Saravanan666/pypi-org huggingface dataset:
Package: ae-progress
progress 0.3.10ae namespace module portion progress: display progress of long running processes.installationexecute the following command to install the
ae.progress module
in the currently active virtual environment:pipinstallae-progressif you want to contribute to this portion then first forkthe ae_progress repository at GitLab.
after that pull it to your machine and finally execute the
following command in the root folder of this repository
(ae_progress):pipinstall-e.[dev]the last command will install this module portion, along with the tools you need
to develop and run tests or to extend the portion documentation. to contribute only to the unit tests or to the
documentation of this portion, replace the setup extras keydevin the above command withtestsordocsrespectively.more detailed explanations on how to contribute to this projectare available herenamespace portion documentationinformation on the features and usage of this portion are available atReadTheDocs.
Package: aepsicro
A python library to perform psychrometrics analysisCalculations are made by implementing equations described in 2009 ASHRAE Handbookâ€”Fundamentals (SI).There are three main classes to use this package: AIRE_HUMEDO, FLUJO, PSICROMETRICO.AIRE_HUMEDO represents a psychrometric state.
FLUJO represents a stream in a particular state.
PSICROMETRICO shows a psychrometric diagram in a window that allows to plot statesInstall:
pip install aepsicroUsage:from aepsicro import aepsicro as ps
s1 = ps.AIRE_HUMEDO(tseca=20, humrel=(0.5, ‘%1’))
s2 = ps.AIRE_HUMEDO(tseca=(30,’ÂºC’), humrel=(0.9, ‘%1’))
print(s1)
print(s2)
f1 = ps.FLUJO(s1,(1000, ‘m3/h’))
f2 = ps.FLUJO(s2,(500, ‘m3/h’))
# Adiabatic mixture
f3 = f1 + f2
psi = ps.PSICROMETRICO()
psi.marca_proceso(f1, f2, f3)
Package: aepsych-client
AEPsych Python client v0.2.0This lets you use Python to interface with the AEPsych server to do model-based adaptive experimentation.InstallationWe recommend installing the client under a virtual environment likeAnaconda.
Once you've created a virtual environment forAEPsychClientand activated it, you can install through pip:pip install aepsych_clientIf you are a developer, you should also install themain AEPsych packageso that you can run the tests.ConfigurationThis interface uses AEPsych's ini-based config, which gets passed as a string to the server:# Instantiate a client
client = AEPsychClient(ip="0.0.0.0", port=5555)

# Send a config message to the server, passing in a configuration filename
filename = 'configs/single_lse_2d.ini'
client.configure(config_path=filename)Ask and tellTo get the next configuration from the server, we callask; we report on the outcome withtell.# Send an ask message to the server
trial_params = client.ask()

# Send a tell back
client.tell(config={"par1": [0], "par2": [1]}, outcome=1)Resume functionalityWe can run multiple interleaved experiments. When we callconfigure, we get back a strategy ID.
The client keeps track of all these strategy IDs and we can use them to resume experiments. By
doing this we can interleave different model runs.# Configure the server using one config
client.configure(config_path=file1, config_name='config1')

# Run some stuff on this config
...

# Configure the server using another config
client.configure(config_path=file2, config_name='config2')

# Run some stuff on this other config
...

# Resume the past config
client.resume(config_name="config1)Ending a sessionWhen you are done with your experiment, you should callclient.finalize(), which will stop the server and save your data to a database.
Package: ae-pvalues
AE-pvaluesThis is the implementation ofAE-pvaluesproposed in the paperTowards Understanding Alerts raised by Unsupervised Network Intrusion Detection Systems, RAID2023(https://doi.org/10.1145/3607199.3607247).
The method allows you to obtain explanations of the anomalies identified by an auto-encoder (AE).Authors@mlanvinInstallationPip InstallationYou can install it through pippipinstallae-pvaluesUsage/Examplesfromae_pvaluesimportae_pvaluesmodel=# Any autoencoderx_train=np.load("./demo/example/x_train.npy")# Normal datax_test=np.load("./demo/example/x_test.npy")# Data to explainorder,pvalues=ae_pvalues(model,normal_data=x_train,data_to_explain=x_test)If you installed the module then you can use ae-pvalues directly from the command line :ae-pvalues-v./demo/example/x_train.npy./demo/example/rec_x_train.npy./demo/example/x_test.npy./demo/example/rec_x_test.npy-ooutputs-v: verbose mode-o: output folderThis produces two output files namely:dimensions_abnormal_order.npy : order of dimension by abnormalitydimensions_abnormal_pvalues.npy : raw pvalue scoresDemoA notebook is available in the demo folder to show an example of use.
Please note that the notebook requires the following dependencies :scikit-learntensorflowseabornplotlywhich can be installed with pip using the following comand line :python3-mpipinstallscikit-learntensorflowseabornplotly
Package: aep-web
Adversary Emulation Planner Web FrontendThis tool can be used to automatically build an ordered set of attack stages
withMITRE ATT&CKtechniques executed during each stage.The output is a set of attack stages that show all possible techniques that an
adversary might execute during each stage.InstallationInstall using pip:pipinstallaep-webYou will also need to clone theaep-datarepository, which contains a starting point witch example data:gitclonehttps://github.com/mnemonic-no/aep-dataRungitclonehttps://github.com/mnemonic-no/aep-data
aep-web--port<PORT>--data-dir/path/to/aep/data-dirOptionsAvailable options--reload            Watch for file changes and reload webserver upon change
                    (Useful for development)
--web-reoot <PATH>  Need if running behind a reverse proxy with another path than /
--data-dir <PATAH>  Path to aep-data directory
--port <PORT>       Listen on PORT (default is 3000)
--host <HOST>       Host intefrace to listen to (default is 127.0.0.1)
Package: aeqtl
AeQTLeQTL analysis using region-based aggregation of rare variants.Requirementspython 3.5pipbx_interval_tree (see installation instructions below)git (optional)InstallationFirst, install IntervalTree from bx-python. We strongly recommend using a standalone package calledbx_interval_treewhich is smaller and easier to compile than bx-python.git clone https://github.com/ccwang002/bx_interval_tree
cd bx_interval_tree
python setup.py install
cd ..Continue to install AeQTL by choosing one of the options below.(1) From PyPIThe easiest way to install AeQTL is from PyPI.pip install aeqtl(2) From source codeAlternatively, download the source code of AeQTLgit clone https://github.com/Huang-lab/AeQTLThen install AeQTLcd AeQTL
pip install .Optional (but recommended)Append the path to AeQTL to your PATH environment variableexport PATH=/path/to/AeQTL/bin:$PATHRunaeqtl -v <vcf file> -b <bed file> -e <expression file> \
	  -cn <numerical covariates> -cc <categorical covariates> -s <covariate file> \
      -o <output directory>Input data formatNote: demo input files with compatible format can be found in the "demo" folderVCF fileA standard multi-sample VCF file with file extension .vcf (or .vcf.gz). Sample IDs in VCF file, expression file, and covariate file should match exactly.BED fileA BED file (tab separated) with at least four columns and without header. The format of the file should follow:<chromosome>	<start>		<end>		<region_name>		<tested_genes>An example row:chr17			41197693	41197821	BRCA1				BRCA1;SLC25A39;HEXIM2The first four columns are required. The fifth column is a list of genes separated by ";". If the fifth column (tested_genes) is not provided, AeQTL by default will test each region with every gene from the expression file.Expression fileA matrix-format, tab separated .tsv file with gene expression from RNA-seq. The first row (header) of the file should follow:gene_id		<sample_id_1>		<sample_id_2>		<sample_id_3>		...and the first column of the file should follow:gene_id
<gene_1>
<gene_2>
...Covariate fileA tab separated .tsv file with column names corresponding to covariates. A column of sample IDs with column name "sample_id" is required. Covariates entered in AeQTL and their corresponding column names must match exactly. However, the covariate file can contain other unused columns as well. If entering a categorical covariate, please make sure each category has the same value throughout the file (i.e. avoid instances such as having both "FEMALE" and "female" in the same column).Output data formatA tab separated .tsv file of summary statistics (up to 5 digits after the decimal point). Each row is an eQTL test between a region and a gene. The file contains the following fields:regiongenecoef_interceptcoef_genotypecoef_<covariate>(for each covariate)pvalue_interceptpvalue_genotypepvalue_<covariate>(for each covariate)
Package: aerarium
A simple command line tool for tracking my finances.FeaturesCheck account balanceUse the--balanceor-bflag to print the current account balance.Withdraw amountUse the--withdraw<amount>or-w<amount>option to withdraw money, along with a description and category prompt.
Prints new account balance.Deposit amountUse the--deposit<amount>or-d<amount>option to deposit money, along with a description and category prompt.
Prints new account balance.List transactionsUse the--transactionsor-tflag to print a list of all transactions.Predict spendingUse the--predict<number_of_days>or-p<number_of_days>flag to print a prediction of spending for next n days.Help informationUse--helpto print usage help.UsageSome examples:$ aerarium --balance
Current balance: $1000

$ aerarium --withdraw 100
Enter withdrawal description: Groceries
Enter withdrawal category: Food
Balance: 900

$ aerarium --deposit 500
Enter deposit description: Salary
Enter deposit category: Income
Balance: 1400The configuration file contains the account name and starting balance.Note: This project is under active development.
Package: aeratable
Nicely format Aera interface-output to a table in ASCII or CSV format.TLDRIf you query a lot from aninterfacein Aera, you might want to format the output in a more readable way. This script does that for you. It takes the output from an interface and formats it into a table in ASCII or CSV format.InstallationIdeally install usingpipx, like any other command line tool:pipxinstallaeratableAlternatively, you can install using pip:pipinstallaeratableExampleIf you copy the output from an interface in Aera like below:It has the tendency to be in a very unusefull format. See the example of what the paste looks like:Theaeratablecommand line tool can be used to format this output into an ASCII (or CSV) table. The command line tool takes the 'ugly' formatted output directly from Windows' clipboard (using the wonderfullpyperclippackage) and creates a table in ASCII format. by default this will replace the clipboard content with the formatted table. If you provide the-vor--verboseflag, the table will additionally be printed to the console.aeratable-vThe output (when pasted back into theinterface) will now look like this:This can be very usefull if you query a lot from an interface in Aera, and want to store your intermediate results in a more readable format directly in the interface. Don't forget to put the table in a comment block (/* ... */) to prevent the interface from interpreting the table as a query.I want CSV instead of ASCIIsimple! use the-for--fmtflag with the valuecsv:aeratable-fcsvHappy SQL!
Package: aerforge
AerForgeA game engine made with pygame.Getting StartedInstall PythonOpen cmd/terminal and type:pip install AerForgeExamplesCreating a windowfromaerforgeimport*forge=Forge()@forge.eventdefupdate():passforge.run()Creating a cubefromaerforgeimport*forge=Forge()classCube(Entity):def__init__(self):super().__init__(window=forge,shape=shape.Rect,width=20,height=20,x=0,y=0,color=color.Color(0,255,255))self.center()cube=Cube()@forge.eventdefupdate():passforge.run()
Package: aerial
A Python library for receiving Unix style signals.This library is meant to be a simple way to deal with handling signals, while avoiding callbacks.Install it with pippipinstallaerialA simple use looks like this:>>>importtime>>>importsignal>>>>>>importaerial>>>defmain_loop():...whilenotaerial.received(signal.SIGTERM):...ifaerial.received(signal.SIGHUP):...print('Got a SIGHUP')...time.sleep(.5)...print('See you later')...>>>And try out the demo by running the module.python-maerial[PID10852]Hello,sendmeaSIGTERMtoexit,oraSIGHUPforatrick#  In another terminal[PID10852]Neathuh?#  kill -SIGHUP 10852[PID10852]Seeyoulater#  kill -SIGTERM 10852
Package: aerics
AericsA networking library for multiplayer games.Getting StartedInstall PythonOpen cmd/terminal and type:pip install AericsExamplesCreating a serverfromaericsimport*server=Server("localhost",5656)@server.eventdefsetup():pass@server.eventdefupdate():pass@server.eventdefon_connection(connection,address,id,clients,globals):print("New connection")return{"x":0,"y":0}@server.eventdefon_disconnection(connection,address,id,clients,globals):print(f"Client{id}disconnected")@server.eventdefon_recv(connection,address,id,clients,globals,data):data=data.split(",")ifdata[0]=="move":clients[id]["x"],clients[id]["y"]=data[1],data[2]returnclientsifdata[0]=="close":server.disconnect(connection)returnNoneserver.listen()Creating a clientfromaerforgeimport*fromaericsimport*defupdate():client.send(f"move,{player.x},{player.y}")players=client.recv()foriinplayers:forge.draw(width=50,height=100,x=int(players[i]["x"]),y=int(players[i]["y"]))if__name__=="__main__":forge=Forge()client=Client("localhost",5656)client.connect()player=prefabs.TopViewController(forge)player.visible=Falseforge.run()
Package: aeripy
Aeripy is a Python adapter for the Aeries Student Information System API.  It’s goal is to simplify interaction with your district’s Aeries database.This library is mostly a wrapper for the popularrequestslibrary.Note: Aeripy is in no way affiliated with Aeries Software, Inc. This module is based on their publicly available documentation.InstallationAeripy is supported on Python 3.8+. The recommended way to install Aeripy is via pip.pip install aeripyQuickstartThe following example uses the Aeries demo database URL and API key.
Both are found in theirdocumentation.To use this module with your own Aeries database, you will need your district’s URL and API key.  See theURL and API Keysection for details.# Aeries Demo URL and API keyhostname='demo.aeries.net/aeries/api/'api_key='477abe9e7d27439681d62f4e0de1f5e1'# In production the URL and API key should not be hardcoded.# For example, they can be retrieved from an environment variablehostname=os.getenv('AERIES_URL')api_key=os.getenv('API_KEY')Create an Aeripy instance and use that to interact with the API.fromaeripyimportAeripyaeries=Aeripy(hostname=hostname,api_key=api_key)# Get all staffstaff_list=aeries.get_staff()# Print the last name of the 55th staff memberprint(staff_list[55].last_Name)# Output:# Barrows# Print the first name of the 55th staff memberprint(staff_list[55].last_Name)# Output:# James# Print the ID of the 55th staff memberprint(staff_list[55].id)# Output:# 994748Check out thefull documentationfor a list of currently supported methods and more examples.URL and API KeyYou must have access to the Security section in Aeries Web to obtain or create the API key.
Package: aerisapisdk
Getting Started$ pip install aerisapisdk
$ aeriscli --help
Usage: aeriscli [OPTIONS] COMMAND [ARGS]...

Options:
  -v, --verbose             Verbose output
  -cfg, --config-file TEXT  Path to config file.
  --help                    Show this message and exit.

Commands:
  aeradmin    AerAdmin API Services
  aerframe    AerFrame API Services
  aertraffic  AerTraffic API Services
  config      Set up the configuration for using this tool
  ping        Simple ping of the api endpointsMore InformationCheck out thewikifor more information.
Package: aerisweather
The AerisWeather Python SDK allows a developer to quickly and easily add
weather content and functionality to their Python applications.It utilizes the Aeris API backend for data loading and is built on
top of an object mapping system that efficiently loads requested
weather content into third-party Python applications, greatly
reducing the amount of code and development needed on the developer end.RequirementsThe AerisWeather Python SDK is built using Python v3.6.You also need:An active AerisWeather API account (don’t have one?sign up here)Python v3.6 or Higheraenum v2.1.0 or Higherrequests libNote: To use the included Jupyter Notebook, you will also need to install the Jupyter kernelHow to get itDownload or link to the aerisweather package via The Python Package Index atpypi.org/project/aerisweatherGet the code at the AerisWeather Python SDK project onGitHubVisit the SDK’s home athttps://www.aerisweather.com/support/docs/toolkits/aeris-python-sdk/.DocsBrowse the code docs online
Package: aerleon
AerleonGenerate firewall configs for multiple firewall platforms from a single platform-agnostic configuration language through a command line tool and Python API.Aerleon is a fork ofCapircawith the following major additions:YAML policy and network definition files andpol2yaml, a converter from Capirca policy DSL to YAML.Network definitions with FQDN data.New firewall platforms can be added through plugins.Typed Python APIs for ACL generation and aclcheck queries.ASLSA-compatible verifiable release process.A detailed regression test suite.Many bug fixes and performance enhancements.InstallAerleon requires Python 3.7 or higher.pipinstallaerleonOverviewAerleon provides a command line tool and a Python API that will generate configs for multiple firewall platforms from a single platform-agnostic configuration language. It can generate configs for Cisco, Juniper, Palo Alto Networks andmany other firewall vendors.Agetting started guidewalking through the basics of using Aerleon is available on the docs website.DocumentationDocumentation can be found athttps://aerleon.readthedocs.io/en/latest/.ContributingContributions are welcome. Please review thecontributing guidelinesandcode of conductfor this project.ContactOfficial channels for communicating issues is viaGithub Issues.General discussions can be had either inGithub Discussionsor in ourSlack Server.Contact MaintainersYou can always reach out to us onSlack.
You many also reach out to us via e-mail.Rob Ankeny (ankenyr@gmail.com)Jason Benterou (jason.benterou@gmail.com)ResourcesBrief Overview (4 slides):Nanog49; Enterprise QoSBlog Post: Safe ACL Change through Model-based AnalysisAerleon Slack#aerleon at NetworkToCode SlackContributors ✨Thanks goes to these wonderful people (emoji key):Ken Celenza📖Axel F📖Brandon Bennett💻Bastian Triller💻Arzhel Younsi💻This project follows theall-contributorsspecification. Contributions of any kind welcome!CreditFiles and code included in this project from Capirca are copyright Google and
are included under the terms of the Apache License, Version 2.0. You may obtain
a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Contributors who wish to modify files bearing a copyright notice are obligated
by the terms of the Apache License, Version 2.0 to include at the top of the
file a prominent notice stating as much. Copyright notices must not be removed
from files in this repository.This README file and other documentation files may contain phrases and sections that are copyright Google.
This file and other documentation files are modified from the original by the Aerleon Project Team.
Package: aermanager
aermanagerPython package for managing AER data, including .aedat4 files, live DV streams, and interfacing with pyTorch.This software is designed for the following purposes:Read aedat files (v2-4) containing DVS spike trains, accumulate them, and save them in a format that can be read by a pyTorch dataset in a faster way.Read the files into a pyTorch datasetReceive frames from the DV software, queue and batch them in order to feed them to a spiking neural network simulation (LiveDv).InstallationThe easiest way to install this repository is to clone it and install it with pip:pipinstallaermanagerAlternatively, you can checkout this repository and install it.gitclonehttps://gitlab.com/synsense/aermanager.gitcdaermanager
pipinstall.Documentationhttps://synsense.gitlab.io/aermanagerYou can generate local documentation by running the below command at the location of the source code.cdpath/to/aermanager
pythonsetup.pybuild_sphinx
Package: aeroapi-python
AeroApi PythonPython wrapper for the FlightAware's AeroAPIDescriptionAeroAPI (formerly FlightXML) is FlightAware's live flight data API that provides powerful, reliable information about real-time and historical flight information. This Python wrapper allows for easier interaction with the AeroAPI from Python applications.FlightAware AeroAPI ReferenceAeroAPIInstallationpipinstallaeroapi-pythonUsing test pypi, install with this command.pipinstall--index-urlhttps://pypi.org/simple/--extra-index-urlhttps://test.pypi.org/simple/aeroapi-pythonUsageAeroAPI ClassThe AeroAPI class is the main class for interacting with the FlightAware AeroAPI. It provides access to various resources such as airports, operators, flights, and more.InitializationTo use the AeroAPI class, you need to create an instance of it by passing your API key as an argument to the constructor:fromAeroAPIimportAeroAPIapi_key='your-api-key'aeroapi=AeroAPI(api_key)AirportsThe Airports class provides methods for retrieving information about airports. You can access an instance of the Airports class through the airports attribute of the AeroAPI instance:airports=aeroapi.airportsMethodsget_airport_info(airport_code: str) -> dict: Returns information about the specified airport.airport_info=airports.get_airport_info('KLAX')search_airports(query: str) -> list: Searches for airports that match the specified query.airport_list=airports.search_airports('Los Angeles')get_nearby_airports(latitude: float, longitude: float, radius: int) -> list: Returns a list of airports near the specified latitude and longitude within the specified radius.airport_list=airports.get_nearby_airports(33.9425,-118.408056,10)Example UsagefromAeroAPIimportAeroAPIapi_key='your-api-key'aeroapi=AeroAPI(api_key)airports=aeroapi.airportsairport_info=airports.get_airport_info('KLAX')print(airport_info)LicenseMITAuthors@derens99
Package: aero-astro-calc
Aero-Astro-CalcA small python library with functions to assist engineers.Getting StartedInstall using pip:pip install aero-astro-calcImport into your Python projectfromaero_astro_calcimportPlaneStressUsageMake plane stress object:stress=PlaneStress(80,-40,25)View the stress on a 2D plane:stress.plane()View Mohr's circle of the stresses:stress.mohr()ContactAnson Biggs -anson@ansonbiggs.com-@Anson_3D
Package: aerobridge
Python client for Aerobridge Management ServerThis repository is the python client for theAerobridge Management Server API.Usage InstructionsInstall requirementspip install aerobridgepyIn your code usefrom aerobridgepy import AerobridgeClient🎉Test driveGo toAerobridge.iofor a test drive of the stack.Change log[0.0.1] - 2022-02-17Initial Commit
Package: aeroet
aeroetSurface energy balance and atmospheric profiling algorithms for calculating
evapotranspiration from thermal imagery.See Morgan, B.E. & Caylor, K.K. (2023). Estimating fine-scale transpiration from
UAV-derived thermal imagery and atmospheric profiles.Water Resources Research,
59, e2023WR035251.https://doi.org/10.1029/2023WR035251Note: The API and documentation are under development. Please contact Bryn Morgan
(brynmorgan@ucsb.edu) with any questions.InstallationCreate a new conda environment.$condacreate-naeroetpython=3.10
$condaactivateaeroetNote: I'm not sure whether specifying the version of python is necessary, but likely depends the version of python install in yourbaseenvironment. Better to specify.pipinstall.$pipinstallaeroetUse as desired!UsageSeehttps://github.com/brynemorgan/uavet-wrrfor example usage.