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Update boolean semantics section.

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Marcell Vazquez-Chanlatte 2020-04-06 10:10:56 -07:00
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@ -156,47 +156,18 @@ phi6 = mtl.parse('(a U[0, 2] b)')
phi7 = mtl.parse('XX a')
```
## Boolean Evaluation
Given a property `phi`, one can evaluate is a timeseries satisifies `phi`. Time Series can either be
defined using a dictionary mapping atomic predicate names to lists of (`time`, `val`) pairs **or** using
the [DiscreteSignals](https://github.com/mvcisback/DiscreteSignals) API (used internally).
There are two types of evaluation. One uses the boolean semantics of MTL and the other uses Signal Temporal Logic like semantics.
```python
# Assumes piece wise constant interpolation.
data = {
'a': [(0, True), (1, False), (3, False)],
'b': [(0, False), (0.2, True), (4, False)]
}
phi = mtl.parse('F(a | b)')
print(phi(data, quantitative=False))
# output: True
phi = mtl.parse('F(a | b)')
print(phi(data))
# output: True
# Note, quantitative parameter defaults to False
# Evaluate at t=3.
print(phi(data, time=3))
# output: False
# Compute sliding satisifaction.
print(phi(data, time=None))
# output: [(0, True), (0.2, True), (4, False)]
# Evaluate with discrete time
phi = mtl.parse('X b')
print(phi(data, dt=0.2))
# output: True
```
## Quantitative Evaluate (Signal Temporal Logic)
Given a property `phi`, one can evaluate is a timeseries satisifies
`phi`. Time Series can either be defined using a dictionary mapping
atomic predicate names to lists of (`time`, `val`) pairs **or** using
the [DiscreteSignals](https://github.com/mvcisback/DiscreteSignals)
API (used internally).
There are two types of evaluation. One uses the boolean semantics of
MTL and the other uses Signal Temporal Logic like semantics.
```python
# Assumes piece wise constant interpolation.
data = {
@ -218,6 +189,45 @@ print(phi(data, dt=0.2))
# output: 2
```
## Boolean Evaluation
To Boolean semantics can be thought of as a special case of the
quantitative semantics where `True ↦ 1` and `False ↦ -1`. This
conversion happens automatically using using the `quantitative=False`
flag.
```python
# Assumes piece wise constant interpolation.
data = {
'a': [(0, True), (1, False), (3, False)],
'b': [(0, False), (0.2, True), (4, False)]
}
phi = mtl.parse('F(a | b)')
print(phi(data, quantitative=False))
# output: True
phi = mtl.parse('F(a | b)')
print(phi(data))
# output: True
# Note, quantitative parameter defaults to False
# Evaluate at t=3.
print(phi(data, time=3, quantitative=False))
# output: False
# Compute sliding satisifaction.
print(phi(data, time=None, quantitative=False))
# output: [(0, True), (0.2, True), (4, False)]
# Evaluate with discrete time
phi = mtl.parse('X b')
print(phi(data, dt=0.2, quantitative=False))
# output: True
```
## Utilities
```python
import mtl