Human Design Project

Overview

This project is a collection of Python scripts and modules that provide functionalities related to Human Design, a system of knowledge that combines astrology, kabbalah, and the I Ching to map the energy flows within a person. The project includes functionalities for geocoding, calculating Human Design features, and providing an API for these calculations.

Project Structure

• api_.py: Flask API for calculating Human Design features.
• convertJSON.py: Functions to convert data into JSON format.
• geocode.py: Functions for geocoding and calculating distances.
• hd_constants.py: Constants used in Human Design calculations.
• hd_features.py: Classes and functions for calculating Human Design features.
• mcp_server.py: MCP server for processing Human Design calculations.

File Descriptions

api_.py

This file contains the Flask API for calculating Human Design features.

Functions

• calculate_hd(): Calculates Human Design features based on the provided input.

convertJSON.py

This file contains functions to convert data into JSON format.

Functions

• general(data): Converts general data into JSON format.
• gatesJSON(data, details=False): Converts gate data into JSON format.
• channelsJSON(data, details=False): Converts channel data into JSON format.

geocode.py

This file contains functions for geocoding and calculating distances.

Classes

• Location: Data class for storing location information.

Functions

• get_latitude_longitude(place: str) -> Tuple[Optional[float], Optional[float]]: Retrieves latitude and longitude for a given place.
• get_address(latitude: float, longitude: float) -> Optional[str]: Retrieves address for given latitude and longitude.
• batch_geocode(places: List[str]) -> List[Location]: Geocodes a list of places.
• calculate_distance(place1: str, place2: str) -> Optional[float]: Calculates the distance between two places.

hd_constants.py

This file contains constants used in Human Design calculations.

Constants

• SWE_PLANET_DICT: Dictionary of planets and their corresponding indices.
• GATES_CHAKRA_DICT: Dictionary of gates and their corresponding chakras.
• CHANNEL_MEANING_DICT: Dictionary of channel meanings.
• IC_CROSS_TYP: Dictionary of IC cross types.
• penta_dict: Dictionary of penta types.
• circuit_typ_dict: Dictionary of circuit types.
• circuit_group_typ_dict: Dictionary of circuit group types.
• awareness_stream_dict: Dictionary of awareness stream types.
• awareness_stream_group_dict: Dictionary of awareness stream group types.

hd_features.py

This file contains classes and functions for calculating Human Design features.

Classes

• hd_features: Class for calculating Human Design features.
• hd_composite: Class for calculating composite Human Design features.

Functions

• get_utc_offset_from_tz(timestamp, zone): Retrieves UTC offset for a given timezone.
• timestamp_to_juldate(self, *time_stamp): Converts timestamp to Julian date.
• calc_create_date(self, jdut): Calculates creation date from Julian date.
• date_to_gate(self, jdut, label): Converts date to gate.
• birth_creat_date_to_gate(self, *time_stamp): Converts birth creation date to gate.
• day_chart(self, *time_stamp): Generates day chart.
• get_inc_cross(date_to_gate_dict): Retrieves incidence cross.
• get_profile(date_to_gate_dict): Retrieves profile.
• get_variables(date_to_gate_dict): Retrieves variables.
• is_connected(active_channels_dict, *args): Checks if channels are connected.
• get_auth(active_chakras, active_channels_dict): Retrieves authentication.
• get_typ_old(active_channels_dict, active_chakras): Retrieves type (old method).
• get_typ(active_channels_dict, active_chakras): Retrieves type.
• get_component(active_channels_dict, chakra): Retrieves component.
• get_channels_and_active_chakras(date_to_gate_dict, meaning=False): Retrieves channels and active chakras.
• get_split(active_channels_dict, active_chakras): Retrieves split.
• calc_full_gates_chakra_dict(gates_chakra_dict): Calculates full gates chakra dictionary.
• calc_full_channel_meaning_dict(): Calculates full channel meaning dictionary.
• chakra_connection_list(chakra_1, chakra_2): Retrieves chakra connection list.
• get_full_chakra_connect_dict(): Retrieves full chakra connection dictionary.
• calc_single_hd_features(timestamp, report=False, channel_meaning=False, day_chart_only=False): Calculates single Human Design features.
• unpack_single_features(single_result): Unpacks single features.
• get_timestamp_list(start_date, end_date, percentage, time_unit, intervall): Retrieves timestamp list.
• calc_mult_hd_features(start_date, end_date, percentage, time_unit, intervall, num_cpu): Calculates multiple Human Design features.
• unpack_mult_features(result, full=True): Unpacks multiple features.
• get_single_hd_features(persons_dict, key, feature): Retrieves single Human Design features.
• composite_chakras_channels(persons_dict, identity, other_person): Retrieves composite chakras and channels.
• get_composite_combinations(persons_dict): Retrieves composite combinations.
• get_penta(persons_dict, report=False): Retrieves penta.

mcp_server.py

This file contains the MCP server for processing Human Design calculations.

Classes

• HumanDesignMCPServer: Class for the MCP server.

Functions

• setup_logging(self): Sets up logging.
• validate_input_parameters(self, request_args: Dict[str, Any]) -> Tuple[Optional[Dict[str, Any]], Optional[Tuple[Dict[str, Any], int]]]: Validates input parameters.
• process_geocoding_timezone(self, birth_time: Tuple[int, ...], birth_place: str) -> Tuple[Optional[float], Optional[float], Optional[str], Optional[Tuple[Dict[str, Any], int]]]: Processes geocoding and timezone.
• calculate_hd_features(self, timestamp: Tuple[int, ...]) -> Tuple[Optional[Any], Optional[Tuple[Dict[str, Any], int]]]: Calculates Human Design features.
• format_output_data(self, single_result: Any) -> Tuple[Optional[Dict[str, Any]], Optional[Tuple[Dict[str, Any], int]]]: Formats output data.
• calculate_hd_wrapper(self): Wrapper for calculating Human Design features.

Usage

To use the project, you can run the Flask API in api_.py and make requests to the /calculate endpoint. The MCP server in mcp_server.py can be used to process Human Design calculations.

Testing the API

Setting Up the Environment

1. Clone the repository.
2. Install the required packages using pip install -r requirements.txt.

Running the API

1. Run the Flask API using python api_.py.
2. The API will be available at http://127.0.0.1:5000/.

Making Sample Requests

You can use tools like curl or Postman to make requests to the /calculate endpoint.

Using curl

curl -X GET "http://127.0.0.1:5000/calculate?year=1990&month=1&day=1&hour=0&minute=0&second=0&tz=UTC&place=New%20York"

Using Postman

1. Open Postman.
2. Create a new GET request.
3. Set the URL to http://127.0.0.1:5000/calculate.
4. Add query parameters:
   • year: 1990
   • month: 1
   • day: 1
   • hour: 0
   • minute: 0
   • second: 0
   • tz: UTC
   • place: New York
5. Send the request and view the response.

Requirements

• Python 3.8 or higher
• Flask
• Geopy
• NumPy

Installation

1. Clone the repository.
2. Install the required packages using pip install -r requirements.txt.
3. Run the Flask API using python api_.py.
4. Run the MCP server using python mcp_server.py.

License

This project is licensed under the MIT License - see the LICENSE file for details.