# -*- coding: utf-8 -*- # Created on Wed Sep 25 14:35:00 2024 # @author: mhebding # Bibliotheques import numpy as np from scipy.stats import linregress import matplotlib.pyplot as plt def exp(VA,VB,pH, couleur, annee): X = VB/VA x = np.log10(VB/VA) y = pH r = np.corrcoef(x, y) [a, b] = np.polyfit(x, y, 1) # (a,b,r,_,_) = linregress(x,y) # a,b,R = round(a,3), round(b,3), round(r**2,3) # arrondis print("y = ax + b") print(" a = {}\n b = {}\n R = {}".format(a,b,r[0][1])) modele = a*x+b plt.scatter(x,y, color=couleur, label=annee) plt.plot(x,modele) # Donnees 2023 VA0_23 = np.array([50,50,50,50,50,50,50,50,50,50]) VB0_23 = np.array([10,20,30,40,50,60,70,80,90,100]) pH0_23 = np.array([4.1,4.4,4.55,4.65,4.77,4.85,4.92,4.97,5.03,5.07]) VA1_23 = np.array([25,30,35,40,45,50,55,60,65,70]) VA2_23 = np.array([25,25,25,25,25,25,25,25,25,25,25]) VA3_23 = np.array([5,10,15,20,20,20]) VA4_23 = np.array([25,25,25,25,25,10,20,30,40,50]) VA5_23 = np.array([5,5,5,5,5,5]) VA6_23 = np.array([25,25,25,25,25,35,45,55,65,70]) VA7_23 = np.array([10,12.5,15,20,30,40,60,80,80]) VA8_23 = np.array([10,10,10,10,10,10,10,10,10,10,10]) VB1_23 = np.array([25,25,25,25,25,25,25,25,25,25]) VB2_23 = np.array([25,30,35,40,45,50,55,60,65,70,75]) VB3_23 = np.array([5,5,5,5,10,20]) VB4_23 = np.array([10,20,30,40,50,25,25,25,25,25]) VB5_23 = np.array([2,5,10,15,20,30]) VB6_23 = np.array([5,15,25,35,45,50,50,50,50,50]) VB7_23 = np.array([10,10,10,10,10,10,10,10,15]) VB8_23 = np.array([10,15,20,25,30,35,40,45,50,55,60]) pH1_23 = np.array([4.5,4.42,4.37,4.33,4.28,4.24,4.20,4.17,4.14,4.10]) pH2_23 = np.array([4.63,4.71,4.79,4.84,4.90,4.95,5,5.04,5.07,5.10,5.13]) pH3_23 = np.array([4.51,4.20,4.0,3.80,4.14,4.48]) pH4_23 = np.array([4.13,4.41,4.60,4.73,4.83,4.93,4.63,4.47,4.34,4.23]) pH5_23 = np.array([4.30,4.65,4.95,5.1,5.25,5.45]) pH6_23 = np.array([3.87,4.62,4.53,4.67,4.78,4.70,4.59,4.51,4.43,4.40]) pH7_23 = np.array([4.55,4.45,4.40,4.27,4.10,3.95,3.76,3.62,3.79]) pH8_23 = np.array([4.8,5.0,5.14,5.24,5.33,5.4,5.46,5.52,5.57,5.60,5.64]) VA23 = np.concatenate((VA0_23, VA1_23, VA2_23, VA3_23, VA4_23, VA5_23, VA6_23, VA7_23, VA8_23), axis=None) VB23 = np.concatenate((VB0_23, VB1_23, VB2_23, VB3_23, VB4_23, VB5_23, VB6_23, VB7_23, VB8_23), axis=None) pH23 = np.concatenate((pH0_23, pH1_23, pH2_23, pH3_23, pH4_23, pH5_23, pH6_23, pH7_23, pH8_23), axis=None) exp(VA23,VB23,pH23,'blue','2023') # Donnees 2024 VA1_24 = np.array([5,10,15,20,25,30,35,40,45]) VA2_24 = np.array([5,10,15,20,25,30,35,40,45]) VA3_24 = np.array([25,20,10,30,40,35,5,45]) VA4_24 = np.array([10,20,25,30,40,5,15]) VA5_24 = np.array([15,22,34,38,43,10,30,40,45]) VA6_24 = np.array([5,10,15,20,25]) VA7_24 = np.array([45,40,35,30,25,20,15,10,5]) VA8_24 = np.array([5,15,25,30,35,45]) VB1_24 = np.array([45,40,35,30,25,20,15,10,5]) VB2_24 = np.array([45,40,35,30,25,20,15,10,5]) VB3_24 = np.array([25,30,40,20,10,15,45,5]) VB4_24 = np.array([40,30,25,20,10,45,35]) VB5_24 = np.array([35,28,16,12,7,40,20,10,5]) VB6_24 = np.array([45,40,35,30,25]) VB7_24 = np.array([5,10,15,20,25,30,35,40,45]) VB8_24 = np.array([45,35,25,20,15,5]) pH1_24 = np.array([5.60,5.20,4.94,4.74,4.62,4.44,4.19,3.98,3.6]) pH2_24 = np.array([3.65,3.28,3,2.82,2.68,2.47,2.36,2.14,1.78]) pH3_24 = np.array([4.6,4.78,5.18,4.6,4,4.1,5.6,3.7]) pH4_24 = np.array([5.13,4.64,4.51,4.43,3.89,5.54,4.95]) pH5_24 = np.array([4.93,4.66,4.20,4.09,4.14,5.19,4.29,3.96,3.61]) pH6_24 = np.array([5.62,5.25,4.93,4.6,4.5]) pH7_24 = np.array([3.72,3.86,4.27,4.19,4.04,4.57,5.04,5.05,5.66]) pH8_24 = np.array([5.45,4.8,4.43,4.35,4,3.53]) VA24 = np.concatenate((VA1_24, VA3_24, VA4_24, VA5_24, VA6_24, VA7_24, VA8_24), axis=None) VB24 = np.concatenate((VB1_24, VB3_24, VB4_24, VB5_24, VB6_24, VB7_24, VB8_24), axis=None) pH24 = np.concatenate((pH1_24, pH3_24, pH4_24, pH5_24, pH6_24, pH7_24, pH8_24), axis=None) exp(VA24,VB24,pH24,'orange','2024') # Donnees 2025 VA1_25 = np.array([25,30,35,40,45,50,55,60,65,70,75]) VA2_25 = np.array([25,25,25,25,25,25,30,35,40,45,50]) VA3_25 = np.array([25,25,35,40,45,45,40,40]) VA4_25 = np.array([25,30,30,35,40,40,40,45,45,50,50]) VA5_25 = np.array([30,35,40,45,50,20,30,20,15,10]) VA6_25 = np.array([5,10,15,20,25,30,35,40,45,50]) VA7_25 = np.array([25,25,25,25,25,25,35,40,45,50,60,70,80,90,100]) VA8_25 = np.array([45,55,65,75,90,100,5,5,5,5,5,5]) VA9_25 = np.array([5,15,25,45,50,50,50,50]) VB1_25 = np.array([25,25,25,25,25,25,25,25,25,25,25]) VB2_25 = np.array([30,35,40,45,50,25,25,25,25,25,25]) VB3_25 = np.array([25,30,30,30,30,60,45,50]) VB4_25 = np.array([25,25,30,30,30,35,40,40,45,45,50]) VB5_25 = np.array([20,20,20,30,50,80,55,45,40,40]) VB6_25 = np.array([50,50,50,50,50,50,50,50,50,50]) VB7_25 = np.array([90,80,70,50,35,25,25,25,25,25,25,25,25,25,25]) VB8_25 = np.array([5,5,5,5,5,5,45,55,65,75,85,100]) VB9_25 = np.array([50,50,50,50,10,20,30,40]) pH1_25 = np.array([4.7,4.6,4.52,4.46,4.41,4.37,4.33,4.30,4.28,4.25,4.22]) pH2_25 = np.array([5.4,5.42,5.46,5.5,5.53,4.37,4.24,4.14,4.09,4.12,4.03]) pH3_25 = np.array([4.37,4.43,4.29,4.22,4.17,4.50,4.42,4.47]) pH4_25 = np.array([4.77,4.79,4.80,4.83,4.89,4.83,4.77,4.76,4.75,4.74,4.72]) pH5_25 = np.array([4.02,3.93,3.86,3.96,4.14,4.8,4.46,4.55,4.67,4.7]) pH6_25 = np.array([5.51,5.13,4.93,4.80,4.69,4.60,4.53,4.47,4.41,4.36]) pH7_25 = np.array([4.98,4.9,4.89,4.71,4.54,4.61,4.45,4.39,4.34,4.29,4.21,4.14,4.08,4.03,3.99]) pH8_25 = np.array([3.84,3.15,3.08,3.02,2.95,2.93,5.18,5.26,5.33,5.40,5.45,5.51]) pH9_25 = np.array([5.57,5.07,4.83,4.53,3.85,4.25,4.40,4.6]) VA25 = np.concatenate((VA1_25, VA2_25, VA3_25, VA4_25, VA5_25, VA6_25, VA7_25, VA8_25, VA9_25), axis=None) VB25 = np.concatenate((VB1_25, VB2_25, VB3_25, VB4_25, VB5_25, VB6_25, VB7_25, VB8_25, VB9_25), axis=None) pH25 = np.concatenate((pH1_25, pH2_25, pH3_25, pH4_25, pH5_25, pH6_25, pH7_25, pH8_25, pH9_25), axis=None) exp(VA25,VB25,pH25,'green','2025') # # Donnees 2026 VA1_26 = np.array([25,35,45,55,55,75,85,95]) VA2_26 = np.array([10,20,30,40,50,50,50,50,50,50]) VA3_26 = np.array([50,50,50,50,50]) VA4_26 = np.array([40,40,40,40,40,40]) VA5_26 = np.array([35.3,35.3,35.3,35.3,14.7]) VA6_26 = np.array([25,30,35,40,45,50,55,60,65]) VA7_26 = np.array([25,25,25,25,25,25,25,25,25,25,25]) VA8_26 = np.array([25,30,35,40,45,50,55,60,65,70,75]) VA9_26 = np.array([25,25,25,25,25,25,25,25,25,25,25]) # VB1_26 = np.array([25,25,25,25,25,25,25,25]) VB2_26 = np.array([50,50,50,50,50,10,20,30,40,50]) VB3_26 = np.array([10,20,30,40,50]) VB4_26 = np.array([10,20,30,40,50,60]) VB5_26 = np.array([14.7,17,35,40.5,49.5]) VB6_26 = np.array([25,25,25,25,25,25,25,25,25]) VB7_26 = np.array([25,30,35,40,45,50,55,60,65,70,75]) VB8_26 = np.array([25,25,25,25,25,25,25,25,25,25,25]) VB9_26 = np.array([25,30,35,40,45,50,55,60,65,70,75]) # # pH1_26 = np.array([4.36,4.23,4.10,4.01,3.94,3.89,3.81,3.75]) pH2_26 = np.array([5.03,4.76,4.60,4.46,4.35,3.63,3.96,4.16,4.25,4.37]) pH3_26 = np.array([3.37,3.92,4.10,4.18,4.28]) pH4_26 = np.array([3.9,4.2,4.38,4.52,4.62,4.71]) pH5_26 = np.array([3.86,3.91,4.17,4.23,4.68]) pH6_26 = np.array([4.2,4.1,4.05,4,3.95,3.9,3.87,3.83,3.80]) pH7_26 = np.array([4.22,4.28,4.36,4.42,4.48,4.53,4.58,4.63,4.67,4.71,4.74]) pH8_26 = np.array([4.40,4.31,4.23,4.18,4.12,4.08,4.04,4.00,3.97,3.92,3.89]) pH9_26 = np.array([4.49,4.56,4.62,4.68,4.73,4.78,4.82,4.86,4.89,4.92,4.95]) # # VA26 = np.concatenate((VA1_26, VA2_26, VA3_26, VA4_26, VA5_26, VA6_26, VA7_26, VA8_26, VA9_26), axis=None) VB26 = np.concatenate((VB1_26, VB2_26, VB3_26, VB4_26, VB5_26, VB6_26, VB7_26, VB8_26, VB9_26), axis=None) pH26 = np.concatenate((pH1_26, pH2_26, pH3_26, pH4_26, pH5_26, pH6_26, pH7_26, pH8_26, pH9_26), axis=None) # exp(VA26,VB26,pH26,'red','2026') # Tout ensemble VA = np.concatenate((VA23, VA24, VA25), axis=None) VB = np.concatenate((VB23, VB24, VB25), axis=None) pH = np.concatenate((pH23, pH24, pH25), axis=None) #exp(VA,VB,pH,'black','232425') # Affichage Donnees plt.title('Determination constante equilibre') plt.xlabel('log(B/A)') plt.ylabel('pH') plt.legend() plt.show()