massive transfer of risk-reliability book

book/code/cosinewave.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+x = np.linspace(0, 2*np.pi, endpoint=True)
+y = np.cos(x)
+
+plt.figure()
+plt.plot(x, y)
+plt.title('$y=\cos(x)$')
+plt.xlabel('$x$')
+plt.ylabel('$y$')
+plt.savefig('cosinewave.svg')

book/code/ex_FN_curve_step_01.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axisartist.axislines import AxesZero
+
+# Probability density/mass function
+
+fig = plt.figure(figsize=(8,6))
+ax = fig.add_subplot(axes_class=AxesZero)
+
+for direction in ["xzero", "yzero"]:
+    ax.axis[direction].set_axisline_style("-|>") # add arrows
+    ax.axis[direction].set_visible(True) # x and y from origin
+    
+for direction in ["left", "right", "bottom", "top"]:
+    ax.axis[direction].set_visible(False) # hide borders
+
+N_fatalities = ['0', '10', '100']
+N_fatalities_loc = [0.05, 0.30, 0.66]
+
+pdf = ['0.989', '$10^{-2}$', '$10^{-3}$']
+pdf_loc = [0.99, 0.66, 0.33]
+
+ax.bar(N_fatalities_loc, pdf_loc, width=0.05, color= 'lightcyan', edgecolor='k')
+
+ax.set_xticks(N_fatalities_loc)
+ax.set_xticklabels(N_fatalities)
+
+ax.set_yticks(pdf_loc)
+ax.set_yticklabels(pdf)
+
+ax.set_title('Probability density/mass function, $f_{N}(n)$', x=0.6, y=1.0)
+ax.set_xlabel('Fatalities')
+ax.set_ylabel('Probability')
+
+ax.axhline(y=0.66, xmin= 0.01, xmax= 0.40, linewidth=1, linestyle='dashed', color='k') # add dashed horizontal lines
+ax.axhline(y=0.33, xmin= 0.01, xmax= 0.90, linewidth=1, linestyle='dashed', color='k')
+
+plt.savefig('ex_FN_curve_step_01_py.svg')

book/code/ex_FN_curve_step_02.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axisartist.axislines import AxesZero
+
+# Cumulative Distribution Function 
+
+fig = plt.figure(figsize=(8,6))
+ax = fig.add_subplot(axes_class=AxesZero)
+
+for direction in ["xzero", "yzero"]:
+    ax.axis[direction].set_axisline_style("-|>") # add arrows
+    ax.axis[direction].set_visible(True) # x and y from origin
+    
+for direction in ["left", "right", "bottom", "top"]:
+    ax.axis[direction].set_visible(False) # hide borders
+
+N_fatalities = ['0', '10', '100', '']
+N_fatalities_loc = [0.03, 0.33, 0.76, 0.95]
+
+pdf = ['0', '0.989', '0.999', '1']
+pdf_loc = [0.03, 0.66, 0.86, 0.99]
+
+ax.step(N_fatalities_loc, pdf_loc, color= 'darkblue',linewidth=4)
+
+ax.set_xticks(N_fatalities_loc)
+ax.set_xticklabels(N_fatalities)
+
+ax.set_yticks(pdf_loc)
+ax.set_yticklabels(pdf)
+
+ax.set_title('Cumulative distribution function, $F_{N}(n) = P(N \leq n)$', x=0.55, y=1.0)
+ax.set_xlabel('Fatalities')
+ax.set_ylabel('Probability')
+
+ax.text(0.8, 0.9, r'} $10^{-3}$', fontsize=20)
+ax.text(0.4, 0.725, r'} $10^{-2}$', fontsize=30)
+
+plt.savefig('ex_FN_curve_step_02_py.svg')

book/code/ex_FN_curve_step_03.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.axisartist.axislines import AxesZero
+
+# FN curve
+
+fig = plt.figure(figsize=(8,6))
+ax = fig.add_subplot(axes_class=AxesZero)
+
+for direction in ["xzero", "yzero"]:
+    ax.axis[direction].set_axisline_style("-|>") # add arrows
+    ax.axis[direction].set_visible(True) # x and y from origin
+    
+for direction in ["left", "right", "bottom", "top"]:
+    ax.axis[direction].set_visible(False) # hide borders
+    
+N_fatalities = ['0', '10', '100','100']
+N_fatalities_loc = [0.03,0.33, 0.95, 0.95]
+
+pdf = ['0','$1.1 * 10^{-2}$', '$10^{-3}$','0']
+pdf_loc = [0.03,0.99, 0.66, 0.03]
+
+ax.step(N_fatalities_loc, pdf_loc, color= 'darkblue',linewidth=4)
+
+ax.set_xticks(N_fatalities_loc)
+ax.set_xticklabels(N_fatalities)
+
+ax.set_yticks(pdf_loc)
+ax.set_yticklabels(pdf)
+
+ax.set_title('FN curve, $1-F_{N}(n) = P(N > n)$', x=0.75, y=1.0)
+ax.set_xlabel('Fatalities')
+ax.set_ylabel('Probability')
+
+plt.savefig('ex_FN_curve_step_03_py.svg')

book/code/present_value.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+t = np.linspace(0, 101)
+y_2 = 1 / (1 + 0.02)**t
+y_4 = 1 / (1 + 0.04)**t
+
+fig = plt.figure()
+plt.plot(t, y_2, label='2%')
+plt.plot(t, y_4, label='4%')
+plt.grid()
+plt.legend()
+plt.xlabel('Year')
+plt.ylabel('Normalized Present Value [$-$]')
+
+plt.savefig('present_value_py.svg')

book/code/risk_types.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+# Given Parameters
+
+N_fatalities = np.linspace(1,1000,1000)  # fatalities
+
+P_df = 0.1; beta = 0.1
+
+alpha = 2
+k = 3
+N_A = 100
+
+r= 0.025
+I = 5*10**6
+B = 0.33
+
+# Individual Risk
+
+Pf_ind_risk = (beta*(10**-4))/P_df  # per year
+
+# print('Acceptable Failure Probability: ', Pf_ind_risk)
+
+# Societal Risk
+
+C = ((beta*100)/(k*np.sqrt(N_A)))**2  # binomial distribution
+
+Pf_SR = (C/(N_fatalities**alpha))
+
+# Economic Risk
+
+D_damage = (5*10**7)*N_fatalities
+
+Pf_ER = (I*B*r)/(D_damage)
+
+# Visualize
+
+my_linewidth = 4
+
+plt.figure(figsize=(8,6))
+
+plt.yscale('log')
+plt.xscale('log')
+plt.plot(N_fatalities, Pf_SR, linewidth=my_linewidth, label='Societal Risk')
+plt.plot(N_fatalities,Pf_ER, linewidth=my_linewidth, color='r', label='Economic Risk')
+plt.axhline(Pf_ind_risk, linewidth=my_linewidth, color='g', label='Individual Risk')
+
+plt.ylabel('Probability of Exceedance, $1-F_N(n)$')
+plt.xlabel('Fatalities, N')
+plt.ylim(10**(-7),1)
+
+plt.legend(loc='upper right')
+
+plt.savefig('risk_types_py.svg')

book/code/sinewave.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+x = np.linspace(0, 2*np.pi, endpoint=True)
+y = np.sin(x)
+
+plt.figure()
+plt.plot(x, y)
+plt.title('$y=\sin(x)$')
+plt.xlabel('$x$')
+plt.ylabel('$y$')
+plt.savefig('sinewave.svg')

book/code/test.py

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+import modulefinder
+
+finder = modulefinder.ModuleFinder()
+finder.run_script('cosinewave.py')
+
+moduleslist = {}
+for name, mod in finder.modules.iteritems():
+    filename = mod.__file__
+    if filename is None:
+        continue
+    if '__' in name:
+        continue
+    #if "python" in filename.lower():
+    #    continue
+    moduleslist[name.split(".")[0]] = True
+    #print '%s: %s' % (name, filename)
+    #print ','.join(mod.globalnames.keys()[:3])
+
+print('Loaded modules:')
+for name, dummy in moduleslist.iteritems():
+    print(name)
+
+input("Press Enter to continue...")