/usr/share/psychtoolbox-3/PsychCal/CalibrateFitGamma.m is in psychtoolbox-3-common 3.0.14.20170103+git6-g605ff5c.dfsg1-1build1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 | function cal = CalibrateFitGamma(cal,nInputLevels)
% cal = CalibrateFitGamma(cal,[nInputLevels])
%
% Fit the gamma function to the calibration measurements. Options for field
% cal.describe.gamma.fitType are:
% simplePower
% crtLinear
% crtPolyLinear
% crtGamma
% crtSumPow
% betacdf
% sigmoid
% weibull
%
% Underlying fit routine is FitGamma for functional forms originally supported,
% and these rely on the optimization toolbox.
%
% Newer functions (e.g, crtSumPow, betacdf) use the curvefit toolbox and that's just
% done locally in this routine. Much less cumbersome.
%
% NOTE (5/27/10, dhb): crtSumPow does not currently appear to normalize the
% measurements to unity, while the older methods do (in FitDeviceGamma).
% This may be a bug, but since we're not currently using crtSumPow I'm not
% going to look into it in detail right now.
%
% See also PsychGamma.
%
% 3/26/02 dhb Pulled out of CalibrateMonDrvr.
% 11/14/06 dhb Define nInputLevels and pass to underlying fit routine.
% 07/22/07 dhb Add simplePower fitType.
% 08/02/07 dhb Optional pass of nInputLevels.
% dhb Don't allow a long string of zeros at the start.
% dhb Reduce redundant code for higher order terms by pulling out of switch
% 08/03/07 dhb Debug. Add call to MakeMonotonic for first three components.
% 11/19/09 dhb Added crtSumPow option, coded to [0-1] world and using curve fit toolbox.
% 3/07/10 dhb Cosmetic to make m-lint happier, including some "|" -> "||"
% 3/07/10 dhb Added crtLinear option.
% dhb contrasthThresh and fitBreakThresh values only set if not already in struct.
% dhb Call MakeGammaMonotonic rather than MakeMonotonic where appropriate.
% dhb Use linear interpolation for higher order linear model weights, rather than
% a polynomial. I now think that ringing is worse than not smoothing enough.
% 3/08/10 dhb Update list of options in comment above.
% 5/26/10 dhb Allow gamma input values to be either a single column or a matrix with same number of columns as devices.
% 6/5/10 dhb Extend fix above to higher order terms in the gamma fit.
% dhb Fix or supress MATLAB lint warnings.
% dhb Add betacdf fit option, which seems to provide a flexible sigmoidally shaped fit.
% 6/8/10 dhb, ar Make sure to set cal.gammaInput in options that use curvefit toolbox method.
% Add a call to MakeGammaMonotonic around input values for higher order linmod fit.
% 6/1010 dhb Fix higher order fit in case where there are multiple gamma input columns. Blew this the other day.
% 6/11/10 dhb Allow passing of weighting parameter as part of cal.describe.gamma structure. Change functional form of betacdf
% to include wrapped power functions.
% 4/12/11 dhb For simplePower option, return vector of exponents in cal.describe.exponents.
% Set nInputLevels
if (nargin < 2 || isempty(nInputLevels))
nInputLevels = 1024;
end
% Fit gamma functions.
switch(cal.describe.gamma.fitType)
case 'simplePower',
mGammaMassaged = cal.rawdata.rawGammaTable(:,1:cal.nDevices);
for i = 1:cal.nDevices
mGammaMassaged(:,i) = MakeGammaMonotonic(HalfRect(mGammaMassaged(:,i)));
end
fitType = 1;
[mGammaFit1a,cal.gammaInput,nil,theExponents] = FitDeviceGamma(...
mGammaMassaged,cal.rawdata.rawGammaInput,fitType,nInputLevels);
mGammaFit1 = mGammaFit1a;
cal.describe.gamma.exponents = theExponents;
case 'crtLinear'
% Set to zero the raw data we believe to be below reliable measurement
% threshold, and then fit the rest by linear interpolation. Force answer
% to be monotonic.
if (~isfield(cal.describe.gamma,'contrastThresh'))
cal.describe.gamma.contrastThresh = 0.001;
end
mGammaMassaged = cal.rawdata.rawGammaTable(:,1:cal.nDevices);
massIndex = find(mGammaMassaged < cal.describe.gamma.contrastThresh);
mGammaMassaged(massIndex) = zeros(length(massIndex),1);
for i = 1:cal.nDevices
mGammaMassaged(:,i) = MakeGammaMonotonic(HalfRect(mGammaMassaged(:,i)));
end
fitType = 6;
[mGammaFit1,cal.gammaInput] = FitDeviceGamma(...
mGammaMassaged,cal.rawdata.rawGammaInput,fitType,nInputLevels);
case 'crtPolyLinear',
% For fitting, we set to zero the raw data we
% believe to be below reliable measurement threshold (contrastThresh).
% Currently we are fitting both with polynomial and a linear interpolation,
% using the latter for low measurement values. The fit break point is
% given by fitBreakThresh. This technique was developed
% through bitter experience and is not theoretically driven.
if (~isfield(cal.describe.gamma,'contrastThresh'))
cal.describe.gamma.contrastThresh = 0.001;
end
if (~isfield(cal.describe.gamma,'fitBreakThresh'))
cal.describe.gamma.fitBreakThresh = 0.02;
end
mGammaMassaged = cal.rawdata.rawGammaTable(:,1:cal.nDevices);
massIndex = find(mGammaMassaged < cal.describe.gamma.contrastThresh);
mGammaMassaged(massIndex) = zeros(length(massIndex),1);
for i = 1:cal.nDevices
mGammaMassaged(:,i) = MakeGammaMonotonic(HalfRect(mGammaMassaged(:,i)));
end
fitType = 7;
[mGammaFit1a,cal.gammaInput] = FitDeviceGamma(...
mGammaMassaged,cal.rawdata.rawGammaInput,fitType,nInputLevels);
fitType = 6;
[mGammaFit1b,cal.gammaInput] = FitDeviceGamma(...
mGammaMassaged,cal.rawdata.rawGammaInput,fitType,nInputLevels);
mGammaFit1 = mGammaFit1a;
for i = 1:cal.nDevices
indexLin = find(mGammaMassaged(:,i) < cal.describe.gamma.fitBreakThresh);
if (~isempty(indexLin))
breakIndex = max(indexLin);
breakInput = cal.rawdata.rawGammaInput(breakIndex);
inputIndex = find(cal.gammaInput <= breakInput);
if (~isempty(inputIndex))
mGammaFit1(inputIndex,i) = mGammaFit1b(inputIndex,i);
end
end
end
case 'crtGamma',
mGammaMassaged = cal.rawdata.rawGammaTable(:,1:cal.nDevices);
for i = 1:cal.nDevices
mGammaMassaged(:,i) = MakeGammaMonotonic(HalfRect(mGammaMassaged(:,i)));
end
fitType = 2;
[mGammaFit1a,cal.gammaInput] = FitDeviceGamma(...
mGammaMassaged,cal.rawdata.rawGammaInput,fitType,nInputLevels);
mGammaFit1 = mGammaFit1a;
case 'crtSumPow',
if (~exist('fit','file'))
error('Fitting with the sum of exponentials requires the curve fitting toolbox\n');
end
if (max(cal.rawdata.rawGammaInput(:)) > 1)
error('crtSumPower option assumes [0-1] specification of input\n');
end
mGammaMassaged = cal.rawdata.rawGammaTable(:,1:cal.nDevices);
for i = 1:cal.nDevices
mGammaMassaged(:,i) = MakeGammaMonotonic(HalfRect(mGammaMassaged(:,i)));
end
fitEqStr = 'a*x^b + (1-a)*x^c';
a = 1;
b = 2;
c = 0;
startPoint = [a b c];
lowerBounds = [0 0.1 0.01];
upperBounds = [1 10 10];
% Fit and predictions
fOptions = fitoptions('Method','NonlinearLeastSquares','Robust','on');
fOptions1 = fitoptions(fOptions,'StartPoint',startPoint,'Lower',lowerBounds,'Upper',upperBounds);
for i = 1:cal.nDevices
if (size(cal.rawdata.rawGammaInput,2) == 1)
fitstruct = fit(cal.rawdata.rawGammaInput,mGammaMassaged(:,i),fitEqStr,fOptions1);
else
fitstruct = fit(cal.rawdata.rawGammaInput(:,i),mGammaMassaged(:,i),fitEqStr,fOptions1);
end
mGammaFit1a(:,i) = feval(fitstruct,linspace(0,1,nInputLevels)); %#ok<*AGROW>
end
mGammaFit1 = mGammaFit1a;
cal.gammaInput = linspace(0,1,nInputLevels)';
case 'betacdf',
if (~exist('fit','file'))
error('Fitting with the betacdf requires the curve fitting toolbox\n');
end
if (~exist('betacdf','file'))
error('Fitting with the betacdf requires the stats toolbox\n');
end
if (max(cal.rawdata.rawGammaInput(:)) > 1)
error('betacdf option assumes [0-1] specification of input\n');
end
mGammaMassaged = cal.rawdata.rawGammaTable(:,1:cal.nDevices);
for i = 1:cal.nDevices
mGammaMassaged(:,i) = MakeGammaMonotonic(HalfRect(mGammaMassaged(:,i)));
end
fitEqStr = 'betacdf(betacdf(x.^f,a,b),c,d).^e';
a = 1;
b = 1;
c = 1;
d = 1;
e = 1;
f = 1;
startPoint = [a b c d e f];
lowerBounds = [1e-3 1e-3 1e-3 1e-3 1e-3 1e-3];
upperBounds = [1e3 1e3 1e3 1e3 1e3 1e3];
% Fit and predictions
fOptions = fitoptions('Method','NonlinearLeastSquares','Robust','on','Display','off');
fOptions1 = fitoptions(fOptions,'StartPoint',startPoint,'Lower',lowerBounds,'Upper',upperBounds,'MaxFunEvals',2000);
for i = 1:cal.nDevices
if (isfield(cal.describe.gamma,'useweight') && cal.describe.gamma.useweight >= 0)
fOptionsUse = fitoptions(fOptions1,'Weights',1./(mGammaMassaged(:,i)+cal.describe.gamma.useweight));
else
fOptionsUse = fOptions1;
end
if (size(cal.rawdata.rawGammaInput,2) == 1)
fitstruct = fit(cal.rawdata.rawGammaInput,mGammaMassaged(:,i),fitEqStr,fOptionsUse);
else
fitstruct = fit(cal.rawdata.rawGammaInput(:,i),mGammaMassaged(:,i),fitEqStr,fOptionsUse);
end
mGammaFit1a(:,i) = feval(fitstruct,linspace(0,1,nInputLevels)); %#ok<*AGROW>
end
mGammaFit1 = mGammaFit1a;
cal.gammaInput = linspace(0,1,nInputLevels)';
case 'sigmoid',
mGammaMassaged = cal.rawdata.rawGammaTable(:,1:cal.nDevices);
for i = 1:cal.nDevices
mGammaMassaged(:,i) = MakeGammaMonotonic(HalfRect(mGammaMassaged(:,i)));
end
fitType = 3;
[mGammaFit1a,cal.gammaInput] = FitDeviceGamma(...
mGammaMassaged,cal.rawdata.rawGammaInput,fitType,nInputLevels);
mGammaFit1 = mGammaFit1a;
case 'weibull',
mGammaMassaged = cal.rawdata.rawGammaTable(:,1:cal.nDevices);
for i = 1:cal.nDevices
mGammaMassaged(:,i) = MakeGammaMonotonic(HalfRect(mGammaMassaged(:,i)));
end
fitType = 4;
[mGammaFit1a,cal.gammaInput] = FitDeviceGamma(...
mGammaMassaged,cal.rawdata.rawGammaInput,fitType,nInputLevels);
mGammaFit1 = mGammaFit1a;
otherwise
error('Unsupported gamma fit string passed');
end
% Fix contingous zeros at start problem
mGammaFit1 = FixZerosAtStart(mGammaFit1);
for j = 1:size(mGammaFit1,2)
mGammaFit1(:,j) = MakeGammaMonotonic(mGammaFit1(:,j));
end
% Handle higher order terms, which are just fit with a polynomial
if (cal.nPrimaryBases > 1)
m = size(mGammaFit1,1);
mGammaFit2 = zeros(m,cal.nDevices*(cal.nPrimaryBases-1));
% OLDFIT path does not contain option of handling data with independent input values
% for measurements for each device primary.
OLDFIT = 0;
if (OLDFIT)
for j = 1:cal.nDevices*(cal.nPrimaryBases-1)
mGammaFit2(:,j) = ...
FitGammaPolyR(cal.rawdata.rawGammaInput,cal.rawdata.rawGammaTable(:,cal.nDevices+j), ...
cal.gammaInput);
end
% This is the code we're currently using. It works for the case where different input levels are specified for
% the measurments for each primary.
else
k = 1;
for j = 1:cal.nDevices*(cal.nPrimaryBases-1)
if (size(cal.rawdata.rawGammaInput,2) > 1)
mGammaFit2(:,j) = interp1(MakeGammaMonotonic([0 ; cal.rawdata.rawGammaInput(:,k)]),[0 ; cal.rawdata.rawGammaTable(:,cal.nDevices+j)],cal.gammaInput,'linear');
else
mGammaFit2(:,j) = interp1(MakeGammaMonotonic([0 ; cal.rawdata.rawGammaInput]),[0 ; cal.rawdata.rawGammaTable(:,cal.nDevices+j)],cal.gammaInput,'linear');
end
k = k+1;
if (k == cal.nDevices+1)
k = 1;
end
end
end
mGammaFit = [mGammaFit1 , mGammaFit2];
else
mGammaFit = mGammaFit1;
end
% Save information in form for calibration routines.
cal.gammaFormat = 0;
cal.gammaTable = mGammaFit;
return
% output = FixZerosAtStart(input)
%
% The OS/X routines need the fit gamma function to be monotonically
% increasing. One way that sometimes fails is when a whole bunch of
% entries at the start are zero. This routine fixes that up.
function output = FixZerosAtStart(input)
output = input;
for j = 1:size(input,2)
for i = 1:size(input,1)
if (input(i,j) > 0)
break;
end
end
if (i == size(input,1))
error('Entire passed gamma function is zero');
end
output(1:i,j) = linspace(0,min([0.0001 input(i+1,j)/2]),i)';
end
return
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