relibmss 0.12.0

A Python package for binary/multi state systems

relibmss

A Python package for binary/multi state systems with BDD/MDD.

Installation

pip install relibmss

Usage

Calculate the probability of a fault tree

import relibmss as ms

# Create a binary system (fault tree)
bss = ms.BSS()

# Define events (This version only supports repeated events)
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

# Make a tree
top = A & B | C # & is AND gate, | is OR gate

# Set probabilities
prob = {
    'A': 0.1,
    'B': 0.2,
    'C': 0.3
}

# Calculate the probability
print(bss.prob(top, prob)) # this style is obsoleted

# Set the interval of the probability
probint = {
    'A': (0.1, 0.2),
    'B': (0.2, 0.3),
    'C': (0.3, 0.4)
}

# Calculate the probability
print(bss.prob_interval(top, probint)) # this style is obsoleted

# new style
topevent = bss.getbdd(top)
print(topevent.prob(prob))
print(topevent.prob_interval(probint))

# An example of the direct use of BddNode
bdd = ms.BDD()

# Define events (This version only supports repeated events)
A = bdd.defvar('A')
B = bdd.defvar('B')
C = bdd.defvar('C')

# Make a tree
top = A & B | C # & is AND gate, | is OR gate

print(top.prob(prob))
print(top.prob_interval(probint))

Boolean operators

In addition to & (AND) and | (OR), events support ~ (NOT) and ^ (XOR).

import relibmss as ms

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')

prob = {'A': 0.1, 'B': 0.2}

# NOT: `~A` and `bss.Not(A)` are equivalent
print(bss.getbdd(~A).prob(prob))         # 0.9
print(bss.getbdd(bss.Not(A)).prob(prob)) # 0.9

# XOR: exactly one of A and B occurs
print(bss.getbdd(A ^ B).prob(prob))      # 0.1*0.8 + 0.9*0.2 = 0.26

Variable order

The variable order determines the size of the BDD/MDD, so it can matter a lot for large models. defvar only declares a variable; the diagram variable itself is created when the expression is first converted (getbdd/getmdd), in order of first appearance in the expression. Variables that never appear are not created at all.

import relibmss as ms

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

# Default: first appearance wins, not declaration order
bss.getbdd(C & A | B)
print(bss.get_varorder())   # ['C', 'A', 'B']

Use set_varorder to pin the order explicitly. It must be called before the first getbdd/getmdd, because the order is fixed once the variables are created (there is no dynamic reordering); calling it afterwards raises an error. Variables you leave out are still created on first appearance, after the ones you listed.

bss = ms.BSS()
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

bss.set_varorder(['C', 'B', 'A'])
bss.getbdd(A & B | C)
print(bss.get_varorder())   # ['C', 'B', 'A']

Passing vars=[...] to the constructor does the same thing: ms.BSS(vars=['C', 'B', 'A']).

get_varorder also lets you carry an order over to another manager. Note the two differ, because MDD variables need their number of states:

bss.get_varorder()   # ['C', 'B', 'A']            -- BSS/BDD: names
mss.get_varorder()   # [('C', 3), ('B', 3), ('A', 2)] -- MSS/MDD: (name, states)

bdd = ms.BDD(bss.get_varorder())   # reuse the order in a raw BDD
mdd = ms.MDD(mss.get_varorder())   # likewise for an MDD

Obtain the minimal cut sets

import relibmss as ms

# Create a binary system
bss = ms.BSS()

# Define events (This version only supports repeated events)
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

# Make a system
top = bss.kofn(2, [A, B, C]) # k-of-n gate

# Convert the ZDD representation to a list of sets
path = bss.getbdd(top).extract(type='bdd')
print('All paths which is to be one')
for x in path:
    print(x)

# Obtain the minimal path vectors
s = bss.minpath(top)

# Convert the ZDD representation to a list of sets
min_path = s.extract()
print('The number of minimal path vectors:', len(min_path))
for x in min_path:
    print(x)

## An example of the direct use of MDD
bdd = ms.BDD()

# Define events (This version only supports repeated events)
A = bdd.defvar('A')
B = bdd.defvar('B')
C = bdd.defvar('C')

# Make a system
top = bdd.kofn(2, [A, B, C]) # k-of-n gate

# Convert the ZDD representation to a list of sets
path = top.extract(type='bdd')
print('All paths which is to be one')
for x in path:
    print(x)

# Obtain the minimal path vectors
s = top.minpath()

# Convert the ZDD representation to a list of sets
min_path = s.extract()
print('The number of minimal path vectors:', len(min_path))
for x in min_path:
    print(x)

Draw a BDD

import relibmss as ms

# Create a binary decision diagram
bss = ms.BSS()

# Define variables
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

# Make a tree
top = A & B | C

# Draw the BDD
bdd = bss.getbdd(top)
source = bdd.dot() # source is a string of the dot language
print(source)

# Example: Display the BDD in Jupyter Notebook
from graphviz import Source
Source(source)

An example of a large fault tree

## This is an example of a large fault tree
## Computational time may be long (about 1 minute)

import relibmss as ms

bss = ms.BSS()
c = [bss.defvar("c" + str(i)) for i in range(61)]

g62 = c[0] & c[1]
g63 = c[0] & c[2]
g64 = c[0] & c[3]
g65 = c[0] & c[4]
g66 = c[0] & c[5]
g67 = c[0] & c[6]
g68 = c[0] & c[7]
g69 = c[0] & c[8]
g70 = g62 | c[9]
g71 = g63 | c[10]
g72 = g64 | c[11]
g73 = g65 | c[12]
g74 = g62 | c[13]
g75 = g63 | c[14]
g76 = g64 | c[15]
g77 = g65 | c[16]
g78 = g62 | c[17]
g79 = g63 | c[18]
g80 = g64 | c[19]
g81 = g65 | c[20]
g82 = g62 | c[21]
g83 = g63 | c[22]
g84 = g64 | c[23]
g85 = g65 | c[24]
g86 = g62 | c[25]
g87 = g63 | c[26]
g88 = g64 | c[27]
g89 = g65 | c[28]
g90 = g66 | c[29]
g91 = g68 | c[30]
g92 = g67 | c[31]
g93 = g69 | c[32]
g94 = g66 | c[33]
g95 = g68 | c[34]
g96 = g67 | c[35]
g97 = g69 | c[36]
g98 = g66 | c[37]
g99 = g68 | c[38]
g100 = g67 | c[39]
g101 = g69 | c[40]
g102 = g66 | c[41]
g103 = g68 | c[42]
g104 = g67 | c[43]
g105 = g69 | c[44]
g106 = bss.kofn(3, [g70, g71, g72, g73])
g107 = bss.kofn(3, [g74, g75, g76, g77])
g108 = bss.kofn(3, [g78, g79, g80, g81])
g109 = bss.kofn(3, [g82, g83, g84, g85])
g110 = bss.kofn(3, [g86, g87, g88, g89])
g111 = bss.kofn(3, [g94, g95, g96, g97])
g112 = bss.kofn(3, [g98, g99, g100, g101])
g113 = g90 & g92
g114 = g91 & g93
g115 = g102 & g104
g116 = g103 & g105
g117 = g113 | c[45]
g118 = g114 | c[46]
g119 = g107 | g108 | c[51]
g120 = g109 | g110
g121 = g66 | g117 | c[47]
g122 = g68 | g118 | c[48]
g123 = g67 | g117 | c[49]
g124 = g69 | g118 | c[50]
g125 = bss.kofn(2, [g121, g123, g122, g124])
g126 = g111 | g112 | g125 | c[52]
g127 = g115 & g120
g128 = g116 & g120
g129 = g62 | g127 | c[53]
g130 = g63 | g128 | c[54]
g131 = g64 | g127 | c[55]
g132 = g65 | g128 | c[56]
g133 = g62 | g129 | c[57]
g134 = g63 | g130 | c[58]
g135 = g64 | g131 | c[59]
g136 = g65 | g132 | c[60]
g137 = bss.kofn(3, [g133, g134, g135, g136])
g138 = g106 | g119 | g137
g139 = g62 | g66 | g117 | g129 | c[47]
g140 = g63 | g68 | g118 | g130 | c[48]
g141 = g64 | g67 | g117 | g131 | c[49]
g142 = g65 | g69 | g118 | g132 | c[50]
g143 = g139 & g140 & g141 & g142
g144 = g111 | g112 | g143 | c[52]
top = g126 & g138 & g144

bdd = bss.getbdd(top)
print(bdd.size()) # The number of nodes in the BDD

s = bdd.minpath() # Obtain the minimal path vectors (minimal cut sets) from the BDD directly
min_path = s.extract()
print('The number of minimal path sets:', len(min_path))

print('Example: 100 minimal path sets')
from itertools import islice
for x in islice(min_path, 0, 100):
    print(x)

## An example of the direct use of MDD

vars = bss.get_varorder()

bdd = ms.BDD(vars) # Create a BDD with the variable order
c = [bdd.defvar("c" + str(i)) for i in range(61)]

g62 = c[0] & c[1]
g63 = c[0] & c[2]
g64 = c[0] & c[3]
g65 = c[0] & c[4]
g66 = c[0] & c[5]
g67 = c[0] & c[6]
g68 = c[0] & c[7]
g69 = c[0] & c[8]
g70 = g62 | c[9]
g71 = g63 | c[10]
g72 = g64 | c[11]
g73 = g65 | c[12]
g74 = g62 | c[13]
g75 = g63 | c[14]
g76 = g64 | c[15]
g77 = g65 | c[16]
g78 = g62 | c[17]
g79 = g63 | c[18]
g80 = g64 | c[19]
g81 = g65 | c[20]
g82 = g62 | c[21]
g83 = g63 | c[22]
g84 = g64 | c[23]
g85 = g65 | c[24]
g86 = g62 | c[25]
g87 = g63 | c[26]
g88 = g64 | c[27]
g89 = g65 | c[28]
g90 = g66 | c[29]
g91 = g68 | c[30]
g92 = g67 | c[31]
g93 = g69 | c[32]
g94 = g66 | c[33]
g95 = g68 | c[34]
g96 = g67 | c[35]
g97 = g69 | c[36]
g98 = g66 | c[37]
g99 = g68 | c[38]
g100 = g67 | c[39]
g101 = g69 | c[40]
g102 = g66 | c[41]
g103 = g68 | c[42]
g104 = g67 | c[43]
g105 = g69 | c[44]
g106 = bdd.kofn(3, [g70, g71, g72, g73])
g107 = bdd.kofn(3, [g74, g75, g76, g77])
g108 = bdd.kofn(3, [g78, g79, g80, g81])
g109 = bdd.kofn(3, [g82, g83, g84, g85])
g110 = bdd.kofn(3, [g86, g87, g88, g89])
g111 = bdd.kofn(3, [g94, g95, g96, g97])
g112 = bdd.kofn(3, [g98, g99, g100, g101])
g113 = g90 & g92
g114 = g91 & g93
g115 = g102 & g104
g116 = g103 & g105
g117 = g113 | c[45]
g118 = g114 | c[46]
g119 = g107 | g108 | c[51]
g120 = g109 | g110
g121 = g66 | g117 | c[47]
g122 = g68 | g118 | c[48]
g123 = g67 | g117 | c[49]
g124 = g69 | g118 | c[50]
g125 = bdd.kofn(2, [g121, g123, g122, g124])
g126 = g111 | g112 | g125 | c[52]
g127 = g115 & g120
g128 = g116 & g120
g129 = g62 | g127 | c[53]
g130 = g63 | g128 | c[54]
g131 = g64 | g127 | c[55]
g132 = g65 | g128 | c[56]
g133 = g62 | g129 | c[57]
g134 = g63 | g130 | c[58]
g135 = g64 | g131 | c[59]
g136 = g65 | g132 | c[60]
g137 = bdd.kofn(3, [g133, g134, g135, g136])
g138 = g106 | g119 | g137
g139 = g62 | g66 | g117 | g129 | c[47]
g140 = g63 | g68 | g118 | g130 | c[48]
g141 = g64 | g67 | g117 | g131 | c[49]
g142 = g65 | g69 | g118 | g132 | c[50]
g143 = g139 & g140 & g141 & g142
g144 = g111 | g112 | g143 | c[52]
top = g126 & g138 & g144

print(top.size()) # The number of nodes in the BDD

s = top.minpath() # Obtain the minimal path vectors (minimal cut sets) from the BDD directly
min_path = s.extract()
print('The number of minimal path sets:', len(min_path))

print('Example: 100 minimal path sets')
from itertools import islice
for x in islice(min_path, 0, 100):
    print(x)

Importance analysis

Compute the Birnbaum importance for each event as the first order derivative of the top event probability with respect to the probability of the event. This is the Birnbaum importance in the case where event occurrences are independent.

import relibmss as ms

# Create a binary system (fault tree)
bss = ms.BSS()

# Define events (This version only supports repeated events)
A = bss.defvar('A')
B = bss.defvar('B')
C = bss.defvar('C')

# Make a tree
top = A & B | C # & is AND gate, | is OR gate

# Set probabilities
prob = {
    'A': 0.1,
    'B': 0.2,
    'C': 0.3
}

# Calculate the probability
print(bss.prob(top, prob))

# top = 1-(1-pa*pb)*(1-pc) = pa*pb+pc-pa*pb*pc
# top / pa = pb - pb*pc = 0.2 - 0.2*0.3 = 0.14
# top / pb = pa - pa*pc = 0.1 - 0.1*0.3 = 0.07
# top / pc = 1 - pa*pb = 1 - 0.1*0.2 = 0.98

print(bss.bmeas(top, prob))

# Set the interval of the probability
interval_prob = {
    'A': (0.1, 0.2),
    'B': (0.2, 0.3),
    'C': (0.3, 0.4)
}

# Calculate the probability
print(bss.prob_interval(top, interval_prob))

print(bss.bmeas_interval(top, interval_prob))

###

prob2 = {
    'A': 0.5,
    'B': 0.5,
    'C': 0.5
}

# structure importance measure
print(bss.bmeas(top, prob2))

# An example of the direct use of BddNode

bdd = ms.BDD()

# Define events (This version only supports repeated events)
A = bdd.defvar('A')
B = bdd.defvar('B')
C = bdd.defvar('C')

# Make a tree
top = A & B | C # & is AND gate, | is OR gate

print(top.prob(prob))
print(top.prob_interval(interval_prob))

print(top.bmeas(prob))
print(top.bmeas_interval(interval_prob))

print(top.bmeas(prob2))

TODO for fault tree analysis

• FTA with MCS
• Importance analysis
• Sensitivity analysis
• Uncertainty analysis; etc.

Multi-state system

Definition of Gate

MSS does not have default gates. Users need to define gates by themselves. The operation that can be used in the definition of a gate is as follows:

• Arithmetic operations: +, -, *, /
• Comparison operations: ==, !=, >, <, >=, <=
• Logical operations:
  • mss.And: AND gate
  • mss.Or: OR gate
  • mss.Not: NOT gate
  • mss.switch: Switch-case structure
  • mss.case: Case structure
• Value operations:
  • mss.Min: minimum of the given expressions (series-like structure)
  • mss.Max: maximum of the given expressions (parallel-like structure)

Min/Max take a list and are handy when a gate is simply the weakest or strongest of its inputs:

import relibmss as ms

mss = ms.MSS()
X = mss.defvar('X', 3)
Y = mss.defvar('Y', 3)
Z = mss.defvar('Z', 3)

# The system state is the worst (Min) / best (Max) of its components
weakest = mss.Min([X, Y, Z])
strongest = mss.Max([X, Y, Z])

prob = {'X': [0.2, 0.3, 0.5], 'Y': [0.2, 0.3, 0.5], 'Z': [0.2, 0.3, 0.5]}

# P(min == 0) = 1 - 0.8^3 = 0.488
print(mss.getmdd(weakest).prob(prob, [0]))
# P(max == 2) = 1 - 0.5^3 = 0.875
print(mss.getmdd(strongest).prob(prob, [2]))

import relibmss as ms

# Define gates
def gate1(mss, x, y):
    return mss.switch([
        mss.case(cond=mss.And([x == 0, y == 0]), then=0),
        mss.case(cond=mss.Or([x == 0, y == 0]), then=1),
        mss.case(cond=mss.Or([x == 2, y == 2]), then=3),
        mss.case(then=2) # default
    ])

def gate2(mss, x, y):
    return mss.switch([
        mss.case(cond=x == 0, then=0),
        mss.case(then=y)
    ])

mss = ms.MSS() # Context for the multi-state system

# Define variables

A = mss.defvar('A', 2) # 2 states
B = mss.defvar('B', 3) # 3 states
C = mss.defvar('C', 3) # 3 states

# Define a multi-state system
sx = gate1(mss, B, C)
ss = gate2(mss, A, sx)

# Define probabilities
prob = {
    'A': [0.1, 0.9],
    'B': [0.2, 0.3, 0.5],
    'C': [0.3, 0.4, 0.3]
}

# Calculate the probability
print(mss.prob(ss, prob, [0,1,2]))

# An example of the direct use of MDD

mdd = ms.MDD()

# Define variables
A = mdd.defvar('A', 2) # 2 states
B = mdd.defvar('B', 3) # 3 states
C = mdd.defvar('C', 3) # 3 states

# Define a multi-state system
sx = gate1(mdd, B, C)
ss = gate2(mdd, A, sx)

# Calculate the probability
print(ss.prob(prob, [0,1,2]))

Draw an MDD

import relibmss as ms

# Define gates
def gate1(mss, x, y):
    return mss.switch([
        mss.case(cond=mss.And([x == 0, y == 0]), then=0),
        mss.case(cond=mss.Or([x == 0, y == 0]), then=1),
        mss.case(cond=mss.Or([x == 2, y == 2]), then=3),
        mss.case(then=2) # default
    ])

def gate2(mss, x, y):
    return mss.switch([
        mss.case(cond=x == 0, then=0),
        mss.case(then=y)
    ])

mss = ms.MSS()

A = mss.defvar('A', 2)
B = mss.defvar('B', 3)
C = mss.defvar('C', 3)

# Define the order of variables -- see "Variable order" above for the details.
# This must be done before making the MDD.
mss.set_varorder(["C", "B", "A"])

sx = gate1(mss, B, C)
ss = gate2(mss, A, sx)

mdd = mss.getmdd(ss)
source = mdd.dot()
print(source)

from graphviz import Source
Source(source)

Obtain the minimal vector sets

import relibmss as ms

# Define gates
def gate1(mss, x, y):
    return mss.switch([
        mss.case(cond=mss.And([x == 0, y == 0]), then=0),
        mss.case(cond=mss.Or([x == 0, y == 0]), then=1),
        mss.case(cond=mss.Or([x == 2, y == 2]), then=3),
        mss.case(then=2) # default
    ])

def gate2(mss, x, y):
    return mss.switch([
        mss.case(cond=x == 0, then=0),
        mss.case(then=y)
    ])

mss = ms.MSS()

A = mss.defvar('A', 2)
B = mss.defvar('B', 3)
C = mss.defvar('C', 3)

sx = gate1(mss, B, C)
ss = gate2(mss, A, sx)

mdd = mss.minpath(ss)
print(mdd.dot())
for path in mdd.extract([0,1,2], type='mdd'):
    print(path)

# An example of the direct use of MDD

mdd = ms.MDD()

A = mdd.defvar('A', 2)
B = mdd.defvar('B', 3)
C = mdd.defvar('C', 3)

sx = gate1(mdd, B, C)
ss = gate2(mdd, A, sx)

mdd = ss.minpath()
print(mdd.dot())
for path in mdd.extract([0,1,2], type='mdd'):
    print(path)

TODO

• Add more examples
• Add more functions for fault tree analysis
• Add more functions for multi-state system analysis

License

MIT License