drop docstrings

src/problems.jl

@@ -1,31 +1,3 @@
-"""
-`SDEProblem`
-
-Wraps the data which defines an SDE problem
-
-```math
-u = f(u,t)dt + Σgᵢ(u,t)dWⁱ
-```
-
-with initial condition ``u0``.
-
-### Constructors
-
-`SDEProblem(f,g,u0;analytic=nothing)` : Defines the SDE with the specified functions and
-defines the solution if analytic is given.
-
-### Fields
-
-* `f`: The drift function in the SDE.
-* `g`: The noise function in the SDE.
-* `u0`: The initial condition.
-* `analytic`: A function which describes the solution.
-* `knownanalytic`: True if the solution is given.
-* `numvars`: The number of variables in the system
-* `sizeu`: The size of the initial condition (and thus `u`)
-* `noise`: The noise process applied to the noise upon generation.
-
-"""
 type SDEProblem <: AbstractSDEProblem
   f::Function
   g::Function

src/sde/sde_solve.jl

@@ -1,41 +1,3 @@
-"""
-`solve(prob::SDEProblem,tspan)`
-
-Solves the SDE as defined by prob on the time interval tspan. If not given, tspan defaults to [0,1].
-
-### Keyword Arguments
-
-* `dt`: Sets the initial stepsize. Defaults to an automatic choice.
-* `save_timeseries`: Saves the result at every timeseries_steps steps. Default is true.
-* `timeseries_steps`: Denotes how many steps between saving a value for the timeseries. Defaults to 1.
-* `adaptive` - Turns on adaptive timestepping for appropriate methods. Default is false.
-* `γ` - The risk-factor γ in the q equation for adaptive timestepping. Default is 2.
-* `qmax` - Defines the maximum value possible for the adaptive q. Default is 1.125.
-* `δ` - The weight-factor in the error estimate. Default is 1/6.
-* `ablstol` - Absolute tolerance in adaptive timestepping. Defaults to 1e-3.
-* `reltol` - Relative tolerance in adaptive timestepping. Defaults to 1e-6.
-* `maxiters` - Maximum number of iterations before stopping. Defaults to 1e9.
-* `dtmax` - Maximum dt for adaptive timestepping. Defaults to half the timespan.
-* `dtmin` - Minimum dt for adaptive timestepping. Defaults to 1e-10.
-* `internalnorm` - The norm for which error estimates are calculated. Default is 2.
-* `progressbar` - Turns on/off the Juno progressbar. Defualt is false.
-* `progress_steps` - Numbers of steps between updates of the progress bar. Default is 1000.
-* `discard_length` - Size at which to discard future information in adaptive. Default is 1e-15.
-* `tableau`: The tableau for an `:SRA` or `:SRI` algorithm. Defaults to SRIW1 or SRA1.
-* `adaptivealg`: The adaptive timestepping algorithm. Default is `:RSwm3`.
-* `alg`: String which defines the solver algorithm. Defult is "SRIW1Optimized". Possibilities are:
-
-    - `:EM`- The Euler-Maruyama method.
-    - `:RKMil` - An explicit Runge-Kutta discretization of the strong Order 1.0 Milstein method.
-    - `:SRA` - The strong Order 2.0 methods for additive SDEs due to Rossler. Not yet implemented.
-      Default tableau is for SRA1.
-    - `:SRI` - The strong Order 1.5 methods for diagonal/scalar SDEs due to Rossler.
-      Default tableau is for SRIW1.
-    - `:SRIW1Optimized` - An optimized version of SRIW1. Strong Order 1.5.
-    - `:SRA1Optimized` - An optimized version of SRIA1. Strong Order 2.0.
-    - `:SRAVectorized` - A vectorized implementation of SRA algorithms. Requires 1-dimensional problem.
-    - `:SRIVectorized` - A vectorized implementation of SRI algorithms. Requires 1-dimensional problem.
-"""
 function solve(prob::AbstractSDEProblem,tspan::AbstractArray=[0,1];dt::Number=0.0,save_timeseries::Bool = true,
               timeseries_steps::Int = 1,alg=nothing,adaptive=false,γ=2.0,alg_hint=nothing,
               abstol=1e-3,reltol=1e-6,qmax=1.125,δ=1/6,maxiters::Int = round(Int,1e9),