Djalim Simaila
8175561250
✨ feat(data_processing.py): remove deprecated get_discrete_vertices2 method and rename get_discrete_vertices3 to get_discrete_vertices. Simplify get_discrete_vertices method by removing the selection of the discretisation method from the settings and always using the Z0-Zi < DeltaZ method. This improves code readability and maintainability. ✨ feat(input.py): remove deprecated result_file_path and bruteforce_discretization_result attributes from ScannedObject class. Add old_delta, old_discrete, and old_discrete_type attributes to cache the results of the get_discrete_vertices method. This improves performance by avoiding unnecessary recomputations of the discretized vertices. ✨ feat(MainWindow.py): add support for selecting a layer to display discrete graphs for. Add two new graph types: "Coupe de la couche" and "Difference entre le rayon de chaque points 🔧 fix(ui): change tab index to display the correct tab on startup ✨ feat(ui): add label and combobox to select layer to display 🔧 fix(worker): add discretisation_value parameter to PreProcessWorker constructor ✨ feat(math): add get_true_teta_from_x_y, get_difference_from_mean_value, and get_distance_between_two_vertices functions The UI fix changes the tab index to display the correct tab on startup. The new label and combobox allow the user to select the layer to display. The worker fix adds a discretisation_value parameter to the PreProcessWorker constructor. The new math functions are get_true_teta_from_x_y, get_difference_from_mean_value, and get_distance_between_two_vertices. These functions are useful for calculating teta, differences from mean values, and distances between vertices.
188 lines
5.1 KiB
Python
188 lines
5.1 KiB
Python
"""
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Created on Mon Apr 17 2023
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@name: data_extraction.py
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@desc: This module contains some utility functions for math operations.
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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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import numpy as np
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import math
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def get_mean(values:list):
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"""
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Get the mean of the values.
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:param values: values
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:return: mean of the values
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:Example:
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>>> get_mean([1,2,3,4,5])
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3.0
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"""
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return np.mean(values)
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def get_standard_deviation(values:list):
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"""
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Get the standard deviation of the values.
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:param values: values
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:return: standard deviation of the values
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:Example:
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>>> get_standard_deviation([1,2,3,4,5])
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1.4142135623730951
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"""
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return np.std(values)
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def get_x_y_z_mean(discrete_values:list):
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"""
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Get the mean of the x and y coordinates in the discrete range.
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:param x: x coordinates
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:param y: y coordinates
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:return: mean of x and y coordinates in the discrete range
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:Example:
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>>> get_x_y_z_mean([(1,2,3),(4,5,6),(7,8,9)])
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(4.0, 5.0, 6.0)
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"""
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x = [vertex[0] for vertex in discrete_values]
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y = [vertex[1] for vertex in discrete_values]
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z = [vertex[2] for vertex in discrete_values]
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return get_mean(x), get_mean(y), get_mean(z)
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def get_radius_from_x_y(xi:float, yi:float, x_mean:float, y_mean:float):
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"""
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Get the radius from the x and y coordinates.
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:param xi: x coordinate
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:param yi: y coordinate
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:param x_mean: mean of x coordinates in the discrete range
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:param y_mean: mean of y coordinates in the discrete range
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:return: radius for this point
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:Example:
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>>> get_radius_from_x_y(1,2,3,4)
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2.8284271247461903
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"""
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return np.sqrt(np.power((xi - x_mean), 2) + np.power((yi - y_mean), 2))
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def get_mean_radius(discrete_values:list):
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"""
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Get the mean of the radius in the discrete range.
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:param discrete_values: discrete values
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:return: mean of the radius in the discrete range
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:Example:
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>>> get_mean_radius([(1,2,3),(4,5,6),(7,8,9)])
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2.82842712474619
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"""
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x_mean, y_mean, z_mean = get_x_y_z_mean(discrete_values)
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radius = []
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for x,y,z in discrete_values:
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radius.append(get_radius_from_x_y(x,y,x_mean,y_mean))
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return get_mean(radius)
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def get_radius_std(discrete_values:list):
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"""
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Get the standard deviation of the radius in the discrete range.
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:param discrete_values: discrete values
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:return: standard deviation of the radius in the discrete range
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:Example:
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>>> get_radius_std([(1,2,3),(4,5,6),(7,8,9)])
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2.8284271247461903
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"""
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x_mean, y_mean, z_mean = get_x_y_z_mean(discrete_values)
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radius = []
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for x,y,z in discrete_values:
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radius.append(get_radius_from_x_y(x,y,x_mean,y_mean))
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return get_standard_deviation(radius)
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def get_mean_teta(discrete_values:list):
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"""
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Get the mean of the teta in the discrete range.
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:param discrete_values: discrete values
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:return: mean of the teta in the discrete range
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:Example:
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>>> get_mean_teta([(1,2,3),(4,5,6),(7,8,9)])
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0.7853981633974483
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"""
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x_mean, y_mean, z_mean = get_x_y_z_mean(discrete_values)
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teta = []
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for x,y,z in discrete_values:
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teta.append(get_teta_from_x_y(x,y,x_mean,y_mean))
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return get_mean(teta)
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def get_teta_from_x_y(xi:float, yi:float, x_mean:float, y_mean:float):
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"""
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Get the teta from the x and y coordinates.
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:param xi: x coordinate
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:param yi: y coordinate
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:param x_mean: mean of x coordinates in the discrete range
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:param y_mean: mean of y coordinates in the discrete range
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:return: teta for this point
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:Example:
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>>> get_teta_from_x_y(1,2,3,4)
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0.7853981633974483
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"""
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return math.atan((yi - y_mean)/(xi-x_mean))
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def get_true_teta_from_x_y(xi:float, yi:float, x_mean:float, y_mean:float):
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"""
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Get the teta from the x and y coordinates.
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:param xi: x coordinate
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:param yi: y coordinate
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:param x_mean: mean of x coordinates in the discrete range
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:param y_mean: mean of y coordinates in the discrete range
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:return: teta for this point
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:Example:
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>>> get_true_teta_from_x_y(1,2,3,4)
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0.7853981633974483
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"""
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return math.atan2((xi-x_mean),(yi-y_mean))
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def get_difference_from_mean_value(values:list, mean_value:float):
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"""
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Get the difference from the mean value.
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:param values: values
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:param mean_value: mean value
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:return: difference from the mean value
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:Example:
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>>> get_difference_from_mean_value([1,2,3,4,5], 3)
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[-2.0, -1.0, 0.0, 1.0, 2.0]
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"""
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return [value - mean_value for value in values]
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def get_distance_between_two_vertices(vertex_1,vertex_2):
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"""
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Get the distance between two vertices.
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:param vertex_1: vertex 1
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:param vertex_2: vertex 2
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:return: distance between two vertices
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:Example:
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>>> get_distance_between_two_vertices((1,2,3),(4,5,6))
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5.196152422706632
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"""
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return np.sqrt(np.power((vertex_1[0] - vertex_2[0]), 2) + np.power((vertex_1[1] - vertex_2[1]), 2) + np.power((vertex_1[2] - vertex_2[2]), 2))
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#todo fix examples
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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