diff --git a/docs/community/probabilistic_macrostability/Tutorial_3_Fragility_Curve_Integration.rst b/docs/community/probabilistic_macrostability/Tutorial_3_Fragility_Curve_Integration.rst index 7531cfb..82d2c24 100644 --- a/docs/community/probabilistic_macrostability/Tutorial_3_Fragility_Curve_Integration.rst +++ b/docs/community/probabilistic_macrostability/Tutorial_3_Fragility_Curve_Integration.rst @@ -301,10 +301,10 @@ interpolation between fragility points) .. code:: ipython3 #calculating alphas in given design point water level (h_Star) - Hs= 11.846 + Hs= 11.067 # Calculating u* for h* - P_us=gumbel_r.pdf(Hs, loc=mu, scale=std) # \Phi^{-1}(F_h(h*)) + P_us=gumbel_r.cdf(Hs, loc=mu, scale=std) # \Phi^{-1}(F_h(h*)) us = st.norm.ppf(P_us) print('u*, design point (genormeerde waterstand) = ', us "\n") @@ -334,43 +334,52 @@ interpolation between fragility points) for i in range(len(values)): Alphas[i]=values[i][idx] - print('α_i |h*` = ', Alphas "\n") - - - print('Sum of influence factors after integrating the probbaility of water level is: ', - sum(Alphas**2)) - - # check is equal to 1 if not we should normalize them. - # warning: The Alphas are not added upt to 1 ( error tolerance 1%) - # And your Alphas after integration are not reliable. + print('α_i |h^* = ', Alphas) + print() + + print('Sum of the squared influence coefficients alpha at the design point of the water level is: ',sum(Alphas**2)) + print('Due to the linear interpolation of alpha values between the fragility points, the sum may not be equal to 1.') + print('The general recommendation in this case is to add an extra fragility point to the fragility curve close to the') + print('design point of the water level. However, for now we normalize the values to 1.') + print() + + Alphas /= np.sqrt(sum(Alphas**2)) + print('Sum of the normalized squared influence coefficients alpha at the design point of the water level is: ',sum(Alphas**2)) .. parsed-literal:: - u*, design point (genormeerde waterstand) = -2.860139676806077 + u*, design point (genormeerde waterstand) = 0.25197669527488287 - 𝛼_ℎ (invloedscoëfficiënt van het waterstand) = 0.7153517909441423 + 𝛼_ℎ (invloedscoëfficiënt van het waterstand) = -0.06302209004084777 - α_i |h* = [ 0.05643247 0. 0. -0.30353334 0.83616773 0.06107594 - 0.34022656 0. 0.17395521] + dict_keys(['Pop.SP 2 Below', 'ShearStrengthRatio.H_Rk_ko', 'ModelFactor', 'StrengthIncreaseExponent.H_Rk_ko', 'StrengthIncreaseExponent.H_Rk_k_shallow', 'StrengthIncreaseExponent.H_vbv_v', 'ShearStrengthRatio.H_vbv_v', 'ShearStrengthRatio.H_Rk_k_shallow', 'Pop.SP 2 Above']) + α_i |h^* = [ 0. 0.25793011 -0.25412093 0.05968704 0. 0. + 0.7000474 0.3986752 0.07902608] - Sum of influence factors after integrating the probbaility of water level is: 0.9442383788975067 + Sum of the squared influence coefficients alpha at the design point of the water level is: 0.7899213344510857 + Due to the linear interpolation of alpha values between the fragility points, the sum may not be equal to 1. + The general recommendation in this case is to add an extra fragility point to the fragility curve close to the + design point of the water level. However, for now we normalize the values to 1. + +Sum of the normalized squared influence coefficients alpha at the design point of the water level is: 0.9999999999999998 .. code:: ipython3 # transformed influence coefficient(s) of parameters to be determined - Alpha_T = Alphas**2*(1-alphaH**2) - print('The influence factors of strength paramters are:\n', Alpha_T "\n") - - print('Influence factor from water level = ', 1-sum(Alpha_T)) + Alpha_T = Alphas**2*(1-alphaH**2) # Alpha_T is the squared, see equation above. + print('The squared influence coefficients of the strength parameters are:\n', Alpha_T) + print() + print('Squared influence coefficient of the water level = ', 1-sum(Alpha_T)) -.. parsed-literal:: - The influence factors of strength paramters are: - [0.00155496 0. 0. 0.0449857 0.34138816 0.00182139 - 0.05651947 0. 0.01477531] +.. parsed-literal:: + + The squared influence coefficients of the strength parameters are: + [0. 0.08388646 0.08142705 0.00449208 0. 0. + 0.61793486 0.20041316 0.0078746 ] - Influence factor from water level = 0.5389550127608109 + Squared influence coefficient of the water level = 0.003971783833116804 diff --git a/docs/community/probabilistic_macrostability/notebooks/Tutorial_3_Fragility_Curve_Integration.ipynb b/docs/community/probabilistic_macrostability/notebooks/Tutorial_3_Fragility_Curve_Integration.ipynb index 322b4c1..dd25131 100644 --- a/docs/community/probabilistic_macrostability/notebooks/Tutorial_3_Fragility_Curve_Integration.ipynb +++ b/docs/community/probabilistic_macrostability/notebooks/Tutorial_3_Fragility_Curve_Integration.ipynb @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 13, "id": "be4c26a5", "metadata": {}, "outputs": [], @@ -266,7 +266,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -412,23 +412,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "u*, design point (genormeerde waterstand) = -2.860139676806077\n", + "u*, design point (genormeerde waterstand) = 0.25197669527488287\n", "\n", - "𝛼_ℎ (invloedscoëfficiënt van het waterstand) = 0.7153517909441423\n", + "𝛼_ℎ (invloedscoëfficiënt van het waterstand) = -0.06302209004084777\n", "\n", - "α_i |h^* = [ 0.05643247 0. 0. -0.30353334 0.83616773 0.06107594\n", - " 0.34022656 0. 0.17395521]\n", + "dict_keys(['Pop.SP 2 Below', 'ShearStrengthRatio.H_Rk_ko', 'ModelFactor', 'StrengthIncreaseExponent.H_Rk_ko', 'StrengthIncreaseExponent.H_Rk_k_shallow', 'StrengthIncreaseExponent.H_vbv_v', 'ShearStrengthRatio.H_vbv_v', 'ShearStrengthRatio.H_Rk_k_shallow', 'Pop.SP 2 Above'])\n", + "α_i |h^* = [ 0. 0.25793011 -0.25412093 0.05968704 0. 0.\n", + " 0.7000474 0.3986752 0.07902608]\n", "\n", - "Sum of influence factors after integrating the probbaility of water level is: 0.9442383788975067\n" + "Sum of the squared influence coefficients alpha at the design point of the water level is: 0.7899213344510857\n", + "Due to the linear interpolation of alpha values between the fragility points, the sum may not be equal to 1.\n", + "The general recommendation in this case is to add an extra fragility point to the fragility curve close to the\n", + "design point of the water level. However, for now we normalize the values to 1.\n", + "\n", + "Sum of the normalized squared influence coefficients alpha at the design point of the water level is: 0.9999999999999998\n" ] } ], "source": [ - "#calculating alphas in given design point water level (h_Star)\n", - "Hs= 11.846 \n", + "#Calculating alphas in given design point water level (h_Star)\n", + "Hs= 11.067 \n", "\n", "# Calculating u* for h*\n", - "P_us=gumbel_r.pdf(Hs, loc=mu, scale=std) # \\Phi^{-1}(F_h(h*))\n", + "P_us=gumbel_r.cdf(Hs, loc=mu, scale=std) # \\Phi^{-1}(F_h(h*))\n", "us = st.norm.ppf(P_us) \n", "\n", "print('u*, design point (genormeerde waterstand) = ', us)\n", @@ -437,14 +443,14 @@ "\n", "print('𝛼_ℎ (invloedscoëfficiënt van het waterstand) =', alphaH)\n", "print()\n", - "#Getting alphas befor integrating for H^star ( interpolated through fragility curve)\n", "\n", + "#Getting alphas befor integrating for H^star ( interpolated through fragility curve)\n", "a=FC_list[0].Alphas \n", "for i in a.keys(): \n", " values = np.array(list(a.values()))\n", "\n", "# to see the list of parameters\n", - "#print(a.keys()) \n", + "print(a.keys()) \n", "\n", "# a function to find the related alpha set interpolated at given h*\n", "def find_nearest(array, value): \n", @@ -461,12 +467,14 @@ "print('α_i |h^* = ', Alphas) \n", "print()\n", "\n", - "print('Sum of influence factors after integrating the probbaility of water level is: ',\n", - " sum(Alphas**2)) \n", + "print('Sum of the squared influence coefficients alpha at the design point of the water level is: ',sum(Alphas**2)) \n", + "print('Due to the linear interpolation of alpha values between the fragility points, the sum may not be equal to 1.')\n", + "print('The general recommendation in this case is to add an extra fragility point to the fragility curve close to the')\n", + "print('design point of the water level. However, for now we normalize the values to 1.')\n", + "print()\n", "\n", - "# check is equal to 1 if not we should normalize them.\n", - "# warning: The Alphas are not added upt to 1 ( error tolerance 1%) \n", - "# And your Alphas after integration are not reliable." + "Alphas /= np.sqrt(sum(Alphas**2))\n", + "print('Sum of the normalized squared influence coefficients alpha at the design point of the water level is: ',sum(Alphas**2))\n" ] }, { @@ -479,21 +487,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "The influence factors of strength paramters are:\n", - " [0.00155496 0. 0. 0.0449857 0.34138816 0.00182139\n", - " 0.05651947 0. 0.01477531]\n", + "The squared influence coefficients of the strength parameters are:\n", + " [0. 0.08388646 0.08142705 0.00449208 0. 0.\n", + " 0.61793486 0.20041316 0.0078746 ]\n", "\n", - "Influence factor from water level = 0.5389550127608109\n" + "Squared influence coefficient of the water level = 0.003971783833116804\n" ] } ], "source": [ "# transformed influence coefficient(s) of parameters to be determined\n", - "Alpha_T = Alphas**2*(1-alphaH**2) \n", - "print('The influence factors of strength paramters are:\\n', Alpha_T)\n", + "Alpha_T = Alphas**2*(1-alphaH**2) # Alpha_T is the squared, see equation above.\n", + "print('The squared influence coefficients of the strength parameters are:\\n', Alpha_T)\n", "\n", "print()\n", - "print('Influence factor from water level = ', 1-sum(Alpha_T))" + "print('Squared influence coefficient of the water level = ', 1-sum(Alpha_T))" ] } ],