# README
## Table of Contents
- [Python](#python)
-- [Installation](#instalation)
-- [Usage](#usage)
## Python
### Instalation
To install as a pip package:
```console
pip install -i https://test.pypi.org/simple/ cloudmodel
```
### Usage
To run the model, first import it from the library
```python
from cloudmodel import CloudModel
```
and call it with the desired parameters
```python
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
```python
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:
```python
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)
```
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"description": "# README\n\n## Table of Contents\n\n- [Python](#python)\n\n-- [Installation](#instalation)\n-- [Usage](#usage)\n\n## Python\n\n### Instalation\n\nTo install as a pip package:\n\n```console\n pip install -i https://test.pypi.org/simple/ cloudmodel\n```\n\n### Usage\n\nTo run the model, first import it from the library\n\n```python\n from cloudmodel import CloudModel\n```\n\nand call it with the desired parameters\n\n```python\nCloudModel(\n simulation_time_minutes, #(int): The total simulation time in minutes.\n save_time_minutes, # (int): The time interval in minutes at which the model state is saved.\n statistic_time_minutes, # (int): The time interval in minutes at which statistics are calculated.\n bacup_time_minutes, # (int): The time interval in minutes at which backups are created.\n)\n```\n\nWhen the model finishes running there will be a new directory, where all the output data from the simulation will be stored.\n\nThe following section describes all the functions that can be applied to those simulation results.\n\n### Features\n\n```python\nclass FileStyle:\n \"\"\"A class representing the file style for cloud data.\n\n Attributes:\n chosen_file (str): The chosen input file type.\n output_data_path (str): The path to the output data folder.\n cmp_output_data_path (str): The path to the comparison output data folder.\n img_path (str): The path to the image folder.\n txt_path (str): The path to the text folder.\n cmp_txt_path (str): The path to the comparison text folder.\n vid_path (str): The path to the video folder.\n img_option (str): The image style option.\n folder_handle (str): The folder handling option.\n\n Methods:\n _get_data(): Get the data from the selected files.\n _get_var_from_data(file_number, var_iterator): Get a specific variable from the data.\n list_var(): List all the variables.\n get_var(var, time): Get a specific variable at a given time.\n show_var_dataframe(var_array, center, axes): Show the variable data as a DataFrame.\n center_var(var_array, center, axes): Center the variable data along a specific axes.\n get_var_max_value_position(var_array): Get the position of the maximum value in the variable data.\n check_path(path, selected_file_name): Check if the path exists and create it if necessary.\n cloud_binary_comparison(): Compare the binary files in the output data and comparison output data folders.\n live_var_animation(variable): Create a live animation of a variable.\n plot_style(variable): Plot the style of a variable.\n generate_image(frame, var_number): Generate an image for a specific frame and variable.\n animate_variables(var_list, save_animation, show_animation): Animate multiple variables.\n animate_variable(var_to_animate, save_animation, show_animation, check_path): Animate a specific variable.\n parse_status_img(): Parse the status images.\n multi_var_img(var_1, var_2, file=\"inis.da\"): Create an image with multiple variables.\n show_file_diff(file): Show the differences between two text files.\n cloud_text_comparison(): Compare text files in the specified paths and display the differences, if any.\n get_unequal_files(): Get the unequal files between two folders.\n parse_text_files(): Parse the text files.\n \"\"\"\n```\n\nA run + image generation for all variables to all the simulated times would look like this:\n\n```python\n from cloudmodel import CloudModel\n \n CloudModel(\n simulation_time_minutes=45,\n save_time_minutes=3,\n statistic_time_minutes=3,\n bacup_time_minutes=3,\n )\n\n \n cloud = FileStyle(\n chosen_file=\"Nube\",\n output_data_path=\"Data/new_code/\",\n cmp_output_data_path=\"outputdata1/\",\n img_path=\"img/new_code/\",\n txt_path=\"txt/\",\n cmp_txt_path=\"txt1/\",\n vid_path=\"vid/\",\n img_option=\"Contour\",\n folder_handle=\"Delete\",\n )\n cloud.parse_status_img()\n theta_base = cloud.get_var(cloud.var_list[3])\n cloud.show_var_dataframe(theta_base, 3)\n```\n",
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