# packages

pkgId: 6d9465e5b6428e63e57b079b8d24ee25e73a52d5899cba16ef6204f528dd6604
name: python-optfunc2-help
arch: noarch
version: 0.1
epoch: 0
release: 1
summary: Development documents and examples for optfunc2
url: https://gitee.com/z-bajer/autocall
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-optfunc2-0.1-1.src.rpm
location_href: Packages/python-optfunc2-help-0.1-1.noarch.rpm
checksum_type: sha256
description:
  1. Allow user call functions directly in command line.
  2. Generate help tips automatically.
  3. Add default called functions if not function was specific.


  1. It's better to add argument type for each autocall functions.
  2. Function with @optfunc_default has @optfunc implicitly.
  3. Arguments of function with @optfunc_default should be optional or no argument.
  4. Not support two type of variadic arguments.


  1. Beautiful print.


  ``` python
  from optfunc import *

  @optfunc
  def arg_test_positional_only(pos_only0, pos_only1: int, pos_only2 = 5, pos_only3: int = 6):
      """summary for the function

      Args:
          pos_only0 (_type_): This is the first positional-only argument.
          pos_only1 (int): This is the second positional-only argument.
          pos_only2 (int, optional): This is the third positional-only argument. Defaults to 5.
          pos_only3 (int, optional): This is the fourth positional-only argument. Defaults to 6.
      """
      " Argument test for positional-only arguments. "
      print(f'pos_only0: {pos_only0}, pos_only1: {pos_only1}, pos_only2: {pos_only2}, pos_only3: {pos_only3}')
      pass

  @optfunc
  def arg_test_positional_or_keyword(pos_or_kw, pos_or_kw1: int, pos_or_kw2 = 3, pos_or_kw3: int = 4):
      " Argument test for positional-or-keyword arguments. "
      print(f'pos_or_kw: {pos_or_kw}, pos_or_kw1: {pos_or_kw1}, pos_or_kw2: {pos_or_kw2}, pos_or_kw3: {pos_or_kw3}')
      pass

  @optfunc
  def arg_test_kw_only(*, kw_only0, kw_only1: int, kw_only2 = 9, kw_only3: int = 10):
      " Argument test for keyword-only arguments. "
      print(f'kw_only0: {kw_only0}, kw_only1: {kw_only1}, kw_only2: {kw_only2}, kw_only3: {kw_only3}')
      pass

  if __name__ == '__main__':
      optfunc_start(globals=globals(), has_abbrev=False, header_doc='This is a test file for the module "autocall".')
  ```

  ``` bash
  ~/:$ python3 test.py -h
  Usage: test.py [command] [<args>|--help]

  This is a test file for the module "autocall".

  commands:
      arg_test_positional_only           summary for the function
      arg_test_positional_or_keyword     Argument test for positional-or-keyword arguments.
      arg_test_kw_only                   Argument test for keyword-only arguments.

  ~/:$ python3 test.py arg_test_positional_only -h
  Usage: test.py arg_test_positional_only [OPTIONS]

  summary for the function


  Arguments:
  +-------------+------+---------+-------------------------------------------------------------+
  |     Opt     | Type | Default |                             Desc                            |
  +-------------+------+---------+-------------------------------------------------------------+
  | --pos_only0 | any  |         |         This is the first positional-only argument.         |
  | --pos_only1 | int  |         |         This is the second positional-only argument.        |
  | --pos_only2 | any  |    5    |  This is the third positional-only argument. Defaults to 5. |
  | --pos_only3 | int  |    6    | This is the fourth positional-only argument. Defaults to 6. |
  +-------------+------+---------+-------------------------------------------------------------+
  ~/:$ python3 test.py arg_test_positional_only --pos_only0 "good day" --pos_only1 2
  pos_only0: good day, pos_only1: 2, pos_only2: 5, pos_only3: 6
  ```

rpm_license:
  Copyright (c) 2016 The Python Packaging Authority (PyPA)  Permission 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.


pkgId: 3a9fa73b5c73c021862645f04ae52cb809515bf640c1fd7e3933f47a3b8e333b
name: python-opthub_client-help
arch: noarch
version: 0.8.1
epoch: 0
release: 1
summary: Development documents and examples for opthub_client
url: https://github.com/opthub-org/opthub-client
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-opthub_client-0.8.1-1.src.rpm
location_href: Packages/python-opthub_client-help-0.8.1-1.noarch.rpm
checksum_type: sha256
description:
  [日本語版](https://github.com/opthub-org/opthub-client/blob/main/README_ja.md) 👈



  ![Skills](https://skillicons.dev/icons?i=py,graphql,vscode,github)

  The Opthub Client is a Python package that provides the following features:

  - Submitting solutions to OptHub competitions
  - Checking the history and status of solutions submitted to OptHub competitions

  This section explains how to install the OptHub Client and provides a tutorial. For more detailed instructions, please refer to the [OptHub Client User Guide](https://opthub.notion.site/OptHub-Client-User-Guide-ece08c9977ec4733b7cbeb2d5bafb797?pvs=4).


  Make sure you have Python 3.10 or higher installed and that you have set up pip as your package manager. Then, run the following command to install opthub-client from PyPI.

  ```bash
  $ pip install opthub-client
  ```



  In this tutorial, we will explain how to submit solutions for the competitions you are participating in and how to review the history of your submissions.

  To submit solutions, you need to create an account and join a competition beforehand.

  👉 [How to Create an Account and Join a Competition](https://opthub.notion.site/Tutorial-23febfdfbb7c41d0893a6681f3e7ae20?pvs=4)



  Execute `opt login` and enter your username and password.

  ```bash
  $ opt login
  Username: [username] # Your username
  Password: [password]   # Your password
  Hello [username]. Successfully logged in.
  ```

  ⚠ You need to create an account and verify your email address beforehand.



  Execute `opt select` and select a competition and match.

  ```bash
  $ opt select
  ? Select a competition: [competition_id] # Use ↑↓ keys to select a competition
  ? Select a match: [match_id] # Use ↑↓ keys to select a match
  You have selected [competition_id]/[match_id]
  ```

  ⚠ You need to join at least one competition beforehand.



  Execute `opt submit` and enter a solution according to the competition's input/output requirements.

  ```bash
  $ opt submit
  ? Write the solution: [your_solution] # Enter your solution
  Submitting to [competition_id]/[match_id]... # Submitting
  ...Submitted # Submission complete
  ```

  👉 [How to submit solutions from files](https://opthub.notion.site/submit-Submit-a-solution-2192134bad9b40c39670b567dd6d8f3f?pvs=4)
  👉 [How to submit solutions by your program](https://opthub.notion.site/How-to-Use-OptHub-API-8f54f4456319430b85039c06ec6cc14e?pvs=4)



  Execute `opt show trials` to display the solutions you have submitted. Press `n` to display the next 20 solutions, or press `e` to exit the check.

  ```bash
  $ opt show trials # Displays 20 solutions in descending order by default
  Trial No: 30, status: success, Score: 0.0001
  Trial No: 29, status: scoring
  Trial No: 28, status: evaluating
  ・・・
  n: next solutions, e: exit

  ```



  Execute `opt download` to download the solutions by specifying the range of trial numbers.

  ```bash
  $ opt download -s 10 -e 30 # Download trial numbers 10 to 30
  Downloading trials  [####################################]  100%
  Trials have been written to trials_match1.json
  ```

  👉 [Format of the Output JSON File](https://opthub.notion.site/download-Download-the-solutions-c77b44f02df24609a8f04490d6036e77?pvs=4)



  Follow these steps to set up the environment:

  1. Clone this repository.
  2. Set up Poetry.
  3. Run `poetry install`.
  4. Download the recommended VSCode Extensions.
  5. Disable the following VS Code Extensions for this workspace to avoid conflicts with other packages:
      - ms-python.pylint
      - ms-python.black-formatter
      - ms-python.flake8
      - ms-python.isort

  Once you have completed the above setup, you can use the `opt` command in the project's root directory.



  If you have any questions or concerns, please feel free to contact us (Email: dev@opthub.ai).

  <img src="https://opthub.ai/assets/images/logo.svg" width="200">


pkgId: 73179bf8f5170f4a01d08be9423685b8676d77c360f41662723ff5dea846c463
name: python-opthub_runner_admin-help
arch: noarch
version: 0.3.11
epoch: 0
release: 1
summary: Development documents and examples for opthub_runner_admin
url: https://github.com/opthub-org/opthub-runner-admin
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-opthub_runner_admin-0.3.11-1.src.rpm
location_href: Packages/python-opthub_runner_admin-help-0.3.11-1.noarch.rpm
checksum_type: sha256
description:
  [日本語版](https://github.com/opthub-org/opthub-runner-admin/blob/main/README_ja.md) 👈



  ![Skills](https://skillicons.dev/icons?i=py,aws,graphql,docker,vscode,github)

  Opthub Runner is a Python package that provides the following two features:

  - Evaluator\*: Feature to evaluate user-submitted solutions on Opthub using Docker Image.
  - Scorer\*: Feature to calculate scores from a series of evaluations using Docker Image.

  Please refer to 4.競技の確認 in [OptHub Tutorial](https://opthub.notion.site/OptHub-1b96e2f4e9424db0934f297ee0351403) for detailed explanations of the Evaluator and Scorer.

  This repository describes how to install and run Opthub Runner.





  We recommend using the following environments:

  - [AWS EC2](https://aws.amazon.com/ec2/).
  - [GCP Compute Engine](https://cloud.google.com/products/compute).
  - [Sakura Cloud](https://www.sakura.ad.jp/?gad_source=1&gclid=Cj0KCQjwmOm3BhC8ARIsAOSbapUD6m2okjosfvKuZvb91rdY4lmgMOZeFtMr2eTZqQLTCrw5naQCE0saAtzWEALw_wcB).
  - Any other environment that supports Docker and can run continuously.



  - Install Python 3.10 or newer.
  - Set up pip for package management.
  - Install and start Docker.*

  \*For Mac users, you can install and start [Docker Desktop](https://docs.docker.com/desktop/install/mac-install/).



  Install `opthub-runner-admin` from PyPI.

  ```bash
  pip install opthub-runner-admin
  ```



  Create a YAML file with the options needed to run the Evaluator and Scorer, based on [config.default.yml](https://github.com/opthub-org/opthub-runner-admin/blob/main/config.default.yml). For details on the options to set in the file, please refer to [YAML File Options](#yaml-file-options).



  At first, start Docker. **Please note that Docker must remain running while the Evaluator/Scorer is active.**

  Then, run the Evaluator/Scorer using the following command. Replace `<evaluator|scorer>` with either `evaluator` or `scorer`. For `--config`, specify the path to the YAML file you created in [2. Configuring YAML File](#2-configuring-yaml-file). If not specified, the `config.yml` in the current directory will be used.

  ```bash
  opthub-runner <evaluator|scorer> --config <yaml file path>
  ```

  You will be prompted to enter the username and password. These must be the ones of the competition administrator.

  ```bash
  Username: (your username)
  Password: (your password)
  ```

  **Please note that the Evaluator/Scorer must remain running during the competition.** If you have any other issues, please refer to [Troubleshooting](#troubleshooting).



  The following table lists the options to include in the YAML file, along with their type, default values, and descriptions. Please confirm the default values for the following options with the OptHub representative. Contact information is [here](#contact).

  - evaluator_queue_url
  - scorer_queue_url
  - access_key_id
  - secret_access_key
  - region_name
  - table_name

  | Option | Type | Default Value | Description |
  | ------ | ---- | ------------- | ----------- |
  | interval | int | 2 | Interval to fetch messages from Amazon SQS. |
  | timeout | int | 43200 | Timeout for evaluation and score calculation using Docker Image. |
  | rm | bool | True | Whether to remove the Docker container after evaluation and score calculation. True is recommended except for debugging. If set to False, the Docker containers created during evaluation and scoring will not be removed, accumulating over time. |
  | log_level | [DEBUG, INFO, WARNING, ERROR, CRITICAL] | INFO | Log level to output. |
  | evaluator_queue_url | path | - | Amazon SQS queue URL used by the Evaluator. |
  | scorer_queue_url | path | - | Amazon SQS queue URL used by the Scorer. |
  | access_key_id | str | - | AWS Access Key ID. |
  | secret_access_key | str | - | AWS Secret Access Key. |
  | region_name | str | - | AWS default Region Name. |
  | table_name | str | - | DynamoDB table name to store solutions, evaluations, and scores. |





  The following error occurs when Docker is not running.

  ```plaintext
  Error: Unable to communicate with Docker. Please ensure Docker is running and accessible. (Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory')))
  ```

  For Mac, you can resolve this issue by starting Docker Desktop. You can install Docker Desktop [here](https://docs.docker.com/desktop/install/mac-install/).

  For Ubuntu, you can resolve this issue by installing Docker via the terminal with the following command.

  ```plaintext
  sudo apt install docker.io
  ```



  The following error occurs when the current user does not have permission to access the Docker socket.

  ```plaintext
  Error: Unable to communicate with Docker. Please ensure Docker is running and accessible. (Error while fetching server API version: ('Connection aborted.', PermissionError(13, 'Permission denied')))
  ```

  For Ubuntu, you can resolve this issue by adding the user to the Docker group and then logging in again. Specifically, follow these steps:

  1. Run the following command in the terminal:

      ```plaintext
      sudo usermod -aG docker $USER
      ```

  2. Either log in again or run the following command in the terminal:

      ```plaintext
      newgrp docker
      ```



  The following error occurs if you did not log in with the competition administrator account when starting the Evaluator/Scorer.

  ```plaintext
  Traceback (most recent call last):
    ･･･
  opthub_runner_admin.models.exception.DockerImageNotFoundError: Cannot access the Docker image. Please check your permissions. If you're not authenticated using the competition administrator's account, please do so.
  ```

  You can resolve this issue by logging in with the competition administrator’s account when starting the Evaluator/Scorer. When the following prompt appears, enter the administrator’s username and password.

  ```plaintext
  Note: Make sure to authenticate using the competition administrator's account.
  Username: (The administrator's username)
  Password: (The administrator's password)
  ```



  The following error occurs when the YAML file containing the Evaluator/Scorer settings does not exist.

  ```plaintext
  Traceback (most recent call last):
    ･･･
  FileNotFoundError: Configuration file not found: config.yml
  ```

  Please create a `config.yml` file by referring to [2. Configuring YAML File](#2-configuring-yaml-file).



  The following error occurs when the AWS Access Key ID (`access_key_id`) set in `config.yml` is incorrect.

  ```plaintext
  Traceback (most recent call last):
    ･･･
  botocore.exceptions.ClientError: An error occurred (InvalidClientTokenId) when calling the ReceiveMessage operation: The security token included in the request is invalid.
  ```

  Please check the AWS Access Key ID set in `config.yml` and enter the correct value.



  The following error occurs when the AWS Secret Access Key (`secret_access_key`) set in `config.yml` is incorrect.

  ```plaintext
  Traceback (most recent call last):
    ･･･
  botocore.exceptions.ClientError: An error occurred (SignatureDoesNotMatch) when calling the ReceiveMessage operation: The request signature we calculated does not match the signature you provided. Check your AWS Secret Access Key and signing method. Consult the service documentation for details.

  ```

  Please check the AWS Secret Access Key set in `config.yml` and enter the correct value.



  The following error occurs when the SQS queue name (`evaluator_queue_name` / `scorer_queue_name`) set in `config.yml` is incorrect.

  ```plaintext
  Traceback (most recent call last):
    ･･･
  botocore.errorfactory.QueueDoesNotExist: An error occurred (AWS.SimpleQueueService.NonExistentQueue) when calling the ReceiveMessage operation: The specified queue does not exist.
  ```

  Please check the SQS queue name set in `config.yml` and enter the correct value.



  The following error occurs when the Amazon DynamoDB table name (`table_name`) set in `config.yml` is incorrect.

  ```plaintext
  Traceback (most recent call last):
    ･･･
  botocore.errorfactory.ResourceNotFoundException: An error occurred (ResourceNotFoundException) when calling the GetItem operation: Requested resource not found

  ```

  Please check the DynamoDB table name set in `config.yml` and enter the correct value.



  The following error occurs when the AWS Default Region Name (`region_name`) set in `config.yml` is incorrect.

  ```plaintext
  Traceback (most recent call last):
    ･･･
  urllib3.exceptions.NameResolutionError: <botocore.awsrequest.AWSHTTPSConnection object at 0x104d8bce0>: Failed to resolve 'sqs.ap-northeas-1.amazonaws.com' ([Errno 8] nodename nor servname provided, or not known)

  During handling of the above exception, another exception occurred:

  Traceback (most recent call last):
    ･･･
  botocore.exceptions.EndpointConnectionError: Could not connect to the endpoint URL: "https://sqs.ap-northeas-1.amazonaws.com/"
  ```

  Please check the AWS Default Region Name set in `config.yml` and enter the correct value.



  The following error is displayed when an error occurs inside the Docker container.

  ```plaintext
  ERROR:opthub_runner_admin.evaluator.main:Error occurred while evaluating solution.
  Traceback (most recent call last):
    File "/home/opthub/.venv/lib/python3.11/site-packages/opthub_runner_admin/evaluator/main.py", line 125, in evaluate
      evaluation_result = execute_in_docker(
                          ^^^^^^^^^^^^^^^^^^
    File "/home/opthub/.venv/lib/python3.11/site-packages/opthub_runner_admin/lib/docker_executor.py", line 88, in execute_in_docker
      raise RuntimeError(msg)
  RuntimeError: Failed to parse stdout.
  ```

  To investigate this error in detail, you need to check the Docker container logs. Modify the `rm` setting in `config.yml` to `False`, then restart the Evaluator/Scorer and, after execution, check the logs of the container. You can view the Docker container logs with the following command:

  ```bash
  $ docker ps # Check Container ID
  $ docker logs <Container ID> # View logs
  ```

  Check the logs and modify the Docker image to ensure the Docker container output is in the correct format.

  Additionally, the following container log shows an error that occurs when the built Docker image is run on an incompatible platform.

  ```plaintext
  exec /usr/bin/sh: exec format error
  ```

  To ensure compatibility, either match the environment used to build the Docker image with the environment used to run the Evaluator/Scorer, or use `buildx` to create a compatible Docker image.



  Follow these steps to set up the environment:

  1. Clone this repository.
  2. Set up Poetry.
  3. Run `poetry install`.
  4. Download the recommended VSCode Extensions.
  5. Disable the following VS Code Extensions for this workspace to avoid conflicts with other packages:
      - ms-python.pylint
      - ms-python.black-formatter
      - ms-python.flake8
      - ms-python.isort
  6. Create `config.yml` with options based on `config.default.yml`.
      - Place `config.yml` directly under opthub-runner-admin.
      - For the values of the options commented out in `config.default.yml`, please contact the Opthub representative. Contact information is [here](#contact).

  Once you have completed the above setup, you can use the `opthub-runner` command in the project's root directory.



  If you have any questions or concerns, please feel free to contact us (Email: dev@opthub.ai).

  <img src="https://opthub.ai/assets/images/logo.svg" width="200">


pkgId: e768aba84bc18f0605cca0d5c7940b3523768486bc0221c08bd99dd76d558840
name: python-optimade-help
arch: noarch
version: 1.1.7
epoch: 0
release: 1
summary: Development documents and examples for optimade
url: https://github.com/Materials-Consortia/optimade-python-tools
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-optimade-1.1.7-1.src.rpm
location_href: Packages/python-optimade-help-1.1.7-1.noarch.rpm
checksum_type: sha256
description:
  <!-- markdownlint-disable MD033 -->
  <!-- markdownlint-disable-next-line MD041 -->
  <div align="center" style="padding-bottom: 1em;">
  <img width="100px" align="center" src="https://matsci.org/uploads/default/original/2X/b/bd2f59b3bf14fb046b74538750699d7da4c19ac1.svg">
  </div>



  <div align="center">

  <a href="https://doi.org/10.21105/joss.03458"><img alt="JOSS DOI" src="https://img.shields.io/badge/JOSS-10.21105%2Fjoss.03458-blueviolet"></a>
  </div>

  <div align="center">

  <table>
  <thead align="center">
  <tr><th align="center">Latest release</th><th align="center">Build status</th><th align="center">Activity</th></tr>
  </thead>

  <tbody>
  <tr>
    <td align="center">
      <a href="https://pypi.org/project/optimade"><img alt="PyPI version" src="https://img.shields.io/pypi/v/optimade?logo=pypi&logoColor=white"></a><br>
      <a href="https://pypi.org/project/optimade"><img alt="PyPI - Python Version"  src="https://img.shields.io/pypi/pyversions/optimade?logo=python&logoColor=white"></a><br>
      <a href="https://github.com/Materials-Consortia/OPTIMADE"><img alt="OPTIMADE version" src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/Materials-Consortia/optimade-python-tools/main/optimade-version.json"></a>
    </td>
    <td align="center">
      <a href="https://github.com/Materials-Consortia/optimade-python-tools/actions?query=branch%3Amain+"><img alt="Build Status" src="https://img.shields.io/github/actions/workflow/status/Materials-Consortia/optimade-python-tools/ci.yml?logo=github"></a><br>
      <a href="https://optimade.org/optimade-python-tools"><img alt="Docs" src="https://img.shields.io/github/actions/workflow/status/Materials-Consortia/optimade-python-tools/ci_cd_updated_main.yml?label=docs&logo=github"></a><br>
      <a href="https://codecov.io/gh/Materials-Consortia/optimade-python-tools"><img alt="Codecov" src="https://img.shields.io/codecov/c/github/Materials-Consortia/optimade-python-tools?logo=codecov&logoColor=white&token=UJAtmqkZZO"></a><br>
    </td>
    <td align="center">
      <a href="https://github.com/Materials-Consortia/optimade-python-tools/pulse"><img alt="Commit Activity" src="https://img.shields.io/github/commit-activity/m/Materials-Consortia/optimade-python-tools?logo=github"></a><br>
      <a href="https://github.com/Materials-Consortia/optimade-python-tools/commits/main"><img alt="Last Commit" src="https://img.shields.io/github/last-commit/Materials-Consortia/optimade-python-tools/main?logo=github"></a><br>
      <a href="https://github.com/Materials-Consortia/optimade-python-tools/graphs/contributors"><img alt="Contributors" src="https://badgen.net/github/contributors/Materials-Consortia/optimade-python-tools?icon=github"></a>
    </td>
  </tr>
  </tbody>
  </table>

  </div>

  The aim of [OPTIMADE](https://optimade.org) is to develop a common API, compliant with the [JSON:API 1.0](http://jsonapi.org/format/1.0/) specification.
  This is to enable interoperability among databases that serve crystal structures and calculated properties of existing and hypothetical materials.

  This repository contains a library of tools for implementing and consuming [OPTIMADE APIs](https://www.optimade.org) using Python:

  1. [pydantic](https://github.com/pydantic/pydantic) data models for all [OPTIMADE entry types](https://www.optimade.org/optimade-python-tools/latest/all_models/) and endpoint responses, and a [Lark](https://github.com/lark-parser/lark) [EBNF grammar](https://en.wikipedia.org/wiki/Extended_Backus%E2%80%93Naur_form) implementation for the OPTIMADE filter language.
  1. Adapters to map OPTIMADE data to and from many commonly used atomistic Python frameworks (e.g., [pymatgen](https://pymatgen.org/), [ASE](https://wiki.fysik.dtu.dk/ase/)) and crystallographic file types (e.g., [CIF](https://www.iucr.org/resources/cif)), using the `optimade.adapters` module.
  1. A configurable reference server implementation that can make use of either MongoDB or Elasticsearch database backends out-of-the-box, and is readily extensible to other backends. Try it out on the [demo site](https://optimade.fly.dev)! The OpenAPI schemas of the server are used to construct the [OPTIMADE schemas](https://schemas.optimade.org/) site.
  1. An [OPTIMADE client](https://www.optimade.org/optimade-python-tools/latest/getting_started/client/) (`optimade-get`) that can query multiple [OPTIMADE providers](https://optimade.org/providers-dashboard) concurrently with a given filter, at the command-line or from Python code.
  1. A fuzzy API validator tool, which may be called from the shell (`optimade-validator`) or used as a GitHub Action from [optimade-validator-action](https://github.com/Materials-Consortia/optimade-validator-action); this validator is used to construct the [providers dashboard](https://optimade.org/providers-dashboard).




  This document, guides, and the full module API documentation can be found online at [https://optimade.org/optimade-python-tools](https://optimade.org/optimade-python-tools).
  In particular, documentation of the OPTIMADE API response data models (implemented here with [pydantic](https://github.com/pydantic/pydantic)) can be found online under [OPTIMADE Data Models](https://optimade.org/optimade-python-tools/latest/all_models).

  The release history and changelog can be found in [the changelog](CHANGELOG.md).



  Detailed installation instructions for different use cases (e.g., using the library or running a server) can be found in [the installation documentation](INSTALL.md).

  The latest stable version of this package can be obtained from [PyPI](https://pypi.org/project/optimade):

  ```shell
  pip install optimade
  ```

  The latest development version of this package can be obtained from the main branch of this repository:

  ```shell
  git clone https://github.com/Materials-Consortia/optimade-python-tools
  ```



  Each release of the `optimade` package from this repository only targets one version of the OPTIMADE specification, summarised in the table below.

  <div align="center">

  <table>

  <thead>
      <tr>
          <th align="center">OPTIMADE API version</th>
          <th align="center"><code>optimade</code> requirements</th>
      </tr>
  </thead>

  <tbody>
      <tr>
          <td align="center"><a href="https://github.com/Materials-Consortia/OPTIMADE/blob/v1.0.0/optimade.rst">v1.0.0</a></td>
          <td align="center"><code>optimade<=0.12.9</code></td>
      </tr>
      <tr>
          <td align="center"><a href="https://github.com/Materials-Consortia/OPTIMADE/blob/v1.1.0/optimade.rst">v1.1.0</a></td>
          <td align="center"><code>optimade>=0.16,<1.2</code></td>
      </tr>
      <tr>
          <td align="center"><a href="https://github.com/Materials-Consortia/OPTIMADE/blob/v1.2.0/optimade.rst">v1.2.0</a></td>
          <td align="center"><code>optimade>=1.2.0</code></td>
      </tr>
  </tbody>
  </table>
  </div>



  All development of this package (bug reports, suggestions, feedback and pull requests) occurs in the [optimade-python-tools GitHub repository](https://github.com/Materials-Consortia/optimade-python-tools).
  Contribution guidelines and tips for getting help can be found in the [contributing notes](CONTRIBUTING.md).



  If you use this package to access or serve OPTIMADE data, we kindly request that you cite the following:

  - Evans *et al.*, Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange, *Digital Discovery*, **3**, 1509-1533 (2024) [10.1039/D4DD00039K](https://doi.org/10.1039/D4DD00039K)
  - Andersen *et al.*, OPTIMADE, an API for exchanging materials data, *Sci. Data* **8**, 217 (2021) [10.1038/s41597-021-00974-z](https://doi.org/10.1038/s41597-021-00974-z)
  - Evans *et al.*, optimade-python-tools: a Python library for serving and consuming materials data via OPTIMADE APIs. *Journal of Open Source Software*, **6**(65), 3458 (2021) [10.21105/joss.03458](https://doi.org/10.21105/joss.03458)



  - [OPTIMADE Specification](https://github.com/Materials-Consortia/OPTIMADE/blob/develop/optimade.rst), the human-readable specification that this library is based on.
  - [optimade-validator-action](https://github.com/Materials-Consortia/optimade-validator-action), a GitHub action that can be used to validate implementations from a URL (using the validator from this repo).
  - [OpenAPI](https://github.com/OAI/OpenAPI-Specification), the machine-readable format used to specify the OPTIMADE API in [`openapi.json`](openapi/openapi.json) and [`index_openapi.json`](openapi/index_openapi.json).
  - [Interactive documentation](https://petstore.swagger.io/?url=https://raw.githubusercontent.com/Materials-Consortia/optimade-python-tools/main/openapi/openapi.json) generated from [`openapi.json`](openapi/openapi.json) (see also [interactive JSON editor](https://editor.swagger.io/?url=https://raw.githubusercontent.com/Materials-Consortia/optimade-python-tools/main/openapi/openapi.json)).
  - [pydantic](https://pydantic-docs.helpmanual.io/), the library used for generating the OpenAPI schema from [Python models](https://www.optimade.org/optimade-python-tools/all_models/).
  - [FastAPI](https://fastapi.tiangolo.com/), the framework used for generating the reference implementation expressed by the [`openapi.json`](openapi/openapi.json) specification.
  - [Lark](https://github.com/lark-parser/lark), the library used to parse the filter language in OPTIMADE queries.


pkgId: 69c060227afafb6520ecb94b8ec9b80f570a44106a1d6ba4d2e9b26073b6706f
name: python-optimum-tpu-help
arch: noarch
version: 0.2.3
epoch: 0
release: 1
summary: Development documents and examples for optimum-tpu
url: https://hf.co/hardware
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-optimum-tpu-0.2.3-1.src.rpm
location_href: Packages/python-optimum-tpu-help-0.2.3-1.noarch.rpm
checksum_type: sha256
description:
  <h4>Take the most out of Google Cloud TPUs with the ease of 🤗 transformers</h4>
  [![Documentation](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://huggingface.co/docs/optimum/index)
  [![license](https://img.shields.io/badge/license-Apache%202-blue)](./LICENSE)
  [![Optimum TPU / Test TGI on TPU](https://github.com/huggingface/optimum-tpu/actions/workflows/test-pytorch-xla-tpu-tgi.yml/badge.svg)](https://github.com/huggingface/optimum-tpu/actions/workflows/test-pytorch-xla-tpu-tgi.yml)
  </div>
  [Tensor Processing Units (TPU)](https://cloud.google.com/tpu) are AI accelerator made by Google to optimize
  performance and cost from AI training to inference.
  This repository exposes an interface similar to what Hugging Face transformers library provides to interact with
  a magnitude of models developed by research labs, institutions and the community.
  We aim at providing our user the best possible performances targeting Google Cloud TPUs for both training and inference
  working closely with Google and Google Cloud to make this a reality.

  We currently support a few LLM models targeting text generation scenarios:
  - 💎 Gemma (2b, 7b)
  - 🦙 Llama2 (7b) and Llama3 (8b). On Text Generation Inference with Jetstream Pytorch, also Llama3.1, Llama3.2 and Llama3.3 (text-only models) are supported, up to 70B parameters.
  - 💨 Mistral (7b)

  `optimum-tpu` comes with an handy PyPi released package compatible with your classical python dependency management tool.
  `pip install optimum-tpu -f https://storage.googleapis.com/libtpu-releases/index.html`
  `export PJRT_DEVICE=TPU`

  `optimum-tpu` provides a set of dedicated tools and integrations in order to leverage Cloud TPUs for inference, especially
  on the latest TPU version `v5e` and `v6e`.
  Other TPU versions will be supported along the way.

  As part of the integration, we do support a [text-generation-inference (TGI)](https://github.com/huggingface/optimum-tpu/tree/main/text-generation-inference) backend allowing to deploy and serve
  incoming HTTP requests and execute them on Cloud TPUs.
  Please see the [TGI specific documentation](text-generation-inference) on how to get started.

  `optimum-tpu` provides an optional support of JetStream Pytorch engine inside of TGI. This support can be installed using the dedicated CLI command:
  ```shell
  optimum-tpu install-jetstream-pytorch
  ```
  To enable the support, export the environment variable `JETSTREAM_PT=1`.

  Fine-tuning is supported and tested on the TPU `v5e`. We have tested so far:
  - 🦙 Llama-2 7B, Llama-3 8B and newer;
  - 💎 Gemma 2B and 7B.
  You can check the examples:
  - [Fine-Tune Gemma on Google TPU](https://github.com/huggingface/optimum-tpu/blob/main/examples/language-modeling/gemma_tuning.ipynb)
  - The [Llama fine-tuning script](https://github.com/huggingface/optimum-tpu/blob/main/examples/language-modeling/llama_tuning.ipynb)

rpm_license:
[cell repeats an earlier one in this file: 10254 characters]


pkgId: bbf103f048c32adcc17b4cb1682c80401ff2c8c454ca9a8cefb9f6de90f2a9e1
name: python-optrabot-help
arch: noarch
version: 0.9.3
epoch: 0
release: 1
summary: Development documents and examples for optrabot
url: http://www.optrabot.com
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-optrabot-0.9.3-1.src.rpm
location_href: Packages/python-optrabot-help-0.9.3-1.noarch.rpm
checksum_type: sha256
description:
  OptraBot is a Options Trading Bot which can be used to run fully automated options trading strategies using your Interactive Brokers trading account.

  Have a look on the **[Roadmap](https://app.loopedin.io/optrabot/roadmap)** and discover what we're currently working on and whats coming next.

  Run through the **[Changelog](https://app.loopedin.io/optrabot/#/updates)** and find out about the most recent updates of the software.


  Microsoft Windows, MacOS or Linux operating system

  Python 3.10 or newer

  Interactive Brokers Trader Workstation or IB Gateway


  Be aware that you're using this software at your own risk. The author is not liable for any losses occuring with using this software.


pkgId: c7bb502efcd483b3d116f040041000fa890a4d67652c00f3a2032885ff0b04eb
name: python-optuna-async-helper-help
arch: noarch
version: 0.5.0
epoch: 0
release: 1
summary: Development documents and examples for optuna-async-helper
url: https://pypi.org/project/optuna-async-helper/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-optuna-async-helper-0.5.0-1.src.rpm
location_href: Packages/python-optuna-async-helper-help-0.5.0-1.noarch.rpm
checksum_type: sha256
description:
  A Helper Library for Optuna Async Optimization



  ```bash
  pip install optuna-async-helper
  ```



  ```python
  from optuna_async_helper import SearchSpace, SearchSpec, optimize


  def rosenbrock(x: float, y: float) -> float:
      return (1 - x) ** 2 + 100 * (y - x**2) ** 2


  search_space: SearchSpace = [
      SearchSpec(var_name="x", var_type="float", low=-5, high=5),
      SearchSpec(var_name="y", var_type="float", low=-5, high=5),
  ]

  with tempfile.TemporaryDirectory() as tempdir:
      study = optimize(
          study_name="rosenbrock",
          storage=f"sqlite:///example.db",
          objective_func=rosenbrock,
          search_space=search_space,
          n_trials=50,
          batch_size=4,
      )

      assert study.best_value < 1.0
      assert abs(study.best_params["x"] - 1) < 1.0
      assert abs(study.best_params["y"] - 1) < 1.0
  ```

  For more detail, please check `optimize` and `SearchSpec` definitions.



  The project is managed by [uv](https://docs.astral.sh/uv/)


pkgId: 662054d9ed77aae0b4ef31c94ae5967f60eff4bef24931d5d6928f8028088c69
name: python-opvious-help
arch: noarch
version: 0.22.1
epoch: 0
release: 1
summary: Development documents and examples for opvious
url: https://github.com/opvious/sdk.py
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-opvious-0.22.1-1.src.rpm
location_href: Packages/python-opvious-help-0.22.1-1.noarch.rpm
checksum_type: sha256
description:
  <div align="center">
    <p>
      Define and deploy optimization models in minutes with <a href="https://www.opvious.io">Opvious</a>.
    </p>
    <a href="https://www.opvious.io/notebooks/retro/notebooks/?path=guides/welcome.ipynb"><img src="https://www.opvious.io/opvious-steps.png" style="height: 600px;"/></a>
      <p>
      <a href="https://www.opvious.io/notebooks/retro/notebooks/?path=guides/welcome.ipynb">Try it out!</a>
    </p>
  </div>




  + Declarative modeling API with __extensive static checks and automatic LaTeX generation__
  + Remote solves with __real-time progress notifications__
  + __Flexible data import/export__ via `pandas`
  + __Advanced multi-objective support__: weighted sums, epsilon constraints, ...
  + __Smart debugging capabilities__: semantic constraint relaxations, annotated LP formatting, ...




  + [Getting started guide](https://www.opvious.io/notebooks/retro/notebooks/?path=guides/welcome.ipynb)
  + [SDK API reference](https://opvious.readthedocs.io)
  + [Interactive optimization notebooks](https://github.com/opvious/notebooks)




  The SDK is licensed under Apache 2.0. The Opvious platform is available as a self-hosted service, free for small projects. See our [plans page](https://www.opvious.io/plans) for more information.


pkgId: 4a3b347a927e93571805450c80791f6a691252ee99721148a63a82d7a4784455
name: python-ora2-help
arch: noarch
version: 6.14.3
epoch: 0
release: 1
summary: Development documents and examples for ora2
url: http://github.com/openedx/edx-ora2
rpm_license: AGPL
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ora2-6.14.3-1.src.rpm
location_href: Packages/python-ora2-help-6.14.3-1.noarch.rpm
checksum_type: sha256
description:
  This repository defines an Open Response Assessment (ORA) XBlock for use within edx-platform: https://github.com/openedx/edx-platform
  `User docs <http://edx.readthedocs.org/projects/edx-partner-course-staff/en/latest/exercises_tools/open_response_assessments/index.html>`_


pkgId: 73f9af8b37f373916b4eb8b72ccba54cf37abc1c178b46819354529c8e0d27cf
name: python-oracle-ads-help
arch: noarch
version: 2.12.9
epoch: 0
release: 1
summary: Development documents and examples for oracle-ads
url: https://pypi.org/project/oracle-ads/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-oracle-ads-2.12.9-1.src.rpm
location_href: Packages/python-oracle-ads-help-2.12.9-1.noarch.rpm
checksum_type: sha256
description:
  [![PyPI](https://img.shields.io/pypi/v/oracle-ads.svg?style=for-the-badge&logo=pypi&logoColor=white)](https://pypi.org/project/oracle-ads/) [![Python](https://img.shields.io/pypi/pyversions/oracle-ads.svg?style=for-the-badge&logo=pypi&logoColor=white)](https://pypi.org/project/oracle-ads/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=for-the-badge&logo=pypi&logoColor=white)](https://github.com/ambv/black)


  The [Oracle Accelerated Data Science (ADS) SDK](https://accelerated-data-science.readthedocs.io/en/latest/index.html) is maintained by the Oracle Cloud Infrastructure (OCI) [Data Science service](https://docs.oracle.com/en-us/iaas/data-science/using/data-science.htm) team. It speeds up common data science activities by providing tools that automate and simplify common data science tasks. Additionally, provides data scientists a friendly pythonic interface to OCI services. Some of the more notable services are OCI Data Science, Model Catalog, Model Deployment, Jobs, ML Pipelines, Data Flow, Object Storage, Vault, Big Data Service, Data Catalog, and the Autonomous Database. ADS gives you an interface to manage the life cycle of machine learning models, from data acquisition to model evaluation, interpretation, and model deployment.

  With ADS you can:

  - Read datasets from Oracle Object Storage, Oracle RDBMS (ATP/ADW/On-prem), AWS S3 and other sources into `Pandas dataframes`.
  - Tune models using hyperparameter optimization with the `ADSTuner` tool.
  - Generate detailed evaluation reports of your model candidates with the `ADSEvaluator` module.
  - Save machine learning models to the [OCI Data Science Model Catalog](https://docs.oracle.com/en-us/iaas/data-science/using/models-about.htm).
  - Deploy models as HTTP endpoints with [Model Deployment](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm).
  - Launch distributed ETL, data processing, and model training jobs in Spark with [OCI Data Flow](https://docs.oracle.com/en-us/iaas/data-flow/using/home.htm).
  - Train machine learning models in OCI Data Science [Jobs](https://docs.oracle.com/en-us/iaas/data-science/using/jobs-about.htm).
  - Define and run an end-to-end machine learning orchestration covering all the steps of machine learning lifecycle in a repeatable, continuous [ML Pipelines](https://accelerated-data-science.readthedocs.io/en/latest/user_guide/pipeline/overview.html#).
  - Manage the life cycle of conda environments through the `ads conda` command line interface (CLI).



  You have various options when installing ADS.



  ```bash
    python3 -m pip install oracle-ads
  ```



  To use the AI Forecast Operator, install the "forecast" dependencies using the following command:

  ```bash
    python3 -m pip install 'oracle_ads[forecast]>=2.9.0'
  ```



  To work with gradient boosting models, install the `boosted` module. This module includes XGBoost and LightGBM model classes.

  ```bash
    python3 -m pip install 'oracle-ads[boosted]'
  ```

  For big data use cases using Oracle Big Data Service (BDS), install the `bds` module. It includes the following libraries, `ibis-framework[impala]`, `hdfs[kerberos]` and `sqlalchemy`.

  ```bash
    python3 -m pip install 'oracle-ads[bds]'
  ```

  To work with a broad set of data formats (for example, Excel, Avro, etc.) install the `data` module. It includes the `fastavro`, `openpyxl`, `pandavro`, `asteval`, `datefinder`, `htmllistparse`, and `sqlalchemy` libraries.

  ```bash
    python3 -m pip install 'oracle-ads[data]'
  ```

  To work with geospatial data install the `geo` module. It includes the `geopandas` and libraries from the `viz` module.

  ```bash
    python3 -m pip install 'oracle-ads[geo]'
  ```

  Install the `notebook` module to use ADS within a OCI Data Science service [notebook session](https://docs.oracle.com/en-us/iaas/data-science/using/manage-notebook-sessions.htm). This module installs `ipywidgets` and `ipython` libraries.

  ```bash
    python3 -m pip install 'oracle-ads[notebook]'
  ```

  To work with ONNX-compatible run times and libraries designed to maximize performance and model portability, install the `onnx` module. It includes the following libraries, `onnx`, `onnxruntime`, `onnxmltools`, `skl2onnx`, `xgboost`, `lightgbm` and libraries from the `viz` module.

  ```bash
    python3 -m pip install 'oracle-ads[onnx]'
  ```

  For infrastructure tasks, install the `opctl` module. It includes the following libraries, `oci-cli`, `docker`, `conda-pack`, `nbconvert`, `nbformat`, and `inflection`.

  ```bash
    python3 -m pip install 'oracle-ads[opctl]'
  ```

  For hyperparameter optimization tasks install the `optuna` module. It includes the `optuna` and libraries from the `viz` module.

  ```bash
    python3 -m pip install 'oracle-ads[optuna]'
  ```

  Install the `tensorflow` module to include `tensorflow` and libraries from the `viz` module.

  ```bash
    python3 -m pip install 'oracle-ads[tensorflow]'
  ```

  For text related tasks, install the `text` module. This will include the `wordcloud`, `spacy` libraries.

  ```bash
    python3 -m pip install 'oracle-ads[text]'
  ```

  Install the `torch` module to include `pytorch` and libraries from the `viz` module.

  ```bash
    python3 -m pip install 'oracle-ads[torch]'
  ```

  Install the `viz` module to include libraries for visualization tasks. Some of the key packages are `bokeh`, `folium`, `seaborn` and related packages.

  ```bash
    python3 -m pip install 'oracle-ads[viz]'
  ```

  See `pyproject.toml` file `[project.optional-dependencies]` section for full list of modules and its list of extra libraries.

  **Note**

  Multiple extra dependencies can be installed together. For example:

  ```bash
    python3 -m pip install  'oracle-ads[notebook,viz,text]'
  ```



    - [Oracle Accelerated Data Science SDK (ADS) Documentation](https://accelerated-data-science.readthedocs.io/en/latest/index.html)
    - [OCI Data Science and AI services Examples](https://github.com/oracle/oci-data-science-ai-samples)
    - [Oracle AI & Data Science Blog](https://blogs.oracle.com/ai-and-datascience/)
    - [OCI Documentation](https://docs.oracle.com/en-us/iaas/data-science/using/data-science.htm)





  ```python
    import ads
    from ads.common.auth import default_signer
    import oci
    import pandas as pd

    ads.set_auth(auth="api_key", oci_config_location=oci.config.DEFAULT_LOCATION, profile="DEFAULT")
    bucket_name = <bucket_name>
    key = <key>
    namespace = <namespace>
    df = pd.read_csv(f"oci://{bucket_name}@{namespace}/{key}", storage_options=default_signer())
  ```



  This example uses SQL injection safe binding variables.

  ```python
    import ads
    import pandas as pd

    connection_parameters = {
        "user_name": "<user_name>",
        "password": "<password>",
        "service_name": "<tns_name>",
        "wallet_location": "<file_path>",
    }

    df = pd.DataFrame.ads.read_sql(
        """
        SELECT *
        FROM SH.SALES
        WHERE ROWNUM <= :max_rows
        """,
        bind_variables={ max_rows : 100 },
        connection_parameters=connection_parameters,
    )
  ```



  This project welcomes contributions from the community. Before submitting a pull request, please [review our contribution guide](./CONTRIBUTING.md)

  Find Getting Started instructions for developers in [README-development.md](https://github.com/oracle/accelerated-data-science/blob/main/README-development.md)



  Consult the security guide [SECURITY.md](https://github.com/oracle/accelerated-data-science/blob/main/SECURITY.md) for our responsible security vulnerability disclosure process.



  Copyright (c) 2020, 2024 Oracle and/or its affiliates. Licensed under the [Universal Permissive License v1.0](https://oss.oracle.com/licenses/upl/)


pkgId: 5ea21f96a114e3664dc7503521cae1106f57e5c8e1b1df4a9d52299cf8011a4b
name: python-oracle-data-studio-help
arch: noarch
version: 1.0.8
epoch: 0
release: 1
summary: Development documents and examples for oracle-data-studio
url: https://pypi.org/project/oracle-data-studio/
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-oracle-data-studio-1.0.8-1.src.rpm
location_href: Packages/python-oracle-data-studio-help-1.0.8-1.noarch.rpm
checksum_type: sha256
description:
  The Python client for Oracle Data Studio on ADB offers various operations for accessing and managing Oracle Databases via Oracle REST Data Services (ORDS). It provides functions to support uploading files from Cloud Storage to the database, importing tables from database links, and manipulating analytic views and insights.

  The Data Studio API works with Python 3.7 or later.



  	pip install oracle-data-studio




  Please visit [Oracle documentation](https://docs.oracle.com/en/database/oracle/sql-developer-web/sdwad/working-python-ords-api-oml.html) for details.

rpm_license:
  Copyright (c) 2023-2024, Oracle and/or its affiliates  The Universal Permissive License (UPL), Version 1.0  Subject to the condition set forth below, permission is hereby granted to any person obtaining a copy of this software, associated documentation and/or data (collectively the "Software"), free of charge and under any and all copyright rights in the Software, and any and all patent rights owned or freely licensable by each licensor hereunder covering either (i) the unmodified Software as contributed to or provided by such licensor, or (ii) the Larger Works (as defined below), to deal in both  (a) the Software, and (b) any piece of software and/or hardware listed in the lrgrwrks.txt file if one is included with the Software (each a "Larger Work" to which the Software is contributed by such licensors),  without restriction, including without limitation the rights to copy, create derivative works of, display, perform, and distribute the Software and make, use, sell, offer for sale, import, export, have made, and have sold the Software and the Larger Work(s), and to sublicense the foregoing rights on either these or other terms.  This license is subject to the following condition:  The above copyright notice and either this complete permission notice or at a minimum a reference to the UPL must 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.


pkgId: ebd0ecf4f5e304a6413d54ce2a95d2cde47655924a0bb1dc15b771c0e41a59e7
name: python-oracle-predeployed-help
arch: noarch
version: 1.0.0a3
epoch: 0
release: 1
summary: Development documents and examples for oracle-predeployed
url: https://github.com/skalenetwork/oracle-contract
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-oracle-predeployed-1.0.0a3-1.src.rpm
location_href: Packages/python-oracle-predeployed-help-1.0.0a3-1.noarch.rpm
checksum_type: sha256
description:
  A tool for generating predeployed oracle smart contract



  ```console
  pip install oracle-predeployed
  ```



  ```python
  from oracle_predeployed import  UpgradeableOracleUpgradeableGenerator, ORACLE_ADDRESS, ORACLE_IMPLEMENTATION_ADDRESS

  OWNER_ADDRESS = '0xd200000000000000000000000000000000000000'
  PROXY_ADMIN_ADDRESS = '0xd200000000000000000000000000000000000001'

  oracle_generator = UpgradeableOracleUpgradeableGenerator()

  genesis = {
      
      'alloc': {
          **oracle_generator.generate_allocation(
              contract_address=ORACLE_ADDRESS,
              implementation_address=ORACLE_IMPLEMENTATION_ADDRESS,
              schain_owner=OWNER_ADDRESS,
              proxy_admin_address=PROXY_ADMIN_ADDRESS
          )
      }
  }

  ```


pkgId: cf19a0822b37b960e65e21f92288487cacfd5e03ed7f6bc3c78c108ff6f1f8ed
name: python-orb-models-help
arch: noarch
version: 4.1.0
epoch: 0
release: 1
summary: Development documents and examples for orb-models
url: https://github.com/orbital-materials/orb-models
rpm_license: Apache License, Version 2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-orb-models-4.1.0-1.src.rpm
location_href: Packages/python-orb-models-help-4.1.0-1.noarch.rpm
checksum_type: sha256
description:
  <p align="center">
    <img src="./logo_color_text.png" alt="Orbital Materials" width="600"/>
  </p>
  <br/>



  ![example workflow](https://github.com/orbital-materials/orb-models/actions/workflows/test.yml/badge.svg)
  [![PyPI version](https://badge.fury.io/py/orb-models.svg)](https://badge.fury.io/py/orb-models)



  ```bash
  pip install orb-models
  pip install "pynanoflann@git+https://github.com/dwastberg/pynanoflann#egg=af434039ae14bedcbb838a7808924d6689274168",
  ```
  Pynanoflann is not available on PyPI, so you must install it from the git repository.

  Orb models are expected to work on MacOS and Linux. Windows support is not guaranteed.



  **Oct 2024**: We have released a new version of the models, `orb-v2`. This version has 2 major changes:
  - v2 models use a smoothed cosine distance cutoff for the attention mechanism. This is a more physically motivated cutoff that is better suited for MPNNs.
  - The force predictions now have net zero forces, meaning they are much more stable for MD simulations.
  - The models are generally more accurate (Increase in 2-3% on the matbench discovery dataset).

  These models are substantially better for all use cases, so we have removed the v1 models from the new orb-models package. To load the v1 models, please install the v0.3.2 version of orb-models.

  **Sept 2024**: v1 models released - state of the art performance on the matbench discovery dataset.




  We provide several pretrained models that can be used to calculate energies, forces & stresses of atomic systems. All models are provided in the `orb_models.forcefield.pretrained` module.

  - `orb-v2` - trained on [MPTraj](https://figshare.com/articles/dataset/Materials_Project_Trjectory_MPtrj_Dataset/23713842?file=41619375) + [Alexandria](https://alexandria.icams.rub.de/).
  - `orb-mptraj-only-v2` - trained on the MPTraj dataset only to reproduce our second Matbench Discovery result. We do not recommend using this model for general use.
  - `orb-d3-v2` - trained on MPTraj + Alexandria with integrated D3 corrections. In general, we recommend using this model, particularly for systems where dispersion interactions are important. This model was trained to predict D3-corrected targets and hence is the same speed as `orb-v2`. Incorporating D3 into the model like this is substantially faster than using analytical D3 corrections.
  - `orb-d3-{sm,xs}-v2` - Smaller versions of `orb-d3-v2`. The `sm` model has 10 layers, whilst the `xs` model has 5 layers.

  For more information on the models, please see the [MODELS.md](MODELS.md) file.




  Note: These examples are designed to run on the `main` branch of orb-models. If you are using a pip installed version of `orb-models`, you may want to look at the corresponding [README.md from that tag](https://github.com/orbital-materials/orb-models/tags).


  ```python

  import ase
  from ase.build import bulk

  from orb_models.forcefield import atomic_system, pretrained
  from orb_models.forcefield.base import batch_graphs

  device = "cpu"  # or device="cuda"
  orbff = pretrained.orb_v2(device=device)
  atoms = bulk('Cu', 'fcc', a=3.58, cubic=True)
  graph = atomic_system.ase_atoms_to_atom_graphs(atoms, device=device)




  result = orbff.predict(graph)


  atoms = atomic_system.atom_graphs_to_ase_atoms(
      graph,
      energy=result["graph_pred"],
      forces=result["node_pred"],
      stress=result["stress_pred"]
  )
  ```



  ```python
  import ase
  from ase.build import bulk

  from orb_models.forcefield import pretrained
  from orb_models.forcefield.calculator import ORBCalculator

  device="cpu" # or device="cuda"
  orbff = pretrained.orb_v2(device=device) # or choose another model using ORB_PRETRAINED_MODELS[model_name]()
  calc = ORBCalculator(orbff, device=device)
  atoms = bulk('Cu', 'fcc', a=3.58, cubic=True)

  atoms.set_calculator(calc)
  atoms.get_potential_energy()
  ```

  You can use this calculator with any ASE calculator-compatible code. For example, you can use it to perform a geometry optimization:

  ```python
  from ase.optimize import BFGS


  atoms.rattle(0.5)
  print("Rattled Energy:", atoms.get_potential_energy())

  calc = ORBCalculator(orbff, device="cpu") # or device="cuda"
  dyn = BFGS(atoms)
  dyn.run(fmax=0.01)
  print("Optimized Energy:", atoms.get_potential_energy())
  ```



  You can finetune the model using your custom dataset.
  The dataset should be an [ASE sqlite database](https://wiki.fysik.dtu.dk/ase/ase/db/db.html#module-ase.db.core).
  ```python
  python finetune.py --dataset=<dataset_name> --data_path=<your_data_path>
  ```
  After the model is finetuned, checkpoints will, by default, be saved to the ckpts folder in the directory you ran the finetuning script from.

  You can use the new model and load the checkpoint by:
  ```python
  from orb_models.forcefield import pretrained

  model = pretrained.orb_v2(weights_path=<path_to_ckpt>)
  ```

  > ⚠ **Caveats**
  >
  > Our finetuning script is designed for simplicity and advanced users may wish to develop it further. Please be aware that:
  > - The script assumes that your ASE database rows contain **energy, forces, and stress** data. To train on molecular data without stress, you will need to edit the code.
  > - **Early stopping** is not implemented. However, you can use the command line argument `save_every_x_epochs` (default is 5), so "retrospective" early stopping can be applied by selecting a suitable checkpoint.
  > - The **learning rate schedule is hardcoded** to be `torch.optim.lr_scheduler.OneCycleLR` with `pct_start=0.05`. The `max_lr`/`min_lr` will be 10x greater/smaller than the `lr` specified via the command line. To get the best performance, you may wish to try other schedulers.
  > - The defaults of `--num_steps=100` and `--max_epochs=50` are small. This may be suitable for very small finetuning datasets (e.g. 100s of systems), but you will likely want to increase the number of steps for larger datasets (e.g. 1000s of datapoints).
  > - The script only tracks a limited set of metrics (energy/force/stress MAEs) which may be insufficient for some downstream use-cases. For instance, if you wish to finetune a model for Molecular Dynamics simulations, we have found (anecdotally) that models that are just on the cusp of overfitting to force MAEs can be substantially worse for simulations. Ideally, more robust "rollout" metrics would be included in the finetuning training loop. In lieu of this, we recommend more aggressive early-stopping i.e. using models several epochs prior to any sign of overfitting.



  We are currently preparing a preprint for publication.




  ORB models are licensed under the Apache License, Version 2.0. Please see the [LICENSE](LICENSE) file for details.

  **If you have an interesting use case or benchmark for an Orb model, please let us know!** We are happy to work with the community to make these models useful for as many applications as possible.



  Please join the discussion on Discord by following [this](https://discord.gg/SyD6vWSSTB) link.


pkgId: f933393d785b9a5b76eedd193f62a362277f50e7efd428261752e0f77edd4684
name: python-orca_nw_lib-help
arch: noarch
version: 1.3.55
epoch: 0
release: 1
summary: Development documents and examples for orca_nw_lib
url: https://pypi.org/project/orca_nw_lib/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-orca_nw_lib-1.3.55-1.src.rpm
location_href: Packages/python-orca_nw_lib-help-1.3.55-1.noarch.rpm
checksum_type: sha256
description:
  <p align="center">
  <a href="https://pypi.org/project/orca_nw_lib/">
        <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/orca_nw_lib?style=for-the-badge&logo=pypi&link=https%3A%2F%2Fpypi.org%2Fproject%2Forca_nw_lib%2F"/>
  </a>
  <a href="https://github.com/STORDIS/orca_backend/actions">
        <img alt="Tests Status" src="https://img.shields.io/github/actions/workflow/status/stordis/orca_nw_lib/publish_to_pypi.yml?style=for-the-badge"/>
  </a>
  <a href="https://github.com/stordis/orca_nw_lib/issues">
        <img alt="Issues" src="https://img.shields.io/github/issues/stordis/orca_nw_lib?style=for-the-badge&logo=github&link=https%3A%2F%2Fgithub.com%2FSTORDIS%2Forca_nw_lib%2Fissues"/>
  </a>
  <a href="https://github.com/stordis/orca_nw_lib/graphs/contributors">
        <img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/stordis/orca_nw_lib?style=for-the-badge&logo=github&link=https%3A%2F%2Fgithub.com%2FSTORDIS%2Forca_nw_lib%2Fgraphs%2Fcontributors" />
  </a>
  <a href="https://github.com/stordis/orca_nw_lib/pulls?q=">
        <img alt="GitHub pull requests" src="https://img.shields.io/github/issues-pr/stordis/orca_nw_lib?color=0088ff&style=for-the-badge&logo=github&link=https%3A%2F%2Fgithub.com%2FSTORDIS%2Forca_nw_lib%2Fpulls" />
  </a>
  <a href="https://github.com/STORDIS/orca_nw_lib?tab=Apache-2.0-1-ov-file#readme">
        <img alt="GitHub License" src="https://img.shields.io/github/license/stordis/orca_nw_lib?style=for-the-badge"/>
  </a>
  </p>


  >_**Note:** If your intension is to just start using ORCA, you need not to get into ORCA Network Library details, instead consider installing [ORCA Backend](https://github.com/STORDIS/orca_backend) and [ORCA UI](https://github.com/STORDIS/orca_ui) directly._

  ORCA Network Library is an open source python package to facilitate CRUD operations on SONiC devices using gNMI interface. orca_nw_lib maintains a graph database with the realtime device configurations and network topology.
  ORCA Network Library can be used to develop the orchestration solutions, NMS applications, network data analytics. [ORCA Backend](https://github.com/STORDIS/orca_backend) and [ORCA UI](https://github.com/STORDIS/orca_ui) are the application developed using APIs and Neo4j DB of ORCA Network Library.


  - [ORCA Network Library](#orca-network-library)
    - [Install orca\_nw\_lib using pip](#install-orca_nw_lib-using-pip)
    - [Prerequisite](#prerequisite)
      - [Install Neo4j](#install-neo4j)
      - [Configuration](#configuration)
        - [orca\_nw\_lib.yml](#orca_nw_libyml)
        - [orca\_nw\_lib\_logging.yml](#orca_nw_lib_loggingyml)
    - [Build and Install orca\_nw\_lib from source](#build-and-install-orca_nw_lib-from-source)
    - [Using the ORCA Network APIs](#using-the-orca-network-apis)
    - [Knowing API call status](#knowing-api-call-status)
    - [Keeping graph DB in sync with realtime Network state.](#keeping-graph-db-in-sync-with-realtime-network-state)
    - [Graph DB Representation](#graph-db-representation)
      - [BGP](#bgp)
    - [Executing Tests](#executing-tests)
    - [Releases of orca\_nw\_lib](#releases-of-orca_nw_lib)
    - [Supported SONiC versions](#supported-sonic-versions)
    - [Contribute](#contribute)


  Latest release of ORCA Network Library can be simply installed using pip as follows :

      pip install orca_nw_lib


  After installing the orca_nw_lib package, orca_nw_lib can be used like any other python package in your application. But before using orca_nw_lib, it is required to install Neo4j and do some basic configurations.

  orca_nw_lib uses neo4j to store the network topology. To install neo4j easiest is to run Neo4j Docker image in container with the following command :

      docker run \
          --name testneo4j \
          -p7474:7474 -p7687:7687 \
          -d \
          -v $HOME/neo4j/data:/data \
          -v $HOME/neo4j/logs:/logs \
          -v $HOME/neo4j/import:/var/lib/neo4j/import \
          -v $HOME/neo4j/plugins:/plugins \
          --env NEO4J_AUTH=neo4j/password \
          neo4j:latest
  Then open https://localhost:7474 with credentials neo4j/password to browse the database.


  There are Following 2 configuration files for orca_nw_lib-

  File contains all required parameters required to perform device and Neo4j DB operations. Also all the config properties defined in the file can be overridden by setting the environment variables with same name.

  In majority of the cases only setting "discover_networks" property is enough i.e.`export discover_networks="10.10.229.50"`


  File contains the standard logging configuration for ORCA Network Library.

  By default above config files from the codebase are used, which is enough for most of the cases. Optionally user can use custom config files by setting the config files path in the environment variables `ORCA_NW_LIB_CONFIG_FILE` and `ORCA_NW_LIB_LOGGING_CONFIG_FILE`.


  Optionally if user want to build and install orca_nw_lib from source, ORCA Network Library uses poetry to build the orca_nw_lib package. As a pre-requisite poetry must be installed in this case. Poetry can be easily installed using the following command :

      pip install poetry

  To build and install orca_nw_lib use the following commands :

      git clone https://github.com/STORDIS/orca_nw_lib.git
      cd orca_nw_lib
      poetry build
      pip install dist/orca_nw_lib-****.whl

  Once installed the orca_nw_lib package, orca_nw_lib can be used like any other python package in your application.


  For normal usage following APIs in python modules in the package [orca_nw_lib](orca_nw_lib) are useful -\
  [utils.py](orca_nw_lib/utils.py) - load_orca_config() function must be called before using any APIs of orca_nw_lib.\
  [discovery.py](orca_nw_lib/discovery.py) - discover_all() function can be used to discover complete topology as per the network defined in orca.yml\
  [bgp.py](orca_nw_lib/bgp.py) - BGP CRUD operations\
  [device.py](orca_nw_lib/device.py) - Get device system info.\
  [interface.py](orca_nw_lib/interface.py) - Interfaces CRUD operations.\
  [lldp.py](orca_nw_lib/lldp.py) - Read LLDP relation, useful while doing discovery.\
  [mclag.py](orca_nw_lib/mclag.py) - MCLAG CRUD operations.\
  [port_chnl.py](orca_nw_lib/port_chnl.py) - Port Channel CRUD operations.\
  [portgroup.py](orca_nw_lib/portgroup.py) - Read port group information.\
  [vlan.py](orca_nw_lib/vlan.py) - VLAN CRUD operations.

  There are modules having suffixes _db and _gnmi, they contain operations to be performed in db or on device using gNMI respectively.\
  e.g. interface.py have general operation on interfaces and users can achieve normal interface configurations by using functions present in interface.py, on the other hand interface_db.py has function to perform CRUD operations in graph DB and interface_gnmi.py has function to configure interfaces on device.
  > [Test cases](./test) are a good starting point to know the usage of APIs in orca_nbw_lib.


  Exception objects raised by APIs in the modules above can be caught to know the API status. Exception object has all necessary details about the error.


  Any configuration done on the device via orca_nw_lib APIs automatically keeps the DB in sync with the realtime network state by triggering the discovery method for the network component although gNMI subscriptions would have been the best way to achieve this, but due lack of support for gNMI subscription for all openconfig models in SONiC currently only pull mechanism can be used to keep graph DB in sync with realtime network state.
  > NOTE : For changes done on the device out side of orca_nw_lib i.e. changes done directly on the device, Best way to keep graph DB in sync with realtime network state is to trigger full discovery at a pre-defined interval.




  ![BGP](bgp.drawio.svg)


  Tests are not only used for regular software testing but are a good example to know the usage of APIs in orca_nw_lib. When starting to use orca_nw_lib referring to tests can be a good starting point. Test cases are located under [test](./orca_nw_lib/test) directory. To execute tests a topology of 3 switches (1-spine, 2-leaves) is required. Prior to execute tests, leaves should be connected to spine and respective interfaces should be enabled, so that by providing one of the switch IP in [orca.yml](./orca_nw_lib/orca.yml) whole topology gets discovered.\
  For performing tests creating a topology in GNS3 can be a good starting point.

  - To execute tests

          pytest orca_nw_lib/test/test_network.py

  - To execute single test cases

          pytest orca_nw_lib/test/test_network.py -k test_remove_port_chnl_members

  - To print console messages from code

          pytest orca_nw_lib/test/test_interface.py -k test_remove_port_chnl_members -s


  orca_nw_lib releases are hosted at PyPI- https://pypi.org/project/orca_nw_lib/#history ,
  To create a new release, increase the release number in pyproject.toml.


  - Broadcom Enterprise SONiC (Latest tested on >=4.0.5)


  You can contribute to the project by opening an issue or sending a pull request.


pkgId: 7ac2f276b3bf1dd8ebb322b0ca1adb29d67016502794c27fdbb0738217b8b15a
name: python-ord-schema-help
arch: noarch
version: 0.3.95
epoch: 0
release: 1
summary: Development documents and examples for ord-schema
url: https://github.com/Open-Reaction-Database/ord-schema
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ord-schema-0.3.95-1.src.rpm
location_href: Packages/python-ord-schema-help-0.3.95-1.noarch.rpm
checksum_type: sha256
description:
  [![DOI:10.1007/978-3-319-76207-4_15](https://zenodo.org/badge/DOI/10.1021/jacs.1c09820.svg)](https://doi.org/10.1021/jacs.1c09820)
  [![PyPI version](https://badge.fury.io/py/ord-schema.svg)](https://badge.fury.io/py/ord-schema)

  This repository contains the schema for the Open Reaction Database initiative; please see the documentation
  at https://docs.open-reaction-database.org.

  This repository does not contain the database itself; that is stored
  in [ord-data](https://github.com/open-reaction-database/ord-data). Rather, `ord-schema` is
  designed to store the database schema and tools for creating, validating, and submitting data to the database.



  ```shell
  $ pip install ord-schema
  ```



  The `examples/` directory contains examples of dataset creation and use. To run locally, install with:

  ```shell
  $ pip install "ord-schema[examples]"
  ```

  Click here to run the examples with Binder:
  [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/open-reaction-database/ord-schema/HEAD?labpath=examples)



  To install in editable/development mode:

  ```shell
  $ git clone https://github.com/open-reaction-database/ord-schema.git
  $ cd ord-schema
  $ pip install -e .
  ```

  If you make changes to the protocol buffer definitions, [install](https://grpc.io/docs/protoc-installation/) `protoc`
  and run `./compile_proto_wrappers.sh` to rebuild the wrappers.










  1. The preferred field for compound stoichiometry is the map `Compound.features` or `ProductCompound.features`.
  2. The key should be "stoichiometric_coefficient" or "stoichiometric_ratio".
  3. The value should be a `Data` message with its `float_value` representing the compound's stoichiometric
  coefficient or ratio.


  [#683](https://github.com/open-reaction-database/ord-schema/issues/683)
  [#684](https://github.com/open-reaction-database/ord-schema/pull/684)


pkgId: 2a58e2dee5b3c3079d439287c862a173004956fadb1368a02ed610986a150ddf
name: python-oreum_core-help
arch: noarch
version: 0.9.10
epoch: 0
release: 1
summary: Development documents and examples for oreum_core
url: https://github.com/oreum-industries/oreum_core
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-oreum_core-0.9.10-1.src.rpm
location_href: Packages/python-oreum_core-help-0.9.10-1.noarch.rpm
checksum_type: sha256
description:
  This is an ever-growing package of core tools for use on client projects by
  Oreum Industries.
  + Provides an essential workflow for data curation, EDA, basic ML using the core
    scientific Python stack incl. `numpy`, `scipy`, `matplotlib`, `seaborn`,
    `pandas`, `scikit-learn`, `umap-learn`
  + Optionally provides an advanced Bayesian modeling workflow in R&D and
    Production using a leading probabilistic programming stack incl. `pymc`,
    `pytensor`, `arviz`
    (do `pip install oreum_core[pymc]`)
  + Optionally enables a generalist black-box ML workflow in R&D using a leading
    Gradient Boosted Trees stack incl. `catboost`, `xgboost`, `optuna`, `shap`
    (do `pip install oreum_core[tree]`)
  + Also includes several utilities for text cleaning, sql scripting, file handling
  This package **is**:
  + A work in progress (v0.y.z) and liable to breaking changes and inconvenience
    to the user
  + Solely designed for ease of use and rapid development by employees of
    Oreum Industries, and selected clients with guidance
  This package **is not**:
  + Intended for public usage and will not be supported for public usage
  + Intended for contributions by anyone not an employee of Oreum Industries,
    and unsolicited contributions will not be accepted.

  + Project began on 2021-01-01
  + The `README.md` is MacOS and POSIX oriented
  + See `LICENCE.md` for licensing and copyright details
  + See `pyproject.toml` for various package details
  + This uses a logger named `'oreum_core'`, feel free to incorporate or ignore
  + Hosting:
    + Source code repo on [GitHub](https://github.com/oreum-industries/oreum_core)
    + Source code release on [GitHub](https://github.com/oreum-industries/oreum_core/releases)
    + Package release on [PyPi](https://pypi.org/project/oreum_core)

  For local development on MacOS

  1. Install Homebrew, see instructions at [https://brew.sh](https://brew.sh)
  2. Install `direnv`, `git`, `git-lfs`, `graphviz`, `zsh`
  ```zsh
  $> brew update && upgrade
  $> brew install direnv git git-lfs graphviz zsh
  ```

  Assumes `direnv`, `git`, `git-lfs` and `zsh` installed as above
  ```zsh
  $> git clone https://github.com/oreum-industries/oreum_core
  $> cd oreum_core
  ```
  Then allow `direnv` on MacOS to autorun file `.envrc` upon directory open

  Notes:
  + We use `conda` virtual envs controlled by `mamba` (quicker than `conda`)
  + We install packages using `miniforge` (sourced from the `conda-forge` repo)
    wherever possible and only use `pip` for packages that are handled better by
    `pip` and/or more up-to-date on [pypi](https://pypi.org)
  + Packages might not be the very latest because we want stability for `pymc`
    which is usually in a state of development flux
  + See [cheat sheet of conda commands](https://conda.io/docs/_downloads/conda-cheatsheet.pdf)
  + The `Makefile` creates a dev env and will also download and preinstall
    `miniforge` if not yet installed on your system

  From the dir above `oreum_core/` project dir:
  ```zsh
  $> make -C oreum_core/ dev
  ```
  This will also create some files to help confirm / diagnose successful installation:
  + `dev/install_log/blas_info.txt` for the `BLAS MKL` installation for `numpy`
  + `dev/install_log/pipdeptree[_rev].txt` lists installed package deps (and reversed)
  + `LICENSES_THIRD_PARTY.md` details the license for each package used

  From the dir above `oreum_core/` project dir:
  ```zsh
  $> make -C oreum_core/ test-dev-env
  ```
  This will also add files `dev/install_log/[numpy|scipy].txt` which detail
  successful installation (or not) for `numpy`, `scipy`

  From the dir above `oreum_core/` project dir:
  ```zsh
  $> make -C oreum_core/ uninstall-env
  ```


  We use [pre-commit](https://pre-commit.com) to run a suite of automated tests
  for code linting & quality control and repo control prior to commit on local
  development machines.
  + Precommit is already installed by the `make dev` command (which itself calls
  `pip install -e .[dev]`)
  + The pre-commit script will then run on your system upon `git commit`
  + See this project's `.pre-commit-config.yaml` for details

  We use [Github Actions](https://docs.github.com/en/actions/using-workflows) aka
  Github Workflows to run:
  1. A suite of automated tests for commits received at the origin (i.e. GitHub)
  2. Publishing to PyPi upon creating a GH Release
  + See `Makefile` for the CLI commands that are issued
  + See `.github/workflows/*` for workflow details

  We use [Git LFS](https://git-lfs.github.com) to store any large files alongside
  the repo. This can be useful to replicate exact environments during development
  and/or for automated tests
  + This requires a local machine install
    (see [Getting Started](https://git-lfs.github.com))
  + See `.gitattributes` for details

  Some notes to help configure local development environment

  ```yaml
  [user]
      name = <YOUR NAME>
      email = <YOUR EMAIL ADDRESS>
  ```

  We strongly recommend using [VSCode](https://code.visualstudio.com) for all
  development on local machines, and this is a hard pre-requisite to use
  the `.devcontainer` environment (see section 3)
  This repo includes relevant lightweight project control and config in:
  ```zsh
  oreum_core.code-workspace
  .vscode/extensions.json
  .vscode/settings.json


pkgId: d287ca7a36ec91eeea5243f2dfc0121198086a59bcfd422c6df2007cafc66a3c
name: python-orion-ml-help
arch: noarch
version: 0.6.1
epoch: 0
release: 1
summary: Development documents and examples for orion-ml
url: https://github.com/sintel-dev/Orion
rpm_license: MIT license
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-orion-ml-0.6.1-1.src.rpm
location_href: Packages/python-orion-ml-help-0.6.1-1.noarch.rpm
checksum_type: sha256
description:
  * Update minimum test images – [Issue #563](https://github.com/signals-dev/Orion/issues/563) by @sarahmish
  * Update benchmark – [Issue #538](https://github.com/signals-dev/Orion/issues/538) by @sarahmish
  * Pin MacOS Version in GH Actions – [Issue #535](https://github.com/signals-dev/Orion/issues/535) by @sarahmish
  * Add anomaly transformer method – [Issue #452](https://github.com/signals-dev/Orion/issues/452) by @sarahmish

  Support for python 3.10 and 3.11

  * Update ``test_core`` file – [Issue #507](https://github.com/signals-dev/Orion/issues/507) by @sarahmish
  * update ARIMA primitive and pipeline – [Issue #503](https://github.com/signals-dev/Orion/issues/503) by @sarahmish
  * Update Dependency – [Issue #497](https://github.com/signals-dev/Orion/issues/497) & [Issue #499](https://github.com/signals-dev/Orion/issues/499) by @sarahmish
  * Update Python for Dependency Test – [Issue #484](https://github.com/signals-dev/Orion/issues/484) by @sarahmish
  * Add python 3.10 and 3.11 & drop 3.6 and 3.7 – [Issue #477](https://github.com/signals-dev/Orion/issues/477) by @sarahmish
  * LNN Pipeline – [Issue #475](https://github.com/signals-dev/Orion/issues/475) by @sarahmish

  Support for python 3.9 and new Matrix Profile pipeline

  * Send pipeline names in benchmark arguments – [Issue #466](https://github.com/signals-dev/Orion/issues/466) by @sarahmish
  * Add continuation parameter for benchmark – [Issue #464](https://github.com/signals-dev/Orion/issues/464) by @sarahmish
  * Fix references in documentation – [Issue #453](https://github.com/signals-dev/Orion/issues/453) by @sarahmish
  * Update documentation – [Issue #448](https://github.com/signals-dev/Orion/issues/448) by @sarahmish
  * Add matrix profiling method – [Issue #446](https://github.com/signals-dev/Orion/issues/446) by @sarahmish
  * Support python 3.9 – [Issue #408](https://github.com/signals-dev/Orion/issues/408) by @sarahmish

  This version introduces a new dataset to the benchmark.

  * Add UCR dataset to the benchmark – [Issue #443](https://github.com/signals-dev/Orion/issues/443) by @sarahmish
  * docker image build failed – [Issue #439](https://github.com/signals-dev/Orion/issues/439) by @sarahmish
  * Edit interval settings in ``azure`` pipeline – [Issue #436](https://github.com/signals-dev/Orion/issues/436) by @sarahmish

  This version uses ``ml-stars`` package instead of ``mlprimitives``.

  * Migrate to ml-stars – [Issue #418](https://github.com/signals-dev/Orion/issues/418) by @sarahmish
  * Updating ``best_cost`` in ``find_anomalies`` primitive – [Issue #403](https://github.com/signals-dev/Orion/issues/403) by @sarahmish
  * Retire ``lstm_dynamic_threshold_gpu`` and ``lstm_autoencoder_gpu`` pipeline maintenance – [Issue #373](https://github.com/signals-dev/Orion/issues/373) by @sarahmish
  * Typo in xlsxwriter dependency specification – [Issue #394](https://github.com/signals-dev/Orion/issues/394) by @sarahmish
  * ``orion.evaluate`` uses fails when fitting – [Issue #384](https://github.com/signals-dev/Orion/issues/384) by @sarahmish
  * AER pipeline with visualization option – [Issue #379](https://github.com/signals-dev/Orion/issues/379) by @sarahmish


  * Move VAE from sandbox to verified – [Issue #377](https://github.com/signals-dev/Orion/issues/377) by @sarahmish
  * Pin ``opencv`` – [Issue #372](https://github.com/signals-dev/Orion/issues/372) by @sarahmish
  * Pin ``scikit-learn`` – [Issue #367](https://github.com/signals-dev/Orion/issues/367) by @sarahmish
  * Fix VAE documentation – [Issue #360](https://github.com/signals-dev/Orion/issues/360) by @sarahmish

  This version introduces several new enhancements:
  * Support to python 3.8
  * Migrating to Tensorflow 2.0
  * New pipeline, namely ``VAE``, a Variational AutoEncoder model.

  * Add python 3.8 – [Issue #342](https://github.com/signals-dev/Orion/issues/342) by @sarahmish
  * VAE (Variational Autoencoders) pipeline implementation – [Issue #349](https://github.com/signals-dev/Orion/issues/349) by @dyuliu
  * Add masking option for ``regression_errors`` – [Issue #352](https://github.com/signals-dev/Orion/issues/352) by @dyuliu
  * Changes in TadGAN for tensorflow 2.0 – [Issue #161](https://github.com/signals-dev/Orion/issues/161) by @lcwong0928
  * Add an automatic dependency checker – [Issue #320](https://github.com/signals-dev/Orion/issues/320) by @sarahmish
  * TadGAN ``batch_size`` cannot be changed – [Issue #313](https://github.com/signals-dev/Orion/issues/313) by @sarahmish

  This version fixes some of the issues in ``aer``, ``ae``, and ``tadgan`` pipelines.

  * Fix AER model predict error after loading – [Issue #304](https://github.com/signals-dev/Orion/issues/304) by @lcwong0928
  * Update AE to work with any `window_size` – [Issue #300](https://github.com/signals-dev/Orion/issues/300) by @sarahmish
  * Updated tadgan_viz.json – [Issue #292](https://github.com/signals-dev/Orion/issues/292) by @Hramir

  This version introduce a new pipeline, namely ``AER``, an AutoEncoder Regressor model.

  * Add AER Model - [Issue #286](https://github.com/signals-dev/Orion/issues/286) by @lcwong0928

  This version deprecates the support of ``OrionDBExplorer``, which has been migrated to
  [sintel](https://github.com/signals-dev/Orion). As a result, ``Orion`` no longer requires
  mongoDB as a dependency.

  * Update dependency  - [Issue #283](https://github.com/signals-dev/Orion/issues/283) by @sarahmish
  * General housekeeping  - [Issue #278](https://github.com/signals-dev/Orion/issues/278) by @sarahmish
  * Fix tutorial testing issue - [Issue #276](https://github.com/signals-dev/Orion/issues/276) by @sarahmish
  * Migrate OrionExplorer to Sintel - [Issue #275](https://github.com/signals-dev/Orion/issues/275) by @dyuliu
  * LSTM viz JSON pipeline added - [Issue #271](https://github.com/signals-dev/Orion/issues/271) by @Hramir

  This version introduces improvements and more testing.

  * Adjusting builds for TadGAN - [Issue #261](https://github.com/signals-dev/Orion/issues/261) by @sarahmish
  * Testing tutorials, dependencies, and OS - [Issue #251](https://github.com/signals-dev/Orion/issues/251) by @sarahmish

  This version supports multivariate timeseries as input. In addition to minor improvements
  and maintenance.

  * `setuptools` no longer supports `lib2to3` breaking `mongoengine` - [Issue #252](https://github.com/signals-dev/Orion/issues/252) by @sarahmish
  * Supporting multivariate input - [Issue #248](https://github.com/signals-dev/Orion/issues/248) by @sarahmish
  * TadGAN pipeline with visualization option - [Issue #240](https://github.com/signals-dev/Orion/issues/240) by @sarahmish
  * Support saving absolute path for add_signals and add_signal when using dbExplorer - [Issue #202](https://github.com/signals-dev/Orion/issues/202) by @sarahmish
  * dynamic scalability of TadGAN primitive based on `window_size` - [Issue #87](https://github.com/signals-dev/Orion/issues/87) by @sarahmish

  This version adds new features to the benchmark function where users can now save pipelines, view results as they are being calculated, and allow a single evaluation to be compared multiple times.

  * Dask issues in benchmark function & improvements - [Issue #225](https://github.com/signals-dev/Orion/issues/225) by @sarahmish
  * Numerical overflow when using contextual metrics - [Issue #212](https://github.com/signals-dev/Orion/issues/212) by @kronerte

  This version introduces two new pipelines: LSTM AE and Dense AE.
  In addition to minor improvements, a bit of code refactoring took place to introduce
  a new primtive: ``reconstruction_errors``.

  * Comparison of DTW library performance - [Issue #205](https://github.com/signals-dev/Orion/issues/205) by @sarahmish
  * Not able to pickle dump tadgan pipeline - [Issue #200](https://github.com/signals-dev/Orion/issues/200) by @sarahmish
  * New pipeline LSTM and Dense autoencoders - [Issue #194](https://github.com/signals-dev/Orion/issues/194) by @sarahmish
  * Readme - [Issue #192](https://github.com/signals-dev/Orion/issues/192) by @pvk-developer
  * Unable to launch cli - [Issue #186](https://github.com/signals-dev/Orion/issues/186) by @sarahmish
  * bullet points not formatted correctly in index.rst - [Issue #178](https://github.com/signals-dev/Orion/issues/178) by @micahjsmith
  * Update notebooks - [Issue #176](https://github.com/signals-dev/Orion/issues/176) by @sarahmish
  * Inaccuracy in README.md file in orion/evaluation/ - [Issue #157](https://github.com/signals-dev/Orion/issues/157) by @sarahmish
  * Dockerfile -- docker does not find orion primitives automatically - [Issue #155](https://github.com/signals-dev/Orion/issues/155) by @sarahmish
  * Primitive documentation - [Issue #151](https://github.com/signals-dev/Orion/issues/151) by @sarahmish
  * Variable name inconsistency in tadgan - [Issue #150](https://github.com/signals-dev/Orion/issues/150) by @sarahmish
  * Sync leaderboard tables between `BENCHMARK.md` and the docs - [Issue #148](https://github.com/signals-dev/Orion/issues/148) by @sarahmish

  This version includes the new style of documentation and a revamp of the `README.md`. In addition to some minor improvements
  in the benchmark code and primitives. This release includes the transfer of `tadgan` pipeline to `verified`.

  * Link with google colab - [Issue #144](https://github.com/signals-dev/Orion/issues/144) by @sarahmish
  * Add `timeseries_anomalies` unittests - [Issue #136](https://github.com/signals-dev/Orion/issues/136) by @sarahmish
  * Update `find_sequences` in converting series to arrays - [Issue #135](https://github.com/signals-dev/Orion/issues/135) by @sarahmish
  * Definition of error/critic smooth window in score anomalies primitive - [Issue #132](https://github.com/signals-dev/Orion/issues/132) by @sarahmish
  * Train-test split in benchmark enhancement - [Issue #130](https://github.com/signals-dev/Orion/issues/130) by @sarahmish

  Minor enhancements to benchmark
  * Load ground truth before try-catch - [Issue #124](https://github.com/signals-dev/Orion/issues/124) by @sarahmish
  * Converting timestamp to datetime in Azure primitive - [Issue #123](https://github.com/signals-dev/Orion/issues/123) by @sarahmish
  * Benchmark exceptions - [Issue #120](https://github.com/signals-dev/Orion/issues/120) by @sarahmish

  New benchmark and Azure primitive.
  * Implement a benchmarking function new feature - [Issue #94](https://github.com/signals-dev/Orion/issues/94) by @sarahmish
  * Add azure anomaly detection as primitive new feature - [Issue #97](https://github.com/signals-dev/Orion/issues/97) by @sarahmish
  * Critic and reconstruction error combination - [Issue #99](https://github.com/signals-dev/Orion/issues/99) by @sarahmish
  * Fixed threshold for `find_anomalies` - [Issue #101](https://github.com/signals-dev/Orion/issues/101) by @sarahmish
  * Add an option to have window size and window step size as percentages of error size - [Issue #102](https://github.com/signals-dev/Orion/issues/102) by @sarahmish
  * Organize pipelines into verified and sandbox - [Issue #105](https://github.com/signals-dev/Orion/issues/105) by @sarahmish
  * Ground truth parameter name enhancement - [Issue #114](https://github.com/signals-dev/Orion/issues/114) by @sarahmish
  * Add benchmark dataset list and parameters to s3 bucket enhancement - [Issue #118](https://github.com/signals-dev/Orion/issues/118) by @sarahmish

  New Evaluation sub-package and refactor TadGAN.
  * Two bugs when saving signalrun if there is no event detected - [Issue #92](https://github.com/signals-dev/Orion/issues/92) by @dyuliu 
  * File encoding/decoding issues about `README.md` and `HISTORY.md` - [Issue #88](https://github.com/signals-dev/Orion/issues/88) by @dyuliu
  * Fix bottle neck of `score_anomaly` in Cyclegan primitive - [Issue #86](https://github.com/signals-dev/Orion/issues/86) by @dyuliu
  * Adjust `epoch` meaning in Cyclegan primitive - [Issue #85](https://github.com/signals-dev/Orion/issues/85) by @sarahmish
  * Rename evaluation to benchmark and metrics to evaluation - [Issue #83](https://github.com/signals-dev/Orion/issues/83) by @sarahmish
  * Scoring function for intervals of size one - [Issue #76](https://github.com/signals-dev/Orion/issues/76) by @sarahmish

  New class and function based interfaces.
  * Implement the Orion Class - [Issue #79](https://github.com/D3-AI/Orion/issues/79) by @csala
  * Implement new functional interface - [Issue #80](https://github.com/D3-AI/Orion/issues/80) by @csala

  First Orion release to PyPI: https://pypi.org/project/orion-ml/


pkgId: 3f4df3ab4bb29a743f4336f8834f6fed43d63f823d8900cd3d89442acbe61590
name: python-orq-ai-sdk-help
arch: noarch
version: 2.13.4
epoch: 0
release: 1
summary: Development documents and examples for orq-ai-sdk
url: https://pypi.org/project/orq-ai-sdk/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-orq-ai-sdk-2.13.4-1.src.rpm
location_href: Packages/python-orq-ai-sdk-help-2.13.4-1.noarch.rpm
checksum_type: sha256
description:
  <p align="left">
    <a href="https://orq.ai" target="_blank">
      <img src="https://asset.brandfetch.io/idtBhDRr2x/idcrPsCm4K.png" alt="Orq">
    </a>
  </p>

  Build AI Applications from Playground to Production

  ![npm](https://img.shields.io/pypi/v/orq-ai-sdk)



  The orq.ai Python library enables easy orq.ai REST API integration in Python 3.7+ apps, offering typed request/response
  elements and httpx-based sync/async clients



  The REST API documentation can be found on [docs.orq.ai](https://docs.orq.ai/reference/authentication).



  ```bash

  pip install orq-ai-sdk
  ```



  _You can get your workspace API key from the settings section in your orq.ai
  workspace. `https://my.orq.ai/<workspace>/settings/developers`_

  ```python
  import os

  from orq_ai_sdk import OrqAI

  client = OrqAI(
      api_key=os.environ.get("ORQ_API_KEY", "__API_KEY__"),
      environment="production"
  )

  generation = client.deployments.invoke(
      key="customer_service",
      context={"environments": "production", "country": "NLD"},
      inputs={"firstname": "John", "city": "New York"},
      metadata={"customer_id": "Qwtqwty90281"},
  )
  ```



  Simply import AsyncOrqAI instead of OrqAI and use await with each API call:

  ```python
  import os
  import asyncio
  from orq_ai_sdk import AsyncOrqAI

  client = AsyncOrqAI(
      api_key=os.environ.get("ORQ_API_KEY", "__API_KEY__"),
      environment="production"
  )


  async def main() -> None:
      generation = await client.deployments.invoke(
          key="customer_service",
          context={"environments": "production", "country": "NLD"},
          inputs={"firstname": "John", "city": "New York"},
          metadata={"customer_id": "Qwtqwty90281"},
      )

      print(generation.choices[0].message.content)


  asyncio.run(main())
  ```



  <div id="deployments"/>

  The Deployments API delivers text outputs, images or tool calls based on the configuration established within Orq
  for your deployments. Additionally, this API supports streaming. To ensure ease of use and minimize errors, using the
  code snippets from the Orq Admin panel is highly recommended.





  ```python
  deployment = await client.deployments.invoke(
      key="customer_service",
      context={"environments": "production", "country": "NLD"},
      inputs={"firstname": "John", "city": "New York"},
      metadata={"customer_id": "Qwtqwty90281"},
  )

  print(deployment.choices[0].message.content)
  ```



  ```python
  stream = client.deployments.invoke_with_stream(
      key="customer_service",
      context={"environments": "production", "country": "NLD"},
      inputs={"firstname": "John", "city": "New York"},
      metadata={"customer_id": "Qwtqwty90281"},
  )

  await
  for chunk in stream:

      if chunk.is_final:
          print("Stream is finished")

      print(chunk.choices[0].message.content or "", end="")
  ```



  If you are using the `invoke` method, you can include `messages` in your request to the model. The `messages` property
  allows you to combine `chat_history` with the prompt configuration in Orq, or to directly send `messages` to the
  model if you are managing the prompt in your code.

  ```python
  generation = client.deployments.invoke(
      key="customer_service",
      context={
          "language": [],
          "environments": []
      },
      metadata={
          "custom-field-name": "custom-metadata-value"
      },
      inputs={"firstname": "John", "city": "New York"},
      messages=[{
          "role": "user",
          "content": "A customer is asking about the latest software update features. Generate a detailed and informative response highlighting the key new features and improvements in the latest update.",
      }]
  )
  ```



  After invoking, streaming or getting the configuration of a deployment, you can use the `add_metrics` method to add
  information to the deployment.

  ```python

  await generation.add_metrics(
      chain_id="c4a75b53-62fa-401b-8e97-493f3d299316",
      conversation_id="ee7b0c8c-eeb2-43cf-83e9-a4a49f8f13ea",
      user_id="e3a202a6-461b-447c-abe2-018ba4d04cd0",
      feedback={"score": 100},
      metadata={
          "custom": "custom_metadata",
          "chain_id": "ad1231xsdaABw",
      },
      messages=[{
          "role": "user",
          "content": "A customer is asking about the latest software update features. Generate a detailed and informative response highlighting the key new features and improvements in the latest update.",
      }]
  )
  ```





  ```python
  prompt_config = client.deployments.get_config(
      key="customer_service",
      context={"environments": "production", "country": "NLD"},
      inputs={"firstname": "John", "city": "New York"},
      metadata={"customer_id": "Qwtqwty90281"},
  )

  print(prompt_config.to_dict())
  ```



  After invoking, streaming or getting the configuration of a deployment, you can use the `add_metrics` method to add
  information to the deployment.

  ```python

  prompt_config.add_metrics(
      chain_id="c4a75b53-62fa-401b-8e97-493f3d299316",
      conversation_id="ee7b0c8c-eeb2-43cf-83e9-a4a49f8f13ea",
      user_id="e3a202a6-461b-447c-abe2-018ba4d04cd0",
      feedback={"score": 100},
      metadata={
          "custom": "custom_metadata",
          "chain_id": "ad1231xsdaABw",
      },
      usage={
          "prompt_tokens": 100,
          "completion_tokens": 900,
          "total_tokens": 1000,
      },
      performance={
          "latency": 9000,
          "time_to_first_token": 250,
      },
  )
  ```



  <div id="logging"/>

  Whether you use the `get_config` or `invoke`, you can log the model generations to the deployment. Here are some
  examples of how to do it.



  ```python
  generation.add_metrics(
      choices=[
          {
              "index": 0,
              "finish_reason": "assistant",
              "message": {
                  "role": "assistant",
                  "content": "Dear customer: Thank you for your interest in our latest software update! We're excited to share with you the new features and improvements we've rolled out. Here's what you can look forward to in this update",
              },
          },
      ]
  )
  ```



  You can save the images generated by the model in Orq. If the image format is `base64` we always store it as
  a `png`.

  ```python
  generation.add_metrics(
      choices=[
          {
              "index": 0,
              "finish_reason": 'stop',
              "message": {
                  "role": "assistant",
                  "url": "<image_url>"
              },
          },
      ],
  )
  ```



  ```python
  generation.add_metrics(
      choices=[
          {
              "index": 0,
              "message": {
                  "role": "assistant",
                  "content": None,
                  "tool_calls": [
                      {
                          "type": "function",
                          "id": "call_pDBPMMacPXOtoWhTWibW1D94",
                          "function": {
                              "name": "get_weather",
                              "arguments": '{"location":"San Francisco, CA"}',
                          },
                      },
                  ],
              },
              "finish_reason": 'tool_calls',
          }
      ]
  )
  ```



  [Contacts](https://github.com/orquestadev/orquesta-python/blob/main/orq_ai_sdk/api_resources/contacts/README.md)

  [Feedback](https://github.com/orquestadev/orquesta-python/blob/main/orq_ai_sdk/api_resources/feedback/README.md)

  [Webhooks](https://github.com/orquestadev/orquesta-python/blob/main/orq_ai_sdk/api_resources/webhooks/README.md)


pkgId: 81843ddde160a3e26128c75901a732a220d991e17c29db01e7ee7e1e3cc2a5ba
name: python-oscn-help
arch: noarch
version: 0.0.0.89
epoch: 0
release: 1
summary: Development documents and examples for oscn
url: https://github.com/codefortulsa/oscn
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-oscn-0.0.0.89-1.src.rpm
location_href: Packages/python-oscn-help-0.0.0.89-1.noarch.rpm
checksum_type: sha256
description:
  A python library for scraping case information from the [Oklahoma State Courts Network](https://www.oscn.net/dockets/).



  oscn > Python package source to provide an api for retrieving and parsing case records.

  scripts > Python scripts showing use of the oscn package

  - example.py: demonstrates use of the request Case and Caselist
  - retrieve-counts.py: saves a list of all counts for a list of counties and years
  - find-counts.py: saves a list of counts passing a test for a list of counties and years
  - soup_test.py: a stub for testing parsing attempts using BeautifulSoup
  - parse_test.py: a stub for developing using saved examples





  - counties: Returns a list of counties.
  - courts: Same as counties but more a accurate description.
  - judges: Returns a list of objects formated as {'name': 'Bond, James', 'number': '007'}
  - types: returns a dict of case type codes and descriptons
  - type: function to return case type description. Usage:
    ```
    >>> oscn.type("AO")
    'CIVIL ADMINISTRATIVE'
    ```



  - Case: Returns a single case. Case can be saved as files using Case.save() and retrieved using Case.open().

  - CaseList: Returns an iterator for retrieving cases for a county and year. CaseLists can be filtered using .find(). See scripts/example.py for details

  - Party: Returns information on parties available on OSCN.

  - Docket: Returns docket of cases for specific judges and date



  Parsers accept the html of an OSCN page and return python objects.



  - filed: returns a string of the filing date (e.g. 12/25/2017)
  - closed: returns a string of the date the case was closed. Return None if not closed.
  - counts: returns of list of count dicts found in a case. Keys include 'description'
    of the count. If available 'violation' and 'disposed' are added.
  - judge: returns a string of the judge's name
  - parties: returns a list of dicts with these keys: id, name, type
  - docket: returns a list of rows in a docket
  - events: returns a list of dicts with these keys: event, party, docket, reporter, date, description. The keys date and description are cleaner versions of the event text. The event key will be deprecated some day so use date and description if you are starting a project.
  - attorneys: returns a list of dicts with these keys: name, address, and representing
  - issues: returns a list of dicts with issue information. Each issues includes a list of dicts for each party



  - name: returns 'Requested Party'
  - alias: returns 'Alias or Alternate Names'
  - profile: returns dict of values in 'Personal Profile'
  - birth_month: returns string of 'Birth Month and Year'
  - addresses: returns a list of dicts for each address



  - cases: returns a list of case indexes
  - tables: returns the html table for each case in the docket



  - CaseIndexes: returns an iterator of case indexes (e.g. tulsa-CF-2019-12).



  Create a CaseIndexes list using these key word arguments:

  - county: defaults to all,
  - last_name: use this for company or organization names
  - first_name: optional
  - middle_name: optional
  - filed_after: More readable than FiledDateL
  - filed_before: More readable than FiledDateH
  - closed_after: More readable than ClosedDateL
  - closed_before: More readable than ClosedDateH



  - The % wild card is added to all words in name, first and middle
  - Date arguments use MM/DD/YYY strings.



  If you are familar with the OSCN search parameters you can initialize CaseIndexes using these as key word arguments: db, number, lname, fname, mname, DoBMin, DoBMax, partytype, apct, dcct, FiledDate, FiledDateH, ClosedDateL, ClosedDateH, iLC, iLCType, iYear, iNumber, and citation

  Using this will override init keyword values such as first or filed_after.



  1. python3 -m venv ~/your_path/oscn
  1. source ~/your_path/oscn/bin/activate
  1. `git clone git@github.com:codefortulsa/oscn.git`
  1. `cd oscn`
  1. `pip install -e .`



  Install with `pip install oscn`

  Script example:

  `import oscn`

  Request a single case:

  `oscn.request.Case(county='tulsa', year='2018', number=84)`

  or use case index notation:

  `oscn.request.Case('love-CF-2019-25')`

  To request a list of cases to iterate:

  `oscn.request.CaseList(county='adair', year='2016')`



  - `pytest tests/`

  or with ipdb:

      - `pytest -s tests/`

  specify a test:

  - `pytest -s tests/test_parse.py -k 'test_events'`



  1. Edit setup.py
  1. `python3 setup.py sdist bdist_wheel`
  1. `twine upload dist/*`



  In some cases a custom user agent is required in the header of requests.
  Setting an environmental varialbe called OSCN_USER_AGENT will override the default.


pkgId: 670dccbca501d3cd7ccdd94eddf511004420b7a441fbc5123d2a254e87667de5
name: python-osef-help
arch: noarch
version: 3.0.0
epoch: 0
release: 1
summary: Development documents and examples for osef
url: https://pypi.org/project/osef/
rpm_license: MIT License
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-osef-3.0.0-1.src.rpm
location_href: Packages/python-osef-help-3.0.0-1.noarch.rpm
checksum_type: sha256
description:
  Library containing utilities to read and parse a stream, live or recorded, retrieved from
  **Shift**.

  The stream is in the **OSEF** format (**O**pen **SE**rialization **F**ormat):
  it's an Outsight-defined serialisation binary format used to encode data streaming out of Shift.
  It is based on *TLV-encoding*.

  For the full documentation, see: [Developer documentation](https://outsight-tech.gitlab.io/common/osef-python-library/).
  You can contact us @ https://support.outsight.ai


  Install from PyPi using pip:
  ```bash
  pip install osef
  ```


  Open and parse an osef file or stream:

  ```python
  import osef

  osef_path = "path/to/my/file.osef"


  for frame_dict in osef.parse(osef_path):
      print(frame_dict)
  ```

  Additional parameters:
  - `first`/`last`: the first and the last frame to parse
  - `auto_reconnect`: enable parser auto_reconnection (default: `True`)
  - `real_frequency`: If False, parse data as fast as your computer can. If True, process the data as the same pace as the real time stream from Shift (default: `False`)
  - `lazy`: If lazy, the dict only unpack the values when they are accessed to save up resources (default: `True`)


  To find more code samples, see [Outsight Code Samples repository](https://gitlab.com/outsight-public/outsight-code-samples/-/tree/master).


pkgId: 1ca1966ae52073daeb475c03eddc4a72636e503311d855d6085355ed47061483
name: python-osism-help
arch: noarch
version: 0.20241213.0
epoch: 0
release: 1
summary: Development documents and examples for osism
url: https://github.com/osism/python-osism
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-osism-0.20241213.0-1.src.rpm
location_href: Packages/python-osism-help-0.20241213.0-1.noarch.rpm
checksum_type: sha256
description:
  [![Quay](https://img.shields.io/badge/Quay-osism%2Fosism-blue.svg)](https://quay.io/repository/osism/osism)
  [![PyPi version](https://badgen.net/pypi/v/osism/)](https://pypi.org/project/osism/)
  [![PyPi license](https://badgen.net/pypi/license/osism/)](https://pypi.org/project/osism/)
  [![Documentation](https://img.shields.io/static/v1?label=&message=documentation&color=blue)](https://osism.tech/docs/references/cli)


pkgId: 482166538ca2efa5d65b9498cf2a0baba2fa7704a0cecc2a28fe80a6ba2f9bf7
name: python-ositah-help
arch: noarch
version: 24.8
epoch: 0
release: 1
summary: Development documents and examples for ositah
url: https://gitlab.in2p3.fr/hito/ositah
rpm_license: BSD 3-Clause License
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ositah-24.8-1.src.rpm
location_href: Packages/python-ositah-help-24.8-1.noarch.rpm
checksum_type: sha256
description:
  OSITAH est une application web, basée sur le framework [Dash](https://dash.plotly.com), qui permet
  le suivi des déclarations de temps dans Hito, leur validation et leur exportation dans NSIP.
  L'accès aux différentes fonctionnalités est soumis à l'authentification de
  l'utilisateur : les droits dans `ositah` sont dérivés de ceux dans Hito.

  OSITAH nécessite un fichier de configuration `ositah.cfg` : par défaut il est recherché dans le
  répertoire courant et s'il n'existe pas, dans le répertoire où est installé l'application OSITAH.
  L'option `--configuration-file` permet de spécifier un autre fichier/localisation, par exemple pour
  utiliser une configuration de test.

  L'instance de production s'exécute normalement à travers [gunicorn](https://gunicorn.org), un serveur
  SWGI écrit en Python et fournit par le module `gunicorn`. Dans ce contexte, le fichier de configuration
  doit être placé dans le répertoire défini comme le répertoire courant de l'application (l'option
  `--configuration-file` n'est pas utilisable).

  L'exécution de `ositah` suppose l'accès à la base de donnée Hito.



  Le déploiement d'OSITAH nécessite le déploiement d'un environnement Python, de préférence distinct
  de ce qui est délivré par l'OS car cela pose de gros problèmes avec les prérequis sur les versions
  des dépendances. Les environnements recommandés sont [pyenv](https://github.com/pyenv/pyenv),
  [poetry](https://python-poetry.org) ou [Anaconda](https://www.anaconda.com/products/individual).
  Pour la création d'un environnement virtuel avec Conda, voir la
  [documentation spécifique](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands).

  Pour installer OSITAH, il faut utiliser les commandes suivantes :

  ```bash
  pip install ositah
  ```



  Pour connaitre la liste des dépendances de l'application OSITAH, voir la propriété `dependencies`
  dans le fichier `pyproject.toml` se trouvant dans les sources de l'application.
  Elles sont automatiquement installées par la commande `pip`.






  Toute la configuration de l'application OSITAH est déclarée dans le fichier `ositah.cfg` qui doit
  se trouver dans le répertoire courant de l'application pour une instance de production gérée par
  le serveur SWGI, `gunicorn`. Pour une instance de test ou de développement qui n'utilise pas
  `gunicorn`, il est possible de spécifier le fichier de configuration à utiliser avec l'option
  `--configuration-file`.



  `gunicorn` est le serveur WSGI recommandé pour exécuter une instance de production. Son installation
  consiste à installer 2 modules Python : `gunicorn` et `greenlet`.

  Le repository Git de'OSITAH contient un répertoire `gunicorn.config` avec les 3 fichiers importants
  pour la configuration de `gunicorn` qu'il faut éditer pour adapter les répertoires à la configuration
  du site :

  * `gunicorn@.service` : script `systemd` à installer pour démarrer l'instance OSITAH. Si le
  l'instance OSITAH s'appelle `ositah`, la systemd unit à utiliser pour gérer le service est
  `gunicorn@ositah`.
  * `gunicorn.ositah` : fichier à placer en `/etc/sysconfig` définissant la configuration spécifique
  à OSITAH (répertoire courant, options `gunicorn`, entry point).
  * `app.conf.py` : options `gunicorn` à utiliser avec l'instance OSITAH



  La validation des déclarations de temps se fait agent par agent, en utilisant le bouton de validation correspondant
  à l'agent. Ce bouton n'est actif qu'à partir de la date définie dans la table `ositah_validation_period` pour la
  période en cours, sauf si on a ajouté des exceptions dans le fichier de configuration, telles que :

  ```
  validation:
    override_period:
      - ROLE_SUPER_ADMIN
  ```

  `override_period` est une liste de roles qui peuvent faire des validations hors de la période standard.

  La validation d'une déclaration a pour effet d'enregistrer le temps déclaré sur chacune des activités de l'agent dans
  la table `ositah_project_declaration`. Cette entrée est associée à une entrée dans la table `ositah_validation` qui
  contient la date de la validation, l'agent concerné par cette validation (son `agent id` Hito), la validation période
  à laquelle correspond cette validation (référence à la table `ositah_validation_period`) ainsi que le statut
  de la validation. Si on invalide cette validation ultérieurement, le statut passe à `0` et la date de la validation
  est copiée dans l'attribut `initial_timestamp`. L'entrée dans `ositah_project_declaration` n'est pas détruite. Lorsque
  la déclaration de  l'agent est à nouveau validée ultérieurement, une nouvelle entrée est créée à la fois dans
  `ositah_project_declaration` et dans `ositah_validation`, comme pour la validation initiale.
  Il est donc possible d'avoir un historique des opérations de validation sur une période donnée (pas exploité
  par l'application OSITAH pour l'instant). Par contre, quand on lit les validations, il faut faire attention à
  prendre la dernière dans une période donnée qui a son statut à `1`.

  La création de l'entrée pour définir une période de déclaration dans `ositah_validation_period` (date de début et
  date de fin de la période, date de  début de la validation) n'est pas gérée par OSITAH actuellement : il faut créer
  une entrée dans la table avec la commande SQL `INSERT INTO`.




  OSITAH permet d'exporter vers NSIP les déclarations validées. La table du menu `Export` indique
  l'état de la synchronisation entre NSIP et OSITAH, agent par agent. Un code couleur permet
  d'identifier facilement si une déclaration est correctement synchronisée ou non. Seules les
  déclarations qui ne sont pas correctement synchronisées peut être exportées. Lors de l'export,
  la déclaration est indiquée comme validée par le responsable dans NSIP, avec la date de sa validation
  dans OSITAH.

  Il est possible d'exporter toutes les déclarations ou de les sélectionner agent par  agent.
  Lorsqu'un agent est sélectionné, toutes ses déclarations non synchronisées sont exportées. Le bouton
  de sélection dans la barre de titre permet de sélectionner tous les agents sélectionnables en un clic.

  Les déclarations d'un agent ne peuvent pas être exportées si l'agent n'existe pas dans NSIP,
  c'est-à-dire s'il est absent de RESEDA. La correction du problème, si on souhaite que les
  déclarations da cet agent soient mises dans NSIP, nécessite une intervention du Service RH
  pour ajouter la personne dans RESEDA.

  Il peut aussi y avoir des déclarations qui ont été faites directement dans NSIP et qui ne sont
  pas encore validées dans OSITAH. Dans ce cas, elles apparaitront comme manquantes dans OSITAH,
  même si elles sont présentes, tant qu'elles ne seront pas validées.


pkgId: ec6fa214d19410b99f7f0e8465e971e34140e9ed336480c3c052439b6ef7f569
name: python-osm2les-dev-help
arch: noarch
version: 1.0.3
epoch: 0
release: 1
summary: Development documents and examples for osm2les-dev
url: https://example.com/osm2les_dev
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-osm2les-dev-1.0.3-1.src.rpm
location_href: Packages/python-osm2les-dev-help-1.0.3-1.noarch.rpm
checksum_type: sha256
description:
  A Python package for processing and analyzing LES data with OpenStreetMap (OSM).



  ```bash
  pip install osm2les_dev
  ```



  <!-- Note: All WSF3D versions require GDAL to be installed -->
  <!--'WSF3D_V02_BuildingHeight.tif' needs to be installed in the current folder to use WSF3D functions  -->

  ```python
  import osm2les_dev



  osm2les_dev.extract_domain_from_points_wsf3d(df)
  osm2les_dev.extract_domain_from_points_osm(df)





  res = 1
  bldH = 16
  name = 'Ber1'
  angleRotate = 0
  extra_geometric_stats = True

  bbox = parseKML(name+'.kml')[0:4]
  domain = extract_domainKML(res,bldH,bbox,name,angleRotate,extra_geometric_stats)

  domain = extract_domain_from_KML_wsf3d(bbox, res, bldH, name, angleRotate, extra_geometric_stats)
  np.savetxt(name+'.pos', bbox ,fmt='%f')
  ```


  MIT License


pkgId: 6cf97e1df13fc5475cf6da5c53f75377e49b40c66170ff13cfe414d9149a15a5
name: python-osm_helper-help
arch: noarch
version: 0.0.8
epoch: 0
release: 1
summary: Development documents and examples for osm_helper
url: https://github.com/mo-waseem/osm-helper
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-osm_helper-0.0.8-1.src.rpm
location_href: Packages/python-osm_helper-help-0.0.8-1.noarch.rpm
checksum_type: sha256
description:
  <p align="center">
      <a href="#"><img src="https://github.com/mo-waseem/osm-helper/blob/main/osm_helper/docs/img/osrm_python.png" alt="OSRM Table service Python Wrapper" /></a><br/>
  </p>
  <h2 align="center">OSRM Table Service Python Wrapper</h2>
  <p align="center">
    <a href="https://pypi.org/project/osm-helper/" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/PyPi-v0.0.5-limegreen">
  </a>
    <a href="https://www.python.org" target="_blank"><img alt="Static Badge" src="https://img.shields.io/badge/python-%5E3.8-limegreen">
  </a>
  	<img alt="Static Badge" src="https://img.shields.io/badge/license-MIT-limegreen">

  </p>


  Do you have multiple python projects for Delivery 🛵, Ride-hailing 🚗 or any logistics service, and each time you need to build the `get_eta` function from scratch ?

  This simple package is your right way! 🚀🚀

  `osm-hepler` is a pretty simple package for making the process of getting eta for a number of locations to be the easiest part in your project (just two lines of code).

  The package built on top of  [`OSRM` (Open Source Routing Machine) project](https://project-osrm.org/). 🗺️
  We can assume it as a python wrapper on top of the `table` service api of the `OSRM` project.



  ```
  pip install osm-helper
  ```



  1- **A default parameters** (that make the performance better) are initialized and they are configurable.

  2- **Caching** the results.

  3- **Load balancing** between more than one `osrm` instance.

  4- **Fault tolerance**: if one of the `osrm` instances is down, it will search for another (this will be done by default in the package).



  After installing the lib you can test it by the following:

  ```python
  from osm_helper.osm_api import OSM

  def simple_example():
      locations = [
          ("25.22386255613285", "55.28397838209264"), # lat, lng
          ("25.356481138224012", "55.404779909052124"),
      ]

      
      osm_api = OSM()

      
      result = osm_api.osm_matrix(
          locations,
          [0], # Source location index
          [1], # Destination location index
      )

      return result

  print(simple_example())
  ```
  By running the code (you will got the following result, the value may be changed with a small changes depending on the OSRM instance you are using):
  ```
  {'duration': [[1128.8]]}
  ```

   

   You need to install redis and set its config in the .yaml config file:
   ```
  REDIS_HOST:
    127.0.0.1

  REDIS_PORT:
    6379

  REDIS_EXPIRATION_TIME: # Optional
    43200

  REDIS_ASYNC_CACHE: # Optional
    False
  ```

  Then you can:


  ```python
  from osm_helper.locations import generate_random_locations
  from osm_helper.osm_api import OSM


  locations = generate_random_locations(
      ("30.222676136092296", "31.455316886214952"), number=10
  )


  osm_api = OSM(load_balance=True, cache_results=True, with_distance=True)


  result = osm_api.osm_matrix(
  			locations,
  			[i for i in  range(len(locations)//2)],
  			[i for i in  range(len(locations)//2, len(locations))],
  			request_id="123"
  		)

  print(result)
  ```
   
   | Name | Default | Explaination |
   |------|---------|--------------|
   |with_time|bool: True|-|
   |with_distance|bool: False|If True, you will get a distance matrix in addition to the time matrix in the result|
   |skip_waypoints|bool: True|This will skip waypoints from the response of osrm api, so you will see a performance enhancement by this|
   |time_scale_factor|int: 1|Scale the resulted times by this scalar|
   |api_url|str: Comes from `PRIMARY_OSRM_URL` config. value|This is the api url of the osrm instance|
   |osm_instances_urls|list: Comes from `OSRM_URLS` config. value|This is a list of osrm urls|
   |load_balance|bool: False|If True, to be not useless we need to set `osm_instances_urls`, so the package will request from a random instance|
   |cache_results|bool: False|If True, you need to pass `request_id` to the `osm_api.osm_matrix` so the key in the redis will be a mix between the info of the function parameters and this `request_id`|
   |max_api_retries|int: 10|If the instance is down, so we will try this number of times before return the result|



  The configuration of this package is done by an .env file and .yaml file like the following (the following files will have the full configs but only `PRIMARY_OSRM_URL` is mandatory)


  ```
  YAML_CONFIG_FILE_NAME=YOUR_OSM_HELPER_CONFIG.yaml
  ```

  | Name                 | Default   | Explaination |
  | -------------------- | ------- |-------  |
  | PRIMARY_OSRM_URL     | http://router.project-osrm.org         | OSRM url, the default value is the public instance of OSRM.         |
  | [Optional] OSRM_URLS               |  None       | OSRM Instances         |
  | [Optional] OSRM_MAX_API_RETRIES | 10 | If the instance is down, so we will try `OSRM_MAX_API_RETRIES` times before return the result |
  | [Optional] REDIS_HOST                     | None        | Redis host url         |
  | [Optional] REDIS_PORT                    | None        | Redis port        |
  | [Optional] REDIS_EXPIRATION_TIME                    |  INFINITY       | Redis records timeout (in seconds)         |
  | [Optional] REDIS_ASYNC_CACHE                   | False        | If True, the caching process will be done in async way (To maximize the performance of getting ETA)          |




  **You're welcome!**
  Fork the project and make a pull request I will see it inshallah.


pkgId: 3a2d28f0b598ddc562164e0c328fe2e086cb6b49d9e7a095f37f9fb0d1fe6eba
name: python-ossa-scanner-help
arch: noarch
version: 0.1.0
epoch: 0
release: 1
summary: Development documents and examples for ossa-scanner
description: Open Source Advisory Scanner (Generator)
url: https://pypi.org/project/ossa-scanner/
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ossa-scanner-0.1.0-1.src.rpm
location_href: Packages/python-ossa-scanner-help-0.1.0-1.noarch.rpm
checksum_type: sha256
rpm_license:
  MIT License  Copyright (c) 2024 Oscar V  Permission 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.


pkgId: ddecb1f6bebfcf3291c5a0be2cd947ff4a606dc6145d4243f537f34250041b66
name: python-osu.py-help
arch: noarch
version: 3.2.1
epoch: 0
release: 1
summary: Development documents and examples for osu.py
url: https://github.com/Sheepposu/osu.py
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-osu.py-3.2.1-1.src.rpm
location_href: Packages/python-osu.py-help-3.2.1-1.noarch.rpm
checksum_type: sha256
description:
  Easy to use API wrapper for osu!api v2 written in Python.
  Has a high-level interface while still allowing ways to do more complex things.


pkgId: 789d2876495270ada3ef90442794b22425276dcb049f3114094fba6d84ca682c
name: python-otdf-python
arch: noarch
version: 0.1.12
epoch: 0
release: 1
summary: Unofficial OpenTDF SDK for Python.
url: https://github.com/b-long/opentdf-python-sdk
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-otdf-python-0.1.12-1.src.rpm
location_href: Packages/python-otdf-python-0.1.12-1.noarch.rpm
checksum_type: sha256
description:
  Unofficial OpenTDF SDK for Python

  [![Tests](https://github.com/b-long/opentdf-python-sdk/workflows/PyPIBuild/badge.svg)](https://github.com/b-long/opentdf-python-sdk/actions?query=workflow%3APyPIBuild)

  This project is powered by gopy, which generates (and compiles) a CPython extension module from a go package.  The `gopy`
  tool unlocks performance, flexibility, and excellent Developer Experience to Python end-users.  Read more about
  [`gopy` on Github](https://github.com/go-python/gopy).



  If you wish to expand the functionality of `otdf-python`:

  1. Create a fork/branch
  1. Add new capabilities (e.g. in `main.go`)
  1. Add a test (e.g. in `otdf_python_test.go`)
  1. Commit your changes, push, and open a Pull Request via
  the Github project: https://github.com/b-long/opentdf-python-sdk



  Install from the [Python Package Index (PyPI)](https://pypi.org):

  ```bash

  pip install otdf_python


  pip install otdf-python==0.0.9


  pip install -i https://test.pypi.org/simple/ otdf-python==0.0.9
  ```



  Simple usage examples are given below.  In addition, we recommend you also:

  1. See the contents of [`main.go` on Github](https://github.com/b-long/opentdf-python-sdk/blob/main/main.go).  ✨ Note that all Upper-case functions are available in Python.
  1. See the contents of [`validate_otdf_python.py` on Github](https://github.com/b-long/opentdf-python-sdk/blob/main/validate_otdf_python.py).



  Creating a helper function may simplify the usage of `otdf-python`.

  For example:

  ```python
  def get_encrypt_config(data_attributes: list | None = None):
      """
      The config object returned here can only be used for encryption.

      While 'otdf_python.gotdf_python' internally can use golang interfaces,
       to normalize config objects, that pattern causes a panic
       when used from Python.

      """
      print("Preparing 'EncryptionConfig' object")
      from otdf_python.gotdf_python import EncryptionConfig
      from otdf_python.go import Slice_string

      if isinstance(data_attributes, list):
          
          da = Slice_string(data_attributes)
          config: EncryptionConfig = EncryptionConfig(
              ClientId="opentdf-sdk",
              ClientSecret="secret",
              PlatformEndpoint=platformEndpoint,
              TokenEndpoint="http://localhost:8888/auth/realms/opentdf/protocol/openid-connect/token",
              KasUrl=f"http://{platformEndpoint}/kas",
              
              
              
              
              DataAttributes=da,
          )
      else:
          
          config: EncryptionConfig = EncryptionConfig(
              ClientId="opentdf-sdk",
              ClientSecret="secret",
              PlatformEndpoint=platformEndpoint,
              TokenEndpoint="http://localhost:8888/auth/realms/opentdf/protocol/openid-connect/token",
              KasUrl=f"http://{platformEndpoint}/kas",
          )

      
      
      print("Returning 'EncryptionConfig'")

      return config
  ```




  ```python
  from otdf_python.gotdf_python import EncryptString



  config: EncryptionConfig = get_encrypt_config()

  tdf_manifest_json = EncryptString(inputText="Hello from Python", config=config)
  ```



  ```python
  from otdf_python.gotdf_python import EncryptFile
  from otdf_python.go import Slice_string



  config: EncryptionConfig = get_encrypt_config()

  with tempfile.TemporaryDirectory() as tmpDir:
      print("Created temporary directory", tmpDir)

      da = Slice_string(["https://example.com/attr/attr1/value/value1", "https://example.com/attr/attr1/value/value2"])

      encrypted_file = Path(tmpDir) / "some-file.tdf"

      if encrypted_file.exists():
          encrypted_file.unlink()

      if encrypted_file.exists():
          raise ValueError(
              "The output path should not exist before calling 'EncryptFile()'."
          )

      outputFilePath = EncryptFile(
          inputFilePath=str(SOME_PLAINTEXT_FILE),
          outputFilePath=str(encrypted_file),
          config=config,
      )

      print(f"The output file was written to destination path: {outputFilePath}")

  ```


pkgId: fbcf4def8cf3f25b0b9c454313b057de9dda63afe5500db001f1ca39dd472631
name: python-oteltest-help
arch: noarch
version: 0.38.0
epoch: 0
release: 1
summary: Development documents and examples for oteltest
url: https://pypi.org/project/oteltest/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-oteltest-0.38.0-1.src.rpm
location_href: Packages/python-oteltest-help-0.38.0-1.noarch.rpm
checksum_type: sha256
description:
  **Table of Contents**
  - [Installation](#installation)
  - [License](#license)

  ```console
  pip install oteltest
  ```

  The `oteltest` package contains utilities for testing OpenTelemetry Python scenarios.

  The `oteltest` command runs black box tests against Python scripts that send telemetry.

  With the virtual environment (into which you've installed `oteltest`) active, run `oteltest` as a shell command and
  provide a directory as an argument:
  ```shell
  oteltest my_script_dir
  ```
  This will attempt to run all [oteltest-eligible](#script-eligibility) scripts in `my_script_dir`, non-recursively.

  Running `oteltest` against a directory containing only `my_script.py`
  1) Starts an OTLP or HTTP listener ([otelsink](#otelsink))
  2) Creates a new Python virtual environment with `requirements()`
  3) In that environment, starts running `my_script.py` in a subprocess
  4) Calls `on_start()`
  5) Depending on the return value from `on_start()`, waits for `my_script.py` to complete
  6) Stops otelsink
  7) Calls `on_stop()` with otelsink's received telemetry and script output
  8) Writes the telemetry to a `.json` file next to the script (script name but with ".{number}.json" instead of ".py")

  For a Python script to be runnable by `oteltest`, it must implement the [OtelTest](src/oteltest/__init__.py)
  abstract base class, either formally by inheriting from `OtelTest`, or informally by merely containing the name
  "OtelTest" and implementing the methods. The script below has an implementation called `MyOtelTest`:
  ```python
  import time
  from opentelemetry import trace
  SERVICE_NAME = "my-otel-test"
  NUM_ADDS = 12
  if __name__ == "__main__":
      tracer = trace.get_tracer("my-tracer")
      for i in range(NUM_ADDS):
          with tracer.start_as_current_span("my-span"):
              print(f"simple_loop.py: {i+1}/{NUM_ADDS}")
              time.sleep(0.5)


  class MyOtelTest:
      def requirements(self):
          return "opentelemetry-distro", "opentelemetry-exporter-otlp-proto-grpc"
      def environment_variables(self):
          return {"OTEL_SERVICE_NAME": SERVICE_NAME}
      def wrapper_command(self):
          return "opentelemetry-instrument"
      def on_start(self):
          return None
      def on_stop(self, telemetry, stdout: str, stderr: str, returncode: int) -> None:
          print(f"script completed with return code {returncode}")
      def is_http(self) -> bool:
          return False
  ```
  Here's a client-server example:
  ```python
  import time
  from typing import Mapping, Optional, Sequence
  PORT = 8002
  HOST = "127.0.0.1"
  if __name__ == "__main__":
      from flask import Flask
      app = Flask(__name__)
      @app.route("/")
      def home():
          return "hello"
      app.run(port=PORT, host=HOST)


  class FlaskOtelTest:
      def environment_variables(self) -> Mapping[str, str]:
          return {}
      def requirements(self) -> Sequence[str]:
          return (
              "flask",
              "opentelemetry-distro",
              "opentelemetry-exporter-otlp-proto-grpc",
              "opentelemetry-instrumentation-flask",
          )
      def wrapper_command(self) -> str:
          return "opentelemetry-instrument"
      def on_start(self) -> Optional[float]:
          import http.client
          
          time.sleep(10)
          conn = http.client.HTTPConnection(HOST, PORT)
          conn.request("GET", "/")
          print("response:", conn.getresponse().read().decode())
          conn.close()
          
          
          
          return 30
      def on_stop(self, telemetry, stdout: str, stderr: str, returncode: int) -> None:
          
          print("done")
      def is_http(self) -> bool:
          return False
  ```

  `otelsink` is a gRPC (or HTTP) server that listens for OTel metrics, traces, and logs.

  You can run otelsink either from the command line by using the `otelsink` command (installed when you
  `pip install oteltest`), or programmatically.
  Either way, `otelsink` runs a gRPC server listening on 0.0.0.0:4317 by default. To run an HTTP server listening on 4318,
  use the `--http` flag.

  ```
  % otelsink
  starting otelsink
  - Set up grpc sink at address 0.0.0.0:4317
  ```
  ```
  % otelsink --http
  - Set up http sink on port 4318
  ```

  ```python
  from oteltest.sink import GrpcSink, RequestHandler
  class MyHandler(RequestHandler):
      def handle_logs(self, request, headers):
          print(f"received log request: {request}")
      def handle_metrics(self, request, headers):
          print(f"received metrics request: {request}")
      def handle_trace(self, request, headers):
          print(f"received trace request: {request}")
  sink = GrpcSink(MyHandler())
  sink.start()
  sink.wait_for_termination()
  ```

  `oteltest` is distributed under the terms of the [Apache-2.0](https://spdx.org/licenses/Apache-2.0.html) license.


pkgId: 28477a0d83cb0d4bcca8368829fbacc471acaad6b0abf5409e4dbea270269e90
name: python-otlmow-model-help
arch: noarch
version: 2.13.8.2
epoch: 0
release: 1
summary: Development documents and examples for otlmow-model
url: https://github.com/davidvlaminck/OTLMOW-Model
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-otlmow-model-2.13.8.2-1.src.rpm
location_href: Packages/python-otlmow-model-help-2.13.8.2-1.noarch.rpm
checksum_type: sha256
description:
  [![PyPI](https://img.shields.io/pypi/v/otlmow-model?label=latest%20release)](https://pypi.org/project/otlmow-model/)
  [![otlmow-model-downloads](https://img.shields.io/pypi/dm/otlmow-model)](https://pypi.org/project/otlmow-model/)
  [![Unittests](https://github.com/davidvlaminck/OTLMOW-Model/actions/workflows/unittest.yml/badge.svg)](https://github.com/davidvlaminck/OTLMOW-Model/actions/workflows/unittest.yml)
  ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/otlmow-model)
  [![GitHub issues](https://img.shields.io/github/issues/davidvlaminck/OTLMOW-Model)](https://github.com/davidvlaminck/OTLMOW-Model/issues)
  [![coverage](https://github.com/davidvlaminck/OTLMOW-Model/blob/master/UnitTests/coverage.svg)](https://htmlpreview.github.io/?https://github.com/davidvlaminck/OTLMOW-Model/blob/master/UnitTests/htmlcov/index.html)



  The main use case of otlmow-model is to provide a class model, allowing instances of OTL compliant classes. The classes have data validation and automatic conversion for attributes. Helper classes assist you to create relations between objects.


  See the [Readme notebook](https://github.com/davidvlaminck/OTLMOW-Model/blob/master/Readme.ipynb). This notebook contains examples on how to use the OTL classes and how to create relations between objects.


  This project aims to implement the Flemish data standard OTL (https://wegenenverkeer.data.vlaanderen.be/) in Python.
  It is split into different packages to reduce compatibility issues.
  - [otlmow_model](https://github.com/davidvlaminck/OTLMOW-Model) (you are currently looking at this package)
  - [otlmow_modelbuilder](https://github.com/davidvlaminck/OTLMOW-ModelBuilder)
  - [otlmow_converter](https://github.com/davidvlaminck/OTLMOW-Converter)
  - [otlmow_template](https://github.com/davidvlaminck/OTLMOW-Template)
  - [otlmow_postenmapping](https://github.com/davidvlaminck/OTLMOW-PostenMapping)
  - [otlmow_davie](https://github.com/davidvlaminck/OTLMOW-DAVIE)
  - [otlmow_visuals](https://github.com/davidvlaminck/OTLMOW-Visuals)
  - [otlmow_gui](https://github.com/davidvlaminck/OTLMOW-GUI)

  The **otlmow-model** project is a Python implementation model of the OTL standard. This is a collection of OTL compliant classes, which can be used to create instances of OTL objects. When assigning data to the attributes of the classes, the data is validated and converted to the correct type (if incorrect). There is support for conversion from and to Python dictionaries.

  A few times during a year a new version of the OTL standard is released. The **otlmow-modelbuilder** project takes an OTL SQLite as input and generates the classes for the new version of the OTL standard. The otlmow-model project is then updated with the new classes. This way the otlmow-model project is always up to date with the latest version of the OTL standard.

  In the **otlmow-converter** project, the instantiated classes can be converted to and from DAVIE compliant file formats (such as CSV, Excel, ...). There is also support for json-ld files. The objects can also be converted to dotnotation dictionaries or loaded into or from a pandas Dataframe. Because of all these possibilities, the converter has multiple dependencies on other Python packages.

  The **otlmow-template** project produces a CSV or Excel template, based on a subset of the OTL. The created template can then be used to input data and upload into the DAVIE platform of AWV.

  The **otlmow-postenmapping** project implements the mapping artifact and allow the creation or modification of OTL objects.

  The **otlmow-davie** project has a REST client to the DAVIE platform to allow automation of deliveries.

  The **otlmow-visuals** project provides a way to visualize OTL objects and their relations. The result is an interactive HTML file that can be opened in any browser.

  The **otlmow-gui** project is a deployable local application that allows the user to create templates, edit, visualize and export data.

rpm_license:
[cell repeats an earlier one in this file: 34486 characters]


pkgId: 899919d17726c615b6eea5fd97a3a60c748e490f4b6eca44e8a1650f147cb122
name: python-otlmow-modelbuilder-help
arch: noarch
version: 0.26
epoch: 0
release: 1
summary: Development documents and examples for otlmow-modelbuilder
url: https://github.com/davidvlaminck/OTLMOW-ModelBuilder
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-otlmow-modelbuilder-0.26-1.src.rpm
location_href: Packages/python-otlmow-modelbuilder-help-0.26-1.noarch.rpm
checksum_type: sha256
description:
  [![PyPI](https://img.shields.io/pypi/v/otlmow-modelbuilder?label=latest%20release)](https://pypi.org/project/otlmow-modelbuilder/)
  [![otlmow-modelbuilder-downloads](https://img.shields.io/pypi/dm/otlmow-modelbuilder)](https://pypi.org/project/otlmow-modelbuilder/)
  [![Unittests](https://github.com/davidvlaminck/OTLMOW-ModelBuilder/actions/workflows/unittest.yml/badge.svg)](https://github.com/davidvlaminck/OTLMOW-ModelBuilder/actions/workflows/unittest.yml)
  ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/otlmow-modelbuilder)
  [![GitHub issues](https://img.shields.io/github/issues/davidvlaminck/OTLMOW-ModelBuilder)](https://github.com/davidvlaminck/OTLMOW-ModelBuilder/issues)
  [![coverage](https://github.com/davidvlaminck/OTLMOW-ModelBuilder/blob/master/UnitTests/coverage.svg)](https://htmlpreview.github.io/?https://github.com/davidvlaminck/OTLMOW-ModelBuilder/blob/master/UnitTests/htmlcov/index.html)


  The main use case of otlmow-modelbuilder is to provide create the class model of otlmow_model, by reading in a OTL SQLite or subset.


  See the [Readme notebook](https://github.com/davidvlaminck/OTLMOW-ModelBuilder/blob/master/Readme.ipynb). This notebook contain an example on how to generate a model given an OTL SQLite.


  This project aims to implement the Flemish data standard OTL (https://wegenenverkeer.data.vlaanderen.be/) in Python.
  It is split into different packages to reduce compatibility issues
  - [otlmow_model](https://github.com/davidvlaminck/OTLMOW-Model)
  - [otlmow_modelbuilder](https://github.com/davidvlaminck/OTLMOW-ModelBuilder) (you are currently looking at this package)
  - [otlmow_converter](https://github.com/davidvlaminck/OTLMOW-Converter)
  - [otlmow_template](https://github.com/davidvlaminck/OTLMOW-Template)
  - [otlmow_postenmapping](https://github.com/davidvlaminck/OTLMOW-PostenMapping)
  - [otlmow_davie](https://github.com/davidvlaminck/OTLMOW-DAVIE)
  - [otlmow_visuals](https://github.com/davidvlaminck/OTLMOW-Visuals)
  - [otlmow_gui](https://github.com/davidvlaminck/OTLMOW-GUI)

rpm_license:
[cell repeats an earlier one in this file: 34486 characters]


pkgId: d48980d6f452779676016dcbba5356bcadd9bf04c146cda8be450c26dc19b554
name: python-otpme-help
arch: noarch
version: 0.3.0a91
epoch: 0
release: 1
summary: Development documents and examples for otpme
url: https://www.otpme.org/redmine
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-otpme-0.3.0a91-1.src.rpm
location_href: Packages/python-otpme-help-0.3.0a91-1.noarch.rpm
checksum_type: sha256
description:
  apt-get install python3.11-venv gobjc++ python3-pybind11 python3-dev build-essential cmake gcc dbus-x11 freeradius libacl1-dev libnss-cache liboath0 liboath-dev libpcsclite1 libpq-dev libre2-9 libre2-dev libsystemd-dev pkg-config postgresql postgresql-server-dev-all pwgen pyflakes3 redis redis-server redis-tools libpcsclite-dev


  systemctl stop redis
  systemctl disable redis
  systemctl stop postgresql
  systemctl disable postgresql
  systemctl stop freeradius
  systemctl disable freeradius




  useradd -r -U -d /var/lib/otpme otpme


  Edit /etc/nsswitch.conf and append 'cache' to the lines passwd and group.


  python3 -m venv /opt/otpme
  . /opt/otpme/bin/activate


  pip3 install cython
  pip3 install otpme


  cp -a /opt/otpme/lib/python3.11/site-packages/etc/otpme /etc/
  cp -a /etc/otpme/otpme.conf.dist /etc/otpme/otpme.conf


  POSTGRES_PG_CTL_BIN="/usr/lib/postgresql/15/bin/pg_ctl"


  /etc/otpme/PYTHONPATH


  otpme-realm --api -ddee --color-logs -f init --ca-key-len 2048 --site-key-len 2048 --node-key-len 2048 --dicts english,en-top10000,common-passwords,us-female,us-male,us-surnames,abbreviations-it --id-ranges "uidNumber:s:100000-200000,gidNumber:s:100000-200000" yourrealm.tld yoursite localhost 127.0.0.1

  Note: Scan the generated QRCode with the "Google Autenticator App" and note the PIN of the admin token.


  otpme-controld start


  You need to input pin+otp.
  otpme-tool login

rpm_license:
  GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007  Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed.  Preamble  The GNU General Public License is a free, copyleft license for software and other kinds of works.  The licenses for most software and other practical works are designed to take away your freedom to share and change the works.  By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users.  We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors.  You can apply it to your programs, too.  When we speak of free software, we are referring to freedom, not price.  Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things.  To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights.  Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others.  For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received.  You must make sure that they, too, receive or can get the source code.  And you must show them these terms so they know their rights.  Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it.  For the developers' and authors' protection, the GPL clearly explains that there is no warranty for this free software.  For both users' and authors' sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions.  Some devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so.  This is fundamentally incompatible with the aim of protecting users' freedom to change the software.  The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable.  Therefore, we have designed this version of the GPL to prohibit the practice for those products.  If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users.  Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary.  To prevent this, the GPL assures that patents cannot be used to render the program non-free.  The precise terms and conditions for copying, distribution and modification follow.  TERMS AND CONDITIONS  0. Definitions.  "This License" refers to version 3 of the GNU General Public License.  "Copyright" also means copyright-like laws that apply to other kinds of works, such as semiconductor masks.  "The Program" refers to any copyrightable work licensed under this License.  Each licensee is addressed as "you".  "Licensees" and "recipients" may be individuals or organizations.  To "modify" a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy.  The resulting work is called a "modified version" of the earlier work or a work "based on" the earlier work.  A "covered work" means either the unmodified Program or a work based on the Program.  To "propagate" a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy.  Propagation includes copying, distribution (with or without modification), making available to the public, and in some countries other activities as well.  To "convey" a work means any kind of propagation that enables other parties to make or receive copies.  Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying.  An interactive user interface displays "Appropriate Legal Notices" to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License.  If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion.  1. Source Code.  The "source code" for a work means the preferred form of the work for making modifications to it.  "Object code" means any non-source form of a work.  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The "Corresponding Source" for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities.  However, it does not include the work's System Libraries, or general-purpose tools or generally available free programs which are used unmodified in performing those activities but which are not part of the work.  For example, Corresponding Source includes interface definition files associated with source files for the work, and the source code for shared libraries and dynamically linked subprograms that the work is specifically designed to require, such as by intimate data communication or control flow between those subprograms and other parts of the work.  The Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source.  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pkgId: 9d680535d8177a765fbfc4235c9e064e9636a4f92f1795f2ca797ab3df784b4a
name: python-otx-help
arch: noarch
version: 2.2.2
epoch: 0
release: 1
summary: Development documents and examples for otx
url: https://pypi.org/project/otx/
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-otx-2.2.2-1.src.rpm
location_href: Packages/python-otx-help-2.2.2-1.noarch.rpm
checksum_type: sha256
description:
  [Key Features](#key-features) •
  [Installation](https://openvinotoolkit.github.io/training_extensions/latest/guide/get_started/installation.html) •
  [Documentation](https://openvinotoolkit.github.io/training_extensions/latest/index.html) •
  [License](#license)
  [![PyPI](https://img.shields.io/pypi/v/otx)](https://pypi.org/project/otx)
  <!-- markdownlint-disable MD042 -->
  [![python](https://img.shields.io/badge/python-3.10%2B-green)]()
  [![pytorch](https://img.shields.io/badge/pytorch-2.1.1%2B-orange)]()
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  <!-- markdownlint-enable  MD042 -->
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  [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/openvinotoolkit/training_extensions/badge)](https://securityscorecards.dev/viewer/?uri=github.com/openvinotoolkit/training_extensions)
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rpm_license:
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Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.  6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.  7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.  8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.  9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.  END OF TERMS AND CONDITIONS  APPENDIX: How to apply the Apache License to your work.  To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!)  The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives.  Copyright (C) 2018-2021 Intel Corporation  Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at  http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.


pkgId: 6117668cd098207bf9c6c5b17e7497b728154f5cfb800f07f99764e09872d128
name: python-oursin-help
arch: noarch
version: 0.7.2
epoch: 0
release: 1
summary: Development documents and examples for oursin
url: https://pypi.org/project/oursin/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-oursin-0.7.2-1.src.rpm
location_href: Packages/python-oursin-help-0.7.2-1.noarch.rpm
checksum_type: sha256
description:
  More details can be found on [our website](https://virtualbrainlab.org/03_unity_neuro/01_urn_intro.html)


pkgId: 5871e9303abded7d58bdd97650bb4d6e7e66ffa32f0ba78b640afa02f5960bde
name: python-ourskyai_astro_api-help
arch: noarch
version: 1.3.5667
epoch: 0
release: 1
summary: Development documents and examples for ourskyai_astro_api
url: https://github.com/GIT_USER_ID/GIT_REPO_ID
rpm_license: NoLicense
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ourskyai_astro_api-1.3.5667-1.src.rpm
location_href: Packages/python-ourskyai_astro_api-help-1.3.5667-1.noarch.rpm
checksum_type: sha256
description:
  No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)

  This Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:

  - API version: 1.3.5667
  - Package version: 1.3.5667
  - Build package: org.openapitools.codegen.languages.PythonClientCodegen



  Python 3.7+




  ```sh
  pip install ourskyai_astro_api
  ```
  (you may need to run `pip` with root permission: `sudo pip install ourskyai_astro_api`)


  If you manage dependencies with [poetry](https://python-poetry.org/) then you can add the SDK with:

  ```sh
  poetry add ourskyai_astro_api
  ```



  Then import the package:
  ```python
  import ourskyai_astro_api
  ```



  Please follow the [installation procedure](#installation--usage) and then run the following:

  ```python

  import os
  import ourskyai_astro_api
  from ourskyai_astro_api.rest import ApiException
  from pprint import pprint



  configuration = ourskyai_astro_api.Configuration(
      host = "https://api.prod.oursky.ai"
  )







  configuration = ourskyai_astro_api.Configuration(
      access_token = os.environ["BEARER_TOKEN"]
  )



  with ourskyai_astro_api.ApiClient(configuration) as api_client:
      
      api_instance = ourskyai_astro_api.DefaultApi(api_client)
      model = 'model_example' # str |  (optional)
      pixel_size_microns = 3.4 # float |  (optional)
      pixels_x = 56 # int |  (optional)
      pixels_y = 56 # int |  (optional)
      megapixels = 3.4 # float |  (optional)
      chilled = True # bool |  (optional)
      adc_bit_depth = 56 # int |  (optional)
      is_color = True # bool |  (optional)

      try:
          api_response = api_instance.v1_camera_match(model=model, pixel_size_microns=pixel_size_microns, pixels_x=pixels_x, pixels_y=pixels_y, megapixels=megapixels, chilled=chilled, adc_bit_depth=adc_bit_depth, is_color=is_color)
          print("The response of DefaultApi->v1_camera_match:\n")
          pprint(api_response)
      except ApiException as e:
          print("Exception when calling DefaultApi->v1_camera_match: %s\n" % e)

  ```



  All URIs are relative to *https://api.prod.oursky.ai/docs/astro*

  <a id="documentation-for-authorization"></a>



  Authentication schemes defined for the API:
  <a id="BearerToken"></a>


  - **Type**: Bearer authentication

  <a id="Roles"></a>


pkgId: cd857901830369e0e0622e0e1ef27a01a08b1a3de9e44ade6dbf9dc8d067c990
name: python-ourskyai_platform_api-help
arch: noarch
version: 1.3.5667
epoch: 0
release: 1
summary: Development documents and examples for ourskyai_platform_api
url: https://github.com/GIT_USER_ID/GIT_REPO_ID
rpm_license: NoLicense
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ourskyai_platform_api-1.3.5667-1.src.rpm
location_href: Packages/python-ourskyai_platform_api-help-1.3.5667-1.noarch.rpm
checksum_type: sha256
description:
  No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator)

  This Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:

  - API version: 1.3.5667
  - Package version: 1.3.5667
  - Build package: org.openapitools.codegen.languages.PythonClientCodegen



  Python 3.7+




  ```sh
  pip install ourskyai_platform_api
  ```
  (you may need to run `pip` with root permission: `sudo pip install ourskyai_platform_api`)


  If you manage dependencies with [poetry](https://python-poetry.org/) then you can add the SDK with:

  ```sh
  poetry add ourskyai_platform_api
  ```



  Then import the package:
  ```python
  import ourskyai_platform_api
  ```



  Please follow the [installation procedure](#installation--usage) and then run the following:

  ```python

  import os
  import ourskyai_platform_api
  from ourskyai_platform_api.rest import ApiException
  from pprint import pprint



  configuration = ourskyai_platform_api.Configuration(
      host = "https://api.prod.oursky.ai"
  )







  configuration = ourskyai_platform_api.Configuration(
      access_token = os.environ["BEARER_TOKEN"]
  )



  with ourskyai_platform_api.ApiClient(configuration) as api_client:
      
      api_instance = ourskyai_platform_api.DefaultApi(api_client)

      try:
          api_response = api_instance.get_client_token()
          print("The response of DefaultApi->get_client_token:\n")
          pprint(api_response)
      except ApiException as e:
          print("Exception when calling DefaultApi->get_client_token: %s\n" % e)

  ```



  All URIs are relative to *https://api.prod.oursky.ai/docs/platform*

  <a id="documentation-for-authorization"></a>



  Authentication schemes defined for the API:
  <a id="BearerToken"></a>


  - **Type**: Bearer authentication

  <a id="Roles"></a>


pkgId: 4f3aa5c0161d8c207ec9c7fa8451a7eba5f8c3504861dde54168b64b01ef2223
name: python-ourskyai_sda_api-help
arch: noarch
version: 1.3.5667
epoch: 0
release: 1
summary: Development documents and examples for ourskyai_sda_api
url: https://github.com/GIT_USER_ID/GIT_REPO_ID
rpm_license: NoLicense
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ourskyai_sda_api-1.3.5667-1.src.rpm
location_href: Packages/python-ourskyai_sda_api-help-1.3.5667-1.noarch.rpm
checksum_type: sha256
description:
  The basic flow for a new organization is as follows:
  1. View the available satellite targets with the [satellite targets](https://api.prod.oursky.ai/docs/sda#tag/satellite-targets/get/v1/satellite-targets) endpoint. Copy the id of the target you want to observe.
  2. Create an organization target with the [organization target](https://api.prod.oursky.ai/docs/sda#tag/organization-targets/get/v1/organization-targets) endpoint. Use the id copied from above.
  3. Create a webhook with the [webhook](https://api.prod.oursky.ai/docs/sda#tag/webhooks/post/v1/communications/webhook) endpoint to receive TDMs automatically (preferred) or use the [tdms](https://api.prod.oursky.ai/docs/sda#tag/tdms/get/v1/tdms) endpoint to poll for TDMs.

  Check out our [examples](https://github.com/ourskyai/oursky-examples) repository to see usage in each language.

  This Python package is automatically generated by the [OpenAPI Generator](https://openapi-generator.tech) project:

  - API version: 1.3.5667
  - Package version: 1.3.5667
  - Build package: org.openapitools.codegen.languages.PythonClientCodegen



  Python 3.7+




  ```sh
  pip install ourskyai_sda_api
  ```
  (you may need to run `pip` with root permission: `sudo pip install ourskyai_sda_api`)


  If you manage dependencies with [poetry](https://python-poetry.org/) then you can add the SDK with:

  ```sh
  poetry add ourskyai_sda_api
  ```



  Then import the package:
  ```python
  import ourskyai_sda_api
  ```



  Please follow the [installation procedure](#installation--usage) and then run the following:

  ```python

  import os
  import ourskyai_sda_api
  from ourskyai_sda_api.rest import ApiException
  from pprint import pprint



  configuration = ourskyai_sda_api.Configuration(
      host = "https://api.prod.oursky.ai"
  )







  configuration = ourskyai_sda_api.Configuration(
      access_token = os.environ["BEARER_TOKEN"]
  )



  with ourskyai_sda_api.ApiClient(configuration) as api_client:
      
      api_instance = ourskyai_sda_api.DefaultApi(api_client)
      v1_create_image_set_request = ourskyai_sda_api.V1CreateImageSetRequest() # V1CreateImageSetRequest |

      try:
          api_response = api_instance.v1_create_image_set(v1_create_image_set_request)
          print("The response of DefaultApi->v1_create_image_set:\n")
          pprint(api_response)
      except ApiException as e:
          print("Exception when calling DefaultApi->v1_create_image_set: %s\n" % e)

  ```



  All URIs are relative to *https://api.prod.oursky.ai/docs/sda*

  <a id="documentation-for-authorization"></a>



  Authentication schemes defined for the API:
  <a id="BearerToken"></a>


  - **Type**: Bearer authentication

  <a id="Roles"></a>


pkgId: 4625db99e25f6691fce58dd3fc9a69ff02a6b7d5cf74feed21cbf105ec98c37c
name: python-outlines-haystack-help
arch: noarch
version: 0.0.2
epoch: 0
release: 1
summary: Development documents and examples for outlines-haystack
url: https://pypi.org/project/outlines-haystack/
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-outlines-haystack-0.0.2-1.src.rpm
location_href: Packages/python-outlines-haystack-help-0.0.2-1.noarch.rpm
checksum_type: sha256
description:
  - [🛠️ Installation](#installation)
  - [📃 Description](#description)
  - [💻 Usage](#usage)

  ```console
  pip install outlines-haystack
  ```

  > Outlines is a Python library that allows you to use Large Language Model in a simple and robust way (with structured generation).  It is built by [.txt](https://dottxt.co).
  >
  > -- <cite>[Outlines docs](https://dottxt-ai.github.io/outlines/latest/welcome/)</cite>
  This library allow you to use [`outlines`](https://dottxt-ai.github.io/outlines/latest/) generators in your [Haystack](https://haystack.deepset.ai) pipelines!
  This library currently supports the following generators:
  - [x] [Text](https://dottxt-ai.github.io/outlines/latest/reference/text/): _simply_ generate text
  - [ ] [JSON](https://dottxt-ai.github.io/outlines/latest/reference/generation/json/): ⚠️ coming soon
  - [ ] [Choices](https://dottxt-ai.github.io/outlines/latest/reference/generation/choices/): ⚠️ coming soon
  - [ ] [Regex](https://dottxt-ai.github.io/outlines/latest/reference/generation/regex/): ⚠️ coming soon
  - [ ] [Format](https://dottxt-ai.github.io/outlines/latest/reference/generation/format/): ⚠️ coming soon
  - [ ] [Grammar](https://dottxt-ai.github.io/outlines/latest/reference/generation/cfg/): ⚠️ coming soon
  `outlines` supports a wide range of models and frameworks, we are currently supporting:
  - [x] [OpenAI/Azure OpenAI](https://dottxt-ai.github.io/outlines/latest/reference/models/openai/)
  - [x] [Transformers](https://dottxt-ai.github.io/outlines/latest/reference/models/transformers/)
  - [x] [`mlx-lm`](https://dottxt-ai.github.io/outlines/latest/reference/models/mlxlm/)

  > [!TIP]
  > See the [Example Notebooks](./notebooks) for complete examples.
  >
  > All below examples only use the `transformers` models.

  ```python
  >>> generator = TransformersTextGenerator(
  >>> generator.warm_up()
  >>> generator.run(prompt="What is the capital of Italy?")
  {'replies': ['\n\n# Answer\nThe capital of Italy is Rome.']}
  ```

  `outlines-haystack` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.

rpm_license:
  MIT License  Copyright (c) 2024-present Edoardo Abati  Permission 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.


pkgId: 09b5bc009309130c807b1fa8aee03f86ecb33a93fdc32299038427683dcf176f
name: python-overdue-help
arch: noarch
version: 0.1.2
epoch: 0
release: 1
summary: Development documents and examples for overdue
url: https://pypi.org/project/overdue/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-overdue-0.1.2-1.src.rpm
location_href: Packages/python-overdue-help-0.1.2-1.noarch.rpm
checksum_type: sha256
description:
  Conveniently stop Python code from running when it is taking too long on Python 3.10+.
  Ships with type annotations and depends at runtime on nothing but Python itself.

  For older Pythons, consider [stopit](https://github.com/glenfant/stopit).


  Without exceptions:
  ```python
  from overdue import timeout_set_to

  with timeout_set_to(0.25) as timeout:
      
  if timeout.triggered:
      
  ```
  With an exception:
  ```python
  from overdue import timeout_set_to, TaskAbortedError

  with timeout_set_to(0.25, raise_exception=True):
      try:
          
      except TaskAbortedError:
          
  ```


  With an exception:
  ```python
  from overdue import timecapped_to, TaskAbortedError

  @timecapped_to(0.25)
  def my_slow_function() -> None:
      

  try:
      my_slow_function()
  except TaskAbortedError:
      
  ```

  Without exceptions:
  ```python
  from overdue import in_time_or_none

  @in_time_or_none(0.25)
  def my_slow_function() -> int:
      
      return 7

  if (result := my_slow_function()) is None:
      
  ```


pkgId: 7e60cc71ecfff231712722239fe8cb31de102d2fcc74cccf3ea4a11856fab44e
name: python-overloadable-help
arch: noarch
version: 1.0.7
epoch: 0
release: 1
summary: Development documents and examples for overloadable
url: https://pypi.org/project/overloadable/
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-overloadable-1.0.7-1.src.rpm
location_href: Packages/python-overloadable-help-1.0.7-1.noarch.rpm
checksum_type: sha256
rpm_license:
  The MIT License (MIT)  Copyright (c) 2024 Johannes  Permission 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.


pkgId: 531d7c61606ccce935ea8f7ebb8c16cf2a91468771b5b28ec92a1186c5a9a80e
name: python-ovinc-client-help
arch: noarch
version: 0.3.13
epoch: 0
release: 1
summary: Development documents and examples for ovinc-client
url: https://www.ovinc.cn/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ovinc-client-0.3.13-1.src.rpm
location_href: Packages/python-ovinc-client-help-0.3.13-1.noarch.rpm
checksum_type: sha256
description:
  Init: `APP_CODE` `APP_SECRET` `OVINC_API_URL`



  Available: `verify_code`



  Available: `mail` `sms` `robot`



  Available: `tcaptcha`


pkgId: 431e2e46ec9e5e226aae0bd53049b1aa7639ac13fd32936b4dc60f679adeddf2
name: python-ovos-core-help
arch: noarch
version: 0.3.5
epoch: 0
release: 1
summary: Development documents and examples for ovos-core
url: https://github.com/OpenVoiceOS/ovos-core
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ovos-core-0.3.5-1.src.rpm
location_href: Packages/python-ovos-core-help-0.3.5-1.noarch.rpm
checksum_type: sha256
description:
  [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE.md)
  ![Unit Tests](https://github.com/OpenVoiceOS/ovos-core/actions/workflows/unit_tests.yml/badge.svg)
  [![codecov](https://codecov.io/gh/OpenVoiceOS/ovos-core/branch/dev/graph/badge.svg?token=CS7WJH4PO2)](https://codecov.io/gh/OpenVoiceOS/ovos-core)
  [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](http://makeapullrequest.com)
  [![Chat](https://img.shields.io/matrix/openvoiceos-general:matrix.org)](https://matrix.to/#/#OpenVoiceOS-general:matrix.org)
  [![GitHub Discussions](https://img.shields.io/github/discussions/OpenVoiceOS/OpenVoiceOS?label=OVOS%20Discussions)](https://github.com/OpenVoiceOS/OpenVoiceOS/discussions)



  [OpenVoiceOS](https://openvoiceos.org/) is an open source platform for smart speakers and other voice-centric devices.

  [Mycroft](https://mycroft.ai) was a hackable, open source voice assistant by the now defunct MycroftAI. OpenVoiceOS continues that work and ovos-core (this repo) is the central component.

  All Mycroft Skills and Plugins should work normally with OVOS-core, with the exception of Common Play and other media-related skills. Other Mycroft-based assistants are also believed, but not guaranteed, to be compatible.

  The biggest difference between OVOS-core and Mycroft-core is that OVOS-core is fully modular. Furthermore, common
  components have been repackaged as plugins. That means it isn't just a great assistant on its own, but also a pretty
  small library!



  - [Installing OVOS](#installing-ovos)
  - [Skills](#skills)
  - [Getting Involved](#getting-involved)
  - [Links](#links)



  If you have an existing system that you would like to install OVOS on, we strongly suggest the [ovos-installer](https://github.com/OpenVoiceOS/ovos-installer) to install OVOS and its dependencies. The full assistant requires several repositories and the installer makes it easy to install them all at once.

  If you would like to install OVOS on a Raspberry Pi, we suggest using the [RaspOVOS](https://github.com/OpenVoiceOS/RaspOVOS) image. This image is based on Raspberry Pi OS and includes OVOS and its dependencies running in a "headless" mode (no GUI). It is designed and optimized for a Raspberry Pi 3B, so on a 4 or higher its performance is even better.

  If you would like to install OVOS on embedded hardware, we suggest using [ovos-buildroot](https://github.com/OpenVoiceOS/ovos-buildroot). This is a buildroot configuration that can be used to build a custom Linux distribution for embedded hardware. It includes OVOS and its dependencies, and is designed to be as small and efficient as possible.

  You can find detailed documentation over at the [community-docs](https://openvoiceos.github.io/community-docs) or [ovos-technical-manual](https://openvoiceos.github.io/ovos-technical-manual)

  This repo can be installed standalone via `pip install ovos-core`, which will install the bare minimum components common to all services. This is useful for developers who want to build their own custom voice assistant. For more details, [please see the community docs](https://openvoiceos.github.io/community-docs/042-install_ovos_core/).



  OVOS is nothing without skills. There are a handful of default skills, but [most need to be installed explicitly](https://openvoiceos.github.io/community-docs/080-ht_skills/). OVOS skills are all pip-installable, and can be found on [PyPI](https://pypi.org/) or by [browsing the OVOS organization on GitHub](https://github.com/orgs/OpenVoiceOS/repositories?language=&q=skill&sort=&type=all). Most classic Mycroft skills will also work on OVOS.

  Please share your own interesting work!



  This is an open source project. We would love your help. We have prepared a [contributing](.github/CONTRIBUTING.md)
  guide to help you get started.

  The easiest way for anyone to contribute is to help with translations! You can help without any programming knowledge via the [translation portal](https://gitlocalize.com/users/OpenVoiceOS)

  If this is your first PR, or you're not sure where to get started,
  say hi in [OpenVoiceOS Chat](https://matrix.to/#/!XFpdtmgyCoPDxOMPpH:matrix.org?via=matrix.org) and a team member would
  be happy to mentor you.
  Join the [Discussions](https://github.com/OpenVoiceOS/OpenVoiceOS/discussions) for questions and answers.



  The OpenVoiceOS team thanks the following entities (in addition to MycroftAI) for making certain code and/or
  manpower resources available to us:

  - [NeonGecko](https://neon.ai)
  - [KDE](https://kde.org) / [Blue Systems](https://blue-systems.com/)



  * [Community Documentation](https://openvoiceos.github.io/community-docs)
  * [ovos-technical-manual](https://openvoiceos.github.io/ovos-technical-manual)
  * [Release Notes](https://github.com/OpenVoiceOS/ovos-core/releases)
  * [OpenVoiceOS Chat](https://matrix.to/#/!XFpdtmgyCoPDxOMPpH:matrix.org?via=matrix.org)
  * [OpenVoiceOS Website](https://openvoiceos.org)
  * [Open Conversational AI Forums](https://community.openconversational.ai/)  (previously mycroft forums)


pkgId: dcc4514cf57b359618bcfff8a577b6471a3ed2438fea1be68ac6298d12bcde06
name: python-ovos-skill-naptime-help
arch: noarch
version: 0.3.10
epoch: 0
release: 1
summary: Development documents and examples for ovos-skill-naptime
url: https://github.com/OpenVoiceOS/skill-ovos-naptime
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ovos-skill-naptime-0.3.10-1.src.rpm
location_href: Packages/python-ovos-skill-naptime-help-0.3.10-1.noarch.rpm
checksum_type: sha256
description:
  Put the assistant to sleep when you don't want to be disturbed.



  Tell the assistant to sleep when you don't want to be disturbed in any way.
  This stops all calls to Speech to Text system, guaranteeing your voice won't be sent anywhere on an accidental activation.

  When sleeping, the assistant will only listen locally for the wake word `Hey Mycroft, wake up`. Otherwise the system will be totally silent and won't bother you.

  On a Mark 1 device this also dims the eyes.

  Skill can mute the audio as well when entering into sleep mode if required.



  The skill utilizes the `~/.config/mycroft/skills/skill-ovos-naptime.openvoiceos/settings.json` file which allows you to configure this skill.

  ```json
  {
    "mute": false
  }
  ```



  - "Go to sleep"
  - "Nap time"
  - "Wake up"



  OpenVoiceOS (@OpenVoiceOS)
  Mycroft AI (@MycroftAI)



  **Daily**
  Configuration


pkgId: 439a4243d9121392495322c9f064b1b188dba29b540364bd65ae34de0b39a969
name: python-ovos-workshop-help
arch: noarch
version: 3.1.3
epoch: 0
release: 1
summary: Development documents and examples for ovos-workshop
url: https://github.com/OpenVoiceOS/OVOS-workshop
rpm_license: apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ovos-workshop-3.1.3-1.src.rpm
location_href: Packages/python-ovos-workshop-help-3.1.3-1.noarch.rpm
checksum_type: sha256
description:
  OVOS Workshop contains skill base classes and supporting tools to build skills
  and applications for OpenVoiceOS systems.


pkgId: d768591da5194ee605fd062c7ef41c9e7afc691fc821e1efb75d3f2a03273180
name: python-ovsgui-help
arch: noarch
version: 1.0.5
epoch: 0
release: 1
summary: Development documents and examples for ovsgui
url: https://github.com/e500ky/ovsgui
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-ovsgui-1.0.5-1.src.rpm
location_href: Packages/python-ovsgui-help-1.0.5-1.noarch.rpm
checksum_type: sha256
description:
  **OVSGUI** is a graphical user interface (GUI) for managing online variables through Firebase. It provides an intuitive interface for users to interact with variables, retrieve, delete, and manage them. The application supports token-based access and displays variable details such as creator information, creation time, and more.



  - **Token Authentication**: Secure access to the variable system using unique tokens.
  - **Variable Management**: View and manage variables, including deletion of variables.
  - **Real-time Updates**: Dynamic interaction with Firebase data.
  - **User Interface**: Modern GUI built with `customtkinter` for a smooth user experience.
  - **Variable Details**: View detailed information about each variable, including creator, IP, creation date, and value.



  To install **OVSGUI** and use it in your Python environment, follow the steps below.



  You can install the package directly from PyPI:

  ```bash
  pip install ovsgui
  ```


pkgId: 74daf965fe8edb2696c1f28758a064393810060f7afcf973ea7be3495e2604bf
name: python-owocr-help
arch: noarch
version: 1.8.3
epoch: 0
release: 1
summary: Development documents and examples for owocr
url: https://github.com/AuroraWright/owocr
rpm_license: Apache License 2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-owocr-1.8.3-1.src.rpm
location_href: Packages/python-owocr-help-1.8.3-1.noarch.rpm
checksum_type: sha256
description:
  Command line client for several Japanese OCR providers derived from [Manga OCR](https://github.com/kha-white/manga-ocr).



  This has been tested with Python 3.11 and 3.12. Newer/older versions might work. It can be installed with `pip install owocr`



  Basic usage is comparable to Manga OCR as in, `owocr` keeps scanning for images and performing text recognition on them. Similarly, by default it will read images from the clipboard and write text back to the clipboard (or optionally, read images from a folder and/or write text to a .txt file if you specify `-r=<folder path>` or `-w=<txt file path>`).

  Additionally:
  - Scanning the clipboard takes basically zero system resources on macOS and Windows
  - Supports reading images and/or writing text to a websocket with the `-r=websocket` and/or `-w=websocket` parameters (the port is 7331 by default, and is configurable in the config file)
  - Supports reading images from a Unix domain socket (`/tmp/owocr.sock`) on macOS and Linux with `-r=unixsocket`
  - Supports capturing the screen directly, or a portion of the screen or a specific window with `-r=screencapture`. By default it will read from the entire main screen every 3 seconds, but you can change it to screenshot a different screen or a portion of a screen (with a set of screen coordinates `x,y,width,height`) or just a specific window (with the window title). You can also change the delay between screenshots or specify a keyboard combo if you don't want screenshots to be taken periodically. Refer to the config file or to `owocr --help` for more details about the screen capture settings
  - You can pause/unpause the image processing by pressing "p" or terminate the script with "t" or "q" inside the terminal window
  - You can switch between OCR providers pressing their corresponding keyboard key inside the terminal window (refer to the list of keys in the providers list below)
  - You can start the script paused with the `-p` option or with a specific provider with the `-e` option (refer to `owocr -h` for the list)
  - Holding ctrl or cmd at any time will pause image processing temporarily, or you can specify keyboard combos in the config file to pause/unpause and switch the OCR provider from anywhere (refer to the config file or `owocr -h`)
  - You can auto pause the script after a successful text recognition with the `-a=seconds` option if you're not using screen capture. 0 (the default) disables it.
  - You can enable notifications in the config file or with `-n` to show the text with a native OS notification. **Important for macOS users:** if you use Python from brew, you need to enter this command in your terminal before the first notification: `codesign -f -s - $(brew --cellar python)/3.*/Frameworks/Python.framework` (works on Ventura/Sonoma). Older macOS versions might require Python to be installed from the [official website](https://www.python.org/downloads/). Nothing can be done about this unfortunately.
  - Optionally, you can speed up the online providers by installing fpng-py: `pip install fpng-py` (requires setting up a developer environment on most operating systems/Python versions)
  - Optionally, you can improve filtering of non-Japanese text for screen capture by installing transformers and sentencepiece: `pip install transformers sentencepiece`
  - A config file (which will be automatically created in `user directory/.config/owocr_config.ini`, on Windows `user directory` is the `C:\Users\yourusername` folder) can be used to configure the script, as an example to limit providers (to reduce clutter/memory usage) as well as specifying provider settings such as api keys etc. A sample config file is also provided [here](https://raw.githubusercontent.com/AuroraWright/owocr/master/owocr_config.ini)
  - For systems where text can be copied to the clipboard at the same time as images, if `*ocr_ignore*` is copied with an image, the image will be ignored (mostly useful for devs making their own sender tool)




  - [Manga OCR](https://github.com/kha-white/manga-ocr): refer to the readme for installation ("m" key)
  - [EasyOCR](https://github.com/JaidedAI/EasyOCR): refer to the readme for installation ("e" key)
  - [RapidOCR](https://github.com/RapidAI/RapidOCR): refer to the readme for installation ("r" key)
  - Apple Vision framework: this will work on macOS Ventura or later. In my experience, the best of the local providers for horizontal text ("a" key)
  - Apple Live Text (VisionKit framework): this will work on macOS Ventura or later. It should be the same as Vision except that in Sonoma Apple added vertical text reading ("d" key)
  - WinRT OCR: this will work on Windows 10 or later if winocr (`pip install winocr`) is installed. It can also be used by installing winocr on a Windows virtual machine and running the server there (`winocr_serve`), and installing requests (`pip install requests`) and specifying the IP address of the Windows VM/machine in the config file ("w" key)


  - Google Lens: Google Vision in disguise (no need for API keys!), however it needs to download a couple megabytes of data for each request. You need to install pyjson5 and requests (`pip install pyjson5 requests`) ("l" key)
  - Google Vision: you need a service account .json file named google_vision.json in `user directory/.config/` and installing google-cloud-vision (`pip install google-cloud-vision`) ("g" key)
  - Azure Image Analysis: you need to specify an api key and an endpoint in the config file and to install azure-ai-vision-imageanalysis (`pip install azure-ai-vision-imageanalysis`) ("v" key)



  This uses code from/references these projects:
  - Viola for working on the Google Lens implementation and helping with the pyobjc VisionKit code!
  - @ronaldoussoren for helping with the pyobjc VisionKit code
  - [Manga OCR](https://github.com/kha-white/manga-ocr)
  - [ocrmac](https://github.com/straussmaximilian/ocrmac) for the Apple Vision framework API
  - [NadeOCR](https://github.com/Natsume-197/NadeOCR) for the Google Vision API
  - [ccylin2000_lipboard_monitor](https://github.com/vaimalaviya1233/ccylin2000_lipboard_monitor) for the Windows clipboard polling code


pkgId: 6fe7121c2542c2774e5251240e5fded01f7ef0064d42573a07e5d0679e92ffab
name: python-p115client-help
arch: noarch
version: 0.0.3.10
epoch: 0
release: 1
summary: Development documents and examples for p115client
url: https://github.com/ChenyangGao/p115client
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-p115client-0.0.3.10-1.src.rpm
location_href: Packages/python-p115client-help-0.0.3.10-1.noarch.rpm
checksum_type: sha256
description:
  ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/p115client)
  ![PyPI - Version](https://img.shields.io/pypi/v/p115client)
  ![PyPI - Downloads](https://img.shields.io/pypi/dm/p115client)
  ![PyPI - Format](https://img.shields.io/pypi/format/p115client)
  ![PyPI - Status](https://img.shields.io/pypi/status/p115client)



  你可以从 [pypi](https://pypi.org/project/p115client/) 安装最新版本

  ```console
  pip install -U p115client
  ```





  导入模块

  ```python
  from p115client import P115Client
  ```

  创建客户端对象，需要传入 <kbd>cookies</kbd>，如果不传，则需要扫码登录

  ```python
  cookies = "UID=...; CID=...; SEID=..."
  client = P115Client(cookies)
  ```

  如果你的 <kbd>cookies</kbd> 保存在 `~/115-cookies.txt`

  ```python
  from pathlib import Path

  client = P115Client(Path("~/115-cookies.txt").expanduser())
  ```

  如果想要在接口返回时自动捕获 405 HTTP 响应码，进行自动扫码，并把更新后的 <kbd>cookies</kbd> 写回文件，然后重试接口调用

  ```python
  client = P115Client(Path("~/115-cookies.txt").expanduser(), check_for_relogin=True)
  ```

  所以综上，推荐的初始化代码为

  ```python
  from p115client import P115Client
  from pathlib import Path

  client = P115Client(Path("~/115-cookies.txt").expanduser(), check_for_relogin=True)
  ```



  所有需要直接或间接执行 HTTP 请求的接口，都有同步和异步的调用方式

  ```python

  client.method(payload)
  client.method(payload, async_=False)


  await client.method(payload, async_=True)
  ```

  从根本上讲，除了几个 `staticmethod`，它们都会调用 `P115Client.request`

  ```python
  url = "https://webapi.115.com/files"
  response = client.request(url=url, params={"cid": 0, "show_dir": 1})
  ```

  当你需要构建自己的扩展模块，以增加一些新的 115 web 接口时，就需要用到此方法了

  ```python
  from collections.abc import Coroutine
  from typing import overload, Any, Literal

  from p115client import P115Client

  class MyCustom115Client(P115Client):

      @overload
      def foo(
          self,
          payload: dict,
          /,
          async_: Literal[False] = False,
          **request_kwargs,
      ) -> dict:
          ...
      @overload
      def foo(
          self,
          payload: dict,
          /,
          async_: Literal[True],
          **request_kwargs,
      ) -> Coroutine[Any, Any, dict]:
          ...
      def foo(
          self,
          payload: dict,
          /,
          async_: bool = False,
          **request_kwargs,
      ) -> dict | Coroutine[Any, Any, dict]:
          api = "https://webapi.115.com/foo"
          return self.request(
              api,
              method="GET",
              params=payload,
              async_=async_,
              **request_kwargs,
          )

      @overload
      def bar(
          self,
          payload: dict,
          /,
          async_: Literal[False] = False,
          **request_kwargs,
      ) -> dict:
          ...
      @overload
      def bar(
          self,
          payload: dict,
          /,
          async_: Literal[True],
          **request_kwargs,
      ) -> Coroutine[Any, Any, dict]:
          ...
      def bar(
          self,
          payload: dict,
          /,
          async_: bool = False,
          **request_kwargs,
      ) -> dict | Coroutine[Any, Any, dict]:
          api = "https://webapi.115.com/bar"
          return self.request(
              api,
              method="POST",
              data=payload,
              async_=async_,
              **request_kwargs,
          )
  ```



  接口被调用后，如果返回的是 dict 类型的数据（说明原本是 JSON），则可以用 `p115client.check_response` 执行检查。首先会查看其中名为 "state" 的键的对应值，如果为  True、1 或不存在，则原样返回被检查的数据；否则，"state" 的对应值大概是 False 或 0，说明有问题出现，会根据实际情况抛出一个异常，但都是 `OSError` 的实例，其中大部分还是 `p115client.P115OSError` 的实例。

  ```python
  from p115client import check_response


  data = check_response(client.method(payload))

  data = check_response(await client.method(payload, async_=True))
  ```



  一些简单的封装工具可能是必要的，特别是那种实现起来代码量比较少，可以封装成单个函数的。我把平常使用过程中，积累的一些经验具体化为一组工具函数。这些工具函数分别有着不同的功能，如果组合起来使用，或许能解决很多问题。

  ```python
  from p115client import tool
  ```



  - 如果你需要更详细的文档，特别是关于各种接口的信息，可以阅读

      [https://p115client.readthedocs.io/en/latest/](https://p115client.readthedocs.io/en/latest/)

  - 如果你想要一组更高级的封装，特别是一组文件系统的操作集合，可以使用

      [https://pypi.org/project/python-115/](https://pypi.org/project/python-115/)


pkgId: bcb336d93c036a0bfd95cbb20b12aa9c6da75cad9f3455fc94e9c94a551d9d32
name: python-p115servedb-help
arch: noarch
version: 0.0.5.2
epoch: 0
release: 1
summary: Development documents and examples for p115servedb
url: https://github.com/ChenyangGao/p115client/tree/main/modules/p115servedb
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-p115servedb-0.0.5.2-1.src.rpm
location_href: Packages/python-p115servedb-help-0.0.5.2-1.noarch.rpm
checksum_type: sha256
description:
  你可以通过 [pypi](https://pypi.org/project/p115servedb/) 安装

  ```console
  pip install -U p115servedb
  ```







  挂载地址为 `/<dav`，无用户名和密码（或者随便瞎填一个）

  ```console
  $ servedb dav -f 115-dbfile.db
  ```

  或者

  ```console
  $ servedb-dav -f 115-dbfile.db
  ```



  ```console
  $ servedb fuse -f 115-dbfile.db
  ```

  或者

  ```console
  $ servedb-fuse -f 115-dbfile.db
  ```


pkgId: 761f1b5b182c285d18f3918ebea4d4283b7b5405552b0a082c9ed1266e0767fa
name: python-package-mikezhou-talk-zhiwo-help
arch: noarch
version: 1.0.0
epoch: 0
release: 1
summary: Development documents and examples for package-mikezhou-talk-zhiwo
url: https://pypi.org/project/package-mikezhou-talk-zhiwo/
rpm_license: BSD License
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-package-mikezhou-talk-zhiwo-1.0.0-1.src.rpm
location_href: Packages/python-package-mikezhou-talk-zhiwo-help-1.0.0-1.noarch.rpm
checksum_type: sha256
description:
  智窝助手sdk，包括以下功能：

  1. 调用数据助手

  2. 发送消息

      2.1 发送websocket消息

      2.2 发送钉钉webhook消息


  3. 细节说明

  ZhiwoAssistant有两个参数，一个是jep传递给main_function的param，必传；

  一个是local，默认为True。如果设置为True，就会调用localhost:8080的服务，说明是在dev、test、uat、prod的本地调用本地。

  如果设置为False，就会根据token调用不同环境的远程服务。

  有三种远程服务：test、uat、prod，由param.authorization决定。param.authorization由代码传递给jep，或者点击助手编辑页的"获取凭证"获取。


pkgId: 108972429595cb5217850af2148b63d5a64afbcb0e046ef6147df8de320df4fa
name: python-packg-help
arch: noarch
version: 0.22.1
epoch: 0
release: 1
summary: Development documents and examples for packg
url: https://pypi.org/project/packg/
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-packg-0.22.1-1.src.rpm
location_href: Packages/python-packg-help-0.22.1-1.noarch.rpm
checksum_type: sha256
description:
  <p align="center">
  <a href="https://github.com/simon-ging/packg/actions/workflows/build-py38.yml">
    <img alt="build 3.8 status" title="build 3.8 status" src="https://img.shields.io/github/actions/workflow/status/simon-ging/packg/build-py38.yml?branch=main&label=python%203.8" />
  </a>
  <a href="https://github.com/simon-ging/packg/actions/workflows/build-py39.yml">
    <img alt="build 3.9 status" title="build 3.9 status" src="https://img.shields.io/github/actions/workflow/status/simon-ging/packg/build-py39.yml?branch=main&label=python%203.9" />
  </a>
  <a href="https://github.com/simon-ging/packg/actions/workflows/build-py310.yml">
    <img alt="build 3.10 status" title="build 3.10 status" src="https://img.shields.io/github/actions/workflow/status/simon-ging/packg/build-py310.yml?branch=main&label=python%203.10" />
  </a>
  <a href="https://github.com/simon-ging/packg/actions/workflows/build-py311.yml">
    <img alt="build 3.11 status" title="build 3.11 status" src="https://img.shields.io/github/actions/workflow/status/simon-ging/packg/build-py311.yml?branch=main&label=python%203.11" />
  </a>
  <a href="https://github.com/simon-ging/packg/actions/workflows/build-py312.yml">
    <img alt="build 3.12 status" title="build 3.12 status" src="https://img.shields.io/github/actions/workflow/status/simon-ging/packg/build-py312.yml?branch=main&label=python%203.12" />
  </a>
  <a href="https://github.com/simon-ging/packg/actions/workflows/build-py313.yml">
    <img alt="build 3.13 status" title="build 3.13 status" src="https://img.shields.io/github/actions/workflow/status/simon-ging/packg/build-py313.yml?branch=main&label=python%203.13" />
  </a>
  <img alt="coverage" title="coverage" src="https://raw.githubusercontent.com/simon-ging/packg/main/docs/coverage.svg" />
  <a href="https://pypi.org/project/packg/">
    <img alt="version" title="version" src="https://img.shields.io/pypi/v/packg?color=success" />
  </a>
  </p>

  Collection of utilities used in other python projects.



  * `caching`: Cache objects to disk (using `joblib`) or to memory
  * `constclass.Const`: Base class for defining constants, as alternative to `enum.Enum`
  * `debugging`: Connect to PyCharm debug server
  * `dtime`: Wrappers and formatters for `datetime` and other timing utilities
  * `log`: Wrapper for `loguru`, utilities for stdlib `logging`
  * `iotools`: Index paths, compress and read files, git utilities, wrappers to load json/yaml
  * `magic`: Wrapper around `importlib`
  * `maths`: Various small mathematical utilities
  * `misc`:
    * `format_exception(e)` outputs the exception as it appears in the stacktrace.
    * `suppress_stdout_stderr` context manager to suppress all output of a block of code.
  * `multiproc`: Multiprocessing utilities
  * `packaging`: Code to handle creation and running of python packages
  * `paths`: Load paths from the global environment or .env files using `python-dotenv`
  * `stats`: Simple statistics utilities
  * `strings`: Base64, quote strings, create random strings, create hashes of objects
  * `system`: Wrapper for `subprocess.Popen`
  * `testing`: Import all modules from a package and other utilities
  * `tqdmext`: Wrapper `tqdm_max_ncols` that limits the width of a `tqdm` progressbar by default
  * `typext`: Type definitions
  * `web`: Download file and resume a partial download, disable web access



  Requires `python>=3.8`

  ```bash
  pip install packg
  ```



  ```bash

  python -m packg.cli.show_env


  packg show_env

  ```

  To override the defaults with your own values:

  - Set the environment variables in your shell e.g. using .bashrc
  - Create a file named `.env` in the root of your project as follows:

  ```bash
  ENV_DATA_DIR=data
  ENV_RESULT_DIR=results
  ENV_ANNO_DIR=annotations
  ENV_CODE_DIR=/home/${USER}/code
  ENV_CACHE_DIR=/home/${USER}/.cache
  ```



  Clone repository and cd into, then:

  ```bash
  pip install -e .
  pip install pytest pytest-cov pylint

  python -m pytest --cov

  pylint packg
  pylint tests
  ```


pkgId: b660d17d7f2fc64ec717a2192f1fcc7381e0cf33ab2da46b7e0bf392ee514219
name: python-packitos-help
arch: noarch
version: 0.106.0
epoch: 0
release: 1
summary: Development documents and examples for packitos
url: https://github.com/packit/packit
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-packitos-0.106.0-1.src.rpm
location_href: Packages/python-packitos-help-0.106.0-1.noarch.rpm
checksum_type: sha256
description:
  See our [documentation](https://packit.dev/docs/guide/)

  The [Contributing Guidelines](CONTRIBUTING.md) hosts all information you need to know to contribute to code and documentation, run tests and additional configuration.

  This list contains workflows covered by packit tool and links to the documentation.
  - [Update Fedora dist-git with an upstream release.](https://packit.dev/docs/cli/propose-downstream/)
  - [Build content of a Fedora dist-git branch in koji.](https://packit.dev/docs/cli/build/)
  - [Create a bodhi update.](https://packit.dev/docs/cli/create-bodhi-update/)
  - [Create a SRPM from the current content in the upstream repository.](https://packit.dev/docs/cli/srpm/)
  - [Sync content of the Fedora dist-git repo into the upstream repository.](https://packit.dev/docs/cli/sync-from-downstream/)

  Packit is written in Python 3 and is supports version 3.9 or later.

  For complete information on how to start using packit, please [click here](https://packit.dev/docs/guide/#have-packit-tooling-installed-locally).

  User configuration file for packit is described [here](http://packit.dev/docs/configuration/#user-configuration-file).

  For the up to date list of projects which are using packit, [click here](https://dashboard.packit.dev/projects).

  Created by `Marián Mrva` - [@surfer19](https://github.com/surfer19)


pkgId: 42f52936d0e6656a61577ecbb7eec7b22a31e3406373b2604e382c528329f51c
name: python-paddle-python-sdk-help
arch: noarch
version: 1.2.2
epoch: 0
release: 1
summary: Development documents and examples for paddle-python-sdk
url: https://developer.paddle.com/api-reference/overview
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-paddle-python-sdk-1.2.2-1.src.rpm
location_href: Packages/python-paddle-python-sdk-help-1.2.2-1.noarch.rpm
checksum_type: sha256
description:
  [![Build Status](https://img.shields.io/github/actions/workflow/status/PaddleHQ/paddle-python-sdk/publish_to_pypi.yml)](https://github.com/PaddleHQ/paddle-python-sdk/actions/?query=branch%3Amain)
  [![PyPI](https://img.shields.io/pypi/v/paddle-python-sdk.svg)](https://pypi.python.org/pypi/paddle-python-sdk)
  [![PyPI pyversions](https://img.shields.io/pypi/pyversions/paddle-python-sdk.svg)](https://pypi.python.org/pypi/paddle-python-sdk/)
  [![License: Apache 2.0](https://img.shields.io/github/license/PaddleHQ/paddle-python-sdk)](http://www.apache.org/licenses/LICENSE-2.0)



  [Paddle Billing](https://www.paddle.com/billing?utm_source=dx&utm_medium=paddle-python-sdk) is a complete digital product sales and subscription management platform, designed for modern software businesses. It helps you increase your revenue, retain customers, and scale your operations.

  This is a [Python](https://www.python.org/) SDK that you can use to integrate Paddle Billing with applications written in Python.

  For working with Paddle in your frontend, use [Paddle.js](https://developer.paddle.com/paddlejs/overview?utm_source=dx&utm_medium=paddle-python-sdk). You can open checkouts, securely collect payment information, build pricing pages, and integrate with Paddle Retain.

  > **Important:** This package works with Paddle Billing. It does not support Paddle Classic. To work with Paddle Classic, see: [Paddle Classic API reference](https://developer.paddle.com/classic/api-reference/1384a288aca7a-api-reference?utm_source=dx&utm_medium=paddle-python-sdk)



  - [Requirements](#Requirements)
  - [Install](#Install)
  - [Usage](#Usage)
  - [Examples](#Examples)
  - [Resources](#Resources)


  Python>=3.11 (for native type hinting, StrEnum, trailing commas, f-strings)

  **Project dependencies** (automatically installed by pip):
  - requests>=2.31
  - urllib3>=2.1.0



  Because `paddle-python-sdk` is [available on PyPi](https://pypi.org/project/paddle-python-sdk/), installation is as simple as running the following `pip` command:

  `pip install paddle-python-sdk`




  To authenticate, you'll need an API key. You can create and manage API keys in **Paddle > Developer tools > Authentication**.

  Pass your API key while initializing a new Paddle client:
  ``` python
  from paddle_billing import Client

  paddle = Client('PADDLE_API_SECRET_KEY')
  ```

  You can pass your Paddle API secret key into the SDK from an environment variable:
  ``` python
  from os             import getenv
  from paddle_billing import Client

  paddle = Client(getenv('PADDLE_API_SECRET_KEY'))
  ```

  You can also pass an environment to work with Paddle's sandbox:
  ``` python
  from paddle_billing import Client, Environment, Options

  paddle = Client('PADDLE_API_SECRET_KEY', options=Options(Environment.SANDBOX))
  ```

  Keep in mind that API keys are separate for your sandbox and live accounts, so you'll need to generate keys for each environment.




  There are examples included in the [examples folder](https://github.com/PaddleHQ/paddle-python-sdk/tree/main/examples). To prevent leaking errors we recommend encapsulating Paddle operations inside Try/Except blocks. For brevity, most of the examples below do not do this.


  You can list supported entities with the `list()` method in the resource. It returns an iterator to help when working with multiple pages.
  ``` python
  from paddle_billing import Client

  paddle = Client('PADDLE_API_SECRET_KEY')

  products = paddle.products.list()


  for product in products:
      print(f"Product's id: {product.id}")
  ```


  You can get an entity with the `get()` method in the resource. It accepts the `id` of the entity to get. The entity is returned.
  ``` python
  from paddle_billing import Client

  paddle = Client('PADDLE_API_SECRET_KEY')

  product = paddle.products.get('PRODUCT_ID')
  ```


  You can create a supported entity with the `create()` method in the resource. It accepts the resource's corresponding `CreateOperation` e.g. `CreateProduct`. The created entity is returned.

  ``` python
  from paddle_billing                               import Client
  from paddle_billing.Entities.Shared.TaxCategory   import TaxCategory
  from paddle_billing.Resources.Products.Operations import CreateProduct

  paddle = Client('PADDLE_API_SECRET_KEY')

  created_product = paddle.products.create(CreateProduct(
      name         = 'My Product',
      tax_category = TaxCategory.Standard,
  ))
  ```


  You can update a supported entity with the `update()` method in the resource. It accepts the `id` of the entity to update and the corresponding `UpdateOperation` e.g. `UpdateProduct`. The updated entity is returned.
  ``` python
  from paddle_billing                        import Client
  from paddle_billing.Resources.Products.Operations import UpdateProduct

  paddle = Client('PADDLE_API_SECRET_KEY')


  updated_product = paddle.products.update('PRODUCT_ID', UpdateProduct(
      name = 'My Improved Product'
  ))
  ```

  Where operations require more than one `id`, the `update()` method accepts multiple arguments. For example, to update an address for a customer, pass the `customerId` and the `addressId`:
  ``` python
  updated_address = paddle.addresses.update(
      'CUSTOMER_ID',
      'ADDRESS_ID',
      operation_goes_here,
  )
  ```


  You can delete an entity with the `delete()` method in the resource. It accepts the `id` of the entity to delete. The deleted entity is returned.
  ``` python
  from paddle_billing import Client

  paddle = Client('PADDLE_API_SECRET_KEY')

  deleted_product = paddle.products.delete('PRODUCT_ID')
  ```





  The SDK includes a helper class to verify webhook signatures sent by Notifications from Paddle.

  ``` python
  from paddle_billing.Notifications import Secret, Verifier

  integrity_check = Verifier().verify(request, Secret('WEBHOOK_SECRET_KEY'))
  ```


  - [Paddle API reference](https://developer.paddle.com/api-reference/overview?utm_source=dx&utm_medium=paddle-python-sdk)
  - [Sign up for Paddle Billing](https://login.paddle.com/signup?utm_source=dx&utm_medium=paddle-python-sdk)


pkgId: c2c81f5a66317f9610158c610f4007d32d21f809bff7f983dab4cf781b9531b2
name: python-pagefind-help
arch: noarch
version: 1.3.0
epoch: 0
release: 1
summary: Development documents and examples for pagefind
url: https://pypi.org/project/pagefind/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pagefind-1.3.0-1.src.rpm
location_href: Packages/python-pagefind-help-1.3.0-1.noarch.rpm
checksum_type: sha256
description:
  An async python API for the [pagefind](https://pagefind.app) binary.



  ```sh
  python3 -m pip install 'pagefind[bin]'
  python3 -m pagefind --help
  ```


  <!--[[[cog
    print("```py")
    print(open('./src/tests/integration.py').read())
    print("```")
  ]]] -->
  ```py
  import asyncio
  import json
  import logging
  import os
  from pagefind.index import PagefindIndex, IndexConfig

  logging.basicConfig(level=os.environ.get("LOG_LEVEL", "INFO"))
  log = logging.getLogger(__name__)
  html_content = (
      "<html>"
      "  <body>"
      "    <main>"
      "      <h1>Example HTML</h1>"
      "      <p>This is an example HTML page.</p>"
      "    </main>"
      "  </body>"
      "</html>"
  )


  def prefix(pre: str, s: str) -> str:
      return pre + s.replace("\n", f"\n{pre}")


  async def main():
      config = IndexConfig(
          root_selector="main", logfile="index.log", output_path="./output", verbose=True
      )
      async with PagefindIndex(config=config) as index:
          log.debug("opened index")
          new_file, new_record, new_dir = await asyncio.gather(
              index.add_html_file(
                  content=html_content,
                  url="https://example.com",
                  source_path="other/example.html",
              ),
              index.add_custom_record(
                  url="/elephants/",
                  content="Some testing content regarding elephants",
                  language="en",
                  meta={"title": "Elephants"},
              ),
              index.add_directory("./public"),
          )
          print(prefix("new_file    ", json.dumps(new_file, indent=2)))
          print(prefix("new_record  ", json.dumps(new_record, indent=2)))
          print(prefix("new_dir     ", json.dumps(new_dir, indent=2)))

          files = await index.get_files()
          for file in files:
              print(prefix("files", f"{len(file['content']):10}B {file['path']}"))


  if __name__ == "__main__":
      asyncio.run(main())

  ```
  <!-- [[[end]]] -->


pkgId: ac2ad4a1feb5eccd073884e8467bca42b99109438a6ac386756fab19f0b63e08
name: python-paibox-help
arch: noarch
version: 1.2.0
epoch: 0
release: 1
summary: Development documents and examples for paibox
url: https://github.com/PAICookers/PAIBox
rpm_license: GPL-3.0-or-later
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-paibox-1.2.0-1.src.rpm
location_href: Packages/python-paibox-help-1.2.0-1.noarch.rpm
checksum_type: sha256
description:
  <div align="center">



  </div>

  <p align="center">
      <a href="https://github.com/PAICookers/PAIBox/blob/master/pyproject.toml">
          <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/paibox">
      </a>
      <a href="https://pypi.org/project/paibox/">
          <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/paibox?color=pink">
      </a>
      <a href="https://www.codefactor.io/repository/github/PAICookers/PAIBox">
          <img alt="CodeFactor Grade" src="https://img.shields.io/codefactor/grade/github/PAICookers/PAIBox?color=orange">
      </a>
      <a href="https://results.pre-commit.ci/latest/github/PAICookers/PAIBox/master">
          <img alt="pre-commit.ci status" src="https://results.pre-commit.ci/badge/github/PAICookers/PAIBox/master.svg">
      </a>
      <a href="https://codecov.io/gh/PAICookers/PAIBox" >
          <img src="https://codecov.io/gh/PAICookers/PAIBox/branch/master/graph/badge.svg?token=949SKVGRMC"/>
      </a>
  </p>

  👉 [用户使用指南](docs/Guide-of-PAIBox.md)

  👉 [支持算子](docs/Support-Ops.md)

  👉 [高效编写测试项目指南](docs/Guide-of-Test.md)

  👉 [Changelog](./CHANGELOG.md)


pkgId: 1d3332f52cc65516b0b1c73f1ba3ab3ca0c0f1b83e68a799aeb5eabf085d59f0
name: python-palom-help
arch: noarch
version: 2024.12.1
epoch: 0
release: 1
summary: Development documents and examples for palom
url: https://pypi.org/project/palom/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-palom-2024.12.1-1.src.rpm
location_href: Packages/python-palom-help-2024.12.1-1.noarch.rpm
checksum_type: sha256
description:
  Installing palom in a fresh conda environment is recommended. [Instruction for
  installing miniconda](https://docs.conda.io/en/latest/miniconda.html)

  Create a named conda environment - _palom_, in the following example, and activate the environment.
  ```
  conda create -n palom python=3.10 pip -c conda-forge
  conda activate palom
  ```

  Install openslide in the conda environment.
  - For MacOS, use conda-forge channel
  ```
  conda install openslide -c conda-forge
  ```
  - For Windows and Linux, use sdvillal channel
  ```
  conda install openslide -c sdvillal
  ```

  Install palom from pypi in the conda environment.
  ```
  python -m pip install palom


pkgId: 57216ef4f3cfe8fe4c54030f54a290ded7f67357d5494aa599cca3abca622305
name: python-pami-help
arch: noarch
version: 2024.12.20.1
epoch: 0
release: 1
summary: Development documents and examples for pami
url: https://github.com/udayLab/PAMI
rpm_license: GPLv3
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pami-2024.12.20.1-1.src.rpm
location_href: Packages/python-pami-help-2024.12.20.1-1.noarch.rpm
checksum_type: sha256
description:
  ![PyPI](https://img.shields.io/pypi/v/PAMI)
  ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/PAMI)
  [![GitHub license](https://img.shields.io/github/license/UdayLab/PAMI)](https://github.com/UdayLab/PAMI/blob/main/LICENSE)
  ![PyPI - Implementation](https://img.shields.io/pypi/implementation/PAMI)
  [![Documentation Status](https://readthedocs.org/projects/pami-1/badge/?version=latest)](https://pami-1.readthedocs.io/en/latest/?badge=latest)
  ![PyPI - Wheel](https://img.shields.io/pypi/wheel/PAMI)
  ![PyPI - Status](https://img.shields.io/pypi/status/PAMI)
  [![GitHub issues](https://img.shields.io/github/issues/UdayLab/PAMI)](https://github.com/UdayLab/PAMI/issues)
  [![GitHub forks](https://img.shields.io/github/forks/UdayLab/PAMI)](https://github.com/UdayLab/PAMI/network)
  [![GitHub stars](https://img.shields.io/github/stars/UdayLab/PAMI)](https://github.com/UdayLab/PAMI/stargazers)
  [![Downloads](https://static.pepy.tech/badge/pami)](https://pepy.tech/project/pami)
  [![Downloads](https://static.pepy.tech/badge/pami/month)](https://pepy.tech/project/pami)
  [![Downloads](https://static.pepy.tech/badge/pami/week)](https://pepy.tech/project/pami)
  [![pages-build-deployment](https://github.com/UdayLab/PAMI/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/UdayLab/PAMI/actions/workflows/pages/pages-build-deployment)
  [![Dependabot Updates](https://github.com/UdayLab/PAMI/actions/workflows/dependabot/dependabot-updates/badge.svg)](https://github.com/UdayLab/PAMI/actions/workflows/dependabot/dependabot-updates)
  [![CodeQL](https://github.com/UdayLab/PAMI/actions/workflows/github-code-scanning/codeql/badge.svg)](https://github.com/UdayLab/PAMI/actions/workflows/github-code-scanning/codeql)

  [Click here for more information](https://pepy.tech/project/pami)


  ***



  - [Introduction](#introduction)
  - [Development process](#process-flow-chart)
  - [Inputs and outputs of a PAMI algorithm](#inputs-and-outputs-of-an-algorithm-in-pami)
  - [Recent updates](#recent-updates)
  - [Features](#features)
  - [Maintenance](#Maintenance)
  - [Try your first PAMI program](#try-your-first-PAMI-program)
  - [Evaluation](#evaluation)
  - [Reading Material](#Reading-Material)
  - [License](#License)
  - [Documentation](#Documentation)
  - [Background](#Background)
  - [Getting Help](#Getting-Help)
  - [Discussion and Development](#Discussion-and-Development)
  - [Contribution to PAMI](#Contribution-to-PAMI)
  - [Tutorials](#tutorials)
    - [Association rule mining](#0-association-rule-mining)
    - [Mining transactional databases](#1-pattern-mining-in-binary-transactional-databases)
    - [Mining temporal databases](#2-pattern-mining-in-binary-temporal-databases)
    - [Mining spatiotemporal databases](#3-mining-patterns-from-binary-geo-referenced-or-spatiotemporal-databases)
    - [Mining utility databases](#4-mining-patterns-from-utility-or-non-binary-databases)
    - [Mining fuzzy databases](#5-mining--patterns-from-fuzzy-transactionaltemporalgeo-referenced-databases)
    - [Mining uncertain databases](#6-mining-patterns-from-uncertain-transactionaltemporalgeo-referenced-databases)
    - [Mining sequence databases](#7-mining-patterns-from-sequence-databases)
    - [Mining multiple timeseries](#8-mining-patterns-from-multiple-timeseries-databases)
    - [Mining streams](#9-mining-interesting-patterns-from-streams)
    - [Mining character sequences](#10-mining-patterns-from-contiguous-character-sequences-eg-dna-genome-and-game-sequences)
    - [Mining graphs](#11-mining-patterns-from-graphs)
    - [Additional features](#12-additional-features)
      - [Synthetic data generator](#121-creation-of-synthetic-databases)
      - [Dataframes to databases](#122-converting-a-dataframe-into-a-specific-database-type)
      - [Gathering database statistics](#123-gathering-the-statistical-details-of-a-database)
  - [Real-World Case Studies](#real-world-case-studies)


  ***


  PAttern MIning (PAMI) is a Python library containing several algorithms to discover user interest-based patterns in a wide-spectrum of datasets across multiple computing platforms. Useful links to utilize the services of this library were provided below:

  1. Youtube tutorial https://www.youtube.com/playlist?list=PLKP768gjVJmDer6MajaLbwtfC9ULVuaCZ

  2. Tutorials (Notebooks) https://github.com/UdayLab/PAMI/tree/main/notebooks

  3. User manual https://udaylab.github.io/PAMI/manuals/index.html

  4. Coders manual https://udaylab.github.io/PAMI/codersManual/index.html

  5. Code documentation https://pami-1.readthedocs.io

  6. Datasets   https://u-aizu.ac.jp/~udayrage/datasets.html

  7. Discussions on PAMI usage https://github.com/UdayLab/PAMI/discussions

  8. Report issues https://github.com/UdayLab/PAMI/issues

  ***


  ![PAMI's production process](./images/pamiDevelopmentSteps.png?raw=true)

  <!--- ![alt text](https://github.com/[username]/[reponame]/blob/[branch]/image.jpg?raw=true) --->
  ***


  ![Inputs and Outputs](./images/inputOutputPAMIalgo.png?raw=true)
  ***


  - **Version 2024.07.02:**
  In this latest version, the following updates have been made:
    - Included one new algorithms, **PrefixSpan**, for Sequential Pattern.
    - Optimized the following pattern mining algorithms: **PFPGrowth, PFECLAT, GPFgrowth and PPF_DFS**.
    - Test cases are implemented for the following algorithms, **Contiguous Frequent patterns, Correlated Frequent Patterns, Coverage Frequent Patterns, Fuzzy Correlated Frequent Patterns, Fuzzy Frequent Patterns, Fuzzy Georeferenced Patterns, Georeferenced Frequent Patterns, Periodic Frequent Patterns, Partial Periodic Frequent Patterns, HighUtility Frequent Patterns, HighUtility Patterns, HighUtility Georeferenced Frequent Patterns, Frequent Patterns, Multiple Minimum Frequent Patterns, Periodic Frequent Patterns, Recurring Patterns, Sequential Patterns, Uncertain Frequent Patterns, Weighted Uncertain Frequent Patterns**.
    - The algorithms mentioned below are automatically tested, **Frequent Patterns, Correlated Frequent Patterns, Contiguous Frequent patterns, Coverage Frequent Patterns, Recurring Patterns, Sequential Patterns**.

  Total number of algorithms: 89

  ***


  - ✅ Tested to the best of our possibility
  - 🔋 Highly optimized to our best effort, light-weight, and energy-efficient
  - 👀 Proper code documentation
  - 🍼 Ample examples of using various algorithms at [./notebooks](https://github.com/UdayLab/PAMI/tree/main/notebooks) folder
  - 🤖 Works with AI libraries such as TensorFlow, PyTorch, and sklearn.
  - ⚡️ Supports Cuda and PySpark
  - 🖥️ Operating System Independence
  - 🔬 Knowledge discovery in static data and streams
  - 🐎 Snappy
  - 🐻 Ease of use

  ***



    __Installation__

    1. Installing basic pami package (recommended)

           pip install pami

    2. Installing pami package in a GPU machine that supports CUDA

           pip install 'pami[gpu]'

    3. Installing pami package in a distributed network environment supporting Spark

           pip install 'pami[spark]'

    4. Installing pami package for developing purpose

           pip install 'pami[dev]'

    5. Installing complete Library of pami

           pip install 'pami[all]'

    __Upgradation__


          pip install --upgrade pami


    __Uninstallation__


          pip uninstall pami


    __Information__


          pip show pami

  ***


  ```shell
  $ python
  ```

  ```python

  from PAMI.frequentPattern.basic import FPGrowth as alg
  fileURL = "https://u-aizu.ac.jp/~udayrage/datasets/transactionalDatabases/Transactional_T10I4D100K.csv"
  minSup=300
  obj = alg.FPGrowth(iFile=fileURL, minSup=minSup, sep='\t')

  obj.mine()
  obj.save('frequentPatternsAtMinSupCount300.txt')
  frequentPatternsDF= obj.getPatternsAsDataFrame()
  print('Total No of patterns: ' + str(len(frequentPatternsDF))) #print the total number of patterns
  print('Runtime: ' + str(obj.getRuntime())) #measure the runtime
  print('Memory (RSS): ' + str(obj.getMemoryRSS()))
  print('Memory (USS): ' + str(obj.getMemoryUSS()))
  ```

  ```
  Output:
  Frequent patterns were generated successfully using frequentPatternGrowth algorithm
  Total No of patterns: 4540
  Runtime: 8.749667644500732
  Memory (RSS): 522911744
  Memory (USS): 475353088
  ```

  ***



  1. we compared three different Python libraries such as PAMI, mlxtend and efficient-apriori for Apriori.
  2. (Transactional_T10I4D100K.csv)is a transactional database downloaded from PAMI and
  used as an input file for all libraries.
  3. Minimum support values and seperator are also same.

  * The performance of the **Apriori algorithm** is shown in the graphical results below:
  1. Comparing the **Patterns Generated** by different Python libraries for the Apriori algorithm:

     <img width="573" alt="Screenshot 2024-04-11 at 13 31 31" src="https://github.com/vanithakattumuri/PAMI/assets/134862983/fd7974bc-ffe2-44dd-82e3-a5306a8a23bd">

  2. Evaluating the **Runtime** of the Apriori algorithm across different Python libraries:

     <img width="567" alt="Screenshot 2024-04-11 at 13 31 20" src="https://github.com/vanithakattumuri/PAMI/assets/134862983/5d615ae3-dc0d-49ba-a880-4890bb1f11c5">

  3. Comparing the **Memory Consumption** of the Apriori algorithm across different Python libraries:

     <img width="570" alt="Screenshot 2024-04-11 at 13 31 08" src="https://github.com/vanithakattumuri/PAMI/assets/134862983/5d5991ca-51ae-442d-9b5e-2d21bbebfedd">

  For more information, we have uploaded the evaluation file in two formats:
  - One **ipynb** file format, please check it here. [Evaluation File ipynb](https://github.com/UdayLab/PAMI/blob/main/notebooks/Evaluation-neverDelete.ipynb)
  - Two **pdf** file format, check here. [Evaluation File Pdf](https://github.com/UdayLab/PAMI/blob/main/notebooks/evaluation.pdf)

  ***


  For more examples, refer this YouTube link [YouTube](https://www.youtube.com/playlist?list=PLKP768gjVJmDer6MajaLbwtfC9ULVuaCZ)

  ***


  [![GitHub license](https://img.shields.io/github/license/UdayLab/PAMI)](https://github.com/UdayLab/PAMI/blob/main/LICENSE)
  ***



  The official documentation is hosted on [PAMI](https://pami-1.readthedocs.io).
  ***



  The idea and motivation to develop PAMI was from [Kitsuregawa Lab](https://www.tkl.iis.u-tokyo.ac.jp/new/resources?lang=en) at the University of Tokyo. Work on ``PAMI`` started at [University of Aizu](https://u-aizu.ac.jp/en/) in 2020 and
  has been under active development since then.

  ***


  For any queries, the best place to go to is Github Issues [GithubIssues](https://github.com/orgs/UdayLab/discussions/categories/q-a).

  ***


  In our GitHub repository, the primary platform for discussing development-related matters is the university lab. We encourage our team members and contributors to utilize this platform for a wide range of discussions, including bug reports, feature requests, design decisions, and implementation details.

  ***


  We invite and encourage all community members to contribute, report bugs, fix bugs, enhance documentation, propose improvements, and share their creative ideas.

  ***



  | Basic                                                                                                                                                                                                                                                |
  |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | Confidence <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/associationRules/basic/confidence.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
  | Lift <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/associationRules/basic/lift.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>             |
  | Leverage <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/associationRules/basic/leverage.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>     |





  | Basic                                                                                                                                                                                                                                                      | Closed                                                                                                                                                                                                                                       | Maximal                                                                                                                                                                                                                                                     | Top-k                                                                                                                                                                                                                                  | CUDA           | pyspark                                                                                                                                                                                                                                                             |
  |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | Apriori <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/basic/Apriori.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>              | CHARM <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/closed/CHARM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | maxFP-growth  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/maximal/MaxFPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | FAE <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/topk/FAE.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | cudaAprioriGCT | parallelApriori <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/pyspark/parallelApriori.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>   |
  | FP-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/basic/FPGrowth.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>           |                                                                                                                                                                                                                                              |                                                                                                                                                                                                                                                             |                                                                                                                                                                                                                                        | cudaAprioriTID | parallelFPGrowth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/pyspark/parallelFPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | ECLAT  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/basic/ECLAT.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>                 |                                                                                                                                                                                                                                              |                                                                                                                                                                                                                                                             |                                                                                                                                                                                                                                        | cudaEclatGCT   | parallelECLAT <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/pyspark/parallelECLAT.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>       |
  | ECLAT-bitSet <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/basic/ECLATbitset.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>   |                                                                                                                                                                                                                                              |                                                                                                                                                                                                                                                             |                                                                                                                                                                                                                                        |                |                                                                                                                                                                                                                                                                     |
  | ECLAT-diffset <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/frequentPattern/basic/ECLATDiffset.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |                                                                                                                                                                                                                                              |                                                                                                                                                                                                                                                             |                                                                                                                                                                                                                                        |                |



  | Basic                                                                                                                                                                                                                                                          |
  |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | RSFP-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/relativeFrequentPattern/basic/RSFPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                                                     |
  |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | CFPGrowth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>       |
  | CFPGrowth++ <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/multipleMinimumFrequentPatterns/basic/CFPGrowthPlus.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                                 |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | CoMine <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/correlatedPattern/basic/CoMine.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>       |
  | CoMine++ <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/correlatedPattern/basic/CoMinePuls.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |



  | Basic                                                                                                                                                                                                                                                                                   |
  |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | FTApriori <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/faultTolerantFrequentPatterns/basic/FTApriori.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>                       |
  | FTFPGrowth (under development) <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/faultTolerantFrequentPatterns/basic/FTFPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |



  | Basic                                                                                                                                                                                                                                             |
  |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | CMine <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/coveragePattern/basic/CMine.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>       |
  | CMine++ <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/coveragePattern/basic/CMinePlus.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |





  | Basic                                                                                                                                                                                                                                                              | Closed                                                                                                                                                                                                                                                  | Maximal                                                                                                                                                                                                                                                           | Top-K                                                                                                                                                                                                                                                      |
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | PFP-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/basic/PFPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>       | CPFP <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/closed/CPFPMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | maxPF-growth<a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/maximal/MaxPFGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | kPFPMiner <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/topk/kPFPMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | PFP-growth++ <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/basic/PFPGrowthPlus.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |                                                                                                                                                                                                                                                         | Topk-PFP<a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/topk/TopKPFP.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>            |
  | PS-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/basic/PSGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>         |                                                                                                                                                                                                                                                         |                                                                                                                                                                                                                                                                   |
  | PFP-ECLAT <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/basic/PFECLAT.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>           |                                                                                                                                                                                                                                                         |                                                                                                                                                                                                                                                                   |
   | PFPM-Compliments <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicFrequentPattern/basic/PFPMC.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>     |                                                                                                                                                                                                                                                         |                                                                                                                                                                                                                                                                   |



  | Basic                                                                                                                                                                                                                                                                             |
  |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | LPPGrowth  (under development) <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/localPeriodicPattern/basic/LPPgrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>     |
  | LPPMBreadth (under development) <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/localPeriodicPattern/basic/LPPM_breadth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | LPPMDepth   (under development) <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/localPeriodicPattern/basic/LPPM_depth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>   |



  | Basic                                                                                                                                                                                                                                                               |
  |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | GPF-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicFrequentPattern/basic/GPFgrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | PPF-DFS <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicFrequentPattern/basic/PPF_DFS.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>      |
  | GPPF-DFS <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicFrequentPattern/basic/GPFgrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>      |




  | Basic                                                                                                                                                                                                                                                           | Closed                                                                                                                                                                                                                                                    | Maximal                                                                                                                                                                                                                                                           | topK                                                                                                                                                                                                                                                                | CUDA                                                                                                                                                                                                                                                                            |
  |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | 3P-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPattern/basic/PPPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>      | 3P-close <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPattern/closed/PPPClose.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | max3P-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPattern/maximal/Max3PGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | topK-3P growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPattern/topk/Topk_PPPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | cuGPPMiner (under development) <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPattern/cuda/cuGPPMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |                                                                                                                                                                                                                                  |                                                                                                                                  |               | |
  | 3P-ECLAT <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPattern/basic/PPP_ECLAT.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>       |                                                                                                                                                                                                                                                           |                                                                                                                                                                                                                                                                   |                                                                                                                                                                                                                                                                     | gPPMiner (under development)  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPattern/cuda/gPPMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>    |
  | G3P-Growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPattern/basic/GThreePGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |                                                                                                                                                                                                                                                           |                                                                                                                                                                                                                                                                   |                                                                                                                                                                                                                                                                     |                                                                                                                                                                                                                                                                                 |




  | Basic                                                                                                                                                                                                                                                            |
  |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | EPCP-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/periodicCorrelatedPattern/basic/EPCPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |



  | Basic                                                                                                                                                                                                                                                           | TopK  |
  |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------|
  | SPP-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/stablePeriodicPatterns/basic/SPPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | TSPIN <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/stablePeriodicPatterns/topk/TSPIN.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>|
  | SPP-ECLAT <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/stablePeriodicPatterns/basic/SPPECLAT.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>   |       |



  | Basic                                                                                                                                                                                                                                               |
  |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | RPgrowth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/recurringPatterns/basic/RPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |






  | Basic                                                                                                                                                                                                                                                                   |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | spatialECLAT  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/georeferencedFrequentPattern/basic/SpatialECLAT.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | FSP-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/georeferencedFrequentPattern/basic/FSPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>       |



  | Basic                                                                                                                                                                                                                                                                    |
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | GPFPMiner <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/georeferencedPeriodicFrequentPattern/basic/GPFPMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | PFS-ECLAT <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/georeferencedPeriodicFrequentPattern/basic/PFS_ECLAT.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | ST-ECLAT <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/georeferencedPeriodicFrequentPattern/basic/STECLAT.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>    |



  | Basic                                                                                                                                                                                                                                                                |
  |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | STECLAT <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/georeferencedPeriodicFrequentPattern/basic/STECLAT.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |






  | Basic    |
  |----------|
  | EFIM  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/highUtilityPattern/basic/EFIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>   |
  | HMiner <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/highUtilityPattern/basic/HMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | UPGrowth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/highUtilityPattern/basic/UPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>|



  | Basic                                                                                                                                                                                                                                                   |
  |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | HUFIM <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/highUtilityFrequentPatterns/basic/HUFIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                                                 |
  |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | SHUFIM <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |



  | Basic                                                                                                                                                                                                                                                    | topk                                                                                                                                                                                                                                                     |
  |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | HDSHIM <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/highUtilitySpatialPattern/basic/HDSHUIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | TKSHUIM <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/highUtilitySpatialPattern/topK/TKSHUIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |
  | SHUIM  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/highUtilitySpatialPattern/basic/SHUIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>   |




  | Basic                                                                                                                                                                                                                                                  |
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | RHUIM <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/relativeHighUtilityPattern/basic/RHUIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                               |
  |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | WFIM  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/weightedFrequentPatterns/basic/WFIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                                          |
  |----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | WFRIMiner <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/weightedFrequentRegularPatterns/basic/WFRI.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic       |
  |-------------|
  | SSWFPGrowth |




  | Basic                                                                                                                                                                                                                                                   |
  |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | FFI-Miner <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/fuzzyFrequentPattern/basic/FFIMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                                       |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | FCP-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/fuzzyCorrelatedPattern/basic/FCPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                                                  |
  |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | FFSP-Miner <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/fuzzyGeoreferencedFrequentPattern/basic/FFSPMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |



  | Basic                                                                                                                                                                                                                                                             |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | FPFP-Miner <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/fuzzyPeriodicFrequentPattern/basic/FPFPMiner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |



  | Basic                                                                                                                                                                                                                                                                                                 |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | FGPFP-Miner (under development) <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/fuzzyGeoreferencedPeriodicFrequentPattern/basic/FGPFP_miner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |






  | Basic                                                                                                                                                                                                                                                   | top-k |
  |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------|
  | PUF <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/uncertainFrequentPatterns/basic/PUFGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | TUFP  |
  | TubeP  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/uncertainFrequentPatterns/basic/TubeP.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>  |       |
  | TubeS  <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/uncertainFrequentPatterns/basic/TubeS.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>  |       |
  | UVEclat                                                                                                                                                                                                                                                 |       |



  | Basic                                                                                                                                                                                                                                                                          |
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | UPFP-growth <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>       |
  | UPFP-growth++ <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/uncertainPeriodicFrequentPatterns/basic/UPFPGrowthPlus.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                                 |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | WUFIM <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/uncertainWeightedFrequent/basic/WUFIM.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |





  | Basic                                                                                                                                                                                                                                                       |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | SPADE <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/sequencePatternMining/basic/SPADE.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>           |
  | PrefixSpan <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/sequencePatternMining/basic/prefixSpan.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                                                                         |
  |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | GFSP-Miner (under development)<a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/georeferencedFrequentSequencePattern/basic/GFSP_miner.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |





  | Basic                                                                                                                                                                                                                                                                                             |
  |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | PP-Growth (under development) <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/partialPeriodicPatternInMultipleTimeSeries/basic/PPGrowth.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |



  1. Frequent pattern mining

  | Basic         |
  |---------------|
   | to be written |

  2. High utility pattern mining

  | Basic |
  |-------|
   | HUPMS |






  | Basic                                                                                                                                                                                                                                                                   |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | PositionMining <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/contiguousFrequentPattern/basic/PositionMining.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |




  | Basic                                                                                                                                                                                                                                      | topk                                                                                                                                                                                                                                  |
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
   | Gspan <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/subgraphMining/basic/gspan.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> | TKG <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/subgraphMining/topk/tkg.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |


  | Basic                                                                                                                                                                                                                                                       |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | GTCP<a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/graphTransactionalCoveragePatterns/basic/GTCP.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> |





  | Database type                                                                                                                                                                                                                                                                        |
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | Transactional database <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/extras/syntheticDataGenerators/TransactionalDatabase.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | |
  | Temporal database <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/extras/syntheticDataGenerators/TemporalDatabase.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>           |
  | Utility database (coming soon)                                                                                                                                                                                                                                                       |
  | spatio-transactional database (coming soon)                                                                                                                                                                                                                                          |
  | spatio-temporal database (coming soon)                                                                                                                                                                                                                                               |
  | fuzzy transactional database (coming soon)                                                                                                                                                                                                                                           |
  | fuzzy temporal database (coming soon)                                                                                                                                                                                                                                                |
  | Sequence database generator (coming soon)                                                                                                                                                                                                                                            |



  | Approaches                                                                                                                                                                                                                                                   |
  |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | Dense dataframe to databases <a target="_blank" href="https://colab.research.google.com/github/udayLab/PAMI/blob/main/notebooks/extras/DF2DB/denseDF2DB.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
  | Sparse dataframe to databases (coming soon)                                                                                                                                                                                                                  |



  | Approaches                                                                                                                                                                                                                                                        |
  |-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | Transactional database <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/extras/stats/TransactionalDatabase.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> |
  | Temporal database <a target="_blank" href="https://colab.research.google.com/github/UdayLab/PAMI/blob/main/notebooks/extras/stats/TemporalDatabase.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>           |
  | Utility database (coming soon)                                                                                                                                                                                                                                    |


  | Approaches                 |
  |----------------------------|
  | Subgraphs2FlatTransactions |
  | CSV2Parquet                |
  | CSV2BitInteger             |
  | CSV2Integer                |




  | Approaches               |
  |--------------------------|
  | Latex code (coming soon) |

  ***



  1. Air pollution analytics <a target="_blank" href="https://colab.research.google.com/github/udayLab/PAMI/blob/main/notebooks/airPollutionAnalytics.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>


  [Go to Top](#table-of-contents)


pkgId: 42333af26637edf6d066cd0f189ac39383b14a3c273c2bf417838db9a0d83d12
name: python-pan-chainguard-help
arch: noarch
version: 0.6.0
epoch: 0
release: 1
summary: Development documents and examples for pan-chainguard
url: https://github.com/PaloAltoNetworks/pan-chainguard
rpm_license: ISC
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pan-chainguard-0.6.0-1.src.rpm
location_href: Packages/python-pan-chainguard-help-0.6.0-1.noarch.rpm
checksum_type: sha256
description:
  ``pan-chainguard`` is a Python3 application which uses
  `CCADB data
  <https://www.ccadb.org/resources>`_
  and allows PAN-OS SSL decryption administrators to:


     Authorities in PAN-OS so they can be `preloaded
     <https://wiki.mozilla.org/Security/CryptoEngineering/Intermediate_Preloading>`_
     as device certificates.


pkgId: c28c3af1711166478347f78a0c2c6e7c449a09d3b172ad226d6dba795845bd17
name: python-pan-scm-sdk-help
arch: noarch
version: 0.3.6
epoch: 0
release: 1
summary: Development documents and examples for pan-scm-sdk
url: https://github.com/cdot65/pan-scm-sdk
rpm_license: Apache 2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pan-scm-sdk-0.3.6-1.src.rpm
location_href: Packages/python-pan-scm-sdk-help-0.3.6-1.noarch.rpm
checksum_type: sha256
description:
  We welcome contributions! To contribute:
  1. Fork the repository.
  2. Create a new feature branch (`git checkout -b feature/your-feature`).
  3. Commit your changes (`git commit -m 'Add new feature'`).
  4. Push to your branch (`git push origin feature/your-feature`).
  5. Open a Pull Request.
  Ensure your code adheres to the project's coding standards and includes tests where appropriate.

  This project is licensed under the Apache 2.0 License. See the [LICENSE](./LICENSE) file for details.


pkgId: 79ce950e89783dd750e07ab442906743a312a7ce504ef3d90e63fa1004635837
name: python-pan3d-help
arch: noarch
version: 0.12.2
epoch: 0
release: 1
summary: Development documents and examples for pan3d
url: https://pypi.org/project/pan3d/
rpm_license: Apache Software License
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pan3d-0.12.2-1.src.rpm
location_href: Packages/python-pan3d-help-0.12.2-1.noarch.rpm
checksum_type: sha256
description:
  <img style="display: inline-block;" src="https://media.githubusercontent.com/media/Kitware/pan3d/main/docs/images/kitware.svg" alt="Kitware Logo" href="https://kitware.com" width="100">

  [![Documentation Status][docs-image]][docs-link]
  [![Binder][binder-image]][binder-link]
  [![Build Status][GHAction-image]][GHAction-link]
  [![PyPI Version][pypi-v-image]][pypi-v-link]
  [![License][apache-license-image]][license-link]

  Pan3D aims to be an utility package for viewing and processing a wide variety of multidimensional datasets. Any dataset that can be interpreted with [xarray][xarray-link] can be explored and rendered with Pan3D. [Read The Doc][docs-link] is available for additional information.

  XArray Viewer is a Pan3D application focused on geospatial rendering use cases. This is a graphical user interface leveraging the Pan3D infrastructure to help geospatial scientists explore data.

  ![](images/xr-viewer-esgf-00.jpg)

  For an introduction to this project, check out our [blog post][blog-post-link].




  To install requirements for the Pan3D VTK mesh builder class only:

      pip install pan3d

  To install requirements for the graphical user interface (viewers + explorers):

      pip install "pan3d[viewer]"

  **Optional**: to install requirements for Pangeo and ESGF catalogs, respectively:

      pip install "pan3d[pangeo]"

      pip install "pan3d[esgf]"

  **Recommended**: To install all requirements, including optional packages:

      pip install "pan3d[all]"



  `xr-viewer` is the main entrypoint for launching XArray Viewer locally. Below are some example usages.

  To launch XArray Viewer without a target dataset to browse XArray examples:

      xr-viewer

  To launch XArray Viewer with a local path to a target dataset:

      xr-viewer --xarray-file ./examples/example_dataset.nc

  To launch XArray Viewer with a remote URL to a target dataset:

      xr-viewer --xarray-url https://host.org/link/to/dataset.zarr

  To launch XArray Viewer with a compatible configuration file (see [examples][examples-link]):

      xr-viewer --import-state ./examples/example_config_xarray.json

  To launch the Catalog browser will allow you to query the Pangeo and ESGF Dataset Catalogs (see [Catalogs Tutorial](tutorials/catalogs.md)) depending on the available dependencies:

      xr-catalog

  You may have to install the required dependencies:

      pip install "pan3d[pangeo]"
      pip install "pan3d[esgf]"


  > The `xr-viewer` entrypoint will automatically launch your default browser to open `localhost:8080`.
  >
  > To launch without opening your browser, add the `--server` argument to your command.




  - [How to use XArray Viewer](tutorials/dataset_viewer.md)
  - [XArray Viewer command line](tutorials/command_line.md)
  - [Catalogs Tutorial](tutorials/catalogs.md)
  - [How to use Pan3D in a Jupyter notebook](tutorials/jupyter_notebook.md)



  Pan3D comes with a set of example configuration files and example Jupyter notebooks in the [examples][examples-link] folder. You can checkout the repository to run these locally, or you can use the [Pan3D Binder instance][binder-link] to run these examples.


  <!-- Links -->
  [docs-image]: https://readthedocs.org/projects/pan3d/badge/?version=latest
  [docs-link]: https://pan3d.readthedocs.io/en/latest
  [binder-image]: https://mybinder.org/badge_logo.svg
  [binder-link]: https://mybinder.org/v2/gh/Kitware/pan3d/main?labpath=examples%2Fjupyter
  [GHAction-image]: https://github.com/Kitware/pan3d/workflows/Test/badge.svg
  [GHAction-link]: https://github.com/Kitware/pan3d/actions?query=event%3Apush+branch%3Amain
  [pypi-v-image]: https://img.shields.io/pypi/v/pan3d.svg
  [pypi-v-link]: https://pypi.org/project/pan3d/
  [apache-license-image]: https://img.shields.io/badge/license-Apache%202-blue.svg
  [license-link]: https://raw.githubusercontent.com/Kitware/pan3d/main/LICENSE
  [xarray-link]: https://docs.xarray.dev/en/stable/user-guide/io.html
  [blog-post-link]: https://www.kitware.com/kitware-introduces-pan3d-a-collaborative-interoperable-visualization-tool/
  [examples-link]: https://github.com/Kitware/pan3d/tree/main/examples


pkgId: 4d9adf3cdc7bc131564c9428138e6d56c28742f061b3b6a877e7ddfff37f8388
name: python-pandare
arch: noarch
version: 1.8.56
epoch: 0
release: 1
summary: Python Interface to PANDA
url: https://github.com/panda-re/panda/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pandare-1.8.56-1.src.rpm
location_href: Packages/python-pandare-1.8.56-1.noarch.rpm
checksum_type: sha256
description:
  Overview
  --
  `pandare` is a Python 3 interface to PANDA. With pandare (also called PyPANDA), you can quickly develop plugins
  to analyze behavior of a running system, record a system and analyze replays, or do
  nearly anything you can do using PANDA's C/C++ APIs.
  Autogenerated documentation is available at [docs.panda.re](https://docs.panda.re).
  Installation
  --
  Note the PANDARE package provided on pypi only a binary distribution and will certainly only work on `x86_64` hosts as pre-built PANDA libraries from Ubuntu 20.04 are included.
  Prior to using pandare **you must install some dependencies**, these are fully described in the code's dependencies folder, i.e., [the list for ubuntu:20.04 is here](https://github.com/panda-re/panda/blob/dev/panda/dependencies/ubuntu_20.04_base.txt). On our development systems, we typically find we just need to install:
  ```
  apt install libvdeplug-dev libpng16-16 libsdl2-2.0-0
  ```
  Then simply install with `pip3 install pandare`. From there, you'll be able to use PANDA with:
  ```
  from pandare import Panda
  panda = Panda(generic='i386')
  ```


pkgId: 21f98de54ade83dddde0c370f760f1a1ca7f41041e4fa7602c9e566976d661c8
name: python-pandas-gbq-help
arch: noarch
version: 0.26.0
epoch: 0
release: 1
summary: Development documents and examples for pandas-gbq
url: https://github.com/googleapis/python-bigquery-pandas
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pandas-gbq-0.26.0-1.src.rpm
location_href: Packages/python-pandas-gbq-help-0.26.0-1.noarch.rpm
checksum_type: sha256
description:
  |preview| |pypi| |versions|
  **pandas-gbq** is a package providing an interface to the Google BigQuery API from pandas.
  -  `Library Documentation`_
  -  `Product Documentation`_


pkgId: a383d22baa4cb561da2e19cd19177a7f62857ec20cefee74d7980e7158fd1cc8
name: python-pandas-numpy-lib-help
arch: noarch
version: 0.1
epoch: 0
release: 1
summary: Development documents and examples for pandas-numpy-lib
url: https://github.com/MaheshMakwana787/pandas_numpy_lib
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pandas-numpy-lib-0.1-1.src.rpm
location_href: Packages/python-pandas-numpy-lib-help-0.1-1.noarch.rpm
checksum_type: sha256
description:
  A simple library to combine functionalities of pandas and numpy.



  You can install the library using pip:

  ```bash
  pip install pandas_numpy_lib


pkgId: 4799d74c0b720b8e135b1946141333060775524d3a04508893ad2c113e04ff64
name: python-pandasai-help
arch: noarch
version: 2.4.1
epoch: 0
release: 1
summary: Development documents and examples for pandasai
url: https://pypi.org/project/pandasai/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pandasai-2.4.1-1.src.rpm
location_href: Packages/python-pandasai-help-2.4.1-1.noarch.rpm
checksum_type: sha256
description:
  Or you can ask more complex questions:
  ```python
  agent.chat(
      "What is the total sales for the top 3 countries by sales?"
  )
  ```
  ```
  The total sales for the top 3 countries by sales is 16500.
  ```

  You can also ask PandasAI to generate charts for you:
  ```python
  agent.chat(
      "Plot the histogram of countries showing for each one the gd. Use different colors for each bar",
  )
  ```
  ![Chart](assets/histogram-chart.png?raw=true)

  You can also pass in multiple dataframes to PandasAI and ask questions relating them.
  ```python
  import os
  import pandas as pd
  from pandasai import Agent
  employees_data = {
      'EmployeeID': [1, 2, 3, 4, 5],
      'Name': ['John', 'Emma', 'Liam', 'Olivia', 'William'],
      'Department': ['HR', 'Sales', 'IT', 'Marketing', 'Finance']
  }
  salaries_data = {
      'EmployeeID': [1, 2, 3, 4, 5],
      'Salary': [5000, 6000, 4500, 7000, 5500]
  }
  employees_df = pd.DataFrame(employees_data)
  salaries_df = pd.DataFrame(salaries_data)


  os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
  agent = Agent([employees_df, salaries_df])
  agent.chat("Who gets paid the most?")
  ```
  ```
  Olivia gets paid the most.
  ```
  You can find more examples in the [examples](examples) directory.

  In order to generate the Python code to run, we take some random samples from the dataframe, we randomize it (using random generation for sensitive data and shuffling for non-sensitive data) and send just the randomized head to the LLM.
  If you want to enforce further your privacy you can instantiate PandasAI with `enforce_privacy = True` which will not send the head (but just column names) to the LLM.

  PandasAI is available under the MIT expat license, except for the `pandasai/ee` directory (which has it's [license here](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE) if applicable.
  If you are interested in managed PandasAI Cloud or self-hosted Enterprise Offering, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).

  - [Docs](https://pandas-ai.readthedocs.io/en/latest/) for comprehensive documentation
  - [Examples](examples) for example notebooks
  - [Discord](https://discord.gg/kF7FqH2FwS) for discussion with the community and PandasAI team

  Contributions are welcome! Please check the outstanding issues and feel free to open a pull request.
  For more information, please check out the [contributing guidelines](CONTRIBUTING.md).

  [![Contributors](https://contrib.rocks/image?repo=sinaptik-ai/pandas-ai)](https://github.com/sinaptik-ai/pandas-ai/graphs/contributors)


pkgId: 76f8caf1a7d3c749fbfaa854414a2c1bef0795b46d084ccf7e92f5e5ba4cab54
name: python-pandashare-help
arch: noarch
version: 0.0.1
epoch: 0
release: 1
summary: Development documents and examples for pandashare
url: https://github.com/yourusername/simple_pkg
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pandashare-0.0.1-1.src.rpm
location_href: Packages/python-pandashare-help-0.0.1-1.noarch.rpm
checksum_type: sha256
description:
  Sharing data focused on financial AI.



  You can install this package via pip:

  pip install pandashare




  ```python
  from pandashare import greet

  print(greet('World'))

  import pandashare as ps


pkgId: 423fe4f2284e8b9009737480bcfaf4e5ef288913928ae366196b5ad0ac3830ef
name: python-pandem-help
arch: noarch
version: 0.1.2
epoch: 0
release: 1
summary: Development documents and examples for pandem
url: https://pypi.org/project/pandem/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pandem-0.1.2-1.src.rpm
location_href: Packages/python-pandem-help-0.1.2-1.noarch.rpm
checksum_type: sha256
description:
  [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

  [Documentation here](https://scigem.github.io/pandem/), or compile it yourself following the details below.


  This package can then be installed via `pip install pandem`. If installing from github, try cloning and then running:
  ```
  pip install -e .
  ```
  If you make any changes to the source code, re-run those two lines to have your changes reflected in your installed package.



  If you are interested in converting a dataset from one DEM file format to antoher, you can use the installed script `pandem`, like so:

  ```
  pandem <path_to_source_file> <path_to_output_file>
  ```

  We support the following file formats:
   - `.bz2` ([YADE](https://yade-dem.org/doc/introduction.html#saving-and-loading))
   - `.data` ([MercuryDPM](https://mercurydpm.org/))
   - `.vtk` ([LIGGGHTS](https://www.cfdem.com/media/DEM/docu/liggghts.html))



  We use `sphinx` to manage the docs. Update documentation with:
  ```
  cd docs
  make html
  ```
  Once these are built, you can commit and push the changes to github to have them refreshed on github pages. You can also view them locally.


pkgId: c9e47719e1ac132d687cd210f4622d04d52299dbad964bf9ccafe082f5952656
name: python-panel-neuroglancer-help
arch: noarch
version: 0.1.0
epoch: 0
release: 1
summary: Development documents and examples for panel-neuroglancer
url: https://github.com/panel-extensions/panel-neuroglancer
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-panel-neuroglancer-0.1.0-1.src.rpm
location_href: Packages/python-panel-neuroglancer-help-0.1.0-1.noarch.rpm
checksum_type: sha256
description:
  Use HoloViz [`Panel`](https://panel.holoviz.org/) to integrate [Neuroglancer](https://www.github.com/google/neuroglancer) into Jupyter notebook workflows and applications for scientifically contextualized, reproducible, and extensible workflows involving large volumetric datasets, such as those from electron and fluorescent microscopy. See the [Overview](#overview) below for more explanation.





  Either use `Neuroglancer(source=<URL>)` or just run `Neuroglancer()` and input the URL in the GUI:
  ![](assets/demo_fromurl.png)




  ```python
  viewer = neuroglancer.Viewer()

  with viewer.txn() as s:
      
      s.layers["image"] = neuroglancer.ImageLayer(
          source="precomputed://gs://neuroglancer-janelia-flyem-hemibrain/emdata/clahe_yz/jpeg",
      )
      
      s.layers["segmentation"] = neuroglancer.SegmentationLayer(
          source="precomputed://gs://neuroglancer-janelia-flyem-hemibrain/v1.1/segmentation",
      )
  Neuroglancer(source=viewer, show_state=True)
  ```

  ![](assets/demo_fromviewer.png)



  **Volumetric imaging** refers to techniques that capture data in three dimensions, allowing researchers to visualize and analyze the internal structures of objects, including depth and spatial relationships. Unlike traditional 2D imaging, volumetric imaging provides a more comprehensive view of specimens.

  One of the most powerful volumetric imaging techniques is **electron microscopy** ([EM](https://en.wikipedia.org/wiki/Electron_microscope)). EM uses a beam of electrons to create high-resolution images at the nanometer scale, enabling the exploration of fine structural details of biological specimens such as cells, tissues, and molecular structures. However, handling and visualizing the massive datasets generated by EM poses significant challenges due to their size and complexity.



  [Neuroglancer](https://github.com/google/neuroglancer) by Google is a WebGL-based viewer designed specifically for volumetric data, offering efficient handling of large-scale datasets through data streaming. Its key features include:

  - **Interactive Visualization:** Smooth, real-time navigation through volumetric data.
  - **Customizable Layers:** Support for raw images, segmented regions, and annotations.
  - **Web-Based Interface:** Accessible directly from browsers without the need for specialized software.

  Originally developed for neuroscience, Neuroglancer empowers researchers to explore complex 3D structures by tracing neural pathways, identifying cellular components, and annotating regions of interest.



  While Neuroglancer is a powerful tool for exploring large volumes, it is typically used as a standalone application. Researchers often utilize **Jupyter Notebooks** to conduct reproducible research and combine code, data analysis, and visualizations.

  By integrating Neuroglancer within Jupyter Notebooks using [HoloViz Panel](https://panel.holoviz.org/), researchers can:

  - **Consolidate Workflow:** Keep code, data analysis, and visualization in a single environment.
  - **Enhance Reproducibility:** Share notebooks that include both computational steps and interactive visualizations.
  - **Facilitate Collaboration:** Allow collaborators to interact with the same data and visualizations within the notebook.



  By using HoloViz Panel, we can:

  - Embed the Neuroglancer viewer directly within a notebook cell.
  - Create interactive widgets and controls to manipulate and report the state of the viewer.
  - Combine Neuroglancer views alongside other visualizations.



  - 2024: Chan Zuckerberg Initiative. Learn more in the [grant announcement](https://blog.bokeh.org/announcing-czi-funding-for-bokeh-for-bioscience-5f74426c011a).


pkgId: afae54fba597eab07a98455f02c02b38afafa52cf52d17ed9b3cf95fec7d9ed8
name: python-pangea-client-help
arch: noarch
version: 0.3.2
epoch: 0
release: 1
summary: Development documents and examples for pangea-client
url: https://pangea.foundation
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pangea-client-0.3.2-1.src.rpm
location_href: Packages/python-pangea-client-help-0.3.2-1.noarch.rpm
checksum_type: sha256
description:
  `pangea-client` is a set of libraries for different languages to interface with [Pangea API](https://docs.pangea.foundation/).

  When using `python` the data is delivered in Arrow or JSON, developers are required to provide their own types.

  <br>



  Access to the API via `pangea-client` requires credentials, please [apply for access](https://pangea.foundation/get-access) first.

  Once credentials are issued, they will need to be set in the environment variables.

  The easiest way to use these credentials is to create a `.env` file in the project root folder and populate it like so:

  ```sh
  PANGEA_USERNAME=xxxxx
  PANGEA_PASSWORD=xxxxx
  ```

  <br>



  Checkout the github repository separately for example code.


pkgId: b0235623f153df327f5376e0ffa2592db19cfd2d3c1df46bca693d09394d782d
name: python-pangea-sdk-help
arch: noarch
version: 5.2.0
epoch: 0
release: 1
summary: Development documents and examples for pangea-sdk
url: https://pangea.cloud/docs/sdk/python/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pangea-sdk-5.2.0-1.src.rpm
location_href: Packages/python-pangea-sdk-help-5.2.0-1.noarch.rpm
checksum_type: sha256
description:
  <a href="https://pangea.cloud?utm_source=github&utm_medium=python-sdk" target="_blank" rel="noopener noreferrer">
    <img src="https://pangea-marketing.s3.us-west-2.amazonaws.com/pangea-color.svg" alt="Pangea Logo" height="40" />
  </a>

  <br />

  [![documentation](https://img.shields.io/badge/documentation-pangea-blue?style=for-the-badge&labelColor=551B76)][Documentation]
  [![Discourse](https://img.shields.io/badge/Discourse-4A154B?style=for-the-badge&logo=discourse&logoColor=white)][Discourse]



  A Python SDK for integrating with Pangea services. Supports Python v3.9 and
  above.





  Via pip:

  ```bash
  $ pip3 install pangea-sdk
  ```

  Via poetry:

  ```bash
  $ poetry add pangea-sdk
  ```

  <a name="beta-releases"></a>



  Pre-release versions may be available with the `b` (beta) denotation in the
  version number. These releases serve to preview Beta and Early Access services
  and APIs. Per Semantic Versioning, they are considered unstable and do not carry
  the same compatibility guarantees as stable releases.
  [Beta changelog](https://github.com/pangeacyber/pangea-python/blob/beta/CHANGELOG.md).

  Via pip:

  ```bash
  $ pip3 install pangea-sdk==5.2.0b1
  ```

  Via poetry:

  ```bash
  $ poetry add pangea-sdk==5.2.0b1
  ```



  - [Documentation][]
  - [GA Examples][]
  - [Beta Examples][]

  General usage would be to create a token for a service through the
  [Pangea Console][] and then construct an API client for that respective service.
  The below example shows how this can be done for [Secure Audit Log][] to log a
  simple event:

  ```python
  import os

  from pangea.config import PangeaConfig
  from pangea.services import Audit



  token = os.getenv("PANGEA_AUDIT_TOKEN")
  domain = os.getenv("PANGEA_DOMAIN")
  config = PangeaConfig(domain=domain)


  audit = Audit(token, config)


  response = audit.log(message="Hello, World!")
  ```



  asyncio support is available through the `pangea.asyncio.services` module. The
  previous example may be rewritten to utilize async/await syntax like so:

  ```python
  import asyncio
  import os

  from pangea.asyncio.services import AuditAsync
  from pangea.config import PangeaConfig



  token = os.getenv("PANGEA_AUDIT_TOKEN")
  domain = os.getenv("PANGEA_DOMAIN")
  config = PangeaConfig(domain=domain)


  audit = AuditAsync(token, config=config)


  async def main():
      
      response = await audit.log(message="Hello, World!")

      await audit.close()


  if __name__ == "__main__":
      asyncio.run(main())
  ```



  The Python Pangea SDK also includes some extra features to validate Audit Service log's integrity. Here we explain how to run them.



  Verify that an event or a list of events has not been tampered with. Usage:

  ```
  usage: python -m pangea.verify_audit [-h] [--file PATH]
  or
  usage: poetry run python -m pangea.verify_audit [-h] [--file PATH]

  Pangea Audit Verifier

  options:
    -h, --help            show this help message and exit
    --file PATH, -f PATH  Input file (default: standard input).
  ```

  It accepts multiple file formats:
  - a Verification Artifact from the Pangea User Console
  - a search response from the REST API:

  ```bash
  $ curl -H "Authorization: Bearer ${PANGEA_TOKEN}" -X POST -H 'Content-Type: application/json'  --data '{"verbose": true}' https://audit.aws.us.pangea.cloud/v1/search
  ```




  Download all audit logs for a given time range. Start and end date should be provided,
  a variety of formats is supported, including ISO-8601. The result is stored in a
  json file (one json per line).

  ```
  usage: python -m pangea.dump_audit [-h] [--token TOKEN] [--domain DOMAIN] [--output OUTPUT] start end
  or
  usage: poetry run python -m pangea.dump_audit [-h] [--token TOKEN] [--domain DOMAIN] [--output OUTPUT] start end

  Pangea Audit Dump Tool

  positional arguments:
    start                 Start timestamp. Supports a variety of formats, including ISO-8601. e.g.: 2023-06-05T18:05:15.030667Z
    end                   End timestamp. Supports a variety of formats, including ISO-8601. e.g.: 2023-06-05T18:05:15.030667Z

  options:
    -h, --help            show this help message and exit
    --token TOKEN, -t TOKEN
                          Pangea token (default: env PANGEA_TOKEN)
    --domain DOMAIN, -d DOMAIN
                          Pangea base domain (default: env PANGEA_DOMAIN)
    --output OUTPUT, -o OUTPUT
                          Output file name. Default: dump-<timestamp>
  ```



  This script performs extensive verification on a range of events of the log stream. Apart from verifying the hash
  and the membership proof, it checks that there are no omissions in the stream, i.e. all the events are present and properly located.

  ```
  usage: python -m pangea.deep_verify [-h] [--token TOKEN] [--domain DOMAIN] --file FILE
  or
  usage: poetry run python -m pangea.deep_verify [-h] [--token TOKEN] [--domain DOMAIN] --file FILE

  Pangea Audit Event Deep Verifier

  options:
    -h, --help            show this help message and exit
    --token TOKEN, -t TOKEN
                          Pangea token (default: env PANGEA_TOKEN)
    --domain DOMAIN, -d DOMAIN
                          Pangea base domain (default: env PANGEA_DOMAIN)
    --file FILE, -f FILE  Event input file. Must be a collection of JSON Objects separated by newlines
  ```

  It accepts multiple file formats:
  - a Verification Artifact from the Pangea User Console
  - a file generated by the `dump_audit` command
  - a search response from the REST API (see `verify_audit`)



     [Documentation]: https://pangea.cloud/docs/sdk/python/
     [GA Examples]: https://github.com/pangeacyber/pangea-python/tree/main/examples
     [Beta Examples]: https://github.com/pangeacyber/pangea-python/tree/beta/examples
     [Pangea Console]: https://console.pangea.cloud/
     [Discourse]: https://l.pangea.cloud/Jd4wlGs
     [Secure Audit Log]: https://pangea.cloud/docs/audit


pkgId: eda313449bb94bb48c9188e6bcd52e5369c7204e1eb4eeb599dcf7f1a47d2847
name: python-panther-net-help
arch: noarch
version: 0.0.1
epoch: 0
release: 1
summary: Development documents and examples for panther-net
url: https://pypi.org/project/panther-net/
rpm_license: MIT
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-panther-net-0.0.1-1.src.rpm
location_href: Packages/python-panther-net-help-0.0.1-1.noarch.rpm
checksum_type: sha256
description:
  - **Extensible Plugin Architecture**: Easily add new implementations, protocols, and environments.
  - **Dynamic Configuration**: Configure experiments using YAML files with structured validation.
  - **Docker Integration**: Seamless environment setup with dynamically built Docker images.
  - **Comprehensive Logging**: Debug and trace experiments with detailed logs.


pkgId: 2d51e1681851b4dc9604683ff7d1d231b264cd5b6d04da9fc29cf7aec73efd0d
name: python-paper-rq-help
arch: noarch
version: 0.8.4
epoch: 0
release: 1
summary: Development documents and examples for paper-rq
url: https://github.com/dldevinc/paper-rq
rpm_license: BSD license
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-paper-rq-0.8.4-1.src.rpm
location_href: Packages/python-paper-rq-help-0.8.4-1.noarch.rpm
checksum_type: sha256
description:
  An administrative interface for managing RQ tasks in Paper Admin.

  ⚠ Default `rq.scheduler` is not supported! Use [rq-scheduler](https://github.com/rq/rq-scheduler) instead.

  [![PyPI](https://img.shields.io/pypi/v/paper-rq.svg)](https://pypi.org/project/paper-rq/)
  [![Build Status](https://github.com/dldevinc/paper-admin/actions/workflows/release.yml/badge.svg)](https://github.com/dldevinc/paper-rq)
  [![Software license](https://img.shields.io/pypi/l/paper-rq.svg)](https://pypi.org/project/paper-rq/)



  -   [`paper-admin`](https://github.com/dldevinc/paper-admin) >= 6.0
  -   [`django-rq`](https://github.com/rq/django-rq) >= 2.4
  -   `python` >= 3.7



  Install the latest release with pip:

  ```shell
  pip install paper-rq
  ```

  Add `paper_rq` to your INSTALLED_APPS in django's `settings.py`:

  ```python
  INSTALLED_APPS = (
      
      "paper_rq",
  )
  ```

  Add `paper_rq` to your `PAPER_MENU`:

  ```python
  from paper_admin.menu import Item

  PAPER_MENU = [
      
      Item(
          app="paper_rq",
          icon="bi-clock-history",
      ),
      
  ]
  ```

  Optionally, set `paper_rq.jobs.Job` as your job class:

  ```python
  RQ = {
      "JOB_CLASS": "paper_rq.jobs.Job",
      
  }
  ```



  [![4d17958f25.png](https://i.postimg.cc/mgzCsHVG/4d17958f25.png)](https://postimg.cc/tsbYd7Lr)



  The same as RQ's `job` decorator, but it automatically works out
  the `connection` argument from RQ_QUEUES. It also respects the
  `RQ.DEFAULT_RESULT_TTL` and `RQ.DEFAULT_FAILURE_TTL` settings.

  Example:
  ```python
  import time

  from paper_rq.decorators import job


  @job("paper:default")
  def sleep(delay):
      time.sleep(delay)
  ```

  ```python
  sleep.delay(5)
  ```



  First, ensure that you have the `rq-scheduler` library installed:

  ```shell
  pip install rq-scheduler
  ```

  If you need to run multiple isolated schedulers on the same server, you should
  use the class `paper_rq.scheduler.Scheduler`. This class reads the Redis keys
  from the `RQ` settings:

  ```python


  RQ = {
      "JOB_CLASS": "paper_rq.jobs.Job",
      "DEFAULT_RESULT_TTL": "7d",
      "DEFAULT_FAILURE_TTL": "30d",
      "SCHEDULER_CLASS": "paper_rq.scheduler.Scheduler",
      "SCHEDULER_LOCK_KEY": "rq:scheduler-1:scheduler_lock",
      "SCHEDULER_JOBS_KEY": "rq:scheduler-1:scheduled_jobs",
  }
  ```


pkgId: d9605e7c2c6e52f5bca4358b23b5b7dd45b463e317ecb373a3846d03ae713d75
name: python-paquo-help
arch: noarch
version: 0.8.2
epoch: 0
release: 1
summary: Development documents and examples for paquo
url: https://github.com/bayer-science-for-a-better-life/paquo
rpm_license: GPLv3
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-paquo-0.8.2-1.src.rpm
location_href: Packages/python-paquo-help-0.8.2-1.noarch.rpm
checksum_type: sha256
description:
  [![PyPI Version](https://img.shields.io/pypi/v/paquo)](https://pypi.org/project/paquo/)
  [![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/paquo?label=conda)](https://anaconda.org/conda-forge/paquo)
  [![Read the Docs](https://img.shields.io/readthedocs/paquo)](https://paquo.readthedocs.io)
  [![paquo ci](https://github.com/bayer-science-for-a-better-life/paquo/actions/workflows/run_pytests.yaml/badge.svg?branch=main)](https://github.com/bayer-science-for-a-better-life/paquo/actions/workflows/run_pytests.yaml)
  [![Codecov](https://img.shields.io/codecov/c/github/bayer-science-for-a-better-life/paquo)](https://codecov.io/gh/bayer-science-for-a-better-life/paquo)
  [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/paquo)](https://github.com/bayer-science-for-a-better-life/paquo)
  [![GitHub issues](https://img.shields.io/github/issues/bayer-science-for-a-better-life/paquo)](https://github.com/bayer-science-for-a-better-life/paquo/issues)

  Welcome to `paquo` :wave:, a library for interacting with [QuPath](https://qupath.github.io/)
  from [Python](https://www.python.org/).

  `paquo`'s goal is to provide a pythonic interface to important features of
  QuPath, and to make creating and working with QuPath projects intuitive for
  Python programmers.

  We strive to make your lives as easy as possible: If `paquo` is not pythonic,
  unintuitive, slow or if its documentation is confusing, it's a bug in
  `paquo`. Feel free to report any issues or feature requests in the issue
  tracker!

  Development
  [happens on GitHub](https://github.com/bayer-science-for-a-better-life/paquo)
  :octocat:



  You can find `paquo`'s documentation at
  [paquo.readthedocs.io](https://paquo.readthedocs.io) :heart:



  paquo's stable releases can be installed via `pip`:

  ```bash
  pip install paquo
  ```

  or via `conda`:

  ```bash
  conda install -c conda-forge paquo
  ```



  After installing, paquo requires a QuPath installation to run. To get QuPath follow the
  [installation instructions](https://qupath.readthedocs.io/en/stable/docs/intro/installation.html).
  If you choose the default installation paths paquo should autodetect your QuPath.

  Or you can run the following command to download a specific version of QuPath
  to a location on your machine. Follow the printed instructions to configure
  paquo to use that version. Currently, paquo supports every version of QuPath from
  `0.2.0` to the most recent. _(We even support older `0.2.0-mX` versions but no guarantees)._

  ```shell
  > paquo get_qupath --install-path "/some/path/on/your/machine" 0.5.0










  /some/path/on/your/machine/QuPath-0.5.0
  ```



  1. Install conda and git
  2. Clone paquo `git clone https://github.com/bayer-science-for-a-better-life/paquo.git`
  3. Run `conda env create -f environment.devenv.yml`
  4. Activate the environment `conda activate paquo`

  Note that in this environment `paquo` is already installed in development mode,
  so go ahead and hack.



  - Please follow [pep-8 conventions](https://www.python.org/dev/peps/pep-0008/) but:
    - We allow 120 character long lines (try anyway to keep them short)
  - Please use [numpy docstrings](https://numpydoc.readthedocs.io/en/latest/format.html#docstring-standard).
  - When contributing code, please try to use Pull Requests.
  - tests go hand in hand with modules on `tests` packages at the same level. We use `pytest`.

  You can set up your IDE to help you to adhere to these guidelines.
  <br>
  _([Santi](https://github.com/sdvillal) is happy to help you to set up pycharm in 5 minutes)_



  Build with love by Andreas Poehlmann and Santi Villalba from the _Machine
  Learning Research_ group at Bayer. In collaboration with the _Pathology Lab 2_
  and the _Mechanistic and Toxicologic Pathology_ group.

  `paquo`: copyright 2020 Bayer AG, licensed under [GPL-3.0](https://github.com/bayer-science-for-a-better-life/paquo/blob/master/LICENSE)


pkgId: 8727ebf9e7b7372735fcf6ee6cb69ac63e77c2e6b988a8601e185242b2c22b62
name: python-para-api-security-help
arch: noarch
version: 0.0.2
epoch: 0
release: 1
summary: Development documents and examples for para-api-security
url: https://github.com/paratwin/para_be_common_lib
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-para-api-security-0.0.2-1.src.rpm
location_href: Packages/python-para-api-security-help-0.0.2-1.noarch.rpm
checksum_type: sha256
description:
  Common Utilities Python library for Para Projects



  Create the setup.py file

  Before uploading to PyPI, you need a few tools to package and upload your project. Run these commands to install them:
  ```bash
  pip install setuptools wheel twine
  ```
  Build Your Package
  ```bash
  python setup.py sdist bdist_wheel
  ```
  This will create a dist/ folder that contains the files you need to upload to PyPI. Inside dist/, you should see files like my_package-0.1.0.tar.gz and my_package-0.1.0-py3-none-any.whl.


  Upload Your Package to PyPI
  ```bash
  twine upload dist/*
  ```

  You can install the package with pip:

  ```bash
  pip install my-package
  ```

  Here's an example of how to use the package:
  ```bash
  from my_package import authenticate
  authenticate_user()
  ```


pkgId: 2b3b5c26480ca030fab448016eb7be2f85e800cd5eea4ec56fc591b16fc1e8cb
name: python-paraffin-help
arch: noarch
version: 0.2.1
epoch: 0
release: 1
summary: Development documents and examples for paraffin
url: https://pypi.org/project/paraffin/
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-paraffin-0.2.1-1.src.rpm
location_href: Packages/python-paraffin-help-0.2.1-1.noarch.rpm
checksum_type: sha256
description:
  [![zincware](https://img.shields.io/badge/Powered%20by-zincware-darkcyan)](https://github.com/zincware)
  [![PyPI version](https://badge.fury.io/py/paraffin.svg)](https://badge.fury.io/py/paraffin)
  [![Discord](https://img.shields.io/discord/1034511611802689557)](https://discord.gg/7ncfwhsnm4)



  Paraffin, derived from the Latin phrase `parum affinis` meaning
  `little related`, is a Python package designed to run [DVC](https://dvc.org)
  stages in parallel. While DVC does not currently support this directly, Paraffin
  provides an effective workaround. For more details, refer to the DVC
  documentation on
  [parallel stage execution](https://dvc.org/doc/command-reference/repro#parallel-stage-execution).

  > [!WARNING]
  > `paraffin` is still very experimental.
  > Do not use it for production workflows.



  Install Paraffin via pip:

  ```bash
  pip install paraffin
  ```



  The `paraffin submit` command mirrors `dvc repro`, enabling you to queue and execute your entire pipeline or selected stages with parallelization.
  If no parameters are specified, the entire graph will be queued and executed via `dvc repro --single-item`.

  ```bash
  paraffin submit <stage name> <stage name> ... <stage name>

  paraffin worker --concurrency=4
  ```


  Due to limitations in Celery’s graph handling (see [Celery discussion](https://github.com/celery/celery/discussions/9376)), complete parallelization is not always achievable. Paraffin will display parallel-ready stages in a flowchart format.
  All stages are visualized in a [Mermaid](https://mermaid.js.org/) flowchart.

  ```mermaid
  flowchart TD
          subgraph Level0:1
                  A_X_ParamsToOuts
                  A_X_ParamsToOuts_1
                  A_Y_ParamsToOuts
                  A_Y_ParamsToOuts_1
          end
          subgraph Level0:2
                  A_X_AddNodeNumbers
                  A_Y_AddNodeNumbers
          end
          subgraph Level0:3
                  A_SumNodeAttributes
          end
          Level0:1 --> Level0:2
          Level0:2 --> Level0:3
          subgraph Level1:1
                  B_X_ParamsToOuts
                  B_X_ParamsToOuts_1
                  B_Y_ParamsToOuts
                  B_Y_ParamsToOuts_1
          end
          subgraph Level1:2
                  B_X_AddNodeNumbers
                  B_Y_AddNodeNumbers
          end
          subgraph Level1:3
                  B_SumNodeAttributes
          end
          Level1:1 --> Level1:2
          Level1:2 --> Level1:3
  ```






  To fine-tune execution, you can assign stages to specific Celery queues, allowing you to manage execution across different environments or hardware setups.
  Define queues in a `paraffin.yaml` file:

  ```yaml
  queue:
      "B_X*": BQueue
      "A_X_AddNodeNumbers": AQueue
  ```
  Then, start a worker with specified queues, such as celery (default) and AQueue:
  ```bash
  paraffin worker -q AQueue,celery
  ```
  All `stages` not assigned to a queue in `paraffin.yaml` will default to the `celery` queue.


  > [!TIP]
  > If you are building Python-based workflows with DVC, consider trying
  > our other project [ZnTrack](https://zntrack.readthedocs.io/) for a more
  > Pythonic way to define workflows.


pkgId: 5b88bb82b7ab0bb222bc9f6950219aa79cf568c78660e82c4dc45662e173b969
name: python-param-help
arch: noarch
version: 2.1.1
epoch: 0
release: 1
summary: Development documents and examples for param
url: https://param.holoviz.org/
rpm_license: BSD-3-Clause
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-param-2.1.1-1.src.rpm
location_href: Packages/python-param-help-2.1.1-1.noarch.rpm
checksum_type: sha256
description:
  <img src="https://raw.githubusercontent.com/holoviz/param/main/doc/_static/logo_horizontal.png" width=250>

  |    |    |
  | --- | --- |
  | Build Status | [![Linux/MacOS/Windows Build Status](https://github.com/holoviz/param/workflows/tests/badge.svg)](https://github.com/holoviz/param/actions/workflows/test.yaml)
  | Coverage | [![codecov](https://codecov.io/gh/holoviz/param/branch/main/graph/badge.svg)](https://codecov.io/gh/holoviz/param) ||
  | Latest dev release | [![Github tag](https://img.shields.io/github/v/tag/holoviz/param.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/param/tags) [![dev-site](https://img.shields.io/website-up-down-green-red/https/holoviz-dev.github.io/param.svg?label=dev%20website)](https://holoviz-dev.github.io/param/) |
  | Latest release | [![Github release](https://img.shields.io/github/release/holoviz/param.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/param/releases) [![PyPI version](https://img.shields.io/pypi/v/param.svg?colorB=cc77dd)](https://pypi.python.org/pypi/param) [![param version](https://img.shields.io/conda/v/pyviz/param.svg?colorB=4488ff&style=flat)](https://anaconda.org/pyviz/param) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/param.svg?label=conda%7Cconda-forge&colorB=4488ff)](https://anaconda.org/conda-forge/param) [![defaults version](https://img.shields.io/conda/v/anaconda/param.svg?label=conda%7Cdefaults&style=flat&colorB=4488ff)](https://anaconda.org/anaconda/param) |
  | Python | [![Python support](https://img.shields.io/pypi/pyversions/param.svg)](https://pypi.org/project/param/)
  | Docs | [![gh-pages](https://img.shields.io/github/last-commit/holoviz/param/gh-pages.svg)](https://github.com/holoviz/param/tree/gh-pages) [![site](https://img.shields.io/website-up-down-green-red/https/param.holoviz.org.svg)](https://param.holoviz.org) |
  | Binder | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/holoviz/param/main?labpath=doc) |
  | Support | [![Discourse](https://img.shields.io/discourse/status?server=https%3A%2F%2Fdiscourse.holoviz.org)](https://discourse.holoviz.org/) |

  Param is a library providing Parameters: Python attributes extended to have features such as type and range checking, dynamically generated values, documentation strings, default values, etc., each of which is inherited from parent classes if not specified in a subclass.

  Param contains only two required Python files, with no external dependencies, and is provided freely for both non-commercial and commercial use under a BSD license, so that it can easily be included as part of other projects.

  Please see [param's website](https://param.holoviz.org) for official releases, installation instructions, documentation, and examples.


pkgId: 2634110d739131b3120c1389ed9989a55b8a6f89c700d129d0c7f4094b1b8bf1
name: python-parepy-toolbox-help
arch: noarch
version: 1.0.0
epoch: 0
release: 1
summary: Development documents and examples for parepy-toolbox
url: https://pypi.org/project/parepy-toolbox/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-parepy-toolbox-1.0.0-1.src.rpm
location_href: Packages/python-parepy-toolbox-help-1.0.0-1.noarch.rpm
checksum_type: sha256
description:
  The PAREpy (Probabilistic Approach to Reliability Engineering) framework is a software developed by the research group headed by Professor Wanderlei M. Pereira Junior in the FENG-UFCAT. It is a framework for applying probabilistic concepts to analyze a system containing random variables. The platform is built in Python and can be used in any environment that supports this programming language.

  See [documentation](https://wmpjrufg.github.io/PAREPY/).



  To use the framework in an Python environment, use the following command:

  ```python
  pip install parepy-toolbox

  ```


pkgId: 967bbeec70a77bc1ee1a52eafa97bf1596aa6837f34d0e9b986519a437659f9a
name: python-parfun-help
arch: noarch
version: 7.0.6
epoch: 0
release: 1
summary: Development documents and examples for parfun
url: https://pypi.org/project/parfun/
rpm_license: Apache 2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-parfun-7.0.6-1.src.rpm
location_href: Packages/python-parfun-help-7.0.6-1.noarch.rpm
checksum_type: sha256
description:
  <div align="center">
    <a href="https://github.com/citi">
      <img src="https://github.com/citi.png" alt="Citi" width="80" height="80">
    </a>

    <h3 align="center">Citi/parfun</h3>

    <p align="center">
      Lightweight parallelisation library for Python.
    </p>

    <p align="center">
      <a href="https://citi.github.io/parfun/">
        <img src="https://img.shields.io/badge/read%20our%20documentation-0f1632">
      </a>
      <a href="./LICENSE">
        <img src="https://img.shields.io/github/license/citi/parfun?label=license&colorA=0f1632&colorB=255be3">
      </a>
      <a href="https://pypi.org/project/parfun/">
        <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/parfun?colorA=0f1632&colorB=255be3">
      </a>
      <img src="https://api.securityscorecards.dev/projects/github.com/Citi/parfun/badge">
    </p>
  </div>

  <br />

  Parfun is a lightweight library providing helpers to **make it easy to write and run a Python function in parallel
  and distributed systems**.

  The main feature of the library is its `@parfun` decorator that transparently executes standard Python functions
  following the [map-reduce](https://en.wikipedia.org/wiki/MapReduce) pattern:

  ```Python
  from parfun import parfun
  from parfun.combine.collection import list_concat
  from parfun.partition.api import per_argument
  from parfun.partition.collection import list_by_chunk

  @parfun(
      split=per_argument(
          values=list_by_chunk
      ),
      combine_with=list_concat,
  )
  def list_pow(values: List[float], factor: float) -> List[float]:
      return [v**factor for v in values]
  ```




  * **Provides significant speedups** to existing Python functions
  * **Does not require any deep knowledge of parallel or distributed computing systems**
  * **Automatically estimates the optimal sub-task splitting** (the *partition size*)
  * **Automatically handles data transmission, caching and synchronization**.
  * **Supports various distributed computing backends**, including Python's multiprocessing,
    [Scaler](https://github.com/citi/scaler) or Dask.




  **Parfun efficiently parallelizes short-duration functions**.

  When running a short 0.28-second ML function on an AMD Epyc 7313 16-Cores Processor, Parfun provides an impressive
  **7.4x speedup**. Source code for this experiment [here](benchmarks/california_housing.py).

  ![Benchmark Results](benchmarks/california_housing_results.svg)




  The official documentation is availaible at [citi.github.io/parfun/](https://citi.github.io/parfun/).

  Alternatively, you can build the HTML documentation from the source code:

  ```bash
  cd docs
  pip install -r requirements.txt
  make html
  ```

  The documentation's main page can then ben found at `docs/build/html/index.html`.

  Take a look at our documentation's [quickstart tutorial](https://citi.github.io/parfun/tutorials/quickstart.html) to get
  more examples and a deeper overview of the library.




  Your contributions are at the core of making this a true open source project. Any contributions you make are
  **greatly appreciated**.

  We welcome you to:

  - Fix typos or touch up documentation
  - Share your opinions on [existing issues](https://github.com/citi/parfun/issues)
  - Help expand and improve our library by [opening a new issue](https://github.com/citi/parfun/issues/new)

  Please review our [community contribution guidelines](https://github.com/Citi/.github/blob/main/CONTRIBUTING.md) and
  [functional contribution guidelines](./CONTRIBUTING.md) to get started 👍.




  We are committed to making open source an enjoyable and respectful experience for our community. See
  [`CODE_OF_CONDUCT`](https://github.com/Citi/.github/blob/main/CODE_OF_CONDUCT.md) for more information.




  This project is distributed under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0). See
  [`LICENSE`](./LICENSE) for more information.




  If you have a query or require support with this project, [raise an issue](https://github.com/Citi/parfun/issues).
  Otherwise, reach out to [opensource@citi.com](mailto:opensource@citi.com).


pkgId: 6bef20cea6616b0d23746114c166e922227bc81754ccb72af1de3da88412a29f
name: python-parlant-help
arch: noarch
version: 1.1.0
epoch: 0
release: 1
summary: Development documents and examples for parlant
url: https://pypi.org/project/parlant/
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-parlant-1.1.0-1.src.rpm
location_href: Packages/python-parlant-help-1.1.0-1.noarch.rpm
checksum_type: sha256
description:
  <div align="center">
  <img alt="Parlant Logo" src="https://github.com/emcie-co/parlant/blob/70757094103f6cc50e311edaeb0729e960fbcb56/logo.png" width="125" />
    <h3>Parlant</h3>
    <p>A structured approach to building and guiding customer-facing AI agents</p>
    <p>
      <a href="https://www.parlant.io/" target="_blank">Website</a> |
      <a href="https://www.parlant.io/docs/quickstart/introduction" target="_blank">Introduction</a> |
      <a href="https://www.parlant.io/docs/quickstart/installation" target="_blank">Installation</a> |
      <a href="https://www.parlant.io/docs/tutorial/getting_started/overview" target="_blank">Tutorial</a> |
      <a href="https://www.parlant.io/docs/about" target="_blank">About</a>
    </p>
    <p>
      <a href="https://pypi.org/project/parlant/" alt="Parlant on PyPi"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/parlant"></a>
      <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/parlant">
      <a href="https://opensource.org/licenses/Apache-2.0"><img alt="Apache 2 License" src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" /></a>
      <img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/emcie-co/parlant">
      <a href="https://discord.gg/duxWqxKk6J"><img alt="Discord" src="https://img.shields.io/discord/1312378700993663007?style=flat&logo=discord&logoColor=white&label=discord">
  </a>
    </p>
    <img alt="Parlant Preview" src="https://github.com/emcie-co/parlant/blob/02c0e11116e03f3622077436ce9d61811bceb519/preview.gif" />
  </div>


  Parlant is a structured way to create and manage guided customer-facing AI agents. It lets you build robust agents from scratch; or, in more advanced cases, a robust interaction layer for your prebuilt agents.

  Structure is what lets Parlant get better accuracy than most other agentic frameworks. It allows Parlant to run pinpointed, real-time conformance checks that ensure your agents adhere to your instruction. It also gives you specific feedback on where agents may have misinterpreted your instructions, so you can quickly troubleshoot and improve.

  Just as importantly, Parlant's way of breaking behavioral guidance down to different elements helps it understand your intentions better. Using this understanding, Parlant actively gives you feedback on your configuration to help you maintain a behavioral configuration for your agents that's coherent and free of confusing contradictions.


  Building conversational AI agents is relatively simple for most developers—at least, it's relatively simple to build an initial prototype.

  But these prototypes are rarely ready to meet customers. Once the prototype is functional, it has to be continually tuned so that its behavior actually provides customers with a good experience that business stakeholders approve.

  With DIY prompt-engineering, reliably incorporating feedback from stakeholders is challenging, as simple implementations tend to be fragile and inconsistent.

  Parlant bridges this gap by making it easy and fast for developers to adjust the behavior of AI agents reliably, allowing you to iterate quickly with feedback from customers and business stakeholders.



  [Revenued](https://www.revenued.com), a business capital provider, could get into trouble if their AI agents make false or misleading claims.

  With Parlant, they've been able to quickly integrate feedback from customer service experts and then test and verify that the agents aren't making problematic promises or statements to customers.




  * **Guidelines**: Control responses by writing contextual rules - like "offer limited time coupons if it's a holiday" or "make it very clear we don't offer loans if a customer asks about it". By using condition/action definitions, you define exactly when and how your agent should respond
  * **Glossary**: Teach your agent your business-specific terminology so that both you and your customers can speak to it naturally in your language
  * **Coherence checks**: Catch conflicts by having Parlant evaluate new guidelines against existing ones before they're applied
  * **Dynamic context**: Make your agent context-aware by setting user-specific variables like customer account or subscription tier. These shape how your agent responds to each user
  * **Guided tool use**: Control API access by linking tools to specific guidelines. This way, your agent only calls APIs when appropriate and with clear intent


  * **See changes instantly**: Modify behavior on the fly by updating guidelines directly, no retraining or redeployment needed
  * **Track changes in Git**: Manage agent behavior like code by storing configuration as JSON in your repo. Review, rollback, branch, and merge just like any other code
  * **Clean architecture**: Separate concerns by keeping business logic in tools and conversation patterns in guidelines. Each piece does what it does best
  * **Type safety**: Build rapidly using native TypeScript/JavaScript SDKs with proper type definitions


  * **Reliable at scale**: Parlant filters and selects guidelines per context, allowing you to scale your agent's complexity and use-cases while maintaining consistent, focused behavior
  * **Debug with ease**: Troubleshoot effectively by tracing which guidelines were applied and why for any given response
  * **Test before deploy**: Validate changes using the built-in chat UI to test new behaviors before they reach customers


  - [OpenAI](https://platform.openai.com/docs/overview) (also via [Azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/))
  - [Gemini](https://ai.google.dev/)
  - [Meta Llama 3](https://www.llama.com/) (via [Together AI](https://www.together.ai/) or [Cerebras](https://cerebras.ai/))
  - [Anthropic](https://www.anthropic.com/api) (also via [AWS Bedrock](https://aws.amazon.com/bedrock/))


  ```bash
  $ pip install parlant
  $ parlant-server
  $ # Open http://localhost:8000 and play
  ```

  Install client SDKs:
  ```bash
  $ # For Python clients:
  $ pip install parlant-client
  $ # For TypeScript clients:
  $ npm install parlant-client
  ```

  To start building with Parlant, visit our [documentation portal](https://parlant.io/docs/quickstart/introduction).

  Need help? Send us a message on [Discord](https://discord.gg/duxWqxKk6J). We're happy to answer questions and help you get up and running!


  Adding a guideline for an agent—for example, to ask a counter-question to get more info when a customer asks a question:
  ```bash
  parlant guideline create \
      --agent-id CUSTOMER_SUCCESS_AGENT_ID
      --condition "a free-tier customer is asking how to use our product" \
      --action "first seek to understsand what they're trying to achieve"
  ```

  In Parlant, Customer-Agent interaction happens asynchronously, to enable more natural customer interactions, rather than forcing a strict and unnatural request-reply mode.

  Here's a basic example of a simple client (using the TypeScript client SDK):

  ```typescript
  import { ParlantClient } from 'parlant-client';

  const client = ParlantClient({ environment: SERVER_ADDRESS });

  session_id = "...";

  // Post customer message
  const customerEvent = await client.sessions.createEvent(session_id, {
     kind: "message",
     source: "customer",
     message: "Hey, I'd like to book a room please",
  });

  // Wait for and get the agent's reply
  const [agentEvent] = (await client.sessions.listEvents(session_id, {
     kinds: "message",
     source: "ai_agent",
     minOffset: customerEvent.offset,
     waitForData: 60 // Wait up to 60 seconds for an answer
  }));

  // Print the agent's reply
  const { agentMessage } = agentEvent.data as { message: string };
  console.log(agentMessage);

  // Inspect the details of the message generation process
  const { trace } = await client.sessions.inspectEvent(
     session_id,
     agentEvent.id
  );
  ```


  We're currently finalizing our Contributor License Agreement (CLA). Full contribution guidelines will be available soon!

  Want to get involved? Join us on [Discord](https://discord.gg/duxWqxKk6J) and let's discuss how you can help shape Parlant. We're excited to work with contributors directly while we set up our formal processes!


pkgId: 7e0b6ac8c7aecf421252b9e2ef3647138d9b89f85ed97fc0cc93d02bb70840b7
name: python-parlant-help
arch: noarch
version: 1.2.0
epoch: 0
release: 1
summary: Development documents and examples for parlant
url: https://pypi.org/project/parlant/
rpm_license: Apache-2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-parlant-1.2.0-1.src.rpm
location_href: Packages/python-parlant-help-1.2.0-1.noarch.rpm
checksum_type: sha256
description:
  <div align="center">
  <img alt="Parlant Logo" src="https://github.com/emcie-co/parlant/blob/70757094103f6cc50e311edaeb0729e960fbcb56/logo.png" width="125" />
    <h3>Parlant</h3>
    <p>A structured approach to building and guiding customer-facing AI agents</p>
    <p>
      <a href="https://www.parlant.io/" target="_blank">Website</a> |
      <a href="https://www.parlant.io/docs/quickstart/introduction" target="_blank">Introduction</a> |
      <a href="https://www.parlant.io/docs/quickstart/installation" target="_blank">Installation</a> |
      <a href="https://www.parlant.io/docs/tutorial/getting_started/overview" target="_blank">Tutorial</a> |
      <a href="https://www.parlant.io/docs/about" target="_blank">About</a>
    </p>
    <p>
      <a href="https://pypi.org/project/parlant/" alt="Parlant on PyPi"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/parlant"></a>
      <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/parlant">
      <a href="https://opensource.org/licenses/Apache-2.0"><img alt="Apache 2 License" src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" /></a>
      <img alt="GitHub last commit" src="https://img.shields.io/github/last-commit/emcie-co/parlant">
      <a href="https://discord.gg/duxWqxKk6J"><img alt="Discord" src="https://img.shields.io/discord/1312378700993663007?style=flat&logo=discord&logoColor=white&label=discord">
  </a>
    </p>
    <img alt="Parlant Preview" src="https://github.com/emcie-co/parlant/blob/02c0e11116e03f3622077436ce9d61811bceb519/preview.gif" />
  </div>


  Parlant is a structured way to create and manage guided customer-facing AI agents. It lets you build robust agents from scratch; or, in more advanced cases, a robust interaction layer for your prebuilt agents.

  Structure is what lets Parlant get better accuracy than most other agentic frameworks. It allows Parlant to run pinpointed, real-time conformance checks that ensure your agents adhere to your instruction. It also gives you specific feedback on where agents may have misinterpreted your instructions, so you can quickly troubleshoot and improve.

  Just as importantly, Parlant's way of breaking behavioral guidance down to different elements helps it understand your intentions better. Using this understanding, Parlant actively gives you feedback on your configuration to help you maintain a behavioral configuration for your agents that's coherent and free of confusing contradictions.


  Building conversational AI agents is relatively simple for most developers—at least, it's relatively simple to build an initial prototype.

  But these prototypes are rarely ready to meet customers. Once the prototype is functional, it has to be continually tuned so that its behavior actually provides customers with a good experience that business stakeholders approve.

  With DIY prompt-engineering, reliably incorporating feedback from stakeholders is challenging, as simple implementations tend to be fragile and inconsistent.

  Parlant bridges this gap by making it easy and fast for developers to adjust the behavior of AI agents reliably, allowing you to iterate quickly with feedback from customers and business stakeholders.



  [Revenued](https://www.revenued.com), a business capital provider, uses Parlant to infuse human-like conversations for their Sales Copilot. They leverage Parlant's structured CLI to modify the agent's behavior quickly and easily based on feedback from company stakeholders.




  * **Guidelines**: Control responses by writing contextual rules - like "offer limited time coupons if it's a holiday" or "make it very clear we don't offer loans if a customer asks about it". By using condition/action definitions, you define exactly when and how your agent should respond
  * **Glossary**: Teach your agent your business-specific terminology so that both you and your customers can speak to it naturally in your language
  * **Coherence checks**: Catch conflicts by having Parlant evaluate new guidelines against existing ones before they're applied
  * **Dynamic context**: Make your agent context-aware by setting user-specific variables like customer account or subscription tier. These shape how your agent responds to each user
  * **Guided tool use**: Control API access by linking tools to specific guidelines. This way, your agent only calls APIs when appropriate and with clear intent


  * **See changes instantly**: Modify behavior on the fly by updating guidelines directly, no retraining or redeployment needed
  * **Track changes in Git**: Manage agent behavior like code by storing configuration as JSON in your repo. Review, rollback, branch, and merge just like any other code
  * **Clean architecture**: Separate concerns by keeping business logic in tools and conversation patterns in guidelines. Each piece does what it does best
  * **Type safety**: Build rapidly using native TypeScript/JavaScript SDKs with proper type definitions


  * **Reliable at scale**: Parlant filters and selects guidelines per context, allowing you to scale your agent's complexity and use-cases while maintaining consistent, focused behavior
  * **Debug with ease**: Troubleshoot effectively by tracing which guidelines were applied and why for any given response
  * **Test before deploy**: Validate changes using the built-in chat UI to test new behaviors before they reach customers


  - [OpenAI](https://platform.openai.com/docs/overview) (also via [Azure](https://learn.microsoft.com/en-us/azure/ai-services/openai/))
  - [Gemini](https://ai.google.dev/)
  - [Meta Llama 3](https://www.llama.com/) (via [Together AI](https://www.together.ai/) or [Cerebras](https://cerebras.ai/))
  - [Anthropic](https://www.anthropic.com/api) (also via [AWS Bedrock](https://aws.amazon.com/bedrock/))


  ```bash
  $ pip install parlant
  $ parlant-server
  $ # Open http://localhost:8000 and play
  ```

  Install client SDKs:
  ```bash
  $ # For Python clients:
  $ pip install parlant-client
  $ # For TypeScript clients:
  $ npm install parlant-client
  ```

  To start building with Parlant, visit our [documentation portal](https://parlant.io/docs/quickstart/introduction).

  Need help? Send us a message on [Discord](https://discord.gg/duxWqxKk6J). We're happy to answer questions and help you get up and running!


  Adding a guideline for an agent—for example, to ask a counter-question to get more info when a customer asks a question:
  ```bash
  parlant guideline create \
      --agent-id CUSTOMER_SUCCESS_AGENT_ID \
      --condition "a free-tier customer is asking how to use our product" \
      --action "first seek to understsand what they're trying to achieve"
  ```

  In Parlant, Customer-Agent interaction happens asynchronously, to enable more natural customer interactions, rather than forcing a strict and unnatural request-reply mode.

  Here's a basic example of a simple client (using the TypeScript client SDK):

  ```typescript
  import { ParlantClient } from 'parlant-client';

  const client = ParlantClient({ environment: SERVER_ADDRESS });

  session_id = "...";

  // Post customer message
  const customerEvent = await client.sessions.createEvent(session_id, {
     kind: "message",
     source: "customer",
     message: "Hey, I'd like to book a room please",
  });

  // Wait for and get the agent's reply
  const [agentEvent] = (await client.sessions.listEvents(session_id, {
     kinds: "message",
     source: "ai_agent",
     minOffset: customerEvent.offset,
     waitForData: 60 // Wait up to 60 seconds for an answer
  }));

  // Print the agent's reply
  const { agentMessage } = agentEvent.data as { message: string };
  console.log(agentMessage);

  // Inspect the details of the message generation process
  const { trace } = await client.sessions.inspectEvent(
     session_id,
     agentEvent.id
  );
  ```


  We're currently finalizing our Contributor License Agreement (CLA). Full contribution guidelines will be available soon!

  Want to get involved? Join us on [Discord](https://discord.gg/duxWqxKk6J) and let's discuss how you can help shape Parlant. We're excited to work with contributors directly while we set up our formal processes!


pkgId: 2c7e5ef1a4a98fda019dccc5fa5da286f983e6bd27a8741c0ccbf4645d779606
name: python-partis-pyproj-help
arch: noarch
version: 0.1.5
epoch: 0
release: 1
summary: Development documents and examples for partis-pyproj
url: https://pypi.org/project/partis-pyproj/
rpm_license: None
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-partis-pyproj-0.1.5-1.src.rpm
location_href: Packages/python-partis-pyproj-help-0.1.5-1.noarch.rpm
checksum_type: sha256
description:
  The backend provides a mechanism to perform an arbitrary operation before any
  files are copied into either the source or binary distribution:
  Each hook must be a python module (a directory with an
  ``__init__.py`` file), either directly importable or relative to the 'pyproject.toml'.
  The hook is specified according to the `entry_points` specification, and
  must resolve to a function that takes the instance of the build system and
  a logger.
  Keyword arguments may also be defined to be passed to the function,
  configured in the same section of the 'pyproject.toml'.
  ```toml

  [tool.pyproj.dist.binary.prep]

  entry = "a_custom_prep_module:a_prep_function"
  [tool.pyproj.dist.binary.prep.kwargs]

  a_custom_argument = 'some value'
  ```
  This will be treated by the backend **equivalent to the
  following code** run from the `pyproject.toml` directory:
  ```python

  import a_custom_prep_module
  a_custom_prep_module.a_prep_function(
    backend,
    logger,
    a_custom_argument = 'some value' )
  ```
  > :warning: Only those requirements listed in `build-system.requires`
  > will be importable during `tool.pyproj.prep`, and only those added to
  > `backend.build_requires` will be available in subsequent hooks.


pkgId: 83edfd1448e39817ac7111a9c79e236212ede32f391677ddb69244bbd7323bb9
name: python-pas.plugins.authomatic-help
arch: noarch
version: 1.4.0
epoch: 0
release: 1
summary: Development documents and examples for pas.plugins.authomatic
url: https://github.com/collective/pas.plugins.authomatic
rpm_license: GPL
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pas.plugins.authomatic-1.4.0-1.src.rpm
location_href: Packages/python-pas.plugins.authomatic-help-1.4.0-1.noarch.rpm
checksum_type: sha256
description:
  **pas.plugins.authomatic** provides OAuth2 and OpenID login capability for Plone sites by integrating the awesome [Authomatic](https://authomatic.github.io/authomatic/) package.
  ```
  Authomatic is a framework agnostic library
  for Python web applications
  with a minimalistic but powerful interface
  which simplifies authentication of users
  by third party providers like Facebook or Twitter
  through standards like OAuth and OpenID.
  ```
  *by author Peter Hudec on Authomatic website*


pkgId: 6c89cd2d9a85b13ecb49ab15502849b584a1add2a404ea79aff97eb21974710e
name: python-pasqal-cloud-help
arch: noarch
version: 0.12.6
epoch: 0
release: 1
summary: Development documents and examples for pasqal-cloud
url: https://github.com/pasqal-io/pasqal-cloud
rpm_license: Apache 2.0
rpm_group: Unspecified
rpm_buildhost: localhost
rpm_sourcerpm: python-pasqal-cloud-0.12.6-1.src.rpm
location_href: Packages/python-pasqal-cloud-help-0.12.6-1.noarch.rpm
checksum_type: sha256
description:
  SDK to be used to access Pasqal Cloud Services.



  | Version | Release Date | End of Support |
  | ------- | ------------ | -------------- |
  | 0.5.0   | 2024-02-05   | 2025-02-05     |
  | 0.6.0   | 2024-02-26   | 2025-02-26     |
  | 0.7.0   | 2024-03-05   | 2025-03-05     |
  | 0.8.0   | 2024-04-15   | 2025-04-15     |
  | 0.9.0   | 2024-05-15   | 2025-05-15     |
  | 0.10.0  | 2024-06-05   | 2025-06-05     |
  | 0.11.0  | 2024-06-27   | 2025-06-27     |
  | 0.12.0  | 2024-09-03   | 2025-09-03     |
  | 0.12.1  | 2024-09-11   | 2025-09-11     |
  | 0.12.2  | 2024-09-11   | 2025-09-11     |
  | 0.12.3  | 2024-10-02   | 2025-10-02     |
  | 0.12.4  | 2024-10-09   | 2025-10-09     |
  | 0.12.5  | 2024-11-12   | 2025-11-12     |
  | 0.12.6  | 2024-12-12   | 2025-12-12     |



  To install the latest release of the `pasqal-cloud` (formerly pasqal-sdk), have Python 3.8.0 or higher installed, then
  use pip:

  ```bash
  pip install pasqal-cloud
  ```

  If you wish to **install the development version of the pasqal-cloud from source** instead, do the following from within
  this repository after cloning it:

  ```bash
  git checkout dev
  pip install -e .
  ```

  Bear in mind that this installation will track the contents of your local
  pasqal-cloud repository folder, so if you check out a different branch (e.g. `master`),
  your installation will change accordingly.



  To run the tutorials or the test suite locally, run the following to install the development requirements:

  ```bash
  pip install -e .[dev]
  ```

  We use pre-commit hooks to enforce some code linting, you can install pre-commit with Python pip:

  ```bash
  python3 -m pip install pre-commit
  pre-commit install
  ```





  There are several ways to authenticate using the SDK:

  ```python
  from pasqal_cloud import SDK

  project_id = "your_project_id"  # Replace this value with your project_id on the PASQAL platform. It can be found on the user-portal, in the 'project' section.
  username = "your_username"  # Replace this value with your username or email on the PASQAL platform.
  password = "your_password"  # Replace this value with your password on the PASQAL platform.
  ```



  If you know your credentials, you can pass them to the SDK instance on creation:

  ```python
  sdk = SDK(username=username, password=password, project_id=project_id)
  ```



  If you only want to insert your username, but want a solution to have your password being secret,
  you can run the SDK without a password. A prompt will then ask for your password:

  ```python
  sdk = SDK(username=username, project_id=project_id)
  ```



  You can define a custom class to provide the token.
  For example, if you have a token, you can use it to authenticate with our APIs:

  ```python
  class CustomTokenProvider(TokenProvider):
      def get_token(self):
          return "your-token"  # Replace this value with your token


  sdk = SDK(token_provider=CustomTokenProvider(), project_id=project_id)
  ```

  Alternatively, create a custom `TokenProvider` that inherits from `ExpiringTokenProvider`.
  You should define a custom '\_query_token' method which fetches your token.
  See `Auth0TokenProvider` implementation for an example.



  The package main component is a python object called `SDK` which can be used to create a `Batch`.

  A `Batch` is a group of jobs with the same sequence that will run on the same QPU. For each job of a given batch, you
  must set a value for each variable, if any, defined in your sequence.
  Once the QPU starts running a batch, only the jobs from that batch will be executed until they all end up in a
  termination status (`DONE`, `ERROR`, `CANCELED`).
  The batch sequence can be generated using [Pulser](https://github.com/pasqal-io/Pulser). See
  their [documentation](https://pulser.readthedocs.io/en/stable/),
  for more information on how to install the library and create your own sequence.

  The sequence should be a pulser sequence object. Once it's created, you can serialize like so:

  ```python
  serialized_sequence = sequence.to_abstract_repr()
  ```

  When creating a job, select a number of runs and set the desired values for the variables defined in the sequence:

  ```python
  job1 = {"runs": 20, "variables": {"omega_max": 6}}
  job2 = {"runs": 50, "variables": {"omega_max": 10.5}}
  ```

  Batches can either be "open" or "closed".
  An "open" batch will accept new jobs after its creation, unlike a "closed" batch.
  Open batch may be used to schedule variational algorithm where the next job parameter are derived from the results of
  the previous jobs, without losing access to the QPU.

  You can create a batch of jobs using the `create_batch` method of the SDK.
  By default, this will create a closed batch, so all jobs should be passed as arguments right away.
  You may set the `wait` argument to `True` to wait for all the jobs to end up in a termination status before proceeding
  to the next statement.

  ```python

  batch = sdk.create_batch(serialized_sequence, [job1, job2], wait=True)
  ```

  To create an open batch, set the `open` argument to `True`, you can then add jobs to your batch.
  Don't forget to mark your batch as closed when you are done adding new jobs to it.

  ```python

  batch = sdk.create_batch(serialized_sequence, [job1], open=True)

  job3 = {"runs": 50, "variables": {"omega_max": 10.5}}
  batch.add_jobs([job2, job3], wait=True)


  batch.close()
  ```

  You can also choose to run your batch on an emulator using the optional argument `emulator`.
  For using a basic single-threaded QPU emulator that can go up to 10 qubits, you can specify the "EMU_FREE" emulator:

  ```python
  from pasqal_cloud.device import EmulatorType

  batch = sdk.create_batch(
      serialized_sequence, [job1, job2], emulator=EmulatorType.EMU_FREE
  )
  ```

  Once the API has returned the results, you can access them with the following:

  ```python
  for job in batch.ordered_jobs:
      print(f"job-id: {job.id}, status: {job.status}, result: {job.result}")
  ```



  It is possible to get all batches or a selection of batches with the `get_batches` method.
  The method returns by default a page of the 100 most recent batches but can be
  configured using the `pagination_params` argument.

  Here are few examples of how to use it:

  ```python
  from datetime import datetime
  from pasqal_cloud import BatchFilters, BatchStatus, PaginationParams, QueuePriority, EmulatorType


  sdk.get_batches()


  sdk.get_batches(pagination_params=PaginationParams(limit=40))


  sdk.get_batches(filters=BatchFilters(device_type=EmulatorType.EMU_TN))


  sdk.get_batches(filters=BatchFilters(status=BatchStatus.DONE, project_id="project_id"))


  sdk.get_batches(filters=BatchFilters(id=["batch_id_1", "batch_id_2"]))


  sdk.get_batches(filters=BatchFilters(queue_priority=QueuePriority.HIGH))


  sdk.get_batches(limit=20,
                  filters=BatchFilters(status=BatchStatus.DONE, start_date=datetime(...), end_date=datetime(...),
                                       user_id=["user_id_1", "user_id_2"]))


  sdk.get_batches(pagination_params=PaginationParams(offset=0)).total


  batches = []
  batches.extend(sdk.get_batches(pagination_params=PaginationParams(offset=0)).results)
  batches.extend(sdk.get_batches(pagination_params=PaginationParams(offset=100)).results)
  batches.extend(sdk.get_batches(pagination_params=PaginationParams(offset=200)).results)
  ```



  It is possible to get all jobs or a selection of jobs with the `get_jobs` method.
  The method returns by default a page of the 100 most recent jobs but can be
  configured using the `pagination_params` argument.

  Here are few examples of how to use it:

  ```python
  from datetime import datetime
  from pasqal_cloud import JobFilters, JobStatus, PaginationParams


  sdk.get_jobs()


  sdk.get_jobs(pagination_params=PaginationParams(limit=40))


  sdk.get_jobs(filters=JobFilters(batch_id="batch_id"))


  sdk.get_jobs(filters=JobFilters(status=JobStatus.ERROR, project_id="project_id"))


  sdk.get_jobs(filters=JobFilters(id=["job_id_1", "job_id_2"]))


  sdk.get_jobs(limit=20,
               filters=JobFilters(status=JobStatus.CANCELED, start_date=datetime(...), end_date=datetime(...),
                                  user_id=["user_id_1", "user_id_2"]))


  sdk.get_jobs(pagination_params=PaginationParams(offset=0)).total


  jobs = []
  jobs.extend(sdk.get_jobs(pagination_params=PaginationParams(offset=0)).results)
  jobs.extend(sdk.get_jobs(pagination_params=PaginationParams(offset=100)).results)
  jobs.extend(sdk.get_jobs(pagination_params=PaginationParams(offset=200)).results)

  ```



  It is possible to retry a selection of jobs from a CLOSED batch with the `rebatch` method.

  ```python
  from datetime import datetime
  from pasqal_cloud import RebatchFilters, JobStatus


  sdk.rebatch(batch_id)


  sdk.rebatch(batch_id, RebatchFilters(id=batch.ordered_jobs[0].id))


  sdk.rebatch(batch_id, RebatchFilters(status=JobStatus.ERROR))


  sdk.rebatch(batch_id, RebatchFilters(status=JobStatus.CANCELED, start_date=datetime(...), end_date=datetime(...)))


  sdk.rebatch(batch_id, RebatchFilters(min_runs=5, max_runs=10))
  ```



  It is possible to retry a single job in a same open batch as an original job using `batch.retry`.
  The batch must be open in order for this method to work.

  ```python

  batch = sdk.create_batch(..., open=True)

  batch.retry(batch.ordered_jobs[0])


  batch.retry(batch.ordered_jobs[0], wait=True)
  ```



  It is possible to cancel a list of batches with the `cancel_batches` method by
  providing a list of batch ids.

  ```python
  sdk.cancel_batches(batch_ids=[...])
  ```



  It is possible to cancel a selection of jobs with the `cancel_jobs` method, using the CancelJobFilters class.

  Here are few examples of how to use it:

  ```python
  from datetime import datetime
  from pasqal_cloud import CancelJobFilters


  sdk.cancel_jobs(batch_id="batch_id", filters=CancelJobFilters(id=["job_id_1", "job_id_2"]))


  sdk.cancel_jobs(batch_id="batch_id", filters=CancelJobFilters(start_date=datetime(...), end_date=datetime(...)))
  ```



  A workload is a unit of work to be executed on Pasqal Cloud Services infrastructure.

  To submit a new workload, select a type, target one of the available
  backends and provide a configuration object to execute it.

  You can create a workload through the SDK with the following command:

  ```python
  workload = sdk.create_workload(workload_type="<WORKLOAD_TYPE>", backend="<BACKEND>", config={"config_param_1": "value"})
  ```

  You can cancel the workload by doing:

  ```python
  sdk.cancel_workload(workload.id)
  ```

  Or refresh the workload status/results by with the following:

  ```python
  workload = sdk.get_workload(workload.id)
  ```

  Once the workload has been processed, you can fetch the result like this:

  ```python
  print(f"workload-id: {workload.id}, status: {workload.status}, result: {workload.result}")
  ```





  Some emulators, such as EMU_TN and EMU_FREE, accept further configuration to control the emulation.
  This is because these emulators are more advanced numerical simulation of the quantum system.

  By default, validation rules are more permissive for jobs targeting an emulator than on the Fresnel QPU when submitting
  jobs to the cloud platform.

  You may however wish to validate that your job running on an emulator is compatible with Fresnel.
  To that extent, set the `strict_validation` key in the configuration to `True`. Defaults to False.

  ```python

  from pasqal_cloud.device import EmulatorType, EmuFreeConfig, EmuTNConfig

  configuration = EmuTNConfig(strict_validation=True)
  batch = sdk.create_batch(serialized_sequence, [job1, job2], emulator=EmulatorType.EMU_TN, configuration=configuration)



  configuration = EmuFreeConfig(strict_validation=True)
  batch = sdk.create_batch(serialized_sequence, [job1, job2], emulator=EmulatorType.EMU_FREE, configuration=configuration)
  ```

  For EMU_TN you may add the integrator timestep in nanoseconds, the numerical accuracy desired in the tensor network
  compression, and the maximal bond dimension of tensor network state.

  ```python
  from pasqal_cloud.device import EmulatorType, EmuTNConfig

  configuration = EmuTNConfig(dt=10.0, precision="normal", max_bond_dim=100)
  batch = sdk.create_batch(serialized_sequence, [job1, job2], emulator=EmulatorType.EMU_TN, configuration=configuration)
  ```

  For EMU_FREE, you may add some default SPAM noise. Beware this makes your job take much longer.

  ```python
  from pasqal_cloud.device import EmulatorType, EmuFreeConfig

  configuration = EmuFreeConfig(with_noise=True)
  batch = sdk.create_batch(serialized_sequence, [job1, job2], emulator=EmulatorType.EMU_FREE, configuration=configuration)
  ```

  Replace the corresponding section in the code examples above with this to add further configuration.



  The SDK provides a method to retrieve the device specs currently defined on PASQAL's cloud platform.
  They define the physical constraints of our QPUs, and these constraints enforce some rules on
  the pulser sequence that can be run on QPUs (e.g., max number of atoms, available pulse channels, ...)

  ```python
  sdk.get_device_specs_dict()
  ```

  The method returns a dict object mapping a device type to a serialized device specs. These specs can be used
  to instantiate a `Device` instance in the `Pulser` library.



  This is intended for the package developers or users which were given access to non-prod
  environments of the PASQAL cloud platform.

  To target a specific environment (`prod`, `preprod` or `dev`), instantiate the SDK class using
  `PASQAL_ENDPOINTS['env']` for the parameter `endpoints` and `AUTH0_CONFIG['env']` for
  `auth0` with env being the environment you want to target.

  Example:

  ```python
  from pasqal_cloud import AUTH0_CONFIG, SDK, PASQAL_ENDPOINTS

  sdk = SDK(..., endpoints=PASQAL_ENDPOINTS['preprod'], auth0=AUTH0_CONFIG['preprod'])
  ```

  By default, the targeted environment for `endpoints` and `auth0` is `prod`.


