructions).xml_codecprovides a meta codec for decoding XML input.Changes in 1.11.1 (released 01/11/2008)Added missing source file_xml_codec_include.cto the source
distributions.
Package: llcsciencesdk
LLC Science SDKA simple way to fetch scientific data from the Science Admin.
Please visit {BASE_URL}/api/schema/swagger-ui/#/ for more information on the API.InstallationpipinstallllcsciencesdkUpdating to a new versionpipinstallllcsciencesdk-UUsageSpecifying environmentfromllcsciencesdk.llc_apiimportScienceSdkllc_api=ScienceSdk()# connect to productionllc_api=ScienceSdk(environment="staging")# connect to stagingllc_api=ScienceSdk(environment="local")# connect to localhostllc_api=ScienceSdk(environment="http://127.0.0.1:8009")# connect to custom urlLogging infromllcsciencesdk.llc_apiimportScienceSdkllc_api=ScienceSdk()llc_api.login("username","password")Using the endpointsfromllcsciencesdk.llc_apiimportScienceSdkllc_api=ScienceSdk()llc_api.login("username","password")model_input=llc_api.get_ft_input(1)planting_design_list=llc_api.get_planting_design_list()planting_design_detail=llc_api.get_planting_design(10)Supported endpointsSee the swagger docs for a complete list. {BASE_URL}/api/schema/swagger-ui/#/For DevelopersUpdating packageMake sure you have supported version of flit installed (see project.toml).python3 -m pip install flitSet env variables for PiPy login:export FLIT_USERNAME=username
    export FLIT_PASSWORD=passwordUpdate the version of the API in theinit.py file.Run command to publish to PiPy:flit publish
Package: lldb-dfsan
lldb-dfsanlldb-dfsanis an LLDB plugin that allows inspecting the DFSan labels of
variables, structures and memory in the target process.Installationpipinstalllldb-dfsan&&echo"command script import lldb_dfsan">>~/.lldbinitHow to useThis plugin provides thelabel VARcommand which prints all labels of the
references local variableVAR. For example:Process38271stopped4inti[3]={1,2,3};56dfsan_labeli_label=1;7dfsan_set_label(i_label,&(i[1]),sizeof(int));(lldb)labeli
i:arrayint[3]{[0]:Notaint[1]:(Taintclass1)[2]:Notaint}
Package: lldblogger-cli
使用指引安装lldbloggerpip install lldblogger指定要测试的app目录地址和断点日志配置文件from lldblogger.runner import Runner

runner = Runner(log_output_path='log file path')
runner.run("path to app", "path to config json")

...

runner.terminate() # 停止调试app文件可以从debug配置的ipa包中解压出来，同时需要将配套的dSYM文件置于同一个目录，可以通过Runner的构造函数指定断点日志输出到哪个文件。断点日志配置文件格式如下:[
  {
    "file": "文件名",
    "line": 48,
    "log": "[测试串行队列] counter： @counter@"
  },
  {
    "symbol": "-[ViewController viewDidLoad]",
    "log": "<From LLDB> [LLDBTest] 使用符号断点 ViewController viewDidLoad"
  }
]目前支持两种形式的断点日志指定文件名和代码行数，file表示文件名，比如AppDelegate.m，line表示代码行数，log表示要打印的日志指定方法的符号名称symbol，同样log表示要打印的日志日志log可以包含动态参数，使用@@包裹动态参数，比如要打印局部变量localVar，可以这么写"log": "局部变量：@localVar@"目前已测试的动态参数支持情况：方法的参数局部变量静态变量类的实例变量简单的表达式
Package: lle7ch
lle7ch (PyLumencorLightEngine7Channel)ControlsSOLA light engine. It coversthis document.Installpip install lle7chUsageFrom Python Script>>>fromlle7chimportLLE>>>lle=LLE()>>>lle.enable_lights_disable_others({LLE.Light.RED,LLE.Light.BLUE})# Enable Red and Blue LED, and disable other lights.>>>lle.tune_intensity(LLE.Light.RED,128)# Set intensity of Red LED as 128. (Min: 0, Max: 255)>>>lle.enable_lights_disable_others({})# Disable all lights.>>>lle.read_temperature()40.0>>>CLI ToolYou can make a plan to control LLE automatically.r: LLE.Light.RED, g: LLE.Light.GREEN, c: LLE.Light.CYAN, u: LLE.Light.UV, b: LLE.Light.BLUE, t: LLE.Light.TEAL$catplan.txtsetg,128
ongwait5setb,255
onb,gwait5$lleplanplan.txtLicenseGNU General Public License v3.0
Package: lleaves
lleaves 🍃A LLVM-based compiler for LightGBM decision trees.lleavesconverts trained LightGBM models to optimized machine code, speeding-up prediction by ≥10x.Examplelgbm_model=lightgbm.Booster(model_file="NYC_taxi/model.txt")%timeitlgbm_model.predict(df)# 12.77sllvm_model=lleaves.Model(model_file="NYC_taxi/model.txt")llvm_model.compile()%timeitllvm_model.predict(df)# 0.90sWhy lleaves?Speed: Both low-latency single-row prediction and high-throughput batch-prediction.Drop-in replacement: The interface oflleaves.Modelis a subset ofLightGBM.Booster.Dependencies:llvmliteandnumpy. LLVM comes statically linked.Installationconda install -c conda-forge lleavesorpip install lleaves(Linux and MacOS only).BenchmarksRan on a dedicated Intel i7-4770 Haswell, 4 cores.
Stated runtime is the minimum over 20.000 runs.Dataset:NYC-taximostly numerical features.batchsize110100LightGBM52.31μs84.46μs441.15μsONNX  Runtime11.00μs36.74μs190.87μsTreelite28.03μs40.81μs94.14μslleaves9.61μs14.06μs31.88μsDataset:MTPL2mix of categorical and numerical features.batchsize10,000100,000678,000LightGBM95.14ms992.47ms7034.65msONNX  Runtime38.83ms381.40ms2849.42msTreelite38.15ms414.15ms2854.10mslleaves5.90ms56.96ms388.88msAdvanced UsageTo avoid expensive recompilation, you can calllleaves.Model.compile()and pass acache=<filepath>argument.
This will store an ELF (Linux) / Mach-O (macOS) file at the given path when the method is first called.
Subsequent calls ofcompile(cache=<same filepath>)will skip compilation and load the stored binary file instead.
For more info, seedocs.To eliminate any Python overhead during inference you can link against this generated binary.
For an example of how to do this seebenchmarks/c_bench/.
The function signature might change between major versions.DevelopmentHigh-level explanation of the inner workings of the lleaves compiler:linkmambaenvcreate
condaactivatelleaves
pipinstall-e.
pre-commitinstall
./benchmarks/data/setup_data.sh
pytest-k"not benchmark"CiteIf you're using lleaves for your research, I'd appreciate if you could cite it. Use:@software{Boehm_lleaves,
  author = {Boehm, Simon},
  title = {lleaves},
  url = {https://github.com/siboehm/lleaves},
  license = {MIT},
}
Package: llfuse
Warning - no longer developed!Python-LLFUSE is no longer actively developed and just receiving
community-contributed maintenance to keep it alive for some time.Python-LLFUSE is a set of Python bindings for the low levelFUSEAPI. It requires at least FUSE 2.8.0 and supports both Python 2.x and
3.x. Like FUSE itself, Python-LLFUSE is developed for Linux systems,
but it should be compatible with OS-X, FreeBSD and NetBSD as well.Python-LLFUSE releases can be downloaded fromPyPi. The documentation
can beread onlineand is also included in thedoc/htmldirectory of the Python-LLFUSE tarball.Getting HelpPlease report any bugs on theissue tracker. For discussion and
questions, please use the generalFUSE mailing list. A searchablemailing list archiveis kindly provided byGmane.ContributingThe Python-LLFUSE source code is available onGitHub.
Package: llg
# LLGThe main objective of this library is to solve the [LLG equation](https://en.wikipedia.org/wiki/Landau%E2%80%93Lifshitz%E2%80%93Gilbert_equation) by using the Heun schema. We also implemented a CLI in order to let you usellgfrom console. You can follow the instructions given on the [wiki](https://github.com/jdalzatec/llg/wiki). Do not hesitate to write us if you have any suggestion/question.Free software: MIT licenseDocumentation:https://github.com/jdalzatec/llg/wiki## CreditsThis package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage) project template.
Package: llhttp
pyllhttpPython wrapper for llhttpA simple Python wrapper aroundllhttp,
the HTTP parser forNode.js.Installllhttpvia PyPI, orpip install llhttpUsageimportllhttpfrompprintimportpprintpprint({"version":llhttp.version})classrequest_parser(llhttp.Request):headers={}url=b''current_header_field=Nonecurrent_header_value=Nonedefon_message_begin(self):print(f"MESSAGE BEGIN")defon_url(self,url):self.url+=urlself.pause()defon_url_complete(self):print(f"URL{self.url}")defon_header_field(self,field):assertself.current_header_valueisNoneifself.current_header_fieldisNone:self.current_header_field=bytearray(field)else:self.current_header_field+=fielddefon_header_field_complete(self):self.current_header_field=self.current_header_field.decode('iso-8859-1').lower()assertself.current_header_fieldnotinself.headersdefon_header_value(self,value):assertself.current_header_fieldisnotNoneifself.current_header_valueisNone:self.current_header_value=bytearray(value)else:self.current_header_value+=valuedefon_header_value_complete(self):assertself.current_header_fieldisnotNoneself.current_header_value=bytes(self.current_header_value)print(f"HEADER{self.current_header_field}:{self.current_header_value}")self.headers[self.current_header_field]=self.current_header_valueself.current_header_field=Noneself.current_header_value=Nonedefon_headers_complete(self):assertself.current_header_fieldisNoneassertself.current_header_valueisNonedefon_message_complete(self):print("MESSAGE COMPLETE")parser=request_parser()assertparser.lenient_headersisnotTrueparser.lenient_headers=Trueparser.reset()assertparser.lenient_headersisTruebuffer=b"GET /test HTTP/1.1\r\nlOl:wut\r\nOH: hai\r\n\r\n"whilebuffer:consumed=parser.execute(buffer[:2])buffer=buffer[consumed:]ifparser.is_paused:print("UNPAUSING")parser.unpause()parser.finish()pprint({"method":parser.method,"url":parser.url,"version":f"{parser.major}.{parser.minor}","headers":parser.headers,}){'version': '3.0.0'}
MESSAGE BEGIN
UNPAUSING
UNPAUSING
UNPAUSING
URL b'/test'
HEADER lol: b'wut'
HEADER oh: b'hai'
MESSAGE COMPLETE
{'headers': {'lol': b'wut', 'oh': b'hai'},
 'method': 'GET',
 'url': b'/test',
 'version': '1.1'}ExtraThis project is a toy, started to reacquaint myself with Pythonc-apimodules.  If you find it useful,
please let me know.The version number tracks the version of the incorporated llhttp.License:MIT
Package: llhub
About allhuballhubis a REST API library for Github REST API v3 written in python. Currently, this library is under heavy
development.  Maybe i will cut a release once i am confident that major part of the library covered with tests.Featuresallhubis heavily inspired by Javascript, meaning that you can access the properties on JSON
object either object.prop or object["prop"]. I feel the later version is kind of verbose, and I recommend
you use the object.prop.I have seen most of the Github libraries are not covered comprehensively. But this library aims to covers
all of REST API v3.I have designed this library keeping programmer ergonomics in mind, so that you create only one object
to access any of the API.LicenseApache License 2.0
MITin case you need  some other license, please let me know.ExamplesfromallhubimportAllHuballhub=AllHub("username","tokenxxxxxxxxxxxxxxx","app_tokenxxxxxxxxxxxxxx","password")response=allhub.xxxxxxxxx()TODOallhubrequires some love. Currently, unit tests are almost non existent. Love to take the coverage to 100%.
But I am severely limited by time and priorities. If someone wants to send unit tests, please fork and
send the patch away.You are more than welcome.