Merge pull request #285 from ArnoStrouwen/LT

[skip ci] docstring spelling

docs/src/faq.md

@@ -65,7 +65,7 @@ jprob = JumpProblem(dprob, Direct(), maj,
 uses the `Xoroshiro128Star` generator from
 [RandomNumbers.jl](https://github.com/JuliaRandom/RandomNumbers.jl).
 
-On version 1.7 and up, JumpProcesses uses Julia's builtin random number generator by
+On version 1.7 and up, JumpProcesses uses Julia's built-in random number generator by
 default. On versions below 1.7 it uses `Xoroshiro128Star`.
 
 ## What are these aggregators and aggregations in JumpProcesses?

docs/src/tutorials/discrete_stochastic_example.md

@@ -731,7 +731,7 @@ Jump](@ref).
 
 ## RegularJumps and τ-Leaping
 The previous parts described how to use `ConstantRateJump`s, `MassActionJump`s,
-and `VariableRateJump`s, however, in many cases one does not require the
+and `VariableRateJump`s. However, in many cases one does not require the
 exactness of stepping to every jump time. Instead, regular jumping (i.e.,
 τ-leaping) allows pooling jumps together, and performing larger updates in a
 statistically-correct but more efficient manner. The trade-off is the

src/SSA_stepper.jl

@@ -58,9 +58,9 @@ mutable struct SSAIntegrator{F, uType, tType, tdirType, P, S, CB, SA, OPT, TS} <
     u::uType
     """The current solution time."""
     t::tType
-    """The previous time a jump occured."""
+    """The previous time a jump occurred."""
     tprev::tType
-    """The direction time is changing in (must be positive indicating time is increasing)"""
+    """The direction time is changing in (must be positive, indicating time is increasing)"""
     tdir::tdirType
     """The current parameters."""
     p::P
@@ -73,7 +73,7 @@ mutable struct SSAIntegrator{F, uType, tType, tdirType, P, S, CB, SA, OPT, TS} <
     cb::CB
     """Times to save the solution at."""
     saveat::SA
-    """Whether to save everytime a jump occurs."""
+    """Whether to save every time a jump occurs."""
     save_everystep::Bool
     """Whether to save at the final step."""
     save_end::Bool

src/aggregators/aggregated_api.jl

@@ -7,7 +7,7 @@ in parameters or such.
 Notes
     - `update_jump_params=true` will recalculate the rates stored within any
       MassActionJump that was built from the parameter vector. If the parameter
-      vector is unchanged this can safely be set to false to improve performance.
+      vector is unchanged, this can safely be set to false to improve performance.
 """
 function reset_aggregated_jumps!(integrator, uprev = nothing; update_jump_params = true,
                                  kwargs...)

src/aggregators/aggregators.jl

@@ -5,7 +5,7 @@
 """
 Gillespie's Direct method. `ConstantRateJump` rates and affects are stored in
 tuples. Fastest for a small (total) number of `ConstantRateJump`s or
-`MassActionJump`s (~10). For larger numbers of possible jumps use other
+`MassActionJump`s (~10). For larger numbers of possible jumps, use other
 methods.
 
 Daniel T. Gillespie, A general method for numerically simulating the stochastic
@@ -71,7 +71,7 @@ struct RSSA <: AbstractAggregatorAlgorithm end
 
 """
 The Rejection SSA Composition-Rejection method. Often the best performer for
-systems with tens of thousands of jumps and sparse depedency graphs.
+systems with tens of thousands of jumps and sparse dependency graphs.
 
 V. H. Thanh, R. Zunino, and C. Priami, Efficient constant-time complexity
 algorithm for stochastic simulation of large reaction networks, IEEE/ACM
@@ -120,7 +120,7 @@ systems with many species and many channels, Journal of Physical Chemistry A,
 struct NRM <: AbstractAggregatorAlgorithm end
 
 """
-An adaptaton of the COEVOLVE algorithm for simulating any compound jump process
+An adaptation of the COEVOLVE algorithm for simulating any compound jump process
 that evolves through time. This method handles variable intensity rates with
 user-defined bounds and inter-dependent processes. It reduces to NRM when rates
 are constant.

src/extended_jump_array.jl

@@ -2,7 +2,7 @@
 $(TYPEDEF)
 
 Extended state definition used within integrators when there are
-`VariableRateJump`s in a system. For detailed examples and usage information see
+`VariableRateJump`s in a system. For detailed examples and usage information, see
 the
 - [Tutorial](https://docs.sciml.ai/JumpProcesses/stable/tutorials/discrete_stochastic_example/)
 

src/jumps.jl

@@ -4,15 +4,15 @@ $(TYPEDEF)
 Defines a jump process with a rate (i.e. hazard, intensity, or propensity) that
 does not *explicitly* depend on time. More precisely, one where the rate
 function is constant *between* the occurrence of jumps. For detailed examples
-and usage information see the
+and usage information, see the
 - [Tutorial](https://docs.sciml.ai/JumpProcesses/stable/tutorials/discrete_stochastic_example/)
 
 ## Fields
 
 $(FIELDS)
 
 ## Examples
-Suppose `u[1]` gives the amount of particles and `p[1]` the probability per time
+Suppose `u[1]` gives the number of particles and `p[1]` the probability per time
 each particle can decay away. A corresponding `ConstantRateJump` for this jump
 process is
 ```julia
@@ -35,11 +35,11 @@ $(TYPEDEF)
 
 Defines a jump process with a rate (i.e. hazard, intensity, or propensity) that may
 explicitly depend on time. More precisely, one where the rate function is allowed to change
-*between* the occurrence of jumps. For detailed examples and usage information see the
+*between* the occurrence of jumps. For detailed examples and usage information, see the
 - [Tutorial](https://docs.sciml.ai/JumpProcesses/stable/tutorials/discrete_stochastic_example/)
 
 Note that two types of `VariableRateJump`s are currently supported, with different
-performance charactertistics.
+performance characteritistics.
 - A general `VariableRateJump` or `VariableRateJump` will refer to one in which only `rate`
   and `affect` functions are specified.
 
@@ -74,16 +74,16 @@ p, t)` require that
 - For `s` in `[t, t + rateinterval(u, p, t)]`, we have that `lrate(u, p, t) <= rate(u, p, s)
   <= urate(u, p, t)`.
 - It is ok if these bounds would be violated during the time window due to another
-  `ConstantRateJump`, `MassActionJump` or bounded `VariableRateJump` occurring, however,
-  they must remaing valid if `u` changes for any other reason (for example, due to
+  `ConstantRateJump`, `MassActionJump` or bounded `VariableRateJump` occurring. However,
+  they must remain valid if `u` changes for any other reason (for example, due to
   continuous dynamics like ODEs, SDEs, or general `VariableRateJump`s).
 
 ## Fields
 
 $(FIELDS)
 
 ## Examples
-Suppose `u[1]` gives the amount of particles and `t*p[1]` the probability per time each
+Suppose `u[1]` gives the number of particles and `t*p[1]` the probability per time each
 particle can decay away. A corresponding `VariableRateJump` for this jump process is
 ```julia
 rate(u,p,t) = t*p[1]*u[1]
@@ -227,7 +227,7 @@ $(TYPEDEF)
 
 Optimized representation for `ConstantRateJump`s that can be represented in mass
 action form, offering improved performance within jump algorithms compared to
-`ConstantRateJump`. For detailed examples and usage information see the
+`ConstantRateJump`. For detailed examples and usage information, see the
 - [Main
   Docs](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#Defining-a-Mass-Action-Jump)
 - [Tutorial](https://docs.sciml.ai/JumpProcesses/stable/tutorials/discrete_stochastic_example/)
@@ -277,7 +277,7 @@ jprob = JumpProblem(prob, Direct(), maj)
 ```
 
 ## Notes
-- By default reaction rates are rescaled when constructing the `MassActionJump`
+- By default, reaction rates are rescaled when constructing the `MassActionJump`
   as explained in the [main
   docs](https://docs.sciml.ai/JumpProcesses/stable/jump_types/#Defining-a-Mass-Action-Jump).
   Disable this with the kwarg `scale_rates=false`.

src/problem.jl

@@ -44,15 +44,15 @@ then be passed within a single [`JumpSet`](@ref) or as subsequent sequential arg
 $(FIELDS)
 
 ## Keyword Arguments
-- `rng`, the random number generator to use. On 1.7 and up defaults to Julia's builtin
+- `rng`, the random number generator to use. On 1.7 and up defaults to Julia's built-in
   generator, below 1.7 uses RandomNumbers.jl's `Xorshifts.Xoroshiro128Star(rand(UInt64))`.
-- `save_positions=(true,true)`, specifies whether to save the system's state (before,after)
+- `save_positions=(true,true)`, specifies whether to save the system's state (before, after)
   the jump occurs.
 - `spatial_system`, for spatial problems the underlying spatial structure.
 - `hopping_constants`, for spatial problems the spatial transition rate coefficients.
 - `use_vrj_bounds = true`, set to false to disable handling bounded `VariableRateJump`s
   with a supporting aggregator (such as `Coevolve`). They will then be handled via the
-  continuous integration interace, and treated like general `VariableRateJump`s.
+  continuous integration interface, and treated like general `VariableRateJump`s.
 
 Please see the [tutorial
 page](https://docs.sciml.ai/JumpProcesses/stable/tutorials/discrete_stochastic_example/) in the