module Variables import JuMP: @variable, Model, @objective, objective_function, value, AbstractJuMPScalar import ..Scanners: gradient_strength, slew_rate import ..BuildSequences: global_model all_variables_symbols = [ :block => [ :duration => "duration of the building block in ms.", ], :sequence => [ :TR => "Time on which an MRI sequence repeats itself in ms.", ], :pulse => [ :flip_angle => "The flip angle of the RF pulse in degrees", :amplitude => "The maximum amplitude of an RF pulse in kHz", :phase => "The angle of the phase of an RF pulse in KHz", :frequency => "The off-resonance frequency of an RF pulse (relative to the Larmor frequency of water) in KHz", :bandwidth => "Bandwidth of the RF pulse in kHz. If you are going to divide by the bandwidth, it can be more efficient to use the [`inverse_bandwidth`](@ref).", :inverse_bandwidth => "Inverse of the [`bandwidth`](@ref) of the RF pulse in ms", :N_left => "The number of zero crossings of the RF pulse before the main peak", :N_right => "The number of zero crossings of the RF pulse after the main peak", :slice_thickness => "Slice thickness of an RF pulse that is active during a gradient in mm.", :inverse_slice_thickness => "Inverse of the [`slice_thickness`](@ref) in 1/mm.", ], # gradients :gradient => [ :qvec => "The spatial range and orientation on which the displacements can be detected due to this gradient in rad/um.", :qval => "The spatial range on which the displacements can be detected due to this gradient in rad/um (i.e., norm of [`qvec`](@ref)).", :δ => "Effective duration of a gradient pulse ([`rise_time`](@ref) + [`flat_time`](@ref)) in ms.", :rise_time => "Time for gradient pulse to reach its maximum value in ms.", :flat_time => "Time of gradient pulse at maximum value in ms.", :gradient_strength => "vector with maximum strength of a gradient along each dimension (kHz/um)", :slew_rate => "vector with maximum slew rate of a gradient along each dimension (kHz/um)", ], :readout => [ :dwell_time => "Time between two samples in an `ADC` in ms.", ] ] symbol_to_func = Dict{Symbol, Function}() for (block_symbol, all_functions) in all_variables_symbols for (func_symbol, description) in all_functions as_string = " $func_symbol($block_symbol)\n\n$description\n\nThis represents a variable within the sequence. Variables can be set during the construction of a [`BuildingBlock`](@ref) or used to create constraints after the fact." new_func = @eval begin function $func_symbol end @doc $as_string $func_symbol $func_symbol end symbol_to_func[func_symbol] = new_func end end """ variables(building_block) variables() Returns all functions representing properties of a [`BuildingBlock`](@ref) object. """ variables() = [values(symbol_to_func)...] # Some universal truths slice_thickness(bb) = inv(inverse_slice_thickness(bb)) bandwidth(bb) = inv(inverse_bandwidth(bb)) function qval_square(bb; kwargs...) vec = qvec(bb; kwargs...) return vec[1]^2 + vec[2]^2 + vec[3]^2 end qval(bb; kwargs...) = sqrt(qval_square(bb)) # These functions are more fully defined in building_blocks.jl function start_time end function end_time end function effective_time end const VariableType = Union{Number, AbstractJuMPScalar} """ get_free_variable(value; integer=false) Get a representation of a given `variable` given a user-defined constraint. """ get_free_variable(value::Number; integer=false) = integer ? Int(value) : Float64(value) get_free_variable(value::VariableType; integer=false) = value get_free_variable(::Nothing; integer=false) = @variable(global_model(), start=0.01, integer=integer) get_free_variable(value::Symbol; integer=false) = integer ? error("Cannot maximise or minimise an integer variable") : get_free_variable(Val(value)) function get_free_variable(::Val{:min}) var = get_free_variable(nothing) model = global_model() @objective model Min objective_function(model) + var return var end function get_free_variable(::Val{:max}) var = get_free_variable(nothing) model = global_model() @objective model Min objective_function(model) - var return var end """ bmat_gradient(gradient::GradientBlock, qstart=(0, 0, 0)) Computes the diffusion-weighting matrix due to a single gradient block in rad^2 ms/um^2. This should be defined for every `GradientBlock`, but not be called directly. Instead, the `bmat` and `bval` should be constrained for specific `Pathways` """ function bmat_gradient end end