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github | zhxing001/DIP_exercise-master | upConv.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/upConv.m | 2,670 | utf_8 | bb548d24157a068adee954f36ad04fbb | % RES = upConv(IM, FILT, EDGES, STEP, START, STOP, RES)
%
% Upsample matrix IM, followed by convolution with matrix FILT. These
% arguments should be 1D or 2D matrices, and IM must be larger (in
% both dimensions) than FILT. The origin of filt
% is assumed to be floor(size(filt)/2)+1.
%
% EDGES is a string determinin... |
github | zhxing001/DIP_exercise-master | range2.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/range2.m | 459 | utf_8 | b5e91506fc6b3e316a8e1671b5af7467 | % [MIN, MAX] = range2(MTX)
%
% Compute minimum and maximum values of MTX, returning them as a 2-vector.
% Eero Simoncelli, 3/97.
function [mn, mx] = range2(mtx)
%% NOTE: THIS CODE IS NOT ACTUALLY USED! (MEX FILE IS CALLED INSTEAD)
fprintf(1,'WARNING: You should compile the MEX code for "range2", found in the MEX su... |
github | zhxing001/DIP_exercise-master | reconFullSFpyr2.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/reconFullSFpyr2.m | 3,257 | utf_8 | 447f5204894b159fbb4ea4de2dccf877 | % RES = reconFullSFpyr2(PYR, INDICES, LEVS, BANDS, TWIDTH)
%
% Reconstruct image from its steerable pyramid representation, in the Fourier
% domain, as created by buildSFpyr.
% Unlike the standard transform, subdivides the highpass band into
% orientations.
function res = reconFullSFpyr2(pyr, pind, levs, bands, twidt... |
github | zhxing001/DIP_exercise-master | steer2HarmMtx.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/steer2HarmMtx.m | 1,803 | utf_8 | 35816be985c308717168260dc97419d8 | % MTX = steer2HarmMtx(HARMONICS, ANGLES, REL_PHASES)
%
% Compute a steering matrix (maps a directional basis set onto the
% angular Fourier harmonics). HARMONICS is a vector specifying the
% angular harmonics contained in the steerable basis/filters. ANGLES
% (optional) is a vector specifying the angular position of... |
github | zhxing001/DIP_exercise-master | subMtx.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/subMtx.m | 420 | utf_8 | c660029ce728dcb8c540c9cf419fa7bc | % MTX = subMtx(VEC, DIMENSIONS, START_INDEX)
%
% Reshape a portion of VEC starting from START_INDEX (optional,
% default=1) to the given dimensions.
% Eero Simoncelli, 6/96.
function mtx = subMtx(vec, sz, offset)
if (exist('offset') ~= 1)
offset = 1;
end
vec = vec(:);
sz = sz(:);
if (size(sz,1) ~= 2)
error('D... |
github | zhxing001/DIP_exercise-master | spyrHt.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/spyrHt.m | 305 | utf_8 | 06e83c1f45c1a99e242fa7ea37093a11 | % [HEIGHT] = spyrHt(INDICES)
%
% Compute height of steerable pyramid with given index matrix.
% Eero Simoncelli, 6/96.
function [ht] = spyrHt(pind)
nbands = spyrNumBands(pind);
% Don't count lowpass, or highpass residual bands
if (size(pind,1) > 2)
ht = (size(pind,1)-2)/nbands;
else
ht = 0;
end
|
github | zhxing001/DIP_exercise-master | spyrBand.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/spyrBand.m | 819 | utf_8 | 7d15b24244e521c9e85c68b3037f569b | % [LEV,IND] = spyrBand(PYR,INDICES,LEVEL,BAND)
%
% Access a band from a steerable pyramid.
%
% LEVEL indicates the scale (finest = 1, coarsest = spyrHt(INDICES)).
%
% BAND (optional, default=1) indicates which subband
% (1 = vertical, rest proceeding anti-clockwise).
% Eero Simoncelli, 6/96.
function res =... |
github | zhxing001/DIP_exercise-master | wpyrBand.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/wpyrBand.m | 873 | utf_8 | 12e08d90ccd6261b737e2d3c5ac5b1e7 | % RES = wpyrBand(PYR, INDICES, LEVEL, BAND)
%
% Access a subband from a separable QMF/wavelet pyramid.
%
% LEVEL (optional, default=1) indicates the scale (finest = 1,
% coarsest = wpyrHt(INDICES)).
%
% BAND (optional, default=1) indicates which subband (1=horizontal,
% 2=vertical, 3=diagonal).
% Eero Simoncelli... |
github | zhxing001/DIP_exercise-master | innerProd.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/innerProd.m | 404 | utf_8 | 29d3b9025830e8641826ce536cdfa63d | % RES = innerProd(MTX)
%
% Compute (MTX' * MTX) efficiently (i.e., without copying the matrix)
function res = innerProd(mtx)
%% NOTE: THIS CODE SHOULD NOT BE USED! (MEX FILE IS CALLED INSTEAD)
fprintf(1,'WARNING: You should compile the MEX version of "innerProd.c",\n found in the MEX subdirectory of matlabPy... |
github | zhxing001/DIP_exercise-master | reconSFpyr.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/reconSFpyr.m | 3,035 | utf_8 | d2797e74c72af97fc0d72123dc91035c | % RES = reconSFpyr(PYR, INDICES, LEVS, BANDS, TWIDTH)
%
% Reconstruct image from its steerable pyramid representation, in the Fourier
% domain, as created by buildSFpyr.
%
% PYR is a vector containing the N pyramid subbands, ordered from fine
% to coarse. INDICES is an Nx2 matrix containing the sizes of
% each subband... |
github | zhxing001/DIP_exercise-master | corrDn.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/corrDn.m | 2,132 | utf_8 | a2140ad2c6dfcb5280f6a594537bc487 | % RES = corrDn(IM, FILT, EDGES, STEP, START, STOP)
%
% Compute correlation of matrices IM with FILT, followed by
% downsampling. These arguments should be 1D or 2D matrices, and IM
% must be larger (in both dimensions) than FILT. The origin of filt
% is assumed to be floor(size(filt)/2)+1.
%
% EDGES is a string dete... |
github | zhxing001/DIP_exercise-master | maxPyrHt.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/maxPyrHt.m | 603 | utf_8 | 019e5ce9d036a893ec979c63f51ff54a | % HEIGHT = maxPyrHt(IMSIZE, FILTSIZE)
%
% Compute maximum pyramid height for given image and filter sizes.
% Specifically: the number of corrDn operations that can be sequentially
% performed when subsampling by a factor of 2.
% Eero Simoncelli, 6/96.
function height = maxPyrHt(imsz, filtsz)
imsz = imsz(:);
filtsz =... |
github | zhxing001/DIP_exercise-master | buildSFpyrLevs.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/buildSFpyrLevs.m | 1,825 | utf_8 | f392f9c8ee1b85c2cf1c86edc383e4af | % [PYR, INDICES] = buildSFpyrLevs(LODFT, LOGRAD, XRCOS, YRCOS, ANGLE, HEIGHT, NBANDS)
%
% Recursive function for constructing levels of a steerable pyramid. This
% is called by buildSFpyr, and is not usually called directly.
% Eero Simoncelli, 5/97.
function [pyr,pind] = buildSFpyrLevs(lodft,log_rad,Xrcos,Yrcos,angl... |
github | zhxing001/DIP_exercise-master | pixelAxes.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/pixelAxes.m | 1,983 | utf_8 | 1b2a2bfddd425fee2ccd276f3948cec7 | % [ZOOM] = pixelAxes(DIMS, ZOOM)
%
% Set the axes of the current plot to cover a multiple of DIMS pixels,
% thereby eliminating screen aliasing artifacts when displaying an
% image of size DIMS.
%
% ZOOM (optional, default='same') expresses the desired number of
% samples displayed per screen pixel. It should be a ... |
github | zhxing001/DIP_exercise-master | pyrBandIndices.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/pyrBandIndices.m | 589 | utf_8 | 28f90484526c294bdb24bdbf8014012d | % RES = pyrBandIndices(INDICES, BAND_NUM)
%
% Return indices for accessing a subband from a pyramid
% (gaussian, laplacian, QMF/wavelet, steerable).
% Eero Simoncelli, 6/96.
function indices = pyrBandIndices(pind,band)
if ((band > size(pind,1)) | (band < 1))
error(sprintf('BAND_NUM must be between 1 and number o... |
github | zhxing001/DIP_exercise-master | pointOp.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/pointOp.m | 1,132 | utf_8 | 6e1737ee18a474ee23f6cf6fb58b45c6 | % RES = pointOp(IM, LUT, ORIGIN, INCREMENT, WARNINGS)
%
% Apply a point operation, specified by lookup table LUT, to image IM.
% LUT must be a row or column vector, and is assumed to contain
% (equi-spaced) samples of the function. ORIGIN specifies the
% abscissa associated with the first sample, and INCREMENT specifi... |
github | zhxing001/DIP_exercise-master | reconWpyr.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/reconWpyr.m | 3,992 | utf_8 | 859a606f214bce204ce3c9ea18c0fc87 | % RES = reconWpyr(PYR, INDICES, FILT, EDGES, LEVS, BANDS)
%
% Reconstruct image from its separable orthonormal QMF/wavelet pyramid
% representation, as created by buildWpyr.
%
% PYR is a vector containing the N pyramid subbands, ordered from fine
% to coarse. INDICES is an Nx2 matrix containing the sizes of
% each sub... |
github | zhxing001/DIP_exercise-master | shift.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/shift.m | 438 | utf_8 | 2ac42b171e4ca683ef1d361eaf331870 | % [RES] = shift(MTX, OFFSET)
%
% Circular shift 2D matrix samples by OFFSET (a [Y,X] 2-vector),
% such that RES(POS) = MTX(POS-OFFSET).
function res = shift(mtx, offset)
dims = size(mtx);
offset = mod(-offset,dims);
res = [ mtx(offset(1)+1:dims(1), offset(2)+1:dims(2)), ...
mtx(offset(1)+1:dims(1), 1:o... |
github | zhxing001/DIP_exercise-master | namedFilter.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/namedFilter.m | 3,207 | utf_8 | 8f45ef65fca60f53e0f734c4f1ae01bf | % KERNEL = NAMED_FILTER(NAME)
%
% Some standard 1D filter kernels. These are scaled such that
% their L2-norm is 1.0.
%
% binomN - binomial coefficient filter of order N-1
% haar: - Haar wavelet.
% qmf8, qmf12, qmf16 - Symmetric Quadrature Mirror Filters [Johnston80]
% daub2,daub3,daub4 -... |
github | zhxing001/DIP_exercise-master | wpyrHt.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/wpyrHt.m | 270 | utf_8 | 18e1a724afee99389db70484333eb05a | % [HEIGHT] = wpyrHt(INDICES)
%
% Compute height of separable QMF/wavelet pyramid with given index matrix.
% Eero Simoncelli, 6/96.
function [ht] = wpyrHt(pind)
if ((pind(1,1) == 1) | (pind(1,2) ==1))
nbands = 1;
else
nbands = 3;
end
ht = (size(pind,1)-1)/nbands;
|
github | zhxing001/DIP_exercise-master | buildSFpyr.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/buildSFpyr.m | 3,260 | utf_8 | 66654d041ea2ff942f5480347bc4c281 | % [PYR, INDICES, STEERMTX, HARMONICS] = buildSFpyr(IM, HEIGHT, ORDER, TWIDTH)
%
% Construct a steerable pyramid on matrix IM, in the Fourier domain.
% This is similar to buildSpyr, except that:
%
% + Reconstruction is exact (within floating point errors)
% + It can produce any number of orientation bands.
% - ... |
github | zhxing001/DIP_exercise-master | modulateFlip.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/modulateFlip.m | 461 | utf_8 | 92f12f8068fcf49b9863851f106a5aa3 | % [HFILT] = modulateFlipShift(LFILT)
%
% QMF/Wavelet highpass filter construction: modulate by (-1)^n,
% reverse order (and shift by one, which is handled by the convolution
% routines). This is an extension of the original definition of QMF's
% (e.g., see Simoncelli90).
% Eero Simoncelli, 7/96.
function [hfilt] = m... |
github | zhxing001/DIP_exercise-master | spyrNumBands.m | .m | DIP_exercise-master/matlab/denoiseBLS_GSM/Simoncelli_PyrTools/spyrNumBands.m | 480 | utf_8 | 4a10cb68438c7dfc197ec00066f48fd4 | % [NBANDS] = spyrNumBands(INDICES)
%
% Compute number of orientation bands in a steerable pyramid with
% given index matrix. If the pyramid contains only the highpass and
% lowpass bands (i.e., zero levels), returns 0.
% Eero Simoncelli, 2/97.
function [nbands] = spyrNumBands(pind)
if (size(pind,1) == 2)
nbands ... |
github | ngcthuong/Caffe-DCS-master | Cal_PSNRSSIM.m | .m | Caffe-DCS-master/utilities/Cal_PSNRSSIM.m | 6,250 | utf_8 | 891b4e57ebcd097592850eecf97f150e | function [psnr_cur, ssim_cur] = Cal_PSNRSSIM(A,B,row,col)
[n,m,ch]=size(B);
A = A(row+1:n-row,col+1:m-col,:);
B = B(row+1:n-row,col+1:m-col,:);
A=double(A); % Ground-truth
B=double(B); %
e=A(:)-B(:);
mse=mean(e.^2);
psnr_cur=10*log10(255^2/mse);
if ch==1
[ssim_cur, ~] = ssim_index(A, B);
else
ssim_cur = -1;... |
github | scilab/scilab2c-master | tols.m | .m | scilab2c-master/toyApplication/tols.m | 4,661 | utf_8 | b67703ed2f505e1b8dd21ac875ee5562 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [y, fullframes, loops] = tols(x, h, L)
% perform time-partitioned overlap save algorithm
% x -- signal (vector)
% h -- impulse response (vector)
% L -- fft size (frame length for impulse response segmentation is L/2)
% y -- convolution result
% fullframes -- numb... |
github | jamessealesmith/nn_trainer_wc-master | prepare_workspace.m | .m | nn_trainer_wc-master/prepare_workspace.m | 294 | utf_8 | 3676b95eaf370e19d1784b9496fb2469 | %% Prepare Workspace
function logS = prepare_workspace()
% get name of log file
formatOut = 'mm_dd_yy';
logS = strcat('Logs/',datestr(now,formatOut));
% add folders to path
addpath('TrainingAlgorithms','BackpropFunctions','SupportFunctions',...
'Data','CustomFunctions');
warning off
end
|
github | BrainardLab/VirtualWorldPsychophysics-master | save2pdf.m | .m | VirtualWorldPsychophysics-master/toolbox/utilities/save2pdf.m | 2,141 | utf_8 | 7332049e1c11bf187f8100ce13f1a901 | %SAVE2PDF Saves a figure as a properly cropped pdf
%
% save2pdf(pdfFileName,handle,dpi)
%
% - pdfFileName: Destination to write the pdf to.
% - handle: (optional) Handle of the figure to write to a pdf. If
% omitted, the current figure is used. Note that handles
% are typically the fi... |
github | BrainardLab/VirtualWorldPsychophysics-master | drawPsychometricFunction.m | .m | VirtualWorldPsychophysics-master/toolbox/utilities/drawPsychometricFunction.m | 7,947 | utf_8 | 8e40e0e5245a3c14afaef29a0eafd0eb | function thresholds = drawPsychometricFunction(varargin)
%%drawPsychometricFunction : draw psychometric function for lightness experiment
%
% Usage:
% drawPsychometricFunction();
%
% Description:
% Draw psychometirc function for the response of subjects for lightness
% experiment. The function needs the directory... |
github | BrainardLab/VirtualWorldPsychophysics-master | runEasyTrials.m | .m | VirtualWorldPsychophysics-master/toolbox/utilities/runEasyTrials.m | 6,719 | utf_8 | 43348671a90b08248dd9abea56e35a31 | function runEasyTrials(nEasyTrials, trialStruct, cal, scaleFactor, LMSStruct, params, controlSignal, win, gamePad, parser)
% This function runs nEasyTrials number of easy trials at the beginning of
% the experiment. These trials are not saved.
keepLooping = 1;
trialToRun = find(trialStruct.cmpYInTrial == max(trialStr... |
github | BrainardLab/VirtualWorldPsychophysics-master | runLightnessExperiment.m | .m | VirtualWorldPsychophysics-master/experiment/runLightnessExperiment.m | 19,604 | utf_8 | 6f4a343b577c46f34c88a3b2454da03d | function acquisitionStatus = runLightnessExperiment(varargin)
%%runExperiment : run lightness estimation experiment and record data
%
% Usage:
% runLightnessExperiment();
%
% Description:
% Run the lightness estimation psychophysics experiment given the
% calibration file, trial struct and LMS stimulus struct. Re... |
github | BrainardLab/VirtualWorldPsychophysics-master | runExperimentArcade.m | .m | VirtualWorldPsychophysics-master/experiment/runExperimentArcade.m | 17,687 | utf_8 | 71670cdef8fd940daed6f91429124bb5 | function results = runExperimentArcade(varargin)
%%runExperiment : run lightness estimation experiment for the arcade game
%
% Usage:
% runExperimentArcade();
%
% Description:
% Run the lightness estimation psychophysics experiment given the
% calibration file, trial struct and LMS stimulus struct. Give the
% r... |
github | BrainardLab/VirtualWorldPsychophysics-master | t_equivNoiseEtc.m | .m | VirtualWorldPsychophysics-master/tutorials/t_equivNoiseEtc.m | 20,673 | utf_8 | f3af826c6c0444ad2c14d8e1869b6e96 | %% Work through basic equivalent noise models
%
% Description:
% Simulates data using a TSD based model, or loads in our actual data,
% and fits with various models.
%
% The fits are for the TSD model used to generate the simulated data and
% a piecewise linear model.
%
% Can also generate curves by simulatin... |
github | BrainardLab/VirtualWorldPsychophysics-master | t_tvnSimpleModel.m | .m | VirtualWorldPsychophysics-master/tutorials/t_tvnSimpleModel.m | 4,385 | utf_8 | 180aa1d005a577de29ce95491d141fdb | % t_tvnSimpleModel
%
% Simple model for thresholds versus noise
%
% See also: t_equivNoiseEtc
% History:
% 11/13/19 dhb Wrote it.
%% Clear
clear; close all;
%% Parameters
sigmasExternal = linspace(0.01,100,1000);
criterionDPrime = 1;
%% Initialize figure
figure; hold on
sigmaInternal = 1; externalIntrusionFacto... |
github | wenbihan/DeepDenoising-master | classification_demo.m | .m | DeepDenoising-master/matlab/demo/classification_demo.m | 5,412 | utf_8 | 8f46deabe6cde287c4759f3bc8b7f819 | function [scores, maxlabel] = classification_demo(im, use_gpu)
% [scores, maxlabel] = classification_demo(im, use_gpu)
%
% Image classification demo using BVLC CaffeNet.
%
% IMPORTANT: before you run this demo, you should download BVLC CaffeNet
% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)
%
% *****... |
github | wenbihan/DeepDenoising-master | MyVOCevalseg.m | .m | DeepDenoising-master/matlab/my_script/MyVOCevalseg.m | 4,625 | utf_8 | 128c24319d520c2576168d1cf17e068f | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | wenbihan/DeepDenoising-master | MyVOCevalsegBoundary.m | .m | DeepDenoising-master/matlab/my_script/MyVOCevalsegBoundary.m | 4,415 | utf_8 | 1b648714e61bafba7c08a8ce5824b105 | %VOCEVALSEG Evaluates a set of segmentation results.
% VOCEVALSEG(VOCopts,ID); prints out the per class and overall
% segmentation accuracies. Accuracies are given using the intersection/union
% metric:
% true positives / (true positives + false positives + false negatives)
%
% [ACCURACIES,AVACC,CONF] = VOCEV... |
github | tahmidzbr/Human-Activities-Gestures-Recognition-using-Channel-State-Information-CSI-of-IEEE-802.11n-master | extract_time_information.m | .m | Human-Activities-Gestures-Recognition-using-Channel-State-Information-CSI-of-IEEE-802.11n-master/extract_time_information.m | 2,094 | utf_8 | a6828b2a8313bc3c5092ea1ac0e5d2ce | % ####################################################################### %
% ####################################################################### %
% Extracting Time Information of packet arrival from CSI Data %
% Author: Tahmid Z. Chowdhury %
% The Univer... |
github | mingloo/DeepSupervisedHashing-master | calcMAP.m | .m | DeepSupervisedHashing-master/scripts/calcMAP.m | 685 | utf_8 | 8fde5825c8e4d5afde81c356458b3354 | % compute mean average precision (MAP)
function [MAP, succRate] = calcMAP (orderH, neighbor)
[Q, N] = size(neighbor);
pos = 1: N;
%pos = [1: 10000];
MAP = 0;
numSucc = 0;
for i = 1: Q
ngb = neighbor(i, orderH(i, :));
nRel = sum(ngb);
if nRel > 0
prec = cumsum(ngb) ./ pos;
ap = mean(... |
github | mingloo/DeepSupervisedHashing-master | calcHammingDist.m | .m | DeepSupervisedHashing-master/scripts/calcHammingDist.m | 242 | utf_8 | 32c41d9f80386fbbddf74076159f9082 | % compute the hamming distance between every pair of data points represented in each row of B1 and B2
function D = calcHammingDist (B1, B2)
P1 = sign(B1 - 0.5);
P2 = sign(B2 - 0.5);
R = size(P1, 2);
D = round((R - P1 * P2') / 2);
end
|
github | CoraliePicoche/Seasonality-master | compute_temperature_season.m | .m | Seasonality-master/script/compute_temperature_season.m | 439 | utf_8 | dcc6f7e84e56969b13f7cb605de42048 | %%% Model first developped by of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Script by Picoche & Barraquand 2017
%%% This function computes a random temperature time series with a seasonal
%%% signal, whose dominance depends on the value of theta
function [tau] = compute_temperature_season(t, theta)
global mu_tau sigma_... |
github | CoraliePicoche/Seasonality-master | growth_response.m | .m | Seasonality-master/script/growth_response.m | 296 | utf_8 | 361a2d909c4de4bca7251485efc9c129 | %%% Model first developped by of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Script by Picoche & Barraquand 2017
%%% This function computes the growth response to temperature
function growth = growth_response(tau)
global a_r_tau0 tau0 E_r k
growth=a_r_tau0*exp(E_r*(tau-tau0)./(k*tau*tau0));
end |
github | CoraliePicoche/Seasonality-master | species_mean_value.m | .m | Seasonality-master/script/species_mean_value.m | 1,132 | utf_8 | 4f0cf05bff8b4c928f84a4f3e511aef0 | %%% Model first developped by of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Script by Picoche & Barraquand 2018
%%% This function computes species mean biomass during the last years of
%%% the simulation
function res=species_mean_value(youtbis, varargin)
S=size(youtbis,2); %number of species
tt=size(youtbis,1)... |
github | CoraliePicoche/Seasonality-master | SV16_ode_integration.m | .m | Seasonality-master/script/SV16_ode_integration.m | 778 | utf_8 | 51d04f73dda0fa88d174539d19010383 | %%% Model first developped by of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Script by Picoche & Barraquand 2017
%%% Function for ODE integration
function dydt = SV16_ode_integration(t,y)
global A m S thresh_min tau b tau_opt r morta_vect
%A interaction matrix
%m mortality
%S number of species
%thresh_min biomass bel... |
github | CoraliePicoche/Seasonality-master | frac_max.m | .m | Seasonality-master/script/frac_max.m | 549 | utf_8 | ef5e371196abe12e6a78ae83b0852bb8 | %%% Model first developped by of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Script by Picoche & Barraquand 2017
%%% This function computes the fraction of the maximum growth rate achieved
%%% for one for the whole temperature time series
function f = frac_max(tau,tau_opt,b)
for t=1:length(tau)
if b<0
f(t)... |
github | CoraliePicoche/Seasonality-master | SV16_ode_integration_no_GR_in_competition.m | .m | Seasonality-master/script/SV16_ode_integration_no_GR_in_competition.m | 774 | utf_8 | 0db131eb4471b9cb439fd0d5a8b94ddf | %%% Model first developped by of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Script by Picoche & Barraquand 2017
%%% Function for ODE integration, removing the storage effect
function dydt = SV16_ode_integration_no_GR_in_competition(t,y)
global A m S r thresh_min morta_vect
%A interaction matrix
%m mortality
%S number ... |
github | CoraliePicoche/Seasonality-master | community_wide_indices.m | .m | Seasonality-master/script/exploratory/community_wide_indices.m | 1,607 | utf_8 | 8cbf83ff91ac0a54f9876a6517fb956f | %%% Developped by Picoche & Barraquand 2018
%%% Compare different community-wide synchrony index (Loreau and Gross, by
%%% year for the last 500 years saved at the end of a simulation)
function [tab_indices] = community_wide_indices(filename)
thresh_min=10^(-6);
%filename='output_simulation/SV_same_temp/iter1_codevers... |
github | CoraliePicoche/Seasonality-master | fun.m | .m | Seasonality-master/script/exploratory/fun.m | 108 | utf_8 | 1f0b15c48554c3bf553bc47cac7d204d | %%%essai
function f = fun(x,b,tau_opt)
%%%%
f= growth_response(x).*frac_max_vectoriel(x,tau_opt,b);
%%%%
end |
github | CoraliePicoche/Seasonality-master | compute_temperature.m | .m | Seasonality-master/script/exploratory/compute_temperature.m | 387 | utf_8 | 3394d20baa7a1ba7f4f2a72615da1f80 | %%% Model of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Developped by Picoche & Barraquand 2017
%%% Function for temperature
function [tau] = compute_temperature(t)
global mu_tau sigma_tau
%for now, we're just using a random value, that's option 1
tau=normrnd(mu_tau,sigma_tau,1,length(t));
%Seasonality
%tau=mu_tau-s... |
github | CoraliePicoche/Seasonality-master | convergence_function.m | .m | Seasonality-master/script/exploratory/convergence_function.m | 1,250 | utf_8 | e3cea003ba549efc2f4eeade661f21c0 | %%% Model of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Developped by Picoche & Barraquand 2018
%%% Function to check stability, given youtbis
function convergence_function(youtbis)
thresh_min=10^(-6);
%First assessments
nb_species=sum(youtbis'>thresh_min);
nb_species_final=nb_species(end)
tot_bioma... |
github | CoraliePicoche/Seasonality-master | figure_1.m | .m | Seasonality-master/script/exploratory/figure_1.m | 789 | utf_8 | 992038085a34ad2e631e1b4f2f9054ca | %%% Model of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Developped by Picoche & Barraquand 2017
%%% fraction of the maximum growth rate achieved for one species
function f = figure_1(b,tau_opt)
global S
temp_min=12;
temp_max=26;
tmp_tot=linspace(temp_min,temp_max,1000)+273;
r=growth_response(tmp_tot);
figure;hold on;... |
github | CoraliePicoche/Seasonality-master | SV16_ode_integration_randomGR_in_competition.m | .m | Seasonality-master/script/exploratory/SV16_ode_integration_randomGR_in_competition.m | 475 | utf_8 | f82af31a4655ccf8e2706469c224ce77 | %%% Model of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Developped by Picoche & Barraquand 2017
%%% Function for ODE integration
function dydt = SV16_ode_integration_randomGR_in_competition(t,y)
global A m S r thresh_min tbis
%A interaction matrix
%m mortality
%S number of species
dydt=zeros(S,1);
comp=zeros(1,S);
m... |
github | CoraliePicoche/Seasonality-master | frac_max_vectoriel.m | .m | Seasonality-master/script/exploratory/frac_max_vectoriel.m | 486 | utf_8 | b78b44f3a6190743120e40f11a3646ad | %%% Model of Scranton & Vasseur 2016 (Theor Ecol.)
%%% Developped by Picoche & Barraquand 2017
%%% fraction of the maximum growth rate achieved for one species
function f = frac_max_vectoriel(tau,tau_opt,b)
for t=1:length(tau)
if b<0
f(t,1:length(b))=10^9; %just a small trick to avoid the Infinite when se... |
github | CoraliePicoche/Seasonality-master | myplot.m | .m | Seasonality-master/script/exploratory/myplot_RAC/myplot.m | 4,459 | utf_8 | 10e958278187c8b7a943a5df9ef58350 | %% Make a nice plot quickly
% Syntax: myplot(X,Y,type,color, style)
% [X],[Y]: vectors of data x,y
% [type]: type of plots
% 'S': scaatter plot, the default
% 'L': line
% 'B': both
% [color]: can ba a numbe... |
github | CoraliePicoche/Seasonality-master | mysubplot.m | .m | Seasonality-master/script/exploratory/myplot_RAC/mysubplot.m | 4,995 | utf_8 | b92e65a273c1e8dfb133be81e1be88e6 | %% Create subplots with Major title
% Usage:
%
% to make subplot:
% mysubplot(L, W, ID, Title, tightL, tightW)
% [L], [W],: the dimension of subplots as in subplot(L,W,ID)
% [ID]: The location of subplot as in subplot(L,W,ID);
% to mak... |
github | CoraliePicoche/Seasonality-master | num2month.m | .m | Seasonality-master/script/exploratory/myplot_RAC/num2month.m | 554 | utf_8 | f6aa671af3fe68e71cebb7b4919ac571 | function monthstr = num2month(monthnum)
[n, p] = size(monthnum);
for i = 1: n
for j = 1: p
monthstr(i, j) = n2mstr(monthnum(i,j));
end
end
if isscalar(monthnum)
monthstr = monthstr{1};
end
end
%% a subfunction
function mstr =n... |
github | CoraliePicoche/Seasonality-master | myplot_RAC.m | .m | Seasonality-master/script/exploratory/myplot_RAC/myplot_RAC.m | 4,588 | utf_8 | 33c81ccad436bc310b0ca9b317467c48 | %% myplot_RAC
% This function plots Rank-Abundance Curve. (each community well be presented in subplots, so cannot be embeded in subplot)
%
% depends on: [mycolor.m], [mysubplot.m]
%% *Syntax*
% myplot_RAC(X)
% X : matrix of size [n, p] ; n communities, p species
%
% myplot_RAC(X, strs, strn, big... |
github | CoraliePicoche/Seasonality-master | mycolor.m | .m | Seasonality-master/script/exploratory/myplot_RAC/mycolor.m | 6,462 | utf_8 | 6b64fa12d5597f8423cb337bb409bd06 | % Color selecter: generating a 3-number vector code for a color.
% Syntax:
% code = mycolor(colorcode,selectplate)
% Input:
% [colorcode]: integer, selecting from the color plate
% e.g. colorcode = 3, means a dark blue from the defaultplate 'color'
% ... |
github | xueshengke/libADMM-master | lrr.m | .m | libADMM-master/algorithms/lrr.m | 3,529 | utf_8 | f415a5263180f31dc35bdec719b7bdf4 | function [X,E,obj,err,iter] = lrr(A,B,lambda,opts)
% Solve the Low-Rank Representation minimization problem by M-ADMM
%
% min_{X,E} ||X||_*+lambda*loss(E), s.t. A=BX+E
% loss(E) = ||E||_1 or 0.5*||E||_F^2 or ||E||_{2,1}
%
% ---------------------------------------------
% Input:
% A - d*na matrix
% ... |
github | xueshengke/libADMM-master | groupl1.m | .m | libADMM-master/algorithms/groupl1.m | 2,730 | utf_8 | 71035c51c2852449c2ddbc3091fe41ed | function [X,obj,err,iter] = groupl1(A,B,G,opts)
% Solve the group l1-minimization problem by ADMM
%
% min_X \sum_{i=1}^n\sum_{g in G} ||(x_i)_g||_2, s.t. AX=B
%
% x_i is the i-th column of X
% ---------------------------------------------
% Input:
% A - d*na matrix
% B - d*nb matrix
% ... |
github | xueshengke/libADMM-master | rpca.m | .m | libADMM-master/algorithms/rpca.m | 2,944 | utf_8 | 1930326cf4bf01c764909897658853ca | function [L,S,obj,err,iter] = rpca(X,lambda,opts)
% Solve the Robust Principal Component Analysis minimization problem by M-ADMM
%
% min_{L,S} ||L||_*+lambda*loss(S), s.t. X=L+S
% loss(S) = ||S||_1 or ||S||_{2,1}
%
% ---------------------------------------------
% Input:
% X - d*n matrix
% lambda ... |
github | xueshengke/libADMM-master | tracelasso.m | .m | libADMM-master/algorithms/tracelasso.m | 2,583 | utf_8 | 536f5ce74c82d5f183c3c967e14d6cf6 | function [x,obj,err,iter] = tracelasso(A,b,opts)
% Solve the trace Lasso minimization problem by ADMM
%
% min_x ||A*Diag(x)||_*, s.t. Ax=b
%
% ---------------------------------------------
% Input:
% A - d*n matrix
% b - d*1 vector
% opts - Structure value in Matlab. The field... |
github | xueshengke/libADMM-master | groupl1R.m | .m | libADMM-master/algorithms/groupl1R.m | 3,417 | utf_8 | daad367680f297bfd5a82197bec8b72d | function [X,E,obj,err,iter] = groupl1R(A,B,G,lambda,opts)
% Solve the group l1 norm regularized minimization problem by M-ADMM
%
% min_{X,E} loss(E)+lambda*\sum_{i=1}^n\sum_{g in G} ||(x_i)_g||_2, s.t. AX+E=B
% x_i is the i-th column of X
% loss(E) = ||E||_1 or 0.5*||E||_F^2
% -----------------------------------------... |
github | xueshengke/libADMM-master | mlap.m | .m | libADMM-master/algorithms/mlap.m | 4,761 | utf_8 | ad408cb013b2ffa24973702254b4d4e0 | function [Z,E,obj,err,iter] = mlap(X,lambda,alpha,opts)
% Solve the Multi-task Low-rank Affinity Pursuit (MLAP) minimization problem by M-ADMM
%
% Reference: Cheng, Bin, Guangcan Liu, Jingdong Wang, Zhongyang Huang, and Shuicheng Yan.
% Multi-task low-rank affinity pursuit for image segmentation. ICCV, 2011.
%
% min_{... |
github | xueshengke/libADMM-master | lrsr.m | .m | libADMM-master/algorithms/lrsr.m | 3,838 | utf_8 | 8bd2f6bd0800a5a346a5a4bfbb011702 | function [X,E,obj,err,iter] = lrsr(A,B,lambda1,lambda2,opts)
% Solve the Low-Rank and Sparse Representation (LRSR) minimization problem by M-ADMM
%
% min_{X,E} ||X||_*+lambda1*||X||_1+lambda2*loss(E), s.t. A=BX+E
% loss(E) = ||E||_1 or 0.5*||E||_F^2 or ||E||_{2,1}
% ---------------------------------------------
% Inpu... |
github | xueshengke/libADMM-master | fusedl1R.m | .m | libADMM-master/algorithms/fusedl1R.m | 3,714 | utf_8 | 145be29163c05b2175bd848ba37d18d1 | function [x,e,obj,err,iter] = fusedl1R(A,b,lambda1,lambda2,opts)
% Solve the fused Lasso regularized minimization problem by ADMM
%
% min_{x,e} loss(e) + lambda1*||x||_1 + lambda2*\sum_{i=2}^p |x_i-x_{i-1}|,
% loss(e) = ||e||_1 or 0.5*||e||_2^2
%
% ---------------------------------------------
% Input:
% A ... |
github | xueshengke/libADMM-master | fusedl1.m | .m | libADMM-master/algorithms/fusedl1.m | 3,027 | utf_8 | e180c20b97ac834bfde5d505c23bdb1e | function [x,obj,err,iter] = fusedl1(A,b,lambda,opts)
% Solve the fused Lasso (Fused L1) minimization problem by ADMM
%
% min_x ||x||_1 + lambda*\sum_{i=2}^p |x_i-x_{i-1}|,
% s.t. Ax=b
%
% ---------------------------------------------
% Input:
% A - d*n matrix
% b - d*1 vector
% la... |
github | xueshengke/libADMM-master | tracelassoR.m | .m | libADMM-master/algorithms/tracelassoR.m | 3,247 | utf_8 | 8bc2e00ce23aaa6478590829303525b6 | function [x,e,obj,err,iter] = tracelassoR(A,b,lambda,opts)
% Solve the trace Lasso regularized minimization problem by M-ADMM
%
% min_{x,e} loss(e)+lambda*||A*Diag(x)||_*, s.t. Ax+e=b
% loss(e) = ||e||_1 or 0.5*||e||_2^2
% ---------------------------------------------
% Input:
% A - d*n matrix
% b... |
github | xueshengke/libADMM-master | latlrr.m | .m | libADMM-master/algorithms/latlrr.m | 3,636 | utf_8 | 51a08d8f2880a125c6d1dda689ba9f7f | function [Z,L,obj,err,iter] = latlrr(X,lambda,opts)
% Solve the Latent Low-Rank Representation by M-ADMM
%
% min_{Z,L,E} ||Z||_*+||L||_*+lambda*loss(E),
% s.t., XZ+LX-X=E.
% loss(E) = ||E||_1 or 0.5*||E||_F^2 or ||E||_{2,1}
% ---------------------------------------------
% Input:
% X - d*n matrix
% ... |
github | xueshengke/libADMM-master | prox_ksupport.m | .m | libADMM-master/proximal_operator/prox_ksupport.m | 1,804 | utf_8 | 028824f0d5fbd7a1023a530eb9fc2265 | function B = prox_ksupport(v,k,lambda)
% The proximal operator of the k support norm of a vector
%
% min_x 0.5*lambda*||x||_{ksp}^2+0.5*||x-v||_2^2
%
% version 1.0 - 27/06/2016
%
% Written by Hanjiang Lai
%
% Reference:
% Lai H, Pan Y, Lu C, et al. Efficient k-support matrix pursuit, ECCV, 2014: 617-631.
%
L = 1/la... |
github | xinario/SAGAN-master | get_ct_window_size.m | .m | SAGAN-master/poisson_noise_simulation/get_ct_window_size.m | 469 | utf_8 | c9540ad023b1cc0ff035bb7f16d3367a | % get_ct_window_size -- get the display window size.
% Paras:
% @type : window type
% Author: Xin Yi (xiy525@mail.usask.ca)
% Date : 03/22/2017
function [center, width] = get_ct_window_size(type)
if strcmp(type, 'lung')
center = -700;
width = 1500;
elseif strcmp(type, 'abdomen')
center = 40... |
github | xinario/SAGAN-master | add_poisson_noise.m | .m | SAGAN-master/poisson_noise_simulation/add_poisson_noise.m | 884 | utf_8 | c5d7164b81d8057fa588e5b4f4e8fb7a | % add_poisson_noise -- add poisson noise to a ct slice.
% Paras:
% @im : attenuation coefficients of a single ct slice
% @N0 : x-ray source influx
% Author: Xin Yi (xiy525@mail.usask.ca)
% Date : 03/22/2017
function im_noise = add_poisson_noise(im_ac, N0)
dtheta = 0.3;
dsensor = 0.1;
D = 500;... |
github | xinario/SAGAN-master | ac2window.m | .m | SAGAN-master/poisson_noise_simulation/ac2window.m | 568 | utf_8 | a1a50589657dc539101e2188ebe364e3 | % ac2window -- rescale the ct slice to a specific window range
% Paras:
% @im_ac : image of attenuation coefficients
% @window_type : dispaly window type
% Author: Xin Yi (xiy525@mail.usask.ca)
% Date : 03/22/2017
function im_dicom = ac2window(im_ac, window_type)
im_dicom_raw = (im_ac - 0.17)/0.17*1... |
github | xinario/SAGAN-master | dicom_read_ac.m | .m | SAGAN-master/poisson_noise_simulation/dicom_read_ac.m | 629 | utf_8 | 41b0e9973a2e7226fea3759367d012a4 | % dicom_read_ac -- transfer the hunsfield units to attenuation coefficents.
% Paras:
% @file_path : path to the dicom image
% Author: Xin Yi (xiy525@mail.usask.ca)
% Date : 03/22/2017
function im_ac = dicom_read_ac(file_path)
im_dicom_raw = dicomread(file_path);
info = dicominfo(file_path);
... |
github | BookIndRandSamplingMethods/code-master | randsrc.m | .m | code-master/CHAPTER_4/ARS_PARS_for_Nakagami_Target/randsrc.m | 3,317 | utf_8 | 40674a451df280dbf8f5fb1d2ad7f2d9 | % %% Copyright (C) 2003 David Bateman
% ##
% ## This program is free software; you can redistribute it and/or modify
% ## it under the terms of the GNU General Public License as published by
% ## the Free Software Foundation; either version 2 of the License, or
% ## (at your option) any later version.
% ##
% ## This pr... |
github | BookIndRandSamplingMethods/code-master | scatterhist.m | .m | code-master/CHAPTER_2/scatterhist.m | 11,895 | utf_8 | d75e1d150a4a9c7f080708aeaf22aa05 | function h = scatterhist(x,y,varargin)
%SCATTERHIST 2D scatter plot with marginal histograms.
% SCATTERHIST(X,Y) creates a 2D scatterplot of the data in the vectors X
% and Y, and puts a univariate histogram on the horizontal and vertical
% axes of the plot. X and Y must be the same length.
%
% SCATTERHIST(...... |
github | mbrbic/Multi-view-LRSSC-master | pairwise_LRSSC_1view.m | .m | Multi-view-LRSSC-master/pairwise_LRSSC_1view.m | 1,954 | utf_8 | b921b76a654d20be613a7eae2eba0d29 | %
% One step of ADMM for one view of pairwise multi-view LRSSC.
%
% -------------------------------------------------------
% Input:
% X: mxn data matrix for one view; columns are samples
% K: nxn kernel matrix current view
% num_views: number of views
% C1: nxn low-rank coefficient matrix for current view
% ... |
github | mbrbic/Multi-view-LRSSC-master | construct_kernel.m | .m | Multi-view-LRSSC-master/construct_kernel.m | 1,724 | utf_8 | e3b4a782ce1e92137069d6861d57f8b6 | % Kernel construction.
%
% -------------------------------------------------------
% Input:
% X1: nxm data matrix (rows are samples)
% X2: nxm data matrix (rows are samples)
% opts: Structure value with the following fields:
% opts.KernelType: kernel type, choices are:
% ... |
github | mbrbic/Multi-view-LRSSC-master | centroid_LRSSC_1view.m | .m | Multi-view-LRSSC-master/centroid_LRSSC_1view.m | 2,128 | utf_8 | c4371c1c1e5c39edccd8f4c2ec66265c | %
% One step of ADMM for one view of centroid multi-view LRSSC.
%
% -------------------------------------------------------
% Input:
% X: mxn data matrix for one view; columns are samples
% K: nxn kernel matrix current view
% C1: nxn low-rank coefficient matrix for current view
% C2: nxn sparse coefficient matr... |
github | mbrbic/Multi-view-LRSSC-master | performance_kmeans.m | .m | Multi-view-LRSSC-master/performance_kmeans.m | 1,303 | utf_8 | 7dc3ce464eda1546d136de65d958a96b | % Run k-means n times and report means and standard deviations of the
% performance measures.
%
% -------------------------------------------------------
% Input:
% X: data matrix (rows are samples)
% k: number of clusters
% truth: truth cluster indicators
%
%
% Output:
% CA: clustering... |
github | mbrbic/Multi-view-LRSSC-master | spectral_clustering.m | .m | Multi-view-LRSSC-master/spectral_clustering.m | 1,100 | utf_8 | 2c499a670f700c1d69379a7c50b3c34a | %--------------------------------------------------------------------------
% This function takes an adjacency matrix of a graph and computes the
% clustering of the nodes using the spectral clustering algorithm of
% Ng, Jordan and Weiss.
% CMat: NxN adjacency matrix
% n: number of groups for clustering
% groups: N-dim... |
github | mbrbic/Multi-view-LRSSC-master | eucl_dist2.m | .m | Multi-view-LRSSC-master/eucl_dist2.m | 1,701 | utf_8 | 29b40873678068324b4647fcdf25e283 | % Euclidean Distance matrix.
%
% -------------------------------------------------------
% Input:
% X1: nxm data matrix (rows are samples)
% X2: nxm data matrix (rows are samples)
% opts: Structure value with the following fields:
% opts.KernelType: kernel type, choices are:
% ... |
github | mbrbic/Multi-view-LRSSC-master | centroid_MLRSSC.m | .m | Multi-view-LRSSC-master/centroid_MLRSSC.m | 4,065 | utf_8 | 24d6aff263cde7054cea083ea5715a03 | %
% Solve the multiview centroid based LRSSC
%
% -------------------------------------------------------
% Input:
% X: cell of nxm matrices (rows are samples)
% k: number of clusters
% truth: truth cluster indicators
% opts: Structure value with following fields:
% opts.lambda: ... |
github | mbrbic/Multi-view-LRSSC-master | pairwise_MLRSSC.m | .m | Multi-view-LRSSC-master/pairwise_MLRSSC.m | 4,316 | utf_8 | d5dc7be4b5c1bd869617ec9043ee03a7 | %
% Solve the multiview pairwise similarities based LRSSC
%
% -------------------------------------------------------
% Input:
% X: cell of nxm matrices (rows are samples)
% k: number of clusters
% truth: truth cluster indicators
% opts: Structure value with the following fields:
% ... |
github | mbrbic/Multi-view-LRSSC-master | compute_CE.m | .m | Multi-view-LRSSC-master/performance/compute_CE.m | 1,955 | utf_8 | f82c9a2da3e44077d754286307840c28 | % Compute the clustering error.
%
%-------------------------------------------------------
% Input:
% A: predicted cluster assignments
% A0 : N true cluster assignments
% truth: truth cluster indicators
%
%
% Output:
% CE: clustering error
%
function CE = compute_CE(A,A0)
if size(A,2) =... |
github | mbrbic/Multi-view-LRSSC-master | compute_nmi.m | .m | Multi-view-LRSSC-master/performance/compute_nmi.m | 1,364 | utf_8 | 60db924b3abc1efdd1fb1731c534eece | % Compute normalized mutual information.
%
function nmi = compute_nmi (T, H)
N = length(T);
classes = unique(T);
clusters = unique(H);
num_class = length(classes);
num_clust = length(clusters);
%%compute number of points in each class
for j=1:num_class
index_class = (T(:)==classes(j));
D(j) = sum... |
github | andreafarina/SOLUS-master | linearity_fit.m | .m | SOLUS-master/example/example_200202_series/linearity_fit.m | 6,499 | utf_8 | 753b17477de52ef6051dbf5991910d79 | %% analyisis
%clear
direc = { 'ex_bulk10_0.1_incl10_0.05',...
'ex_bulk10_0.1_incl10_0.4',...
'ex_bulk10_0.2_incl10_0.2',...
'ex_bulk10_0.1_incl10_0.1',...
'ex_bulk10_0.1_incl10_0.6',...
'ex_bulk10_0.2_incl10_0.4',...
'ex_bulk10_0.1_incl05_0.1',...
'ex_bulk10_0.1_incl10_0.2',...
'e... |
github | andreafarina/SOLUS-master | mutiple_run_script.m | .m | SOLUS-master/example/example_200202_series/mutiple_run_script.m | 1,527 | utf_8 | adeeec982ba14695f0f8cb8644281cf4 | %% run series of experiments
direc = { 'ex_bulk10_0.1_incl10_0.05',...
'ex_bulk10_0.1_incl10_0.4',...
'ex_bulk10_0.2_incl10_0.2',...
'ex_bulk10_0.1_incl10_0.1',...
'ex_bulk10_0.1_incl10_0.6',...
'ex_bulk10_0.2_incl10_0.4',...
'ex_bulk10_0.1_incl05_0.1',...
'ex_bulk10_0.1_incl10_0.2',..... |
github | andreafarina/SOLUS-master | createExp.m | .m | SOLUS-master/data/202002/createExp.m | 24,556 | utf_8 | a4e4fc8456687115a5988c858451ae9d | function EXP = createExp(DAT_STR, type_phantom)
%
% EXP = CreateMatlabExp(DAT_STR, type_phantom)
% Takes as input the path to a *.dat file and return a MAT variable
% suitable for operations in the SOLUS software
% DATA_STR is the pathname of a *.dat file
% type_phantom is a str defining from which phantom the experime... |
github | andreafarina/SOLUS-master | DatRead3.m | .m | SOLUS-master/data/202002/DatRead3.m | 22,460 | utf_8 | 7a8e8f566dc60d850973521c257867aa | function [ Data, varargout ] = DatRead3(FileName,varargin)
%DatRead3('FileName')
%Can be as input a selection of the following parameters
%DatRead3(...,'loop4',numloop4,'loop5',numloop5,'datatype','uint32','compilesub',true/false,'forcereading',true/false)
%[Data,Header,EasyReadableHead,SubHeaders,EasyReadableSubHe... |
github | andreafarina/SOLUS-master | pdftops.m | .m | SOLUS-master/src/util/export_fig/pdftops.m | 3,687 | utf_8 | 43c139e49fce63cb78060895bd13137a | function varargout = pdftops(cmd)
%PDFTOPS Calls a local pdftops executable with the input command
%
% Example:
% [status result] = pdftops(cmd)
%
% Attempts to locate a pdftops executable, finally asking the user to
% specify the directory pdftops was installed into. The resulting path is
% stored for futur... |
github | andreafarina/SOLUS-master | crop_borders.m | .m | SOLUS-master/src/util/export_fig/crop_borders.m | 3,791 | utf_8 | 2c8fc83f142f1d5b28b99080556c791e | function [A, vA, vB, bb_rel] = crop_borders(A, bcol, padding)
%CROP_BORDERS Crop the borders of an image or stack of images
%
% [B, vA, vB, bb_rel] = crop_borders(A, bcol, [padding])
%
%IN:
% A - HxWxCxN stack of images.
% bcol - Cx1 background colour vector.
% padding - scalar indicating how much paddi... |
github | andreafarina/SOLUS-master | isolate_axes.m | .m | SOLUS-master/src/util/export_fig/isolate_axes.m | 4,851 | utf_8 | 611d9727e84ad6ba76dcb3543434d0ce | function fh = isolate_axes(ah, vis)
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
% fh = isolate_axes(ah)
% fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The o... |
github | andreafarina/SOLUS-master | im2gif.m | .m | SOLUS-master/src/util/export_fig/im2gif.m | 6,234 | utf_8 | 8ee74d7d94e524410788276aa41dd5f1 | %IM2GIF Convert a multiframe image to an animated GIF file
%
% Examples:
% im2gif infile
% im2gif infile outfile
% im2gif(A, outfile)
% im2gif(..., '-nocrop')
% im2gif(..., '-nodither')
% im2gif(..., '-ncolors', n)
% im2gif(..., '-loops', n)
% im2gif(..., '-delay', n)
%
% This function c... |
github | andreafarina/SOLUS-master | read_write_entire_textfile.m | .m | SOLUS-master/src/util/export_fig/read_write_entire_textfile.m | 961 | utf_8 | 775aa1f538c76516c7fb406a4f129320 | %READ_WRITE_ENTIRE_TEXTFILE Read or write a whole text file to/from memory
%
% Read or write an entire text file to/from memory, without leaving the
% file open if an error occurs.
%
% Reading:
% fstrm = read_write_entire_textfile(fname)
% Writing:
% read_write_entire_textfile(fname, fstrm)
%
%IN:
% fn... |
github | andreafarina/SOLUS-master | pdf2eps.m | .m | SOLUS-master/src/util/export_fig/pdf2eps.m | 1,522 | utf_8 | 4c8f0603619234278ed413670d24bdb6 | %PDF2EPS Convert a pdf file to eps format using pdftops
%
% Examples:
% pdf2eps source dest
%
% This function converts a pdf file to eps format.
%
% This function requires that you have pdftops, from the Xpdf suite of
% functions, installed on your system. This can be downloaded from:
% http://www.foolabs.c... |
github | andreafarina/SOLUS-master | print2array.m | .m | SOLUS-master/src/util/export_fig/print2array.m | 9,613 | utf_8 | e398a6296734121e6e1983a45298549a | function [A, bcol] = print2array(fig, res, renderer, gs_options)
%PRINT2ARRAY Exports a figure to an image array
%
% Examples:
% A = print2array
% A = print2array(figure_handle)
% A = print2array(figure_handle, resolution)
% A = print2array(figure_handle, resolution, renderer)
% A = print2array(figur... |
github | andreafarina/SOLUS-master | append_pdfs.m | .m | SOLUS-master/src/util/export_fig/append_pdfs.m | 2,759 | utf_8 | 9b52be41aff48bea6f27992396900640 | %APPEND_PDFS Appends/concatenates multiple PDF files
%
% Example:
% append_pdfs(output, input1, input2, ...)
% append_pdfs(output, input_list{:})
% append_pdfs test.pdf temp1.pdf temp2.pdf
%
% This function appends multiple PDF files to an existing PDF file, or
% concatenates them into a PDF file if the o... |
github | andreafarina/SOLUS-master | using_hg2.m | .m | SOLUS-master/src/util/export_fig/using_hg2.m | 1,037 | utf_8 | 3303caab5694b040103ccb6b689387bf | %USING_HG2 Determine if the HG2 graphics engine is used
%
% tf = using_hg2(fig)
%
%IN:
% fig - handle to the figure in question.
%
%OUT:
% tf - boolean indicating whether the HG2 graphics engine is being used
% (true) or not (false).
% 19/06/2015 - Suppress warning in R2015b; cache result for i... |
github | andreafarina/SOLUS-master | eps2pdf.m | .m | SOLUS-master/src/util/export_fig/eps2pdf.m | 8,543 | utf_8 | a63a364925b89dac21030d36b0dd29a3 | function eps2pdf(source, dest, crop, append, gray, quality, gs_options)
%EPS2PDF Convert an eps file to pdf format using ghostscript
%
% Examples:
% eps2pdf source dest
% eps2pdf(source, dest, crop)
% eps2pdf(source, dest, crop, append)
% eps2pdf(source, dest, crop, append, gray)
% eps2pdf(source, de... |
github | andreafarina/SOLUS-master | ghostscript.m | .m | SOLUS-master/src/util/export_fig/ghostscript.m | 7,683 | utf_8 | 4def5873a1621e793bd59aaa23f3fc25 | function varargout = ghostscript(cmd)
%GHOSTSCRIPT Calls a local GhostScript executable with the input command
%
% Example:
% [status result] = ghostscript(cmd)
%
% Attempts to locate a ghostscript executable, finally asking the user to
% specify the directory ghostcript was installed into. The resulting path... |
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