object Spatial
All functions and utilities relating to spatial simulation
- Alphabetic
- By Inheritance
- Spatial
- AnyRef
- Any
- Hide All
- Show All
- Public
- All
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
cle1d(n: Spn[DoubleState], d: DoubleState, dt: Double = 0.01): (GenSeq[DoubleState], Time, Time) ⇒ GenSeq[DoubleState]
The 1d spatial CLE algorithm
The 1d spatial CLE algorithm
- n
A
Spn[DoubleState]
model for simulation- d
A vector of diffusion coefficients - one for each species
- dt
Time step of the simulation algorithm
- returns
A function with type signature
(x0: GenSeq[DoubleState], t0: Time, deltat: Time) => GenSeq[DoubleState]
which will simulate the state of the system at timet0+deltat
given initial statex0
and initial timet0
-
def
cle2d(n: Spn[DoubleState], d: DoubleState, dt: Double = 0.01): (PMatrix[DoubleState], Time, Time) ⇒ PMatrix[DoubleState]
The 2d spatial CLE algorithm
The 2d spatial CLE algorithm
- n
A
Spn[DoubleState]
model for simulation- d
A vector of diffusion coefficients - one for each species
- dt
Time step of the simulation algorithm
- returns
A function with type signature
(x0: PMatrix[DoubleState], t0: Time, deltat: Time) => PMatrix[DoubleState]
which will simulate the state of the system at timet0+deltat
given initial statex0
and initial timet0
-
def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
def
euler1d(n: Spn[DoubleState], d: DoubleState, dt: Double = 0.01): (GenSeq[DoubleState], Time, Time) ⇒ GenSeq[DoubleState]
The 1d spatial Euler algorithm
The 1d spatial Euler algorithm
- n
A
Spn[DoubleState]
model for simulation- d
A vector of diffusion coefficients - one for each species
- dt
Time step of the simulation algorithm
- returns
A function with type signature
(x0: GenSeq[DoubleState], t0: Time, deltat: Time) => GenSeq[DoubleState]
which will simulate the state of the system at timet0+deltat
given initial statex0
and initial timet0
-
def
euler2d(n: Spn[DoubleState], d: DoubleState, dt: Double = 0.01): (PMatrix[DoubleState], Time, Time) ⇒ PMatrix[DoubleState]
The 2d spatial Euler algorithm
The 2d spatial Euler algorithm
- n
A
Spn[DoubleState]
model for simulation- d
A vector of diffusion coefficients - one for each species
- dt
Time step of the simulation algorithm
- returns
A function with type signature
(x0: PMatrix[DoubleState], t0: Time, deltat: Time) => PMatrix[DoubleState]
which will simulate the state of the system at timet0+deltat
given initial statex0
and initial timet0
-
def
finalize(): Unit
- Attributes
- protected[lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
gillespie1d(n: Spn[IntState], d: DoubleState, minH: Double = 1e-20, maxH: Double = 1e6): (GenSeq[IntState], Time, Time) ⇒ GenSeq[IntState]
The 1d spatial Gillespie algorithm
The 1d spatial Gillespie algorithm
- n
A
Spn[IntState]
model for simulation- d
A vector of diffusion coefficients - one for each species
- minH
Threshold for treating hazard as zero
- maxH
Threshold for terminating simulation early
- returns
A function with type signature
(x0: GenSeq[IntState], t0: Time, deltat: Time) => GenSeq[IntState]
which will simulate the state of the system at timet0+deltat
given initial statex0
and initial timet0
-
def
gillespie2d(n: Spn[IntState], d: DoubleState, minH: Double = 1e-20, maxH: Double = 1e6): (PMatrix[IntState], Time, Time) ⇒ PMatrix[IntState]
The 2d spatial Gillespie algorithm
The 2d spatial Gillespie algorithm
- n
A
Spn[IntState]
model for simulation- d
A vector of diffusion coefficients - one for each species
- minH
Threshold for treating hazard as zero
- maxH
Threshold for terminating simulation early
- returns
A function with type signature
(x0: PMatrix[IntState], t0: Time, deltat: Time) => PMatrix[IntState]
which will simulate the state of the system at timet0+deltat
given initial statex0
and initial timet0
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
def
plotTs1d[S](ts: Ts[GenSeq[S]])(implicit arg0: State[S]): Unit
Plot the output of a 1d time series simulation.
Plot the output of a 1d time series simulation. Called solely for the side-effect of rendering a plot on the console.
- ts
Output from a 1d spatial time series simulation
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... ) @native()