CloudModel

This repository hosts the re-engineered cloud simulation model, now implemented in Modern Fortran and wrapped for Python integration. Below are the instructions for setting up and running the model, as well as additional requirements for animation functionality and compilation with different Fortran compilers.

Table of Contents

• Python

  • Installation
  • Usage
  • Extra Functionality Requirements

• Fortran

  • Fortran Only
  • F2Py

• Contributing

• License

Python

Instalation

To install as a pip package:

    pip install cloudmodel

Usage

To run the model, first import it from the library

from cloudmodel.cloud_read import CloudSimulation, ImageStyle

and call it with the desired parameters

CloudModel(
    simulation_time_minutes,  #(int): The total simulation time in minutes.
    save_time_minutes,  # (int): The time interval in minutes at which the model state is saved.
    statistic_time_minutes,  # (int): The time interval in minutes at which statistics are calculated.
    backup_time_minutes,  # (int): The time interval in minutes at which backups are created.
    directory,  # (str): Output Data Destination.
)

When the model finishes running there will be a new directory, where all the output data from the simulation will be stored.

The following section describes all the functions that can be applied to those simulation results.

Features

class FileStyle:
    """A class representing the file style for cloud data.

    Attributes:
        chosen_file (str): The chosen input file type.
        output_data_path (str): The path to the output data folder.
        cmp_output_data_path (str): The path to the comparison output data folder.
        img_path (str): The path to the image folder.
        txt_path (str): The path to the text folder.
        cmp_txt_path (str): The path to the comparison text folder.
        vid_path (str): The path to the video folder.
        img_option (str): The image style option.
        folder_handle (str): The folder handling option.

    Methods:
        _get_data(): Get the data from the selected files.
        _get_var_from_data(file_number, var_iterator): Get a specific variable from the data.
        _get_var_max_value_position(var_array): Get the position of the maximum value in the variable data.
        _plot_style(variable): Plot the style of a variable.
        get_var(var, time): Get a specific variable at a given time.
        get_var_dataframe(var_array, center, axes): return the variable data as a DataFrame.
        center_var(var_array, center, axes): Center the variable data along a specific axes.
        parse_status_img(): Parse the status images.
        multi_var_img(var_1, var_2, file="inis.da"): Create an image with multiple variables.
    """

A run + image generation for all variables to all the simulated times would look like this:

    from cloudmodel import CloudModel
    
    CloudModel(
        simulation_time_minutes,  #(int): The total simulation time in minutes.
        save_time_minutes,  # (int): The time interval in minutes at which the model state is saved.
        statistic_time_minutes,  # (int): The time interval in minutes at which statistics are calculated.
        backup_time_minutes,  # (int): The time interval in minutes at which backups are created.
        directory,  # (str): Output Data Destination.
    )
        
    cloud = FileStyle(
        chosen_file="Nube",
        output_data_path="Data/new_code/",
        cmp_output_data_path="outputdata1/",
        img_path="img/new_code/",
        txt_path="txt/",
        cmp_txt_path="txt1/",
        vid_path="vid/",
        img_option="Contour",
        folder_handle="Delete",
    )
    cloud.parse_status_img()
    theta_base = cloud.get_var(cloud.var_list[3])
    cloud.show_var_dataframe(theta_base, 3)

Extra Functionality Requirements

To display animations, follow these steps:

1. Install ffmpeg:

   sudo apt install ffmpeg

2. If you're using Windows Subsystem for Linux (WSL), you can show Matplotlib plots and other GUI elements by following these steps:

   • Install Xming X Server for Windows.

   • Run the following command in your WSL2 terminal:

     sudo apt-get install python-tk

Fortran for devs

This section is focused on the steps needed to re-compile the model to mantain usability with the Python package.

F2Py Integration

To compile for F2Py, from the python directory:

    pip install .

---

Fortran-only compiling

gfortran

Linux:

Install gfortran:

sudo apt install gfortran

MacOS:

Install gfortran using Homebrew:

brew install gfortran
xcode-select --install

---

Execution:

Run the following command with fpm:

fpm run --profile release

nvfortran

Installation

1. Install the appropriate Nvidia drivers for your system.
2. Install the Nvidia CUDA toolkit.
3. Install the Nvidia HPC SDK. The installation path is usually /opt/nvidia/hpc_sdk/Linux_x86_64/(version)/compilers/bin. Add it to your PATH.

Execution:

Run the following command with fpm:

fpm run --compiler "/opt/nvidia/hpc_sdk/Linux_x86_64/(version)/compilers/bin/nvfortran" --flag "-O3 -cuda"

Contributing

1. Fork the repository.
2. Create a new branch: git checkout -b feature-name.
3. Make your changes.
4. Push your branch: git push origin feature-name.
5. Create a pull request.

License

This project is licensed under the MIT License.