AetherOnePySocial

Group Analysis with AetherOnePy Integration

This API serves as a backend for extending the functionality of AetherOnePy, a powerful program for conducting individual analyses and broadcasts. While AetherOnePy excels in individual work, this project is designed to enable collaborative group analysis and broadcasting efforts by:

Combining Analysis Results:

When multiple users perform analyses on the same subject, this API collects and consolidates their results.

Group Broadcasting:

Facilitates group collaboration by merging intentions and ensuring coordinated broadcasts.

Team Collaboration:

The API is built to support distributed teams working together on shared projects, enhancing the efficiency of group efforts.

Getting started

install all packages

go to tha map AetherOnePySocial/py and run

pip install -e .

then in command line in the same map run

 uvicorn app.main:app --reload

If you want to run tests on PostMan

AetherOnePySocial_Postman_Collection.json

To run tests

pytest

To build frontend you could use openAPI

python ./generateOpenAPI.py

you will generate

openapi.json file

Introduction to the AetherOnePySocial Project

Overview

This project is a modular and scalable web application built using FastAPI, designed with a clear Model-View-Controller (MVC) architecture. It serves as a foundation for developing APIs with features like authentication, data management, and analysis. The application includes:

User authentication (register and login).
Management of machines, catalogs, rates, cases, sessions, analyses, and comparisons.
Unit tests to ensure application reliability.
Integration with an SQLite database (or any other database of your choice).

Key Components

1. Application Structure

The project follows an MVC structure:

models/: Database models for each entity.
schemas/: Pydantic schemas for request and response validation.
routers/: FastAPI routers that define the API endpoints.
tests/: Unit tests for all endpoints to validate functionality.

2. Database

The project uses SQLAlchemy for ORM and database management. It also includes an alembic integration for database migrations (not implemented here but can be added).

3. Endpoints

The application has endpoints for:

Authentication (/api/auth/)
Machines (/api/machines/)
Catalogs (/api/catalog/)
Rates (/api/rates/)
Cases (/api/cases/)
Sessions (/api/sessions/)
Analyses (/api/analysis/)
Merged Analyses (/api/merged-analysis/)
Comparisons (/api/comparison/)

4. Unit Testing

All endpoints are tested using pytest. The tests/ directory includes test cases for creating, retrieving, and validating data for all entities.

How to Use

1. Clone the Repository

git clone <repository-url>
cd AetherOnePySocial

2. Set Up the Environment

Create a virtual environment and install dependencies:

python -m venv env
source env/bin/activate  # On Windows: env\Scripts\activate
pip install -e .

3. Run the Application

Start the FastAPI server:

uvicorn app.main:app --reload

Access the API documentation at http://127.0.0.1:8000/docs.

4. Run Unit Tests

Run the unit tests to verify functionality:

pytest

Features

Automatic documentation: Interactive API documentation is available with Swagger UI.
Validation: Input and output validation using Pydantic.
Extensibility: Easily add new models, endpoints, and features.
Testing: Comprehensive unit tests for all entities.

Example Usage

Register a User

Endpoint: POST /api/auth/register

{
  "username": "testuser",
  "email": "[email protected]",
  "password": "password123"
}

Response:

{
  "id": 1,
  "username": "testuser",
  "email": "[email protected]"
}

Create a Machine

Endpoint: POST /api/machines/

{
  "machine_name": "Machine1",
  "description": "Test machine",
  "api_key": "1234"
}

Response:

{
  "id": 1,
  "machine_name": "Machine1",
  "description": "Test machine",
  "api_key": "1234"
}

Entity Relationship Diagram (ERD)

[User] 1---* [SessionKey] *---1 [Session] *---1 [Analysis] *---1 [RateAnalysis]
   |                |                |                |
   |                |                |                |
   |                |                |                |
   *                *                *                *
[Case]           [Catalog]        [Rate]           [Machine]

User: Has many SessionKeys, Sessions, and Cases.
SessionKey: Belongs to a User, referenced by Sessions.
Session: Belongs to a User, Case, and SessionKey. Has many Analyses.
Analysis: Belongs to a Session and Catalog. Has many RateAnalyses.
RateAnalysis: Belongs to an Analysis and Catalog.
Case: Belongs to a User. Has many Sessions.
Catalog: Has many Analyses, Rates, and RateAnalyses.
Rate: Belongs to a Catalog.
Machine: Can be referenced by Sessions, Cases, etc.

Endpoint Data Requirements

Register a User

POST /api/auth/register

{
  "username": "testuser",
  "email": "[email protected]",
  "password": "password123"
}

Login

POST /api/auth/login

Content-Type: application/x-www-form-urlencoded
Body:
- username: your email (e.g., [email protected])
- password: your password

Create a Machine

POST /api/machines/

{
  "machine_name": "Machine1",
  "description": "Test machine",
  "api_key": "1234"
}

Create a Session Key (Detailed)

POST /api/keys/

Request Body Structure:

user_id (int): The user ID for whom the session key is being created (required)
local_session_id (int): The local session ID (required)
key (str, UUID, optional): The session key (if not provided, a new UUID will be generated)
session_id (UUID, optional): The session ID (optional, rarely needed)

Example Request:

{
  "user_id": 1,
  "local_session_id": 123
}

Possible Responses:

Success (new key created):

{
  "status": "created",
  "message": "New session key created successfully",
  "key": "b3e1c2d4-5f6a-7b8c-9d0e-1f2a3b4c5d6e",
  "user_id": 1,
  "local_session_id": 123
}

Key already exists for this user/local_session_id:

{
  "status": "exists",
  "message": "Session key already exists for this user in combination with local_session_id",
  "key": "b3e1c2d4-5f6a-7b8c-9d0e-1f2a3b4c5d6e",
  "user_id": 1,
  "local_session_id": 123
}

Combination of user_id, key, and local_session_id already exists:

{
  "status": "error",
  "message": "This combination of user_id, key, and local_session_id already exists. This is not allowed.",
  "user_id": 1,
  "key": "b3e1c2d4-5f6a-7b8c-9d0e-1f2a3b4c5d6e",
  "local_session_id": 123
}

Not authorized (user mismatch):

{
  "detail": "Not authorized to create/update session keys for other users"
}

Missing required fields:

{
  "detail": "local_session_id and user_id are required fields"
}

Invalid key format:

{
  "detail": "Invalid key format. Must be a valid UUID"
}

Share Analysis

POST /api/analysis/share

Request Body Structure:

data (object)
- user_id (int): The user ID submitting the analysis
- machine_id (str): The machine identifier
- key (str): The session key (UUID string)
- session_id (int): The local session ID
- analyses (object)
  - analyses (list of objects): Each contains:
    - analysis (object):
      - catalog_id (int)
      - created (str, ISO datetime)
      - id (int)
      - name (str or null)
      - session_id (int)
      - target_gv (int)
    - catalog (object):
      - description (str)
      - id (int)
      - name (str)
    - rate_analysis (list of objects): Each contains:
      - analysis_id (int)
      - catalog_id (int)
      - description (str or null)
      - energetic_value (int)
      - gv (int)
      - id (int)
      - level (int)
      - note (str)
      - potency (int)
      - potencyType (str)
      - signature (str)
  - case (object):
    - color (str)
    - created (str, ISO datetime)
    - description (str)
    - email (str)
    - id (int)
    - last_change (str, RFC 1123 datetime)
    - name (str)
  - session (object):
    - case_id (int)
    - created (str, ISO datetime)
    - description (str)
    - id (int)
    - intention (str)
message (str): Status or info message
status (str): Status indicator (e.g., "success")

Example:

{
  "data": {
    "user_id": 1,
    "machine_id": "machine_id_1",
    "key": "session-key-uuid",
    "session_id": 7,
    "analyses": {
      "analyses": [
        {
          "analysis": {
            "catalog_id": 2,
            "created": "2025-01-27T14:11:40",
            "id": 9,
            "name": null,
            "session_id": 7,
            "target_gv": 699
          },
          "catalog": {
            "description": "radionics-rates",
            "id": 2,
            "name": "emotions"
          },
          "rate_analysis": [
            {
              "analysis_id": 9,
              "catalog_id": 2,
              "description": null,
              "energetic_value": 1002,
              "gv": 962,
              "id": 1,
              "level": 0,
              "note": "",
              "potency": 0,
              "potencyType": "8989",
              "signature": "wary"
            }
            // ... more rate_analysis objects ...
          ]
        }
        // ... more analyses ...
      ],
      "case": {
        "color": "#303de8",
        "created": "2025-01-27T14:10:50",
        "description": "test6",
        "email": "[email protected]",
        "id": 6,
        "last_change": "Mon, 27 Jan 2025 14:10:50 GMT",
        "name": "test6"
      },
      "session": {
        "case_id": 6,
        "created": "2025-01-27T14:11:18",
        "description": "test6",
        "id": 7,
        "intention": "test6"
      }
    }
  },
  "message": "Found 3 analyses with their related data",
  "status": "success"
}

Get Analysis by Key (Detailed Response)

GET /api/analysis/key/{key}

Response Structure:

status (str): Status indicator (e.g., "success")
status_code (int): HTTP status code (e.g., 200)
message (str): Status or info message
data (list of session objects): Each session contains:
- id (int): Session ID
- local_id (int): Local session ID
- machine_id (str): Machine identifier
- user_id (int): User ID
- description (str): Session description
- intention (str): Session intention
- created (str): ISO datetime string
- case (object):
  - id (int)
  - local_id (int)
  - name (str)
  - description (str)
  - email (str)
  - color (str)
  - created (str, ISO datetime)
  - last_change (str, ISO datetime)
- analyses (list of objects): Each contains:
  - id (int)
  - local_id (int)
  - machine_id (str)
  - catalog_id (int)
  - target_gv (int)
  - created (str, ISO datetime)
  - catalog (object):
    - id (int)
    - name (str)
    - description (str)
  - rate_analyses (list of objects): Each contains:
    - id (int)
    - local_id (int)
    - machine_id (str)
    - catalog_id (int)
    - signature (str)
    - description (str)
    - energetic_value (int)
    - gv (int)
    - level (int)
    - potencyType (str)
    - potency (int)
    - note (str)

Example:

{
  "status": "success",
  "status_code": 200,
  "message": "Sessions retrieved successfully",
  "data": [
    {
      "id": 1,
      "local_id": 101,
      "machine_id": "machine_id_1",
      "user_id": 1,
      "description": "Session description",
      "intention": "Session intention",
      "created": "2025-01-27T14:11:18",
      "case": {
        "id": 6,
        "local_id": 201,
        "name": "test6",
        "description": "test6",
        "email": "[email protected]",
        "color": "#303de8",
        "created": "2025-01-27T14:10:50",
        "last_change": "2025-01-27T14:10:50"
      },
      "analyses": [
        {
          "id": 9,
          "local_id": 301,
          "machine_id": "machine_id_1",
          "catalog_id": 2,
          "target_gv": 699,
          "created": "2025-01-27T14:11:40",
          "catalog": {
            "id": 2,
            "name": "emotions",
            "description": "radionics-rates"
          },
          "rate_analyses": [
            {
              "id": 1,
              "local_id": 401,
              "machine_id": "machine_id_1",
              "catalog_id": 2,
              "signature": "wary",
              "description": null,
              "energetic_value": 1002,
              "gv": 962,
              "level": 0,
              "potencyType": "8989",
              "potency": 0,
              "note": ""
            }
            // ... more rate_analyses ...
          ]
        }
        // ... more analyses ...
      ]
    }
    // ... more sessions ...
  ]
}

Other Endpoints

See the OpenAPI docs at /docs for full details on all endpoints and their required/requested data.

Contribution

Feel free to fork the repository and submit pull requests. For major changes, open an issue to discuss what you would like to change.

License

This project is licensed under the MIT License.

files schema

Directory Structure:

 project/
 ├── app/
 │   ├── __init__.py
 │   ├── main.py
 │   ├── config.py
 │   ├── database.py
 │   ├── models/
 │   │   ├── __init__.py
 │   │   ├── user.py
 │   │   ├── machine.py
 │   │   ├── catalog.py
 │   │   ├── rate.py
 │   │   ├── case.py
 │   │   ├── session.py
 │   │   ├── analysis.py
 │   │   ├── merged_analysis.py
 │   │   ├── comparison.py
 │   ├── routers/
 │   │   ├── __init__.py
 │   │   ├── auth.py
 │   │   ├── machines.py
 │   │   ├── catalog.py
 │   │   ├── rates.py
 │   │   ├── cases.py
 │   │   ├── sessions.py
 │   │   ├── analysis.py
 │   │   ├── merged_analysis.py
 │   │   ├── comparison.py
 │   ├── schemas/
 │   │   ├── __init__.py
 │   │   ├── user.py
 │   │   ├── machine.py
 │   │   ├── catalog.py
 │   │   ├── rate.py
 │   │   ├── case.py
 │   │   ├── session.py
 │   │   ├── analysis.py
 │   │   ├── merged_analysis.py
 │   │   ├── comparison.py
 ├── setup.py
 ├── tests/
 │   ├── __init__.py
 │   ├── test_auth.py
 │   ├── test_machines.py
 │   ├── test_catalog.py
 │   ├── test_rates.py
 │   ├── test_cases.py
 │   ├── test_sessions.py
 │   ├── test_analysis.py
 │   ├── test_merged_analysis.py
 │   ├── test_comparison.py
 ├── .gitignore

Name		Name	Last commit message	Last commit date
Latest commit History 39 Commits
.railway		.railway
alembic		alembic
app		app
tests		tests
ui		ui
.dockerignore		.dockerignore
.gitignore		.gitignore
.python-version		.python-version
AetherOnePySocial_Postman_Collection.json		AetherOnePySocial_Postman_Collection.json
Procfile		Procfile
README.md		README.md
alembic.ini		alembic.ini
conftest.py		conftest.py
generateOpenAPI.py		generateOpenAPI.py
openapi.json		openapi.json
pyproject.toml		pyproject.toml
pytest.ini		pytest.ini
requirements.txt		requirements.txt

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Latest commit

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AetherOnePySocial

Group Analysis with AetherOnePy Integration

Combining Analysis Results:

Group Broadcasting:

Team Collaboration:

Getting started

install all packages

If you want to run tests on PostMan

To run tests

To build frontend you could use openAPI

you will generate

Introduction to the AetherOnePySocial Project

Overview

Key Components

1. Application Structure

2. Database

3. Endpoints

4. Unit Testing

How to Use

1. Clone the Repository

2. Set Up the Environment

3. Run the Application

4. Run Unit Tests

Features

Example Usage

Register a User

Create a Machine

Entity Relationship Diagram (ERD)

Endpoint Data Requirements

Register a User

Login

Create a Machine

Create a Session Key (Detailed)

Share Analysis

Get Analysis by Key (Detailed Response)

Other Endpoints

Contribution

License

Directory Structure:

About

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