AnalyseMorphologique/utils/data_processing/data_processing.py

"""
Created on Mon Apr 24 2023
@name:   data_processing.py
@desc:   A module to process the data
@auth:   Djalim Simaila
@e-mail: djalim.simaila@inrae.fr
"""
from utils.math import data_extraction as de
import numpy as np
from utils.files.input import ScannedObject
from utils.settings.SettingManager import SettingManager

def progressbar_placeholder(percent:int):
    """
    This function is a placeholder for a progressbar function
    """

def get_raw_data(obj:ScannedObject, ndigits:int,delta_z:float=1,update_progress_bar = progressbar_placeholder)->dict:
    """
    Calculates data from the given object

    :param obj: Object to analyse
    :param ndigits: Number of digits to keep after the comma
    :param delta_z: Delta z to use for the discretisation
    :param update_progress_bar: Function to update the progress bar
    :return: dict(str:list) with the following keys:
        - X (en mm)     : list of x values
        - Y (en mm)     : list of y values
        - Z (en mm)     : list of z values
        - teta (en rad) : list of teta values
        - rayon (en mm) : list of radius values
        - Xi-Xmoy       : list of Xi-Xmoy values
        - Yi-Ymoy       : list of Yi-Ymoy values
    """

    # Create the data dict
    colones = ["X (en mm)", "Y (en mm)", "Z (en mm)", "teta (en rad)", "rayon (en mm)","Xi-Xmoy","Yi-Ymoy"]
    data = {}
    for colone in colones:
        data[colone] = []

    # Get the discrete vertices
    discrete_vertices = obj.get_discrete_vertices(delta_z)
    progress = 0

    # Calculate the data for each discrete vertex
    for discrete_values in discrete_vertices:
        mean_x ,mean_y, mean_z = de.get_x_y_z_mean(discrete_values)
        for x,y,z in discrete_values:
            data["X (en mm)"].append(round(x, ndigits))
            data["Y (en mm)"].append(round(y, ndigits))
            data["Z (en mm)"].append(round(z, ndigits))
            data["teta (en rad)"].append(round(de.get_teta_from_x_y(x,y,mean_x,mean_y), ndigits))
            data["rayon (en mm)"].append(round(de.get_radius_from_x_y(x,y,mean_x,mean_y), ndigits))
            data["Xi-Xmoy"].append(round(x-mean_x, ndigits))
            data["Yi-Ymoy"].append(round(y-mean_y, ndigits))
        update_progress_bar(int(progress/len(discrete_vertices)*100))
        progress += 1
    return data

def get_discrete_data(obj:ScannedObject, ndigits:int, delta_z:float=1, update_progress_bar= progressbar_placeholder)->dict:
    """
    Calculates data from the given object

    :param obj: Object to analyse
    :param ndigits: Number of digits to keep after the comma
    :param delta_z: Delta z to use for the discretisation
    :param update_progress_bar: Function to update the progress bar
    :return: dict(str:list) with the following keys:
        - X moy (en mm)             : list of x mean values
        - Y moy (en mm)             : list of y mean values
        - Z moy (en mm)             : list of z mean values
        - Rayon moyen (en mm)       : list of mean radius values
        - Rayon ecart type (en mm)  : list of radius standard deviation values
    """
    # Create the data dict
    colones = ["X moy (en mm)", "Y moy (en mm)", "Z moy (en mm)","Discretisation(en mm)","Rayon moyen (en mm)","Rayon ecart type (en mm)"]
    data = {}
    for colone in colones:
        data[colone] = []

    # Get the discrete vertices
    discrete_vertices = obj.get_discrete_vertices(delta_z)
    progress = 0
    for discrete_values in discrete_vertices:
        x,y,z = de.get_x_y_z_mean(discrete_values)
        data["X moy (en mm)"].append(round(x, ndigits))
        data["Y moy (en mm)"].append(round(y, ndigits))
        data["Z moy (en mm)"].append(round(z, ndigits))
        first = discrete_values[0]
        last = discrete_values[-1]
        data["Discretisation(en mm)"].append(round(last[2]-first[2],ndigits))
        data["Rayon moyen (en mm)"].append(round(de.get_mean_radius(discrete_values), ndigits))
        data["Rayon ecart type (en mm)"].append(round(de.get_radius_std(discrete_values), ndigits))
        update_progress_bar(int(progress/len(discrete_vertices)*100))
        progress += 1
    return data

def get_advanced_data(discrete_data:dict, update_progress_bar= progressbar_placeholder):
    """
    """
    # Tortusity
    l = 0
    L = 0
    vertices = list(zip(discrete_data["X moy (en mm)"], discrete_data["Y moy (en mm)"], discrete_data["Z moy (en mm)"]))
    for i in range(len(vertices)-1):
        l += de.get_distance_between_two_vertices(vertices[i], vertices[i+1])
    L = de.get_distance_between_two_vertices(vertices[0], vertices[-1])
    T = l/L
    update_progress_bar(10)
    # Volume and surface
    H = discrete_data["Z moy (en mm)"][-1] - discrete_data["Z moy (en mm)"][0]
    R = de.get_mean([np.power(r,2) for r in discrete_data["Rayon moyen (en mm)"]])
    V = np.pi * R * H
    S = 2 * np.pi * R * H
    update_progress_bar(30)
    #
    R_mean = de.get_mean(discrete_data["Rayon moyen (en mm)"])
    R_mean_std = de.get_standard_deviation(discrete_data["Rayon moyen (en mm)"])
    mean_sigma_r_squared = de.get_mean([np.power(r,2) for r in discrete_data["Rayon ecart type (en mm)"]])
    sigma_r_tot = np.sqrt(np.power(R_mean_std,2) + mean_sigma_r_squared )
    MI_l = R_mean_std/R_mean
    MI_p = np.sqrt(mean_sigma_r_squared)/R_mean
    update_progress_bar(100)
    return {
        "Tortuosité":T,
        "Volume":V,
        "Surface":S,
        "Moyenne des rayons moyens":R_mean,
        "Ecart-type des rayons moyens":R_mean_std,
        "Sigma r tot":sigma_r_tot,
        "MI_l":MI_l,
        "MI_p":MI_p
    }