Saravanan666/pypi-org huggingface dataset:
Package: amle-py
Amstrad Learning EnvironmentThe Amstrad Learning Environment is a Python interface that is meant to be used with AIs and OpenAi Gym. The Python library is
available on Linux and Windows.InstallationUnixFirst you’ll need to install Python3 (version >= 3.6). To install Python :$sudoapt-getinstallpython3To check the version :$python3--versionThen, you’ll need to install pip, so you can install the amle-py package :$curlhttps://bootstrap.pypa.io/get-pip.py|pythonWith pip, you’ll need to install two dependencies :$pipinstall--upgradesetuptools$pipinstall--upgradenumpyFinally you can install amle-py :$pipinstall--upgradeamle-pyWindowsInstall Python by going on theirwebsite. Make sure to add Python to your path and install pip during the installation.How to useCompile and use the sources (Linux only)To compile the sources you will need to install a few dependencies :SDL 1.2libpnglibzipcppunitOnce you have all these dependencies, you can compile the project. You may also want to generate the documentation of the project, this is done in the doc/ folder. Make sure to have doxygen installed :$sudoapt-getinstalldoxygenAnd then :$doxygendoxygen.configUse the Python libraryFirst you will need to create a new Python file in which you import the library :importamle_pyThen you will need to create a new amle object :amle=amle_py.AmLEInterface()And you will need to load a game :amle.loadSnapshot("Arkanoid","snap/arka.sna")Note that the first string has to a name the AmLE can understand. If you have a doubt you can get the list of all possible strings with :games=amle.getSupportedGames()Moreover, the second argument is a path to YOUR .sna file. You have to import one from the internet and generate one yourself with an emulator. Also, it doesn’t have to be in a snap/ folder, this is just cleaner.Then you may want to run the game :whilenot(amle.gameOver()):amle.step()This doesn’t do anything interesting though, you can also interact with the game. To do so, before the loop you can do :nbLegalActions=amle.getNbLegalActions()legalActions=np.empty(nbLegalActions,dtype=np.int32)amle.getLegalActions(legalActions)legalActions=legalActions.tolist()And then in the loop :importamle_pyimportrandom# The previous code discussedwhilenot(amle.gameOver()):amle.act(random.choice(legalActions))amle.step()Finally, you may want to generate the documentation for the library. To do so go in the amle_py folder and run :$pydoc-wamle_python_interface.py
Package: amlhpc
amlhpcPackage to provide a -just enough- Slurm or PBS experience on Azure Machine Learning. Use the infamous sbatch/qsub/sinfo to submit
jobs and get insight into the state of the HPC system through a familiar way. Allow applications to interact with AML without
the need to re-program another integration.For the commands to function, the following environment variables have to be set:SUBSCRIPTION=<guid of you Azure subscription e.g. 12345678-1234-1234-1234-1234567890ab>
CI_RESOURCE_GROUP=<name of the resource group where your Azure Machine Learning Workspace is created>
CI_WORKSPACE=<name of your Azure MAchine Learning Workspace>In the Azure Machine Learning environment, the CI_RESOURCE_GROUP and CI_WORKGROUP are normally set, so you only need to export SUBSCRIPTION.sinfoShow the available partitions. sinfo does not take any options.(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sinfo
PARTITION       AVAIL   VM_SIZE                 NODES   STATE
f16s            UP      STANDARD_F16S_V2        37
hc44            UP      STANDARD_HC44RS         3
hbv2            UP      STANDARD_HB120RS_V2     4
login-vm        UP      STANDARD_DS12_V2        NonesqueueShow the queue with historical jobs. squeue does not take any options.(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ squeue
JOBID                           NAME            PARTITION       STATE   TIME
crimson_root_52y4l9yfjd         sbatch  	f16s
polite_lock_v8wyc9gnx9          runscript.sh    f16ssbatchSubmit a job, either as a command through the--wrapoption or a (shell) script. sbatch uses several options, which are explained in sbatch --help.
Quite a bit of sbatch options are supported such as running multi-node MPI jobs with the option to set the amount of nodes to be used.
Also array jobs are supported with the default--arrayoption.Some additional options are introduced to support e.g. the data-handling methods available in AML. These are explaned indata.md.(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sbatch -p f16s --wrap="hostname"
gifted_engine_yq801rygm2(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sbatch --help
usage: sbatch [-h] [-a ARRAY] -p PARTITION [-N NODES] [-w WRAP] [script]

sbatch: submit jobs to Azure Machine Learning

positional arguments:
  script                script to be executed

optional arguments:
  -h, --help            show this help message and exit
  -a ARRAY, --array ARRAY
                        index for array jobs
  -p PARTITION, --partition PARTITION
                        set compute partition where the job should be run. Use <sinfo> to view available partitions
  -N NODES, --nodes NODES
                        amount of nodes to use for the job
  -w WRAP, --wrap WRAP  command line to be executed, should be enclosed with quotesIf you encounter a scenario or option that is not supoprted yet or behaves unexpected, please create an issue and explain the option and the scenario.
Package: aml-pipeline
Azure-ML-PipelineAzure Machine Learning Pipeline high level APIThis project defines a set of high level APIs to define and publish Machine Learning Pipeline fromAzure Machine Learning Service.Azure Machine Learning Service supports 2 types of pipelines:Http triggered pipeline: expose a constant REST endpoint to be triggered by authenticated Http requestSchedule based pipeline: pipeline is triggered by a predefined time interval.Use the libaryfor Http triggered pipeline: inherentHttpTriggeredPipelineclass and override two methods:registerDataStores() and definePipelineSteps() . Example:HttpSklearnAzureFunctionPipeline.pyfor schedule based pipeline: (developing)Contribute to this repoBuild locallyUse conda (recommended)conda create -p .env python=3.7conda activate ./.env.env/bin/pip install -r src/requirements.txtUse pippython3.6 -m venv .envsource .env/bin/activate.env/bin/pip install -r src/requirements.txt
Package: amlpp
Descriptionlibrary wrapper for ml libraries.СapabilitiesThe main class is a conveyor as an analogue of a pipeline. There is a partial interchangeability. The focus is on expanding built-in capabilities such as auto-typing predictive models, feature importance inference, scores, and various future features. There are also adapted transformers, classes for customizing the structure of projects.StatusAt the moment, the functionality is not wide, designed for expansion in the future
Package: amm
AMMThe AI model manager.Use it# Initialize a new projectamminit# Probe an existing directory to build a map fileammprobe# Install a model from Civitaiamminstallhttps://civitai.com/models/7240/meinamix# Install a model to a specific placeamminstallhttps://civitai.com/models/7240/meinamix./models/meinamix# Install a Lora from Civitai, and automatically pair it with previewsamminstallhttps://civitai.com/models/13213# Install a model from Hugging Faceamminstallhttps://huggingface.co/THUDM/chatglm-6b/blob/main/pytorch_model-00001-of-00008.bin# Download a model by nameamminstallbloom# Install from an existing amm.jsonamminstall
amminstall-rnon-default-named.amm.jsonLicenseMIT
Package: ammcpc
This command-line application and python module is a simple wrapper around the
MediaConch tool which takes a file and a MediaConch policy file as input and
prints to stdout a JSON object indicating, in a way that Archivematica likes,
whether the file passes the policy check.Install with Pip:$ pip install ammcpcInstall from source:$ python setup.py installCommand-line usage:$ ammcpc <PATH_TO_FILE> <PATH_TO_POLICY>Python usage with a policy file path:>>> from ammcpc import MediaConchPolicyCheckerCommand
>>> policy_checker = MediaConchPolicyCheckerCommand(
        policy_file_path='/path/to/my-policy.xml')
>>> exitcode = policy_checker.check('/path/to/file.mkv')Python usage with a policy as a string:>>> policy_checker = MediaConchPolicyCheckerCommand(
        policy='<?xml><policy> ... </policy>',
        policy_file_name='my-policy.xml')
>>> exitcode = policy_checker.check('/path/to/file.mkv')System dependencies:MediaConch version 16.12To run the tests, make sure tox is installed, then:$ tox
Package: ammeter-logger
Python Ammeter LoggerWorks in conjunction with theMicropython ADC Amperage MonitorView the details of our load testing series here:https://www.learningtopi.com/category/load-testing/IntroductionThis Python module works with theMicropython ADC Amperage Monitorto collect data from a amperage monitor. The module will control the micropython microcontroller to run a baseline of the current sensor or collect data.The module can be run of the command line or imported into other projects to record amperage data.InstallationThe package is available on PyPi or can be installed manually using the why/tar.gz file in the dist folder.pip3 install ammeter_loggerCLI Usage(venv) $ python3 -m ammeter_logger 
usage: __main__.py [-h] [--get-config] [--get-status] [--skip-init] [--force-init] [--init-only] [--sample-interval SAMPLE_INTERVAL] [--capture-time CAPTURE_TIME] [--baudrate BAUDRATE] [--log-level LOG_LEVEL]
               DEVICE OUTPUT_FILE
__main__.py: error: the following arguments are required: DEVICE, OUTPUT_FILE
(venv) $The --get-config and --get-status parameters can be used to get the current status of the microcontroller.It is recommended to run the following the initialize the ammeter (get a baseline 0 reading) with no load on the ammeter before connecting the device you intend to monitor.  Initialization can be run using the --force-init option.After the ammeter is initialized, the capture can be started using the --capture-time interval. Logged data will be stored in the specified output_file (as a CSV).
Package: ammod-blob-detector
Blob DetectorClone me withgit clone https://git.inf-cv.uni-jena.de/AMMOD/blob_detector.gitDescriptionBlob detection algorithms for insects on a single-color (white) screen.Installationcondacreate-ndetectorpython~=3.9.0opencv~=4.5
condaactivatedetector
pipinstall--upgradepip
pipinstall-rrequirements.txtLicenceThis work is licensed under aCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Package: ammolite
Ammolite - From Biotite to PyMOL and back againThis package enables the transfer of structure related objects
fromBiotitetoPyMOLfor visualization,
via PyMOL’s Python API:ImportAtomArrayandAtomArrayStackobjects intoPyMOL-
without intermediate structure files.ConvertPyMOLobjects intoAtomArrayandAtomArrayStackinstances.UseBiotite’s boolean masks for atom selection inPyMOL.Display images rendered withPyMOLinJupyternotebooks.Have a look atthis example:PyMOL is a trademark of Schrodinger, LLC.
Package: ammonite
Ammonite: Geometry calculation cheatsheetUnit aware calculation of geometrical primitives' area, volumes, etc. It is base
onpintfor a seamless unit management and
easy conversion.This package is at an early stage of development, it currently only include3D:BoxCubeSphere2D:RectangleSquareCircleUsagefromammoniteimporturegfromammonite.Object3DimportSpheremy_sphere=Sphere(radius=3*ureg.meter)print(my_sphere.volume)# 113.09733552923254 meter ** 3print(my_sphere.area)# 113.09733552923255 meter ** 2ContributionsAny comments to report a bug, improve this package, make it follow best practices which
I might not know of or improve its automation are welcome.
Package: ammonyte
AmmonytePython package designed for conducting non-linear time series analysis of paleoclimate data. Recommended for usage in tandem withPyleoclim. Developed byAlexander Jameswith theUniversity of Southern California Climate Dynamics Lab Group. Currently under heavy construction.Much of Ammonyte's capability is enabled byPyRQA, a tool designed to conduct recurrence analysis in a massively parallel manner. Many of our functions are essentially wrappers around PyRQA functions, so we recommend looking into that package if you're curious about the bones of how recurrence analysis is done in Ammonyte.Install instructions:It's recommended you create a new environment using anaconda before installing.Inside your environment install cartopy with the commandconda install -c conda-forge cartopyRunpip install ammonyteThings to note:Current releases of Ammonyte are highly experimental. These are mainly done to facilitate our research, though others are welcome to use the functionality. Just be aware that the package is currently subject to constant change and will remain unstable for some time.Certain functionalities such as RecurrenceNetwork and the synthetic_series utilities are currently non-functional and are acting as placeholders for features that will be included in the future.This function has only been tested on macOS/Unix based operating systems, so if you encounter errors they may be system specific.Raising issues/feature requests is appreciated but there is no guarantee they'll be addressed hastily at this stage of the package's development as we are focused on building core features that are of use to our lab.
Package: amms_planop2xls
AMMS PlanOp2XLSImporter/konwerter plików PDF z planem operacyjnym z systemu Asseco Medical
Management Solutions.AMMS-PlanOp2XLS to otwarte oprogramowanie: licencja MIT. Oznacza to, iż jest
dostępne za darmo.Program w wersji binarnej stabilnej 0.7.3 można pobrać na dwa sposoby:format ZIP: wersja bez instalatora, nie wymaga uprawnień administratorado
instalacji, powinna działać z pendrive USB (78.6 MB)format EXE: pełnoprawny instalator, wymaga uprawnień administratora (63.6 MB)Wersja 0.7.3 dostępna jest tutaj:https://github.com/mpasternak/amms-planop2xls/releases/tag/v0.7.3Możliwości programugenerowanie plików ODT (OpenOffice, LibreOffice) według zadanych templatek,importowanie danych z plików PDF z planem operacyjnym generowanym przez system AMMS.CreditsAutorem ikony programu jest Edward Boatman, którego to inne prace można pobrać
ze stronyhttps://thenounproject.com/edward/collection/health-care-symbols/.Program jest niezależnym projektem firmy IPL i nie jest w żaden sposób związany
lub wspierany przez firmę Asseco.This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-05-02)First release on PyPI.
Package: amnes
Adaptive Meta-Framework for Network Experiment Studies (AMNES)AMNES is a highly modular tool that aims to simplify the execution of reproducible
network experiments of all kinds.
With its easy configurability, the possibility to develop comprehensive extensions and
the connectivity to different storage backends, this framework helps you to spend less
time on testbed configuration and more time on your network research.For Usage and Development Guide consultAMNES Documentation.
Package: amnesia
Amnesia reloads modules on-the-fly as an aid for developmentof WSGI compliant applications.
Amnesia is meant to be used like a standalone executable,
running on the shell likeamnesia mymodule my_wsgi_appor as a WSGI Middleware as follows:>>> from wsgiref.simple_server import make_server
>>> from amnesia import Amnesia
>>>
>>> my_reloading_app = Amnesia("mymodule", "wsgi_app")
>>>
>>> if __name__ == "__main__":
>>>     httpd = make_server("", 8080, my_reloading_app)
>>>     print("Running on localhost:8080...")
>>>     print("Modify your app and refresh your browser's page.")
>>>     httpd.serve_forever()
>>>
Package: amniotic
AmnioticA multi-output ambient sound mixer for Home Assistant.Amniotic lets you use a single device to create on-the-fly, custom ambient audio mixes - e.g. mixing Waterfall sounds
with Birdsong on one set of speakers, while playing Fireplace sounds from a second audio device - to suit your tastes
and environment.The library integrates with Home Assistant via MQTT as a new device, allowing you to create and control ambient mixes
from the Home Assistant interface.Why Would I Want Such a Thing?I won't explain the general reasons for introducing non-musical sounds into one's environment, but if you
findsound maskinghelps you concentrate in noisy environments, if
you're (as I am) slightlymisophonic, if you
usewhite noise machinesto induce sleep or relaxation, or if you
just think sound is an important factor in setting a pleasant ambience, then you might find Amniotic useful.Can't I do This with Spotify, Volumio, HifiBerry etc.?Since those systems are intended for music, they aren't designed for playing or mixing multiple streams simultaneously
with a single device, even if set up in multi-room configurations. Also, the streaming services often won't allow a
single account to play multiple streams, evenifmultiple devices are used.Anyway, those limitations motivated this library.There are two ways to install and run Amniotic:On the Home Assistant machine itself, as an add-on.Install manually, on a separate machine.Home Assistant AddonTo add as an Addon, click here:DocumentationFor non-addon installs, and general documentation, seethe project wiki.
Package: am-num2word
Amharic Number to WordConverters Numbers from decimal representation to Amharic language word representation. The package supports numbers up-to 999,999,999,999,999. These are the numbers that could often comeup in real world.UsageThis package provides one class(am_num2word.Number2WordsConverter) to handle number to words conversion.importam_num2wordnumber=45232945075converter=am_num2word.Number2WordsConverter()words,coma_separated=converter.convert(number)print(words)# አርባ አምስት ቢሊዮን ሁለት መቶ ሰላሳ ሁለት ሚሊዮን ዘጠኝ መቶ አርባ አምስት ሺህ ሰባ አምስትprint(coma_separated)# 45,232,945,075'How to installpip install am-num2wordLincenseMIT LicenseCopyright (c) 2019 Mitiku YohannesPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: amodel
amodelamodelis Apollo's model deployment and management tool.Set environmentIf you are running in Apollo docker, you can skip this step. If you are running outside of docker, the following environment needs to be set up.sourcedocker/setup_host/host_env.shHow to workamodelprovides the following commands:list. Show models installed in Apollo.info. Show details of the model.install. Install the model to Apollo.remove. Remove the model from Apollo.ListYou can get the installed models in Apollo through thelistcommand.$amodellist
Name|Task_type|Sensor_type|Framework|Date
mask_pillars|3d_detection|lidar|paddlepaddle|2021-07-30
center_point|3d_detection|lidar|paddlepaddle|2022-07-22
point_pillars|3d_detection|lidar|paddlepaddle|2020-12-15
cnnseg16|3d_segmentation|lidar|paddlepaddle|2018-10-14
cnnseg128|3d_segmentation|lidar|paddlepaddle|2020-06-17
cnnseg64|3d_segmentation|lidar|paddlepaddle|2019-05-29
smoke|3d_detection|camera|paddlepaddle|2019-06-27
3d-yolo|3d_detection|camera|paddlepaddle|2019-12-08
denseline|lane_detection|camera|paddlepaddle|2019-05-29
darkSCNN|lane_detection|camera|paddlepaddle|2020-12-15
tl_detection|tl_detection|camera|paddlepaddle|2021-01-15
tl_recognition|tl_recognition|camera|paddlepaddle|2021-01-15InfoThen you can use theinfocommand to show the details of the model.$amodelinfopoint_pillars
name:point_pillars
date:2020-12-15
task_type:3d_detection
sensor_type:lidar
framework:paddlepaddle
model_files:
-name:pfe.onnxsize:4125-name:pts_backbone.zipsize:16945051-name:pts_bbox_head.zipsize:121150-name:pts_middle_encoder.zipsize:3763-name:pts_neck.zipsize:2420625-name:pts_voxel_encoder.zipsize:17575-name:rpn.onnxsize:18300546dataset:
-waymo
-kitti
-nusenseInstallYou can deploy the model through theinstallcommand.# Install from localamodelinstallxxx.zip# Install from httpamodelinstallhttps://xxx.zipRemoveYou can delete the model installed in Apollo through theremovecommand.amodelremovepoint_pillars
Package: amogus
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Package: amole
Amole pay integrationThis python library is to help you integrate with amole api. Contact amole team for integration.importamoleimportjson,requestsamole=Amole(url="https://uatc.api.myamole.com:8076/amole/pay",signature='<SEGNITURE>',ip_address='<YOUR IP>',username='<USERNAME>',password='<TEST>')response=amole.send_otp(card_number='<CARD OR PHONENUMBER>',merchant_id='<MERCHANT ID>')print(response)response=amole.pay(card_number='<CARD OR PHONENUMBER>',merchant_id='<MERCHANT ID>',amount='200',OTP='9999')print(json.loads(response.content))