cloudmodel 1.0.1

A simple, parameterized cloud model

README

Table of Contents

• Python

-- Installation -- Usage

Python

Instalation

To install as a pip package:

    pip install -i https://test.pypi.org/simple/ cloudmodel

Usage

To run the model, first import it from the library

    from cloudmodel import CloudModel

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.
    bacup_time_minutes,  # (int): The time interval in minutes at which backups are created.
)

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.
        list_var(): List all the variables.
        get_var(var, time): Get a specific variable at a given time.
        show_var_dataframe(var_array, center, axes): Show the variable data as a DataFrame.
        center_var(var_array, center, axes): Center the variable data along a specific axes.
        get_var_max_value_position(var_array): Get the position of the maximum value in the variable data.
        check_path(path, selected_file_name): Check if the path exists and create it if necessary.
        cloud_binary_comparison(): Compare the binary files in the output data and comparison output data folders.
        live_var_animation(variable): Create a live animation of a variable.
        plot_style(variable): Plot the style of a variable.
        generate_image(frame, var_number): Generate an image for a specific frame and variable.
        animate_variables(var_list, save_animation, show_animation): Animate multiple variables.
        animate_variable(var_to_animate, save_animation, show_animation, check_path): Animate a specific variable.
        parse_status_img(): Parse the status images.
        multi_var_img(var_1, var_2, file="inis.da"): Create an image with multiple variables.
        show_file_diff(file): Show the differences between two text files.
        cloud_text_comparison(): Compare text files in the specified paths and display the differences, if any.
        get_unequal_files(): Get the unequal files between two folders.
        parse_text_files(): Parse the text files.
    """

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

    from cloudmodel import CloudModel
    
    CloudModel(
        simulation_time_minutes=45,
        save_time_minutes=3,
        statistic_time_minutes=3,
        bacup_time_minutes=3,
    )

    
    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)