Djalim Simaila
aeb8c6ed44
The function parameter `update_progress_bar` has been renamed to `progressbar_placeholder` to improve the semantics of the function. This change does not affect the functionality of the code.
✨ feat(data_processing.py): add new morphological indicators to the output of the `get_advanced_data` function
The `get_advanced_data` function now calculates and returns the following morphological indicators:
- MI_mR: the ratio of the maximum radius to the mean radius
- MI_mH: the ratio of the maximum radius to the height of the object
- V_scan: the volume of the scanned object
- R_V_scan: the radius of a sphere with the same volume as the scanned object
- S_V_scan: the surface area of a sphere with the same volume as the scanned object
- R_h: the hydraulic radius of the object
- HI: the hydraulic index of the object
188 lines
7.0 KiB
Python
188 lines
7.0 KiB
Python
"""
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Created on Mon Apr 24 2023
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@name: data_processing.py
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@desc: A module to process the data
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@auth: Djalim Simaila
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@e-mail: djalim.simaila@inrae.fr
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"""
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from utils.math import data_extraction as de
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import numpy as np
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from utils.files.input import ScannedObject
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def progressbar_placeholder(percent:int):
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"""
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This function is a placeholder for a progressbar function
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"""
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def get_raw_data(obj:ScannedObject, ndigits:int,delta_z:float=1,update_progress_bar = progressbar_placeholder)->dict:
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"""
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Calculates data from the given object
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:param obj: Object to analyse
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:param ndigits: Number of digits to keep after the comma
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:param delta_z: Delta z to use for the discretisation
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:param update_progress_bar: Function to update the progress bar
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:return: dict(str:list) with the following keys:
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- X (en mm) : list of x values
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- Y (en mm) : list of y values
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- Z (en mm) : list of z values
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- theta (en rad) : list of theta values
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- rayon (en mm) : list of radius values
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- Xi-Xmoy : list of Xi-Xmoy values
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- Yi-Ymoy : list of Yi-Ymoy values
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"""
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# Create the data dict
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colones = ["X (en mm)",
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"Y (en mm)",
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"Z (en mm)",
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"theta (en rad)",
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"rayon (en mm)",
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"Xi-Xmoy",
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"Yi-Ymoy"]
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data = {}
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for colone in colones:
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data[colone] = []
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# Get the discrete vertices
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discrete_vertices = obj.get_discrete_vertices(delta_z)
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progress = 0
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# Calculate the data for each discrete vertex
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for discrete_values in discrete_vertices:
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mean_x ,mean_y, mean_z = de.get_x_y_z_mean(discrete_values)
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for x,y,z in discrete_values:
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data["X (en mm)"].append(round(x, ndigits))
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data["Y (en mm)"].append(round(y, ndigits))
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data["Z (en mm)"].append(round(z, ndigits))
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data["theta (en rad)"].append(round(de.get_theta_from_x_y(x,y,mean_x,mean_y), ndigits))
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data["rayon (en mm)"].append(round(de.get_radius_from_x_y(x,y,mean_x,mean_y), ndigits))
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data["Xi-Xmoy"].append(round(x-mean_x, ndigits))
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data["Yi-Ymoy"].append(round(y-mean_y, ndigits))
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update_progress_bar(int(progress/len(discrete_vertices)*100))
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progress += 1
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return data
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def get_discrete_data(obj:ScannedObject, ndigits:int, delta_z:float=1, update_progress_bar= progressbar_placeholder)->dict:
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"""
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Calculates data from the given object
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:param obj: Object to analyse
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:param ndigits: Number of digits to keep after the comma
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:param delta_z: Delta z to use for the discretisation
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:param update_progress_bar: Function to update the progress bar
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:return: dict(str:list) with the following keys:
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- X moy (en mm) : list of x mean values
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- Y moy (en mm) : list of y mean values
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- Z moy (en mm) : list of z mean values
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- Rayon moyen (en mm) : list of mean radius values
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- Rayon ecart type (en mm) : list of radius standard deviation values
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"""
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# Create the data dict
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colones = ["X moy (en mm)",
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"Y moy (en mm)",
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"Z moy (en mm)",
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"Discretisation(en mm)",
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"Rayon moyen (en mm)",
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"Rayon ecart type (en mm)"]
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data = {}
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for colone in colones:
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data[colone] = []
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# Get the discrete vertices
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discrete_vertices = obj.get_discrete_vertices(delta_z)
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progress = 0
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for discrete_values in discrete_vertices:
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x,y,z = de.get_x_y_z_mean(discrete_values)
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data["X moy (en mm)"].append(round(x, ndigits))
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data["Y moy (en mm)"].append(round(y, ndigits))
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data["Z moy (en mm)"].append(round(z, ndigits))
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first = discrete_values[0]
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last = discrete_values[-1]
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data["Discretisation(en mm)"].append(round(last[2]-first[2],ndigits))
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data["Rayon moyen (en mm)"].append(round(de.get_mean_radius(discrete_values), ndigits))
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data["Rayon ecart type (en mm)"].append(round(de.get_radius_std(discrete_values), ndigits))
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update_progress_bar(int(progress/len(discrete_vertices)*100))
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progress += 1
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return data
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def get_advanced_data(discrete_data:dict, V_scan = 0, update_progress_bar= progressbar_placeholder)->dict:
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"""
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Calculates morphological indicators from the given discrete data
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:param discrete_data: dict(str:list) with the following keys:
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- X moy (en mm) : list of x mean values
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- Y moy (en mm) : list of y mean values
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- Z moy (en mm) : list of z mean values
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- Rayon moyen (en mm) : list of mean radius values
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- Rayon ecart type (en mm) : list of radius standard deviation values
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:param update_progress_bar: Function to update the progress bar
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:return: dict with the following keys:
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- Tortuosite
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- Volume en mm3
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- Surface en mm2
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- Moyenne des rayons moyens 〈R〉
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- Ecart-type des rayons moyens σ_〈R〉
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- σ_〈R〉^tot
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- MI_l
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- MI_p
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- Rayon hydraulique R_h
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"""
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all_R = discrete_data["Rayon moyen (en mm)"]
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# Tortusity
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l = 0
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L = 0
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vertices = list(zip(discrete_data["X moy (en mm)"],
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discrete_data["Y moy (en mm)"],
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discrete_data["Z moy (en mm)"]))
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for index in range(len(vertices)-1):
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l += de.get_distance_between_two_vertices(vertices[index], vertices[index+1])
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L = de.get_distance_between_two_vertices(vertices[0], vertices[-1])
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T = l/L
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update_progress_bar(10)
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# Volume and surface
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H = discrete_data["Z moy (en mm)"][-1] - discrete_data["Z moy (en mm)"][0]
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R2_mean = de.get_mean([np.power(r,2) for r in all_R])
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V = np.pi * R2_mean * H
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S = 2 * np.pi * R2_mean * H
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update_progress_bar(30)
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# Morphological indicators
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R_mean = de.get_mean(all_R)
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R_mean_std = de.get_standard_deviation(all_R)
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mean_sigma_r_squared = de.get_mean([np.power(r,2) for r in discrete_data["Rayon ecart type (en mm)"]])
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sigma_r_tot = np.sqrt(np.power(R_mean_std,2) + mean_sigma_r_squared )
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MI_l = R_mean_std/R_mean
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MI_p = np.sqrt(mean_sigma_r_squared)/R_mean
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R_max = max(all_R)
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MI_mR = R_max/R_mean
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MI_mH = R_max/H
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#MI_mr_in = R_max/R_in
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# TODO understand what is R_in
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R_V_scan = np.sqrt(V_scan/np.pi*H)
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S_V_scan = 2 * np.sqrt(np.pi * H * V_scan)
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R_h = R2_mean/ R_mean
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HI = R_mean * R_V_scan / R2_mean
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update_progress_bar(100)
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return {
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"Tortuosité":T,
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"Volume en mm3":V,
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"Surface en mm2":S,
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"Moyenne des rayons moyens 〈R〉":R_mean,
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"Ecart-type des rayons moyens σ_〈R〉":R_mean_std,
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"σ_〈R〉^tot":sigma_r_tot,
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"MI_l":MI_l,
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"MI_p":MI_p,
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"MI_mR":MI_mR,
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"MI_mH":MI_mH,
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# "MI_mr_in":MI_mr_in,
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"V_scan":V_scan,
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"R_V_scan":R_V_scan,
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"S_V_scan":S_V_scan,
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"Rayon hydraulique R_h":R_h,
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"HI":HI
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}
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