{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNs2ZExOzTiiHmDxWCipgMs"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["Comment vérifier que l'incertitude-type d'une variable $C_b$ décrite par une loi Gaussienne (normale) est égale à l'écart-type de la distribution  ?\n","\n"," avec $C_b=0.10$ et $u(C_b)=0.01*C_b$"],"metadata":{"id":"lc2fQZ7a3D66"}},{"cell_type":"code","source":["import numpy as np\t#Importe la bibliothèque pour faire divers calculs\n","import numpy.random as rd\t#Importe la fonction de tirage aléatoire rectangulaire\n","import matplotlib.pyplot as plt\n","C_b = 0.10 #valeur de C_b\n","u_C_b=0.01*C_b #incertitude de C_b\n","N_sim =10000 \t#Nombre de tirage\n","C_b_list=rd.normal(C_b, u_C_b, N_sim) #vecteur de N_sim valeurs aléatoires de C_b\n","C_b_moy=np.mean(C_b_list) #moyenne de C_b\n","u_C_b_calc= np.std(C_b_list, ddof=1) #écart-type de C_b\n","print (\"C_b =\",format(C_b_moy,\"#.4f\"),\" avec écart-type = \",format(u_C_b_calc,\"#.1e\"),\" à comparer à u_C_b =\",format(u_C_b,\"#.1e\"))\n","#Affiche C_b et incertitude type\n","\n","plt.hist(C_b_list, bins='rice', histtype = 'step') # Tracé histogramme\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"TcINuLBb3Vzu","executionInfo":{"status":"ok","timestamp":1662647014017,"user_tz":-120,"elapsed":575,"user":{"displayName":"Lionnel Malara","userId":"16342666118884215780"}},"outputId":"503144ff-0259-46df-81c3-a4108071c17f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["C_b = 0.1000  avec écart-type =  9.9e-04  à comparer à u_C_b = 1.0e-03\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["Comment vérifier que l'incertitude-type d'une variable V décrite par une loi uniforme est égale à la demi-étendue de V divisée par racine de 3 ?\n","\n","avec V = 10,0 mL et m = 0,1 mL"],"metadata":{"id":"WYGQtfzmb4fi"}},{"cell_type":"code","source":["import numpy as np\t#Importe la bibliothèque pour faire divers calculs\n","import numpy.random as rd\t#Importe la fonction de tirage aléatoire rectangulaire\n","import matplotlib.pyplot as plt\n","V = 10.0 #valeur de V\n","m_V=0.05 #demi-étendu de V\n","N_sim =10000 \t#Nombre de tirage\n","Vlist=rd.uniform(V-m_V, V+m_V, N_sim) #vecteur de N_sim valeurs aléatoires de V\n","V_moy=np.mean(Vlist) #moyenne de V\n","u_V= np.std(Vlist, ddof=1) #écart-type de V\n","print (\"V_moy =\",format(V_moy,\"#.4f\"),\" avec u(V) = \",format(u_V,\"#.1e\"),\" à comparer à m/racine(3) =\",format(m_V/np.sqrt(3),\"#.1e\"))#Affiche V et incertitude type\n","\n","plt.hist(Vlist, bins='rice', histtype = 'step') # Tracé histogramme\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"687XkbjXc6BO","executionInfo":{"status":"ok","timestamp":1757680809466,"user_tz":-120,"elapsed":293,"user":{"displayName":"Lionnel Malara","userId":"16342666118884215780"}},"outputId":"c37ec0b0-8c8c-43c5-8c67-926ef410011e"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["V_moy = 9.9997  avec u(V) =  2.9e-02  à comparer à m/racine(3) = 2.9e-02\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","metadata":{"id":"PuzzptT0SLQz"},"source":["Comment déterminer l'incertitude-type d'un produit ?\n","\n","$\\frac{C_b \\times Ve}{V}$"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"id":"YnIUpliBzG21","executionInfo":{"status":"ok","timestamp":1757680830963,"user_tz":-120,"elapsed":205,"user":{"displayName":"Lionnel Malara","userId":"16342666118884215780"}},"outputId":"6326d92d-ead8-4c1b-8880-183188bdb770"},"source":["import numpy as np\t#Importe la bibliothèque pour faire divers calculs\n","import numpy.random as rd\t#Importe la fonction de tirage aléatoire rectangulaire\n","import matplotlib.pyplot as plt\n","C_b = 0.10 #valeur de C_b\n","u_C_b=0.01*C_b #incertitude de C_b\n","V = 10.0 #valeur de V\n","Ve = 12.4 #valeur de Ve\n","m_V=0.05 #demi-étendu de V et Ve\n","N_sim =10000 \t#Nombre de tirage\n","\n","C_b_list=rd.normal(C_b, u_C_b, N_sim) #vecteur de N_sim valeurs aléatoires de C_b\n","Vlist=rd.uniform(V-m_V, V+m_V, N_sim) #vecteur de N_sim valeurs aléatoires de V\n","Velist=rd.uniform(Ve-m_V, Ve+m_V, N_sim) #vecteur de N_sim valeurs aléatoires de Ve\n","C_a=C_b_list*Velist/Vlist # calcul des N_sim valeurs de Ca\n","C_a_moy=np.mean(C_a) #moyenne de C_a\n","u_C_a= np.std(C_a, ddof=1) #écart-type de C_a\n","print (\"C_a =\",format(C_a_moy,\"#.4f\"),\" avec u(C_a) = \",format(u_C_a,\"#.1e\")) #Affiche résultat et incertitude type\n","\n","plt.hist(C_a, bins='rice', histtype = 'step') # Tracé histogramme\n","plt.show()\n"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["C_a = 0.1240  avec u(C_a) =  1.3e-03\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}