puff-model/TwoTubesSimple.m at master · sg-s/puff-model · GitHub

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% This implements a simpler 2-ODe model
% where the key assumption is that the interaction with
% surfaces equilibrates rapidly

classdef TwoTubesSimple < model & ConstructableHandle


properties

end % props


methods


	function self = TwoTubesSimple(varargin)

		self = self@ConstructableHandle(varargin{:});

		if isempty(self.Parameters)
			self.Parameters = struct;
			self.Parameters.t_offset = 1;
			self.Parameters.k_d = 1;
			self.Parameters.w = 1;
			self.Parameters.tau_s = 1e-1;
		end

	end % constructor


	function evaluate(self)

		assert(~isempty(self.Parameters),'Parameters should not be empty')
		assert(~isempty(self.Stimulus),'Stimulus should not be empty')


		% geometrical parameters
		q1 = 3; % mL/s
		q2 = 30; % mL/s
		r2 = 2.425; % mm
		r1 = 2.7; % mm

		V2 = pi*r2*r2*90/1000;  % mL, /1000 comes from converting to mL

		% Pasteur pipette
		V1 = pi*r1*r1*90/1000; % mL

		% fixed parameters
		phi = q1/q2; % 3mL/s / 30 mL/s
		tau_2 = (V2/q2); % seconds
		L = V1/V2;
		L = 1;


		% unpack parameters
		w = self.Parameters.w;
		k_d = self.Parameters.k_d;
		tau_s = self.Parameters.tau_s;


		ic = [1 0];

		S = self.Stimulus;

		time = 1e-2*(1:length(S)); % 10 ms timestep
		Tspan = [min(time) max(time)];

		options = odeset('MaxStep',.1);
		warning off
		[T, Y] = ode23t(@(t,y) SimplifedODE(t,y,time,S),Tspan,ic,options); % Solve ODE
		warning on


		% re-interpolate the solution to fit the stimulus
		x2 = corelib.vectorise(interp1(T,Y(:,2),time));
		x1 = corelib.vectorise(interp1(T,Y(:,1),time));

		% normalise
		if max(x2) == 0
		else
			x2 = x2/max(x2(find(time>1,1,'first'):find(time<3,1,'last')));
		end

		% allow for some offset in time
		x1 = circshift(x1,floor(self.Parameters.t_offset));
		x2 = circshift(x2,floor(self.Parameters.t_offset));


		f2 = (1 + S(:)*phi).*x2(:);

		if max(f2) == 0
		else
			f2 = f2/max(f2(find(time>1,1,'first'):find(time<3,1,'last')));
		end

		self.Prediction = f2;

		function dy = SimplifedODE(t,y,time,odor)
			% calculate the odor at the time point
			stim = interp1(time,odor,t); % Interpolate the data set (ft,f) at time t

			% unpack variables
			x2 = y(2);
			x1 = y(1);


			effective_tau = tau_2*(1 + (w/k_d)/((1+(x2/k_d))^2));

			dx2 =  (stim*phi*x1 - ((1 + stim*phi)*x2))/effective_tau;

			effective_tau = 1 + (w/k_d)/((1+(x1/k_d))^2);

			dx1 = ((1-x1)/tau_s - (stim*phi*x1)/(tau_2*L))/effective_tau;


			dy = 0*y;
			dy(2) = dx2;
			dy(1) = dx1;

			dy = dy(:);
		end


	end % evaluate


end % methods

end % classdef