Updating the README.md

# PyThermoGIS

# pyThermoGIS

This is a python API to the ThermoGIS Java code calculations

pyThermoGIS is a Python package that provides API access to the ThermoGIS Geothermal simulation (https://www.thermogis.nl/en). The simulations are conducted in java and this code uses the JPype package as a python-java

binding.

It uses xarray (https://docs.xarray.dev/en/stable/index.html) Datasets as input to the simulation and can be combined with the pygridsio package (https://pypi.org/project/pygridsio/) to read in and process 2D rasters

### Install via pip from GitLab

To install `pyThermoGIS` into your own project directly from GitLab, run:

```sh

pip install git+https://gitlab.com/your-repo/pythermogis.git

```

This will fetch the latest version from the public GitLab repository.

## Usage

Here’s a simple example demonstrating how to use `pyThermoGIS` for a single set of input values:

```python

from pythermogis.pythermogis import calculate_doublet_performance

import xarray as xr

# Initialize an input Dataset with the required input variables:

input_data = xr.Dataset({

         "thickness_mean": ((), 300),

         "thickness_sd": ((), 50),

         "ntg": ((), 0.5),

         "porosity": ((), 0.5),

         "depth": ((), 5000),

         "ln_permeability_mean": ((), 5),

         "ln_permeability_sd": ((), 0.5),

     })

# Simulate a Geothermal doublet using the ThermoGIS methodology

doublet_simulation_results = calculate_doublet_performance(input_data)

# Display results

print(doublet_simulation_results)

```

## Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

## License

This project is licensed under the MIT License. See `LICENSE` for details.

## Installation for further development

### Install from Source with Pixi for further Development

To install `pyThermoGIS` from source using Pixi, follow these steps:

1. Clone the repository:

   ```sh

   git clone https://gitlab.com/your-repo/pythermogis.git

   cd pythermogis

   ```

2. Ensure Pixi is installed:

   ```sh

   curl -sSL https://pixi.sh/install.sh | bash

   ```

3. Install dependencies and create an isolated environment:

   ```sh

   pixi install

   ```

4. Activate the environment:

   ```sh

   pixi run python

   ```

from pythermogis.thermogis_classes.doublet import calculate_doublet_performance

from typing import List

from pythermogis.thermogis_classes.doublet import calculate_doublet_performance_across_dimensions

import xarray as xr

def calculate_doublet_performance(input_data):

    """Testing API"""

    return calculate_doublet_performance(input_data)

def calculate_doublet_performance(input_data: xr.Dataset,

                                  input_params: dict = None,

                                  rng_seed: int = None,

                                  p_values: List[float] = None) -> xr.Dataset:

    """

    An access point to the calculate_doublet_performance_across_dimensions function in pythermogis.thermogis_classes.doublet, see that method for more detailed instruction

    """

    return calculate_doublet_performance_across_dimensions(input_data, input_params=input_params, rng_seed=rng_seed, p_values=p_values)

        "cophp"], output_values["pres"], output_values["flow_rate"], output_values["welld"]

def calculate_doublet_performance(input_data: xr.Dataset,

def calculate_doublet_performance_across_dimensions(input_data: xr.Dataset,

                                  input_params: dict =None,

                                  rng_seed: int =None,

                                  p_values: List[float] =None) -> xr.Dataset: