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github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_lpf_MOD2_f2.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_lpf_MOD2_f2.m
| 5,547 |
utf_8
|
7123a8788999fc93894d61afd875da5e
|
% write_lpf_MOD
% 11/17/16
function write_lpf_MOD2_f2(GSFLOW_indir, infile_pre, surfz_fil, NLAY)
% % =========== TO RUN AS SCRIPT ===========================================
% clear all, close all, fclose all;
% % - directories
% % MODFLOW input files
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
%
% % infile_pre = 'test1lay';
% % NLAY = 1;
% % DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% infile_pre = 'test2lay';
% NLAY = 2;
% DZ = [50; 50]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
%
% % for various files: ba6, dis, uzf, lpf
% surfz_fil = [GIS_indir, 'topo.asc'];
% % for various files: ba6, uzf
% mask_fil = [GIS_indir, 'basinmask_dischargept.asc'];
%
% % for sfr
% reach_fil = [GIS_indir, 'reach_data.txt'];
% segment_fil_all = cell(3,1);
% segment_fil_all{1} = [GIS_indir, 'segment_data_4A_INFORMATION.txt'];
% segment_fil_all{2} = [GIS_indir, 'segment_data_4B_UPSTREAM.txt'];
% segment_fil_all{3} = [GIS_indir, 'segment_data_4C_DOWNSTREAM.txt'];
% % ====================================================================
% - write to this file
% GSFLOW_dir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% lpf_file = 'test.lpf';
lpf_file = [infile_pre, '.lpf'];
slashstr = '/';
% - domain dimensions, maybe already in surfz_fil and botm_fil{}?
% NLAY = 2;
% surfz_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/topo.asc';
fid = fopen(surfz_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
fclose(fid);
% -- Base hydcond, Ss (all layers), and Sy (top layer only) on data from files
% (temp place-holder)
hydcond = ones(NROW,NCOL,NLAY)*2; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
% hydcond(:,:,2) = 0.5; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
% hydcond(:,:,2) = 0.1; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
hydcond(:,:,1) = 0.1; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
% hydcond(:,:,1) = 0.01; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
hydcond(:,:,2) = 0.01; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
Ss = ones(NROW,NCOL,NLAY)* 2e-6; % constant 2e-6 /m for Sagehen
Sy = ones(NROW,NCOL,NLAY)*0.15; % 0.08-0.15 in Sagehen (lower Sy under ridges for volcanic rocks)
WETDRY = Sy; % = Sy in Sagehen (lower Sy under ridges for volcanic rocks)
% -- assumed input values
flow_filunit = 34; % make sure this matches namefile!!
hdry = 1e30; % head assigned to dry cells
nplpf = 0; % number of LPF parameters (if >0, key words would follow)
laytyp = zeros(NLAY,1); laytyp(1) = 1; % flag, top>0: "covertible", rest=0: "confined"
layave = zeros(NLAY,1); % flag, layave=1: harmonic mean for interblock transmissivity
chani = ones(NLAY,1); % flag, chani=1: constant horiz anisotropy mult factor (for each layer)
layvka = zeros(NLAY,1); % flag, layvka=0: vka is vert K; >0 is vertK/horK ratio
VKA = hydcond;
laywet = zeros(NLAY,1); laywet(1)=1; % flag, 1: wetting on for top convertible cells, 0: off for confined
fl_Tr = 1; % flag, 1 for at least 1 transient stress period (for Ss and Sy)
WETFCT = 1.001; % 1.001 for Sagehen, wetting (convert dry cells to wet)
IWETIT = 4; % number itermations for wetting
IHDWET = 0; % wetting scheme, 0: equation 5-32A is used: h = BOT + WETFCT (hn - BOT)
%% ------------------------------------------------------------------------
fmt1 = repmat('%2d ', 1, NLAY);
fil_lpf_0 = [GSFLOW_indir, slashstr, lpf_file];
fid = fopen(fil_lpf_0, 'wt');
fprintf(fid, '# LPF package inputs\n');
fprintf(fid, '%d %g %d ILPFCB,HDRY,NPLPF\n', flow_filunit, hdry, nplpf);
fprintf(fid, [fmt1, ' LAYTYP\n'], laytyp);
fprintf(fid, [fmt1, ' LAYAVE\n'], layave);
fprintf(fid, [fmt1, ' CHANI \n'], chani);
fprintf(fid, [fmt1, ' LAYVKA\n'], layvka);
fprintf(fid, [fmt1, ' LAYWET\n'], laywet);
if ~isempty(find(laywet,1))
fprintf(fid, '%g %d %d WETFCT, IWETIT, IHDWET\n', WETFCT, IWETIT, IHDWET);
end
% -- Write HKSAT and Ss, Sy (if Tr) in .lpf file
format0 = [repmat(' %4.2f ', 1, NCOL), '\n'];
format1 = [repmat(' %4.2e ', 1, NCOL), '\n'];
% loop thru layers (different entry for each layer)
for lay = 1: NLAY
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 HY layer %d\n', lay); % horizontal hyd cond
fprintf(fid, format1, hydcond(:,:,lay)');
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 VKA layer %d\n', lay); % vertical hyd cond
fprintf(fid, format1, VKA(:,:,lay)');
if fl_Tr
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 Ss layer %d\n', lay);
fprintf(fid, format1, Ss(:,:,lay)');
if laytyp(lay) > 0 % convertible, i.e. unconfined
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 Sy layer %d\n', lay);
fprintf(fid, format1, Sy(:,:,lay)');
if laywet(lay) > 0
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 WETDRY layer %d\n', lay);
fprintf(fid, format0, WETDRY(:,:,lay)');
end
end
end
end
fprintf(fid, '\n');
fclose(fid);
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_ba6_MOD3.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_ba6_MOD3.m
| 6,046 |
utf_8
|
c967103aeca207643dcd775bdb4760b4
|
% write_ba6_MOD
% 11/17/16
function write_ba6_MOD3(GSFLOW_indir, infile_pre, mask_fil)
% % ==== TO RUN AS SCRIPT ===================================================
% clear all, close all, fclose all;
% % - directories
% % MODFLOW input files
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
%
% % infile_pre = 'test1lay';
% % NLAY = 1;
% % DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% infile_pre = 'test2lay';
% NLAY = 2;
% DZ = [50; 50]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
%
% % for various files: ba6, dis, uzf, lpf
% surfz_fil = [GIS_indir, 'topo.asc'];
% % for various files: ba6, uzf
% mask_fil = [GIS_indir, 'basinmask_dischargept.asc'];
% % =========================================================================
%%
% - write to this file
% GSFLOW_dir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
ba6_file = [infile_pre, '.ba6'];
slashstr = '/';
% - domain dimensions, maybe already in surfz_fil and botm_fil{}?
% NLAY = 1;
% NROW = 50;
% NCOL = 50;
% -- IBOUND(NROW,NCOL,NLAY): <0 const head, 0 no flow, >0 variable head
% use basin mask (set IBOUND>0 within watershed, =0 outside watershed, <0 at discharge point and 2 neighboring pixels)
% mask_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/basinmask_dischargept.asc';
fid = fopen(mask_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
D = textscan(fid, '%f');
IBOUND = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
D = textscan(fid, '%s %s %f %s %f');
dischargePt_rowi = D{3};
dischargePt_coli = D{5};
fclose(fid);
% - force some cells to be active to correspond to stream reaches
IBOUND(14,33) = 1;
IBOUND(11,35) = 1;
IBOUND(12,34) = 1;
IBOUND(7,43) = 1;
% find boundary cells
IBOUNDin = IBOUND(2:end-1,2:end-1);
IBOUNDu = IBOUND(1:end-2,2:end-1); % up
IBOUNDd = IBOUND(3:end,2:end-1); % down
IBOUNDl = IBOUND(2:end-1,1:end-2); % left
IBOUNDr = IBOUND(2:end-1,3:end); % right
% - inner boundary is constant head
ind_bound = IBOUNDin==1 & (IBOUNDin-IBOUNDu==1 | IBOUNDin-IBOUNDd==1 | ...
IBOUNDin-IBOUNDl==1 | IBOUNDin-IBOUNDr==1);
% - outer boundary is constant head
% ind_bound = IBOUNDin==0 & (IBOUNDin-IBOUNDu==-1 | IBOUNDin-IBOUNDd==-1 | ...
% IBOUNDin-IBOUNDl==-1 | IBOUNDin-IBOUNDr==-1);
% -- init head: base on TOP and BOTM
dis_file = [GSFLOW_indir, '/', infile_pre, '.dis'];
fid = fopen(dis_file);
for ii = 1:2, cmt = fgets(fid); end
line0 = fgets(fid);
D = textscan(line0, '%d', 6);
NLAY = D{1}(1); NROW = D{1}(2); NCOL = D{1}(3);
NPER = D{1}(4); ITMUNI = D{1}(5); LENUNI = D{1}(6);
line0 = fgets(fid);
D = textscan(line0, '%d');
LAYCBD = D{1}; % 1xNLAY (0 if no confining layer)
line0 = fgets(fid);
D = textscan(line0, '%s %d'); DELR = D{2}; % width of column
line0 = fgets(fid);
D = textscan(line0, '%s %d'); DELC = D{2}; % height of row
TOP = nan(NROW,NCOL);
line0 = fgets(fid);
for irow = 1: NROW
line0 = fgets(fid);
D = textscan(line0, '%f');
TOP(irow,:) = D{1}(1:NCOL);
end
BOTM = nan(NROW, NCOL, NLAY);
for ilay = 1: NLAY
line0 = fgets(fid);
for irow = 1: NROW
line0 = fgets(fid);
D = textscan(line0, '%f');
BOTM(irow,:,ilay) = D{1}(1:NCOL);
end
end
fclose(fid);
% - make boundary cells constant head above a certain elevation
% IBOUNDin(ind_bound & TOP(2:end-1,2:end-1) > 4500) = -1;
IBOUNDin(ind_bound & TOP(2:end-1,2:end-1) > 3500) = -1;
IBOUND(2:end-1,2:end-1,1) = IBOUNDin;
% - make discharge point and neighboring cells constant head
IBOUND(dischargePt_rowi,dischargePt_coli,1) = -2; % downgrad of discharge pt
% IBOUND(dischargePt_rowi-1,dischargePt_coli,1) = -1; % neighbor points
IBOUND(dischargePt_rowi+1,dischargePt_coli,1) = -1;
IBOUND(dischargePt_rowi,dischargePt_coli+1,1) = -2; % downgrad of discharge pt
IBOUND(dischargePt_rowi-1,dischargePt_coli+1,1) = -1; % neighbor points
IBOUND(dischargePt_rowi+1,dischargePt_coli+1,1) = -1;
IBOUND(dischargePt_rowi,dischargePt_coli,1) = 1; % downgrad of discharge pt
IBOUND = repmat(IBOUND, [1 1 NLAY]);
% - initHead(NROW,NCOL,NLAY)
initHead = BOTM(:,:,1) + (TOP-BOTM(:,:,1))*0.9; % within top layer
% % (no more than 10m below top):
% Y = nan(NROW,NCOL,2); Y(:,:,1) = initHead; Y(:,:,2) = TOP-10;
% initHead = max(Y,[],3);
initHead = repmat(initHead, [1, 1, NLAY]);
% - assumed values
HNOFLO = -999.99;
%% ------------------------------------------------------------------------
% -- Write ba6 file
fil_ba6_0 = [GSFLOW_indir, slashstr, ba6_file];
fmt1 = [repmat('%4d ', 1, NCOL), '\n']; % for IBOUND
fmt2 = [repmat('%7g ', 1, NCOL), '\n']; % for initHead
fid = fopen(fil_ba6_0, 'wt');
fprintf(fid, '# basic package file --- %d layers, %d rows, %d columns\n', NLAY, NROW, NCOL);
fprintf(fid, 'FREE\n');
for ilay = 1: NLAY
fprintf(fid, 'INTERNAL 1 (FREE) 3 IBOUND for layer %d \n', ilay); % 1: CNSTNT multiplier, 3: IPRN>0 to print input to list file
fprintf(fid, fmt1, IBOUND(:,:,ilay)');
end
fprintf(fid, ' %f HNOFLO\n', HNOFLO);
for ilay = 1: NLAY
fprintf(fid, 'INTERNAL 1 (FREE) 3 init head for layer %d \n', ilay); % 1: CNSTNT multiplier, 3: IPRN>0 to print input to list file
fprintf(fid, fmt2, initHead(:,:,ilay)');
end
fclose(fid);
% -- Plot basics
for ii = 1:2
if ii == 1,
X0 = IBOUND; ti0 = 'IBOUND';
elseif ii == 2
X0 = initHead; ti0 = 'init head';
end
figure
for ilay = 1:NLAY
subplot(2,2,double(ilay))
X = X0(:,:,ilay);
m = X(X>0); m = min(m(:));
imagesc(X), %caxis([m*0.9, max(X(:))]),
cm = colormap;
% cm(1,:) = [1 1 1];
colormap(cm);
colorbar
title([ti0, ' lay', num2str(ilay)]);
end
end
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_ba6_MOD2_bu.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_ba6_MOD2_bu.m
| 5,863 |
utf_8
|
1f12d52d0b1416b1514618c0791f04d0
|
% write_ba6_MOD
% 11/17/16
function write_ba6_MOD2(GSFLOW_indir, infile_pre, surfz_fil, mask_fil, NLAY, DZ)
% % ==== TO RUN AS SCRIPT ===================================================
% clear all, close all, fclose all;
% % - directories
% % MODFLOW input files
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
%
% % infile_pre = 'test1lay';
% % NLAY = 1;
% % DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% infile_pre = 'test2lay';
% NLAY = 2;
% DZ = [50; 50]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
%
% % for various files: ba6, dis, uzf, lpf
% surfz_fil = [GIS_indir, 'topo.asc'];
% % for various files: ba6, uzf
% mask_fil = [GIS_indir, 'basinmask_dischargept.asc'];
% % =========================================================================
%%
% - write to this file
% GSFLOW_dir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
ba6_file = [infile_pre, '.ba6'];
slashstr = '/';
% - domain dimensions, maybe already in surfz_fil and botm_fil{}?
% NLAY = 1;
% NROW = 50;
% NCOL = 50;
% -- IBOUND(NROW,NCOL,NLAY): <0 const head, 0 no flow, >0 variable head
% use basin mask (set IBOUND>0 within watershed, =0 outside watershed, <0 at discharge point and 2 neighboring pixels)
% mask_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/basinmask_dischargept.asc';
fid = fopen(mask_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
D = textscan(fid, '%f');
IBOUND = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
D = textscan(fid, '%s %s %f %s %f');
dischargePt_rowi = D{3};
dischargePt_coli = D{5};
fclose(fid);
% - force some cells to be active to correspond to stream reaches
IBOUND(14,33) = 1;
IBOUND(11,35) = 1;
IBOUND(12,34) = 1;
IBOUND(7,43) = 1;
% find boundary cells
IBOUNDin = IBOUND(2:end-1,2:end-1);
IBOUNDu = IBOUND(1:end-2,2:end-1); % up
IBOUNDd = IBOUND(3:end,2:end-1); % down
IBOUNDl = IBOUND(2:end-1,1:end-2); % left
IBOUNDr = IBOUND(2:end-1,3:end); % right
ind_bound = IBOUNDin==1 & (IBOUNDin-IBOUNDu==1 | IBOUNDin-IBOUNDd==1 | ...
IBOUNDin-IBOUNDl==1 | IBOUNDin-IBOUNDr==1);
% IBOUNDin(ind) = -1;
% IBOUND(2:end-1,2:end-1) = IBOUNDin;
% -- init head: base on TOP and BOTM
% surfz_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/topo.asc';
fid = fopen(surfz_fil, 'r');
D = textscan(fid, '%s %f', 6);
if ~isempty(find(NSEW ~= D{2}(1:4),1)) || NROW ~= D{2}(5) || NCOL ~= D{2}(6);
fprintf('Error!! NSEW, NROW, or NCOL in data files do not match!\n');
fprintf(' (files: %d and %d\n', mask_fil, surfz_fil);
fprintf('exiting...\n');
return
end
% - space discretization
DELR = (NSEW(3)-NSEW(4))/NCOL; % width of column [m]
DELC = (NSEW(1)-NSEW(2))/NROW; % height of row [m]
% DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
% DZ = [5; 5]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
% - set TOP to surface elevation [m]
D = textscan(fid, '%f');
fclose(fid);
TOP = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
BOTM = zeros(NROW, NCOL, NLAY);
BOTM(:,:,1) = TOP-DZ(1);
for ilay = 2:NLAY
BOTM(:,:,ilay) = BOTM(:,:,ilay-1)-DZ(ilay);
end
% - make boundary cells constant head above a certain elevation
% IBOUNDin(ind_bound & TOP(2:end-1,2:end-1) > 4500) = -1;
IBOUNDin(ind_bound & TOP(2:end-1,2:end-1) > 3500) = -1;
IBOUND(2:end-1,2:end-1,1) = IBOUNDin;
% - make discharge point and neighboring cells constant head
IBOUND(dischargePt_rowi,dischargePt_coli,1) = -2; % downgrad of discharge pt
% IBOUND(dischargePt_rowi-1,dischargePt_coli,1) = -1; % neighbor points
IBOUND(dischargePt_rowi+1,dischargePt_coli,1) = -1;
IBOUND(dischargePt_rowi,dischargePt_coli+1,1) = -2; % downgrad of discharge pt
IBOUND(dischargePt_rowi-1,dischargePt_coli+1,1) = -1; % neighbor points
IBOUND(dischargePt_rowi+1,dischargePt_coli+1,1) = -1;
IBOUND(dischargePt_rowi,dischargePt_coli,1) = 1; % downgrad of discharge pt
IBOUND = repmat(IBOUND, [1 1 NLAY]);
% - initHead(NROW,NCOL,NLAY)
initHead = BOTM(:,:,1) + (TOP-BOTM(:,:,1))*0.9; % within top layer
initHead = repmat(initHead, [1, 1, NLAY]);
% - assumed values
HNOFLO = -999.99;
%% ------------------------------------------------------------------------
% -- Write ba6 file
fil_ba6_0 = [GSFLOW_indir, slashstr, ba6_file];
fmt1 = [repmat('%4d ', 1, NCOL), '\n']; % for IBOUND
fmt2 = [repmat('%7g ', 1, NCOL), '\n']; % for initHead
fid = fopen(fil_ba6_0, 'wt');
fprintf(fid, '# basic package file --- %d layers, %d rows, %d columns\n', NLAY, NROW, NCOL);
fprintf(fid, 'FREE\n');
for ilay = 1: NLAY
fprintf(fid, 'INTERNAL 1 (FREE) 3 IBOUND for layer %d \n', ilay); % 1: CNSTNT multiplier, 3: IPRN>0 to print input to list file
fprintf(fid, fmt1, IBOUND(:,:,ilay)');
end
fprintf(fid, ' %f HNOFLO\n', HNOFLO);
for ilay = 1: NLAY
fprintf(fid, 'INTERNAL 1 (FREE) 3 init head for layer %d \n', ilay); % 1: CNSTNT multiplier, 3: IPRN>0 to print input to list file
fprintf(fid, fmt2, initHead(:,:,ilay)');
end
fclose(fid);
% -- Plot basics
for ii = 1:2
if ii == 1,
X0 = IBOUND; ti0 = 'IBOUND';
elseif ii == 2
X0 = initHead; ti0 = 'init head';
end
figure
for ilay = 1:NLAY
subplot(2,2,double(ilay))
X = X0(:,:,ilay);
m = X(X>0); m = min(m(:));
imagesc(X), %caxis([m*0.9, max(X(:))]),
cm = colormap;
% cm(1,:) = [1 1 1];
colormap(cm);
colorbar
title([ti0, ' lay', num2str(ilay)]);
end
end
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_lpf_MOD2_f2_2.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_lpf_MOD2_f2_2.m
| 7,349 |
utf_8
|
1987d3c8d54b48bd223a82357bdba31d
|
% write_lpf_MOD
% 11/17/16
function write_lpf_MOD2_f2_2(GSFLOW_indir, infile_pre, surfz_fil, NLAY)
% % =========== TO RUN AS SCRIPT ===========================================
% clear all, close all, fclose all;
% % - directories
% % MODFLOW input files
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
%
% % infile_pre = 'test1lay';
% % NLAY = 1;
% % DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% infile_pre = 'test2lay';
% NLAY = 2;
% DZ = [50; 50]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
%
% % for various files: ba6, dis, uzf, lpf
% surfz_fil = [GIS_indir, 'topo.asc'];
% % for various files: ba6, uzf
% mask_fil = [GIS_indir, 'basinmask_dischargept.asc'];
%
% % for sfr
% reach_fil = [GIS_indir, 'reach_data.txt'];
% segment_fil_all = cell(3,1);
% segment_fil_all{1} = [GIS_indir, 'segment_data_4A_INFORMATION.txt'];
% segment_fil_all{2} = [GIS_indir, 'segment_data_4B_UPSTREAM.txt'];
% segment_fil_all{3} = [GIS_indir, 'segment_data_4C_DOWNSTREAM.txt'];
buffer_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/segments_buffer2.asc';
% % ====================================================================
% - write to this file
% GSFLOW_dir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% lpf_file = 'test.lpf';
lpf_file = [infile_pre, '.lpf'];
slashstr = '/';
% - domain dimensions, maybe already in surfz_fil and botm_fil{}?
% NLAY = 2;
% surfz_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/topo.asc';
fid = fopen(surfz_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
% - space discretization
DELR = (NSEW(3)-NSEW(4))/NCOL; % width of column [m]
DELC = (NSEW(1)-NSEW(2))/NROW; % height of row [m]
% - set TOP to surface elevation [m]
D = textscan(fid, '%f');
fclose(fid);
fprintf('Done reading...\n');
TOP = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
% get buffer info
fid = fopen(buffer_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
D = textscan(fid, '%f');
buffer = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
fclose(fid);
% -- Base hydcond, Ss (all layers), and Sy (top layer only) on data from files
% (temp place-holder)
hydcond = ones(NROW,NCOL,NLAY)*2; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
% hydcond(:,:,2) = 0.5; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
% hydcond(:,:,2) = 0.1; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
hydcond(:,:,1) = 0.1; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
% hydcond(:,:,1) = 0.01; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
hydcond(:,:,2) = 0.01; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
Ss = ones(NROW,NCOL,NLAY)* 2e-6; % constant 2e-6 /m for Sagehen
Sy = ones(NROW,NCOL,NLAY)*0.15; % 0.08-0.15 in Sagehen (lower Sy under ridges for volcanic rocks)
WETDRY = Sy; % = Sy in Sagehen (lower Sy under ridges for volcanic rocks)
% % use buffer (and elev?)
% K = buffer; % 0: very far from stream, 5: farthest from stream in buffer, 1: closest to stream
% K(buffer==0) = 0.04; % very far from streams
% K(buffer==0 & TOP>5000) = 0.03; % very far from streams
% K(buffer>=1) = 0.5; % close to streams
% K(buffer>=2) = 0.4;
% K(buffer>=3) = 0.3;
% K(buffer>=4) = 0.15;
% K(buffer==5) = 0.08; % farthest from stream and high
% hydcond(:,:,1) = K;
% hydcond(:,:,2) = 0.01;
% use buffer (and elev?)
K = buffer; % 0: very far from stream, 5: farthest from stream in buffer, 1: closest to stream
K(buffer==0) = 0.04; % very far from streams
K(buffer==0 & TOP>5000) = 0.03; % very far from streams
% K(buffer>=1) = 0.5; % close to streams
K(buffer>=1) = 0.25; % close to streams
K(buffer>=2) = 0.15;
K(buffer>=3) = 0.08;
K(buffer>=4) = 0.08;
K(buffer==5) = 0.08; % farthest from stream and high
hydcond(:,:,1) = K;
hydcond(:,:,2) = 0.01;
% -- assumed input values
flow_filunit = 34; % make sure this matches namefile!!
hdry = 1e30; % head assigned to dry cells
nplpf = 0; % number of LPF parameters (if >0, key words would follow)
laytyp = zeros(NLAY,1); laytyp(1) = 1; % flag, top>0: "covertible", rest=0: "confined"
layave = zeros(NLAY,1); % flag, layave=1: harmonic mean for interblock transmissivity
chani = ones(NLAY,1); % flag, chani=1: constant horiz anisotropy mult factor (for each layer)
layvka = zeros(NLAY,1); % flag, layvka=0: vka is vert K; >0 is vertK/horK ratio
VKA = hydcond;
laywet = zeros(NLAY,1); laywet(1)=1; % flag, 1: wetting on for top convertible cells, 0: off for confined
fl_Tr = 1; % flag, 1 for at least 1 transient stress period (for Ss and Sy)
WETFCT = 1.001; % 1.001 for Sagehen, wetting (convert dry cells to wet)
IWETIT = 4; % number itermations for wetting
IHDWET = 0; % wetting scheme, 0: equation 5-32A is used: h = BOT + WETFCT (hn - BOT)
%% ------------------------------------------------------------------------
fmt1 = repmat('%2d ', 1, NLAY);
fil_lpf_0 = [GSFLOW_indir, slashstr, lpf_file];
fid = fopen(fil_lpf_0, 'wt');
fprintf(fid, '# LPF package inputs\n');
fprintf(fid, '%d %g %d ILPFCB,HDRY,NPLPF\n', flow_filunit, hdry, nplpf);
fprintf(fid, [fmt1, ' LAYTYP\n'], laytyp);
fprintf(fid, [fmt1, ' LAYAVE\n'], layave);
fprintf(fid, [fmt1, ' CHANI \n'], chani);
fprintf(fid, [fmt1, ' LAYVKA\n'], layvka);
fprintf(fid, [fmt1, ' LAYWET\n'], laywet);
if ~isempty(find(laywet,1))
fprintf(fid, '%g %d %d WETFCT, IWETIT, IHDWET\n', WETFCT, IWETIT, IHDWET);
end
% -- Write HKSAT and Ss, Sy (if Tr) in .lpf file
format0 = [repmat(' %4.2f ', 1, NCOL), '\n'];
format1 = [repmat(' %4.2e ', 1, NCOL), '\n'];
% loop thru layers (different entry for each layer)
for lay = 1: NLAY
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 HY layer %d\n', lay); % horizontal hyd cond
fprintf(fid, format1, hydcond(:,:,lay)');
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 VKA layer %d\n', lay); % vertical hyd cond
fprintf(fid, format1, VKA(:,:,lay)');
if fl_Tr
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 Ss layer %d\n', lay);
fprintf(fid, format1, Ss(:,:,lay)');
if laytyp(lay) > 0 % convertible, i.e. unconfined
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 Sy layer %d\n', lay);
fprintf(fid, format1, Sy(:,:,lay)');
if laywet(lay) > 0
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 WETDRY layer %d\n', lay);
fprintf(fid, format0, WETDRY(:,:,lay)');
end
end
end
end
fprintf(fid, '\n');
fclose(fid);
figure
for ilay = 1:NLAY
subplot(2,2,double(ilay))
X = hydcond(:,:,ilay);
m = X(X>0); m = min(m(:));
imagesc(X), %caxis([m*0.9, max(X(:))]),
cm = colormap;
% cm(1,:) = [1 1 1];
caxis([0 max(X(:))])
colormap(cm);
colorbar
title(['hydcond', num2str(ilay)]);
end
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
make_sfr2_f_Mannings.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/make_sfr2_f_Mannings.m
| 17,011 |
utf_8
|
e13e86b33d9983c53be1b54cc3a79c20
|
% make_sfr.m
% 1/8/16
% Leila Saberi
%
% 2 - gcng
function make_sfr2_f_Mannings(GSFLOW_indir, infile_pre, reach_fil, segment_fil_all)
% Note: assume .dis file already created!! (reads in TOP for setting STRTOP)
% % ======== TO RUN AS SCRIPT ===============================================
% % clear all, close all, fclose all;
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% infile_pre = 'test2lay';
%
% % for sfr
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
% reach_fil = [GIS_indir, 'reach_data.txt'];
% segment_fil_all = cell(3,1);
% segment_fil_all{1} = [GIS_indir, 'segment_data_4A_INFORMATION.txt'];
% segment_fil_all{2} = [GIS_indir, 'segment_data_4B_UPSTREAM.txt'];
% segment_fil_all{3} = [GIS_indir, 'segment_data_4C_DOWNSTREAM.txt'];
% % =========================================================================
%%
sfr_file = [infile_pre, '.sfr'];
% -- Refer to GSFLOW manual p.202, SFR1 manual, and SFR2 manual
% - Refer to Fig. 1 of SFR1 documentation for segment vs. reach numbering
% You need the following inputs (with corresponding structures)
% the followings are used to write item 1
fl_nstrm = -1; % flag for stream reaches, <0: include unsaturated zone below (sagehen: >0)
nsfrpar = 0; %Always Zero
nparseg = 0; %Always Zero
const = 86400.; %Conversion factor used in calculating depth for a stream reach (86400 in sagehen example)
dleak = 0.0001; %Tolerance level of stream depth used in computing leakage between each stream (0.0001 in sagehen example)
istcb1 = -1; %Flag for writing stream-aquifer leakage values (>0: file unit, <0: write to listing file)
istcb2 = 0; %Flag for writing to a seperate formatted file information on inflows&outflows
isfropt = 3; %defines input structure; saturated or non-saturated zone (1: No UZ; 3: UZ, unsat prop at start of simulation), sagehen uses 3
nstrail = 10; %Number of trailing-waive increments, incr for better mass balance (10-20 rec'd, sagehen uses 8)
isuzn = 1; %Maximum number of vertical cells used to define the unsaturated zone beneath a stream reach (for icalc=1 (Mannings for depth): use isuzn=1)
nsfrsets = 40; %Maximum number of different sets of trailing waves used to allocate arrays.
irtflg = 0; %Flag whether transient streamflow routing is active
project_name = 'TestProject'; % used to name the output file (.sfr)
% data_indir = '/home/gcng/workspace/matlab_files/GSFLOW_pre-processor/MODFLOW_scripts/sfr_final/data/';
% data_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
% items
reach_data_all = importdata(reach_fil); % used to write item 2: assumes
NPER = 2; % used for item 3
% items 4a: # NSEG ICALC OUTSEG IUPSEG IPRIOR NSTRPTS FLOW RUNOFF ETSW PPTSW ROUGHCH ROUGHBK CDPTH FDPTH AWDTH BWDTH
segment_data_4A = importdata(segment_fil_all{1}); % used to write items 4a
segment_data_4B = importdata(segment_fil_all{2}); % used to write items 4b (ignored for ICALC=3 in 4a)
segment_data_4C = importdata(segment_fil_all{3}); % used to write items 4c (ignored for ICALC=3 in 4a)
% -------------------------------------------------------------------------
% In case the input text files (e.g. reach_data.txt) contain header lines (comments)
if isstruct(reach_data_all)
reach_data_all0 = reach_data_all.data;
nstrm = size(reach_data_all0,1);
if fl_nstrm < 0, nstrm = -nstrm; end
% sort rows according to increasing segment numbers
[~, ind] = sort(reach_data_all0(:,strcmp(reach_data_all.colheaders, 'ISEG')), 'ascend');
reach_data_all0 = reach_data_all0(ind,:);
nss = max(reach_data_all0(:,strcmp(reach_data_all.colheaders, 'ISEG')));
% sort rows according to increasing reach numbers
for ii = 1: nss
ind1 = find(reach_data_all0(:,strcmp(reach_data_all.colheaders, 'ISEG')) == ii);
[~, ind2] = sort(reach_data_all0(ind1,strcmp(reach_data_all.colheaders, 'IREACH')), 'ascend');
reach_data_all0(ind1,:) = reach_data_all0(ind1(ind2),:);
% renumber IREACH to start at 1 for each segment
reach_data_all0(ind1,strcmp(reach_data_all.colheaders, 'IREACH')) = [1:length(ind1)];
end
X = mat2cell(reach_data_all0, abs(nstrm), ones(size(reach_data_all0,2),1));
[KRCH,IRCH,JRCH,ISEG,IREACH,RCHLEN,STRTOP,SLOPE,STRTHICK,STRHC1,THTS,THTI,EPS,UHC] = X{:};
% -- make sure STRTOP is within 1st layer
% - read in TOP and BOTM from .dis file
dis_file = [GSFLOW_indir, '/', infile_pre, '.dis'];
fid = fopen(dis_file);
for ii = 1:2, cmt = fgets(fid); end
line0 = fgets(fid);
D = textscan(line0, '%d', 6);
NLAY = D{1}(1); NROW = D{1}(2); NCOL = D{1}(3);
NPER = D{1}(4); ITMUNI = D{1}(5); LENUNI = D{1}(6);
line0 = fgets(fid);
D = textscan(line0, '%d');
LAYCBD = D{1}; % 1xNLAY (0 if no confining layer)
line0 = fgets(fid);
D = textscan(line0, '%s %d'); DELR = D{2}; % width of column
line0 = fgets(fid);
D = textscan(line0, '%s %d'); DELC = D{2}; % height of row
TOP = nan(NROW,NCOL);
line0 = fgets(fid);
for irow = 1: NROW
line0 = fgets(fid);
D = textscan(line0, '%f');
TOP(irow,:) = D{1}(1:NCOL);
end
BOTM = nan(NROW, NCOL, NLAY);
for ilay = 1: NLAY
line0 = fgets(fid);
for irow = 1: NROW
line0 = fgets(fid);
D = textscan(line0, '%f');
BOTM(irow,:,ilay) = D{1}(1:NCOL);
end
end
fclose(fid);
% TOP for cells corresponding to reaches
TOP_RCH = nan(abs(nstrm),1);
for ii = 1:abs(nstrm), TOP_RCH(ii) = TOP(IRCH(ii),JRCH(ii)); end
% BOTM for cells corresponding to reaches
BOTM_RCH = nan(abs(nstrm),1);
for ii = 1:abs(nstrm), BOTM_RCH(ii) = BOTM(IRCH(ii),JRCH(ii),KRCH(ii)); end
% - set STRTOP to be just below TOP
STRTOP = TOP_RCH - 2;
if ~isempty(find(STRTOP-STRTHICK < BOTM_RCH,1))
fprintf('Error! STRTOP is below BOTM of the corresponding layer! Exiting...\n');
end
reach_data_all0(:,strcmp(reach_data_all.colheaders, 'STRTOP')) = STRTOP;
% -- plot stream reaches
RCH_mask = TOP;
for ii = 1:abs(nstrm), RCH_mask(IRCH(ii),JRCH(ii)) = max(TOP(:))*2; end
figure
subplot(2,2,1)
imagesc(TOP), colorbar,
cm = colormap;
cm(end,:) = [1 1 1];
caxis([min(TOP(:)) max(TOP(:))* 1.25]);
colormap(cm);
subplot(2,2,2)
imagesc(RCH_mask), colorbar,
cm = colormap;
cm(end,:) = [1 1 1];
caxis([min(TOP(:)) max(TOP(:))* 1.25]);
colormap(cm);
RCH_NUM = zeros(NROW,NCOL); SEG_NUM = zeros(NROW,NCOL);
for ii = 1:abs(nstrm), RCH_NUM(IRCH(ii),JRCH(ii)) = IREACH(ii); end
for ii = 1:abs(nstrm), SEG_NUM(IRCH(ii),JRCH(ii)) = ISEG(ii); end
figure
imagesc(RCH_NUM), colorbar,
colormap(jet(1+max(IREACH)));
caxis([-0.5 max(IREACH)+0.5])
figure
imagesc(SEG_NUM), colorbar,
colormap(jet(1+max(ISEG)));
caxis([-0.5 max(ISEG)+0.5])
% % when running as script: to visualize segments one at a time
% for j = 1: max(ISEG)
% RCH_NUM = zeros(NROW,NCOL); SEG_NUM = zeros(NROW,NCOL);
% ind = find(ISEG==j);
% for ii = ind(:)'
% RCH_NUM(IRCH(ii),JRCH(ii)) = IREACH(ii);
% SEG_NUM(IRCH(ii),JRCH(ii)) = ISEG(ii);
% end
%
% figure(100)
% imagesc(RCH_NUM), colorbar,
% colormap(jet(1+max(RCH_NUM(:))));
% caxis([-0.5 max(RCH_NUM(:))+0.5])
% title(['reaches for seg ', num2str(j)]);
% figure(101)
% imagesc(SEG_NUM), colorbar,
% colormap(jet(1+max(SEG_NUM(:))));
% caxis([-0.5 max(SEG_NUM(:))+0.5])
% title(['seg ', num2str(j)]);
% pause
% end
% -- threshold slope at minimum 0.001
ind = reach_data_all0(:,strcmp(reach_data_all.colheaders, 'SLOPE')) < 0.001;
reach_data_all0(ind,strcmp(reach_data_all.colheaders, 'SLOPE')) = 0.001;
% -- set streambed thickness (Sagehen uses constant 1m)
ind = strcmp(reach_data_all.colheaders, 'STRTHICK');
reach_data_all0(:,ind) = 1; % [m]
% -- set streambed hydraulic conductivity (Sagehen example: 5 m/d)
ind = strcmp(reach_data_all.colheaders, 'STRHC1');
reach_data_all0(:,ind) = 5;
% set streambed theta_s
ind = strcmp(reach_data_all.colheaders, 'THTS');
reach_data_all0(:,ind) = 0.35;
% set streambed initial theta
ind = strcmp(reach_data_all.colheaders, 'THTI');
reach_data_all0(:,ind) = 0.3;
% set streambed Brooks-Corey exp (sagehen example is 3.5)
ind = strcmp(reach_data_all.colheaders, 'EPS');
reach_data_all0(:,ind) = 3.5;
% set streambed unsaturated zone saturated hydraulic conductivity
% (sagehen example is 0.3 m/d)
ind = strcmp(reach_data_all.colheaders, 'UHC');
reach_data_all0(:,ind) = 0.3;
reach_data_all = reach_data_all0;
end
if isstruct(segment_data_4A)
segment_data_4A = segment_data_4A.data;
nss = size(segment_data_4A,1); %Number of stream segments
end
if isstruct(segment_data_4B)
segment_data_4B = segment_data_4B.data;
end
if isstruct(segment_data_4C)
segment_data_4C = segment_data_4C.data;
end
% if isstruct(stress_periods)
% stress_periods = stress_periods.data;
% end
% - specify only for 2 stress periods:
stress_periods = zeros(NPER, 3); % itmp, irdflg, iptflg (latter 2 are set to 0)
stress_periods(1,1) = nss;
if NPER > 1, stress_periods(2:end,1) = -1; end
% -------------------------------------------------------------------------
% First put 4A, 4B and 4C data all together in a cell array
% size(cell) = nitems x 1 x nperiods
% In this case, nitems is 3 (i.e. 4A, 4B and 4C)
nitems = 3;
nperiods = size(stress_periods, 1);
segment_data_all = cell(nitems, 1, nperiods);
segment_data_all{1, 1, 1} = segment_data_4A;
segment_data_all{2, 1, 1} = segment_data_4B;
segment_data_all{3, 1, 1} = segment_data_4C;
% -------------------------------------------------------------------------
% validate some of the input data
msg_invalidISFROPT = ['Error: ISFROPT should be set to an integer of', ...
'1, 2, 3, 4 or 5.'];
if (nstrm < 0)
if ~ismember(isfropt, [1, 2, 3, 4, 5])
error(msg_invalidISFROPT);
end
end
msg_notSupport = ['Error: %s: this variable must be zero because ', ...
'parameters are not supported in GSFLOW.'];
if (nsfrpar ~= 0)
error(msg_notSupport, 'NSFRPAR');
end
if (nparseg ~= 0)
error(msg_notSupport, 'NPARSEG');
end
% -------------------------------------------------------------------------
% Ouput file
fid = fopen([GSFLOW_indir, '/', sfr_file], 'wt');
% Write header lines (item 0)
heading = '# Streamflow-Routing (SFR7) input file.\n';
fprintf(fid, heading);
fprintf(fid, '# %s simulation -- created on %s.\n', upper(project_name), date);
% Item 1
fprintf(fid, ' %5d %5d %5d %5d %8.2f %8.4f %5d %5d', ...
nstrm, nss, nsfrpar, nparseg, const, dleak, istcb1, istcb2);
if (isfropt >= 1)
fprintf(fid, ' %5d', isfropt);
if (isfropt == 1)
fprintf(fid, ' %5d\n', irtflg);
elseif (isfropt > 1)
fprintf(fid, ' %5d %5d %5d %5d\n', nstrail, isuzn, nsfrsets, irtflg);
end
else
fprintf(fid, '\n');
end
% Item 2
if (isfropt == 1)
ncols_reach = 10;
elseif (isfropt == 2)
ncols_reach = 13;
elseif (isfropt == 3)
ncols_reach = 14;
else
ncols_reach = 6;
end
reach_data_copy = reach_data_all(:, 1:ncols_reach);
p = ncols_reach - 5;
fmt_reach = [repmat(' %5d', 1, 5), repmat(' %8.3f', 1, p), '\n'];
for istrm=1:abs(nstrm)
dummy = reach_data_copy(istrm, :);
fprintf(fid, fmt_reach, dummy);
end
% Item 3 and 4
nper = size(stress_periods, 1);
for iper=1:nper
% write item 3 to the file
dummy3 = num2cell(stress_periods(iper, :));
[itmp, irdflg, iptflg] = dummy3{:};
fprintf(fid, ' %5d %5d %5d\n', itmp, irdflg, iptflg);
if (itmp > 0)
seg_inf_4a = segment_data_all{1, 1, iper};
seg_inf_4b = segment_data_all{2, 1, iper};
seg_inf_4c = segment_data_all{3, 1, iper};
for iitmp=1:itmp % start loop over itmp (num_segments)
% write item 4a to the file
dummy4a = num2cell(seg_inf_4a(iitmp, :));
[nseg, icalc, outseg, iupseg, iprior, nstrpts, ...
flow, runoff, etsw, pptsw, roughch, roughbk, ...
cdpth, fdpth, awdth, bwdth] = dummy4a{:};
fmt = [' ', repmat(' %5d', 1, 4)];
fprintf(fid, fmt, nseg, icalc, outseg, iupseg);
if (iupseg > 0)
fprintf(fid, ' %5d', iprior);
end
if (icalc == 4)
fprintf(fid, ' %5d', nstrpts);
end
fmt = repmat(' %8.3f', 1, 4);
fprintf(fid, fmt, flow, runoff, etsw, pptsw);
if ((icalc == 1) || (icalc == 2))
fprintf(fid, ' %8.3f', roughch);
end
if (icalc == 2)
fprintf(fid, ' %8.3f', roughbk);
end
if (icalc == 3)
fmt = repmat(' %8.3f', 1, 4);
fprintf(fid, fmt, cdpth, fdpth, awdth, bwdth);
end
fprintf(fid, '\n');
% write items 4b and 4c to the file
suffixes = 'bc';
for i=1:2 % start loop over i
suffix = suffixes(i);
var_str = ['seg_inf_4', suffix];
var = eval(var_str);
dummy4bc = num2cell(var(iitmp, :));
[hcond, thickm, elevupdn, width, ...
depth, thts, thti, eps, uhc] = dummy4bc{:};
fl_no_4bc = 0;
if (ismember(isfropt, [0, 4, 5]) && (icalc <= 0))
fmt = [' ', repmat(' %8.3f', 1, 5)];
fprintf(fid, fmt, hcond, thickm, elevupdn, width, depth);
elseif (ismember(isfropt, [0, 4, 5]) && (icalc == 1))
fprintf(fid, ' %8.3f', hcond);
if (iper == 1) % only for the first period
fmt = repmat(' %8.3f', 1, 3);
fprintf(fid, fmt, thickm, elevupdn, width);
if ((isfropt == 4) || (isfropt == 5))
fmt = repmat(' %8.3f', 1, 3);
fprintf(fid, fmt, thts, thti, eps);
end
if (isfropt == 5)
fprintf(fid, ' %8.3f', uhc);
end
elseif ((iper > 1) && (isfropt == 0))
fmt = repmat(' %8.3f', 1, 3);
fprintf(fid, fmt, thickm, elevupdn, width);
end
elseif (ismember(isfropt, [0, 4, 5]) && (icalc >= 2))
fprintf(fid, ' %8.3f', hcond);
if ~(ismember(isfropt, [4, 5]) && (iper > 1) && (icalc == 2))
fprintf(fid, ' %8.3f %8.3f', thickm, elevupdn);
if (ismember(isfropt, [4, 5]) && (iper == 1) && (icalc == 2))
fmt = repmat(' %8.3f', 1, 3);
fprintf(fid, fmt, thts, thti, eps);
if (isfropt == 5)
fprintf(fid, ' %8.3f', uhc);
end
end
end
elseif ((isfropt == 1) && (icalc <= 1))
fprintf(fid, ' %8.3f', width);
if (icalc <= 0)
fprintf(fid, ' %8.3f', depth);
end
elseif (ismember(isfropt, [2, 3]) && (icalc <= 1))
if (iper == 1)
fprintf(fid, ' %8.3f', width);
if (icalc <= 0)
fprintf(fid, ' %8.3f', depth);
end
end
else
fl_no_4bc = 1;
end
if ~fl_no_4bc
fprintf(fid, '\n');
end
end % terminate loop over i (4b and 4c, respectively)
end % terminate loop over itmp (num_segments)
end % enf if (itmp > 0)
end % terminate loop over iper (num_periods)
fclose(fid);
% -------------------------------------------------------------------------
% End of the script
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
make_sfr2_f.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/make_sfr2_f.m
| 16,998 |
utf_8
|
0ad0153cb953fb0dc4dfac073cf5d6b4
|
% make_sfr.m
% 1/8/16
% Leila Saberi
%
% 2 - gcng
function make_sfr2_f(GSFLOW_indir, infile_pre, reach_fil, segment_fil_all)
% Note: assume .dis file already created!! (reads in TOP for setting STRTOP)
% % ======== TO RUN AS SCRIPT ===============================================
% clear all, close all, fclose all;
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% infile_pre = 'test2lay';
%
% % for sfr
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
% reach_fil = [GIS_indir, 'reach_data.txt'];
% segment_fil_all = cell(3,1);
% segment_fil_all{1} = [GIS_indir, 'segment_data_4A_INFORMATION.txt'];
% segment_fil_all{2} = [GIS_indir, 'segment_data_4B_UPSTREAM.txt'];
% segment_fil_all{3} = [GIS_indir, 'segment_data_4C_DOWNSTREAM.txt'];
% % =========================================================================
%%
sfr_file = [infile_pre, '.sfr'];
% -- Refer to GSFLOW manual p.202, SFR1 manual, and SFR2 manual
% - Refer to Fig. 1 of SFR1 documentation for segment vs. reach numbering
% You need the following inputs (with corresponding structures)
% the followings are used to write item 1
fl_nstrm = -1; % flag for stream reaches, <0: include unsaturated zone below (sagehen: >0)
nsfrpar = 0; %Always Zero
nparseg = 0; %Always Zero
const = 86400.; %Conversion factor used in calculating depth for a stream reach (86400 in sagehen example)
dleak = 0.0001; %Tolerance level of stream depth used in computing leakage between each stream (0.0001 in sagehen example)
istcb1 = -1; %Flag for writing stream-aquifer leakage values (>0: file unit, <0: write to listing file)
istcb2 = 0; %Flag for writing to a seperate formatted file information on inflows&outflows
isfropt = 3; %defines input structure; saturated or non-saturated zone (1: No UZ; 3: UZ, unsat prop at start of simulation), sagehen uses 3
nstrail = 10; %Number of trailing-waive increments, incr for better mass balance (10-20 rec'd, sagehen uses 8)
isuzn = 1; %Maximum number of vertical cells used to define the unsaturated zone beneath a stream reach (for icalc=1 (Mannings for depth): use isuzn=1)
nsfrsets = 40; %Maximum number of different sets of trailing waves used to allocate arrays.
irtflg = 0; %Flag whether transient streamflow routing is active
project_name = 'TestProject'; % used to name the output file (.sfr)
% data_indir = '/home/gcng/workspace/matlab_files/GSFLOW_pre-processor/MODFLOW_scripts/sfr_final/data/';
data_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
% items
reach_data_all = importdata(reach_fil); % used to write item 2: assumes
NPER = 2; % used for item 3
% items 4a: # NSEG ICALC OUTSEG IUPSEG IPRIOR NSTRPTS FLOW RUNOFF ETSW PPTSW ROUGHCH ROUGHBK CDPTH FDPTH AWDTH BWDTH
segment_data_4A = importdata(segment_fil_all{1}); % used to write items 4a
segment_data_4B = importdata(segment_fil_all{2}); % used to write items 4b (ignored for ICALC=3 in 4a)
segment_data_4C = importdata(segment_fil_all{3}); % used to write items 4c (ignored for ICALC=3 in 4a)
% -------------------------------------------------------------------------
% In case the input text files (e.g. reach_data.txt) contain header lines (comments)
if isstruct(reach_data_all)
reach_data_all0 = reach_data_all.data;
nstrm = size(reach_data_all0,1);
if fl_nstrm < 0, nstrm = -nstrm; end
% sort rows according to increasing segment numbers
[~, ind] = sort(reach_data_all0(:,strcmp(reach_data_all.colheaders, 'ISEG')), 'ascend');
reach_data_all0 = reach_data_all0(ind,:);
nss = max(reach_data_all0(:,strcmp(reach_data_all.colheaders, 'ISEG')));
% sort rows according to increasing reach numbers
for ii = 1: nss
ind1 = find(reach_data_all0(:,strcmp(reach_data_all.colheaders, 'ISEG')) == ii);
[~, ind2] = sort(reach_data_all0(ind1,strcmp(reach_data_all.colheaders, 'IREACH')), 'ascend');
reach_data_all0(ind1,:) = reach_data_all0(ind1(ind2),:);
% renumber IREACH to start at 1 for each segment
reach_data_all0(ind1,strcmp(reach_data_all.colheaders, 'IREACH')) = [1:length(ind1)];
end
X = mat2cell(reach_data_all0, abs(nstrm), ones(size(reach_data_all0,2),1));
[KRCH,IRCH,JRCH,ISEG,IREACH,RCHLEN,STRTOP,SLOPE,STRTHICK,STRHC1,THTS,THTI,EPS,UHC] = X{:};
% -- make sure STRTOP is within 1st layer
% - read in TOP and BOTM from .dis file
dis_file = [GSFLOW_indir, '/', infile_pre, '.dis'];
fid = fopen(dis_file);
for ii = 1:2, cmt = fgets(fid); end
line0 = fgets(fid);
D = textscan(line0, '%d', 6);
NLAY = D{1}(1); NROW = D{1}(2); NCOL = D{1}(3);
NPER = D{1}(4); ITMUNI = D{1}(5); LENUNI = D{1}(6);
line0 = fgets(fid);
D = textscan(line0, '%d');
LAYCBD = D{1}; % 1xNLAY (0 if no confining layer)
line0 = fgets(fid);
D = textscan(line0, '%s %d'); DELR = D{2}; % width of column
line0 = fgets(fid);
D = textscan(line0, '%s %d'); DELC = D{2}; % height of row
TOP = nan(NROW,NCOL);
line0 = fgets(fid);
for irow = 1: NROW
line0 = fgets(fid);
D = textscan(line0, '%f');
TOP(irow,:) = D{1}(1:NCOL);
end
BOTM = nan(NROW, NCOL, NLAY);
for ilay = 1: NLAY
line0 = fgets(fid);
for irow = 1: NROW
line0 = fgets(fid);
D = textscan(line0, '%f');
BOTM(irow,:,ilay) = D{1}(1:NCOL);
end
end
fclose(fid);
% TOP for cells corresponding to reaches
TOP_RCH = nan(abs(nstrm),1);
for ii = 1:abs(nstrm), TOP_RCH(ii) = TOP(IRCH(ii),JRCH(ii)); end
% BOTM for cells corresponding to reaches
BOTM_RCH = nan(abs(nstrm),1);
for ii = 1:abs(nstrm), BOTM_RCH(ii) = BOTM(IRCH(ii),JRCH(ii),KRCH(ii)); end
% - set STRTOP to be just below TOP
STRTOP = TOP_RCH - 2;
if ~isempty(find(STRTOP-STRTHICK < BOTM_RCH,1))
fprintf('Error! STRTOP is below BOTM of the corresponding layer! Exiting...\n');
end
reach_data_all0(:,strcmp(reach_data_all.colheaders, 'STRTOP')) = STRTOP;
% -- plot stream reaches
RCH_mask = TOP;
for ii = 1:abs(nstrm), RCH_mask(IRCH(ii),JRCH(ii)) = max(TOP(:))*2; end
figure
subplot(2,2,1)
imagesc(TOP), colorbar,
cm = colormap;
cm(end,:) = [1 1 1];
caxis([min(TOP(:)) max(TOP(:))* 1.25]);
colormap(cm);
subplot(2,2,2)
imagesc(RCH_mask), colorbar,
cm = colormap;
cm(end,:) = [1 1 1];
caxis([min(TOP(:)) max(TOP(:))* 1.25]);
colormap(cm);
RCH_NUM = zeros(NROW,NCOL); SEG_NUM = zeros(NROW,NCOL);
for ii = 1:abs(nstrm), RCH_NUM(IRCH(ii),JRCH(ii)) = IREACH(ii); end
for ii = 1:abs(nstrm), SEG_NUM(IRCH(ii),JRCH(ii)) = ISEG(ii); end
figure
imagesc(RCH_NUM), colorbar,
colormap(jet(1+max(IREACH)));
caxis([-0.5 max(IREACH)+0.5])
figure
imagesc(SEG_NUM), colorbar,
colormap(jet(1+max(ISEG)));
caxis([-0.5 max(ISEG)+0.5])
% % when running as script: to visualize segments one at a time
% for j = 1: max(ISEG)
% RCH_NUM = zeros(NROW,NCOL); SEG_NUM = zeros(NROW,NCOL);
% ind = find(ISEG==j);
% for ii = ind(:)'
% RCH_NUM(IRCH(ii),JRCH(ii)) = IREACH(ii);
% SEG_NUM(IRCH(ii),JRCH(ii)) = ISEG(ii);
% end
%
% figure(100)
% imagesc(RCH_NUM), colorbar,
% colormap(jet(1+max(RCH_NUM(:))));
% caxis([-0.5 max(RCH_NUM(:))+0.5])
% title(['reaches for seg ', num2str(j)]);
% figure(101)
% imagesc(SEG_NUM), colorbar,
% colormap(jet(1+max(SEG_NUM(:))));
% caxis([-0.5 max(SEG_NUM(:))+0.5])
% title(['seg ', num2str(j)]);
% pause
% end
% -- threshold slope at minimum 0.001
ind = reach_data_all0(:,strcmp(reach_data_all.colheaders, 'SLOPE')) < 0.001;
reach_data_all0(ind,strcmp(reach_data_all.colheaders, 'SLOPE')) = 0.001;
% -- set streambed thickness (Sagehen uses constant 1m)
ind = strcmp(reach_data_all.colheaders, 'STRTHICK');
reach_data_all0(:,ind) = 1; % [m]
% -- set streambed hydraulic conductivity (Sagehen example: 5 m/d)
ind = strcmp(reach_data_all.colheaders, 'STRHC1');
reach_data_all0(:,ind) = 5;
% set streambed theta_s
ind = strcmp(reach_data_all.colheaders, 'THTS');
reach_data_all0(:,ind) = 0.35;
% set streambed initial theta
ind = strcmp(reach_data_all.colheaders, 'THTI');
reach_data_all0(:,ind) = 0.3;
% set streambed Brooks-Corey exp (sagehen example is 3.5)
ind = strcmp(reach_data_all.colheaders, 'EPS');
reach_data_all0(:,ind) = 3.5;
% set streambed unsaturated zone saturated hydraulic conductivity
% (sagehen example is 0.3 m/d)
ind = strcmp(reach_data_all.colheaders, 'UHC');
reach_data_all0(:,ind) = 0.3;
reach_data_all = reach_data_all0;
end
if isstruct(segment_data_4A)
segment_data_4A = segment_data_4A.data;
nss = size(segment_data_4A,1); %Number of stream segments
end
if isstruct(segment_data_4B)
segment_data_4B = segment_data_4B.data;
end
if isstruct(segment_data_4C)
segment_data_4C = segment_data_4C.data;
end
% if isstruct(stress_periods)
% stress_periods = stress_periods.data;
% end
% - specify only for 2 stress periods:
stress_periods = zeros(NPER, 3); % itmp, irdflg, iptflg (latter 2 are set to 0)
stress_periods(1,1) = nss;
if NPER > 1, stress_periods(2:end,1) = -1; end
% -------------------------------------------------------------------------
% First put 4A, 4B and 4C data all together in a cell array
% size(cell) = nitems x 1 x nperiods
% In this case, nitems is 3 (i.e. 4A, 4B and 4C)
nitems = 3;
nperiods = size(stress_periods, 1);
segment_data_all = cell(nitems, 1, nperiods);
segment_data_all{1, 1, 1} = segment_data_4A;
segment_data_all{2, 1, 1} = segment_data_4B;
segment_data_all{3, 1, 1} = segment_data_4C;
% -------------------------------------------------------------------------
% validate some of the input data
msg_invalidISFROPT = ['Error: ISFROPT should be set to an integer of', ...
'1, 2, 3, 4 or 5.'];
if (nstrm < 0)
if ~ismember(isfropt, [1, 2, 3, 4, 5])
error(msg_invalidISFROPT);
end
end
msg_notSupport = ['Error: %s: this variable must be zero because ', ...
'parameters are not supported in GSFLOW.'];
if (nsfrpar ~= 0)
error(msg_notSupport, 'NSFRPAR');
end
if (nparseg ~= 0)
error(msg_notSupport, 'NPARSEG');
end
% -------------------------------------------------------------------------
% Ouput file
fid = fopen([GSFLOW_indir, '/', sfr_file], 'wt');
% Write header lines (item 0)
heading = '# Streamflow-Routing (SFR7) input file.\n';
fprintf(fid, heading);
fprintf(fid, '# %s simulation -- created on %s.\n', upper(project_name), date);
% Item 1
fprintf(fid, ' %5d %5d %5d %5d %8.2f %8.4f %5d %5d', ...
nstrm, nss, nsfrpar, nparseg, const, dleak, istcb1, istcb2);
if (isfropt >= 1)
fprintf(fid, ' %5d', isfropt);
if (isfropt == 1)
fprintf(fid, ' %5d\n', irtflg);
elseif (isfropt > 1)
fprintf(fid, ' %5d %5d %5d %5d\n', nstrail, isuzn, nsfrsets, irtflg);
end
else
fprintf(fid, '\n');
end
% Item 2
if (isfropt == 1)
ncols_reach = 10;
elseif (isfropt == 2)
ncols_reach = 13;
elseif (isfropt == 3)
ncols_reach = 14;
else
ncols_reach = 6;
end
reach_data_copy = reach_data_all(:, 1:ncols_reach);
p = ncols_reach - 5;
fmt_reach = [repmat(' %5d', 1, 5), repmat(' %8.3f', 1, p), '\n'];
for istrm=1:abs(nstrm)
dummy = reach_data_copy(istrm, :);
fprintf(fid, fmt_reach, dummy);
end
% Item 3 and 4
nper = size(stress_periods, 1);
for iper=1:nper
% write item 3 to the file
dummy3 = num2cell(stress_periods(iper, :));
[itmp, irdflg, iptflg] = dummy3{:};
fprintf(fid, ' %5d %5d %5d\n', itmp, irdflg, iptflg);
if (itmp > 0)
seg_inf_4a = segment_data_all{1, 1, iper};
seg_inf_4b = segment_data_all{2, 1, iper};
seg_inf_4c = segment_data_all{3, 1, iper};
for iitmp=1:itmp % start loop over itmp (num_segments)
% write item 4a to the file
dummy4a = num2cell(seg_inf_4a(iitmp, :));
[nseg, icalc, outseg, iupseg, iprior, nstrpts, ...
flow, runoff, etsw, pptsw, roughch, roughbk, ...
cdpth, fdpth, awdth, bwdth] = dummy4a{:};
fmt = [' ', repmat(' %5d', 1, 4)];
fprintf(fid, fmt, nseg, icalc, outseg, iupseg);
if (iupseg > 0)
fprintf(fid, ' %5d', iprior);
end
if (icalc == 4)
fprintf(fid, ' %5d', nstrpts);
end
fmt = repmat(' %8.3f', 1, 4);
fprintf(fid, fmt, flow, runoff, etsw, pptsw);
if ((icalc == 1) || (icalc == 2))
fprintf(fid, ' %8.3f', roughch);
end
if (icalc == 2)
fprintf(fid, ' %8.3f', roughbk);
end
if (icalc == 3)
fmt = repmat(' %8.3f', 1, 4);
fprintf(fid, fmt, cdpth, fdpth, awdth, bwdth);
end
fprintf(fid, '\n');
% write items 4b and 4c to the file
suffixes = 'bc';
for i=1:2 % start loop over i
suffix = suffixes(i);
var_str = ['seg_inf_4', suffix];
var = eval(var_str);
dummy4bc = num2cell(var(iitmp, :));
[hcond, thickm, elevupdn, width, ...
depth, thts, thti, eps, uhc] = dummy4bc{:};
fl_no_4bc = 0;
if (ismember(isfropt, [0, 4, 5]) && (icalc <= 0))
fmt = [' ', repmat(' %8.3f', 1, 5)];
fprintf(fid, fmt, hcond, thickm, elevupdn, width, depth);
elseif (ismember(isfropt, [0, 4, 5]) && (icalc == 1))
fprintf(fid, ' %8.3f', hcond);
if (iper == 1) % only for the first period
fmt = repmat(' %8.3f', 1, 3);
fprintf(fid, fmt, thickm, elevupdn, width);
if ((isfropt == 4) || (isfropt == 5))
fmt = repmat(' %8.3f', 1, 3);
fprintf(fid, fmt, thts, thti, eps);
end
if (isfropt == 5)
fprintf(fid, ' %8.3f', uhc);
end
elseif ((iper > 1) && (isfropt == 0))
fmt = repmat(' %8.3f', 1, 3);
fprintf(fid, fmt, thickm, elevupdn, width);
end
elseif (ismember(isfropt, [0, 4, 5]) && (icalc >= 2))
fprintf(fid, ' %8.3f', hcond);
if ~(ismember(isfropt, [4, 5]) && (iper > 1) && (icalc == 2))
fprintf(fid, ' %8.3f %8.3f', thickm, elevupdn);
if (ismember(isfropt, [4, 5]) && (iper == 1) && (icalc == 2))
fmt = repmat(' %8.3f', 1, 3);
fprintf(fid, fmt, thts, thti, eps);
if (isfropt == 5)
fprintf(fid, ' %8.3f', uhc);
end
end
end
elseif ((isfropt == 1) && (icalc <= 1))
fprintf(fid, ' %8.3f', width);
if (icalc <= 0)
fprintf(fid, ' %8.3f', depth);
end
elseif (ismember(isfropt, [2, 3]) && (icalc <= 1))
if (iper == 1)
fprintf(fid, ' %8.3f', width);
if (icalc <= 0)
fprintf(fid, ' %8.3f', depth);
end
end
else
fl_no_4bc = 1;
end
if ~fl_no_4bc
fprintf(fid, '\n');
end
end % terminate loop over i (4b and 4c, respectively)
end % terminate loop over itmp (num_segments)
end % enf if (itmp > 0)
end % terminate loop over iper (num_periods)
fclose(fid);
% -------------------------------------------------------------------------
% End of the script
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_ba6_MOD2_ok.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_ba6_MOD2_ok.m
| 5,863 |
utf_8
|
1f12d52d0b1416b1514618c0791f04d0
|
% write_ba6_MOD
% 11/17/16
function write_ba6_MOD2(GSFLOW_indir, infile_pre, surfz_fil, mask_fil, NLAY, DZ)
% % ==== TO RUN AS SCRIPT ===================================================
% clear all, close all, fclose all;
% % - directories
% % MODFLOW input files
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
%
% % infile_pre = 'test1lay';
% % NLAY = 1;
% % DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% infile_pre = 'test2lay';
% NLAY = 2;
% DZ = [50; 50]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
%
% % for various files: ba6, dis, uzf, lpf
% surfz_fil = [GIS_indir, 'topo.asc'];
% % for various files: ba6, uzf
% mask_fil = [GIS_indir, 'basinmask_dischargept.asc'];
% % =========================================================================
%%
% - write to this file
% GSFLOW_dir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
ba6_file = [infile_pre, '.ba6'];
slashstr = '/';
% - domain dimensions, maybe already in surfz_fil and botm_fil{}?
% NLAY = 1;
% NROW = 50;
% NCOL = 50;
% -- IBOUND(NROW,NCOL,NLAY): <0 const head, 0 no flow, >0 variable head
% use basin mask (set IBOUND>0 within watershed, =0 outside watershed, <0 at discharge point and 2 neighboring pixels)
% mask_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/basinmask_dischargept.asc';
fid = fopen(mask_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
D = textscan(fid, '%f');
IBOUND = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
D = textscan(fid, '%s %s %f %s %f');
dischargePt_rowi = D{3};
dischargePt_coli = D{5};
fclose(fid);
% - force some cells to be active to correspond to stream reaches
IBOUND(14,33) = 1;
IBOUND(11,35) = 1;
IBOUND(12,34) = 1;
IBOUND(7,43) = 1;
% find boundary cells
IBOUNDin = IBOUND(2:end-1,2:end-1);
IBOUNDu = IBOUND(1:end-2,2:end-1); % up
IBOUNDd = IBOUND(3:end,2:end-1); % down
IBOUNDl = IBOUND(2:end-1,1:end-2); % left
IBOUNDr = IBOUND(2:end-1,3:end); % right
ind_bound = IBOUNDin==1 & (IBOUNDin-IBOUNDu==1 | IBOUNDin-IBOUNDd==1 | ...
IBOUNDin-IBOUNDl==1 | IBOUNDin-IBOUNDr==1);
% IBOUNDin(ind) = -1;
% IBOUND(2:end-1,2:end-1) = IBOUNDin;
% -- init head: base on TOP and BOTM
% surfz_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/topo.asc';
fid = fopen(surfz_fil, 'r');
D = textscan(fid, '%s %f', 6);
if ~isempty(find(NSEW ~= D{2}(1:4),1)) || NROW ~= D{2}(5) || NCOL ~= D{2}(6);
fprintf('Error!! NSEW, NROW, or NCOL in data files do not match!\n');
fprintf(' (files: %d and %d\n', mask_fil, surfz_fil);
fprintf('exiting...\n');
return
end
% - space discretization
DELR = (NSEW(3)-NSEW(4))/NCOL; % width of column [m]
DELC = (NSEW(1)-NSEW(2))/NROW; % height of row [m]
% DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
% DZ = [5; 5]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
% - set TOP to surface elevation [m]
D = textscan(fid, '%f');
fclose(fid);
TOP = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
BOTM = zeros(NROW, NCOL, NLAY);
BOTM(:,:,1) = TOP-DZ(1);
for ilay = 2:NLAY
BOTM(:,:,ilay) = BOTM(:,:,ilay-1)-DZ(ilay);
end
% - make boundary cells constant head above a certain elevation
% IBOUNDin(ind_bound & TOP(2:end-1,2:end-1) > 4500) = -1;
IBOUNDin(ind_bound & TOP(2:end-1,2:end-1) > 3500) = -1;
IBOUND(2:end-1,2:end-1,1) = IBOUNDin;
% - make discharge point and neighboring cells constant head
IBOUND(dischargePt_rowi,dischargePt_coli,1) = -2; % downgrad of discharge pt
% IBOUND(dischargePt_rowi-1,dischargePt_coli,1) = -1; % neighbor points
IBOUND(dischargePt_rowi+1,dischargePt_coli,1) = -1;
IBOUND(dischargePt_rowi,dischargePt_coli+1,1) = -2; % downgrad of discharge pt
IBOUND(dischargePt_rowi-1,dischargePt_coli+1,1) = -1; % neighbor points
IBOUND(dischargePt_rowi+1,dischargePt_coli+1,1) = -1;
IBOUND(dischargePt_rowi,dischargePt_coli,1) = 1; % downgrad of discharge pt
IBOUND = repmat(IBOUND, [1 1 NLAY]);
% - initHead(NROW,NCOL,NLAY)
initHead = BOTM(:,:,1) + (TOP-BOTM(:,:,1))*0.9; % within top layer
initHead = repmat(initHead, [1, 1, NLAY]);
% - assumed values
HNOFLO = -999.99;
%% ------------------------------------------------------------------------
% -- Write ba6 file
fil_ba6_0 = [GSFLOW_indir, slashstr, ba6_file];
fmt1 = [repmat('%4d ', 1, NCOL), '\n']; % for IBOUND
fmt2 = [repmat('%7g ', 1, NCOL), '\n']; % for initHead
fid = fopen(fil_ba6_0, 'wt');
fprintf(fid, '# basic package file --- %d layers, %d rows, %d columns\n', NLAY, NROW, NCOL);
fprintf(fid, 'FREE\n');
for ilay = 1: NLAY
fprintf(fid, 'INTERNAL 1 (FREE) 3 IBOUND for layer %d \n', ilay); % 1: CNSTNT multiplier, 3: IPRN>0 to print input to list file
fprintf(fid, fmt1, IBOUND(:,:,ilay)');
end
fprintf(fid, ' %f HNOFLO\n', HNOFLO);
for ilay = 1: NLAY
fprintf(fid, 'INTERNAL 1 (FREE) 3 init head for layer %d \n', ilay); % 1: CNSTNT multiplier, 3: IPRN>0 to print input to list file
fprintf(fid, fmt2, initHead(:,:,ilay)');
end
fclose(fid);
% -- Plot basics
for ii = 1:2
if ii == 1,
X0 = IBOUND; ti0 = 'IBOUND';
elseif ii == 2
X0 = initHead; ti0 = 'init head';
end
figure
for ilay = 1:NLAY
subplot(2,2,double(ilay))
X = X0(:,:,ilay);
m = X(X>0); m = min(m(:));
imagesc(X), %caxis([m*0.9, max(X(:))]),
cm = colormap;
% cm(1,:) = [1 1 1];
colormap(cm);
colorbar
title([ti0, ' lay', num2str(ilay)]);
end
end
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_OC_PCG_MOD_f_ok.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_OC_PCG_MOD_f_ok.m
| 2,464 |
utf_8
|
1d9ed9195d98acdaca000e193ada7d77
|
% write_OC_PCG_MOD.m
% 11/20/16
function write_OC_PCG_MOD_f(GSFLOW_indir, infile_pre)
% clear all, close all, fclose all;
% - write to this file
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
fil_pcg = [infile_pre, '.pcg'];
fil_oc = [infile_pre, '.oc'];
slashstr = '/';
% -- shoud match .dis
NPER = 2; % 1 SS then 1 transient
PERLEN = [1; 365]; % 2 periods: 1-day steady-state and multi-day transient
NSTP = PERLEN;
% -- pcg and oc files are not changed with this script
% fil_pcg_0 = fullfile(MODtest_dir0, fil_pcg);
fil_pcg_0 = [GSFLOW_indir, slashstr, fil_pcg];
fid = fopen(fil_pcg_0, 'wt');
% fprintf(fid, '# Preconditioned conjugate-gradient package\n');
% fprintf(fid, ' 50 30 1 MXITER, ITER1, NPCOND\n');
% fprintf(fid, ' 0000.001 .001 1. 2 1 1 1.00\n');
% fprintf(fid, ' HCLOSE, RCLOSE, RELAX, NBPOL, IPRPCG, MUTPCG damp\n');
% % sagehen example:
% fprintf(fid, '# Preconditioned conjugate-gradient package\n');
% fprintf(fid, ' 1000 450 1 MXITER, ITER1, NPCOND\n');
% fprintf(fid, ' 0.001 0.08 1.0 2 1 0 -0.05 0.70\n');
% fprintf(fid, ' HCLOSE, RCLOSE, RELAX, NBPOL, IPRPCG, MUTPCG damp\n');
% sagehen example:
fprintf(fid, '# Preconditioned conjugate-gradient package\n');
fprintf(fid, ' 5000 450 1 MXITER, ITER1, NPCOND\n');
fprintf(fid, ' 0.001 0.08 1.0 2 1 0 -0.05 0.70\n');
fprintf(fid, ' HCLOSE, RCLOSE, RELAX, NBPOL, IPRPCG, MUTPCG damp\n');
fclose(fid);
% fil_oc_0 = (MODtest_dir0, fil_oc);
% "PRINT": to listing file
% "SAVE": to file with unit number in name file
fil_oc_0 = [GSFLOW_indir, slashstr, fil_oc];
fid = fopen(fil_oc_0, 'wt');
fprintf(fid, 'HEAD PRINT FORMAT 20\n');
fprintf(fid, 'HEAD SAVE UNIT 51\n');
fprintf(fid, 'COMPACT BUDGET AUX\n');
fprintf(fid, 'IBOUND SAVE UNIT 52\n');
for per_i = 1:NPER
for stp_i = 1:30:NSTP(per_i)
fprintf(fid, 'PERIOD %d STEP %d\n', per_i, stp_i);
if stp_i == NSTP(per_i) % only at end of stress period
fprintf(fid, ' PRINT HEAD\n');
fprintf(fid, ' SAVE IBOUND\n');
fprintf(fid, ' PRINT BUDGET\n');
end
fprintf(fid, ' SAVE HEAD\n');
fprintf(fid, ' SAVE BUDGET\n');
end
end
fclose(fid);
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_nam_MOD_f2.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_nam_MOD_f2.m
| 2,336 |
utf_8
|
b3ca150a14dec00616051b95a14ea82c
|
% write_nam_MOD
% 11/20/16
function write_nam_MOD_f2(GSFLOW_indir, GSFLOW_outdir, infile_pre, fil_res_in)
% v2 - allows for restart option (init)
% clear all, close all, fclose all;
% % - directories
% % MODFLOW input filesfil_res_in
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
% - write to this file (within indir)
fil_nam = [infile_pre, '.nam'];
slashstr = '/';
% all assumed to be in GSFLOW_dir
fil_ba6 = [infile_pre, '.ba6'];
fil_lpf = [infile_pre, '.lpf'];
fil_pcg = [infile_pre, '.pcg'];
fil_oc = [infile_pre, '.oc'];
fil_dis = [infile_pre, '.dis'];
fil_uzf = [infile_pre, '.uzf'];
fil_sfr = [infile_pre, '.sfr'];
fil_res_out = [infile_pre, '.out']; % write to restart file
%% ------------------------------------------------------------------------
% -- .nam file with full paths
% fil_nam_0 = fullfile(MODtest_dir0, fil_nam);
fil_nam_0 = [GSFLOW_indir, slashstr, fil_nam];
fid = fopen(fil_nam_0, 'wt');
fprintf(fid, 'LIST 7 %s \n', [GSFLOW_outdir, slashstr, 'test.lst']); % MODFLOW output file
fprintf(fid, 'BAS6 8 %s \n', [GSFLOW_indir, slashstr, fil_ba6]);
fprintf(fid, 'LPF 11 %s \n', [GSFLOW_indir, slashstr, fil_lpf]);
fprintf(fid, 'PCG 19 %s \n', [GSFLOW_indir, slashstr, fil_pcg]);
fprintf(fid, 'OC 22 %s \n', [GSFLOW_indir, slashstr, fil_oc]);
fprintf(fid, 'DIS 10 %s \n', [GSFLOW_indir, slashstr, fil_dis]);
fprintf(fid, 'UZF 12 %s \n', [GSFLOW_indir, slashstr, fil_uzf]);
fprintf(fid, 'SFR 13 %s \n', [GSFLOW_indir, slashstr, fil_sfr]);
if ~isempty(fil_res_in)
fprintf(fid, 'IRED 90 %s \n', fil_res_in);
end
fprintf(fid, 'IWRT 91 %s \n', [GSFLOW_outdir, slashstr, fil_res_out]);
fprintf(fid, 'DATA(BINARY) 34 %s \n', fullfile(GSFLOW_outdir, 'test.bud')); % MODFLOW LPF output file, make sure 34 is unit listed in lpf file!!
fprintf(fid, 'DATA(BINARY) 51 %s \n', [GSFLOW_outdir, slashstr, 'testhead.dat']); % MODFLOW output file
fprintf(fid, 'DATA(BINARY) 61 %s \n', [GSFLOW_outdir, slashstr, 'uzf.dat']); % MODFLOW output file
fprintf(fid, 'DATA 52 %s \n', [GSFLOW_outdir, slashstr, 'ibound.dat']); % MODFLOW output file
fclose(fid);
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_ba6_MOD3_2.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_ba6_MOD3_2.m
| 6,046 |
utf_8
|
c967103aeca207643dcd775bdb4760b4
|
% write_ba6_MOD
% 11/17/16
function write_ba6_MOD3(GSFLOW_indir, infile_pre, mask_fil)
% % ==== TO RUN AS SCRIPT ===================================================
% clear all, close all, fclose all;
% % - directories
% % MODFLOW input files
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
%
% % infile_pre = 'test1lay';
% % NLAY = 1;
% % DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% infile_pre = 'test2lay';
% NLAY = 2;
% DZ = [50; 50]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
%
% % for various files: ba6, dis, uzf, lpf
% surfz_fil = [GIS_indir, 'topo.asc'];
% % for various files: ba6, uzf
% mask_fil = [GIS_indir, 'basinmask_dischargept.asc'];
% % =========================================================================
%%
% - write to this file
% GSFLOW_dir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
ba6_file = [infile_pre, '.ba6'];
slashstr = '/';
% - domain dimensions, maybe already in surfz_fil and botm_fil{}?
% NLAY = 1;
% NROW = 50;
% NCOL = 50;
% -- IBOUND(NROW,NCOL,NLAY): <0 const head, 0 no flow, >0 variable head
% use basin mask (set IBOUND>0 within watershed, =0 outside watershed, <0 at discharge point and 2 neighboring pixels)
% mask_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/basinmask_dischargept.asc';
fid = fopen(mask_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
D = textscan(fid, '%f');
IBOUND = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
D = textscan(fid, '%s %s %f %s %f');
dischargePt_rowi = D{3};
dischargePt_coli = D{5};
fclose(fid);
% - force some cells to be active to correspond to stream reaches
IBOUND(14,33) = 1;
IBOUND(11,35) = 1;
IBOUND(12,34) = 1;
IBOUND(7,43) = 1;
% find boundary cells
IBOUNDin = IBOUND(2:end-1,2:end-1);
IBOUNDu = IBOUND(1:end-2,2:end-1); % up
IBOUNDd = IBOUND(3:end,2:end-1); % down
IBOUNDl = IBOUND(2:end-1,1:end-2); % left
IBOUNDr = IBOUND(2:end-1,3:end); % right
% - inner boundary is constant head
ind_bound = IBOUNDin==1 & (IBOUNDin-IBOUNDu==1 | IBOUNDin-IBOUNDd==1 | ...
IBOUNDin-IBOUNDl==1 | IBOUNDin-IBOUNDr==1);
% - outer boundary is constant head
% ind_bound = IBOUNDin==0 & (IBOUNDin-IBOUNDu==-1 | IBOUNDin-IBOUNDd==-1 | ...
% IBOUNDin-IBOUNDl==-1 | IBOUNDin-IBOUNDr==-1);
% -- init head: base on TOP and BOTM
dis_file = [GSFLOW_indir, '/', infile_pre, '.dis'];
fid = fopen(dis_file);
for ii = 1:2, cmt = fgets(fid); end
line0 = fgets(fid);
D = textscan(line0, '%d', 6);
NLAY = D{1}(1); NROW = D{1}(2); NCOL = D{1}(3);
NPER = D{1}(4); ITMUNI = D{1}(5); LENUNI = D{1}(6);
line0 = fgets(fid);
D = textscan(line0, '%d');
LAYCBD = D{1}; % 1xNLAY (0 if no confining layer)
line0 = fgets(fid);
D = textscan(line0, '%s %d'); DELR = D{2}; % width of column
line0 = fgets(fid);
D = textscan(line0, '%s %d'); DELC = D{2}; % height of row
TOP = nan(NROW,NCOL);
line0 = fgets(fid);
for irow = 1: NROW
line0 = fgets(fid);
D = textscan(line0, '%f');
TOP(irow,:) = D{1}(1:NCOL);
end
BOTM = nan(NROW, NCOL, NLAY);
for ilay = 1: NLAY
line0 = fgets(fid);
for irow = 1: NROW
line0 = fgets(fid);
D = textscan(line0, '%f');
BOTM(irow,:,ilay) = D{1}(1:NCOL);
end
end
fclose(fid);
% - make boundary cells constant head above a certain elevation
% IBOUNDin(ind_bound & TOP(2:end-1,2:end-1) > 4500) = -1;
IBOUNDin(ind_bound & TOP(2:end-1,2:end-1) > 3500) = -1;
IBOUND(2:end-1,2:end-1,1) = IBOUNDin;
% - make discharge point and neighboring cells constant head
IBOUND(dischargePt_rowi,dischargePt_coli,1) = -2; % downgrad of discharge pt
% IBOUND(dischargePt_rowi-1,dischargePt_coli,1) = -1; % neighbor points
IBOUND(dischargePt_rowi+1,dischargePt_coli,1) = -1;
IBOUND(dischargePt_rowi,dischargePt_coli+1,1) = -2; % downgrad of discharge pt
IBOUND(dischargePt_rowi-1,dischargePt_coli+1,1) = -1; % neighbor points
IBOUND(dischargePt_rowi+1,dischargePt_coli+1,1) = -1;
IBOUND(dischargePt_rowi,dischargePt_coli,1) = 1; % downgrad of discharge pt
IBOUND = repmat(IBOUND, [1 1 NLAY]);
% - initHead(NROW,NCOL,NLAY)
initHead = BOTM(:,:,1) + (TOP-BOTM(:,:,1))*0.9; % within top layer
% % (no more than 10m below top):
% Y = nan(NROW,NCOL,2); Y(:,:,1) = initHead; Y(:,:,2) = TOP-10;
% initHead = max(Y,[],3);
initHead = repmat(initHead, [1, 1, NLAY]);
% - assumed values
HNOFLO = -999.99;
%% ------------------------------------------------------------------------
% -- Write ba6 file
fil_ba6_0 = [GSFLOW_indir, slashstr, ba6_file];
fmt1 = [repmat('%4d ', 1, NCOL), '\n']; % for IBOUND
fmt2 = [repmat('%7g ', 1, NCOL), '\n']; % for initHead
fid = fopen(fil_ba6_0, 'wt');
fprintf(fid, '# basic package file --- %d layers, %d rows, %d columns\n', NLAY, NROW, NCOL);
fprintf(fid, 'FREE\n');
for ilay = 1: NLAY
fprintf(fid, 'INTERNAL 1 (FREE) 3 IBOUND for layer %d \n', ilay); % 1: CNSTNT multiplier, 3: IPRN>0 to print input to list file
fprintf(fid, fmt1, IBOUND(:,:,ilay)');
end
fprintf(fid, ' %f HNOFLO\n', HNOFLO);
for ilay = 1: NLAY
fprintf(fid, 'INTERNAL 1 (FREE) 3 init head for layer %d \n', ilay); % 1: CNSTNT multiplier, 3: IPRN>0 to print input to list file
fprintf(fid, fmt2, initHead(:,:,ilay)');
end
fclose(fid);
% -- Plot basics
for ii = 1:2
if ii == 1,
X0 = IBOUND; ti0 = 'IBOUND';
elseif ii == 2
X0 = initHead; ti0 = 'init head';
end
figure
for ilay = 1:NLAY
subplot(2,2,double(ilay))
X = X0(:,:,ilay);
m = X(X>0); m = min(m(:));
imagesc(X), %caxis([m*0.9, max(X(:))]),
cm = colormap;
% cm(1,:) = [1 1 1];
colormap(cm);
colorbar
title([ti0, ' lay', num2str(ilay)]);
end
end
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_dis_MOD2_f.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_dis_MOD2_f.m
| 5,326 |
utf_8
|
2a486d8aebe770037a17329382652f18
|
% write_dis_MOD (for 3D domains)
% 11/17/16
%
% v1 - 11/30/16 start to include GIS data for Chimborazo's Gavilan Machay
% watershed; topo.asc for surface elevation (fill in bottom elevation
% based on uniform thickness of single aquifer)
function write_dis_MOD2_f(GSFLOW_indir, infile_pre, surfz_fil, NLAY, DZ, perlen_tr)
% % ==== TO RUN AS SCRIPT ===================================================
% % - directories
% % MODFLOW input files
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
%
% % infile_pre = 'test1lay';
% % NLAY = 1;
% % DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% infile_pre = 'test2lay';
% NLAY = 2;
% DZ = [50; 50]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% perlen_tr = 365; % ok if too long
%
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
%
% % for various files: ba6, dis, uzf, lpf
% % surfz_fil = [GIS_indir, 'topo.asc'];
% surfz_fil = [GIS_indir, 'SRTM_new_20161208.asc'];
%
% % for various files: ba6, uzf
% mask_fil = [GIS_indir, 'basinmask_dischargept.asc'];
% % =========================================================================
%%
% clear all, close all, fclose all;
% - write to this file
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
dis_file = [infile_pre, '.dis'];
% - read in this file for surface elevation (for TOP(NROW,NCOL))
% surfz_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/topo.asc';
% - read in this file for elevation of layer bottoms (for BOTM(NROW,NCOL,NLAY))
% (layer 1 is top layer)
botmz_fil = '';
% - domain dimensions, maybe already in surfz_fil and botm_fil{}?
% NLAY = 1;
% NROW = 1058;
% NCOL = 1996;
% % - domain boundary (UTM zone 17S, outer boundaries)
% north = 9841200;
% south = 9835900;
% east = 751500;
% west = 741500;
% % - space discretization
% DELR = (east-west)/NCOL; % width of column [m]
% DELC = (north-south)/NROW; % height of row [m]
% DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
% DZ = [5; 5]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
% - time discretization
PERLEN = [1; perlen_tr]; % 2 periods: 1-day steady-state and multi-day transient
comment1 = '# test file for Gavilan Machay';
comment2 = '# test file';
% - The following will be assumed:
LAYCBD = zeros(NLAY,1); % no confining layer below layer
ITMUNI = 4; % [d]
LENUNI = 2; % [m]
NPER = 2; % 1 SS then 1 transient
NSTP = PERLEN; TSMULT = 1; % must have daily time step to correspond with PRMS
SsTr_flag = ['ss'; 'tr'];
%% ------------------------------------------------------------------------
% -- Read in data from files
fid = fopen(surfz_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
% - space discretization
DELR = (NSEW(3)-NSEW(4))/NCOL; % width of column [m]
DELC = (NSEW(1)-NSEW(2))/NROW; % height of row [m]
% - set TOP to surface elevation [m]
D = textscan(fid, '%f');
fclose(fid);
fprintf('Done reading...\n');
TOP = reshape(D{1}, NCOL, NROW)'; % NROW x NCOL
BOTM = zeros(NROW, NCOL, NLAY);
BOTM(:,:,1) = TOP-DZ(1);
for ilay = 2:NLAY
BOTM(:,:,ilay) = BOTM(:,:,ilay-1)-DZ(ilay);
end
% -- Discretization file:
fmt1 = [repmat('%4d ', 1, NCOL), '\n']; %
fmt2 = [repmat('%10g ', 1, NCOL), '\n']; %
fid = fopen([GSFLOW_indir, '/', dis_file], 'wt');
fmt3 = [repmat(' %d', 1, NLAY), '\n']; % for LAYCBD
fprintf(fid, '%s\n', comment1);
fprintf(fid, '%s\n', comment2);
fprintf(fid, ' %d %d %d %d %d %d ', NLAY, NROW, NCOL, NPER, ITMUNI, LENUNI);
fprintf(fid, ' NLAY,NROW,NCOL,NPER,ITMUNI,LENUNI\n');
fprintf(fid, fmt3, LAYCBD);
fprintf(fid, 'CONSTANT %14g DELR\n', DELR);
fprintf(fid, 'CONSTANT %14g DELC\n', DELC);
fprintf(fid, 'INTERNAL 1.0 (FREE) 0 TOP ELEVATION OF LAYER 1 \n');
fprintf(fid, fmt2, TOP');
for ii = 1: NLAY
fprintf(fid, 'INTERNAL 1.0 (FREE) 0 BOTM ELEVATION OF LAYER %d \n', ii);
fprintf(fid, fmt2, BOTM(:,:,ii)');
end
for ii = 1: NPER
fprintf(fid, ' %g %d %g %c%c PERLEN, NSTP, TSMULT, Ss/Tr (stress period %4d)\n', ...
PERLEN(ii), NSTP(ii), TSMULT, SsTr_flag(ii,:), ii);
end
fclose(fid);
% -- plot domain discretization
figure
subplot(2,2,1)
imagesc(TOP),
m = TOP(TOP>0); m = min(m(:));
caxis([m*0.9, max(TOP(:))]),
cm = colormap;
cm(1,:) = [1 1 1];
colormap(cm);
colorbar
title('TOP')
for ilay = 1:NLAY
subplot(2,2,1+double(ilay))
m = BOTM(BOTM>0); m = min(m(:));
imagesc(BOTM(:,:,ilay)), caxis([m*0.9, max(BOTM(:))]),
cm = colormap;
cm(1,:) = [1 1 1];
colormap(cm);
colorbar
title(['BOTM', ' lay', num2str(ilay)]);
end
figure
for ilay = 1:NLAY
subplot(2,2,double(ilay))
if ilay == 1
X = TOP - BOTM(:,:,ilay);
else
X = BOTM(:,:,ilay-1)-BOTM(:,:,ilay);
end
% m = X(X>0); m = min(m(:));
imagesc(X), %caxis([m*0.9, max(X(:))]),
cm = colormap;
cm(1,:) = [1 1 1];
colormap(cm);
colorbar
title(['DZ', ' lay', num2str(ilay)]);
end
|
github
|
UMN-Hydro/GSFLOW_pre-processor-master
|
write_lpf_MOD2_f2_ok.m
|
.m
|
GSFLOW_pre-processor-master/matlab_scripts/MODFLOW_scripts/write_lpf_MOD2_f2_ok.m
| 5,446 |
utf_8
|
78b4bbbf2eb4c445215734e7c60a624a
|
% write_lpf_MOD
% 11/17/16
function write_lpf_MOD2_f2(GSFLOW_indir, infile_pre, surfz_fil, NLAY)
% % =========== TO RUN AS SCRIPT ===========================================
% clear all, close all, fclose all;
% % - directories
% % MODFLOW input files
% GSFLOW_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% % MODFLOW output files
% GSFLOW_outdir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/outputs/MODFLOW/';
%
% % infile_pre = 'test1lay';
% % NLAY = 1;
% % DZ = 10; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% infile_pre = 'test2lay';
% NLAY = 2;
% DZ = [50; 50]; % [NLAYx1] ***temporary: constant 10m thick single aquifer (consider 2-layer?)
%
% GIS_indir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/';
%
% % for various files: ba6, dis, uzf, lpf
% surfz_fil = [GIS_indir, 'topo.asc'];
% % for various files: ba6, uzf
% mask_fil = [GIS_indir, 'basinmask_dischargept.asc'];
%
% % for sfr
% reach_fil = [GIS_indir, 'reach_data.txt'];
% segment_fil_all = cell(3,1);
% segment_fil_all{1} = [GIS_indir, 'segment_data_4A_INFORMATION.txt'];
% segment_fil_all{2} = [GIS_indir, 'segment_data_4B_UPSTREAM.txt'];
% segment_fil_all{3} = [GIS_indir, 'segment_data_4C_DOWNSTREAM.txt'];
% % ====================================================================
% - write to this file
% GSFLOW_dir = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/GSFLOW/inputs/MODFLOW/';
% lpf_file = 'test.lpf';
lpf_file = [infile_pre, '.lpf'];
slashstr = '/';
% - domain dimensions, maybe already in surfz_fil and botm_fil{}?
% NLAY = 2;
% surfz_fil = '/home/gcng/workspace/ProjectFiles/AndesWaterResources/Data/GIS/topo.asc';
fid = fopen(surfz_fil, 'r');
D = textscan(fid, '%s %f', 6);
NSEW = D{2}(1:4);
NROW = D{2}(5);
NCOL = D{2}(6);
fclose(fid);
% -- Base hydcond, Ss (all layers), and Sy (top layer only) on data from files
% (temp place-holder)
hydcond = ones(NROW,NCOL,NLAY)*2; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
% hydcond(:,:,2) = 0.5; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
% hydcond(:,:,2) = 0.1; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
hydcond(:,:,1) = 0.1; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
hydcond(:,:,2) = 0.01; % m/d (Sagehen: 0.026 to 0.39 m/d, lower K under ridges for volcanic rocks)
Ss = ones(NROW,NCOL,NLAY)* 2e-6; % constant 2e-6 /m for Sagehen
Sy = ones(NROW,NCOL,NLAY)*0.15; % 0.08-0.15 in Sagehen (lower Sy under ridges for volcanic rocks)
WETDRY = Sy; % = Sy in Sagehen (lower Sy under ridges for volcanic rocks)
% -- assumed input values
flow_filunit = 34; % make sure this matches namefile!!
hdry = 1e30; % head assigned to dry cells
nplpf = 0; % number of LPF parameters (if >0, key words would follow)
laytyp = zeros(NLAY,1); laytyp(1) = 1; % flag, top>0: "covertible", rest=0: "confined"
layave = zeros(NLAY,1); % flag, layave=1: harmonic mean for interblock transmissivity
chani = ones(NLAY,1); % flag, chani=1: constant horiz anisotropy mult factor (for each layer)
layvka = zeros(NLAY,1); % flag, layvka=0: vka is vert K; >0 is vertK/horK ratio
VKA = hydcond;
laywet = zeros(NLAY,1); laywet(1)=1; % flag, 1: wetting on for top convertible cells, 0: off for confined
fl_Tr = 1; % flag, 1 for at least 1 transient stress period (for Ss and Sy)
WETFCT = 1.001; % 1.001 for Sagehen, wetting (convert dry cells to wet)
IWETIT = 4; % number itermations for wetting
IHDWET = 0; % wetting scheme, 0: equation 5-32A is used: h = BOT + WETFCT (hn - BOT)
%% ------------------------------------------------------------------------
fmt1 = repmat('%2d ', 1, NLAY);
fil_lpf_0 = [GSFLOW_indir, slashstr, lpf_file];
fid = fopen(fil_lpf_0, 'wt');
fprintf(fid, '# LPF package inputs\n');
fprintf(fid, '%d %g %d ILPFCB,HDRY,NPLPF\n', flow_filunit, hdry, nplpf);
fprintf(fid, [fmt1, ' LAYTYP\n'], laytyp);
fprintf(fid, [fmt1, ' LAYAVE\n'], layave);
fprintf(fid, [fmt1, ' CHANI \n'], chani);
fprintf(fid, [fmt1, ' LAYVKA\n'], layvka);
fprintf(fid, [fmt1, ' LAYWET\n'], laywet);
if ~isempty(find(laywet,1))
fprintf(fid, '%g %d %d WETFCT, IWETIT, IHDWET\n', WETFCT, IWETIT, IHDWET);
end
% -- Write HKSAT and Ss, Sy (if Tr) in .lpf file
format0 = [repmat(' %4.2f ', 1, NCOL), '\n'];
format1 = [repmat(' %4.2e ', 1, NCOL), '\n'];
% loop thru layers (different entry for each layer)
for lay = 1: NLAY
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 HY layer %d\n', lay); % horizontal hyd cond
fprintf(fid, format1, hydcond(:,:,lay)');
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 VKA layer %d\n', lay); % vertical hyd cond
fprintf(fid, format1, VKA(:,:,lay)');
if fl_Tr
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 Ss layer %d\n', lay);
fprintf(fid, format1, Ss(:,:,lay)');
if laytyp(lay) > 0 % convertible, i.e. unconfined
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 Sy layer %d\n', lay);
fprintf(fid, format1, Sy(:,:,lay)');
if laywet(lay) > 0
fprintf(fid, 'INTERNAL 1.000E-00 (FREE) 0 WETDRY layer %d\n', lay);
fprintf(fid, format0, WETDRY(:,:,lay)');
end
end
end
end
fprintf(fid, '\n');
fclose(fid);
|
github
|
victorlei/libermate-master
|
fox_rabbit.m
|
.m
|
libermate-master/Tests/fox_rabbit.m
| 1,178 |
utf_8
|
602c5dee8301607688c2500a9c52510e
|
function fox_rabbit
%FOX_RABBIT Fox-rabbit pursuit simulation.
% Uses relative speed parameter, K.
k = 1.1;
tspan = [0 10]; yzero = [3;0];
options = odeset('RelTol',1e-6,'AbsTol',1e-6,'Events',@events);
[tfox,yfox,te,ye,ie] = ode45(@fox2,tspan,yzero,options);
plot(yfox(:,1),yfox(:,2)), hold on
plot(sqrt(1+tfox).*cos(tfox),sqrt(1+tfox).*sin(tfox),'--')
plot([3 1],[0 0],'o'), plot(yfox(end,1),yfox(end,2),'*')
axis equal, axis([-3.5 3.5 -2.5 3.1])
legend('Fox','Rabbit'), hold off
function yprime = fox2(t,y)
%FOX2 Fox-rabbit pursuit simulation ODE.
r = sqrt(1+t)*[cos(t); sin(t)];
r_p = (0.5/sqrt(1+t)) * [cos(t)-2*(1+t)*sin(t); sin(t)+2*(1+t)*cos(t)];
dist = max(norm(r-y),1e-6);
factor = k*norm(r_p)/dist;
yprime = factor*(r-y);
end
end
function [value,isterminal,direction] = events(t,y)
%EVENTS Events function for FOX2.
% Locate when fox is close to rabbit.
r = sqrt(1+t)*[cos(t); sin(t)];
value = norm(r-y) - 1e-4; % Fox close to rabbit.
isterminal = 1; % Stop integration.
direction = -1; % Value must be decreasing through zero.
end
|
github
|
victorlei/libermate-master
|
functiontest2.m
|
.m
|
libermate-master/Tests/functiontest2.m
| 50 |
utf_8
|
e53e67b09926ff991d5a1ee2224940c2
|
% Comment
function ret=myfunction(a,b,c)
ret=a
|
github
|
victorlei/libermate-master
|
lcrun.m
|
.m
|
libermate-master/Tests/lcrun.m
| 1,252 |
utf_8
|
5a6e1f2365db43e3c78240595f9dcd04
|
function lcrun
%LCRUN Liquid crystal BVP.
% Solves the liquid crystal BVP for four different lambda values.
lambda_vals = [2.4, 2.5, 3, 10];
lambda_vals = lambda_vals(end:-1:1); % Necessary order for continuation.
solinit = bvpinit(linspace(-1,1,20),@lcinit);
lambda = lambda_vals(1); sola = bvp4c(@lc,@lcbc,solinit);
lambda = lambda_vals(2); solb = bvp4c(@lc,@lcbc,sola);
lambda = lambda_vals(3); solc = bvp4c(@lc,@lcbc,solb);
lambda = lambda_vals(4); sold = bvp4c(@lc,@lcbc,solc);
plot(sola.x,sola.y(1,:),'-', 'LineWidth',4), hold on
plot(solb.x,solb.y(1,:),'--','LineWidth',2)
plot(solc.x,solc.y(1,:),'--','LineWidth',4)
plot(sold.x,sold.y(1,:),'--','LineWidth',6), hold off
legend([repmat('\lambda = ',4,1) num2str(lambda_vals')])
xlabel('x','FontSize',16)
ylabel('\theta','Rotation',0,'FontSize',16)
ylim([-0.1 1.5])
function yprime = lc(x,y)
%LC ODE/BVP liquid crystal system.
yprime = [y(2); -lambda*sin(y(1))*cos(y(1))];
end
end
function res = lcbc(ya,yb)
%LCBC ODE/BVP liquid crystal boundary conditions.
res = [ya(1); yb(1)];
end
function yinit = lcinit(x)
%LCINIT ODE/BVP liquid crystal initial guess.
yinit = [sin(0.5*(x+1)*pi); 0.5*pi*cos(0.5*(x+1)*pi)];
end
|
github
|
victorlei/libermate-master
|
neural.m
|
.m
|
libermate-master/Tests/neural.m
| 1,142 |
utf_8
|
230a2509d261ffe4bdba2ce6d3856ba1
|
function neural
%NEURAL Neural network model with delays.
tspan = [0 40];
sol = dde23(@f,[0.2,0.5],@history,tspan);
subplot(2,2,1)
plot(sol.x,sol.y(1,:),'r-', sol.x,sol.y(2,:),'g--', 'LineWidth',2)
legend('y_1','y_2')
title('\tau_1 = 0.2, \tau_2 = 0.5','FontSize',12)
xlabel t, ylabel('y','Rotation',0), ylim([-0.2,0.2])
subplot(2,2,3)
plot(sol.y(1,:),sol.y(2,:),'r-')
xlabel y_1, ylabel('y_2','Rotation',0)
xlim([-0.2,0.2]), ylim([-0.1,0.1])
sol = dde23(@f,[0.325,0.525],@history,tspan);
subplot(2,2,2)
plot(sol.x,sol.y(1,:),'r-', sol.x,sol.y(2,:),'g--', 'LineWidth',2)
legend('y_1','y_2')
title('\tau_1 = 0.325, \tau_2 = 0.525','FontSize',12)
xlabel t, ylabel('y','Rotation',0), ylim([-0.2,0.2])
subplot(2,2,4)
plot(sol.y(1,:),sol.y(2,:),'r-')
xlabel y_1, ylabel('y_2','Rotation',0)
xlim([-0.2,0.2]), ylim([-0.1,0.1])
function v = f(t,y,Z)
%F Neural network differential equation.
ylag1 = Z(:,1);
ylag2 = Z(:,2);
v = [-y(1) + 2*tanh(ylag2(2))
-y(2) - 1.5*tanh(ylag1(1))];
function v = history(t)
%HISTORY Initial function for neural network model
v = 0.1*[sin(t/10);cos(t/10)];
|
github
|
victorlei/libermate-master
|
poly1err.m
|
.m
|
libermate-master/Tests/poly1err.m
| 530 |
utf_8
|
af1e12065da2b7d0735699f20a6e0022
|
function max_err = poly1err(n)
%POLY1ERR Error in linear interpolating polynomial.
% POLY1ERR(N) is an approximation based on N sample points
% to the maximum difference between subfunction F and its
% linear interpolating polynomial at 0 and 1.
max_err = 0;
f0 = f(0); f1 = f(1);
for x = linspace(0,1,n)
p = x*f1 + (x-1)*f0;
err = abs(f(x)-p);
max_err = max(max_err,err);
end
% Subfunction.
function y = f(x)
%F Function to be interpolated, F(X).
y = sin(x);
|
github
|
victorlei/libermate-master
|
rosy.m
|
.m
|
libermate-master/Tests/rosy.m
| 694 |
utf_8
|
779115362bc6de592272293c187aff40
|
function rosy(a, b)
%ROSY "Rose" figures.
% ROSY(A, B) plots the curve
% X = R*COS(A*theta), Y = R*SIN(A*theta), where
% R = SIN(A*B*theta) and 0 <= theta <= 2*PI (360 values).
% Suggestions: ROSY(97, 5); ROSY(43, 4); ROSY(79, n9), n a digit.
% P. M. Maurer, A rose is a rose..., Amer. Math. Monthly, 94 (1987),
% pp. 631-645.
if nargin < 2, b = 1; end
if nargin < 1, a = 1; end
c = 0; d = 1; p = a*b;
[x, y] = spiro(a, a, c, d, p, .5);
plot(x,y)
axis square, axis off
% Subfunction.
function [x, y] = spiro(a, b, c, d, p, k)
h = k*2*pi/180;
t = (0:h:2*pi)';
r = c + d*sin(t*p);
x = r.*cos(a*t);
y = r.*sin(b*t);
|
github
|
victorlei/libermate-master
|
rossler_ex0.m
|
.m
|
libermate-master/Tests/rossler_ex0.m
| 947 |
utf_8
|
5a8cb634db4f93b743a6d06be24b133c
|
function rossler_ex0
%ROSSLER_EX0 Run Rossler example.
% This is the recommended approach for MATLAB 6.5 and earlier.
% ROSSLER_EX0 runs in MATLAB 7, but ROSSLER_EX1 illustrates the style of
% coding now recommended for MATLAB 7.
tspan = [0 100]; yzero = [1;1;1];
options = odeset('AbsTol',1e-7,'RelTol',1e-4);
a = 0.2; b = 0.2; c = 2.5;
[t,y] = ode45(@rossler,tspan,yzero,options,a,b,c);
subplot(221), plot3(y(:,1),y(:,2),y(:,3)), mytitle(c), zlabel y_3(t), grid
subplot(223), plot(y(:,1),y(:,2)), mytitle(c)
c = 5;
[t,y] = ode45(@rossler,tspan,yzero,options,a,b,c);
subplot(222), plot3(y(:,1),y(:,2),y(:,3)), mytitle(c), zlabel y_3(t), grid
subplot(224), plot(y(:,1),y(:,2)), mytitle(c)
function yprime = rossler(t,y,a,b,c)
%ROSSLER Rossler system, parameterized.
yprime = [-y(2)-y(3); y(1)+a*y(2); b+y(3)*(y(1)-c)];
function mytitle(c)
title(sprintf('c = %2.1f',c),'FontSize',14)
xlabel y_1(t), ylabel y_2(t)
|
github
|
victorlei/libermate-master
|
skiprun.m
|
.m
|
libermate-master/Tests/skiprun.m
| 851 |
utf_8
|
029ef542f0f4a4b7f1a4396178755b86
|
function sol = skiprun
%SKIPRUN Skipping rope BVP/eigenvalue example.
solinit = bvpinit(linspace(0,1,10),@skipinit,5);
sol = bvp4c(@skip,@skipbc,solinit);
plot(sol.x,sol.y(1,:),'-', sol.x,sol.yp(1,:),'--', 'LineWidth',4)
xlabel('x','FontSize',12)
legend('y_1','y_2')
% ------------------------ Subfunctions ------------------------
function yprime = skip(x,y,mu)
%SKIP ODE/BVP skipping rope example.
% YPRIME = SKIP(X,Y,MU) evaluates derivative.
yprime = [y(2); -mu*y(1)];
function res = skipbc(ya,yb,mu)
%SKIPBC ODE/BVP skipping rope boundary conditions.
% RES = SKIPBC(YA,YB,MU) evaluates residual.
res = [ya(1); ya(2)-1; yb(1)+yb(2)];
function yinit = skipinit(x)
%SKIPINIT ODE/BVP skipping rope initial guess.
% YINIT = SKIPINIT(X) evaluates initial guess at X.
yinit = [sin(x); cos(x)];
|
github
|
victorlei/libermate-master
|
fisher.m
|
.m
|
libermate-master/Tests/fisher.m
| 1,812 |
utf_8
|
e0e4fe550562f2bce6a2895fc2219ea4
|
function fisher
%FISHER Displays solutions to Fisher PDE.
m = 0; a = -50; b = 50; t0 = 0; tf = 20;
xvals = linspace(a,b,101); tvals = linspace(t0,tf,51);
[xmesh, tmesh] = meshgrid(xvals,tvals);
figure(1), subplot(2,2,1)
sol = pdepe(m,@fpde,@fica,@fbc,xvals,tvals);
ua = sol(:,:,1); mesh(xmesh,tmesh,ua)
xlabel('x'), ylabel('t'), zlabel('u','Rotation',0), title('u(x,t)')
text_set, view(30,30)
subplot(2,2,2), contour(xmesh,tmesh,ua,[0.2:0.2:0.8])
xlabel('x'), ylabel('t','Rotation',0), title('Contour Plot')
text_set, hold on
plot([10,20,20,10],[8,13,8,8],'r--'), text(0,6,'Ref. slope = 2')
hold off
subplot(2,2,3), sol = pdepe(m,@fpde,@ficb,@fbc,xvals,tvals);
ub = sol(:,:,1); mesh(xmesh,tmesh,ub)
xlabel('x'), ylabel('t'), zlabel('u','Rotation',0), title('u(x,t)')
text_set, view(30,30)
subplot(2,2,4), contour(xmesh,tmesh,ub,[0.2:0.2:0.8])
xlabel('x'), ylabel('t','Rotation',0), title('Contour Plot')
text_set, hold on
plot([25,35,35,25],[5,10,5,5],'r--'), text(15,3,'Ref. slope = 2')
hold off
figure(2), zmesh = xmesh - 2*diag(tvals)*ones(size(xmesh));
waterfall(zmesh,tmesh,ua)
xlabel('x-2t'), ylabel('t'), zlabel('u','Rotation',0), title('u(x-2t,t)')
zlim([0 1]), text_set, view(15,30)
%-------------------------- Subfunctions ------------------------------%
function [c,f,s] = fpde(x,t,u,DuDx)
%FDE Fisher PDE.
c = 1; f = DuDx; s = u*(1-u);
function u0 = fica(x)
%FIC Fisher initial condition: 1st case.
u0 = 0.99*(x<=-20);
function [pa,qa,pb,qb] = fbc(xa,ua,xb,ub,t)
%FBC Fisher boundary conditions.
pa = 0; qa = 1; pb = 0; qb = 1;
function u0 = ficb(x)
%FIC2 Fisher initial condition: 2nd case.
u0 = 0.25*(cos(0.1*pi*x).^2).*(abs(x)<=5);
function text_set
h = findall(gca,'type','text'); set(h,'FontSize',12,'FontWeight','Bold')
|
github
|
victorlei/libermate-master
|
ode_pp.m
|
.m
|
libermate-master/Tests/ode_pp.m
| 2,537 |
utf_8
|
2556cbfbbd3a419a06d408a4f2038dce
|
function T = ode_pp
%ODE_PP Performance profile of three ODE solvers.
solvers = {@ode23, @ode45, @ode113}; nsolvers = length(solvers);
nproblems = 6;
nruns = 5; % Number of times to run solver to get more reliable timing.
for j = 1:nsolvers
code = solvers{j}
for i = 1:nproblems
options = [];
switch i
case 1
fun = @fox1; tspan = [0 10]; yzero = [3;0];
case 2
fun = @rossler; tspan = [0 100]; yzero = [1;1;1];
options = odeset('AbsTol',1e-7,'RelTol',1e-4);
case 3
fun = @fvdpol; tspan = [0 20]; yzero = [2;1]; mu = 10;
case 4
fun = @fvdpol; tspan = [0 20]; yzero = [2;1]; mu = 1000;
case 5
fun = @drug_transport; tspan = [0 6]; yzero = [0;0];
case 6
fun = @knee; tspan = [0 2]; yzero = 1;
end
t0 = clock;
for k = 1:nruns
[t,y] = code(fun,tspan,yzero,options);
end
T(i,j) = etime(clock,t0)/nruns;
end
end
perfprof(T);
ylim([0 1.05]), grid
yvals = 0:1/nproblems:1;
set(gca,'YTick',yvals)
set(gca,'YTickLabel',[' 0 ';num2str(yvals(2:end-1)','%4.2f ');' 1 '])
f = findall(gcf,'type','line'); % Handles of the three lines.
legend('ode23','ode45','ode113','Location','SE')
set(f,{'Marker'},{'*','s','o'}') % Vectorized set.
set(f,'MarkerSize',10)
set(f,'MarkerFaceColor','auto') % Make marker interiors non-transparent.
set(f,{'LineStyle'},{'-',':','--'}') % Vectorized set.
set(f,'LineWidth',2)
set(gca,'FontSize',14)
function yprime = fvdpol(x,y)
%FVDPOL Van der Pol equation written as first order system.
% Parameter MU.
yprime = [y(2); mu*y(2)*(1-y(1)^2)-y(1)];
end
end
function yprime = rossler(t,y)
%ROSSLER Rossler system, parameterized.
a = 0.2; b = 0.2; c = 2.5;
yprime = [-y(2)-y(3); y(1)+a*y(2); b+y(3)*(y(1)-c)];
end
function yprime = drug_transport(t,y)
%DRUG_TRANSPORT Two-compartment pharmacokinetics example.
% Reference: Shampine (1994, p. 105).
yprime = [-5.6*y(1) + 48*pulse(t,1/48,0.5); 5.6*y(1) - 0.7*y(2)];
function pls = pulse(t,w,p)
%PULSE Pulse of height 1, width W, period P.
pls = (rem(t,p) <= w);
end
end
function yprime = knee(t,y)
%KNEE Knee problem.
% Reference: Shampine (1994, p. 115).
epsilon = 1e-4;
yprime = (1/epsilon)*((1-t)*y - y^2);
end
|
github
|
victorlei/libermate-master
|
functiontest3.m
|
.m
|
libermate-master/Tests/functiontest3.m
| 189 |
utf_8
|
0a3c2f15e8b5f4c945982a4e1f9e1938
|
% Comment
function ret=myfunction(a,b,c)
ret=a
% Comment
function [ret,b]=myfunction1(a,b,c)
ret=a
% Comment
function [ret,ret2,ret3]=myfunction1(a,b,c)
if(1)
return
end
ret=a
|
github
|
victorlei/libermate-master
|
mbiol.m
|
.m
|
libermate-master/Tests/mbiol.m
| 1,110 |
utf_8
|
e2c5cb177e5361aaeaa8bc6ebfcf5b23
|
function mbiol
%MBIOL Reaction-diffusion system from mathematical biology.
% Solves the PDE and tests the energy decay condition.
m = 0;
xmesh = linspace(0,1,15);
tspan = linspace(0,0.2,10);
sol = pdepe(m,@mbpde,@mbic,@mbbc,xmesh,tspan);
u1 = sol(:,:,1);
u2 = sol(:,:,2);
subplot(221)
surf(xmesh,tspan,u1)
xlabel('x','FontSize',12)
ylabel('t','FontSize',12)
title('u_1','FontSize',16)
subplot(222)
surf(xmesh,tspan,u2)
xlabel('x','FontSize',12)
ylabel('t','FontSize',12)
title('u_2','FontSize',16)
% Estimate energy integral.
dx = xmesh(2) - xmesh(1); % Constant spacing.
energy = 0.5*sum( (diff(u1,1,2)).^2 + (diff(u2,1,2)).^2, 2)/dx;
subplot(212)
plot(tspan',energy)
xlabel('t','FontSize',12)
title('Energy','FontSize',16)
% ----------------------- Subfunctions -----------------------
function [c,f,s] = mbpde(x,t,u,DuDx)
c = [1; 1];
f = DuDx/2;
s = [1/(1+u(2)^2); 1/(1+u(1)^2)];
function u0 = mbic(x);
u0 = [1+0.5*cos(2*pi*x); 1-0.5*cos(2*pi*x)];
function [pa,qa,pb,qb] = mbbc(xa,ua,xb,ub,t)
pa = [0; 0];
qa = [1; 1];
pb = [0; 0];
qb = [1; 1];
|
github
|
joe-of-all-trades/ImageM-master
|
ImageM.m
|
.m
|
ImageM-master/ImageM.m
| 2,264 |
utf_8
|
c8b4371869f9375fa52c9346bcf8ee99
|
function ImageM
% ImageM is a GUI program that aims to provide ImageJ-like experience in
% MATLAB.
%
% In this very first version, the only usable function is to allow drag and
% drop display of image file. Users can drage a file from file explorer and
% drop it over the text area. If the file is an image file supported by
% MATLAB, it'll be displayed in a new figure.
%
% Copyright, Chao-Yuan Yeh, 2016
hFig = figure('Name','ImageM', 'NumberTitle', 'off', 'position', ...
[300 600 500 20], 'MenuBar', 'None');
tbh = uitoolbar(hFig);
a = .20:.05:0.95;
img1(:,:,1) = repmat(a,16,1)';
img1(:,:,2) = repmat(a,16,1);
img1(:,:,3) = repmat(flip(a),16,1);
pth = uipushtool(tbh,'CData',img1, 'TooltipString','My push tool',...
'HandleVisibility','off','ClickedCallBack', 'disp(''clicked'')');
f = uimenu('Label','File');
uimenu(f,'Label','Open','Callback','disp(''Open'')');
uimenu(f,'Label','Save','Callback','disp(''Save'')');
uimenu(f,'Label','Quit','Callback','disp(''Exit'')',...
'Separator','on','Accelerator','Q');
dndcontrol.initJava();
% Create figure
% Create Java Swing JTextArea
jTextArea = javaObjectEDT('javax.swing.JTextArea', ...
sprintf('ImageM version 1.0'));
% Create Java Swing JScrollPane
% jScrollPane = javaObjectEDT('javax.swing.JScrollPane', jTextArea);
% jScrollPane.setVerticalScrollBarPolicy(jScrollPane.VERTICAL_SCROLLBAR_ALWAYS);
% Add Scrollpane to figure
[~,hContainer] = javacomponent(jTextArea,[],hFig);
set(hContainer,'Units','normalized','Position',[0 0 1 1]);
% Create dndcontrol for the JTextArea object
dndobj = dndcontrol(jTextArea);
% Set Drop callback functions
dndobj.DropFileFcn = @demoDropFcn;
dndobj.DropStringFcn = @demoDropFcn;
% Callback function
function demoDropFcn(~,evt)
txt = '';
switch evt.DropType
case 'file'
% jTextArea.setText('ImageM version 1.0')
for n = 1:numel(evt.Data)
jTextArea.setText(['Opening ', evt.Data{n}]);
figure;
imshow(evt.Data{1})
end
case 'string'
jTextArea.append(sprintf('Dropped text:\n%s\n',evt.Data));
end
jTextArea.append(sprintf('\n'));
end
end
|
github
|
pfrommerd/tag-tracking-matlab-master
|
algorithm.m
|
.m
|
tag-tracking-matlab-master/algorithm.m
| 3,064 |
utf_8
|
b6b1b580d072f2db73a6da2e9f86f8b3
|
function algorithm(tracker, detector, images, initial_skip, skip_rate, save_images, save_poses)
disp('Initializing figures');
fig1 = sfigure(1);
fig2 = sfigure(2);
fig3 = sfigure(3);
fig4 = sfigure(4);
fig5 = sfigure(5);
fig6 = sfigure(6);
figure(1);
disp('Entering main loop');
counter = initial_skip;
pose_history = [];
while images.hasImage()
if mod(counter, 10) == 0
%clc;
end
fprintf('------------------------------\n');
fprintf(':: Reading image\n');
tic();
img = images.readImage();
fprintf('// Took %f\n', toc());
fprintf(':: Processing image');
[detector_tags] = detector.process(img);
[tags, x] = tracker.process(img, detector_tags);
x
fprintf(':: Clearing figures\n');
tic();
clf(fig1);
clf(fig2);
clf(fig3);
clf(fig4);
clf(fig5);
clf(fig6);
fprintf('// Took %f\n', toc());
fprintf(':: Displaying result\n');
tic();
sfigure(1);
colormap(gray(255));
image(img);
hold on;
% project the tags, will be stored in the
% tags array
tags = project_tags(tracker.tagParams.K, tags);
%drawTags(detector_tags, 'symbol', 'x');
%drawTags(reproj_tags, 'symbol', 'o');
drawTags(tags);
sfigure(2);
fprintf('// Took %f\n', toc());
fprintf(':: Displaying debug stuff\n');
tic();
tracker.debug(img, x, fig2, fig3, fig4, fig5, fig6);
% Draw the pose history
sfigure(7);
pose_history = [pose_history x];
visualize_poses(pose_history);
sfigure(1);
fprintf('// Took %f\n', toc());
drawnow;
if ~(counter == initial_skip && initial_skip > 0)
if save_images
img = getframe(fig1);
file = sprintf('../tmp/images/frame_%04d.png', counter);
fprintf('Saving image to file %s\n', file);
imwrite(img.cdata, file);
end
if save_poses
% Save the poses
file = sprintf('../tmp/poses/poses_%04d.mat', counter);
fprintf('Saving pose to file %s\n', file);
save(file, 'x');
end
end
counter = counter + skip_rate;
end
end
function drawTags(tags, varargin)
for i=1:length(tags)
drawTag(tags{i}, varargin);
end
end
% Draws a (projected!) tag
function drawTag(tag, vars)
symbol = '.-';
for i=1:length(vars)
if strcmp(vars{i},'symbol')
symbol = vars{i + 1};
end
end
if max(size(tag)) < 1
return
end
color = tag.color;
points = tag.corners';
x = points(1, :);
y = points(2, :);
plot([x x(1)], [y y(1)], symbol, 'Color', color);
end
|
github
|
pfrommerd/tag-tracking-matlab-master
|
measure_patch_error.m
|
.m
|
tag-tracking-matlab-master/utils/measure_patch_error.m
| 1,003 |
utf_8
|
314720b90b8acc0540c992f1399e9e55
|
% Use squared error
%{
function [ err ] = measure_patch_error(patchA, patchB)
if ((size(patchA, 1) ~= size(patchB,1)) || ...
(size(patchA, 2) ~= size(patchB,2)) || ...
(size(patchA,1) == 0 || size(patchA,2) == 0))
err = 1;
return;
else
[M, N] = size(patchA);
diff = double(patchA) - double(patchB);
%err = sum(sum(diff) .* diff)) / (M * N);
% Divide by 255^2 to get the error from 0-1
err = sum(sum(diff .* diff)) / (M * N * 255 * 255);
return;
end
end
%}
% Use correlation
%%{
function [ err ] = measure_patch_error(patchA, patchB, default)
if (size(patchA) ~= size(patchB))
err = default;
return;
else
a = double(patchA);
b = double(patchB);
correlation = min(max(corr2(a, b), 0.0001), 1);
err = -log(correlation);
if isnan(err) % Some crazy value, like -Inf, Inf, NaN
err = default;
end
end
end
%}
|
github
|
pfrommerd/tag-tracking-matlab-master
|
homography_project.m
|
.m
|
tag-tracking-matlab-master/utils/homography_project.m
| 253 |
utf_8
|
94e504b39cf3c7350ec41941a3627707
|
function [ x ] = homography_project(H, X)
t = H * X;
if any(t(3,:) <= 0)
x = ones([2 size(t,2)]) * -1;
end
% Divide by the last row
x_x = t(1, :) ./ t(3, :);
x_y = t(2, :) ./ t(3, :);
x = [x_x; x_y];
end
|
github
|
pfrommerd/tag-tracking-matlab-master
|
homography_solve.m
|
.m
|
tag-tracking-matlab-master/utils/homography_solve.m
| 2,397 |
utf_8
|
af74ae155f6fd32916b4660743d84634
|
%{
function v = homography_solve(pin, pout)
% HOMOGRAPHY_SOLVE finds a homography from point pairs
% V = HOMOGRAPHY_SOLVE(PIN, POUT) takes a 2xN matrix of input vectors and
% a 2xN matrix of output vectors, and returns the homogeneous
% transformation matrix that maps the inputs to the outputs, to some
% approximation if there is noise.
%
% This uses the SVD method of
% http://www.robots.ox.ac.uk/%7Evgg/presentations/bmvc97/criminispaper/node3.html
% David Young, University of Sussex, February 2008
pin = pin';
pout = pout';
if ~isequal(size(pin), size(pout))
error('Points matrices different sizes');
end
if size(pin, 1) ~= 2
error('Points matrices must have two rows');
end
n = size(pin, 2);
if n < 4
error('Need at least 4 matching points');
end
% Solve equations using SVD
x = pout(1, :); y = pout(2,:); X = pin(1,:); Y = pin(2,:);
rows0 = zeros(3, n);
rowsXY = -[X; Y; ones(1,n)];
hx = [rowsXY; rows0; x.*X; x.*Y; x];
hy = [rows0; rowsXY; y.*X; y.*Y; y];
h = [hx hy];
if n == 4
[U, ~, ~] = svd(h);
else
[U, ~, ~] = svd(h, 'econ');
end
v = (reshape(U(:,9), 3, 3)).';
end
%}
%%{
function [ H ] = homography_solve(in_pts, out_pts)
% est_homography estimates the homography to transform each of the
% in_pts to out_pts
% Inputs:
% in_pts
% out_pts
% Outputs:
% H: a 3x3 homography matrix such that outpts ~ H*video_pts
% Scale the out_pts to prevent problems with small numbers
%{
out_mean = mean(out_pts, 1);
out_pts = out_pts - out_mean(ones(size(out_pts,1),1),:);
out_scale = max(abs(out_pts(:)));
out_pts = out_pts ./ out_scale;
%}
A = [];
for p=1:size(in_pts, 1)
i = in_pts(p,:);
o = out_pts(p,:);
a_x = [ -i(1) -i(2) -1 0 0 0 i(1) * o(1) i(2) * o(1) o(1) ];
a_y = [ 0 0 0 -i(1) -i(2) -1 i(1) * o(2) i(2) * o(2) o(2) ];
A = [A; a_x; a_y];
end
[U, S, V] = svd(A);
H = V(:, end);
H = transpose(reshape(H, 3, 3));
% Redo the scaling that we did before
%{
S = [out_scale 0 out_mean(1); ...
0 out_scale out_mean(2);
0 0 1];
H = S * H;
%}
% H33 (Tz) must be positive
% if it is negative, take the negative of the matrix
% as H is only known up to a scale
if H(3, 3) < 0
H = -1 * H;
end
end
%%}
|
github
|
pfrommerd/tag-tracking-matlab-master
|
cosyvio_pose_to_std.m
|
.m
|
tag-tracking-matlab-master/cs_conv/cosyvio_pose_to_std.m
| 796 |
utf_8
|
49be4974100959a1218ccf1d72bcbb30
|
% Converts a cosyvio pose (where x = z_std, y = -x_std, z = -y_std) to a
% standard pose with the conversion
% A = [0 -1 0; 0 0 -1; 1 0 0];
% X_std_cam = A * X_cosyvio_cam
% X_std_world = B * X_cosyvio_world
% The cosyvio dataset uses the form
% X_c = R * X_w + T
% We use
% R * X_c + T = X_w
% it can be solved that therefore
% R_std = B * inv(R_cosyvio) * inv(A)
% and
% T_std = -B * inv(R_cosyvio) * T_cosyvio
function [ std ] = cosyvio_pose_to_std(cosvio)
A = [1 0 0; 0 1 0; 0 0 1];
B = [0 -1 0; 0 0 -1 ; 1 0 0];
R_cos = [quat_to_rotm(cosvio(4:7))];
T_cos = [cosvio(1); cosvio(2); cosvio(3)];
R_std = B * inv(R_cos) * inv(A);
T_std = - B * inv(R_cos) * T_cos;
quat = rotm_to_quat(R_std);
std = [T_std(1); T_std(2); T_std(3); quat'];
end
|
github
|
Nekooeimehr/MATLAB-Source-Code-Oversampling-Methods-master
|
Safe_Level_SMOTE.m
|
.m
|
MATLAB-Source-Code-Oversampling-Methods-master/Safe_Level_SMOTE.m
| 2,224 |
utf_8
|
7bcc91902154ffd812e529eb4bb2ec0c
|
function [final_features ,final_mark] = Safe_Level_SMOTE(original_features, original_mark, KNN)
ind = find(original_mark == -1);
Min_ins = original_features(ind,:);
KNN = KNN + 1;
final_features = original_features;
Limit = size(Min_ins,1);
Num_Ov = ceil(max(size(find(original_mark == -1),1) - size(find(original_mark == 1),1),size(find(original_mark == 1),1) - size(find(original_mark == -1),1)));
j2 = 1;
Safe_Level = safe_level_Finder(Min_ins, original_features, original_mark, KNN);
while j2 <= Num_Ov
%find nearest K samples from S2(i,:)
[FirstCand idx] = datasample(Min_ins,1);
Safe_Level_cand1 = Safe_Level(idx);
Condidates = nearestneighbour(FirstCand', Min_ins', 'NumberOfNeighbours', min(KNN,Limit));
Condidates(:,1) = [] ;
rn=ceil(rand(1)*(size(Condidates,2)));
Sel_index = Condidates(:,rn);
SecondCand = Min_ins(Sel_index,:);
Safe_Level_cand2 = Safe_Level(Sel_index);
if Safe_Level_cand2 ~= 0
Safe_level_ratio = Safe_Level_cand1/Safe_Level_cand2;
else
Safe_level_ratio = inf;
end
if (Safe_level_ratio == inf && Safe_Level_cand1 == 0)
else
if (Safe_level_ratio == inf && Safe_Level_cand1 ~= 0)
gap = 0;
else if Safe_level_ratio == 1
gap = rand(1);
else if Safe_level_ratio > 1
gap = rand(1)*(1/Safe_level_ratio);
else if Safe_level_ratio < 1
gap = rand(1) * Safe_level_ratio + 1 - Safe_level_ratio;
end
end
end
end
snew = FirstCand(1,:) + gap.*(SecondCand - FirstCand(1,:));
final_features = [final_features;snew];
j2=j2+1;
end
end
mark = -1 * ones(Num_Ov,1);
final_mark = [original_mark; mark];
end
function Safe_Level = safe_level_Finder(Minority_features, WholeDataInst, WholeDataLable, KNN)
Ins_neighbors = nearestneighbour(Minority_features', WholeDataInst', 'NumberOfNeighbours', KNN);
Safe_Level = zeros(1,size(Minority_features,1));
for i = 1:size(Minority_features,1)
for j = 2:KNN
if(WholeDataLable(Ins_neighbors(j,i),1)== -1)
Safe_Level(1,i) = Safe_Level(1,i) + 1;
end
end
end
end
|
github
|
Nekooeimehr/MATLAB-Source-Code-Oversampling-Methods-master
|
Orig_agg_cluster.m
|
.m
|
MATLAB-Source-Code-Oversampling-Methods-master/Orig_agg_cluster.m
| 1,763 |
utf_8
|
bcf7eb3c45362da02adbfe1337455cfa
|
function labels = Orig_agg_cluster(data, CThresh)
N = size(data,2);
% Clusters is a cell array of vectors. Each vector contains the
% indicies of the points belonging to that cluster.
% Initially, each point is in it's own cluster.
clusters = cell(N,1);
for cc = 1:length(clusters)
clusters{cc} = [cc];
end
% the distance between each pair of points
% point_dist = point_distance(data);
D = pdist(data,'euclidean');
point_dist = squareform(D);
point_dist2 = point_dist;
for i=1:N
point_dist2(i,i) = 100;
end
thresh = mean(median(point_dist2)).* CThresh;
Z = linkage(D,'complete');
labels = cluster(Z,'cutoff',thresh, 'criterion', 'distance');
function d = point_distance(X)
N = size(X,2);
d = sum(X.^2,1);
d = ones(N,1)*d + d'*ones(1,N) - 2*X'*X;
%//////////////////////////////////////////////////////////
% d = cluster_distance(c1,c2,point_dist,linkage)
% Computes the pairwise distances between clusters c1
% and c2, using the point distance info in point_dist.
%----------------------------------------------------------
function d = cluster_distance(c1,c2,point_dist,version)
M1 = length(c1);
M2 = length(c2);
MaxM = max([M1,M2]);
d = point_dist(c1,c2);
if version == 1
d = min(d(:))*MaxM^0;
else if version == 2
d = mean(d(:))*MaxM^0;
else
d = max(d(:))*MaxM^0;
end
end
%//////////////////////////////////////////////////////////
% clusters = merge_clusters(clusters, indicies)
% Merge the clusters indicated by the entries indicies(1)
% and indicies(2) of cell array 'clusters'.
%----------------------------------------------------------
function clusters = merge_clusters(clusters, indicies)
clusters{indicies(1)} = [clusters{indicies(1)} clusters{indicies(2)}];
clusters(indicies(2)) = [];
|
github
|
Nekooeimehr/MATLAB-Source-Code-Oversampling-Methods-master
|
nearestneighbour.m
|
.m
|
MATLAB-Source-Code-Oversampling-Methods-master/nearestneighbour.m
| 13,779 |
utf_8
|
8156790f42c7c9e5eba34274cd7ccbaa
|
function [idx, tri] = nearestneighbour(varargin)
%NEARESTNEIGHBOUR find nearest neighbours
% IDX = NEARESTNEIGHBOUR(X) finds the nearest neighbour by Euclidean
% distance to each point (column) in X from X. X is a matrix with points
% as columns. IDX is a vector of indices into X, such that X(:, IDX) are
% the nearest neighbours to X. e.g. the nearest neighbour to X(:, 2) is
% X(:, IDX(2))
%
% IDX = NEARESTNEIGHBOUR(P, X) finds the nearest neighbour by Euclidean
% distance to each point in P from X. P and X are both matrices with the
% same number of rows, and points are the columns of the matrices. Output
% is a vector of indices into X such that X(:, IDX) are the nearest
% neighbours to P
%
% IDX = NEARESTNEIGHBOUR(I, X) where I is a logical vector or vector of
% indices, and X has at least two rows, finds the nearest neighbour in X
% to each of the points X(:, I).
% I must be a row vector to distinguish it from a single point.
% If X has only one row, the first input is treated as a set of 1D points
% rather than a vector of indices
%
% IDX = NEARESTNEIGHBOUR(..., Property, Value)
% Calls NEARESTNEIGHBOUR with the indicated parameters set. Property
% names can be supplied as just the first letters of the property name if
% this is unambiguous, e.g. NEARESTNEIGHBOUR(..., 'num', 5) is equivalent
% to NEARESTNEIGHBOUR(..., 'NumberOfNeighbours', 5). Properties are case
% insensitive, and are as follows:
% Property: Value:
% --------- ------
% NumberOfNeighbours natural number, default 1
% NEARESTNEIGHBOUR(..., 'NumberOfNeighbours', K) finds the closest
% K points in ascending order to each point, rather than the
% closest point. If Radius is specified and there are not
% sufficient numbers, fewer than K neighbours may be returned
%
% Radius positive, default +inf
% NEARESTNEIGHBOUR(..., 'Radius', R) finds neighbours within
% radius R. If NumberOfNeighbours is not set, it will find all
% neighbours within R, otherwise it will find at most
% NumberOfNeighbours. The IDX matrix is padded with zeros if not
% all points have the same number of neighbours returned. Note
% that specifying a radius means that the Delaunay method will
% not be used.
%
% DelaunayMode {'on', 'off', |'auto'|}
% DelaunayMode being set to 'on' means NEARESTNEIGHBOUR uses the
% a Delaunay triangulation with dsearchn to find the points, if
% possible. Setting it to 'auto' means NEARESTNEIGHBOUR decides
% whether to use the triangulation, based on efficiency. Note
% that the Delaunay triangulation will not be used if a radius
% is specified.
%
% Triangulation Valid triangulation produced by
% delaunay or delaunayn
% If a triangulation is supplied, NEARESTNEIGHBOUR will attempt
% to use it (in conjunction with dsearchn) to find the
% neighbours.
%
% [IDX, TRI] = NEARESTNEIGHBOUR( ... )
% If the Delaunay Triangulation is used, TRI is the triangulation of X'.
% Otherwise, TRI is an empty matrix
%
% Example:
%
% % Find the nearest neighbour in X to each column of X
% x = rand(2, 10);
% idx = nearestneighbour(x);
%
% % Find the nearest neighbours to each point in p
% p = rand(2, 5);
% x = rand(2, 20);
% idx = nearestneighbour(p, x)
%
% % Find the five nearest neighbours to points x(:, [1 6 20]) in x
% x = rand(4, 1000)
% idx = nearestneighbour([1 6 20], x, 'NumberOfNeighbours', 5)
%
% % Find all neighbours within radius of 0.1 of the points in p
% p = rand(2, 10);
% x = rand(2, 100);
% idx = nearestneighbour(p, x, 'r', 0.1)
%
% % Find at most 10 nearest neighbours to point p from x within a
% % radius of 0.2
% p = rand(1, 2);
% x = rand(2, 30);
% idx = nearestneighbour(p, x, 'n', 10, 'r', 0.2)
%
%
% See also DELAUNAYN, DSEARCHN, TSEARCH
%TODO Allow other metrics than Euclidean distance
%TODO Implement the Delaunay mode for multiple neighbours
% Copyright 2006 Richard Brown. This code may be freely used and
% distributed, so long as it maintains this copyright line
error(nargchk(1, Inf, nargin, 'struct'));
% Default parameters
userParams.NumberOfNeighbours = [] ; % Finds one
userParams.DelaunayMode = 'auto'; % {'on', 'off', |'auto'|}
userParams.Triangulation = [] ;
userParams.Radius = inf ;
% Parse inputs
[P, X, fIndexed, userParams] = parseinputs(userParams, varargin{:});
% Special case uses Delaunay triangulation for speed.
% Determine whether to use Delaunay - set fDelaunay true or false
nX = size(X, 2);
nP = size(P, 2);
dim = size(X, 1);
switch lower(userParams.DelaunayMode)
case 'on'
%TODO Delaunay can't currently be used for finding more than one
%neighbour
fDelaunay = userParams.NumberOfNeighbours == 1 && ...
size(X, 2) > size(X, 1) && ...
~fIndexed && ...
userParams.Radius == inf;
case 'off'
fDelaunay = false;
case 'auto'
fDelaunay = userParams.NumberOfNeighbours == 1 && ...
~fIndexed && ...
size(X, 2) > size(X, 1) && ...
userParams.Radius == inf && ...
( ~isempty(userParams.Triangulation) || delaunaytest(nX, nP, dim) );
end
% Try doing Delaunay, if fDelaunay.
fDone = false;
if fDelaunay
tri = userParams.Triangulation;
if isempty(tri)
try
tri = delaunayn(X');
catch
msgId = 'NearestNeighbour:DelaunayFail';
msg = ['Unable to compute delaunay triangulation, not using it. ',...
'Set the DelaunayMode parameter to ''off'''];
warning(msgId, msg);
end
end
if ~isempty(tri)
try
idx = dsearchn(X', tri, P')';
fDone = true;
catch
warning('NearestNeighbour:DSearchFail', ...
'dsearchn failed on triangulation, not using Delaunay');
end
end
else % if fDelaunay
tri = [];
end
% If it didn't use Delaunay triangulation, find the neighbours directly by
% finding minimum distances
if ~fDone
idx = zeros(userParams.NumberOfNeighbours, size(P, 2));
% Loop through the set of points P, finding the neighbours
Y = zeros(size(X));
for iPoint = 1:size(P, 2)
x = P(:, iPoint);
% This is the faster than using repmat based techniques such as
% Y = X - repmat(x, 1, size(X, 2))
for i = 1:size(Y, 1)
Y(i, :) = X(i, :) - x(i);
end
% Find the closest points, and remove matches beneath a radius
dSq = sum(abs(Y).^2, 1);
iRad = find(dSq < userParams.Radius^2);
if ~fIndexed
iSorted = iRad(minn(dSq(iRad), userParams.NumberOfNeighbours));
else
iSorted = iRad(minn(dSq(iRad), userParams.NumberOfNeighbours + 1));
iSorted = iSorted(2:end);
end
% Remove any bad ones
idx(1:length(iSorted), iPoint) = iSorted';
end
%while ~isempty(idx) && isequal(idx(end, :), zeros(1, size(idx, 2)))
% idx(end, :) = [];
%end
idx( all(idx == 0, 2), :) = [];
end % if ~fDone
if isvector(idx)
idx = idx(:)';
end
end % nearestneighbour
%DELAUNAYTEST Work out whether the combination of dimensions makes
%fastest to use a Delaunay triangulation in conjunction with dsearchn.
%These parameters have been determined empirically on a Pentium M 1.6G /
%WinXP / 512MB / Matlab R14SP3 platform. Their precision is not
%particularly important
function tf = delaunaytest(nx, np, dim)
switch dim
case 2
tf = np > min(1.5 * nx, 400);
case 3
tf = np > min(4 * nx , 1200);
case 4
tf = np > min(40 * nx , 5000);
% if the dimension is higher than 4, it is almost invariably better not
% to try to use the Delaunay triangulation
otherwise
tf = false;
end % switch
end % delaunaytest
%MINN find the n most negative elements in x, and return their indices
% in ascending order
function I = minn(x, n)
% Make sure n is no larger than length(x)
n = min(n, length(x));
% Sort the first n
[xsn, I] = sort(x(1:n));
% Go through the rest of the entries, and insert them into the sorted block
% if they are negative enough
for i = (n+1):length(x)
j = n;
while j > 0 && x(i) < xsn(j)
j = j - 1;
end
if j < n
% x(i) should go into the (j+1) position
xsn = [xsn(1:j), x(i), xsn((j+1):(n-1))];
I = [I(1:j), i, I((j+1):(n-1))];
end
end
end %minn
%PARSEINPUTS Support function for nearestneighbour
function [P, X, fIndexed, userParams] = parseinputs(userParams, varargin)
if length(varargin) == 1 || ~isnumeric(varargin{2})
P = varargin{1};
X = varargin{1};
fIndexed = true;
varargin(1) = [];
else
P = varargin{1};
X = varargin{2};
varargin(1:2) = [];
% Check the dimensions of X and P
if size(X, 1) ~= 1
% Check to see whether P is in fact a vector of indices
if size(P, 1) == 1
try
P = X(:, P);
catch
error('NearestNeighbour:InvalidIndexVector', ...
'Unable to index matrix using index vector');
end
fIndexed = true;
else
fIndexed = false;
end % if size(P, 1) == 1
else % if size(X, 1) ~= 1
fIndexed = false;
end
if ~fIndexed && size(P, 1) ~= size(X, 1)
error('NearestNeighbour:DimensionMismatch', ...
'No. of rows of input arrays doesn''t match');
end
end
% Parse the Property/Value pairs
if rem(length(varargin), 2) ~= 0
error('NearestNeighbour:propertyValueNotPair', ...
'Additional arguments must take the form of Property/Value pairs');
end
propertyNames = {'numberofneighbours', 'delaunaymode', 'triangulation', ...
'radius'};
while length(varargin) ~= 0
property = varargin{1};
value = varargin{2};
% If the property has been supplied in a shortened form, lengthen it
iProperty = find(strncmpi(property, propertyNames, length(property)));
if isempty(iProperty)
error('NearestNeighbour:InvalidProperty', 'Invalid Property');
elseif length(iProperty) > 1
error('NearestNeighbour:AmbiguousProperty', ...
'Supplied shortened property name is ambiguous');
end
property = propertyNames{iProperty};
switch property
case 'numberofneighbours'
if rem(value, 1) ~= 0 || ...
value > length(X) - double(fIndexed) || ...
value < 1
error('NearestNeighbour:InvalidNumberOfNeighbours', ...
'Number of Neighbours must be an integer, and smaller than the no. of points in X');
end
userParams.NumberOfNeighbours = value;
case 'delaunaymode'
fOn = strcmpi(value, 'on');
if strcmpi(value, 'off')
userParams.DelaunayMode = 'off';
elseif fOn || strcmpi(value, 'auto')
if userParams.NumberOfNeighbours ~= 1
if fOn
warning('NearestNeighbour:TooMuchForDelaunay', ...
'Delaunay Triangulation method works only for one neighbour');
end
userParams.DelaunayMode = 'off';
elseif size(X, 2) < size(X, 1) + 1
if fOn
warning('NearestNeighbour:TooFewDelaunayPoints', ...
'Insufficient points to compute Delaunay triangulation');
end
userParams.DelaunayMode = 'off';
elseif size(X, 1) == 1
if fOn
warning('NearestNeighbour:DelaunayDimensionOne', ...
'Cannot compute Delaunay triangulation for 1D input');
end
userParams.DelaunayMode = 'off';
else
userParams.DelaunayMode = value;
end
else
warning('NearestNeighbour:InvalidOption', ...
'Invalid Option');
end % if strcmpi(value, 'off')
case 'radius'
if isscalar(value) && isnumeric(value) && isreal(value) && value > 0
userParams.Radius = value;
if isempty(userParams.NumberOfNeighbours)
userParams.NumberOfNeighbours = size(X, 2) - double(fIndexed);
end
else
error('NearestNeighbour:InvalidRadius', ...
'Radius must be a positive real number');
end
case 'triangulation'
if isnumeric(value) && size(value, 2) == size(X, 1) + 1 && ...
all(ismember(1:size(X, 2), value))
userParams.Triangulation = value;
else
error('NearestNeighbour:InvalidTriangulation', ...
'Triangulation not a valid Delaunay Triangulation');
end
end % switch property
varargin(1:2) = [];
end % while
if isempty(userParams.NumberOfNeighbours)
userParams.NumberOfNeighbours = 1;
end
end %parseinputs
|
github
|
Nekooeimehr/MATLAB-Source-Code-Oversampling-Methods-master
|
Mod_AggCluster.m
|
.m
|
MATLAB-Source-Code-Oversampling-Methods-master/Mod_AggCluster.m
| 4,951 |
utf_8
|
5625c0e6a852c1dc8f6c1ddf39c5f24b
|
function [min_clusters] = Mod_AggCluster(Majority_features, Minority_features ,CThresh)
% This code is a modification of the source code for Hierachical Clustering
% implemented by David Ross
% The source code for the original Hierachical Clustering can be found in:
% http://www.cs.toronto.edu/~dross/code/
SizeMin = size(Minority_features,1);
min_clusters = (1:SizeMin)';
%% Clustering the majority class using Hierachical Clustering
maj_clusters = Orig_agg_cluster(Majority_features, CThresh);
% Kmaj = size(unique(maj_clusters),1);
% m_each_maj = histc(maj_clusters,1:Kmaj);
Whole_data_min = [Minority_features; Majority_features];
D = pdist(Whole_data_min,'euclidean');
point_dist_min = squareform(D);
%% Clustering the Minority instances using majority clusters
min_clusters = inside_AggCluster(Minority_features', min_clusters, maj_clusters, point_dist_min, CThresh);
function labels = inside_AggCluster(data, same_clusters, other_clusters, point_dist_whole, CThresh)
Num_Reject = 0;
N = size(data,2);
Exist_Clus = unique(same_clusters);
M = size(Exist_Clus ,1);
% the distance between each pair of points
point_dist = point_dist_whole(1:N,1:N);
point_dist2 = point_dist;
for i=1:N
point_dist2(i,i) = 100;
end
% Measuring the threshold
thresh = mean(median(point_dist2)).* CThresh;
% Clusters is a cell array of vectors. Each vector contains the
% indicies of the points belonging to that cluster.
% Initially, each point is in it's own cluster.
clusters = cell(M,1);
for cc = 1:M
clusters{cc} = find(same_clusters == Exist_Clus(cc))';
end
% until the termination condition is met
mm = 0;
while mm < thresh
% compute the distances between all pairs of clusters
cluster_dist = inf*ones(length(clusters));
for c1 = 1:length(clusters)
for c2 = (c1+1):length(clusters)
cluster_dist(c1,c2) = cluster_distance(clusters{c1}, clusters{c2}, point_dist, 3);
end
end
% merge the two nearest clusters
[mm ii] = min(cluster_dist(:));
[ii(1) ii(2)] = ind2sub(size(cluster_dist), ii(1));
if mm > thresh || length(clusters) < 3,
break
end
% find the distance of nearest clusters to other class clusters:
Unique_Other = unique(other_clusters);
num_clus = size(Unique_Other,1);
for k = 1:num_clus
MN2other(k) = cluster_distance_maj(clusters{ii(1)}, N + find(other_clusters == Unique_Other(k)), point_dist_whole, 3);
end
flag = 1;
Distr = histc(other_clusters,1:max(other_clusters));
Distr(Distr == 0) = [] ;
near_other_ind = find(MN2other < mm & Distr' > 3);
for t = 1:length(near_other_ind)
check_dis = cluster_distance_maj(clusters{ii(2)}, N + find(other_clusters == Unique_Other(near_other_ind(t))) , point_dist_whole, 3);
if check_dis <mm
flag = 0;
Num_Reject = Num_Reject + 1;
A = clusters{ii(1)};
B = clusters{ii(2)};
point_dist (A(1,1),B(1,1)) = inf;
point_dist (B(1,1),A(1,1)) = inf;
end
end
% Place the if condition if there exist a majority cluster between them or not
if flag == 1;
clusters = merge_clusters(clusters, ii);
end
end
% assign labels to the points, based on their cluster membership
Num_Reject
labels = zeros(N,1);
for cc = 1:length(clusters)
labels(clusters{cc}) = cc;
end
%//////////////////////////////////////////////////////////
% d = point_distance(X)
% Computes the pairwise distances between columns of X.
%----------------------------------------------------------
function d = Point_Distance(X)
N = size(X,2);
d = sum(X.^2,1);
d = ones(N,1)*d + d'*ones(1,N) - 2*X'*X;
%//////////////////////////////////////////////////////////
% d = cluster_distance(c1,c2,point_dist,linkage)
% Computes the pairwise distances between clusters c1
% and c2, using the point distance info in point_dist.
%----------------------------------------------------------
function d = cluster_distance(c1,c2,point_dist,version)
M1 = length(c1);
M2 = length(c2);
MaxM = max([M1,M2]);
d = point_dist(c1,c2);
if version == 1
d = min(d(:))*MaxM^0.04;
else if version == 2
d = mean(d(:))*MaxM^0.04;
else
d = max(d(:))*MaxM^0.04;
end
end
function d = cluster_distance_maj(c1,c2,point_dist,version)
d = point_dist(c1,c2);
if version == 1
d = min(d(:));
else if version == 2
d = mean(d(:));
else
d = max(d(:));
end
end
%//////////////////////////////////////////////////////////
% clusters = merge_clusters(clusters, indicies)
% Merge the clusters indicated by the entries indicies(1)
% and indicies(2) of cell array 'clusters'.
%----------------------------------------------------------
function clusters = merge_clusters(clusters, indicies)
clusters{indicies(1)} = [clusters{indicies(1)} clusters{indicies(2)}];
clusters(indicies(2)) = [];
|
github
|
snoopyisadog/Chinese_Stroke_Extraction-master
|
extract_rho.m
|
.m
|
Chinese_Stroke_Extraction-master/extract_rho.m
| 1,538 |
utf_8
|
5f36bbd40f6c0289fa94fec6621dfdd0
|
function [ pics ] = extract_rho( rho, pts )
global map img space hei wid ang
space = rho;
[ hei, wid, ang] = size( rho);
map = zeros( hei, wid, ang);
P = size(pts,1);
pics = zeros( 1, hei, wid);
for i = 1:P
x = pts(i,1); y = pts(i,2);
for k = 1:ang
if ( rho( x, y, k) == 1 ) & ( map( x, y, k) == 0 )
img = ones( hei, wid);
DFS(x,y,k);
%fprintf('pic=%d\n',hei*wid - sum(sum(img)));
if hei*wid - sum(sum(img)) > 50 % a stroke should bigger than 50 pixels
pics(end+1,:,:) = img;
end
end
end
end
end
function DFS( x, y, z)
global map img space hei wid ang
img( x, y) = 0;
map( x, y, z) = 1;
for i = x-1 : 1 : x+1
for j = y-1 : 1 : y+1
for k = z-1 : 1 : z+1
a = i; b = j; c = k;
if i <1
a = 1;
elseif i>hei
a = hei;
end
if j <1
b = 1;
elseif j>wid
b = wid;
end
if k<1
c = ang; % wrap
elseif k>ang
c = 1;
end
%fprintf('i=%djy=%d,k=%d,a=%d,b=%d,c=%d\n',i,j,k,a,b,c);
if ( space( a, b, c) == 1 ) & ( map( a, b, c) == 0 )
DFS(a,b,c);
end
end
end
end
end
|
github
|
snoopyisadog/Chinese_Stroke_Extraction-master
|
get_PBOD.m
|
.m
|
Chinese_Stroke_Extraction-master/get_PBOD.m
| 1,139 |
utf_8
|
e89aa1e17b4707bfa9945b0ed62b909b
|
function [ ret, pt ] = get_PBOD( im )
global pic hei wid
pic = im;
[ hei, wid ] = size(im);
gap = 3;
range = 360/gap;
ang = linspace(0,2*pi,360/gap);
ret = zeros(hei, wid, 360/gap);
pt = zeros(1,2)
for i = 1:hei
for j = 1:wid
if im(i,j) == 0 % if this pixel is black
pt(end+1,1:2) = [ i j ];
for k = 1:size(ang,2)
ret(i,j,k) = distance2boundary(i,j,ang(k));
%fprintf('ret(%d,%d,%d) = %f\n',i,j,k,ret(i,j,k));
end
end
%{
for k = 1:range % start from deg 1 because MATLAB
ret(i,j,k) = distance2boundary(i,j,k*gap);
end
%}
end
end
pt(1,:) = [];
end
function ret = distance2boundary(i,j,deg)
global pic hei wid
for d = 1:hei
%fprintf('deg=%f\n',deg);
x = round( i + d * cos(deg) );
y = round( j + d * sin(deg) );
%fprintf('i=%d,j=%d,deg=%f\n',i,j,deg);
%fprintf('d=%d,x=%f,y=%f,deg=%f\n',d,x,y,deg);
if pic(x,y) == 1
break;
end
end
ret = d;
end
|
github
|
mainster/matlabCodes-master
|
EMW_d.m
|
.m
|
matlabCodes-master/EMW_d.m
| 5,530 |
utf_8
|
f71f44e53bfe2723a3d6a621d63e3e06
|
function varargout = EMW_d(varargin)
% EMW_D M-file for EMW_d.fig
% EMW_D, by itself, creates a new EMW_D or raises the existing
% singleton*.
%
% H = EMW_D returns the handle to a new EMW_D or the handle to
% the existing singleton*.
%
% EMW_D('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in EMW_D.M with the given input arguments.
%
% EMW_D('Property','Value',...) creates a new EMW_D or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before EMW_d_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to EMW_d_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help EMW_d
% Wellengleichung, elektromagnetische welle 05-11-2015 @@@MDB
%
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @EMW_d_OpeningFcn, ...
'gui_OutputFcn', @EMW_d_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before EMW_d is made visible.
function EMW_d_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to EMW_d (see VARARGIN)
% Choose default command line output for EMW_d
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes EMW_d wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = EMW_d_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
x = -400:400;
t = 0:10000;
lambda =100;
T = 50;
w = 2*pi/T;
k = 2*pi/lambda;
c = lambda/T;
phi0 = 180;
for l = 1:length(t)
% E(1:400) = sin(w*t(l) + k*x(1:400) + phi0);
% E(401:801) = sin(w*t(l) - k*x(401:801) + phi0);
% E = sin(w*t(l) - sign((1:801)-400.5).*k.*x + phi0); %Welle von Mitte
E1(1:801) = sin(w*t(l) - k*x(1:801) + phi0); %Welle nach rechts
E2(1:801) = sin(w*t(l) + k*x(1:801) + phi0); %Welle nach links
E3 = E1+ E2;
% plot(x,E3, 'b');
plot(x, E1, 'g', x, E2, 'r',x,E3, 'b');
% grid on
xlim([-400 400]) % Bereich für x Achse
set(gca, 'xtick', min(xlim):100:max(xlim)); % Bestimme die Einteilung der x Achse
ylim([-2.4 2.4]) % Bereich für y Achse
set(gca, 'ytick', min(xlim):0.5:max(xlim)); % Bestimme die Einteilung der y Achse
% xlabel('this goes across')
% ylabel('this goes up')
pause(0.1);
end
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
pause;
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
exit;
% --- Executes on slider movement.
function slider1_Callback(hObject, eventdata, handles)
% hObject handle to slider1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'Value') returns position of slider
% get(hObject,'Min') and get(hObject,'Max') to determine range of slider
% --- Executes during object creation, after setting all properties.
function slider1_CreateFcn(hObject, eventdata, handles)
% hObject handle to slider1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: slider controls usually have a light gray background.
if isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor',[.9 .9 .9]);
end
|
github
|
mainster/matlabCodes-master
|
BodePlotGui.m
|
.m
|
matlabCodes-master/BodePlotGui.m
| 51,136 |
utf_8
|
857358906042b6788eef8bf402d2758f
|
function varargout = BodePlotGui(varargin)
% BODEPLOTGUI Application M-file for BodePlotGui.fig
% FIG = BODEPLOTGUI launch BodePlotGui GUI.
% BODEPLOTGUI('callback_name', ...) invoke the named callback.
% Last Modified by GUIDE v2.5 18-Oct-2011 14:11:08
%Written by Erik Cheever (Copyright 2002)
%Contact: [email protected]
% Erik Cheever
% Dept. of Engineering
% Swarthmore College
% 500 College Avenue
% Swarthmore, PA 19081 USA
%This function acts as a switchyard for several callbacks. It also intializes
%the variables used by the GUI. Note that all variables are initialized here to
%default values. A brief description of each variable is included.
if (nargin == 0) || (isa(varargin{1},'tf')) %If no arguments, or first
fig = openfig(mfilename,'new');
handles = guihandles(fig); %Get handles structure.
initBodePlotGui(handles);
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
if nargout > 0 %If output argument is used, set it to figure.
varargout{1} = fig;
end
if ((nargin~=0) && (isa(varargin{1},'tf'))),
%Transfer function chosen.
handles.Sys=varargin{1}; %The variable Sys is the transfer function.
doBodeGUI(handles);
end
elseif ischar(varargin{1}) % INVOKE NAMED SUBFUNCTION OR CALLBACK
try
[varargout{1:nargout}] = feval(varargin{:}); % FEVAL switchyard
catch
disp(lasterr);
end
end
end
% ------------------End of function BodePlotGui ----------------------
function initBodePlotGui(handles)
handles.IncludeString=[]; %An array of strings representing terms to include in the plot.
handles.ExcludeString=[]; %An array of strings representing terms to exclude from plot.
handles.IncElem=[]; %An array of indices of terms corresponding to their
% location in the IncludeString array.
handles.ExcElem=[]; %An array of indices of terms corresponding to their
% location in the ExcludeString array.
handles.FirstPlot=1; %This term is 1 the first time a plot is made. This lets
% Matlab do the original autoscaling. The scales are then
% saved and reused.
handles.MagLims=[]; %The limits on the magnitude plot determined by MatLab autoscaling.
handles.PhaseLims=[]; %The limits on the phase plot determined by MatLab autoscaling.
handles.LnWdth=2;
set(handles.LineWidth,'String',num2str(handles.LnWdth));
%Set the color of lines used in gray scale. The plotting functions
%cycle through these colors (and then cycle through the linestyles).
handles.Gray=[0.75 0.75 0.75; 0.5 0.5 0.5; 0.25 0.25 0.25];
handles.GrayZero=[0.9 0.9 0.9]; %This is the color used for the zero reference.
%Set the color of lines used in color plots. The plotting functions
%cycle through these colors (and then cycle through the linestyles).
handles.Color=[0 1 1; 0 0 1; 0 1 0; 1 0 0; 1 0 1;1 0.52 0.40];
handles.ColorZero=[1 1 0]; %Yellow, this is the color used for the zero reference.
%Sets order of linestyles used.
handles.linestyle={':','--','-.'};
%This sets the default scheme to color (GUI can set them to gray scale).
handles.colors=handles.Color;
handles.zrefColor=handles.ColorZero;
handles.exactColor=[0 0 0]; %Black
handles.Sys=[];
handles.SysInc=[];
handles.Terms=[];
%The structure "Term" has three elements.
% type: this can be any of the 7 types listed below.
% 1) The multiplicative constant.
% 2) Real poles
% 3) Real zeros
% 4) Complex poles
% 5) Complex zeros
% 6) Poles at the origin
% 7) Zeros at the origin
% value: this is the location of the pole or zero (or in the case
% of the multiplicative constant, its value).
% multiplicity: this gives the multiplicity of the pole or zero. It has
% no meaning in the case of the multiplicative constant.
%The variable "Acc" is a relative accuracy used to determine whether or not
%two poles (or zeros) are the same. Because Matlab uses an approximate
%technique to find roots of an equation, it is likely to give slightly
%different locations to identical roots.
handles.Acc=1E-3;
set(handles.TransferFunctionText,'String',...
{' ','No transfer function chosen',' '});
guidata(handles.AsymBodePlot, handles); %save changes to handles.
loadSystems(handles);
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
guidata(handles.AsymBodePlot, handles); %save changes to handles.
end
function simpleTF = makeSimple(origTF)
simpleTF=minreal(origTF); %Get minimum realization.
% Get numerator and denominator of two realizations. If their
% lengths are unequal, it means that there were poles and zeros that
% cancelled.
[n1,d1]=tfdata(simpleTF,'v');
[n2,d2]=tfdata(origTF,'v');
if (length(n1)~=length(n2)),
disp(' ');
disp(' ');
disp(' ');
disp('************Warning******************');
disp('Original transfer function was:');
origTF
disp('Some poles and zeros were equal. After cancellation:');
simpleTF
disp('The simplified transfer function is the one that will be used.');
disp('*************************************');
disp(' ');
beep;
waitfor(warndlg('System has poles and zeros that cancel. See Command Window for caveats.'));
end
end
function doBodeGUI(handles)
handles.Sys=makeSimple(handles.Sys);
handles.SysInc=handles.Sys; %The variable sysInc is that part of the transfer
% function that will be plotted (with no poles or zeros excluded). Start with
% it equal to Sys. This variable is modified in BodePlotSys
%The function BodePlotTerms separates the transfer function into its consituent parts.
% The variable DoQuit will come back as non-zero if there was a problem.
DoQuit=BodePlotTerms(handles);
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
%if DoQuit is zero, there were no problems and we may continue.
if ~DoQuit,
BodePlotter(handles); %Make plot
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
%DoQuit was non-zero, so there was a problem. Quit program.
else
CloseButton_Callback(fig,'',handles,'');
end
end
%| ABOUT CALLBACKS:
%| GUIDE automatically appends subfunction prototypes to this file, and
%| sets objects' callback properties to call them through the FEVAL
%| switchyard above. This comment describes that mechanism.
%|
%| Each callback subfunction declaration has the following form:
%| <SUBFUNCTION_NAME>(H, EVENTDATA, HANDLES, VARARGIN)
%|
%| The subfunction name is composed using the object's Tag and the
%| callback type separated by '_', e.g. 'slider2_Callback',
%| 'figure1_CloseRequestFcn', 'axis1_ButtondownFcn'.
%|
%| H is the callback object's handle (obtained using GCBO).
%|
%| EVENTDATA is empty, but reserved for future use.
%|
%| HANDLES is a structure containing handles of components in GUI using
%| tags as fieldnames, e.g. handles.figure1, handles.slider2. This
%| structure is created at GUI startup using GUIHANDLES and stored in
%| the figure's application data using GUIDATA. A copy of the structure
%| is passed to each callback. You can store additional information in
%| this structure at GUI startup, and you can change the structure
%| during callbacks. Call guidata(h, handles) after changing your
%| copy to replace the stored original so that subsequent callbacks see
%| the updates. Type "help guihandles" and "help guidata" for more
%| information.
%|
%| VARARGIN contains any extra arguments you have passed to the
%| callback. Specify the extra arguments by editing the callback
%| property in the inspector. By default, GUIDE sets the property to:
%| <MFILENAME>('<SUBFUNCTION_NAME>', gcbo, [], guidata(gcbo))
%| Add any extra arguments after the last argument, before the final
%| closing parenthesis.
% --------------------------------------------------------------------
function varargout = IncludedElements_Callback(~, ~, handles, varargin)
% Stub for Callback of the uicontrol handles.IncludedElements.
% If a term in the "Included Elements" box is clicked, this callback is invoked.
%Get index of element in box that is chosen.%If the index corresponds to one of the terms of the transfer function, deal with it.
i=get(handles.IncludedElements,'Value');
% The alternative is that it corresponds to another string in the box (there is a blank
% line, a line with dashes "----" and a line instructing the user to click on an element
% to include it).
if i<=length(handles.IncElem)
TermsInd=handles.IncElem(i); %Get the index of the included element.
handles.Terms(TermsInd).display=0; %Set display to 0 (to exclude it)
guidata(handles.AsymBodePlot, handles); %save changes to handles.
BodePlotter(handles); %Plot the Transfer function.
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
end
end
% ------------------End of function IncludedElements_Callback --------
% --------------------------------------------------------------------
function varargout = ExcludedElements_Callback(~, ~, handles, varargin)
% Callback of the uicontrol handles.ExcludedElements.
% If a term in the "Excluded Elements" box is clicked, this callback is invoked.
i=get(handles.ExcludedElements,'Value'); %Get index of element in box that is chosen.
%If the index corresponds to one of the terms of the transfer function, deal with it.
% The alternative is that it corresponds to another string in the box (there is a blank
% line, a line with dashes "----" and a line instructing the user to click on an element
% to exclude it).
if i<=length(handles.ExcElem)
TermsInd=handles.ExcElem(i); %Get the index of the excluded element.
handles.Terms(TermsInd).display=1; %Set display to 1 (to include it)
guidata(handles.AsymBodePlot, handles); %save changes to handles.
BodePlotter(handles); %Plot the Transfer function.
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
end
end
% ------------------End of function ExcludedElements_Callback --------
% --------------------------------------------------------------------
function varargout = CloseButton_Callback(~, ~, handles, varargin)
% Callback for the uicontrol handles.CloseButton.
%This function closes the window, and displays a message.
disp(' '); disp('Asymptotic Bode Plotter closed.'); disp(' '); disp(' ');
delete(handles.AsymBodePlot);
end
% ------------------End of function CloseButton_Callback -------------
% --------------------------------------------------------------------
function DoQuit=BodePlotTerms(handles)
%This function takes a system and splits it up into terms that are plotted
%individually when making a Bode plot by hand (it finds
%1) The multiplicative constant.
%2) All real poles
%3) All real zeros
%4) All complex poles
%5) All complex zeros
%6) All poles at the origin
%7) All zeros at the origin
%
%In addition to finding the poles and zeros, it determines their multiplicity.
%If two poles or zeros are very close they are determined to be the same pole
%or zero.
sys=handles.Sys;
Acc=handles.Acc; %Relative accuracy.
[z,p,k]=zpkdata(sys,'v'); %Get pole and zero data.
%Find gain term.
[n,d]=tfdata(sys,'v');
k=n(max(find(n~=0)))/d(max(find(d~=0)));
term(1).type='Constant';
term(1).value=k;
term(1).multiplicity=1;
%Get all poles.
j=length(term);
for i=1:length(p),
term(j+i).value=p(i);
term(j+i).multiplicity=1;
term(j+i).type='Pole';
end
%Get all zeros.
j=length(term);
for i=1:length(z),
term(j+i).value=z(i);
term(j+i).multiplicity=1;
term(j+i).type='Zero';
end
%Check for multiplicity
for i=2:length(term),
for k=(i+1):length(term),
%Handle pole or zero at origin as special case.
if (term(i).value==0),
%Multiple pole or zero at origin.
if (term(k).value==0),
term(i).multiplicity=term(i).multiplicity+term(k).multiplicity;
%Set multiplicity of kth term to 0 to signify that it has been
% subsumed by term(i) (by increasing the ith term's multiplicity).
term(k).multiplicity=0;
end
%We know term is not at origin, so check for (approximate) equality
% Since we know this term is not at origin, we can divide by value.
elseif (abs((term(i).value-term(k).value)/term(i).value) < Acc),
term(i).multiplicity=term(i).multiplicity+term(k).multiplicity;
%Set multiplicity of kth term to 0 to signify that it has been
% subsumed by term(i) (by increasing the ith term's multiplicity).
term(k).multiplicity=0;
end
end
end
%Check for location of poles and zeros (and remove complex conjugates).
i=2;
while (i<=length(term)),
%If root is at origin, handle it separately
if (term(i).value==0),
term(i).type=['Origin' term(i).type];
%If imaginary part is sufficiently small...
elseif (abs(imag(term(i).value)/term(i).value)<Acc),
term(i).type=['Real' term(i).type]; %...Add "Real" to type
term(i).value=real(term(i).value); %...And set imaginary part=0
%If imaginary part is *not* small...
else
term(i).type=['Complex' term(i).type]; %...Add "Complex" to type
term(i+1).multiplicity=0; %...Remove complex conjugate
i=i+1; %...And skip conjugate.
end
i=i+1; %Go to next root.
end
%Remove all terms with multiplicity 0.
j=0;
for i=1:length(term),
if (term(i).multiplicity~=0)
j=j+1;
term(j)=term(i);
term(j).display=1;
end
end
term=term(1:j);
DoQuit=0;
%Check for poles or zeros in right half plane,
% or on imaginary axis. Poles and zeros at origin are OK.
if any(real(p)>0), %Poles in RHP.
beep;
waitfor(errordlg('System has poles with positive real part, cannot make plot.'));
DoQuit=1;
return;
end
if any(real(z)>0), %Zeros in RHP.
disp(' ');
disp(' ');
disp(' ');
disp('************Warning******************');
handles.Sys
disp('is a nonminimum phase system (zeros in right half plane).');
disp('The standard rules for phase do not apply.');
disp(' ');
disp('Also - The plots produced may be different than the Matlab Bode plot');
disp(' by a factor of 360 degrees. So though the graphs don''t look');
disp(' the same, they are equivalent');
disp(' ');
disp('Location(s) of zero(s):');
disp(z);
disp('*************************************');
disp(' ');
beep;
waitfor(warndlg('System has zeros with positive real part. See Command Window for caveats.'));
end
%Check for terms near imaginary axis, or multiple poles or zeros at origin.
for i=2:length(term),
if (term(i).value~=0),
if (abs(real(term(i).value)/term(i).value)<Acc),
disp(' ');
disp(' ');
disp(' ');
disp('************Warning******************');
handles.Sys
disp('has a pole or zero near, or on, the imaginary axis.');
disp('The plots may be inaccurate near that frequency.')
disp(' ');
disp('--------');
disp('Pole(s):');
disp(p)
disp('--------');
disp('Zero(s):');
disp(z);
disp('*************************************');
disp(' ');
disp(' ');
disp(' ');
beep;
waitfor(warndlg('System has poles or zeros with real part near zero. See Command Window.'));
end
elseif (term(i).multiplicity>1),
disp(' ');
disp(' ');
disp(' ');
disp('************Warning******************');
handles.Sys
disp('has multiple poles or zeros at the origin.');
disp('Components of the phase plot may appear to disagree.');
disp('This is because the phase of a complex number is');
disp('not unique; the phase of -1 could be +180 degrees');
disp('or -180 or +/-540... Likewise the phase of 1/s^2');
disp('could be +180 degrees, or -180 degrees (or +/-540');
disp('Keep this in mind when looking at the phase plots.');
disp('*************************************');
disp(' ');
beep;
waitfor(warndlg('System has multiple poles or zeros at origin. See Command Window.'));
end
end
handles.Terms=term;
guidata(handles.AsymBodePlot, handles); %save changes to handles.
end
% ------------------End of function BodePlotTerms --------------------
% --------------------------------------------------------------------
function BodePlotter(handles)
%Get the constituent terms and the system itself.
Terms=handles.Terms;
sys=handles.Sys;
%Call function to get a system with only included poles and zeros.
BodePlotSys(handles);
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
%Get systems with included poles and zeros.
sysInc=handles.SysInc;
%Find min and max freq.
MinF=realmax;
MaxF=-realmax;
for i=2:length(Terms),
if Terms(i).value~=0,
MinF=min(MinF,abs(Terms(i).value));
MaxF=max(MaxF,abs(Terms(i).value));
end
end
%If there is exclusively a pole or zero at origin, MinF and MaxF will
% not have changed. So set them arbitrarily to be near unity.
if MaxF==-realmax,
MinF=0.9;
MaxF=1.1;
end
%MinFreq is a bit more than an order of magnitude below lowest break.
MinFreq=10^(floor(log10(MinF)-1.01));
%MaxFreq is a bit more than an order of magnitude above highest break.
MaxFreq=10^(ceil(log10(MaxF)+1.01));
%Calculate 500 frequency points for plotting.
w=logspace(log10(MinFreq),log10(MaxFreq),1000);
%%%%%%%%%%%%%%%%%%%% Start Magnitude Plot %%%%%%%%%%
axes(handles.MagPlot);
cla;
%Plot line at 0 dB for reference.
semilogx([MinFreq MaxFreq],[0 0],...
'Color',handles.zrefColor,...
'LineWidth',1.5);
hold on;
%For each term, plot the magnitude accordingly.
%The variable mag_a has the combined asymptotic magnitude.
mag_a=zeros(size(w));
%The variable peakinds holds the indices at which peaks in underdamped
%responses occur.
peakinds=[];
for i=1:length(Terms),
if Terms(i).display,
switch Terms(i).type,
case 'Constant',
%A constant term is unchanging from beginning to end.
f=[MinFreq MaxFreq];
m=20*log10(abs([Terms(i).value Terms(i).value]));
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
case 'RealPole',
%A real pole has a single break frequency and then
%decreases at 20 dB per decade (Or more if pole is multiple).
wo=-Terms(i).value;
f=[MinFreq wo MaxFreq];
m=-20*log10([1 1 MaxFreq/wo])*Terms(i).multiplicity;
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
case 'RealZero',
%Similar to real pole, but increases instead of decreasing.
wo=abs(Terms(i).value);
f=[MinFreq wo MaxFreq];
m=20*log10([1 1 MaxFreq/wo])*Terms(i).multiplicity;
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
case 'ComplexPole',
%A complex pole has a single break frequency and then
%decreases at 40 dB per decade (Or more if pole is multiple).
%There is also a peak value whose height and location are
%determined by the natural frequency and damping coefficient.
%We will plot a circle ('o') at the location of the peak.
wn=abs(Terms(i).value);
theta=atan(abs(imag(Terms(i).value)/real(Terms(i).value)));
zeta=cos(theta);
if (zeta < 0.5), %Show peaking if zeta<0.5
peak=2*zeta;
f=[MinFreq wn wn wn MaxFreq];
m=-20*log10([1 1 peak 1 (MaxFreq/wn)^2])*Terms(i).multiplicity;
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
semilogx(wn,-20*log10(peak),'o','Color',GetLineColor(i,handles));
% Set up for interpolation (w/o peak)
f=[MinFreq wn MaxFreq];
m=-20*log10([1 1 (MaxFreq/wn)^2])*Terms(i).multiplicity;
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
%Find location closest to peak, and adjust its amplitude.
index=find(w>=wn,1,'first');
mag_a(index)=mag_a(index)-20*log10(peak);
peakinds=[peakinds index]; %Save this index.
else
f=[MinFreq wn MaxFreq];
m=-20*log10([1 1 (MaxFreq/wn)^2])*Terms(i).multiplicity;
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
% Set up for interpolation (w/o peak)
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
end
case 'ComplexZero',
%Similar to complex pole, but increases instead of decreasing.
wn=abs(Terms(i).value);
theta=atan(abs(imag(Terms(i).value)/real(Terms(i).value)));
zeta=cos(theta);
if (zeta < 0.5), %Show peaking if zeta<0.5
peak=2*zeta;
f=[MinFreq wn wn wn MaxFreq];
m=20*log10([1 1 peak 1 (MaxFreq/wn)^2])*Terms(i).multiplicity;
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
semilogx(wn,20*log10(peak),'o','Color',GetLineColor(i,handles));
% Set up for interpolation (w/o peak)
f=[MinFreq wn MaxFreq];
m=20*log10([1 1 (MaxFreq/wn)^2])*Terms(i).multiplicity;
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
%Find location closest to peak, and adjust its amplitude.
index=find(w>=wn,1,'first');
mag_a(index)=mag_a(index)+20*log10(peak);
peakinds=[peakinds index]; %Save this index.
else
f=[MinFreq wn MaxFreq];
m=20*log10([1 1 (MaxFreq/wn)^2])*Terms(i).multiplicity;
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
% Set up for interpolation (w/o peak)
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
end
case 'OriginPole',
%A pole at the origin is a monotonically decreasing straigh line.
f=[MinFreq MaxFreq];
m=-20*log10([MinFreq MaxFreq])*Terms(i).multiplicity;
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
case 'OriginZero',
%Similar to pole at origin, but increases instead of decreasing.
f=[MinFreq MaxFreq];
m=20*log10([MinFreq MaxFreq])*Terms(i).multiplicity;
semilogx(f,m,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
mag_a=mag_a+interp1(log(f),m,log(w)); %Build up asymptotic approx.
end
end
end
%Set the x-axis limits to the minimum and maximum frequency.
set(gca,'XLim',[MinFreq MaxFreq]);
[mg,ph,w]=bode(sysInc,w); %Calculate the exact bode plot.
semilogx(w,20*log10(mg(:)),...
'Color',handles.exactColor,...
'LineWidth',handles.LnWdth/2); %Plot it.
if (get(handles.ShowAsymptoticCheckBox,'Value')~=0),
semilogx(w,mag_a,...
'Color',handles.exactColor,...
'LineStyle',':',...
'LineWidth',handles.LnWdth); %Plot asymptotic approx.
semilogx(w(peakinds),mag_a(peakinds),'o','Color',handles.exactColor);
end
if handles.FirstPlot,
%If this is the first time, let Matlab do autoscaling, but save
%the y-axis limits so that they will be unchanged as the plot changes.
ylims=get(gca,'YLim')/20;
ylims(1)=min(-20,ceil(ylims(1))*20);
ylims(2)=max(20,floor(ylims(2))*20);
handles.MagLims=ylims;
else
%If this is not the first time, retrieve the old y-axis limits.
ylims=handles.MagLims;
end
set(gca,'YLim',ylims);
set(gca,'YTick',ylims(1):20:ylims(2)); %Make ticks every 20 dB.
ylabel('Magnitude - dB');
xlabel('Frequency - \omega, rad-sec^{-1}')
title('Magnitude Plot','color','b','FontWeight','bold');
if get(handles.GridCheckBox,'Value')==1,
grid on
end
hold off;
%%%%%%%%%%%%%%%%%%%% End Magnitude Plot %%%%%%%%%%
%%%%%%%%%%%%%%%%%%%% Start Phase Plot %%%%%%%%%%
%Much of this section mirrors the previous section and is not commented.
%One difference is that phase is calculated explicitly, rather than use
%Matlab's "bode" command. Since phase is not unique (you can add or
%subtract multiples of 360 degrees) There were sometimes discrepancies
%between Matlab's phase calculations and mine
axes(handles.PhasePlot);
cla;
%Plot line at 0 degrees for reference.
semilogx([MinFreq MaxFreq],[0 0],...
'Color',handles.zrefColor,...
'LineWidth',1.5);
hold on;
%The variable phs_a has the combined asymptotic phase.
phs_a=zeros(size(w));
for i=1:length(Terms),
if Terms(i).display,
switch Terms(i).type,
case 'Constant',
f=[MinFreq MaxFreq];
if Terms(i).value>0,
p=[0 0];
else
p=[180 180];
end
if get(handles.RadianCheckBox,'Value')==1,
p=p/180;
end
semilogx(f,p,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
case 'RealPole',
wo=-Terms(i).value;
f=[MinFreq wo/10 wo*10 MaxFreq];
p=[0 0 -90 -90]*Terms(i).multiplicity;
if get(handles.RadianCheckBox,'Value')==1,
p=p/180;
end
semilogx(f,p,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
case 'RealZero',
if Terms(i).value>0, %Non-minimum phase
wo=Terms(i).value;
%Uncomment the next section to get agreement with Matlab plots.
%if Terms(1).value>0, %Choose 0 or 360 to agree with MatLab plots
% p=[0 0 0 0]; % (based on sign of constant term).
% else
% p=[360 360 360 360];
%end
p=[0 0 -90 -90]*Terms(i).multiplicity;
else
%Minimum phase
wo=-Terms(i).value;
p=[0 0 90 90]*Terms(i).multiplicity;
end
f=[MinFreq wo/10 wo*10 MaxFreq];
if get(handles.RadianCheckBox,'Value')==1,
p=p/180;
end
semilogx(f,p,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
case 'ComplexPole',
wo=abs(Terms(i).value);
bf=0.2^zeta;
f=[MinFreq wo*bf wo/bf MaxFreq];
p=[0 0 -180 -180]*Terms(i).multiplicity;
if get(handles.RadianCheckBox,'Value')==1,
p=p/180;
end
semilogx(f,p,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
case 'ComplexZero',
wo=abs(Terms(i).value);
bf=0.2^zeta;
f=[MinFreq wo*bf wo/bf MaxFreq];
if real(Terms(i).value)>0, %Non-minimum phase
p=[0 0 -180 -180]*Terms(i).multiplicity;
else
p=[0 0 180 180]*Terms(i).multiplicity;
end
if get(handles.RadianCheckBox,'Value')==1,
p=p/180;
end
semilogx(f,p,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
case 'OriginPole',
f=[MinFreq MaxFreq];
p=[-90 -90]*Terms(i).multiplicity;
if get(handles.RadianCheckBox,'Value')==1,
p=p/180;
end
semilogx(f,p,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
case 'OriginZero',
f=[MinFreq MaxFreq];
p=[90 90]*Terms(i).multiplicity;
if get(handles.RadianCheckBox,'Value')==1,
p=p/180;
end
semilogx(f,p,...
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
end
phs_a=phs_a+interp1(log(f),p,log(w)); %Build up asymptotic approx.
end
end
if get(handles.RadianCheckBox,'Value')==1,
ph=ph/180;
end
semilogx(w,ph(:),...
'Color',handles.exactColor,...
'LineWidth',handles.LnWdth/2); %Plot it.
if (get(handles.ShowAsymptoticCheckBox,'Value')~=0),
% There can be discrepancies between Matlabs calculation of phase and
% the quantity "phs_a." This is because phase is not unique (you can
% add or subtract multiples of 360 degrees). The next line shifts the
% value of phs_a so that phs_a(1)=ph(1). Ensuring they are the same
% at the beginning of the plot ensures that they align elsewhere. Note
% that if the plots are already aligned (ph(1)=phs_a(1)), that the next
% line does nothing.
phs_a=phs_a+(ph(1)-phs_a(1));
semilogx(w,phs_a,...
'Color',handles.exactColor,...
'LineStyle',':',...
'LineWidth',handles.LnWdth); %Plot asymptotic approx.
end
set(gca,'XLim',[MinFreq MaxFreq]);
if handles.FirstPlot,
ylims=get(gca,'YLim')/45;
ylims(1)=ceil(ylims(1)-1)*45;
ylims(2)=floor(ylims(2)+1)*45;
handles.DPhaseLims=ylims; %Find phase limits in degrees
handles.RPhaseLims=ylims/180; %Find phase limits in radians/pi
else
if get(handles.RadianCheckBox,'Value')==1,
ylims=handles.RPhaseLims;
else
ylims=handles.DPhaseLims;
end
end
if get(handles.RadianCheckBox,'Value')==1,
set(gca,'YLim',ylims);
set(gca,'YTick',ylims(1):0.25:ylims(2))
ylabel('Phase - radians/\pi');
else
set(gca,'YLim',ylims);
set(gca,'YTick',ylims(1):45:ylims(2))
ylabel('Phase - degrees');
end
xlabel('Frequency - \omega, rad-sec^{-1}');
title('Phase Plot','color','b','FontWeight','bold');
if get(handles.GridCheckBox,'Value')==1,
grid on
end
hold off;
%%%%%%%%%%%%%%%%%%%% End Phase Plot %%%%%%%%%%
BodePlotDispTF(handles); %Display the transfer function
BodePlotLegend(handles); %Display the legend.
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
%Set first plot to zero (so Matlab won't autoscale on subsequent calls).
handles.FirstPlot=0;
guidata(handles.AsymBodePlot, handles); %save changes to handles.
end
% ----------------- End of function BodePlotter ----------------------
% --------------------------------------------------------------------
function BodePlotSys(handles)
%This function makes up a transfer function of all the terms that are not in
%the "Excluded Elements" box in the GUI.
Terms=handles.Terms; %Get all terms from original transfer function.
p=[]; %Start with no poles, or zeros, and a constant of 1
z=[];
k=1;
for i=1:length(Terms), %For each term.
%If the term is not in "Excluded Elements". then we want to display it.
if Terms(i).display,
switch Terms(i).type,
case 'Constant',
k=Terms(i).value; %This is the constant.
case 'RealPole',
for j=1:Terms(i).multiplicity,
p=[p Terms(i).value]; %Add poles.
end
case 'RealZero',
for j=1:Terms(i).multiplicity,
z=[z Terms(i).value]; %Add zeros.
end
case 'ComplexPole',
for j=1:Terms(i).multiplicity,
p=[p Terms(i).value]; %Add poles.
p=[p conj(Terms(i).value)];
end
case 'ComplexZero',
for j=1:Terms(i).multiplicity,
z=[z Terms(i).value]; %Add zeros.
z=[z conj(Terms(i).value)];
end
case 'OriginPole',
for j=1:Terms(i).multiplicity,
p=[p 0]; %Add poles.
end
case 'OriginZero',
for j=1:Terms(i).multiplicity,
z=[z 0]; %Add zeros.
end
end
end
end
%Determine multiplicative constant in standard Bode Plot form.
for i=1:length(p),
if p(i)~=0
k=-k*p(i);
end
end
for i=1:length(z),
if z(i)~=0
k=-k/z(i);
end
end
%If poles and/or zeros were complex conjugate pairs, there may be
%some residual imaginary part due to finite precision. Remove it.
k=real(k);
handles.SysInc=zpk(z,p,k);
guidata(handles.AsymBodePlot, handles); %save changes to handles.
end
% ------------------End of function BodePlotSys ----------------------
% --------------------------------------------------------------------
function BodePlotDispTF(handles)
% This function displays a tranfer function that is a helper function for the
% BodePlotGui routine. It takes the transfer function of the numerator and
% splits it into three lines so that it can be displayed nicely. For example:
% " s + 1"
% "H(s) = ---------------"
% " s^2 + 2 s + 1"
%
% The numerator string is in the variable nStr,
% the second line is in divStr,
% and the denominator string is in dStr.
% Get numerator and denominator.
[n,d]=tfdata(handles.SysInc,'v');
% Get string representations of numerator and denominator
nStr=poly2str(n,'s');
dStr=poly2str(d,'s');
% Find length of strings.
LnStr=length(nStr);
LdStr=length(dStr);
if LnStr>LdStr,
%the numerator is longer than denominator string, so pad denominator.
n=LnStr; %n is the length of the longer string.
nStr=[' ' nStr]; %add spaces for characters at beginning of divStr.
dStr=[' ' blanks(floor((LnStr-LdStr)/2)) dStr]; %pad denominator.
else
%the demoninator is longer than numerator, pad numerator.
n=LdStr;
nStr=[' ' blanks(floor((LdStr-LnStr)/2)) nStr];
dStr=[' ' dStr];
end
divStr=[];
for i=1:n,
divStr=[divStr '-'];
end
divStr=['H(s) = ' divStr];
set(handles.TransferFunctionText,'String',strvcat(nStr,divStr,dStr));
%Change type font and size.
set(handles.TransferFunctionText,'FontName','Courier New')
%set(handles.TransferFunctionText,'FontSize',10)
guidata(handles.AsymBodePlot, handles); %save changes to handles.
end
% ------------------End of function BodePlotDispTF -------------------
% --------------------------------------------------------------------
function BodePlotLegend(handles)
%This function creates the legends for the Bode plot being displayed.
%It also makes four changes to the "handles" structure.
% 1) It updates the array "IncElem" that holds the indices that determine
% which elements are included in the Bode plot.
% 2) It updates the sring array "IncStr" that hold the description of
% each included elements.
% 3) Updates ExcElem that holds indices of excluded elements.
% 4) Updates ExcStr that hold descriptions of excluded elements.
%Load the terms and the plotting strings into local variables for convenience.
Terms=handles.Terms;
axes(handles.LegendPlot); %Set axes to the legend widow,
cla; % and clear it.
Xleg=[0 0.1]; %Xleg holds start and end of line segment for legend.
XlegText=0.125; %XlegText is location of text.
FntSz=8; %Font Size of text.
y=1-1/(length(Terms)+6); %Vertical location of first text item
plot(Xleg,[y y],...
'Color',handles.exactColor,...
'LineWidth',handles.LnWdth/2); %Plot line for legend.
text(XlegText,y,'Exact Bode Plot','FontSize',FntSz); %Place text
hold on;
if (get(handles.ShowAsymptoticCheckBox,'Value')~=0),
y=1-2/(length(Terms)+6); %Vertical location of second item
plot(Xleg,[y y],...
'Color',handles.exactColor,...
'LineStyle',':',...
'LineWidth',handles.LnWdth); %Plot line for legend.
text(XlegText,y,'Asymptotic Plot','FontSize',FntSz); %Place text
end
y=1-3/(length(Terms)+6); %Vertical location of third item.
plot(Xleg,[y y],... %Line.
'Color',handles.zrefColor,...
'LineWidth',2);
text(XlegText,y,'Zero Value (for reference only)','FontSize',FntSz); %Text.
IncElem=[]; %The indices of elements to be included in plot.
ExcElem=[]; %The indices of elements to be excluded from plot.
IncStr=''; %An array of strings describing included elements.
ExcStr=''; %An array of strings describing excluded elements.
%These variables are used as local counters later. Here they are initialized.
i1=1;
i2=1;
for i=1:length(Terms), %For each term,
%Tv is a local variable representing the pole location. It is used solely
% for convenience.
Tv=Terms(i).value;
%Tm is a local variable representing the pole multiplicity. It is used solely
% for convenience.
Tm=Terms(i).multiplicity;
%S2 is a blank string to be added to later in the loop.
S2='';
%The next section of code ("switch" statement) plus a few lines, creates
%a string that describes the pole or zero, its location, muliplicity...
%The variable "DescStr" hold a Descriptive String for the pole or zero. The
%string "S2" is a Second String that holds additional information (if needed)
switch Terms(i).type,
case 'Constant',
%If the term is a consant, print its value in a string.
DescStr=sprintf('Constant = %0.2g (%0.2g dB)',Tv,20*log10(abs(Tv)));
if Tv>=0,
DescStr=[DescStr ' phi=0'];
else
DescStr=[DescStr ' phi=180 (pi/2)'];
end;
case 'RealPole',
%If the term is a real pole, print its value in string.
DescStr=sprintf('Real Pole at %0.2g',Tv);
case 'RealZero',
%If the term is a real zero, print its value in string.
DescStr=sprintf('Real Zero at %0.2g',Tv);
if real(Tv)>0,
DescStr=[DescStr ' RHP (Non-min phase)'];
end;
case 'ComplexPole',
%If the term is a complex pole, print its value in string.
%However, do this in terms of natural frequency and damping, as
%well as the actual location of the pole (in S2).
wn=abs(Tv);
theta=atan(abs(imag(Tv)/real(Tv)));
zeta=cos(theta);
DescStr=sprintf('Complex Pole at wn=%0.2g, zeta=%0.2g',wn,zeta);
if (zeta < 0.5), %peaking only if zeta<0.5
S2=sprintf('(%0.2g +/- %0.2gj) Circle shows peak height.',real(Tv),imag(Tv));
else
S2=sprintf('(%0.2g +/- %0.2gj) (no peaking shown, zeta>0.5)',real(Tv),imag(Tv));
end
case 'ComplexZero',
%If the term is a complex zero, print its value in string.
%However, do this in terms of natural frequency and damping, as
%well as the actual location of the zero (in S2).
%Also note if it is a non-minimum phase zero.
wn=abs(Tv);
theta=atan(abs(imag(Tv)/real(Tv)));
zeta=cos(theta);
DescStr=sprintf('Complex Zero at wn=%0.2g, zeta=%0.2g',wn,zeta);
if real(Tv)>0,
DescStr=[DescStr ' (RHP, Non-min phase)'];
end;
if (zeta < 0.5), %peaking only if zeta<0.5
S2=sprintf('(%0.2g +/- %0.2gj) Circle shows peak height.',real(Tv),imag(Tv));
else
S2=sprintf('(%0.2g +/- %0.2gj) (no peaking shown, zeta>0.5)',real(Tv),imag(Tv));
end
case 'OriginPole',
%If pole is at origin, not this.
DescStr=sprintf('Pole at origin');
case 'OriginZero',
%If zero is at origin, not this.
DescStr=sprintf('Zero at origin');
end
%If multiplicity is greater than one, not this as well.
if Tm>1,
DescStr=[DescStr sprintf(', mult=%d',Tm)];
end
%At this point we have a string (in "DescStr" and "S2").
if Terms(i).display, %If the term is to be included in plot....
IncStr=strvcat(IncStr,DescStr); %Add the Desriptive String to IncStr
IncElem(i1)=i; %Add the appropriate index to the Included Elements list.
i1=i1+1; %Increment the index counter
y=1-(i1+2)/(length(Terms)+6); %Calculate the vertical position.
plot(Xleg,[y y],... %Plot the line.
'LineWidth',handles.LnWdth,...
'LineStyle',GetLineStyle(i,handles),...
'Color',GetLineColor(i,handles));
text(XlegText,y,strvcat(DescStr,S2),'FontSize',FntSz); %Add the text.
else %The term is *not* to be included in plot, so...
ExcStr=strvcat(ExcStr,DescStr); %Add its Desriptive String to ExcStr
ExcElem(i2)=i; %Add the appropriate index to the Excluded Elements list.
i2=i2+1; %Increment the index counter.
end
end
hold off;
%Get rid of ticks around plot.
axis([0 1 0 1]);
set(gca,'Xtick',[]);
set(gca,'Ytick',[]);
%At this point the legend is completed. Next we will make up the strings
%for the boxes that separately list included and excluded elements.
IncStr=strvcat(IncStr,' '); %Add a blank line to IncStr.
IncStr=strvcat(IncStr,'-------'); %Add a series of dashes.
%If there are any included elements, we can click on box to exclude it.
if i1~=1
IncStr=strvcat(IncStr,'Select element to exclude from plot');
end
ExcStr=strvcat(ExcStr,' '); %Add a blank line to ExcStr
ExcStr=strvcat(ExcStr,'-------'); %Add a series of dashes.
%If there are any excluded elements, we can click on box to include it.
if i2~=1
ExcStr=strvcat(ExcStr,'Select element to include in plot');
end
%Set the strings for included and excluded elements.
set(handles.IncludedElements,'String',IncStr);
set(handles.ExcludedElements,'String',ExcStr);
%Change the arrays holding included and excluded elements in the handles array.
handles.IncElem=IncElem;
handles.ExcElem=ExcElem;
guidata(handles.AsymBodePlot, handles); %save changes to handles.
end
% ------------------End of function BodePlotLegend --------
% --------------------------------------------------------------------
% --- Executes during object creation, after setting all properties.
function LineWidth_CreateFcn(hObject, ~, ~)
% hObject handle to LineWidth (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
if ispc
set(hObject,'BackgroundColor','white');
else
set(hObject,'BackgroundColor',get(0,'defaultUicontrolBackgroundColor'));
end
end
% ------------------End of function BodePlotLegend --------
% --------------------------------------------------------------------
% Set the width of the lines used in plots.
function LineWidth_Callback(~, ~, handles)
% hObject handle to LineWidth (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
handles.LnWdth=str2num(get(handles.LineWidth,'String'));
guidata(handles.AsymBodePlot, handles); %save changes to handles.
BodePlotter(handles); %Plot the Transfer function.
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
end
% ------------------End of function BodePlotLegend --------
% --------------------------------------------------------------------
% Determines the line color of the ith plot.
function linecolor=GetLineColor(i,handles)
numColors=size(handles.colors,1);
%Cycle through colors by using mod operator.
linecolor=handles.colors(mod(i-1,numColors)+1,:);
end
% ------------------End of function GetLineColor --------
% --------------------------------------------------------------------
% Determines the line style of the ith plot.
function linestyle=GetLineStyle(i,handles)
numColors=size(handles.colors,1);
numLnStl=size(handles.linestyle,2);
%Cycle through line styles, incrementing after all colors are used.
linestyle=handles.linestyle{mod(ceil(i/numColors)-1,numLnStl)+1};
end
% ------------------End of function GetLineStyle --------
% --------------------------------------------------------------------
% --- Executes on button press in GrayCheckBox.
% This function sets the sequence of colors used in plotting to gray scales.
function GrayCheckBox_Callback(~, ~, handles)
if get(handles.GrayCheckBox,'Value')==1, %If button is not set,
handles.colors=handles.Gray; %and set colors to gray scale
handles.zrefColor=handles.GrayZero;
else
handles.colors=handles.Color; %and set colors to RGB
handles.zrefColor=handles.ColorZero;
end
guidata(handles.AsymBodePlot, handles); %save changes to handles.
BodePlotter(handles); %Plot the Transfer function.
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
end
% ------------------End of function BodePlotLegend --------
% --- Executes on button press in GridCheckBox.
function GridCheckBox_Callback(~, ~, handles)
BodePlotter(handles); %Plot the Transfer function.
end
% --- Executes on button press in RadianCheckBox.
function RadianCheckBox_Callback(~, ~, handles)
BodePlotter(handles); %Plot the Transfer function.
end
% --- Executes on button press in WebButton.
function WebButton_Callback(~, ~, ~)
web('http://lpsa.swarthmore.edu/Bode/Bode.html','-browser')
end
% --- Executes on button press in ShowAsymptoticCheckBox.
function ShowAsymptoticCheckBox_Callback(~, ~, handles)
BodePlotter(handles); %Plot the Transfer function.
end
% --- Executes on button press in limitButton.
function limitButton_Callback(~, ~, ~)
s{1}='Restrictions on systems:';
s{2}=' 1) SISO (Single Input Single Output);';
s{3}=' 2) Proper systems (order of num <= order of den);';
s{4}=' 4) System must be a transfer function (i.e., not state space...)';
s{5}=' 5) Time delays are ignored.';
helpdlg(s,'Valid Systems');
end
% --- Executes on selection change in popupSystems.
function popupSystems_Callback(hObject, ~, handles)
i=get(hObject,'Value');
if i ~= 1, %If this is not the "User Systems" choice
if i==2, %This is the refresh choice
loadSystems(handles);
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
else %THis is a valid choice, pick transfer function.
initBodePlotGui(handles);
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
handles.Sys=handles.WorkSpaceTFs{i};
doBodeGUI(handles);
handles=guidata(handles.AsymBodePlot); %Reload handles after function call.
end
end
guidata(hObject, handles);
end
% --- Executes during object creation, after setting all properties.
function popupSystems_CreateFcn(hObject, ~, ~)
% hObject handle to popupSystems (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
end
function loadSystems(handles)
[v_name, v_tf]=getBaseTFs;
set(handles.popupSystems,'String',v_name);
handles.WorkSpaceTFs=v_tf;
guidata(handles.AsymBodePlot, handles); %save changes to handles.
end
function [varName, varTF]=getBaseTFs
% Find all valid transfer functions in workspace
s=evalin('base','whos(''*'')');
tfs=strvcat(s.class); %x=class of all variable
tfs=strcmp(cellstr(tfs),'tf'); %Convert to cell array and find tf's
s=s(tfs); %Get just tf's
vname=strvcat(s.name);
varName{1}='User Systems';
varTF{1}=[];
varName{2}='Refresh Systems';
varTF{2}=[];
j=2;
for i=1:length(s)
myTF=evalin('base',vname(i,:));
if (size(myTF.num)==[1 1]), %Check for siso
j=j+1;
varName{j}=vname(i,:);
varTF{j}=myTF;
end
end
end
|
github
|
mainster/matlabCodes-master
|
CustomMDBdistribution.m
|
.m
|
matlabCodes-master/CustomMDBdistribution.m
| 14,354 |
utf_8
|
e0b9947e4c6d31e4383fdd19d958129b
|
classdef CustomMDBdistribution < prob.ToolboxFittableParametricDistribution
% This is a sample implementation of the Laplace distribution. You can use
% this template as a model to implement your own distribution. Create a
% directory called '+prob' somewhere on your path, and save this file in
% that directory using a name that matches your distribution name.
%
% An object of the LaplaceDistribution class represents a Laplace
% probability distribution with a specific location parameter MU and
% scale parameter SIGMA. This distribution object can be created directly
% using the MAKEDIST function or fit to data using the FITDIST function.
%
% LaplaceDistribution methods:
% cdf - Cumulative distribution function
% fit - Fit distribution to data
% icdf - Inverse cumulative distribution function
% iqr - Interquartile range
% mean - Mean
% median - Median
% paramci - Confidence intervals for parameters
% pdf - Probability density function
% proflik - Profile likelihood function
% random - Random number generation
% std - Standard deviation
% truncate - Truncation distribution to an interval
% var - Variance
%
% LaplaceDistribution properties:
% DistributionName - Name of the distribution
% mu - Value of the mu parameter
% sigma - Value of the sigma parameter
% NumParameters - Number of parameters
% ParameterNames - Names of parameters
% ParameterDescription - Descriptions of parameters
% ParameterValues - Vector of values of parameters
% Truncation - Two-element vector indicating truncation limits
% IsTruncated - Boolean flag indicating if distribution is truncated
% ParameterCovariance - Covariance matrix of estimated parameters
% ParameterIsFixed - Two-element boolean vector indicating fixed parameters
% InputData - Structure containing data used to fit the distribution
% NegativeLogLikelihood - Value of negative log likelihood function
%
% See also fitdist, makedist.
% All ProbabilityDistribution objects must specify a DistributionName
properties(Constant)
%DistributionName Name of distribution
% DistributionName is the name of this distribution.
DistributionName = 'laplace';
end
% Optionally add your own properties here. For this distribution it's convenient
% to be able to refer to the mu and sigma parameters by name, and have them
% connected to the proper element of the ParameterValues property. These are
% dependent properties because they depend on ParameterValues.
properties(Dependent=true)
%MU Location parameter
% MU is the location parameter for this distribution.
mu
%SIGMA Scale parameter
% SIGMA is the scale parameter for this distribution.
sigma
end
% All ParametricDistribution objects must specify values for the following
% constant properties (they are the same for all instances of this class).
properties(Constant)
%NumParameters Number of parameters
% NumParameters is the number of parameters in this distribution.
NumParameters = 2;
%ParameterName Name of parameter
% ParameterName is a two-element cell array containing names
% of the parameters of this distribution.
ParameterNames = {'mu' 'sigma'};
%ParameterDescription Description of parameter
% ParameterDescription is a two-element cell array containing
% descriptions of the parameters of this distribution.
ParameterDescription = {'location' 'scale'};
end
% All ParametricDistribution objects must include a ParameterValues property
% whose value is a vector of the parameter values, in the same order as
% given in the ParameterNames property above.
properties(GetAccess='public',SetAccess='protected')
%ParameterValues Values of the distribution parameters
% ParameterValues is a two-element vector containing the mu and sigma
% values of this distribution.
ParameterValues
end
methods
% The constructor for this class can be called with a set of parameter
% values or it can supply default values. These values should be
% checked to make sure they are valid. They should be stored in the
% ParameterValues property.
function pd = LaplaceDistribution(mu,sigma)
if nargin==0
mu = 0;
sigma = 1;
end
checkargs(mu,sigma);
pd.ParameterValues = [mu sigma];
% All FittableParametricDistribution objects must assign values
% to the following two properties. When an object is created by
% the constructor, all parameters are fixed and the covariance
% matrix is entirely zero.
pd.ParameterIsFixed = [true true];
pd.ParameterCovariance = zeros(pd.NumParameters);
end
% Implement methods to compute the mean, variance, and standard
% deviation.
function m = mean(this)
m = this.mu;
end
function s = std(this)
s = sqrt(2)*this.sigma;
end
function v = var(this)
v = 2*this.sigma^2;
end
end
methods
% If this class defines dependent properties to represent parameter
% values, their get and set methods must be defined. The set method
% should mark the distribution as no longer fitted, because any
% old results such as the covariance matrix are not valid when the
% parameters are changed from their estimated values.
function this = set.mu(this,mu)
checkargs(mu,this.sigma);
this.ParameterValues(1) = mu;
this = invalidateFit(this);
end
function this = set.sigma(this,sigma)
checkargs(this.mu,sigma);
this.ParameterValues(2) = sigma;
this = invalidateFit(this);
end
function mu = get.mu(this)
mu = this.ParameterValues(1);
end
function sigma = get.sigma(this)
sigma = this.ParameterValues(2);
end
end
methods(Static)
% All FittableDistribution classes must implement a fit method to fit
% the distribution from data. This method is called by the FITDIST
% function, and is not intended to be called directly
function pd = fit(x,varargin)
%FIT Fit from data
% P = prob.LaplaceDistribution.fit(x)
% P = prob.LaplaceDistribution.fit(x, NAME1,VAL1, NAME2,VAL2, ...)
% with the following optional parameter name/value pairs:
%
% 'censoring' Boolean vector indicating censored x values
% 'frequency' Vector indicating frequencies of corresponding
% x values
% 'options' Options structure for fitting, as create by
% the STATSET function
% Get the optional arguments. The fourth output would be the
% options structure, but this function doesn't use that.
[x,cens,freq] = prob.ToolboxFittableParametricDistribution.processFitArgs(x,varargin{:});
% This distribution was not written to support censoring or to process
% a frequency vector. The following utility expands x by the frequency
% vector, and displays an error message if there is censoring.
x = prob.ToolboxFittableParametricDistribution.removeCensoring(x,cens,freq,'laplace');
freq = ones(size(x));
% Estimate the parameters from the data. If this is an iterative procedure,
% use the values in the opt argument.
m = median(x);
s = mean(abs(x-m));
% Create the distribution by calling the constructor.
pd = prob.LaplaceDistribution(m,s);
% Fill in remaining properties defined above
pd.ParameterIsFixed = [false false];
[nll,acov] = prob.CustomMDBdistribution.likefunc([m s],x);
pd.ParameterCovariance = acov;
% Assign properties required for the FittableDistribution class
pd.NegativeLogLikelihood = nll;
pd.InputData = struct('data',x,'cens',[],'freq',freq);
end
% The following static methods are required for the
% ToolboxParametricDistribution class and are used by various
% Statistics and Machine Learning Toolbox functions. These functions operate on
% parameter values supplied as input arguments, not on the
% parameter values stored in a CustomMDBdistribution object. For
% example, the cdf method implemented in a parent class invokes the
% cdffunc static method and provides it with the parameter values.
function [nll,acov] = likefunc(params,x) % likelihood function
n = length(x);
mu = params(1);
sigma = params(2);
nll = -sum(log(prob.CustomMDBdistribution.pdffunc(x,mu,sigma)));
acov = (sigma^2/n) * eye(2);
end
function y = cdffunc(x,mu,sigma) % cumulative distribution function
if sigma==0
y = double(x>=mu);
else
z = (x-mu) ./ sigma;
y = 0.5 + sign(z).*(1-exp(-abs(z)))/2;
end
y(isnan(x)) = NaN;
end
function y = pdffunc(x,mu,sigma) % probability density function
y = exp(-abs(x - mu)/sigma) / (2*sigma);
end
function y = invfunc(p,mu,sigma) % inverse cdf
if nargin<2, mu = 0; end
if nargin<2, sigma = 1; end
if sigma==0
y = mu + zeros(size(p));
else
u = p-0.5;
y = mu - sigma.*sign(u).*log(1-2*abs(u));
end
y(p < 0 | 1 < p) = NaN;
end
function y = randfunc(mu,sigma,varargin) % random number generator
y = prob.CustomMDBdistribution.invfunc(rand(varargin{:}),mu,sigma);
end
end
methods(Static,Hidden)
% All ToolboxDistributions must implement a getInfo static method
% so that Statistics and Machine Learning Toolbox functions can get information about
% the distribution.
function info = getInfo
% First get default info from parent class
info = [email protected]('prob.CustomMDBdistribution');
% Then override fields as necessary
info.name = 'MDBdistribution';
info.code = 'MDBdistribution';
% info.pnames is obtained from the ParameterNames property
% info.pdescription is obtained from the ParameterDescription property
% info.prequired = [false false] % Change if any parameter must
% be specified before fitting.
% An example would be the N
% parameter of the binomial
% distribution.
% info.hasconfbounds = false % Set to true if the cdf and
% icdf methods can return
% lower and upper bounds as
% their 2nd and 3rd outputs.
% censoring = false % Set to true if the fit
% method supports censoring.
% info.support = [-Inf, Inf] % Set to other lower and upper
% bounds if the distribution
% doesn't cover the whole real
% line. For example, for a
% distribution on positive
% values use [0, Inf].
% info.closedbound = [false false] % Set the Jth value to
% true if the distribution
% allows x to be equal to the
% Jth element of the support
% vector.
% info.iscontinuous = true % Set to false if x can take
% only integer values.
info.islocscale = true; % Set to true if this is a
% location/scale distribution
% (no other parameters).
% info.uselogpp = false % Set to true if a probability
% plot should be drawn on the
% log scale.
% info.optimopts = false % Set to true if the fit
% method can be called with an
% options structure.
info.logci = [false true]; % Set to true for a parameter
% that should have its Wald
% confidence interval computed
% using a normal approximation
% on the log scale.
end
end
end % classdef
% The following utilities check for valid parameter values
function checkargs(mu,sigma)
if ~(isscalar(mu) && isnumeric(mu) && isreal(mu) && isfinite(mu))
error('MU must be a real finite numeric scalar.')
end
if ~(isscalar(sigma) && isnumeric(sigma) && isreal(sigma) && sigma>=0 && isfinite(sigma))
error('SIGMA must be a positive finite numeric scalar.')
end
end
|
github
|
mainster/matlabCodes-master
|
LTspiceParamImport.m
|
.m
|
matlabCodes-master/LTspiceParamImport.m
| 9,811 |
utf_8
|
31cb370f57cc42db4b4d21a689e367bb
|
function varargout = LTspiceParamImport (ascfile, varargin)
% LTSPICEPARAMIMPORT Scan and import parameters from LTspice *.asc files.
% Place pattern ".param MATLAB_PARAM=1" at the beginning of '+ (...)' extended
% parameter list as spice directive, for example:
%
% .param MATLAB_PARAM=1
% + Ve = 5V
% + Rc = 1k
% + R1 = 2u
% + R2 = 2m
%
% LTSPICEPARAMIMPORT(ASCFILE) scan file 'ASCFILE' and evaluate params in
% "base" workspace.
%
% LTSPICEPARAMIMPORT(___ 'strucnam', STRUCNAM) scan file 'ASCFILE'
% and evaluate params in "base" workspace using 'STRUCNAM' as a structure
% name prefix.
%
% STRUC_OUT = LTSPICEPARAMIMPORT(___ ['strucnam', STRUCNAM]) scan file
% 'ASCFILE' and evaluate params in "base" workspace using 'STRUC_OUT' as a
% structure name prefix, ignoring 'strucnam', STRUCNAM if given.
%
% [STRUC_OUT, STRUC_ORIG] = LTSPICEPARAMIMPORT(___) scan file
% 'ASCFILE' and evaluate params in "base" workspace using 'STRUC_OUT' as a
% structure name prefix, ignoring 'strucnam', STRUCNAM if given. Retrun
% original spice param strings in STRUC_OUT
%
% LTSPICEPARAMIMPORT(___ 'evalin', EVALIN) ... and evaluate params in
% workspace EVALIN using 'STRUCNAM' as a structure name prefix.
%
% EVALIN: 'base' Default, eval param set in base workspace.
% 'none' Do not evaluate param set in base workspace. Return
% structure containing the unevaluated param strings.
%
% ------------------------------------------------------------------------------
% Check function arguments by inputParser
% ------------------------------------------------------------------------------
p = inputParser;
vEvalin = { 'base','global','caller','local'};
valEvalin = @(x) ischar(x) && any(validatestring(x,vEvalin));
addRequired(p,'ascfile', @checkpath);
addParameter(p,'strucnam', '', @ischar);
addParameter(p,'evalin', 'base', valEvalin);
parse(p, ascfile, varargin{:});
P = p.Results;
P.argout = false;
if nargout > 0
% If outarg is sourced AND param value pair 'strucnam' is not an empty
% string, throw warning that 'strucnam' gets ignored
if ~strcmpi(P.strucnam, '')
warning(['nargout > 0\nThis means that param value pair "strucnam",',...
' %s is beeing ignored!\n', P.strucnam]);
P.strucnam = '';
end
P.argout = true;
varargout{1}=[];
end
% ------------------------------------------------------------------------------
% Read lines from input file
fid = fopen(P.ascfile, 'r');
Cin = textscan(fid, '%s', 'Delimiter', '\n');
fclose(fid);
% search pattern is MATLAB_PARAM=1
C1 = strfind(Cin{1}, 'MATLAB_PARAM=1');
% find index and create cell containing all lines that matches MATLAB...
lines = ~cellfun('isempty', C1);
cp=Cin{1}(lines);
if isempty(cp)
error('Keywords "MATLAB_PARAM=1" no where found!\n');
else
% cast cp to datatype cell if only one param set found
if ~iscell(cp)
cp{1}=cp;
disp('Single parameter set found...');
else
disp('Cell array of parameter set found...');
cp
end
end
% parse each param set into cell
cpk={zeros(1,length(cp))};
for k=1:length(cp)
cpk{k} = strsplit(cp{k},'\\n+')';
% remove first cell entry due its the search pattern
cpk{k}(1,:)=[];
% cpk holds k subcells with n cellstrings like 'R1 = 1k'
for n=1:length(cpk{k})
% remove all whitespaces
cpk{k}{n} = cpk{k}{n}(cpk{k}{n} ~= ' ');
try
pset{k}{1, n} = strsplit(cpk{k}{n},'=');
% psets.([f num2str(n)]) = strtrim( strsplit(cpk{k}{n},'=') );
% pset{k}{1, n-1}
catch err
disp (err.message)
disp ([k, n])
end
end
end
% Create delimiter cell array
delims = repmat({'='},length(cpk{1}),1);
tmp1 = cellfun(@strsplit, cpk{1}, delims,'UniformOutput',false);
% malloc
parn=cell(length(tmp1), 2*length(tmp1{k})+1 );
% cell ENUMS
enpar.EQ = 2; % idx of equality '=' signs
enpar.ORIG = 3; % idx of original param strings
enpar.EE = 4; % idx of EE non-calc evaluations
enpar.EECLC = 5; % idx of calc and non-calc evaluations
enpar.COL = 6; % idx of semicolons
for k=1:length(tmp1)
try
parn(k, [1 enpar.ORIG]) = tmp1{k};
catch erra
disp(erra.message)
end
end
% Parse different LTspice notations like
% 'Ve=5V' 'Ve=5' 'Ve=5.'
% 'I0 = 1mA' 'I0 = 1m' 'I0 = .001A' 'I0 = .001'
%
% Search rhs cell fields for physical extensions like 'V' (Volt) or 'A'
% cpk{1}
%
% 'Ve = 5V'
% 'Rc = 1k'
% 'R2 = 4.7k'
% 'Vbe0 = .65V'
% 'Ic0 = 3.88mA'
%
% find indices of numeric char 0-9 or \. followed by patterns_A
%
% idx=regexp(cpk{1},'[0-9\.][VAva]')
% extA = { 'fF' ,'pP' ,'nN' ,'uU' ,'mM' ,'kK','[mM]eg','gG','tT' };
% ext = {'[fF]','[pP]','[nN]','[uU]','[mM]','[kK]','[[mM]eg]','[gG]','[tT]'};
% EE = { 'e-15','e-12','e-9','e-6','e-3','e3','e6' ,'e9','e12' };
% Store index of faulty params here and remove them from the evaluation string
% cell
idxFaulty = [];
%%
EE ={'[fF]', 'e-15';...
'[pP]','e-12';...
'[nN]','e-09';...
'[uU]','e-06';...
'([mM]eg)','e06';...
'[mM]','e-03';...
'[kK]','e03';...
'[gG]','e09';...
'[tT]','e12';...
'[vVaA]','' }; % remove physical V (Volt) and A (Ampere) extensions
c = parn(:, enpar.ORIG);
% find index of non-calc LTspice parameters, this means all members that are NOT
% of type {...}
try
idxNonCalc = cellfun(@isempty,strfind(c,'{'));
c(idxNonCalc) = regexprep( c(idxNonCalc), EE(:,1)', EE(:,2)');
parn(:, [enpar.EE, enpar.EECLC]) = [c, c];
catch err
disp(err.message)
end
% Construct CALLER evaluation string. Evaluate non-calc params locally, eg. in
% CALLER workspace. This is necessary because there might be some LTspice calc
% params defined inside spice command ".param" For example:
% .param MATLAB ....
% + Ve = 5V
% + Ve2 = {Ve/2}
parn(:,[enpar.EQ, enpar.COL]) = [repmat({'='}, size(parn(:,2))),...
repmat({'; '}, size(parn(:,2))) ];
% Eval non-calc params locally
try
eval( strjoin(parn( idxNonCalc, [1, enpar.EQ, enpar.EE, enpar.COL] )',''));
catch err0
disp(err0.message)
end
% index of calc params
iCalc = find(~idxNonCalc);
parn{end-2,3}((parn{end-2,3}~='{')' & (parn{end-2,3}~='}')');
% Normaly, logical indexing should be preferred. In this case, the eval command
% could only be processed if there are no calc-params which are function off
% other calc-params.
% Solve this problem by sequentially try to eval one calc param after the other
%
% Try to localy evaluate calc param eg. 'alpha = {beta/(beta+1)}'
for KK=1:length(iCalc)
try
% parn{iCalc(KK), enpar.EECLC } = num2str(eval( parn{iCalc(KK),3}(2:end-1) ));
% idxt2 = ((parn{iCalc(KK),enpar.EECLC}~='{')' & (parn{iCalc(KK),enpar.EECLC}~='}')');
parn{iCalc(KK), enpar.EECLC} = ...
regexprep(parn{iCalc(KK), enpar.EECLC},'(\*\*)', '^');
parn{iCalc(KK), enpar.EECLC} = ...
regexprep(parn{iCalc(KK), enpar.EECLC},'[{}]', '');
% { parn{ iCalc(KK),enpar.EECLC }(idxt2) };
eval( [strjoin(parn( iCalc(KK), [1, enpar.EQ, enpar.EECLC] )','') ';']);
% evalin('base',strjoin(parn( iCalc(KK), [1, enpar.EQ, enpar.EECLC] )',''));
parn{ iCalc(KK),enpar.EECLC } = ...
num2str( eval([parn{ iCalc(KK),enpar.EECLC } ';']) );
catch err;
disp(err.message)
% If err message contains substring 'Undefined function or variable',
% then display "Remove all..." error message string.
if ~isempty(strfind(err.message,'Undefined function or variable'))
% Find the "undefined" variable.
[~, B] = strtok(err.message,'\''');
undef = B(B~='''' & B~='.');
% Add index to faulty param entrys index storage.
idxFaulty = [idxFaulty iCalc(KK)];
end
% Insert 'NaN' into faulty EECLC-row using index variable 'idxFaulty'
parn{iCalc(KK), enpar.EECLC} = 'nan';
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% Base workspace evaluation | return struct generation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
evLogic = logical( strcmpi( P.evalin, {'base','global'} ));
if evLogic(1) || evLogic(2) % this means evalin('base','...)
if ~strcmpi( P.strucnam, '' )
pparn = [ repmat({[P.strucnam '.']}, size(parn(:,2))),...
parn(:, [1, enpar.EQ, enpar.EECLC, enpar.COL]) ];
else
pparn = parn(:, [1, enpar.EQ, enpar.EECLC, enpar.COL]);
end
if (P.argout == false)
evalin('base', strjoin(pparn(1:end,:)','') );
else
for k=1:length(pparn)
outA.(pparn{k,1}) = str2double(pparn{k,3});
end
end
if nargout == 1
varargout{1} = outA;
return;
end
if nargout == 2
for k=1:length(pparn)
outB.(pparn{k,1}) = parn{k, enpar.ORIG};
end
varargout{1} = outA;
varargout{2} = outB;
return;
end
else
warning('Not implemented yet...\n')
end
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Callback functions for input parser
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function TF = checkpath (x)
TF = false;
if ~ischar(x)
error(['\n"ascfile" must be a string containing a relative or absolut',...
'path to a LTspice *.asc file']);
end
if exist(x,'file') == 0
error( '\nFile %s not found!', x );
else
if exist(x,'file') ~= 2
error( '\nReturn value of exist( %s ,''file'') = %g ~= 2 \n',...
x, exist(x,'file'));
else
TF = true;
end
end
|
github
|
mainster/matlabCodes-master
|
AudioDisplay.m
|
.m
|
matlabCodes-master/AudioDisplay.m
| 5,016 |
utf_8
|
e8de62c2d879c6dcbd6204046ee00ab2
|
function varargout = AudioDisplay(varargin)
% AUDIODISPLAY M-file for AudioDisplay.fig
% AUDIODISPLAY, by itself, creates a new AUDIODISPLAY or raises the existing
% singleton*.
%
% H = AUDIODISPLAY returns the handle to a new AUDIODISPLAY or the handle to
% the existing singleton*.
%
% AUDIODISPLAY('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in AUDIODISPLAY.M with the given input arguments.
%
% AUDIODISPLAY('Property','Value',...) creates a new AUDIODISPLAY or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before AudioDisplay_OpeningFunction gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to AudioDisplay_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help AudioDisplay
% Last Modified by GUIDE v2.5 10-May-2010 17:37:29
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @AudioDisplay_OpeningFcn, ...
'gui_OutputFcn', @AudioDisplay_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before AudioDisplay is made visible.
function AudioDisplay_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to AudioDisplay (see VARARGIN)
% Choose default command line output for AudioDisplay
handles.output = hObject;
handles.r = hObject;
backgroundImage = importdata('leaf.jpg');
axes(handles.axes2);
image(backgroundImage)
% Update handles structure
guidata(hObject, handles);
global flag;
global FClose;
flag = 1;
% UIWAIT makes AudioDisplay wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = AudioDisplay_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
% --- Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global flag;
global FClose;
handles.r = audiorecorder(8000,16,1);
flag = 1;
FClose = 1;
StopExe = 0;
buffSize = 100000;
buffer1(1:buffSize,1) = 0;
while(1)
while(~flag);
pause(0.1);
if(~FClose)
pause(0.01)
close AudioDisplay;
StopExe = 1;
break;
else
break;
end
end
if(flag)
recordblocking(handles.r,0.1);
buff = getaudiodata(handles.r,'int16');
[n m] = size(buff);
% buff = buff(1:n-mod(n,64));
% a(:,1) = 1:3;
% [n m] = size(a);
% b(:,1) = buffSize:-1:1
% b(1 : n,1 ) = a(1 : n,1)
% b(n + 1 : buffSize,1) = b(1 : buffSize - n,1)
buffer1(1:n,1) = buff(1:n,1);
buffer1(n + 1 : buffSize,1) = buffer1(1 : (buffSize) - n,1);
axes(handles.axes1)
plot(buffer1);
guidata(hObject,handles);
if(~FClose)
pause(.1);
close AudioDisplay;
break;
end
end
if(StopExe)
break;
end
end
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global flag;
flag = 0;
% --- Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
global FClose;
FClose = 0;
|
github
|
mainster/matlabCodes-master
|
GalvoBIGMatlab_cortex_Link.m
|
.m
|
matlabCodes-master/RT_projects/GalvoProjekt/GalvoBIGMatlab_cortex_Link.m
| 8,877 |
utf_8
|
c230be7a86d7cc7b8fae1c6406a24707
|
function GalvoMatlab_cortex_Link
uicontrol('Style', 'pushbutton', 'String', 'OpenPort',...
'Position', [20 20 70 30],...
'Callback', @MDBopenPort);
%%
uicontrol('Style', 'slider',...
'Min',1,'Max',50,'Value',41,...
'Position', [400 20 120 20],...
'Callback', {@surfzlim,hax});
% Uses cell array function handle callback
% Implemented as a local function with an argument
uicontrol('Style','text',...
'Position',[400 45 120 20],...
'String','Vertical Exaggeration')
return
function MDBopenPort()
availableDevices = ls('/dev/ttyUSB*')
USBport = availableDevices;
USBport = '/dev/ttyUSB0';
obj=serial(USBport);
obj.BaudRate=115200;
% Termination character for data sequences
obj.Terminator='CR';
% The byte order is important for interpreting binary data
obj.ByteOrder='bigEndian';
%
% The serial port object must be opened for communication
try
if strcmp(obj.Status,'closed'), fopen(obj); end
catch err
s = instrfind;
delete(s);
if strcmp(obj.Status,'closed'), fopen(obj); end
end
return
function dummy ()
%% 1. Identify the serial port
availableDevices = ls('/dev/ttyUSB*')
USBport = availableDevices;
USBport = '/dev/ttyUSB0';
obj=serial(USBport);
obj.BaudRate=115200;
% Termination character for data sequences
obj.Terminator='CR';
% The byte order is important for interpreting binary data
obj.ByteOrder='bigEndian';
%
% The serial port object must be opened for communication
try
if strcmp(obj.Status,'closed'), fopen(obj); end
catch err
s = instrfind;
delete(s);
if strcmp(obj.Status,'closed'), fopen(obj); end
end
%%
% Send a command. The terminator character set above will be appended.
for k=1:25
str = input('$:','s');
fprintf(obj, [str '\r\n']);
flushoutput(obj)
flushoutput(obj)
end
%%
fclose(obj);
delete(obj);
%%
zo=instrfind;
delete(zo)
%%
%% Read serial port objects from memory to MATLAB workspace
% out = instrfind
% out = instrfind('PropertyName',PropertyValue,...)
% out = instrfind(S)
% out = instrfind(obj,'PropertyName',PropertyValue,...)
%%
spz=instrfind
%%
% clear (serial)
% Remove serial port object from MATLAB workspace
% s = serial('COM1');
% scopy = s;
% clear s
% s = instrfind;
% isequal(scopy,s)
s = serial('/dev/ttyUSB1');
scopy = s;
clear s
s = instrfind
isequal(scopy,s)
%%
obj.InputBufferSize=2^18; % in bytes
% The serial port object must be opened for communication
if strcmp(obj.Status,'closed'), fopen(obj); end
response = fscanf(obj)
prompt = {'Enter matrix size:','Enter colormap name:'};
dlg_title = 'Input';
num_lines = 1;
def = {'20','hsv'};
% Send a command. The terminator character set above will be appended.
for k=1:5
str = input('$:','s');
fprintf(obj, str);
end
% Read the response
response = fscanf(obj);
%% 2. Create the serial object
% The serial port object represents the connection to the device. In the
% MATLB documentation, this variable is typically called "obj". You will
% read and write data from your USB device through the serial port object.
% You will have to set the serial port settings according to your device
% requirements. Some common settings are shown here. See the MATLAB
% Documentation for all possibilities for your device.
%
% The baudrate is the data transmission rate in bytes per second
% Most USB devices can support a baudrate of up to 1.5e6, but some
% operating systems do not support "nonstandard" baudrates for serial ports.
% "standard" baud rates include:
% 300, 600, 1200, 2400, 4800, 9600, 14400, 19200, 28800, 38400, 57600, 115200, 128000, 230400, 460800, 921600
%% 3. Setup your device
% Your USB device may require some setup through interface commands. To
% send and receive commands, use fprintf(obj) and fscanf(obj).
% obj=s1;
obj.InputBufferSize=2^18; % in bytes
% The serial port object must be opened for communication
if strcmp(obj.Status,'closed'), fopen(obj); end
response = fscanf(obj)
prompt = {'Enter matrix size:','Enter colormap name:'};
dlg_title = 'Input';
num_lines = 1;
def = {'20','hsv'};
% Send a command. The terminator character set above will be appended.
for k=1:5
fprintf(obj,'!EnableBeam~~~~~~~~~~~~');
answer = inputdlg(prompt,dlg_title,num_lines,def);
fprintf(obj,'!DisableBeam~~~~~~~~~~~');
answer = inputdlg(prompt,dlg_title,num_lines,def);
end
% Read the response
response = fscanf(obj);
%% 4. Prepare for the data stream
% The data arriving from the USB device will be handled by a serial port
% function which is called automatically when a certain number of bytes
% have been received in the
input buffer. Your serial port function will
% remove the data from the buffer and process it. Adjust the buffer size
% and the function byte count to suit your application.
% The input buffer must be large enough to accomodate the amount of data
% that will be received while your program is busy processing previously
% received chunks of data. Having a buffer that is too large is not a
% problem for most modern computers.
% The "BytesAvailableFcn" function will be called whenever
% BytesAvailableFcnCount number of bytes have been received from the USB
% device.
obj.BytesAvailableFcnMode='byte';
obj.BytesAvailableFcnCount=2^10; % 1 kB of data
% The name of the BytesAvailableFcn function in this example is
% "getNewData", and it has one additional input argument ("arg1").
obj.BytesAvailableFcn = {@getNewData,arg1};
% Use the serial port object to pass data between your main function
% and the serial port function ("getNewData").
% You could include things like total number of data points read,
% timestamps, etc, here as well.
obj.UserData.newData=[];
obj.UserData.isNew=0;
%% 5. Process the incoming data
% In this example, we use a loop to plot the data stream that is sent by
% the USB device.
% A global variable is used to exit the loop
global PLOTLOOP; PLOTLOOP=1;
% Initialize data for plotting. "plotWindow" will be the length of the
% x-axis in the data plot.
plotData=zeros(plotWindow);
newData=[];
% Create figure for plotting
pfig = figure;
% This allows us to stop the test by pressing a key
set(pfig,'KeyPressFcn', @stopStream);
% Send commands to the device to start the data stream.
fprintf(obj,'START');
while PLOTLOOP
% wait until we have new data
if obj.UserData.isNew==1
% get the data from serial port object (data will be row-oriented)
newData=mr.UserData.newData';
% indicate that data has been read
mr.UserData.isNew=0;
% concatenate new data for plotting
plotData=[plotData(size(newData,1)+1:end,:); newData];
% plot the data
plot(pfig,plotData);
drawnow;
end
% The loop will exit when the user presses return, using the
% KeyPressFcn of the plot window
end
%% 6. Finish & Cleanup
% Add whatever commands are required for closing your device.
% Send commands to the device stop the data transmission
fprintf(obj,'STOP');
% flush the input buffer
ba=get(obj,'BytesAvailable');
if ba > 0, fread(mr,ba); end
% Close the serial port
fclose(obj);
delete(obj);
return
%% Data Processing Function
function getNewData(obj,event,arg1)
% GETNEWDATA processes data that arrives at the serial port.
% GETNEWDATA is the "BytesAvailableFcn" for the serial port object, so it
% is called automatically when BytesAvailableFcnCount bytes of data have
% been received at the serial port.
% Read the data from the port.
% For binary data, use fread. You will have to supply the number of bytes
% to read and the format for the data. See the MATLAB documentation.
% For ASCII data, you might still use fread with format of 'char', so that
% you do not have to handle the termination characters.
[Dnew, Dcount, Dmsg]=fread(obj);
% You can do some initial processing of the data here in this function.
% However, I recommend keeping processing here to a minimum and doing
% most of the work in the main loop for best performance.
% Return the data to the main loop for plotting/processing
if obj.UserData.isNew==0
% indicate that we have new data
obj.UserData.isNew=1;
obj.UserData.newData=Dnew;
else
% If the main loop has not had a chance to process the previous batch
% of data, then append this new data to the previous "new" data
obj.UserData.newData=[obj.UserData.newData Dnew];
end
return
%% Loop Control Function
function [] = stopStream(src,evnt)
% STOPSTREAM is a local function that stops the main loop by setting the
% global variable to 0 when the user presses return.
global PLOTLOOP;
if strcmp(evnt.Key,'return')
PLOTLOOP = 0;
fprintf(1,'Return key pressed.');
end
return
% % % %
|
github
|
mainster/matlabCodes-master
|
GalvoMatlab_cortex_Link.m
|
.m
|
matlabCodes-master/RT_projects/GalvoProjekt/GalvoMatlab_cortex_Link.m
| 3,199 |
utf_8
|
25abb87130f441e60e5c75f49348e4ed
|
function GalvoMatlab_cortex_Link()
% GalvoMatlab_cortex_Link Link handling to cortex_m4 serial interface.
%
% See also SUM, PLUS.
global s;
evalin('base','global s');
uicontrol('Style', 'pushbutton', 'String', 'OpenPort',...
'Position', [20 20 70 30],...
'Callback', {@clf});
uicontrol('Style', 'pushbutton', 'String', 'OpenPort',...
'Position', [120 120 70 30],...
'Callback', {@MdbOpenPortSub});
s = MdbOpenPort();
MdbSerialGets(s)
end
%%
function [varargout] = MdbDeleteAllPorts()
delete(instrfind);
if (nargout > 0)
varargout{1}=1;
end
end
%%
function MdbSerialGets (obj)
while (1)
if obj.BytesAvailable
str = fscanf(obj);
disp(str)
end
end
end
function [openPortHandle] = MdbOpenPort()
% [openPortHandle] = MdbOpenPort Open a serial port.
% MdbOpenPort() Error, senseless function call.
% [openPortHandle] = MdbOpenPort returns a opened port object.
%
% See also SUM, PLUS.
%% Read serial port objects from memory to MATLAB workspace
% out = instrfind
% out = instrfind('PropertyName',PropertyValue,...)
% out = instrfind(S)
% out = instrfind(obj,'PropertyName',PropertyValue,...)
%%
global obj;
evalin('base','global obj')
spz=instrfind
if ~isempty(spz)
delete(spz);
clear spz;
end
%%
availableDevices = ls('/dev/ttyUSB*') ;
USBports = strsplit(availableDevices, ' ');
USBport = USBports{1}
obj=serial(USBport);
obj.BaudRate=115200
% Termination character for data sequences
obj.Terminator='LF';
% The byte order is important for interpreting binary data
obj.ByteOrder='bigEndian';
disp(obj.Status)
%%
% The serial port object must be opened for communication
try
if strcmp(obj.Status,'closed'), fopen(obj); end
printf('opened');
catch err
if strcmp(obj.Status,'closed'), fopen(obj); end
end
if ~nargout
warning('Too much Output, serial port object destroyed\n!')
else
disp(obj.Status)
openPortHandle = obj;
end
end
function MdbOpenPortSub()
disp('Sub!!!\n');
end
% %%
% global in
% fd=fopen('/media/storage/kabelBW_longtimeSpeedtest/analysed');
% in=textscan(fd,'%f %s %s %s');
% fclose(fd);
% f1=figure(1); clf;
% ax=axes;
% br=bar(in{1})
% %%
% startDate = datenum(in{3}(1));
% endDate = datenum(in{3}(end));
% xData = linspace(startDate,endDate,10);
% set(ax,'XTick',xData)
%
% datetick(ax,'x','mm/dd','keepticks')
%
% %%
% function GalvoMatlab_cortex_Link ()
%
% MDBopenPort();
%
% uicontrol('Style', 'pushbutton', 'String', 'OpenPort',...
% 'Position', [20 20 70 30],...
% 'Callback', @MDBopenPort);
% %%
%
% % uicontrol('Style', 'slider',...
% % 'Min',1,'Max',50,'Value',41,...
% % 'Position', [400 20 120 20],...
% % 'Callback', {@surfzlim,hax});
% % Uses cell array function handle callback
% % Implemented as a local function with an argument
%
% uicontrol('Style','text',...
% 'Position',[400 45 120 20],...
% 'String','Vertical Exaggeration')
% return
%
|
github
|
mainster/matlabCodes-master
|
RootRaisedCosShaper.m
|
.m
|
matlabCodes-master/NT_projects/RootRaisedCosShaper.m
| 887 |
utf_8
|
ebc26eb85cefc0ff1dfae5bcc57f49ad
|
% Root Raised-Cosine Filter / Pulsform
%
% t: timevector
% Ts: Symbol time
% r: Role- off faktor
% dom: Domain, Time or Frequency
%
function [res] = RootRaisedCosShaper(x,Ts,r,dom)
jump=@(xx) (0.5*sign(xx)+0.5);
if dom=='time'
fs=1/Ts;
fn=fs;
% sig=@(x) 2*fn*sinc(2*pi*fn*x).*cos(2*pi*r*fn*x)./(1-(4*r*fn*x).^1);
sig=@(x) ( 4*r/(pi*sqrt(Ts))* (cos((1+r)*pi*x/Ts)+Ts./(4*r*x).*sin((1-r)*pi*x/Ts))./(1-(4*r*x/Ts).^2) )
res = sig(x);
else
res=-1;
end
if dom=='freq'
fs=1/Ts;
fn=fs/2 % Nyquist frequency --> fn=fs/2 (half the symbol frequency)
% Hrc= @(x) (cos(pi/4*(abs(x)-(1-r)*fn)/(r*fn))).^2 .* (jump(x+(1+r)*fn)-jump(x-(1+r)*fn));
% Hrc2= @(x) -( ((cos(pi/4*(abs(x)-(1-r)*fn)/(r*fn))).^2 -1).* (jump(x+(1-r)*fn)-jump(x-(1-r)*fn)));
res= 0;
end
end
|
github
|
mainster/matlabCodes-master
|
RaisedCosShaper.m
|
.m
|
matlabCodes-master/NT_projects/RaisedCosShaper.m
| 954 |
utf_8
|
e476fe526ad6bbf8fba7c051cdef1139
|
% Raised-Cosine Filter / Pulsform
%
% t: timevector
% Ts: Symbol time
% r: Role- off faktor
% dom: Domain, Time or Frequency
%
function [res] = RaisedCosShaper(x,Ts,r,dom)
% syms n k;
% step = abs(abs(x(2))-abs(x(1)));
jump=@(xx) (0.5*sign(xx)+0.5);
k=1;
res=[1:length(x)];
if dom=='time'
fs=1/Ts;
fn=fs;
%// sig=@(x) 2*fn*sinc(2*pi*fn*x).*cos(2*pi*r*fn*x)./(1-(4*r*fn*x).^1);
sig=@(x) ( sinc(x*fn).*(cos(pi*r*x*fn))./(1-4*(r*x*fn).^2) );
res = sig(x);
else
res=-1;
end
if dom=='freq'
fs=1/Ts;
fn=fs/2 % Nyquist frequency --> fn=fs/2 (half the symbol frequency)
Hrc= @(x) (cos(pi/4*(abs(x)-(1-r)*fn)/(r*fn))).^2 .* (jump(x+(1+r)*fn)-jump(x-(1+r)*fn));
Hrc2= @(x) -( ((cos(pi/4*(abs(x)-(1-r)*fn)/(r*fn))).^2 -1).* (jump(x+(1-r)*fn)-jump(x-(1-r)*fn)));
res= Hrc(x)+Hrc2(x);
end
end
|
github
|
mainster/matlabCodes-master
|
genComplSinFS.m
|
.m
|
matlabCodes-master/NT_projects/genComplSinFS.m
| 1,045 |
ibm852
|
af9fbb8c127b618397e63c583decf164
|
% Function generate Complex Sinusodial
%
% fc: Frequenz der Schwingung
% n: n Perioden von 1/fc werden gesamplt??
% Are: Amplitude Re
% Aim: Amplitude Im
% phi: Phase zwischen Re und Im
% DCre: Gleichanteil von Re
% DCim: Gleichanteil von Im
%
% [time,fsam,res]:
% time: ... ist der Zeitvektor der erzeugt wurde unter einhaltung von
% - n*Tc mit n el. Integers+
% - length(t) modulo 32 = 0
% fsam: ... ist Rückgabewert der Samplefrequenz
% res: ... vektor mit den erzeugten Funktionswerten
%
% function [time,res] = genComplSin(fc,fs,Are,Aim,phideg,DCre,DCim)
% Tc=1/fc;
% Ts=1/fs;
% n=20;
%
% phi=phideg*pi/180;
%
% t=[0:Ts:n*Tc-Ts];
% time=t;
%
% sig=DCre+Are*cos(2*pi*fc*t) + i*(DCim+Aim*sin(2*pi*fc*t+phi));
% res=sig.';
%
% end
function [time,res] = genComplSin(fc,fs,n,Are,Aim,phideg,DCre,DCim)
Tc=1/fc;
Ts=1/fs;
phi=phideg*pi/180;
t=[0:Ts:(n-1)*Ts];
time=t;
sig=DCre+Are*cos(2*pi*fc*t) + i*(DCim+Aim*sin(2*pi*fc*t+phi));
res=sig.';
end
|
github
|
mainster/matlabCodes-master
|
zbb.m
|
.m
|
matlabCodes-master/NT_projects/zbb.m
| 736 |
utf_8
|
da2f47bee8c3d37db2c3ea0ddbb392fe
|
% Get complex Baseband in time domain
%
% t: time- vector
% M: M- valued PSK
% Ts: Symbol- Time in[s] --> 1/Ts = Baudrate
% symbolBits: mapped symbol vector --> sizeof(symbol) = 1
% Ampl: Baseband Amplitude
%
function [res] = zbb(t,M,Ts,symbolBits,Ampl)
rect=@(t) (0.5*sign(t)+0.5);
% Baseband- Pulse: For M-PSK g(t)=cos(2*pi/(2*Ts))*(sigma(t+Ts)-sigma(t-Ts))
g=@(t) cos((pi*t)/(2*Ts)).*(rect(t+Ts)-rect(t-Ts));
% Summing complex I-Q pointer
res=0;
for k=0:M-1
res=res + g(t-k*Ts)*( cos(pi/M*(2*(symbolBits)-1)) + i*sin(pi/M*(2*(symbolBits)-1)) );
end;
res=Ampl*res;
if real(res)==0
warndlg('No Real signal part')
elseif imag(res)==0
warndlg('No Imaginary signal part')
end
end
|
github
|
mainster/matlabCodes-master
|
genComplSin.m
|
.m
|
matlabCodes-master/NT_projects/genComplSin.m
| 1,130 |
ibm852
|
639178c2b3b60daf8c549f7dc096b24b
|
% Function generate Complex Sinusodial
%
% fc: Frequenz der Schwingung
% n: n Perioden von 1/fc werden gesamplt??
% Are: Amplitude Re
% Aim: Amplitude Im
% phi: Phase zwischen Re und Im
% DCre: Gleichanteil von Re
% DCim: Gleichanteil von Im
%
% [time,fsam,res]:
% time: ... ist der Zeitvektor der erzeugt wurde unter einhaltung von
% - n*Tc mit n el. Integers+
% - length(t) modulo 32 = 0
% fsam: ... ist Rückgabewert der Samplefrequenz
% res: ... vektor mit den erzeugten Funktionswerten
%
% function [time,res] = genComplSin(fc,fs,Are,Aim,phideg,DCre,DCim)
% Tc=1/fc;
% Ts=1/fs;
% n=20;
%
% phi=phideg*pi/180;
%
% t=[0:Ts:n*Tc-Ts];
% time=t;
%
% sig=DCre+Are*cos(2*pi*fc*t) + i*(DCim+Aim*sin(2*pi*fc*t+phi));
% res=sig.';
%
% end
function [time,fsam,res] = genComplSin(fc,n,Are,Aim,phideg,DCre,DCim)
Tc=1/fc;
fs=(fc*(32*8-1))/n; % über Abtastfreq lenth(t)%32=0 einhalten
%fs=160e6;
Ts=1/fs;
phi=phideg*pi/180;
t=[0:Ts:n*Tc];
time=t;
fsam=fs;
sig=DCre+Are*cos(2*pi*fc*t) + i*(DCim+Aim*sin(2*pi*fc*t+phi));
res=sig.';
end
|
github
|
mainster/matlabCodes-master
|
zbbBPSK.m
|
.m
|
matlabCodes-master/NT_projects/zbbBPSK.m
| 813 |
utf_8
|
173bec77a32a45d39d22488412bc1d46
|
% Get complex Baseband in time domain
%
% t: time- vector
% Ts: Symbol- Time in[s] --> 1/Ts = Baudrate
% dk: mapped symbol vector
% Ampl: Baseband Amplitude
%
function [res] = zbbBPSK(t,Ts,dk,Ampl,shape,rolloff)
rect=@(t) (0.5*sign(t)+0.5);
% Baseband- Pulse: For M-PSK g(t)=cos(2*pi/(2*Ts))*(sigma(t+Ts)-sigma(t-Ts))
if shape=='rect'
g=@(t) (rect(t)-rect(t-1));
elseif shape=='rcos'
g=@(t) RaisedCosShaper(t,Ts,rolloff,'time');
else
errordlg('Unknown shape format')
g=@(t) -1;
end
% Summing complex I-Q pointer
res=0;
for k=0:size(dk,2)-1
res=res + dk(k+1)*g(t-k*Ts);
end;
res=Ampl*res;
% if real(res)==0
% warndlg('No Real signal part')
% elseif imag(res)==0
% warndlg('No Imaginary signal part')
% end
end
|
github
|
mainster/matlabCodes-master
|
NyquistGui.m
|
.m
|
matlabCodes-master/NyquistGui/NyquistGui.m
| 26,670 |
utf_8
|
020bca963b04660214fdac5ea9e1a183
|
function varargout = NyquistGui(varargin)
% Doesn't handle multiple poles on axes (except at origin).
% Rounds to nearest 0.001 (if near origin or axis
%
% NYQUISTGUI MATLAB code for NyquistGui.fig
% NYQUISTGUI, by itself, creates a new NYQUISTGUI or raises the existing
% singleton*.
%
% H = NYQUISTGUI returns the handle to a new NYQUISTGUI or the handle to
% the existing singleton*.
%
% NYQUISTGUI('CALLBACK',hObject,eventData,handles,...) calls the local
% function named CALLBACK in NYQUISTGUI.M with the given input arguments.
%
% NYQUISTGUI('Property','Value',...) creates a new NYQUISTGUI or raises the
% existing singleton*. Starting from the left, property value pairs are
% applied to the GUI before NyquistGui_OpeningFcn gets called. An
% unrecognized property name or invalid value makes property application
% stop. All inputs are passed to NyquistGui_OpeningFcn via varargin.
%
% *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one
% instance to run (singleton)".
%
% See also: GUIDE, GUIDATA, GUIHANDLES
% Edit the above text to modify the response to help NyquistGui
% Last Modified by GUIDE v2.5 10-Oct-2011 17:58:14
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @NyquistGui_OpeningFcn, ...
'gui_OutputFcn', @NyquistGui_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before NyquistGui is made visible.
function NyquistGui_OpeningFcn(hObject, ~, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to NyquistGui (see VARARGIN)
% Choose default command line output for NyquistGui
handles.output = hObject;
handles.axNumLim=10; %limit for axes
handles.axDelt=2; %spacing on grid
handles.axInf=12; %"Infinity" on graphs
handles.circRad=5; %radius of circle for plot
set(handles.pauseTButton,'Value',0);
set(handles.pauseTButton,'String','Pause');
handles.tf=[]; dispTF(handles); %Clear TF
handles.BItfs=[];
handles.nth=32; %number of points for circular s-domain plot
set(handles.pathDrawPanel,'Visible','off'); %Disable plotting
set(handles.expPanel,'Visible','off');
set(handles.zoomButton,'String','Zoom out');
initSysLists(handles); % Initialize lists of systems
handles=guidata(hObject); % Reload handles (changed in getTFInfor)
guidata(hObject, handles); % Update handles structure
initAxes(handles);
% UIWAIT makes NyquistGui wait for user response (see UIRESUME)
% uiwait(handles.NyqGuiFig);
% --- Outputs from this function are returned to the command line.
function varargout = NyquistGui_OutputFcn(~, ~, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
function initAxes(handles)
myGrid(handles.sDomAx, handles); %Initialize axes
axes(handles.sDomAx);
title('s-domain plot (w/ poles and zeros of L(s))');
ylabel('imag(s)'); xlabel('real(s)');
myGrid(handles.ghDomAx, handles); %Initialize axes
ylabel('imag( L(s) )'); xlabel('real( L(s) )');
if get(handles.NyqPathRButt,'Value'),
title('L(s)-domain plot (w/ the point -1 shown)); L(s)=G(s)H(s)');
text(-1,0,'+','HorizontalAlignment','Center',...
'FontSize',14,'FontWeight','demi');
thet=linspace(0,2*pi,32);
plot(cos(thet),sin(thet),':','Color',0.8*[1 1 1]);
else
title('L(s)-domain plot');
end
% --- Initialize graph
function myGrid(myAx, handles)
axes(myAx);
lm=handles.axNumLim; %limit for axes
delt=handles.axDelt; %spacing on grid
lmi=handles.axInf; %"infinity"
g=0.8*[1 1 1]; %Color for grid.
cla;
set(gca,'XLim',[-lmi lmi]); set(gca,'YLim',[-lmi lmi]);
set(gca,'XTick',-lmi:delt:lmi);
set(gca,'XTickLabel',{'-inf';-lm:delt:lm;'+inf'});
set(gca,'YTick',-lmi:delt:lmi);
set(gca,'YTickLabel',{'-inf';-lm:delt:lm;'+inf'}); box on;
hold on;
thet = 0:0.01:2*pi;
x=lmi*cos(thet); y=lmi*sin(thet); %patch(x,y,'w');
patch([x lmi lmi -lmi -lmi lmi],[y -lmi lmi lmi -lmi -lmi],...
g+0.5*(1-g),'EdgeColor',g);
plot(lm*sin(thet),lm*cos(thet),':','Color',g);
for x=delt:delt:lm, % Make grid
isct=sqrt(lm*lm-x*x); %intersect of line an circle
plot([x x],[-isct isct],':','Color',g);
plot([-isct isct],[-x -x],':','Color',g);
plot([-isct isct],[x x],':','Color',g);
plot([-x -x],[-isct isct],':','Color',g);
end
plot([-lm lm],[0 0],':','Color',0.8*g); %axis at zero is darker
plot([0 0],[-lm lm],':','Color',0.8*g);
plot([-lm -lmi],[0 0],':','Color',0.5*g); %axis out to infinity
plot([0 0],[-lm -lmi],':','Color',0.5*g);
plot([lm lmi],[0 0],':','Color',0.5*g);
plot([0 0],[lm lmi],':','Color',0.5*g);
zoomButton_Callback([], [], handles); %Zoom gh plot
% --- Executes on button press in StartButton.
function StartButton_Callback(~, ~, handles)
sPath(handles, 0); %Circular path, fast
function SlowButton_Callback(~, ~, handles)
sPath(handles, 0.1); %Circular path, slow
% --- Executes on selection change in BISysMenu.
function BISysMenu_Callback(~, ~, handles)
% hObject handle to BISysMenu (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
tfNum=get(handles.BISysMenu,'value');
myTF=handles.BItfs{tfNum};
if (tfNum==1),
set(handles.pathDrawPanel,'Visible','off');
set(handles.expPanel,'Visible','off');
warndlg('No transfer function chosen');
else
initAxes(handles);
axes(handles.sDomAx);
mappz(myTF);
set(handles.pathDrawPanel,'Visible','on');
set(handles.userSysMenu,'value',1);
end
handles.tf=myTF;
dispTF(handles);
guidata(handles.NyqGuiFig, handles); % Update handles structure
% --- Executes during object creation, after setting all properties.
function BISysMenu_CreateFcn(hObject, ~, ~)
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in limitationButton.
function limitationButton_Callback(~, ~, ~)
s={ '* Time delays are ignored. (You can add them by altering the',...
' code in function ''pathInterp'', but do so at your own risk.)',...
'* The drawing of the angle subtended by the path in "L(s)"',...
' is not meaningful in the case of marginally stable systems).',...
' (i.e., marginally stable systems.',...
'* The imaginary part of poles very near the imaginary axis are ',...
' rounded to zero (i.e., the poles are placed on the axis).',...
'* User-defined functions are loaded when program starts. Any ',...
' functions added while program runs do not appear in the list.',...
' ',...
'* Axes are fixed; poles, zeros and gains must be chosen',...
' accordingly.',...
' This is not meant to be a general tool for plotting Nyquist',...
' diagrams, but rather a tool for learning the associated',...
' concepts.'};
helpdlg(s,'Program Limitations');
function initSysLists(handles)
% Builtins
x={ 'Built-in Systems',[];...
'PoleOrig',tf(10,[1 0]);...
'ZeroOrig',tf([1 0],1);...
'PoleNeg4',tf(10,[1 4]);...
'ZeroNeg4',tf([1 4],1);...
'PoleNeg6',tf(10,[1 6]);...
'ZeroNeg6',tf([1 6],1);...
'PolesConj',tf(100,[1 4 20]);...
'PolesP6M4',tf(48,[1 -2 -24]);...
'Pole4_8',tf(10,[1 4.8]);...
'PZ',tf([1 -2],[1 4]);...
'Polesjw',tf(16,[1 0 16]);...
'DoubleInt',zpk([],[0 0],1);...
'CondStable',zpk([],[-1 -2 -3],30);...
'SlightlyUnstable',zpk(-3,[2j -2j -2],10);...
'BarelyStable',zpk(-2,[2j -2j -3],10);...
'Example 1',tf(90,[1 9 18]);...
'Example 2',tf(20,[1 5 6]);...
'Example 2b',tf(100,[1 5 6]);...
'Example 3',tf(10*[1 3],[1 0 -4]);...
'Example 4',tf(50*[1 3],[1 -1 11 -51]);...
'Example 5',tf(10*[1 2],[1 3 0 0]);...
'Example 5b',tf(10*[1 4],[1 3 0 0])};
set(handles.BISysMenu,'String',x(:,1));
handles.BItfs=x(:,2);
set(handles.BISysMenu,'Value',1); % Put top value in menu
s=evalin('base','whos(''*'')');
tfs=char(s.class); %x=class of all variable
tfs=strcmp(cellstr(tfs),'tf'); %Convert to cell array and find tf's
s=s(tfs); %Get just tf's
vname=char(s.name);
x=cell(length(s)+1,2);
j=1;
x{j,1}='User Systems';
for i=1:length(s)
myTF=evalin('base',vname(i,:));
if (size(myTF.num,1)==1),
j=j+1;
x{j,1}=vname(i,:);
x{j,2}=myTF;
end
end
set(handles.userSysMenu,'String',x(1:j,1));
handles.Usertfs=x(1:j,2);
set(handles.userSysMenu,'Value',1); % Put top value in menu
guidata(handles.NyqGuiFig, handles); % Update handles structure
function dispTF(handles)
% This function displays a tranfer function that is a helper function.
% It takes the transfer function of the and splits it
% into three lines so that it can be displayed nicely. For example:
% " s + 1"
% "H(s) = ---------------"
% " s^2 + 2 s + 1"
% The numerator string is in the variable nStr,
% the second line is in divStr,
% and the denominator string is in dStr.
if isempty(handles.tf),
nStr=blanks(50);
dStr=blanks(50);
divStr='No system to display, choose one';
else
% Get numerator and denominator.
[n,d]=tfdata(handles.tf,'v');
% Set very small values to zero
n=n.*(abs(n)>1e-6);
d=d.*(abs(d)>1e-6);
% Get string representations of numerator and denominator
nStr=poly2str(n,'s'); dStr=poly2str(d,'s');
% Find length of strings.
LnStr=length(nStr); LdStr=length(dStr);
if LnStr>LdStr,
%the numerator is longer than denominator string, so pad denominator.
n=LnStr; %n is the length of the longer string.
nStr=[' ' nStr]; %add spaces for characters at start of divStr.
dStr=[' ' blanks(floor((LnStr-LdStr)/n)) dStr]; %pad denominator.
else
%the demoninator is longer than numerator, pad numerator.
n=LdStr;
nStr=[' ' blanks(floor((LdStr-LnStr)/n)) nStr];
dStr=[' ' dStr];
end
divStr='L(s)= ';
divStr=[divStr strrep(blanks(n),' ','-')];
end
set(handles.tfText,'String',{nStr,divStr,dStr});
%Change type font and size.
set(handles.tfText,'FontName','Courier New')
set(handles.tfText,'FontSize',10)
function mappz(myTF)
[z,p]=zpkdata(myTF,'v');
plot(real(z),imag(z),'bo','Markersize',8)
plot(real(p),imag(p),'bx','Markersize',10);
% --- Executes on button press in zoomButton.
function zoomButton_Callback(~, ~, handles)
% hObject handle to zoomButton (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
axes(handles.ghDomAx);
if get(handles.zoomButton, 'Value')==0
axis((handles.axInf)*[-1 1 -1 1]);
set(handles.zoomButton,'String','Zoom in');
else
axis(handles.axNumLim/5*[-1 1 -1 1]);
set(handles.zoomButton,'String','Zoom out');
end
guidata(handles.NyqGuiFig, handles); % Update handles structure
% --- Executes on selection change in userSysMenu.
function userSysMenu_Callback(~, ~, handles)
tfNum=get(handles.userSysMenu,'value');
myTF=handles.Usertfs{tfNum};
if (tfNum==1),
set(handles.pathDrawPanel,'Visible','off');
set(handles.expPanel,'Visible','off');
warndlg('No transfer function chosen');
else
initAxes(handles);
axes(handles.sDomAx);
mappz(myTF);
set(handles.pathDrawPanel,'Visible','on');
set(handles.BISysMenu,'value',1);
end
handles.tf=myTF;
dispTF(handles);
guidata(handles.NyqGuiFig, handles); % Update handles structure
% --- Executes during object creation, after setting all properties.
function userSysMenu_CreateFcn(hObject, ~, ~)
% hObject handle to userSysMenu (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function sPath(handles, del)
rinf=1000;
rDetour=0.4; % This is the drawn radius of a detour around a pole.
epsln = 0.001; % This is the precision for rounding location of any
% poles near jw axis (imag part of pole must be greater
% than epsln).
set(handles.pauseTButton,'Value',0);
set(handles.pauseTButton,'String','Pause');
axis((handles.axInf)*[-1 1 -1 1]);
set(handles.expPanel,'Visible','off');
[z,p,k]=zpkdata(handles.tf,'v');
% If pole is near imag axis, put it on the axis (set real part=0)
is_onaxis=abs(real(p))<epsln; % is_onaxis=1 if pole is near axis.
p=complex(real(p).*(~is_onaxis),imag(p)); %If on axis, make p pure imaginary.
handles.tf=zpk(z,p,k);
dispTF(handles);
handles.gh_path_inf=0; %path in gh gets to inf?
guidata(handles.NyqGuiFig, handles); % Update handles structure
initAxes(handles);
axes(handles.sDomAx);
mappz(handles.tf);
n=handles.nth;
hold on;
if get(handles.NyqPathRButt,'Value')
% The Nyquist path is the hardest to create, because we must accomodate
% detours around poles on the jw axis. To make the plot look right
% the detours will be large enough to see on the s-domain plot.
% However, we'll keep track of when the path is on the detour and use
% this information in the plot (we'll simply extend the radius of the
% GH domain plot to infinity.
p_at_origin=sum(abs(p)<epsln)~=0; %See if we have poles at origin.
p_gt_0=p(imag(p(is_onaxis))>=epsln); %find all positive poles on axis.
p_onax=sort(p_gt_0); %sort all of the poles.
n_onax=length(p_onax); %number on axis.
% If there is a pole at the origin, put detour on path.
if p_at_origin,
theta=linspace(0,pi/2,n/4);
x=rDetour*cos(theta); %Plot detour (large)
y=rDetour*sin(theta);
ondet=ones(size(x)); %This variable indicates we are
%on detour.
else
x=0; %else start path at origin (for both plot and calc)
y=0;
ondet=zeros(size(x)); %We are not on detour.
end
% Path now goes up the imag axis
thet=linspace(-pi/2,pi/2,n);
for i=1:n_onax, %Loop through poles on axis
%First extend path up to the current pole (plot),
ynew = linspace(y(end), imag(p_onax(i))-rDetour, n/(n_onax+1));
y=[y ynew];
x=[x zeros(size(ynew))];
ondet=[ondet zeros(size(ynew))]; %Not on detour
%Now make detour
y=[y imag(p_onax(i))+rDetour*sin(thet)];
x=[x rDetour*cos(thet)];
ondet=[ondet ones(size(thet))]; %On detour
end
% Now complete path up ro the top of the axis.
ynew = linspace(y(end),2*handles.axNumLim,2*n);
y=[y ynew];
x=[x zeros(size(ynew))];
ondet=[ondet zeros(size(ynew))];
% Now make entire path - add quarter of circle at infinity (to get path
% back to real axis, thereby completing the path on the upper half of
% the s plane. We then complete path by extending the path with its
% complex conjugate to get lower half of s-plane.
thet=linspace(pi/2,0,n/2); % Angles for quarter circle.
x=[x rinf*cos(thet)]; % Add quarter circle.
y=[y rinf*sin(thet)];
% x=[x fliplr(x)]; % Add lower half of s-plane.
% y=[y -fliplr(y)];
% We know the rest of the path won't have detours, so extend array
% that indicates detours with zeros.
% ondet = [ondet zeros(size(thet))]; ondet=[ondet fliplr(ondet)];
ondet=[ondet zeros(size(thet))];
pathInterp(x,y,ondet,del,handles);
axes(handles.ghDomAx);
text(-1,0,'+','HorizontalAlignment','Center',...
'FontSize',14,'FontWeight','demi');
if(~get(handles.pauseTButton,'Value')),
nyqPathExplain(handles);
end
elseif get(handles.circPathRButt,'Value')
r=handles.circRad;
thet=linspace(0,pi,n+1);
x0=r*cos(-thet); y0=r*sin(-thet);
pathInterp(x0,y0,zeros(size(x0)),del,handles);
if(~get(handles.pauseTButton,'Value')),
nyqCircExplain(handles);
end
else
beep;
errordlg('One radio button must be pushed.');
end
function pathInterp(x0,y0,ondet0,del,handles)
maxlen=10000;
x=zeros(1,maxlen); y=zeros(1,maxlen); ondet=zeros(1,maxlen);
j=1; %Counter for x0,y0
i=1; %Counter for x,y
x(1)=x0(1); y(1)=y0(1); ondet(1)=ondet0(1);
dx=x0(2)-x0(1); dy=y0(2)-y0(1);
dx0=abs(dx); dy0=abs(dy);
s=complex(x(1),y(1));
%If you want a time delay, uncomment the next line and add desired delay.
%handles.tf=handles.tf*tf(1,1,'InputDelay',0.5);
gh_old=freqresp(handles.tf,s); %gha_old=angle(gh_old);
while ( (i<maxlen) && (j<(length(x0))) ),
x_nxt=x(i)+dx; y_nxt=y(i)+dy;
s=complex(x_nxt,y_nxt);
gh=freqresp(handles.tf,s); %gha=angle(gh);
% diffAngle=abs(gha-gha_old);
% if diffAngle>1.5*pi,
% diffAngle=abs(diffAngle-2*pi);
% end
% diffAngle=0;
if abs(gh)<handles.axInf,
maxDiff=0.05;
else
maxDiff=0.2;
end
% if (((abs((gh-gh_old)/gh)>maxDiff) || (diffAngle>0.001)) && (abs(gh)>0.02)),
if ((abs((gh-gh_old)/gh)>maxDiff) && (abs(gh)>0.02)),
dx=dx/2; dy=dy/2;
else
if ( (abs(x_nxt-x0(j))>=dx0) || (abs(y_nxt-y0(j))>=dy0) ),
j=j+1;
x_nxt=x0(j); y_nxt=y0(j);
s=complex(x_nxt,y_nxt);
gh=freqresp(handles.tf,s); % gha=angle(gh);
if j~=length(x0),
dx=x0(j+1)-x0(j); dy=y0(j+1)-y0(j);
dx0=abs(dx); dy0=abs(dy);
end
end
i=i+1;
x(i)=x_nxt; y(i)=y_nxt; % Here we assign the values of x and y
ondet(i)=ondet0(j);
gh_old=gh; %gha_old=gha;
dx=dx*1.4; dy=dy*1.4;
end
end
if i==maxlen,
warndlg('Large changes in L(s), plot inaccurate.');
end
ondet=ondet(1:i);
s=complex(x(1:i),y(1:i));
gh=freqresp(handles.tf,s);
gh=transpose(gh(:));
mltp = 1+999*ondet; %multiplier array - if ondet, multiply by 1000
gh=gh.*mltp;
s(end+1)=s(end);
gh(end+1)=gh(end);
plotPath(handles,s,gh,del); %Make plot
handles=guidata(handles.NyqGuiFig); % Reload handles
function plotPath(handles,s,gh,del)
alph=0.25; %Colors (and transparency) used in plots
zcol=[1 0 0];
pcol=[0 0 1];
gcol=[1 1 1]*0.25;
arr=zeros(size(s));
arr([10 floor((1:4)*(length(s)/5)) end-12 end-4])=1;
s=[s fliplr(conj(s)) s(1)];
gh=[gh fliplr(conj(gh)) gh(1)];
arr=[arr fliplr(arr) 0];
% Get real and imaginary parts of s, and truncate large values.
r_s=abs(s);
i=find(r_s>(handles.axInf)); % too large
s(i)=s(i)*(handles.axInf)./r_s(i);
sr=real(s); si=imag(s);
% Get real and imaginary parts of gh, and truncate large values.
r_gh=abs(gh);
i=find(r_gh>(handles.axInf)); % too large
gh(i)=gh(i)*(handles.axInf)./r_gh(i);
if ~isempty(i),
handles.gh_path_inf=1; %path in gh gets to inf?
guidata(handles.NyqGuiFig, handles); % Update handles structure
end
gr=real(gh); gi=imag(gh);
% % Calculate the cummulative arclength (while s<inf)
% arclen=[0 sqrt(cumsum((diff(sr).^2+diff(si).^2).*...
% ((r_s(2:end)<handles.axInf))))];
arclen=[0 cumsum(abs(diff(s))+abs(diff(gh)))];
maxarclen=max(arclen);
c=hsv2rgb([arclen/maxarclen; ones(2,length(arclen))]'); %colormap
if get(handles.NyqPathRButt,'Value')
ghx0=-1;
else
ghx0=0;
end
%Get poles and zeros
[z,p]=zpkdata(handles.tf,'v');
% Create patches for showing angle of zeros (s domain) and precalculate
% angles.
azs=zeros(length(z),length(s));
%za=zeros(length(z),length(s)-1);
zp=zeros(length(z)); zl=zeros(length(z));
axes(handles.sDomAx);
for j=1:length(z),
azs(j,:)=unwrap(angle(s(:)-z(j))); %angle from s to z(j)
% za(j,:)=cumsum(diff(azs(j,:)));
% zp is patch showhing cumulative angle from zero to s. It is defined
% here but not used until later.
% zl is a line from origin to s.
zp(j)=patch([0 0 ],[0 0],zcol,...
'FaceColor',zcol,'FaceAlpha',alph,...
'EdgeColor',zcol,'EdgeAlpha',alph*0.5);
zl(j)=line([0 0],[0 0],'Color',zcol,'Linestyle',':');
end
% Create patches for showing angle of poles (s domain), and precalculate
% angles.
aps=zeros(length(p),length(s));
%pa=zeros(length(p),length(s)-1);
pp=zeros(length(p)); pl=zeros(length(p));
for j=1:length(p),
aps(j,:)=unwrap(angle(s(:)-p(j)));
% pa(j,:)=cumsum(diff(aps(j,:)));
pp(j)=patch([0 0 ],[0 0],pcol,...
'FaceColor',pcol,'FaceAlpha',alph,...
'EdgeColor',pcol,'EdgeAlpha',alph*0.5);
pl(j)=line([0 0],[0 0],'Color',pcol,'Linestyle',':');
end
% Create patches for showing angle of gh path
axes(handles.ghDomAx);
agh=unwrap(angle(gh-ghx0));
%ga=cumsum(diff(agh));
gp=patch([0 0 ],[0 0],gcol,'FaceAlpha',alph,'EdgeAlpha',alph*0.5);
gl=line([0 0],[0 0],'Color',gcol,'Linestyle',':');
zr=real(z); zi=imag(z); pr=real(p); pi=imag(p);
i=1;
while i<(length(s)-1),
if get(handles.pauseTButton,'Value'),
pause(0.5);
else
axes(handles.sDomAx);
% Plot s path (changing color as we go)
plot([sr(i) sr(i+1)],[si(i) si(i+1)],'Color',c(i,:),'Linewidth',1.5);
% Draw lines from z(j) to s, and fill in patch showing subtended angle.
for j=1:length(z),
%th=azs(j,1)+linspace(0,za(j,i),20);
th=linspace(azs(j,1),azs(j,i+1),20);
set(zp(j),'XData',[zr(j) zr(j)+cos(th)],'YData',[zi(j) zi(j)+sin(th)])
set(zl(j),'XData',[zr(j) sr(i+1)],'YData',[zi(j) si(i+1)]);
end
for j=1:length(p),
%th=aps(j,1)+linspace(0,pa(j,i),20);
th=linspace(aps(j,1),aps(j,i+1),20);
set(pp(j),'XData',[pr(j) pr(j)+cos(th)],'YData',[pi(j) pi(j)+sin(th)])
set(pl(j),'XData',[pr(j) sr(i+1)],'YData',[pi(j) si(i+1)]);
end
axes(handles.ghDomAx);
plot([gr(i) gr(i+1)],[gi(i) gi(i+1)],'Color',c(i,:),'Linewidth',2);
%th=agh(1)+linspace(0,ga(i),abs(ga(i))*2+20);
th=linspace(agh(1),agh(i+1),abs(agh(i+1)-agh(1))*2+20);
if (agh(i+1)>0),
gcol=pcol;
else
gcol=zcol;
end
set(gp,'XData',ghx0+[0 cos(th)],'YData',[0 sin(th)],...
'EdgeColor',gcol,'FaceColor',gcol);
set(gl,'XData',[ghx0 gr(i+1)],'YData',[0 gi(i+1)],...
'Color',gcol);
if arr(i)~=0,
sarr_a=atan2(si(i+1)-si(i),sr(i+1)-sr(i));
garr_a=atan2(gi(i+1)-gi(i),gr(i+1)-gr(i));
axes(handles.sDomAx);
angleArrow(sr(i), si(i), sarr_a, c(i,:));
axes(handles.ghDomAx);
angleArrow(gr(i), gi(i), garr_a, c(i,:));
end
pause(del);
i=i+1;
end
end
function angleArrow(tx,ty,thet,c)
x=0.5*[-0.75 1 -0.75];
y=0.3*[-1 0 1];
pts=[ cos(thet) -sin(thet) tx;
sin(thet) cos(thet) ty;
0 0 1;]*[x; y; ones(size(x))];
x=pts(1,:); y=pts(2,:);
patch(x,y,c,'EdgeColor',c,'FaceAlpha',0.5,'EdgeAlpha',0.5);
% --- Executes on button press in pauseTButton.
function pauseTButton_Callback(~, ~, handles)
% hObject handle to pauseTButton (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
if get(handles.pauseTButton,'Value'),
set(handles.pauseTButton,'String','Play');
else
set(handles.pauseTButton,'String','Pause');
end
function nyqCircExplain(handles)
r=handles.circRad;
[z,p]=zpkdata(handles.tf,'v');
nz=length(z);
nzi=sum(abs(z)<r);
np=length(p);
npi=sum(abs(p)<r);
N=nzi-npi;
s1=['The transfer function has ' num2str(nz) ' zero(s) and '...
num2str(np) ' pole(s).'];
s2=['The s-domain path encircles ' num2str(nzi) ' zero(s) and '...
num2str(npi) ' pole(s) in a CW direction.'];
s3=['Z=' num2str(nzi) ', P=' num2str(npi) ', and N=Z-P=' num2str(N)];
s4=' ';
s5=['Thus the path in GH encirles the origin ' num2str(N) ' time(s) '...
' CW, or ' num2str(-N) ' time(s) CCW.'];
if handles.gh_path_inf~=0
s6=' ';
s7=['Note: GH was large enought that it had to be shown at '...
'infinity for part of the path.'];
s={s1;s2;s3;s4;s5;s6;s7};
else
s={s1;s2;s3;s4;s5};
end
set(handles.expText,'String',s);
set(handles.expPanel,'Visible','on');
function nyqPathExplain(handles)
r=handles.circRad;
[z,p]=zpkdata(handles.tf,'v');
% Assume poles very near jw axis are on jw axis
p_offax=abs(real(p))>1e-5;
p=complex(real(p).*p_offax,imag(p));
P=sum(real(p)>0);
tfcl=minreal(feedback(handles.tf,1)); %System with feedback.
[zcl,pcl]=zpkdata(tfcl,'v');
% Assume poles very near jw axis are on jw axis
p_offax=abs(real(pcl))>1e-5;
p_onax=~p_offax;
pcl=complex(real(pcl).*p_offax,imag(pcl));
Z=sum(real(pcl)>0);
N=Z-P;
s1=['The open-loop transfer function, L(s), has P=' num2str(P)...
' pole(s) in the RHP.'];
s2=['The s-domain path encircles the origin N=' num2str(N) ' time(s) '...
'in a CW direction.'];
s3=['Therefore the closed loop transfer function has Z=N+P='...
num2str(Z) ' pole(s) in the RHP (' num2str(Z)...
' zero(s) of c.e. in RHP)'];
if (Z>0)
s4 = 'The system is unstable.';
else
s4 = 'The system is stable.';
end
s5=' ';
if sum(p_onax)~=0,
s6=['"L(s)" path goes through -1+j0, so there are some closed ',...
'loop poles on jw axis.'];
s7='The angles drawn on the "L(s)" plot may be inaccurate!';
s={s1;s2;s3;s4;s5;s6;s7};
else
s={s1;s2;s3;s4};
end
set(handles.expText,'String',s);
set(handles.expPanel,'Visible','on');
% --- Executes on button press in clearPButton.
function clearPButton_Callback(~, ~, handles)
initAxes(handles);
axes(handles.sDomAx);
mappz(handles.tf);
% --- Executes on button press in webPB.
function webPB_Callback(hObject, eventdata, handles)
web('http://lpsa.swarthmore.edu/Nyquist/Nyquist.html','-browser')
|
github
|
mainster/matlabCodes-master
|
gershband.m
|
.m
|
matlabCodes-master/CUSTOM_LIBRARY/Mimotools/gershband.m
| 3,076 |
utf_8
|
1fc6d3bb4118fc7975d6ebd6fee3f398
|
function gershband(a,b,c,d,e)
%GERSHBAND - Finds the Gershorin Bands of a nxn LTI MIMO SYS model
% The use of the Gershorin Bands along the Nyquist plot is helpful for
% finding the coupling grade of a MIMO system.
%
% Syntax: gershband(SYS) - computes the Gershgorin bands of SYS
% gershband(SYS,'v') - computes the Gershgorin bands and the
% Nyquist array of SYS
% Inputs:
% SYS - LTI MIMO system, either in State Space or Transfer Function
% representation.
%
% Example:
% g11=tf(2,[1 3 2]);
% g12=tf(0.1,[1 1]);
% g21=tf(0.1,[1 2 1]);
% g22=tf(6,[1 5 6]);
% G=[g11 g12; g21 g22];
% gershband(G);
%
% Other m-files required: sym2tf, ss2sym
% Subfunctions: center, radio
% See also: rga
%
% Author: Oskar Vivero Osornio
% email: [email protected]
% Created: February 2006;
% Last revision: 11-May-2006;
% May be distributed freely for non-commercial use,
% but please leave the above info unchanged, for
% credit and feedback purposes
%------------- BEGIN CODE --------------
%--------- Determines Syntax -----------
ni=nargin;
switch ni
case 1
%Transfer Function Syntax
switch class(a)
case 'tf'
%Numeric Transfer Function Syntax
g=a;
case 'sym'
%Symbolic Transfer Function Syntax
g=sym2tf(a);
end
e=0;
case 2
%Transfer Function Syntax with Nyquist Array
switch class(a)
case 'tf'
%Numeric Transfer Function Syntax
g=a;
case 'sym'
%Symbolic Transfer Function Syntax
g=sym2tf(a);
end
e=1;
case 4
%State Space Syntax
g=ss2sym(a,b,c,d);
g=sym2tf(g);
e=0;
case 5
%State Space Syntax
g=ss2sym(a,b,c,d);
g=sym2tf(g);
e=1;
end
%---------------------------------------
[n,m]=size(g);
w=logspace(-1,6,200);
q=0:(pi/50):(2*pi);
for i=1:n
for j=1:m
if i==j
figure(i)
nyquist(g(i,i));
grid on
title(['Nyquist Diagram of G(',num2str(i),',',num2str(j),')'])
for iest=1:n
for jest=1:m
if iest~=jest
hold on
C=center(g(i,j),w);
R=radio(g(iest,jest),w);
for k=1:length(C)
plot((R(k)*cos(q))+real(C(k)),(R(k)*sin(q))+imag(C(k)),'g-')
end
hold off
end
end
end
end
end
end
if e==1
figure(n+1)
nyquist(g);
grid on
end
%------------ Subfunction --------------
function C = center(g,w)
g=tf2sym(g);
C=subs(g,complex(0,w));
function R = radio(g,w)
g=tf2sym(g);
R=abs(subs(g,complex(0,w)));
%------------- END OF CODE --------------
|
github
|
mainster/matlabCodes-master
|
arrowh.m
|
.m
|
matlabCodes-master/CUSTOM_LIBRARY/Mimotools/arrowh.m
| 6,921 |
utf_8
|
a6ac3ee76572ce60d5bd4be9dd7ee4c0
|
% ARROWH Draws a solid 2D arrow head in current plot.
% ARROWH(X,Y,COLOR,SIZE,LOCATION) draws a solid arrow head into
% the current plot to indicate a direction. X and Y must contain
% a pair of x and y coordinates ([x1 x2],[y1 y2]) of two points:
%
% The first point is only used to tell (in conjunction with the
% second one) the direction and orientation of the arrow -- it
% will point from the first towards the second.
%
% The head of the arrow will be located in the second point. An
% example of use is plot([0 2],[0 4]); ARROWH([0 1],[0 2],'b')
%
% You may also give two vectors of same length > 2. The routine
% will then choose two consecutive points from "about" the middle
% of each vectors. Useful if you don't want to worry each time
% about where to put the arrows on a trajectory. If x1 and x2
% are the vectors x1(t) and x2(t), simply put ARROWH(x1,x2,'r')
% to have the right direction indicated in your x2 = f(x1) phase
% plane.
%
% (x2,y2)
% --o
% \ |
% \|
%
%
% o
% (x1,y1)
%
% Please note that the following optional arguments need -- if
% you want to use them -- to be given in that exact order.
%
% The COLOR argument is exactely the same as for plots, eg. 'r';
% if not given, blue is default.
%
% The SIZE argument allows you to tune the size of the arrows.
%
% The LOCAITON argument only applies, if entire solution vectors
% have been passed on. With this argument you can indicate where
% abouts inside those vectors to take the two points from.
% Can be a vector, if you want to have more than one arrow drawn.
%
% Both arguments, SIZE and LOCATION must be given in percent,
% where 100 means standard size, 50 means half size, respectively
% 100 means end of the vector, 48 means about middle, 0 beginning.
% Note that those "locations" correspond to the cardinal position
% "inside" the vector, say "index-wise".
%
% This routine is mainely intended to be used for indicating
% "directions" on trajectories -- just give two consecutive times
% and the corresponding values of a flux and the proper direction
% of the trajectory will be shown on the plot. You may also pass
% on two solution vectors, as described above.
%
% Note, that the arrow only looks good on the specific axis
% settings when the routine was actually started. If you zoom in
% afterwards, the triangle gets distorted.
%
% Examples of use:
% x1 = [0:.2:2]; x2 = [0:.2:2]; plot(x1,x2); hold on;
% arrowh(x1,x2,'r',100,20); % passing on entire vectors
% arrowh([0 1],[0 1],'g',300); % passing on 2 points
% Author: Florian Knorn
% Email: [email protected]
% Version: 1.10
% Filedate: Dec 1st, 2005
%
% History: 1.10 - Buxfix
% 1.09 - Possibility to chose *several* locations
% 1.08 - Possibility to chose location
% 1.07 - Choice of color
% 1.06 - Bug fixes
% 1.00 - Release
%
% ToDos: - More specific shaping-possibilities,
% - Keep proportions when zooming or resizing;
% has to be done with callback functions, I guess.
%
% Bugs: None discovered yet, those discovered were fixed
%
% Thanks: I haven't used the function in ages, but the
% last time I modified something in a hurry, I
% introduced a stupid bug, which Kesh Ikum was so
% kind to point out ;-) Thanks!
%
% If you have suggestions for this program, if it doesn't work for
% your "situation" or if you change something in it - please send
% me an email! This is my very first "public" program and I'd like
% to improve it where I can -- your help is kindely appreciated!
% Thank you!
function arrowh(x,y,clr,ArSize,Where)
%-- errors
if nargin < 2
error('Please give enough coordinates !');
end
if (length(x) < 2) || (length(y) < 2),
error('X and Y vectors must each have "length" >= 2 !');
end
if (x(1) == x(2)) && (y(1) == y(2)),
error('Points superimposed - cannot determine direction !');
end
if nargin < 3
clr = 'b';
end
if nargin < 4
ArSize = 100 / 10000; %-- 10000 is an arbitrary value...
else
ArSize = ArSize / 10000;
end
if nargin < 5
Where = 50;
end
%-- determine and remember the hold status, toggle if necessary
if ishold,
WasHold = 1;
else
WasHold = 0;
hold on;
end
%-- start for-loop in case several arrows are wanted
for Loop = 1:length(Where),
%-- if vectors "longer" then 2 are given we're dealing with time series
if (length(x) == length(y)) && (length(x) > 2),
j = floor(length(x)*Where(Loop)/100); %-- determine that location
if j >= length(x), j = length(x) - 1; end
if j == 0, j = 1; end
x1 = x(j); x2 = x(j+1); y1 = y(j); y2 = y(j+1);
else %-- just two points given - take those
x1 = x(1); x2 = x(2); y1 = y(1); y2 = y(2);
end
%-- get axe ranges and their norm
OriginalAxis = axis;
Xextend = abs(OriginalAxis(2)-OriginalAxis(1));
Yextend = abs(OriginalAxis(4)-OriginalAxis(3));
%-- determine angle for the rotation of the triangle
if x2 == x1, %-- line vertical, no need to calculate slope
if y2 > y1,
p = pi/2;
else
p= -pi/2;
end
else %-- line not vertical, go ahead and calculate slope
%-- using normed differences (looks better like that)
m = ( (y2 - y1)/Yextend ) / ( (x2 - x1)/Xextend );
if x2 > x1, %-- now calculate the resulting angle
p = atan(m);
else
p = atan(m) + pi;
end
end
%-- the arrow is made of a transformed "template triangle".
%-- it will be created, rotated, moved, resized and shifted.
%-- the template triangle (it points "east", centered in (0,0)):
xt = [1 -sin(pi/6) -sin(pi/6)];
yt = [0 cos(pi/6) -cos(pi/6)];
%-- rotate it by the angle determined above:
xd=[];
yd=[];
for i=1:3,
xd(i) = cos(p)*xt(i) - sin(p)*yt(i);
yd(i) = sin(p)*xt(i) + cos(p)*yt(i);
end
%-- move the triangle so that its "head" lays in (0,0):
xd = xd - cos(p);
yd = yd - sin(p);
%-- stretch/deform the triangle to look good on the current axes:
xd = xd*Xextend*ArSize;
yd = yd*Yextend*ArSize;
%-- move the triangle to the location where it's needed
xd = xd + x2;
yd = yd + y2;
%-- draw the actual triangle
patch(xd,yd,clr,'EdgeColor',clr);
end % Loops
%-- restore original axe ranges and hold status
axis(OriginalAxis);
if ~WasHold,
hold off
end
%-- work done. good bye.
|
github
|
mainster/matlabCodes-master
|
icdtool.m
|
.m
|
matlabCodes-master/CUSTOM_LIBRARY/Mimotools/icdtool.m
| 19,918 |
utf_8
|
44e8f0f1d83bf6807bdfacc3078fd998
|
function varargout = icdtool(varargin)
%ICDTOOL - Individual Channel Design utility for 2x2 MIMO systems
%
% Syntax:
% icdtool(G) - Starts icdtool for G, where G is a transfer function matrix
%
% Example:
% g11=tf(2,[1 3 2]);
% g12=tf(-2,[1 1]);
% g21=tf(-1,[1 2 1]);
% g22=tf(6,[1 5 6]);
% G=[g11 g12; g21 g22];
%
% Other m-files required: nyqmimo
%
% Author: Oskar Vivero Osornio
% email: [email protected]
% Created: February 2006;
% Last revision: 12-April-2006;
% May be distributed freely for non-commercial use,
% but please leave the above info unchanged, for
% credit and feedback purposes
% Last Modified by GUIDE v2.5 20-Apr-2006 00:42:51
%------------- BEGIN CODE --------------
% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name', mfilename, ...
'gui_Singleton', gui_Singleton, ...
'gui_OpeningFcn', @icdtool_OpeningFcn, ...
'gui_OutputFcn', @icdtool_OutputFcn, ...
'gui_LayoutFcn', [] , ...
'gui_Callback', []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end
% End initialization code - DO NOT EDIT
% --- Executes just before icdtool is made visible.
function icdtool_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to icdtool (see VARARGIN)
% Determining Input
ni=nargin;
switch ni
case 4
%Input is a matrix transfer function
G=varargin{1};
end
g11=G(1,1);
g12=G(1,2);
g21=G(2,1);
g22=G(2,2);
gamma=minreal((g12*g21)/(g11*g22));
setappdata(0,'hMainGui',gcf);
setappdata(gcf,'G',G);
setappdata(gcf,'gamma',gamma);
% Choose default command line output for icdtool
handles.output = hObject;
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes icdtool wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% --- Outputs from this function are returned to the command line.
function varargout = icdtool_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
varargout{1} = handles.output;
function gain_K1_Callback(hObject, eventdata, handles)
% hObject handle to gain_K1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of gain_K1 as text
% str2double(get(hObject,'String')) returns contents of gain_K1 as a double
% --- Executes during object creation, after setting all properties.
function gain_K1_CreateFcn(hObject, eventdata, handles)
% hObject handle to gain_K1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function poles_K1_Callback(hObject, eventdata, handles)
% hObject handle to poles_K1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of poles_K1 as text
% str2double(get(hObject,'String')) returns contents of poles_K1 as a double
% --- Executes during object creation, after setting all properties.
function poles_K1_CreateFcn(hObject, eventdata, handles)
% hObject handle to poles_K1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function zeros_K1_Callback(hObject, eventdata, handles)
% hObject handle to zeros_K1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of zeros_K1 as text
% str2double(get(hObject,'String')) returns contents of zeros_K1 as a double
% --- Executes during object creation, after setting all properties.
function zeros_K1_CreateFcn(hObject, eventdata, handles)
% hObject handle to zeros_K1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function gain_K2_Callback(hObject, eventdata, handles)
% hObject handle to gain_K2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of gain_K2 as text
% str2double(get(hObject,'String')) returns contents of gain_K2 as a double
% --- Executes during object creation, after setting all properties.
function gain_K2_CreateFcn(hObject, eventdata, handles)
% hObject handle to gain_K2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function poles_K2_Callback(hObject, eventdata, handles)
% hObject handle to poles_K2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of poles_K2 as text
% str2double(get(hObject,'String')) returns contents of poles_K2 as a double
% --- Executes during object creation, after setting all properties.
function poles_K2_CreateFcn(hObject, eventdata, handles)
% hObject handle to poles_K2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
function zeros_K2_Callback(hObject, eventdata, handles)
% hObject handle to zeros_K2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: get(hObject,'String') returns contents of zeros_K2 as text
% str2double(get(hObject,'String')) returns contents of zeros_K2 as a double
% --- Executes during object creation, after setting all properties.
function zeros_K2_CreateFcn(hObject, eventdata, handles)
% hObject handle to zeros_K2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: edit controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on selection change in popupmenu_C1.
function popupmenu_C1_Callback(hObject, eventdata, handles)
% hObject handle to popupmenu_C1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = get(hObject,'String') returns popupmenu_C1 contents as cell array
% contents{get(hObject,'Value')} returns selected item from popupmenu_C1
% Get user input from GUI
% System data
hMainGui = getappdata(0,'hMainGui');
G = getappdata(hMainGui,'G');
gamma = getappdata(hMainGui,'gamma');
g11 = G(1,1);
g12 = G(1,2);
g21 = G(2,1);
g22 = G(2,2);
val = get(hObject,'Value');
str = get(hObject, 'String');
figure(1)
switch str{val};
case 'Nyquist of Gamma' % User selects peaks
% Status window
[num11,den11]=tfdata(g11,'v');
[num22,den22]=tfdata(g22,'v');
zeros11=roots(num11);
zeros22=roots(num22);
RHPP11=0;
RHPP22=0;
for i=1:length(zeros11)
if sign(real(zeros(i)))==1
RHPP11=RHPP11+1;
end
end
for i=1:length(zeros22)
if sign(real(zeros22(i)))==1
RHPP22=RHPP22+1;
end
end
s1=sprintf('%-d RHPP in g11',RHPP11);
s2=sprintf('%-d RHPP in g22',RHPP22);
vars{1}='RHPP of Gamma';
vars{2}=s1;
vars{3}=s2;
set(handles.status_window,'String',vars)
% Plot
syms p
g=tf2sym(gamma);
nyqmimo(gamma);
title('Nyquist Diagram of Gamma')
case 'Bode k1*g11' % User selects membrane
k1=getappdata(hMainGui,'k1');
margin(k1*g11);
case 'Bode h1'
h1=getappdata(hMainGui,'h1');
% Status Window
[num,den]=tfdata(h1,'v');
den=roots(den);
RHPP=0;
for i=1:length(den)
if sign(real(den))==1
RHPP=RHPP+1;
end
end
vars{1}=sprintf('%-d RHPP in h1',RHPP);
set(handles.status_window,'String',vars)
% Plot
bode(h1);
title('Bode Diagram h1')
case 'Nyquist Gamma*h1' % User selects sinc
h1=getappdata(hMainGui,'h1');
syms p
g=tf2sym(gamma*h1);
nyqmimo(gamma*h1);
title('Nyquist Diagram of Gamma*h1')
case 'Bode Gamma*h1'
h1=getappdata(hMainGui,'h1');
margin(minreal(gamma*h1))
case 'Nyquist C1'
C1=getappdata(hMainGui,'C1');
% Status Window
[num,den]=tfdata(C1,'v');
den=roots(den);
RHPP=0;
for i=1:length(den)
if sign(real(den))==1
RHPP=RHPP+1;
end
end
vars{1}=sprintf('%-d RHPP in C1',RHPP);
set(handles.status_window,'String',vars)
% Plot
syms p
g=tf2sym(C1);
nyqmimo(C1);
title('Nyquist Diagram of C1')
case 'Bode C1'
C1=getappdata(hMainGui,'C1');
margin(C1);
case 'Step of C1->R1'
C1=getappdata(hMainGui,'C1');
C1cl=C1/(1+C1);
step(C1cl);
grid on
title('Step Response of C1->R1')
case 'Step of C1->R2'
h2=getappdata(hMainGui,'h2');
S1=getappdata(hMainGui,'S1');
Pref1=minreal(((g12/g22)*h2)*S1);
step(Pref1);
grid on
title('Step Response of C1->R2')
end
% --- Executes during object creation, after setting all properties.
function popupmenu_C1_CreateFcn(hObject, eventdata, handles)
% hObject handle to popupmenu_C1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on selection change in popupmenu_C2.
function popupmenu_C2_Callback(hObject, eventdata, handles)
% hObject handle to popupmenu_C2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = get(hObject,'String') returns popupmenu_C2 contents as cell array
% contents{get(hObject,'Value')} returns selected item from popupmenu_C2
hMainGui = getappdata(0,'hMainGui');
G = getappdata(hMainGui,'G');
gamma = getappdata(hMainGui,'gamma');
g11 = G(1,1);
g12 = G(1,2);
g21 = G(2,1);
g22 = G(2,2);
val = get(hObject,'Value');
str = get(hObject, 'String');
figure(2)
switch str{val};
case 'Nyquist of Gamma' % User selects peaks
% Status window
[num11,den11]=tfdata(g11,'v');
[num22,den22]=tfdata(g22,'v');
zeros11=roots(num11);
zeros22=roots(num22);
RHPP11=0;
RHPP22=0;
for i=1:length(zeros11)
if sign(real(zeros(i)))==1
RHPP11=RHPP11+1;
end
end
for i=1:length(zeros22)
if sign(real(zeros22(i)))==1
RHPP22=RHPP22+1;
end
end
s1=sprintf('%-d RHPP in g11',RHPP11);
s2=sprintf('%-d RHPP in g22',RHPP22);
vars{1}='RHPP of Gamma';
vars{2}=s1;
vars{3}=s2;
set(handles.status_window,'String',vars)
%Plot
syms p
g=tf2sym(gamma);
nyqmimo(gamma);
title('Nyquist Diagram of Gamma')
case 'Bode k2*g22' % User selects membrane
k2=getappdata(hMainGui,'k2');
margin(k2*g22);
case 'Bode h2'
h2=getappdata(hMainGui,'h2');
% Status Window
[num,den]=tfdata(h2,'v');
den=roots(den);
RHPP=0;
for i=1:length(den)
if sign(real(den))==1
RHPP=RHPP+1;
end
end
vars{1}=sprintf('%-d RHPP in h2',RHPP);
set(handles.status_window,'String',vars)
% Plot
bode(h2);
title('Bode Diagram h2')
case 'Nyquist Gamma*h2' % User selects sinc
h2=getappdata(hMainGui,'h2');
syms p
g=tf2sym(gamma*h2);
nyqmimo(gamma*h2);
title('Nyquist Diagram of Gamma*h2')
case 'Bode Gamma*h2'
h2=getappdata(hMainGui,'h2');
margin(minreal(gamma*h2))
case 'Nyquist C2'
C2=getappdata(hMainGui,'C2');
% Status Window
[num,den]=tfdata(C2,'v');
den=roots(den);
RHPP=0;
for i=1:length(den)
if sign(real(den))==1
RHPP=RHPP+1;
end
end
vars{1}=sprintf('%-d RHPP in C2',RHPP);
set(handles.status_window,'String',vars)
% Plot
syms p
g=tf2sym(C2);
nyqmimo(C2);
title('Nyquist Diagram of C2')
case 'Bode C2'
C2=getappdata(hMainGui,'C2');
margin(C2);
case 'Step of C2->R2'
C2=getappdata(hMainGui,'C2');
C2cl=C2/(1+C2);
step(C2cl);
grid on
title('Step response of C2->R2')
case 'Step of C2->R1'
h1=getappdata(hMainGui,'h1');
S2=getappdata(hMainGui,'S2');
Pref2=minreal(((g21/g11)*h1)*S2);
step(Pref2);
grid on
title('Step response of C2->R1')
end
% --- Executes during object creation, after setting all properties.
function popupmenu_C2_CreateFcn(hObject, eventdata, handles)
% hObject handle to popupmenu_C2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: popupmenu controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in updatebutton.
function updatebutton_Callback(hObject, eventdata, handles)
% hObject handle to updatebutton (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get user input from GUI
% System data
hMainGui = getappdata(0,'hMainGui');
G = getappdata(hMainGui,'G');
gamma = getappdata(hMainGui,'gamma');
g11 = G(1,1);
g12 = G(1,2);
g21 = G(2,1);
g22 = G(2,2);
% K1 CONTROLLER
gain_k1 = str2double(get(handles.gain_K1,'String'));
poles_k1 = strread(get(handles.poles_K1,'String'));
zeros_k1 = strread(get(handles.zeros_K1,'String'));
% K2 CONTROLLER
gain_k2 = str2double(get(handles.gain_K2,'String'));
poles_k2 = strread(get(handles.poles_K2,'String'));
zeros_k2 = strread(get(handles.zeros_K2,'String'));
% Calculating data
% C1
num_k1=poly(zeros_k1);
den_k1=poly(poles_k1);
k1=tf(gain_k1*num_k1,den_k1);
% C2
num_k2=poly(zeros_k2);
den_k2=poly(poles_k2);
k2=tf(gain_k2*num_k2,den_k2);
% Subsystems and channels
h1=minreal((k1*g11)/(1+k1*g11));
h2=minreal((k2*g22)/(1+k2*g22));
C1=minreal((k1*g11)*(1-(gamma*h2)));
C2=minreal((k2*g22)*(1-(gamma*h1)));
% C1cl=C1/(1+C1);
% C2cl=C2/(1+C2);
% Sensibility channels
S1=minreal(1/(1+C1));
T1=minreal(C1/(1+C1));
S2=minreal(1/(1+C2));
T2=minreal(C2/(1+C2));
% Exporting data
setappdata(hMainGui,'k1',k1);
setappdata(hMainGui,'k2',k2);
setappdata(hMainGui,'h1',h1);
setappdata(hMainGui,'h2',h2);
setappdata(hMainGui,'C1',C1);
setappdata(hMainGui,'C2',C2);
setappdata(hMainGui,'S1',S1);
setappdata(hMainGui,'S2',S2);
setappdata(hMainGui,'T1',T1);
setappdata(hMainGui,'T2',T2);
% --- Executes on selection change in status_window.
function status_window_Callback(hObject, eventdata, handles)
% hObject handle to status_window (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Hints: contents = get(hObject,'String') returns status_window contents as cell array
% contents{get(hObject,'Value')} returns selected item from status_window
% --- Executes during object creation, after setting all properties.
function status_window_CreateFcn(hObject, eventdata, handles)
% hObject handle to status_window (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles empty - handles not created until after all CreateFcns called
% Hint: listbox controls usually have a white background on Windows.
% See ISPC and COMPUTER.
if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))
set(hObject,'BackgroundColor','white');
end
% --- Executes on button press in pushbutton2.
function pushbutton2_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton2 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
|
github
|
mainster/matlabCodes-master
|
LiveRecording.m
|
.m
|
matlabCodes-master/LiveRecordingWave/LiveRecording.m
| 10,220 |
utf_8
|
abbd8965ff6e8ea28c0d4966d99778fd
|
function LiveRecording
%Syntax: LiveRecording
% Run LiveRecording by typing "LiveRecording" in your command line
%
% Marcus Vollmer
% alpha 13.06.2014
% Initialize and hide the GUI as it is being constructed.
aud = audiodevinfo;
if isempty(ver('Signal'))
errordlg('Signal Processing Toolbox required','!! Error !!')
elseif isempty(aud.input)
errordlg('No input device found for audio recording. After plugged in you have to restart Matlab.' ,'!! Error !!')
else
f=figure('Visible','off','Position',[0,0,900,600],'Units','normalized','Toolbar','figure');%,'PaperSize',[20,13]
hp1 = uipanel('Title','Length of record','FontSize',14,'BackgroundColor','white','Units','normalized','Position',[0.1 0.5 .6 .2],'FontUnits','normalized','visible','on');
hp2 = uipanel('Title','Display length','FontSize',14,'BackgroundColor','white','Units','normalized','Position',[0.1 0.25 .6 .2],'FontUnits','normalized','visible','on');
hp3 = uipanel('Title','Frequency window','FontSize',14,'BackgroundColor','white','Units','normalized','Position',[0.1 0.1 .25 .1],'FontUnits','normalized','visible','on');
% Construct the components.
%Text
htext=uicontrol('Parent',hp3,'Style','text','String','-','FontSize',10,'Units','normalized','Position',[.4,.1,.1,.6],'FontUnits','normalized','HorizontalAlignment','center');
htextHz=uicontrol('Parent',hp3,'Style','text','String','Hz','FontSize',10,'Units','normalized','Position',[.9,.1,.1,.6],'FontUnits','normalized','HorizontalAlignment','center');
%Button
hbuttonStart = uicontrol('String','Start recording','FontSize',14,'Units','normalized','Position',[.75,.25,.2,.2],'FontUnits','normalized','Callback', @buttonStart_Callback);
hbuttonStartAgain = uicontrol('String','new record','FontSize',10,'Units','normalized','Position',[.85,.01,.125,.05],'FontUnits','normalized','visible','off','Callback', @buttonStartAgain_Callback);
hbuttonSave = uicontrol('String','save','FontSize',10,'Units','normalized','Position',[.65,.01,.1,.05],'FontUnits','normalized','visible','off','Callback', @buttonSave_Callback);
hbuttonSaveAs = uicontrol('String','save as','FontSize',10,'Units','normalized','Position',[.75,.01,.1,.05],'FontUnits','normalized','visible','off','Callback', @buttonSaveAs_Callback);
hbuttonPlay = uicontrol('String','play','FontSize',10,'Units','normalized','Position',[.05,.01,.1,.05],'FontUnits','normalized','visible','off','Callback', @buttonPlay_Callback);
hbuttonPlayAll = uicontrol('String','play all','FontSize',10,'Units','normalized','Position',[.15,.01,.1,.05],'FontUnits','normalized','visible','off','Callback', @buttonPlayAll_Callback);
hbuttonShowFigures = uicontrol('String','open figures','FontSize',10,'Units','normalized','Position',[.3,.01,.125,.05],'FontUnits','normalized','visible','off','Callback', @buttonShowFigures_Callback);
%Slider
hsliderRecordLength = uicontrol('Parent',hp1,'Style','slider','Min',1,'Max',30,'SliderStep',[1 1]./29,'Value',10,'Units','normalized','Position',[.1 .5 .8 .3],'FontUnits','normalized','Callback',@sliderRecordLength_Callback);
hsliderShowLength = uicontrol('Parent',hp2,'Style','slider','Min',1,'Max',30,'SliderStep',[1 1]./29,'Value',5,'Units','normalized','Position',[.1 .5 .8 .3],'FontUnits','normalized','Callback',@sliderShowLength_Callback);
%Edit fields
heditRecordLength=uicontrol('Parent',hp1,'Style','edit','String',10,'Units','normalized','Position',[.5 .2 .4 .3],'FontUnits','normalized','Callback',@editRecordLength_Callback);
heditShowLength=uicontrol('Parent',hp2,'Style','edit','String',5,'Units','normalized','Position',[.5 .2 .4 .3],'FontUnits','normalized','Callback',@editShowLength_Callback);
heditFrequencyWindow1=uicontrol('Parent',hp3,'Style','edit','String',0,'Units','normalized','Position',[.1 .1 .3 .8],'FontUnits','normalized','Callback',@editFrequencyWindow1_Callback);
heditFrequencyWindow2=uicontrol('Parent',hp3,'Style','edit','String',3000,'Units','normalized','Position',[.5 .1 .4 .8],'FontUnits','normalized','Callback',@editFrequencyWindow2_Callback);
ha1=axes('Units','Pixels','Position',[100,300,750,250],'Units','normalized','FontUnits','normalized','Layer','top','visible','off');
ha2=axes('Units','Pixels','Position',[100,50,750,200],'Units','normalized','FontUnits','normalized','Layer','top','visible','off');
% Global variables
global RecordLength ShowLength FrequencyWindow1 FrequencyWindow2 myRecording fs mag;
%Initialisation
RecordLength = str2double(get(heditRecordLength,'String'));
ShowLength = str2double(get(heditShowLength,'String'));
FrequencyWindow1 = str2double(get(heditFrequencyWindow1,'String'));
FrequencyWindow2 = str2double(get(heditFrequencyWindow2,'String'));
% Initialize the GUI.
set(f,'Name','Audio recording and live visualisation') % Assign the GUI a name to appear in the window title.
movegui(f,'center') % Move the GUI to the center of the screen.
set(f,'Visible','on'); % Make the GUI visible.
end
%% BUTTONS
% Push button callbacks. Each callback plots current_data in the
% specified plot type.
function buttonStart_Callback(~,~)
RecordLength = str2double(get(heditRecordLength,'String'));
ShowLength = str2double(get(heditShowLength,'String'));
FrequencyWindow1 = str2double(get(heditFrequencyWindow1,'String'));
FrequencyWindow2 = str2double(get(heditFrequencyWindow2,'String'));
set(hp1,'visible','off');
set(hp2,'visible','off');
set(hp3,'visible','off');
set(ha1,'visible','on');
set(ha2,'visible','on');
set(hbuttonStart,'visible','off')
set(hbuttonPlay,'visible','off')
set(hbuttonPlayAll,'visible','off')
set(hbuttonShowFigures,'visible','off')
set(hbuttonSave,'visible','off')
set(hbuttonSaveAs,'visible','off')
set(hbuttonStartAgain,'visible','on')
liverecording
end
function buttonStartAgain_Callback(~,~)
set(ha1,'visible','off');
set(ha2,'visible','off');
cla(ha1)
cla(ha2)
set(hp1,'visible','on');
set(hp2,'visible','on');
set(hp3,'visible','on');
set(hbuttonStart,'visible','on')
set(hbuttonPlay,'visible','off')
set(hbuttonPlayAll,'visible','off')
set(hbuttonShowFigures,'visible','off')
set(hbuttonSave,'visible','off')
set(hbuttonSaveAs,'visible','off')
set(hbuttonStartAgain,'visible','off')
end
function buttonSave_Callback(~,~)
[y,m,d,h,min,sec]=datevec(now);
audiowrite([num2str(y,'%04.0f') num2str(m,'%02.0f') num2str(d,'%02.0f') '-' num2str(h,'%02.0f') '''' num2str(min,'%02.0f') '''''' num2str(floor(sec),'%02.0f') '.wav'],myRecording,fs);
end
function buttonSaveAs_Callback(~,~)
[y,m,d,h,min,sec]=datevec(now);
[file,path] = uiputfile([num2str(y,'%04.0f') num2str(m,'%02.0f') num2str(d,'%02.0f') '-' num2str(h,'%02.0f') '''' num2str(min,'%02.0f') '''''' num2str(floor(sec),'%02.0f') '.wav'],'Save record');
audiowrite([path file],myRecording,fs);
end
function buttonPlay_Callback(~,~)
xx = round(get(ha1,'Xlim')*fs);
sound(myRecording(max(1,xx(1)):min(xx(2),size(myRecording,1))), fs);
end
function buttonPlayAll_Callback(~,~)
sound(myRecording, fs);
end
function buttonShowFigures_Callback(~,~)
figure
plot((1:size(myRecording,1))./fs,myRecording)
ylim([-1.2 1.2]*mag)
xlim([0 max(size(myRecording,1)/fs,ShowLength)])
figure
spectrogram(myRecording,2^9,2^7,2^12,fs)
xlim([FrequencyWindow1 FrequencyWindow2])
view(-90,90)
set(gca,'ydir','reverse')
set(gca, 'YTick', []);
ylim([0 max(size(myRecording,1)/fs,ShowLength)])
end
%% EDITFIELDS
function editRecordLength_Callback(~,~)
RecordLength = str2double(get(heditRecordLength,'String'));
set(hsliderRecordLength,'value',RecordLength);
end
function editShowLength_Callback(~,~)
ShowLength = str2double(get(heditShowLength,'String'));
set(hsliderShowLength,'value',ShowLength);
end
function editFrequencyWindow1_Callback(~,~)
FrequencyWindow1 = str2double(get(heditFrequencyWindow1,'String'));
end
function editFrequencyWindow2_Callback(~,~)
FrequencyWindow2 = str2double(get(heditFrequencyWindow2,'String'));
end
%% SLIDER
function sliderRecordLength_Callback(~,~)
RecordLength = round(get(hsliderRecordLength,'value'));
set(heditRecordLength,'string',num2str(RecordLength));
end
function sliderShowLength_Callback(~,~)
ShowLength = round(get(hsliderShowLength,'value'));
set(heditShowLength,'string',num2str(ShowLength));
end
%% GENERAL FUNCTIONS
function liverecording
fs = 44100;
nBits = 16;
mag = 1.05;
plot(ha1,0,0);
ylim(ha1,[-mag mag])
xlim(ha1,[0 RecordLength])
xlabel(ha1,'Time [s]')
idx_last = 1;
recObj = audiorecorder(fs,nBits,1);
record(recObj,RecordLength);
tic
while toc<.1
end
tic
bit = 2;
while toc<RecordLength
myRecording = getaudiodata(recObj);
idx = round(toc*fs);
while idx-idx_last<.1*fs
idx = round(toc*fs);
end
plot(ha1,(max(1,size(myRecording,1)-fs*ShowLength):(2^bit):size(myRecording,1))./fs,myRecording(max(1,size(myRecording,1)-fs*ShowLength):(2^bit):end))
mag = max(abs(myRecording));
ylim(ha1,[-1.2 1.2]*mag)
xlim(ha1,[max(0,size(myRecording,1)/fs-ShowLength) max(size(myRecording,1)/fs,ShowLength)])
spectrogram(myRecording(max(1,size(myRecording,1)-fs*ShowLength):(2^bit):end),2^9/(2^bit),2^7/(2^bit),2^12/(2^bit),fs/(2^bit))
xlim(ha2,[FrequencyWindow1 FrequencyWindow2])
ylim(ha2,[0 ShowLength])
view(ha2,-90,90)
set(gca,'ydir','reverse')
set(gca, 'YTick', []);
drawnow
idx_last = idx;
end
endrecording
end
function endrecording
set(hbuttonPlay,'visible','on')
set(hbuttonPlayAll,'visible','on')
set(hbuttonShowFigures,'visible','on')
set(hbuttonSave,'visible','on')
set(hbuttonSaveAs,'visible','on')
end
end
|
github
|
terejanu/AdaptiveGaussianSumFilter-master
|
error_ellipse.m
|
.m
|
AdaptiveGaussianSumFilter-master/error_ellipse.m
| 8,394 |
utf_8
|
89c9afde8aeb5b09c8a1fa777a9a8b9b
|
function h=error_ellipse(varargin)
% ERROR_ELLIPSE - plot an error ellipse, or ellipsoid, defining confidence region
% ERROR_ELLIPSE(C22) - Given a 2x2 covariance matrix, plot the
% associated error ellipse, at the origin. It returns a graphics handle
% of the ellipse that was drawn.
%
% ERROR_ELLIPSE(C33) - Given a 3x3 covariance matrix, plot the
% associated error ellipsoid, at the origin, as well as its projections
% onto the three axes. Returns a vector of 4 graphics handles, for the
% three ellipses (in the X-Y, Y-Z, and Z-X planes, respectively) and for
% the ellipsoid.
%
% ERROR_ELLIPSE(C,MU) - Plot the ellipse, or ellipsoid, centered at MU,
% a vector whose length should match that of C (which is 2x2 or 3x3).
%
% ERROR_ELLIPSE(...,'Property1',Value1,'Name2',Value2,...) sets the
% values of specified properties, including:
% 'C' - Alternate method of specifying the covariance matrix
% 'mu' - Alternate method of specifying the ellipse (-oid) center
% 'conf' - A value betwen 0 and 1 specifying the confidence interval.
% the default is .5 which is the 50% error ellipse.
% 'scale' - Allow the plot the be scaled to difference units.
% 'style' - A plotting style used to format ellipses.
% 'clip' - specifies a clipping radius. Portions of the ellipse, -oid,
% outside the radius will not be shown.
%
% NOTES: C must be positive definite for this function to work properly.
default_properties = struct(...
'C', [], ... % The covaraince matrix (required)
'mu', [], ... % Center of ellipse (optional)
'conf', .5, ... % Percent confidence/100
'scale', 1, ... % Scale factor, e.g. 1e-3 to plot m as km
'style', '', ... % Plot style
'clip', inf); % Clipping radius
if length(varargin) >= 1 & isnumeric(varargin{1})
default_properties.C = varargin{1};
varargin(1) = [];
end
if length(varargin) >= 1 & isnumeric(varargin{1})
default_properties.mu = varargin{1};
varargin(1) = [];
end
if length(varargin) >= 1 & isnumeric(varargin{1})
default_properties.conf = varargin{1};
varargin(1) = [];
end
if length(varargin) >= 1 & isnumeric(varargin{1})
default_properties.scale = varargin{1};
varargin(1) = [];
end
if length(varargin) >= 1 & ~ischar(varargin{1})
error('Invalid parameter/value pair arguments.')
end
prop = getopt(default_properties, varargin{:});
C = prop.C;
if isempty(prop.mu)
mu = zeros(length(C),1);
else
mu = prop.mu;
end
conf = prop.conf;
scale = prop.scale;
style = prop.style;
if conf <= 0 | conf >= 1
error('conf parameter must be in range 0 to 1, exclusive')
end
[r,c] = size(C);
if r ~= c | (r ~= 2 & r ~= 3)
error(['Don''t know what to do with ',num2str(r),'x',num2str(c),' matrix'])
end
x0=mu(1);
y0=mu(2);
% Compute quantile for the desired percentile
%k = sqrt(qchisq(conf,r)); % r is the number of dimensions (degrees of freedom)
%%%%%%%%%%%%%%%%
k = 1; % GT
%%%%%%%%%%%%%%%%%
hold_state = get(gca,'nextplot');
if r==3 & c==3
z0=mu(3);
% Make the matrix has positive eigenvalues - else it's not a valid covariance matrix!
if any(eig(C) <=0)
error('The covariance matrix must be positive definite (it has non-positive eigenvalues)')
end
% C is 3x3; extract the 2x2 matricies, and plot the associated error
% ellipses. They are drawn in space, around the ellipsoid; it may be
% preferable to draw them on the axes.
Cxy = C(1:2,1:2);
Cyz = C(2:3,2:3);
Czx = C([3 1],[3 1]);
[x,y,z] = getpoints(Cxy,prop.clip);
h1=plot3(x0+k*x,y0+k*y,z0+k*z,prop.style);hold on
[y,z,x] = getpoints(Cyz,prop.clip);
h2=plot3(x0+k*x,y0+k*y,z0+k*z,prop.style);hold on
[z,x,y] = getpoints(Czx,prop.clip);
h3=plot3(x0+k*x,y0+k*y,z0+k*z,prop.style);hold on
[eigvec,eigval] = eig(C);
[X,Y,Z] = ellipsoid(0,0,0,1,1,1);
XYZ = [X(:),Y(:),Z(:)]*sqrt(eigval)*eigvec';
X(:) = scale*(k*XYZ(:,1)+x0);
Y(:) = scale*(k*XYZ(:,2)+y0);
Z(:) = scale*(k*XYZ(:,3)+z0);
h4=surf(X,Y,Z);
colormap gray
alpha(0.3)
camlight
if nargout
h=[h1 h2 h3 h4];
end
elseif r==2 & c==2
% Make the matrix has positive eigenvalues - else it's not a valid covariance matrix!
if any(eig(C) <=0)
error('The covariance matrix must be positive definite (it has non-positive eigenvalues)')
end
[x,y,z] = getpoints(C,prop.clip);
h1=plot(scale*(x0+k*x),scale*(y0+k*y),prop.style);
set(h1,'zdata',z+1)
if nargout
h=h1;
end
else
error('C (covaraince matrix) must be specified as a 2x2 or 3x3 matrix)')
end
%axis equal
set(gca,'nextplot',hold_state);
%---------------------------------------------------------------
% getpoints - Generate x and y points that define an ellipse, given a 2x2
% covariance matrix, C. z, if requested, is all zeros with same shape as
% x and y.
function [x,y,z] = getpoints(C,clipping_radius)
n=100; % Number of points around ellipse
p=0:pi/n:2*pi; % angles around a circle
[eigvec,eigval] = eig(C); % Compute eigen-stuff
xy = [cos(p'),sin(p')] * sqrt(eigval) * eigvec'; % Transformation
x = xy(:,1);
y = xy(:,2);
z = zeros(size(x));
% Clip data to a bounding radius
if nargin >= 2
r = sqrt(sum(xy.^2,2)); % Euclidian distance (distance from center)
x(r > clipping_radius) = nan;
y(r > clipping_radius) = nan;
z(r > clipping_radius) = nan;
end
%---------------------------------------------------------------
function x=qchisq(P,n)
% QCHISQ(P,N) - quantile of the chi-square distribution.
if nargin<2
n=1;
end
s0 = P==0;
s1 = P==1;
s = P>0 & P<1;
x = 0.5*ones(size(P));
x(s0) = -inf;
x(s1) = inf;
x(~(s0|s1|s))=nan;
for ii=1:14
dx = -(pchisq(x(s),n)-P(s))./dchisq(x(s),n);
x(s) = x(s)+dx;
if all(abs(dx) < 1e-6)
break;
end
end
%---------------------------------------------------------------
function F=pchisq(x,n)
% PCHISQ(X,N) - Probability function of the chi-square distribution.
if nargin<2
n=1;
end
F=zeros(size(x));
if rem(n,2) == 0
s = x>0;
k = 0;
for jj = 0:n/2-1;
k = k + (x(s)/2).^jj/factorial(jj);
end
F(s) = 1-exp(-x(s)/2).*k;
else
for ii=1:numel(x)
if x(ii) > 0
F(ii) = quadl(@dchisq,0,x(ii),1e-6,0,n);
else
F(ii) = 0;
end
end
end
%---------------------------------------------------------------
function f=dchisq(x,n)
% DCHISQ(X,N) - Density function of the chi-square distribution.
if nargin<2
n=1;
end
f=zeros(size(x));
s = x>=0;
f(s) = x(s).^(n/2-1).*exp(-x(s)/2)./(2^(n/2)*gamma(n/2));
%---------------------------------------------------------------
function properties = getopt(properties,varargin)
%GETOPT - Process paired optional arguments as 'prop1',val1,'prop2',val2,...
%
% getopt(properties,varargin) returns a modified properties structure,
% given an initial properties structure, and a list of paired arguments.
% Each argumnet pair should be of the form property_name,val where
% property_name is the name of one of the field in properties, and val is
% the value to be assigned to that structure field.
%
% No validation of the values is performed.
%
% EXAMPLE:
% properties = struct('zoom',1.0,'aspect',1.0,'gamma',1.0,'file',[],'bg',[]);
% properties = getopt(properties,'aspect',0.76,'file','mydata.dat')
% would return:
% properties =
% zoom: 1
% aspect: 0.7600
% gamma: 1
% file: 'mydata.dat'
% bg: []
%
% Typical usage in a function:
% properties = getopt(properties,varargin{:})
% Process the properties (optional input arguments)
prop_names = fieldnames(properties);
TargetField = [];
for ii=1:length(varargin)
arg = varargin{ii};
if isempty(TargetField)
if ~ischar(arg)
error('Propery names must be character strings');
end
f = find(strcmp(prop_names, arg));
if length(f) == 0
error('%s ',['invalid property ''',arg,'''; must be one of:'],prop_names{:});
end
TargetField = arg;
else
% properties.(TargetField) = arg; % Ver 6.5 and later only
properties = setfield(properties, TargetField, arg); % Ver 6.1 friendly
TargetField = '';
end
end
if ~isempty(TargetField)
error('Property names and values must be specified in pairs.');
end
|
github
|
masumhabib/quest-master
|
importBandResult.m
|
.m
|
quest-master/utils/matlab/importBandResult.m
| 899 |
utf_8
|
7ff4feab1a9082e50da7c2b663fd9af9
|
%
% Copyright (C) 2014 K M Masum Habib <[email protected]>
%
function out = importBandResult(fileName)
fid = fopen(fileName, 'rt');
out = [];
while (~feof(fid))
type = fscanf(fid, '%s[^\n]');
if strfind(type, 'EK') == 1
out.EK = scan();
elseif strfind(type, 'EIGENVECTOR') == 1
out.DOS = scan();
end
end
fclose(fid);
function M = scan()
Nk = fscanf(fid, '%d[^\n]');
data = fscanf(fid, '%d %d[^\n]');
m = data(1);
n = data(2);
for ik = 1:Nk
M.k(ik,:) = fscanf(fid, '%f %f %f[^\n]');
M.M{ik} = zeros(m,n);
for ii = 1:m
for jj = 1:n
data = fscanf(fid, '%*[ \n\t]%e', 1);
M.M{ik}(ii,jj) = data(1);
end
end
end
end
end
|
github
|
masumhabib/quest-master
|
importTransResult.m
|
.m
|
quest-master/utils/matlab/importTransResult.m
| 1,603 |
utf_8
|
fcadd5373709cc91cec1eed7e966bb96
|
%
% Copyright (C) 2014 K M Masum Habib <[email protected]>
%
function out = importTransResult(fileName)
fid = fopen(fileName, 'rt');
out = [];
ibIE = 1;
ibn = 1;
ibneq = 1;
while (~feof(fid))
type = fscanf(fid, '%s[^\n]');
if strfind(type, 'ENERGY') == 1
[out.NE, out.E] = scanE();
elseif strfind(type, 'TRANSMISSION') == 1
out.TE = scan();
elseif strfind(type, 'CURRENT') == 1
out.IE{ibIE} = scan();
ibIE = ibIE + 1;
elseif strfind(type, 'DOS') == 1
out.DOS = scan();
elseif strfind(type, 'n') == 1
out.n{ibn} = scan();
ibn = ibn + 1;
elseif strfind(type, 'neq') == 1
out.neq{ibneq} = scan();
ibneq = ibneq + 1;
end
end
fclose(fid);
function M = scan()
NE = fscanf(fid, '%d[^\n]');
%M.NE = NE;
tmp = fscanf(fid, '%d %d[^\n]');
M.ib = tmp(1); M.jb = tmp(2);
N = fscanf(fid, '%d[^\n]');
%M.N = N;
for iE = 1:NE
%M.E(iE) = fscanf(fid, '%f[^\n]');
M.M{iE} = zeros(N,N);
for ii = 1:N
for jj = 1:N
data = fscanf(fid, '%*[ \n\t](%e,%e)', 2);
M.M{iE}(ii,jj) = data(1) + 1i*data(2);
end
end
end
end
function [NE, EE] = scanE()
NE = fscanf(fid, '%d[^\n]');
EE = zeros(NE, 1);
for iE = 1:NE
EE(iE) = fscanf(fid, '%f[^\n]');
end
end
end
|
github
|
masumhabib/quest-master
|
importPotential.m
|
.m
|
quest-master/utils/matlab/importPotential.m
| 334 |
utf_8
|
bcd8483c35f0173a6b1605c72bd0258f
|
%
% Copyright (C) 2014 K M Masum Habib <[email protected]>
%
function [X, Y, Z, V] = importPotential(fileName)
fid = fopen(fileName, 'rt');
X = [];
Y = [];
Z = [];
V = [];
data = load(fileName);
X = data(:,1);
Y = data(:,2);
Z = data(:,3);
V = data(:,4);
fclose(fid);
end
|
github
|
NYU-DiffusionMRI/mppca_denoise-master
|
MPdenoising.m
|
.m
|
mppca_denoise-master/MPdenoising.m
| 8,173 |
utf_8
|
e486f43bc82b9c4a0ebf3bd3a095b504
|
function [Signal, Sigma] = MPdenoising(data, mask, kernel, sampling, centering)
%
% "MPPCA": 4d image denoising and noise map estimation by exploiting data redundancy in the PCA domain using universal properties of the eigenspectrum of
% random covariance matrices, i.e. Marchenko Pastur distribution
%
% [Signal, Sigma] = MPdenoising(data, mask, kernel, sampling)
% output:
% - Signal: [x, y, z, M] denoised data matrix
% - Sigma: [x, y, z] noise map
% input:
% - data: [x, y, z, M] data matrix
% - mask: (optional) region-of-interest [boolean]
% - kernel: (optional) window size, typically in order of [5 x 5 x 5]
% - sampling:
% 1. full: sliding window (default for noise map estimation, i.e. [Signal, Sigma] = MPdenoising(...) )
% 2. fast: block processing (default for denoising, i.e. [Signal] = MPdenoising(...))
%
% Authors: Jelle Veraart ([email protected])
% Copyright (c) 2016 New York Universit and University of Antwerp
%
% Permission is hereby granted, free of charge, to any non-commercial entity
% ('Recipient') obtaining a copy of this software and associated
% documentation files (the 'Software'), to the Software solely for
% non-commercial research, including the rights to use, copy and modify the
% Software, subject to the following conditions:
%
% 1. The above copyright notice and this permission notice shall be
% included by Recipient in all copies or substantial portions of the
% Software.
%
% 2. THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
% EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIESOF
% MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
% NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BELIABLE FOR ANY CLAIM,
% DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
% OTHERWISE, ARISING FROM, OUT OF ORIN CONNECTION WITH THE SOFTWARE OR THE
% USE OR OTHER DEALINGS IN THE SOFTWARE.
%
% 3. In no event shall NYU be liable for direct, indirect, special,
% incidental or consequential damages in connection with the Software.
% Recipient will defend, indemnify and hold NYU harmless from any claims or
% liability resulting from the use of the Software by recipient.
%
% 4. Neither anything contained herein nor the delivery of the Software to
% recipient shall be deemed to grant the Recipient any right or licenses
% under any patents or patent application owned by NYU.
%
% 5. The Software may only be used for non-commercial research and may not
% be used for clinical care.
%
% 6. Any publication by Recipient of research involving the Software shall
% cite the references listed below.
%
% REFERENCES
% Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping
% using random matrix theory Magn. Res. Med., 2016, early view, doi:
% 10.1002/mrm.26059
if isa(data,'integer')
data = single(data);
end
[sx, sy, sz, M] = size(data);
if ~exist('mask', 'var') || isempty(mask)
mask = true([sx, sy, sz]);
end
if ~isa(mask,'boolean')
mask = mask>0;
end
if ~exist('kernel', 'var') || isempty(kernel)
kernel = [5 5 5];
end
if isscalar(kernel)
kernel = [kernel, kernel, kernel];
end
kernel = kernel + (mod(kernel, 2)-1); % needs to be odd.
k = (kernel-1)/2; kx = k(1); ky = k(2); kz = k(3);
N = prod(kernel);
if ~exist('sampling', 'var') || isempty(sampling)
if nargout > 1
sampling = 'full';
else
sampling = 'fast';
end
end
% create mask
if ~exist('mask', 'var') || isempty(mask)
mask = true(sx, sy, sz);
end
if ~exist('centering', 'var') || isempty(centering)
centering = false;
end
if strcmp(sampling, 'fast')
if nargout>1
warning('undersampled noise map will be returned')
end
% compute center points of patches
stats = regionprops(mask, 'BoundingBox');
n = ceil(stats.BoundingBox(4:6) ./ kernel);
x = linspace(ceil(stats.BoundingBox(1))+k(1), floor(stats.BoundingBox(1))-k(1) + stats.BoundingBox(4), n(1)); x = round(x);
y = linspace(ceil(stats.BoundingBox(2))+k(2), floor(stats.BoundingBox(2))-k(2) + stats.BoundingBox(5), n(2)); y = round(y);
z = linspace(ceil(stats.BoundingBox(3))+k(3), floor(stats.BoundingBox(3))-k(3) + stats.BoundingBox(6), n(3)); z = round(z);
[y, x, z] = meshgrid(x, y, z); x = x(:); y = y(:); z = z(:);
end
if strcmp(sampling, 'full')
warning('image bounderies are not processed.')
mask(1:k(1), :, :) = 0;
mask(sx-k(1)+1:sx, :, :) = 0;
mask(:, 1:k(2), :) = 0;
mask(:, sy-k(2)+1:sy, :, :) = 0;
mask(:,:,1:k(3)) = 0;
mask(:,:,sz-k(3)+1:sz) = 0;
x = []; y = []; z = [];
for i = k(3)+1:sz-k(3)
[x_, y_] = find(mask(:,:,i) == 1);
x = [x; x_]; y = [y; y_]; z = [z; i*ones(size(y_))];
end
x = x(:); y = y(:); z = z(:);
end
% Declare variables:
sigma = zeros(1, numel(x), 'like', data);
npars = zeros(1, numel(x), 'like', data);
signal = zeros(M, prod(kernel), numel(x), 'like', data);
Sigma = zeros(sx, sy, sz, 'like', data);
Npars = zeros(sx, sy, sz, 'like', data);
Signal = zeros(sx, sy, sz, M, 'like', data);
% compute scaling factor for in case N<M
R = min(M, N);
scaling = (max(M, N) - (0:R-centering-1)) / N;
scaling = scaling(:);
% start denoising
for nn = 1:numel(x)
% create data matrix
X = data(x(nn)-kx:x(nn)+kx, y(nn)-ky:y(nn)+ky, z(nn)-kz:z(nn)+kz, :);
X = reshape(X, N, M); X = X';
if centering
colmean = mean(X, 1);
X = X - repmat(colmean, [M, 1]);
end
% compute PCA eigenvalues
[u, vals, v] = svd(X, 'econ');
vals = diag(vals).^2 / N;
% First estimation of Sigma^2; Eq 1 from ISMRM presentation
csum = cumsum(vals(R-centering:-1:1)); cmean = csum(R-centering:-1:1)./(R-centering:-1:1)'; sigmasq_1 = cmean./scaling;
% Second estimation of Sigma^2; Eq 2 from ISMRM presentation
gamma = (M - (0:R-centering-1)) / N;
rangeMP = 4*sqrt(gamma(:));
rangeData = vals(1:R-centering) - vals(R-centering);
sigmasq_2 = rangeData./rangeMP;
% sigmasq_2 > sigma_sq1 if signal-components are represented in the
% eigenvalues
t = find(sigmasq_2 < sigmasq_1, 1);
if isempty(t)
sigma(nn) = NaN;
signal(:, :, nn) = X;
t = R+1;
else
sigma(nn) = sqrt(sigmasq_1(t));
vals(t:R) = 0;
s = u*diag(sqrt(N*vals))*v';
if centering
s = s + repmat(colmean, [M, 1]);
end
signal(:, :, nn) = s;
end
npars(nn) = t-1;
end
for nn = 1:numel(x)
Sigma(x(nn), y(nn), z(nn)) = sigma(nn);
Npars(x(nn), y(nn), z(nn)) = npars(nn);
if strcmp(sampling, 'fast')
Signal(x(nn)-k(1):x(nn)+k(1),y(nn)-k(2):y(nn)+k(2),z(nn)-k(3):z(nn)+k(3), :) = unpatch(signal(:,:,nn), k);
elseif strcmp(sampling, 'full')
Signal(x(nn), y(nn),z(nn), :) = signal(:,ceil(prod(kernel)/ 2),nn);
end
end
end
function data = unpatch(X, k)
kernel=k+k+1;
data = zeros([kernel, size(X, 1)]);
tmp = zeros(kernel);
for i = 1:size(X, 1);
tmp(:) = X(i, :);
data(:,:,:,i) = tmp;
end
end
|
github
|
NYU-DiffusionMRI/mppca_denoise-master
|
MP.m
|
.m
|
mppca_denoise-master/MP.m
| 5,946 |
utf_8
|
3b5355d0743ba9e04980fb6755227d1d
|
function [Xdn, sigma, npars] = MP(X, nbins, centering)
% "MP": matrix denoising and noiseestimation by exploiting data redundancy in the PCA domain using universal properties of the eigenspectrum of
% random covariance matrices, i.e. Marchenko Pastur distribution
%
% [Xdn, Sigma, npars] = MP(X, nbins)
% output:
% - Xdn: [MxN] denoised data matrix
% - sigma: [1x1] noise level
% - npars: [1x1] number of significant components
% input:
% - X: [MxN] data matrix
% - nbins: number of histogram bins for visualization. If
% empty or not provided, no graphs will be shown.
%
% Author: Jelle Veraart ([email protected])
% Copyright (c) 2016 New York University
%
% Permission is hereby granted, free of charge, to any non-commercial entity
% ('Recipient') obtaining a copy of this software and associated
% documentation files (the 'Software'), to the Software solely for
% non-commercial research, including the rights to use, copy and modify the
% Software, subject to the following conditions:
%
% 1. The above copyright notice and this permission notice shall be
% included by Recipient in all copies or substantial portions of the
% Software.
%
% 2. THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
% EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIESOF
% MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
% NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BELIABLE FOR ANY CLAIM,
% DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
% OTHERWISE, ARISING FROM, OUT OF ORIN CONNECTION WITH THE SOFTWARE OR THE
% USE OR OTHER DEALINGS IN THE SOFTWARE.
%
% 3. In no event shall NYU be liable for direct, indirect, special,
% incidental or consequential damages in connection with the Software.
% Recipient will defend, indemnify and hold NYU harmless from any claims or
% liability resulting from the use of the Software by recipient.
%
% 4. Neither anything contained herein nor the delivery of the Software to
% recipient shall be deemed to grant the Recipient any right or licenses
% under any patents or patent application owned by NYU.
%
% 5. The Software may only be used for non-commercial research and may not
% be used for clinical care.
%
% 6. Any publication by Recipient of research involving the Software shall
% cite the references listed below.
%
% REFERENCES
% Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping
% using random matrix theory Magn. Res. Med., 2016, early view, doi:
% 10.1002/mrm.26059
% Veraart, J.; Novikov, D.S.; Christiaens, D.; Ades-Aron, B.; Sijbers, J. & Fieremans, E.
% Denoising of diffusion MRI using random matrix theory, NeuroImage, Magn. Res. Med., 2016, early view,
% DOI: 10.1016/j.neuroimage.2016.08.016
if ~exist('nbins', 'var') || isempty(nbins)
nbins=0;
end
[M, N] = size(X);
if ~exist('centering', 'var') || isempty(centering)
centering = false;
end
if centering
colmean = mean(X, 1);
X = X - repmat(colmean, [M, 1]);
end
R = min(M, N);
scaling = ones(R-centering, 1);
if M>N
%scaling = M/N;
scaling = (M - (0:R-centering-1)) / N;
scaling(scaling<1) = 1;
scaling = scaling(:);
end
[u, vals, v] = svd(X, 'econ');
vals = diag(vals).^2 / N;
csum = cumsum(vals(R-centering:-1:1)); cmean = csum(R-centering:-1:1)./(R-centering:-1:1)'; sigmasq_1 = cmean./scaling;
gamma = (M - (0:R-centering-1)) / N;
rangeMP = 4*sqrt(gamma(:));
rangeData = vals(1:R-centering) - vals(R-centering);
sigmasq_2 = rangeData./rangeMP;
t = find(sigmasq_2 < sigmasq_1, 1);
sigma = sqrt(sigmasq_1(t));
npars = t-1;
if nbins>0
[~, range] = MarchenkoPasturDistribution(rand(), sigma, M-npars, N);
[p, ~] = MarchenkoPasturDistribution([range(1):diff(range)/100:range(2)], sigma, M-npars, N);
figure;
hold on
range_ = [vals(R-centering), vals(npars+1)];
binwidth = diff(range_)/nbins; % Finds the width of each bin.
scale = M * binwidth;
x = histc(vals(1:R-centering), [range_(1):diff(range_)/(nbins-1):range_(2)]);
bar([range_(1):diff(range_)/(nbins-1):range_(2)], x/nansum(p))
plot([range(1):diff(range)/100:range(2)], real(p)*scale/nansum(p), 'r', 'LineWidth', 3)
xlabel('$\lambda$', 'FontName', 'Times', 'FontSize', 20, 'Interpreter', 'Latex')
ylabel('$p(\lambda$)', 'FontName', 'Times', 'FontSize', 20, 'Interpreter', 'Latex')
set(gca, 'FontSize', 20, 'box', 'on', 'LineWidth', 2, 'FontSize', 20);
title(['sigma = ', num2str(sigma), ' and npars = ', num2str(npars)])
end
vals(t:R) = 0;
Xdn = u*diag(sqrt(N*vals))*v';
if centering
Xdn = Xdn + repmat(colmean, [M, 1]);
end
end
function [p, range] = MarchenkoPasturDistribution(lambda, sigma, M, N)
Q = M/N;
lambda_p = sigma^2*(1 + sqrt(Q)).^2;
lambda_m = sigma^2*(1 - sqrt(Q)).^2;
p = sqrt((lambda_p - lambda).*(lambda-lambda_m))./(2*pi*Q*lambda*sigma.^2);
p(lambda < lambda_m) = 0;
p(lambda > lambda_p) = 0;
range = [lambda_m, lambda_p];
end
|
github
|
NYU-DiffusionMRI/mppca_denoise-master
|
MPnonlocal.m
|
.m
|
mppca_denoise-master/MPnonlocal.m
| 12,883 |
utf_8
|
9df62ed192b176dcf7fa65ac94b73ddb
|
function [Signal, varargout] = MPnonlocal(data, varargin)
% MPnonlocal Denoise 4d magnitude data (x, y, z, dirs) or 5d complex data
% (x, y, z, coils, dirs) and estimate 3d noise maps and significant
% parameter maps using nonlocal patching and eigenvalue shrinkage in
% the MPPCA framework
%
% [Signal, Sigma, Nparams] = MPnonlocal(data, kernel, patchsize, norm)
% output:
% - Signal: [x, y, z, N] for 4d (real or complex) [x, y, z, C, N] for 5d
% (complex)
% - Sigma: [x, y, z] noise map
% - Npars: [x, y, z] map of the number of signal carrying
% components
% - Sigma_after: [x, y, z] noise map (sigma after denoiosing) estimated using Jespersen et al
% method
% input:
% - data: [x, y, z, N] for 4d (real or complex) [x, y, z, C, N] for 5d
% (complex)
% - kernel: (optional) default smallest isotropic box window where prod(kernel) > n volumes. Must be odd.
% - patchtype: (optional) default is 'box'. Can alternatively be
% set to 'nonlocal'
% - patchsize: (optional) Number of voxels to include in nonlocal
% patch. For nonlocal denoising only. n volumes < patch size <
% prod(kernel). If it is not set a default option of a nonlocal
% patch 20% smaller than prod(kernel) will be used.
% - shrink: (optional) default 'threshold'. Can be set to 'threshold' for hard
% thresholding of eigenvalues or 'frob' to implement eigenvalue
% shrinkage using the frobenius norm.
% - exp: (optional) default is 1. Options are 1, 2 and 3
% correspoinding to Veraart 2016, Cordero-Grande, and the
% "in-between method" respectively.
%
% usage:
% - box patch denoising:
% [Signal, Sigma, Npars] = MPnonlocal(data, [5,5,5])
% - shrinkage denoisng:
% [Signal, Sigma, Npars] = MPnonlocal(data, [5,5,5], 'patchtype','box','shrink','frob')
% - nonlocal denoisng:
% [Signal, Sigma, Npars] = MPnonlocal(data, [5,5,5], 'patchtype','nonlocal','patchsize',100)
%
% Authors: Benjamin Ades-Aron ([email protected])
% Jelle Veraart ([email protected])
% Gregory Lemberskiy ([email protected])
% Copyright (c) 2020 New York University
%
% Permission is hereby granted, free of charge, to any non-commercial entity
% ('Recipient') obtaining a copy of this software and associated
% documentation files (the 'Software'), to the Software solely for
% non-commercial research, including the rights to use, copy and modify the
% Software, subject to the following conditions:
%
% 1. The above copyright notice and this permission notice shall be
% included by Recipient in all copies or substantial portions of the
% Software.
%
% 2. THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
% EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIESOF
% MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
% NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BELIABLE FOR ANY CLAIM,
% DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
% OTHERWISE, ARISING FROM, OUT OF ORIN CONNECTION WITH THE SOFTWARE OR THE
% USE OR OTHER DEALINGS IN THE SOFTWARE.
%
% 3. In no event shall NYU be liable for direct, indirect, special,
% incidental or consequential damages in connection with the Software.
% Recipient will defend, indemnify and hold NYU harmless from any claims or
% liability resulting from the use of the Software by recipient.
%
% 4. Neither anything contained herein nor the delivery of the Software to
% recipient shall be deemed to grant the Recipient any right or licenses
% under any patents or patent application owned by NYU.
%
% 5. The Software may only be used for non-commercial research and may not
% be used for clinical care.
%
% 6. Any publication by Recipient of research involving the Software shall
% cite the references listed below.
%
% REFERENCES
% Veraart, J.; Fieremans, E. & Novikov, D.S. Diffusion MRI noise mapping
% using random matrix theory Magn. Res. Med., 2016, early view, doi:
% 10.1002/mrm.26059
% set defaults
if isreal(data)
data = single(data);
else
data = complex(single(data));
end
if ndims(data) > 4
coil = true;
else
coil = false;
end
defaultShrink = 'threshold';
defaultExp = 1;
nvols = size(data, ndims(data));
p_ = (1:2:nvols);
pf_ = find(p_.^3 >= nvols, 1);
defaultKernel = p_(pf_);
defaultPatchtype = 'box';
defaultPatchsize = defaultKernel^3;
defaultCrop = 0;
% parse input arguments
p = inputParser;
addRequired(p,'data');
addOptional(p,'kernel', defaultKernel);
addOptional(p,'patchtype', defaultPatchtype);
addOptional(p,'patchsize', defaultPatchsize);
addOptional(p,'shrink', defaultShrink);
addOptional(p,'exp', defaultExp);
addOptional(p,'crop',defaultCrop);
parse(p, data, varargin{:});
if isscalar(p.Results.kernel)
kernel = [p.Results.kernel, p.Results.kernel, p.Results.kernel];
else
kernel = p.Results.kernel;
end
kernel = kernel + (mod(kernel, 2)-1);
if any(kernel > [size(data,1),size(data,2),size(data,3)])
error(['kernel size of ',num2str(kernel), ' exceeds data size along dimention ',...
num2str(find(kernel>size(data,[1,2,3]))),', specify a smaller kernel extent']);
end
if strcmp(p.Results.patchtype,'box')
psize = prod(kernel);
nonlocal = false;
center_idx = ceil(prod(kernel)/2);
pos_img = [];
elseif strcmp(p.Results.patchtype,'nonlocal')
if p.Results.patchsize >= prod(kernel)
warning('selecting sane default nonlocal patch size')
psize = floor(prod(kernel) - 0.2*prod(kernel));
if psize <= nvols
psize = nvols + 1;
end
else
psize = p.Results.patchsize;
end
nonlocal = true;
center_idx = 1;
else
error('patchtype options are "box" or "nonlocal"');
end
nrm = p.Results.shrink;
exp = p.Results.exp;
cropdist = p.Results.crop;
if p.Results.patchsize ~= prod(kernel) && strcmp(p.Results.patchtype,'box')
warning('patchsize argument does not affect box kernel');
end
disp('Denoising data using parameters:')
disp(['kernel = [',num2str(kernel),']'])
disp(['patch type = ',p.Results.patchtype]);
disp(['patch size = ',num2str(psize)]);
disp(['shrinkage = ',p.Results.shrink]);
disp(['algorithm = ',num2str(exp)]);
disp(['cropdist = ',num2str(cropdist)]);
% begin processing here
k = (kernel-1)/2;
kx = k(1);
ky = k(2);
kz = k(3);
% pad the data in first 3 dimentions
if coil
data = padarray(data, [kx, ky, kz, 0, 0], 'circular');
[sx, sy, sz, sc, N] = size(data);
M = psize*sc;
else
data = padarray(data, [kx, ky, kz, 0], 'circular');
[sx, sy, sz, N] = size(data);
M = psize;
sc = 1;
end
% define a mask that excludes padded values and extract coordinates
[x,y,z] = get_voxel_coords(sx,sy,sz,kx,ky,kz);
if nonlocal
[pi, pj, pk] = ind2sub(kernel, find(ones(kernel)));
patchcoords = cat(2,pi,pj,pk);
pos_img = 1/prod(kernel) * sum((patchcoords - ceil(kernel/2)).^2, 2);
end
% Declare variables:
sigma = zeros(1, numel(x), 'like', data);
sigma_after = zeros(1, numel(x), 'like', data);
npars = zeros(1, numel(x), 'like', data);
Sigma = zeros(sx, sy, sz, 'like', data);
Sigma_after = zeros(sx, sy, sz, 'like', data);
Npars = zeros(sx, sy, sz, 'like', data);
if coil
signal = zeros(sc, N, numel(x), 'like', data);
Signal = zeros(sx, sy, sz, sc, N, 'like', data);
else
signal = zeros(1, N, numel(x), 'like', data);
Signal = zeros(sx, sy, sz, N, 'like', data);
end
% start denoising
%xi = floor(4*length(x)/7);
parfor nn = 1:numel(x)
X = data(x(nn)-kx:x(nn)+kx, y(nn)-ky:y(nn)+ky, z(nn)-kz:z(nn)+kz, :, :);
if coil
X = reshape(X, prod(kernel), sc, N);
else
X = reshape(X, prod(kernel), N);
end
if nonlocal
Xn = normalize(X);
min_idx = refine_patch(Xn, kernel, psize, pos_img, coil);
X = X(min_idx,:,:);
end
X = reshape(X,[M, N]);
[s, sigma(nn), npars(nn), sigma_after(nn)] = denoise(X, nrm, exp, cropdist);
if coil
signal(:,:,nn) = s(center_idx:psize:end,:);
else
signal(:,:,nn) = s(center_idx,:);
end
end
for nn = 1:numel(x)
Sigma(x(nn), y(nn), z(nn)) = sigma(nn);
Sigma_after(x(nn), y(nn), z(nn)) = sigma_after(nn);
Npars(x(nn), y(nn), z(nn)) = npars(nn);
Signal(x(nn), y(nn),z(nn), :, :) = signal(:,:,nn);
end
Sigma = unpad(Sigma,kernel);
Sigma_after = unpad(Sigma_after,kernel);
Npars = unpad(Npars,kernel);
Signal = unpad(Signal,kernel);
varargout{1} = Sigma;
varargout{2} = Npars;
varargout{3} = Sigma_after;
end
function [min_idx] = refine_patch(data, kernel, M, pos_img, coil)
refval = data(ceil(prod(kernel)/2),:,:);
if coil
refval = repmat(refval,[prod(kernel),1,1]);
int_img = 1/(size(data,2)*size(data,3)) * sum((data - refval).^2, [2,3]);
else
refval = repmat(refval,[prod(kernel),1]);
%int_img = 1/size(data,2) * sum((data(:,1) - refval(1)).^2, [2]);
int_img = 1/size(data,2) * sum((data - refval).^2, [2]);
end
wdists = (pos_img .* int_img);
[~,min_idx] = mink(wdists, M);
end
function data_norm = normalize(data)
data_norm = zeros(size(data));
for i = 1:size(data,4)
data_ = data(:,:,:,i);
data_norm(:,:,:,i) = abs(data_./max(data_(:)));
end
end
function [x,y,z] = get_voxel_coords(sx,sy,sz,kx,ky,kz)
mask = true([sx, sy, sz]);
mask(1:kx, :, :) = 0;
mask(:, 1:ky, :) = 0;
mask(:, :, 1:kz) = 0;
mask(sx-kx+1:sx, :, :) = 0;
mask(:, sy-ky+1:sy, :) = 0;
mask(:, :, sz-kz+1:sz) = 0;
maskinds = find(mask);
[x,y,z] = ind2sub(size(mask),maskinds);
end
function data = unpad(data,kernel)
k = (kernel-1)/2;
data = data(k(1)+1:end-k(1),k(2)+1:end-k(2),k(3)+1:end-k(3),:,:);
end
function s = shrink(y, gamma)
% Frobenius norm optimal shrinkage
% Gavish & Donoho IEEE 63, 2137 (2017)
% DOI: 10.1109/TIT.2017.2653801
% Eq (7)
t = 1 + sqrt(gamma);
s = zeros(size(y));
x = y(y > t);
s(y > t) = sqrt((x.^2-gamma-1).^2 - 4*gamma)./x;
end
function [s, sigma, npars, sigma_after] = denoise(X, nrm, exp, tn)
N = size(X,2);
M = size(X,1);
Mp = min(M,N);
Np = max(M,N);
if M < N
X = X.';
end
% compute PCA eigenvalues
[u, vals, v] = svd(X, 'econ');
vals = diag(vals).^2;
[vals, order] = sort(vals,'descend');
u = u(:,order); v = v(:,order);
ptn = (0:Mp-1-tn)';
p = (0:Mp-1)';
csum = cumsum(vals,'reverse');
if exp == 1 % veraart 2016
sigmasq_1 = csum./((Mp-p).*Np);
rangeMP = 4*sqrt((Mp-ptn).*(Np-tn));
elseif exp == 2 % cordero-grande
sigmasq_1 = csum./((Mp-p).*(Np-p));
rangeMP = 4*sqrt((Mp-ptn).*(Np-ptn));
elseif exp == 3 % jespersen
sigmasq_1 = csum./((Mp-p).*(Np-p));
rangeMP = 4*sqrt((Np-tn).*(Mp));
end
rangeData = vals(1:Mp-tn) - vals(Mp-tn);
sigmasq_2 = rangeData./rangeMP;
t = find(sigmasq_2 < sigmasq_1(1:end-tn),1);
if isempty(t)
sigma = NaN;
npars = NaN;
s = X;
sigma_after = NaN;
else
sigma = sqrt(sigmasq_1(t));
npars = t-1;
if strcmp(nrm,'threshold')
vals(t:end) = 0;
s = u*diag(sqrt(vals))*v';
elseif strcmp(nrm,'frob')
vals_frob= sqrt(Mp)*sigma * shrink(sqrt(vals)./(sqrt(Mp)*sigma), Np/Mp);
s = u*(diag(vals_frob))*v';
end
s2_after = sigma.^2 - csum(t)/(Mp*Np);
sigma_after = sqrt(s2_after);
end
if M < N
s = s.';
end
end
|
github
|
CUAir/ardupilot-master
|
RotToQuat.m
|
.m
|
ardupilot-master/libraries/AP_NavEKF/Models/Common/RotToQuat.m
| 288 |
utf_8
|
9239706354267c8f5f2a29f992c07de9
|
% convert froma rotation vector in radians to a quaternion
function quaternion = RotToQuat(rotVec)
vecLength = sqrt(rotVec(1)^2 + rotVec(2)^2 + rotVec(3)^2);
if vecLength < 1e-6
quaternion = [1;0;0;0];
else
quaternion = [cos(0.5*vecLength); rotVec/vecLength*sin(0.5*vecLength)];
end
|
github
|
CUAir/ardupilot-master
|
NormQuat.m
|
.m
|
ardupilot-master/libraries/AP_NavEKF/Models/Common/NormQuat.m
| 198 |
utf_8
|
ed913e87efc9194a2c52b266fced8da7
|
% normalise the quaternion
function quaternion = normQuat(quaternion)
quatMag = sqrt(quaternion(1)^2 + quaternion(2)^2 + quaternion(3)^2 + quaternion(4)^2);
quaternion(1:4) = quaternion / quatMag;
|
github
|
CUAir/ardupilot-master
|
QuatToEul.m
|
.m
|
ardupilot-master/libraries/AP_NavEKF/Models/Common/QuatToEul.m
| 436 |
utf_8
|
c19c9235052d99b8b943a7157e83fc94
|
% Convert from a quaternion to a 321 Euler rotation sequence in radians
function Euler = QuatToEul(quat)
Euler = zeros(3,1);
Euler(1) = atan2(2*(quat(3)*quat(4)+quat(1)*quat(2)), quat(1)*quat(1) - quat(2)*quat(2) - quat(3)*quat(3) + quat(4)*quat(4));
Euler(2) = -asin(2*(quat(2)*quat(4)-quat(1)*quat(3)));
Euler(3) = atan2(2*(quat(2)*quat(3)+quat(1)*quat(4)), quat(1)*quat(1) + quat(2)*quat(2) - quat(3)*quat(3) - quat(4)*quat(4));
|
github
|
skulumani/foucault-master
|
load_constants.m
|
.m
|
foucault-master/matlab/load_constants.m
| 1,176 |
utf_8
|
14da85c48f205f137f1fb2e74e55db1f
|
% load constants for Foucault pendulum
function [constants] = load_constants()
%% define constants
constants.eom = 'full'; % full or len or rot for simplifications
% constants.Omega = 7.2921158553e-5; % rad/sec earth angular velocity
constants.Omega = 7.2921158553e-5;
constants.mu = 3.986004418e14; % m^3/sec
% % original Foucault Pendulum
% constants.L = 67; % meters
% constants.m = 28; % kilograms
% constants.beta = 48.846222*pi/180; % Latitude for the Pantheon, Paris
constants.L = 100;
constants.m = 100;
constants.beta = 40*pi/180; % latitude of pivot location on Earth
constants.Re = 6378.137 * 1e3; % meters radius of the Earth
% constants.g = 9.7976432222; % mean g at equator in meters/sec^2
constants.g = 9.7976432222;
constants.Cbeta = [cos(constants.beta)^2 0 -sin(constants.beta)*cos(constants.beta);...
0 1 0 ;...
-sin(constants.beta)*cos(constants.beta) 0 sin(constants.beta)^2];
constants.S = hat_map(constants.Omega*(ROT2(-constants.beta)'*[0;0;1]));
constants.ode_options = odeset('RelTol',1e-13,'AbsTol',1e-13);
|
github
|
skulumani/foucault-master
|
hat_map.m
|
.m
|
foucault-master/matlab/hat_map.m
| 246 |
utf_8
|
a1515ff3e5d34892df65ff78d2340d60
|
% 8 June 15
% skew symmetric operator
function mat = hat_map(vec)
% maps a 3-vec to a skew symmetric matrix
mat = zeros(3,3);
mat(1,2) = -vec(3);
mat(1,3) = vec(2);
mat(2,1) = vec(3);
mat(2,3) = -vec(1);
mat(3,1) = -vec(2);
mat(3,2) = vec(1);
|
github
|
skulumani/foucault-master
|
ROT2.m
|
.m
|
foucault-master/matlab/ROT2.m
| 594 |
utf_8
|
cc21aff60c155554ebcbed98170a584e
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Purpose: Rotation matrix about second axis
% b = dcm*a
%
% Inputs:
% - beta - rotation angle (rad)
%
% Outpus:
% - rot2 - rotation matrix (3x3)
%
% Dependencies:
% - none
%
% Author: Shankar Kulumani 23 September 2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function rot2 = ROT2(beta)
cos_beta = cos(beta);
sin_beta = sin(beta);
rot2 = [cos_beta 0 sin_beta; ...
0 1 0 ; ...
-sin_beta 0 cos_beta ];
end
|
github
|
skulumani/foucault-master
|
body_animation.m
|
.m
|
foucault-master/matlab/body_animation.m
| 3,976 |
utf_8
|
bde6196a4c3f3ae7ea82c2b5fb05d57c
|
% 23 September 2016
% animation for foucault pendulum
function body_animation(t,q,qd,constants,type,filename)
% draw position of pendulum in the body frame
% body frame reference frame
% Rotate body frame to match matlab figure (gravity is downward -z
% direction)
b1 = constants.L*[1;0;0];
b2 = constants.L*[0;1;0];
b3 = constants.L*[0;0;1];
b1 = ROT3(-pi/2)*ROT2(-pi/2)*b1;
b2 = ROT3(-pi/2)*ROT2(-pi/2)*b2;
b3 = ROT3(-pi/2)*ROT2(-pi/2)*b3;
fig_handle = figure();
range=1.1*(constants.L);
axis([-range range -range range -range range]);
axis square;
grid on,hold on,
title('Foucault Pendulum - Body-fixed frame')
xlabel('b_2')
ylabel('b_3')
zlabel('b_1')
traj = constants.L*q;
% draw the inertial frame
axis_data = get(gca);
xmin = axis_data.XLim(1);
xmax = axis_data.XLim(2);
ymin = axis_data.YLim(1);
ymax = axis_data.YLim(2);
zmin = axis_data.ZLim(1);
zmax = axis_data.ZLim(2);
switch type
case 'gif'
f = getframe;
[im,map] = rgb2ind(f.cdata,256,'nodither');
case 'movie'
% M(1:length(tspan))= struct('cdata',[],'colormap',[]);
nFrames = length(t);
vidObj = VideoWriter([filename '.avi']);
vidObj.Quality = 100;
vidObj.FrameRate = 60;
open(vidObj);
end
% loop over time
for ii = 1:1:length(t)
cla
% compute the position of the pendulum mass (L q)
pos = traj(ii,:);
xcoord = pos(2);
ycoord = pos(3);
zcoord = pos(1);
% draw the rotating frame
line([0 b1(1)],[0 b1(2)],[0 b1(3)],'color','r','linewidth',1)
text(b1(1),b1(2),b1(3),'$\hat{b}_1$','interpreter','latex')
line([0 b2(1)],[0 b2(2)],[0 b2(3)],'color','g','linewidth',1)
text(b2(1),b2(2),b2(3),'$\hat{b}_2$','interpreter','latex')
line([0 b3(1)],[0 b3(2)],[0 b3(3)],'color','b','linewidth',1)
text(b3(1),b3(2),b3(3),'$\hat{b}_3$','interpreter','Latex')
% arrow head
plot3(b1(1),b1(2),b1(3),'r>','Linewidth',1.5)
plot3(b2(1),b2(2),b2(3),'g>','Linewidth',1.5)
plot3(b3(1),b3(2),b3(3),'b^','Linewidth',1.5)
% inertial frame
% plot3([xmin,xmax],[0 0],[0 0],'red','Linewidth',1); plot3(xmax,0,0,'r>','Linewidth',1.5);
% plot3([0 0],[ymin,ymax],[0 0],'green','Linewidth',1); plot3(0,ymax,0,'g>','Linewidth',1.5);
% plot3([0 0],[0 0],[zmin,zmax],'blue','Linewidth',1); plot3(0,0,zmax,'b^','Linewidth',1.5);
% plot trajectory through space
if ii < 100
ind = 1:1:ii;
else
ind = ii-100+1:1:ii;
end
% pendulum mass
plot3([0 xcoord],[0 ycoord],[0 zcoord],'MarkerSize',20,'Marker','.','LineWidth',1,'Color','b');
plot3(traj(ind,2),traj(ind,3),traj(ind,1),'Marker','.','MarkerSize',1,'color','k');
% ground trace
plot3(traj(1:ii,2),traj(1:ii,3),(-constants.L-0.1*constants.L)*ones(ii,1),'Marker','.','color','g','MarkerSize',1);
% add the current simulation time to a window someplace
text(-1*constants.L,1*constants.L,1*constants.L,sprintf('%5.2f s',t(ii)))
drawnow;
% save animation
switch type
case 'gif'
frame = getframe(1);
im = frame2im(frame);
[imind,cm] = rgb2ind(im,256);
outfile = [filename '.gif'];
% On the first loop, create the file. In subsequent loops, append.
if ii==1
imwrite(imind,cm,outfile,'gif','DelayTime',0,'loopcount',inf);
else
imwrite(imind,cm,outfile,'gif','DelayTime',0,'writemode','append');
end
case 'movie'
% M(ii)=getframe(gcf,[0 0 560 420]); % leaving gcf out crops the frame in the movie.
writeVideo(vidObj,getframe(gca));
otherwise
end
end
% Output the movie as an avi file
switch type
case 'gif'
fprintf('Finished animation\n')
case 'movie'
%movie2avi(M,[filename '.avi']);
close(vidObj);
fprintf('Finished animation - movie saved\n')
otherwise
end
|
github
|
skulumani/foucault-master
|
inertial_animation.m
|
.m
|
foucault-master/matlab/inertial_animation.m
| 3,766 |
utf_8
|
59d22cc3a33cf76d7a8d9577cd3512c9
|
% 23 September 2016
% animation for foucault pendulum expressed in inertial frame
function inertial_animation(t,q,qd,constants,type,filename)
% draw position of pendulum in the body frame
fig_handle = figure();
range=1.1*(constants.L);
axis([-range range -range range -range range]);
axis square;
grid on,hold on,
title('Foucault Pendulum - Inertial Frame')
xlabel('e_1')
ylabel('e_2')
zlabel('e_3')
% body frame reference frame
e1 = constants.L*[1;0;0];
e2 = constants.L*[0;1;0];
e3 = constants.L*[0;0;1];
traj = constants.L*q;
traj_inertial = zeros(size(traj));
% draw the inertial frame
axis_data = get(gca);
xmin = axis_data.XLim(1);
xmax = axis_data.XLim(2);
ymin = axis_data.YLim(1);
ymax = axis_data.YLim(2);
zmin = axis_data.ZLim(1);
zmax = axis_data.ZLim(2);
switch type
case 'gif'
f = getframe;
[im,map] = rgb2ind(f.cdata,256,'nodither');
case 'movie'
% M(1:length(tspan))= struct('cdata',[],'colormap',[]);
nFrames = length(tspan);
vidObj = VideoWriter([filename '.avi']);
vidObj.Quality = 100;
vidObj.FrameRate = 8;
open(vidObj);
end
% loop over time
for ii = 1:1:length(t)
cla
% compute the rotation from body frame to inertial frame
R_i2b = ROT3(constants.Omega*t(ii))*ROT2(-constants.beta);
R_b2i = R_i2b';
% compute the position of the pendulum mass (L q)
pos = R_b2i'*traj(ii,:)';
traj_inertial(ii,:) = pos';
xcoord = pos(2);
ycoord = pos(3);
zcoord = pos(1);
% rotate the body frame axes and plot
b1 = R_i2b*e1;
b2 = R_i2b*e2;
b3 = R_i2b*e3;
% draw the rotating frame
line([0 b1(1)],[0 b1(2)],[0 b1(3)],'color','r','linewidth',1)
text(b1(1),b1(2),b1(3),'$\hat{b}_1$','interpreter','latex')
line([0 b2(1)],[0 b2(2)],[0 b2(3)],'color','g','linewidth',1)
text(b2(1),b2(2),b2(3),'$\hat{b}_2$','interpreter','latex')
line([0 b3(1)],[0 b3(2)],[0 b3(3)],'color','b','linewidth',1)
text(b3(1),b3(2),b3(3),'$\hat{b}_3$','interpreter','Latex')
% inertial frame
plot3([xmin,xmax],[0 0],[0 0],'red','Linewidth',1); plot3(xmax,0,0,'r>','Linewidth',1.5);
plot3([0 0],[ymin,ymax],[0 0],'green','Linewidth',1); plot3(0,ymax,0,'g>','Linewidth',1.5);
plot3([0 0],[0 0],[zmin,zmax],'blue','Linewidth',1); plot3(0,0,zmax,'b^','Linewidth',1.5);
% draw the pendulum mass in the inertial frame
plot3([0 pos(1)],[0 pos(2)],[0 pos(3)],'MarkerSize',20,'Marker','.','LineWidth',1,'Color','b')
% plot trajectory through space
if ii < 100
ind = 1:1:ii;
else
ind = ii-100+1:1:ii;
end
plot3(traj_inertial(ind,1),traj_inertial(ind,2),traj_inertial(ind,3),'Marker','.','MarkerSize',1,'color','k');
drawnow;
% save animation
switch type
case 'gif'
frame = getframe(1);
im = frame2im(frame);
[imind,cm] = rgb2ind(im,256);
outfile = [filename '.gif'];
% On the first loop, create the file. In subsequent loops, append.
if ii==1
imwrite(imind,cm,outfile,'gif','DelayTime',0,'loopcount',inf);
else
imwrite(imind,cm,outfile,'gif','DelayTime',0,'writemode','append');
end
case 'movie'
% M(ii)=getframe(gcf,[0 0 560 420]); % leaving gcf out crops the frame in the movie.
writeVideo(vidObj,getframe(gca));
otherwise
end
end
% Output the movie as an avi file
switch type
case 'gif'
fprintf('Finished animation\n')
case 'movie'
%movie2avi(M,[filename '.avi']);
close(vidObj);
fprintf('Finished animation - movie saved\n')
otherwise
end
|
github
|
skulumani/foucault-master
|
plot_outputs.m
|
.m
|
foucault-master/matlab/plot_outputs.m
| 2,708 |
utf_8
|
35bb5bdc3d3fc9f4b3011e76dba4f584
|
% 23 September 2016
% plot simulation
function plot_outputs(t,q,qd,constants)
% extract constants
Cbeta = constants.Cbeta;
S = constants.S;
Omega = constants.Omega;
Len = constants.L;
m = constants.m;
% calculate the total energy of the pendulum and make sure it's consistent
T = zeros(length(t),1);
V = zeros(length(t),1);
L = zeros(length(t),1);
E = zeros(length(t),1);
pend_pos = constants.L*q;
% calculate total energy
for ii = 1:length(t)
body_pos = constants.Re*[1;0;0]+constants.L*q(ii,:)';
% need the kinetic energy in the inertial frame
switch constants.eom
case 'full'
T(ii) = 1/2*m*Len^2*norm(qd(ii,:))^2 + ...
m*Len*qd(ii,:)*S*body_pos + ...
1/2*m*Omega^2*body_pos'*Cbeta*body_pos;
case 'rot'
T(ii) = 1/2*m*Len^2*norm(qd(ii,:))^2;
case 'len'
T(ii) = 1/2*m*Len^2*norm(qd(ii,:))^2 + ...
m*Len*qd(ii,:)*S*body_pos + ...
1/2*m*Omega^2*body_pos'*Cbeta*body_pos;
end
V(ii) = - constants.m*constants.g*constants.Re^2 / norm(body_pos);
L(ii) = T(ii)-V(ii);
E(ii) = T(ii)+V(ii);
end
% energy variation
E_diff = abs(E - E(1));
%% plot outputs
fontsize = 18;
fontname = 'Times';
e_fig = figure;
grid on;hold on
plot(t,abs(E_diff./E))
title('Relative Energy Difference','interpreter','latex','FontName',fontname,'FontSize',fontsize);
xlabel('Time (sec)','interpreter','latex','FontName',fontname,'FontSize',fontsize);
ylabel('$\Delta E / E$','interpreter','latex','FontName',fontname,'FontSize',fontsize);
set(gca,'FontName',fontname,'FontSize',fontsize);
% 2-D projections
pos_fig = figure;
% ground track of pendulum (b2 vs b3 frame)
subplot(2,2,1)
grid on;hold all
plot(pend_pos(:,2),pend_pos(:,3))
title('$b_2$ vs $b_3$','interpreter','latex','FontName',fontname,'FontSize',fontsize);
set(gca,'FontName',fontname,'FontSize',fontsize);
% vertical frame
subplot(2,2,3)
grid on;hold all
plot(pend_pos(:,2),pend_pos(:,1))
title('$b_2$ vs $b_1$','interpreter','latex','FontName',fontname,'FontSize',fontsize);
set(gca,'FontName',fontname,'FontSize',fontsize);
subplot(2,2,4)
grid on;hold all
plot(pend_pos(:,3),pend_pos(:,1))
title('$b_3$ vs $b_1$','interpreter','latex','FontName',fontname,'FontSize',fontsize);
set(gca,'FontName',fontname,'FontSize',fontsize);
% plot the norm of the position vector
norm_plot = figure;
grid on
hold on
norm_q = sqrt(sum(q.^2,2));
plot(t,norm_q)
title('$||q||$','interpreter','latex','FontName',fontname,'FontSize',fontsize);
xlabel('Time (sec)','interpreter','latex','FontName',fontname,'FontSize',fontsize);
ylabel('$||q||$','interpreter','latex','FontName',fontname,'FontSize',fontsize);
|
github
|
skulumani/foucault-master
|
vee_map.m
|
.m
|
foucault-master/matlab/vee_map.m
| 217 |
utf_8
|
03c2d87aa5e1f2a683080adf744e0e27
|
% 11 June 15
% vee map function to take a skew symmetric matrix and map it to a 3 vector
function [vec] = vee_map(mat)
x1 = mat(3,2)-mat(2,3);
x2 = mat(1,3) - mat(3,1);
x3 = mat(2,1)-mat(1,2);
vec = 1/2*[x1;x2;x3];
|
github
|
skulumani/foucault-master
|
foucault_ode_rot.m
|
.m
|
foucault-master/matlab/foucault_ode_rot.m
| 648 |
utf_8
|
39743fd03e852988bf26ffb954f37a1d
|
% 8 September 2016
% Assuming length of pendulum is much less than the Earth and that the
% rotation coriolis force is negligible r \Omega^@ << g
function [state_dot] = foucault_ode_rot(t,state,constants)
% extract constants
Omega = constants.Omega;
L = constants.L;
m = constants.m;
Re = constants.Re;
g = constants.g;
Cbeta = constants.Cbeta;
S = constants.S;
% redefine the state
pos = state(1:3,1);
vel = state(4:6,1);
proj_mat = eye(3,3) - pos*pos';
pos_dot = vel;
vel_dot = -1/m/L^2 * (m*L^2*norm(vel)^2*pos + 2*m*L^2*proj_mat*S*vel ...
+ m*g*L*proj_mat*[1;0;0]);
state_dot = [pos_dot;vel_dot];
|
github
|
skulumani/foucault-master
|
foucault_ode.m
|
.m
|
foucault-master/matlab/foucault_ode.m
| 692 |
utf_8
|
3c2f639b066df7d1619969e89d260f42
|
% 8 September 2016
% Full NL ODE function for foucault pendulum
function [state_dot] = foucault_ode(t,state,constants)
% extract constants
Omega = constants.Omega;
L = constants.L;
m = constants.m;
Re = constants.Re;
g = constants.g;
Cbeta = constants.Cbeta;
S = constants.S;
% redefine the state
pos = state(1:3,1);
vel = state(4:6,1);
proj_mat = eye(3,3) - pos*pos';
body_pos = Re*[1;0;0]+L*pos; % position of pendulum in body frame
pos_dot = vel;
vel_dot = -1/m/L^2 * (m*L^2*norm(vel)^2*pos + 2*m*L^2*proj_mat*S*vel - m*L*Omega^2*proj_mat*Cbeta*body_pos ...
+ m*g*Re^2*L*proj_mat*body_pos/norm(body_pos)^3);
state_dot = [pos_dot;vel_dot];
|
github
|
skulumani/foucault-master
|
ROT3.m
|
.m
|
foucault-master/matlab/ROT3.m
| 611 |
utf_8
|
72379dd4a63db93ef3483a5b142b68c3
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Purpose: Rotation matrix about thrid axis
% b = dcm*a
%
% Inputs:
% - gamma - rotation angle (rad)
%
% Outpus:
% - rot3 - rotation matrix (3x3)
%
% Dependencies:
% - none
%
% Author: Shankar Kulumani 23 September 2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function rot3 = ROT3(gamma)
cos_gamma = cos(gamma);
sin_gamma = sin(gamma);
rot3 = [ cos_gamma -sin_gamma 0 ; ...
sin_gamma cos_gamma 0 ; ...
0 0 1 ];
end
|
github
|
skulumani/foucault-master
|
ROT1.m
|
.m
|
foucault-master/matlab/ROT1.m
| 606 |
utf_8
|
8f5386316502a83577c44e5d15de58e8
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Purpose: Rotation matrix about first axis
% b = dcm*a
%
% Inputs:
% - alpha - rotation angle (rad)
%
% Outpus:
% - rot1 - rotation matrix (3x3)
%
% Dependencies:
% - none
%
% Author: Shankar Kulumani 23 September 2016
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function rot1 = ROT1(alpha)
cos_alpha = cos(alpha);
sin_alpha = sin(alpha);
rot1 = [1 0 0 ; ...
0 cos_alpha -sin_alpha ; ...
0 sin_alpha cos_alpha ];
end
|
github
|
skulumani/foucault-master
|
foucault_ode_length.m
|
.m
|
foucault-master/matlab/foucault_ode_length.m
| 620 |
utf_8
|
45cffa2ccb1d9618df1ebb10cf953ee3
|
% 8 September 2016
% Assuming length of pendulum is much less than the Earth
function [state_dot] = foucault_ode_length(t,state,constants)
% extract constants
Omega = constants.Omega;
L = constants.L;
m = constants.m;
Re = constants.Re;
g = constants.g;
Cbeta = constants.Cbeta;
S = constants.S;
% redefine the state
pos = state(1:3,1);
vel = state(4:6,1);
proj_mat = eye(3,3) - pos*pos';
pos_dot = vel;
vel_dot = -1/m/L^2 * (m*L^2*norm(vel)^2*pos + 2*m*L^2*proj_mat*S*vel ...
- m*L*Re*Omega^2*proj_mat*Cbeta*[1;0;0] + m*g*L*proj_mat*[1;0;0]);
state_dot = [pos_dot;vel_dot];
|
github
|
CALFEM/calfem-matlab-iga-master
|
bspdegelev.m
|
.m
|
calfem-matlab-iga-master/NURBS/bspdegelev.m
| 20,513 |
utf_8
|
5a7638accd22f943a5ac4278ab8176b6
|
function [ic,ik] = bspdegelev(d,c,k,t)
% BSPDEGELEV: Degree elevate a univariate B-Spline.
%
% Calling Sequence:
%
% [ic,ik] = bspdegelev(d,c,k,t)
%
% INPUT:
%
% d - Degree of the B-Spline.
% c - Control points, matrix of size (dim,nc).
% k - Knot sequence, row vector of size nk.
% t - Raise the B-Spline degree t times.
%
% OUTPUT:
%
% ic - Control points of the new B-Spline.
% ik - Knot vector of the new B-Spline.
%
% Copyright (C) 2000 Mark Spink, 2007 Daniel Claxton
%
% 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 program is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with this program. If not, see <http://www.gnu.org/licenses/>.
[mc,nc] = size(c);
%
% int bspdegelev(int d, double *c, int mc, int nc, double *k, int nk,
% int t, int *nh, double *ic, double *ik)
% {
% int row,col;
%
% int ierr = 0;
% int i, j, q, s, m, ph, ph2, mpi, mh, r, a, b, cind, oldr, mul;
% int n, lbz, rbz, save, tr, kj, first, kind, last, bet, ii;
% double inv, ua, ub, numer, den, alf, gam;
% double **bezalfs, **bpts, **ebpts, **Nextbpts, *alfs;
%
% double **ctrl = vec2mat(c, mc, nc);
% ic = zeros(mc,nc*(t)); % double **ictrl = vec2mat(ic, mc, nc*(t+1));
%
n = nc - 1; % n = nc - 1;
%
bezalfs = zeros(d+1,d+t+1); % bezalfs = matrix(d+1,d+t+1);
bpts = zeros(mc,d+1); % bpts = matrix(mc,d+1);
ebpts = zeros(mc,d+t+1); % ebpts = matrix(mc,d+t+1);
Nextbpts = zeros(mc,d+1); % Nextbpts = matrix(mc,d+1);
alfs = zeros(d,1); % alfs = (double *) mxMalloc(d*sizeof(double));
%
m = n + d + 1; % m = n + d + 1;
ph = d + t; % ph = d + t;
ph2 = floor(ph / 2); % ph2 = ph / 2;
%
% // compute bezier degree elevation coefficeients
bezalfs(1,1) = 1; % bezalfs[0][0] = bezalfs[ph][d] = 1.0;
bezalfs(d+1,ph+1) = 1; %
for i=1:ph2 % for (i = 1; i <= ph2; i++) {
inv = 1/bincoeff(ph,i); % inv = 1.0 / bincoeff(ph,i);
mpi = min(d,i); % mpi = min(d,i);
%
for j=max(0,i-t):mpi % for (j = max(0,i-t); j <= mpi; j++)
bezalfs(j+1,i+1) = inv*bincoeff(d,j)*bincoeff(t,i-j); % bezalfs[i][j] = inv * bincoeff(d,j) * bincoeff(t,i-j);
end
end % }
%
for i=ph2+1:ph-1 % for (i = ph2+1; i <= ph-1; i++) {
mpi = min(d,i); % mpi = min(d, i);
for j=max(0,i-t):mpi % for (j = max(0,i-t); j <= mpi; j++)
bezalfs(j+1,i+1) = bezalfs(d-j+1,ph-i+1); % bezalfs[i][j] = bezalfs[ph-i][d-j];
end
end % }
%
mh = ph; % mh = ph;
kind = ph+1; % kind = ph+1;
r = -1; % r = -1;
a = d; % a = d;
b = d+1; % b = d+1;
cind = 1; % cind = 1;
ua = k(1); % ua = k[0];
%
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
ic(ii+1,1) = c(ii+1,1); % ictrl[0][ii] = ctrl[0][ii];
end %
for i=0:ph % for (i = 0; i <= ph; i++)
ik(i+1) = ua; % ik[i] = ua;
end %
% // initialise first bezier seg
for i=0:d % for (i = 0; i <= d; i++)
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
bpts(ii+1,i+1) = c(ii+1,i+1); % bpts[i][ii] = ctrl[i][ii];
end
end %
% // big loop thru knot vector
while b < m % while (b < m) {
i = b; % i = b;
while b < m && k(b+1) == k(b+2) % while (b < m && k[b] == k[b+1])
b = b + 1; % b++;
end %
mul = b - i + 1; % mul = b - i + 1;
mh = mh + mul + t; % mh += mul + t;
ub = k(b+1); % ub = k[b];
oldr = r; % oldr = r;
r = d - mul; % r = d - mul;
%
% // insert knot u(b) r times
if oldr > 0 % if (oldr > 0)
lbz = floor((oldr+2)/2); % lbz = (oldr+2) / 2;
else % else
lbz = 1; % lbz = 1;
end %
if r > 0 % if (r > 0)
rbz = ph - floor((r+1)/2); % rbz = ph - (r+1)/2;
else % else
rbz = ph; % rbz = ph;
end %
if r > 0 % if (r > 0) {
% // insert knot to get bezier segment
numer = ub - ua; % numer = ub - ua;
for q=d:-1:mul+1 % for (q = d; q > mul; q--)
alfs(q-mul) = numer / (k(a+q+1)-ua); % alfs[q-mul-1] = numer / (k[a+q]-ua);
end
for j=1:r % for (j = 1; j <= r; j++) {
save = r - j; % save = r - j;
s = mul + j; % s = mul + j;
%
for q=d:-1:s % for (q = d; q >= s; q--)
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
tmp1 = alfs(q-s+1)*bpts(ii+1,q+1);
tmp2 = (1-alfs(q-s+1))*bpts(ii+1,q);
bpts(ii+1,q+1) = tmp1 + tmp2; % bpts[q][ii] = alfs[q-s]*bpts[q][ii]+(1.0-alfs[q-s])*bpts[q-1][ii];
end
end %
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
Nextbpts(ii+1,save+1) = bpts(ii+1,d+1); % Nextbpts[save][ii] = bpts[d][ii];
end
end % }
end % }
% // end of insert knot
%
% // degree elevate bezier
for i=lbz:ph % for (i = lbz; i <= ph; i++) {
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
ebpts(ii+1,i+1) = 0; % ebpts[i][ii] = 0.0;
end
mpi = min(d, i); % mpi = min(d, i);
for j=max(0,i-t):mpi % for (j = max(0,i-t); j <= mpi; j++)
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
tmp1 = ebpts(ii+1,i+1);
tmp2 = bezalfs(j+1,i+1)*bpts(ii+1,j+1);
ebpts(ii+1,i+1) = tmp1 + tmp2; % ebpts[i][ii] = ebpts[i][ii] + bezalfs[i][j]*bpts[j][ii];
end
end
end % }
% // end of degree elevating bezier
%
if oldr > 1 % if (oldr > 1) {
% // must remove knot u=k[a] oldr times
first = kind - 2; % first = kind - 2;
last = kind; % last = kind;
den = ub - ua; % den = ub - ua;
bet = floor((ub-ik(kind)) / den); % bet = (ub-ik[kind-1]) / den;
%
% // knot removal loop
for tr=1:oldr-1 % for (tr = 1; tr < oldr; tr++) {
i = first; % i = first;
j = last; % j = last;
kj = j - kind + 1; % kj = j - kind + 1;
while j-i > tr % while (j - i > tr) {
% // loop and compute the new control points
% // for one removal step
if i < cind % if (i < cind) {
alf = (ub-ik(i+1))/(ua-ik(i+1)); % alf = (ub-ik[i])/(ua-ik[i]);
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
tmp1 = alf*ic(ii+1,i+1);
tmp2 = (1-alf)*ic(ii+1,i);
ic(ii+1,i+1) = tmp1 + tmp2; % ictrl[i][ii] = alf * ictrl[i][ii] + (1.0-alf) * ictrl[i-1][ii];
end
end % }
if j >= lbz % if (j >= lbz) {
if j-tr <= kind-ph+oldr % if (j-tr <= kind-ph+oldr) {
gam = (ub-ik(j-tr+1)) / den; % gam = (ub-ik[j-tr]) / den;
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
tmp1 = gam*ebpts(ii+1,kj+1);
tmp2 = (1-gam)*ebpts(ii+1,kj+2);
ebpts(ii+1,kj+1) = tmp1 + tmp2; % ebpts[kj][ii] = gam*ebpts[kj][ii] + (1.0-gam)*ebpts[kj+1][ii];
end % }
else % else {
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
tmp1 = bet*ebpts(ii+1,kj+1);
tmp2 = (1-bet)*ebpts(ii+1,kj+2);
ebpts(ii+1,kj+1) = tmp1 + tmp2; % ebpts[kj][ii] = bet*ebpts[kj][ii] + (1.0-bet)*ebpts[kj+1][ii];
end
end % }
end % }
i = i + 1; % i++;
j = j - 1; % j--;
kj = kj - 1; % kj--;
end % }
%
first = first - 1; % first--;
last = last + 1; % last++;
end % }
end % }
% // end of removing knot n=k[a]
%
% // load the knot ua
if a ~= d % if (a != d)
for i=0:ph-oldr-1 % for (i = 0; i < ph-oldr; i++) {
ik(kind+1) = ua; % ik[kind] = ua;
kind = kind + 1; % kind++;
end
end % }
%
% // load ctrl pts into ic
for j=lbz:rbz % for (j = lbz; j <= rbz; j++) {
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
ic(ii+1,cind+1) = ebpts(ii+1,j+1); % ictrl[cind][ii] = ebpts[j][ii];
end
cind = cind + 1; % cind++;
end % }
%
if b < m % if (b < m) {
% // setup for next pass thru loop
for j=0:r-1 % for (j = 0; j < r; j++)
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
bpts(ii+1,j+1) = Nextbpts(ii+1,j+1); % bpts[j][ii] = Nextbpts[j][ii];
end
end
for j=r:d % for (j = r; j <= d; j++)
for ii=0:mc-1 % for (ii = 0; ii < mc; ii++)
bpts(ii+1,j+1) = c(ii+1,b-d+j+1); % bpts[j][ii] = ctrl[b-d+j][ii];
end
end
a = b; % a = b;
b = b+1; % b++;
ua = ub; % ua = ub;
% }
else % else
% // end knot
for i=0:ph % for (i = 0; i <= ph; i++)
ik(kind+i+1) = ub; % ik[kind+i] = ub;
end
end
end % }
% End big while loop % // end while loop
%
% *nh = mh - ph - 1;
%
% freevec2mat(ctrl);
% freevec2mat(ictrl);
% freematrix(bezalfs);
% freematrix(bpts);
% freematrix(ebpts);
% freematrix(Nextbpts);
% mxFree(alfs);
%
% return(ierr);
end % }
function b = bincoeff(n,k)
% Computes the binomial coefficient.
%
% ( n ) n!
% ( ) = --------
% ( k ) k!(n-k)!
%
% b = bincoeff(n,k)
%
% Algorithm from 'Numerical Recipes in C, 2nd Edition' pg215.
% double bincoeff(int n, int k)
% {
b = floor(0.5+exp(factln(n)-factln(k)-factln(n-k))); % return floor(0.5+exp(factln(n)-factln(k)-factln(n-k)));
end % }
function f = factln(n)
% computes ln(n!)
if n <= 1, f = 0; return, end
f = gammaln(n+1); %log(factorial(n));
end
|
github
|
otroblogdetecno/matlabExamples-master
|
spatial_calibration_demo.m
|
.m
|
matlabExamples-master/caracteristicas/spatial_calibration_demo.m
| 10,512 |
utf_8
|
f6a8ac89a25e8f0720bdadb7a30c016a
|
function spatial_calibration_demo()
% spatial_calibration_demo This demo allows you to
% spatially calibrate your image and then
% make distance or area measurements.
global originalImage;
% Check that user has the Image Processing Toolbox installed.
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
hasIPT = license('test', 'image_toolbox');
if ~hasIPT
% User does not have the toolbox installed.
message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
if strcmpi(reply, 'No')
% User said No, so exit.
return;
end
end
% Read in a standard MATLAB gray scale demo image.
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
button = menu('Use which demo image?', 'CameraMan', 'Moon', 'Eight', 'Coins', 'Peppers', 'My own...', 'Exit');
switch button
case 1
baseFileName = 'cameraman.tif';
case 2
baseFileName = 'moon.tif';
case 3
baseFileName = 'eight.tif';
case 4
baseFileName = 'coins.png';
case 5
baseFileName = 'peppers.png';
case 6
% Get the name of the file that the user wants to use.
defaultFileName = fullfile(cd, '*.*');
[baseFileName, folder] = uigetfile(defaultFileName, 'Select an image file');
if baseFileName == 0
% User clicked the Cancel button.
return;
end
case 7
return;
end
% Get the full filename, with path prepended.
fullFileName = fullfile(folder, baseFileName);
% Check if file exists.
if ~exist(fullFileName, 'file')
% File doesn't exist -- didn't find it there. Check the search path for it.
fullFileName = baseFileName; % No path this time.
if ~exist(fullFileName, 'file')
% Still didn't find it. Alert user.
errorMessage = sprintf('Error: %s does not exist in the search path folders.', fullFileName);
uiwait(warndlg(errorMessage));
return;
end
end
% Read in the chosen standard MATLAB demo image.
originalImage = imread(fullFileName);
% Get the dimensions of the image.
% numberOfColorBands should be = 1.
[rows columns numberOfColorBands] = size(originalImage);
% Display the original gray scale image.
figureHandle = figure;
subplot(1,2, 1);
imshow(originalImage, []);
axis on;
title('Original Grayscale Image', 'FontSize', fontSize);
% Enlarge figure to full screen.
set(gcf, 'units','normalized','outerposition',[0 0 1 1]);
% Give a name to the title bar.
set(gcf,'name','Demo by ImageAnalyst','numbertitle','off')
message = sprintf('First you will be doing spatial calibration.');
reply = questdlg(message, 'Calibrate spatially', 'OK', 'Cancel', 'OK');
if strcmpi(reply, 'Cancel')
% User said Cancel, so exit.
return;
end
button = 1; % Allow it to enter loop.
while button ~= 4
if button > 1
% Let them choose the task, once they have calibrated.
button = menu('Select a task', 'Calibrate', 'Measure Distance', 'Measure Area', 'Exit Demo');
end
switch button
case 1
success = Calibrate();
% Keep trying if they didn't click properly.
while ~success
success = Calibrate();
end
% If they get to here, they clicked properly
% Change to something else so it will ask them
% for the task on the next time through the loop.
button = 99;
case 2
DrawLine();
case 3
DrawArea();
otherwise
close(figureHandle);
break;
end
end
end
%=====================================================================
function success = Calibrate()
global lastDrawnHandle;
global calibration;
try
success = false;
instructions = sprintf('Left click to anchor first endpoint of line.\nRight-click or double-left-click to anchor second endpoint of line.\n\nAfter that I will ask for the real-world distance of the line.');
title(instructions);
msgboxw(instructions);
[cx, cy, rgbValues, xi,yi] = improfile(1000);
% rgbValues is 1000x1x3. Call Squeeze to get rid of the singleton dimension and make it 1000x3.
rgbValues = squeeze(rgbValues);
distanceInPixels = sqrt( (xi(2)-xi(1)).^2 + (yi(2)-yi(1)).^2);
if length(xi) < 2
return;
end
% Plot the line.
hold on;
lastDrawnHandle = plot(xi, yi, 'y-', 'LineWidth', 2);
% Ask the user for the real-world distance.
userPrompt = {'Enter real world units (e.g. microns):','Enter distance in those units:'};
dialogTitle = 'Specify calibration information';
numberOfLines = 1;
def = {'microns', '500'};
answer = inputdlg(userPrompt, dialogTitle, numberOfLines, def);
if isempty(answer)
return;
end
calibration.units = answer{1};
calibration.distanceInPixels = distanceInPixels;
calibration.distanceInUnits = str2double(answer{2});
calibration.distancePerPixel = calibration.distanceInUnits / distanceInPixels;
success = true;
message = sprintf('The distance you drew is %.2f pixels = %f %s.\nThe number of %s per pixel is %f.\nThe number of pixels per %s is %f',...
distanceInPixels, calibration.distanceInUnits, calibration.units, ...
calibration.units, calibration.distancePerPixel, ...
calibration.units, 1/calibration.distancePerPixel);
uiwait(msgbox(message));
catch ME
errorMessage = sprintf('Error in function Calibrate().\nDid you first left click and then right click?\n\nError Message:\n%s', ME.message);
fprintf(1, '%s\n', errorMessage);
WarnUser(errorMessage);
end
return; % from Calibrate()
end
%=====================================================================
% --- Executes on button press in DrawLine.
function success = DrawLine()
try
global lastDrawnHandle;
global calibration;
fontSize = 14;
instructions = sprintf('Draw a line.\nFirst, left-click to anchor first endpoint of line.\nRight-click or double-left-click to anchor second endpoint of line.\n\nAfter that I will ask for the real-world distance of the line.');
title(instructions);
msgboxw(instructions);
subplot(1,2, 1); % Switch to image axes.
[cx,cy, rgbValues, xi,yi] = improfile(1000);
% Get the profile again but spaced at the number of pixels instead of 1000 samples.
hImage = findobj(gca,'Type','image');
theImage = get(hImage, 'CData');
lineLength = round(sqrt((xi(1)-xi(2))^2 + (yi(1)-yi(2))^2))
[cx,cy, rgbValues] = improfile(theImage, xi, yi, lineLength);
% rgbValues is 1000x1x3. Call Squeeze to get rid of the singleton dimension and make it 1000x3.
rgbValues = squeeze(rgbValues);
distanceInPixels = sqrt( (xi(2)-xi(1)).^2 + (yi(2)-yi(1)).^2);
distanceInRealUnits = distanceInPixels * calibration.distancePerPixel;
if length(xi) < 2
return;
end
% Plot the line.
hold on;
lastDrawnHandle = plot(xi, yi, 'y-', 'LineWidth', 2);
% Plot profiles along the line of the red, green, and blue components.
subplot(1,2,2);
[rows, columns] = size(rgbValues);
if columns == 3
% It's an RGB image.
plot(rgbValues(:, 1), 'r-', 'LineWidth', 2);
hold on;
plot(rgbValues(:, 2), 'g-', 'LineWidth', 2);
plot(rgbValues(:, 3), 'b-', 'LineWidth', 2);
title('Red, Green, and Blue Profiles along the line you just drew.', 'FontSize', 14);
else
% It's a gray scale image.
plot(rgbValues, 'k-', 'LineWidth', 2);
end
xlabel('X', 'FontSize', fontSize);
ylabel('Gray Level', 'FontSize', fontSize);
title('Intensity Profile', 'FontSize', fontSize);
grid on;
% Inform use via a dialog box.
txtInfo = sprintf('Distance = %.1f %s, which = %.1f pixels.', ...
distanceInRealUnits, calibration.units, distanceInPixels);
msgboxw(txtInfo);
% Print the values out to the command window.
fprintf(1, '%\n', txtInfo);
catch ME
errorMessage = sprintf('Error in function DrawLine().\n\nError Message:\n%s', ME.message);
fprintf(1, '%s\n', errorMessage);
WarnUser(errorMessage);
end
end % from DrawLine()
%=====================================================================
function DrawArea()
global originalImage;
global lastDrawnHandle;
global calibration;
try
txtInfo = sprintf('Left click to anchor vertices.\nDouble left click to anchor final point of polygon.');
title(txtInfo);
msgboxw(txtInfo);
% Get size information.
[rows, columns, numberOfColorBands] = size(originalImage);
% Get a gray scale version.
if numberOfColorBands > 1
grayImage = rgb2gray(originalImage);
else
grayImage = originalImage;
end
subplot(1,2, 1); % Switch to image axes.
% Ask user to draw a polygon.
[maskImage, xi, yi] = roipolyold();
% Draw the polygon over the image on the main screen.
hold on;
lastDrawnHandle = plot(xi, yi, 'r-', 'LineWidth', 2);
numberOfPixels = sum(maskImage(:));
area = numberOfPixels * calibration.distancePerPixel^2;
% Get the mean gray level of the gray scale image.
mean_GL = mean(grayImage(maskImage)); % Of the gray scale version.
% Print the area values out to the command window.
txtInfo = sprintf('Area = %8.1f square %s.\nMean gray level = %.2f.', ...
area, calibration.units, mean_GL);
fprintf(1,'%s\n', txtInfo);
title(txtInfo, 'FontSize', 14);
% Done with measurement of area.
% Now, just for fun, get the mean value and display the histogram.
if numberOfColorBands >= 3
% It's a color image. Get the mean RGB Values.
redPlane = double(originalImage(:, :, 1));
greenPlane = double(originalImage(:, :, 2));
bluePlane = double(originalImage(:, :, 3));
mean_RGB_GL(1) = mean(redPlane(maskImage));
mean_RGB_GL(2) = mean(greenPlane(maskImage));
mean_RGB_GL(3) = mean(bluePlane(maskImage));
fprintf('%s\nRed mean = %.2f\nGreen mean = %.2f\nBlue mean = %.2f', ...
txtInfo, mean_RGB_GL(1), mean_RGB_GL(2), mean_RGB_GL(3));
end
% Just for fun, let's get its histogram within the masked region.
[pixelCount, grayLevels] = imhist(grayImage(maskImage));
subplot(1,2, 2); % Switch to plot axes.
cla;
bar(pixelCount);
grid on;
caption = sprintf('Histogram within area. Mean gray level = %.2f', mean_GL);
title(caption, 'FontSize', 14);
xlim([0 grayLevels(end)]); % Scale x axis manually.
% Show the mean as a vertical red bar on the histogram.
hold on;
maxYValue = ylim;
line([mean_GL, mean_GL], [0 maxYValue(2)], 'Color', 'r', 'linewidth', 2);
catch ME
errorMessage = sprintf('Error in function DrawArea().\n\nError Message:\n%s', ME.message);
fprintf(1, '%s\n', errorMessage);
WarnUser(errorMessage);
end
end % od DrawArea()
%=====================================================================
function msgboxw(message)
uiwait(msgbox(message));
end
%=====================================================================
function WarnUser(message)
uiwait(msgbox(message));
end
|
github
|
otroblogdetecno/matlabExamples-master
|
DeltaE.m
|
.m
|
matlabExamples-master/DeltaE/DeltaE.m
| 20,283 |
utf_8
|
117e2bac667be64d3a0c96dac4c7b853
|
% Demo macro to very, very simple color detection in LAB color space.
% The RGB image is converted to LAB color space and then the user draws
% some freehand-drawn irregularly shaped region to identify a color.
% The Delta E (the color difference in LAB color space) is then calculated
% for every pixel in the image between that pixel's color and the average
% LAB color of the drawn region. The user can then specify a number that
% says how close to that color would they like to be. The software will
% then find all pixels within that specified Delta E of the color of the drawn region.
%
% Note: This demo differs from my demo on color detection by thresholding in the
% hsv color space because with that one you are essentially extracting out a
% pie-shaped sector out of the LAB color space gamut while in this demo
% we're extracting out a sphere centered at the mean LAB color of the user-drawn region.
% by ImageAnalyst, Ph.D.
function DeltaE()
clc; % Clear command window.
clear; % Delete all variables.
close all; % Close all figure windows except those created by imtool.
% imtool close all; % Close all figure windows created by imtool.
workspace; % Make sure the workspace panel is showing.
% Change the current folder to the folder of this m-file.
if(~isdeployed)
cd(fileparts(which(mfilename))); % From Brett
end
try
% Check that user has the Image Processing Toolbox installed.
hasIPT = license('test', 'image_toolbox');
if ~hasIPT
% User does not have the toolbox installed.
message = sprintf('Sorry, but you do not seem to have the Image Processing Toolbox.\nDo you want to try to continue anyway?');
reply = questdlg(message, 'Toolbox missing', 'Yes', 'No', 'Yes');
if strcmpi(reply, 'No')
% User said No, so exit.
return;
end
end
% Continue with the demo. Do some initialization stuff.
close all;
fontSize = 14;
figure;
% Maximize the figure.
set(gcf, 'Position', get(0, 'ScreenSize'));
set(gcf,'name','Color Matching Demo by ImageAnalyst','numbertitle','off')
% Change the current folder to the folder of this m-file.
% (The line of code below is from Brett Shoelson of The Mathworks.)
if(~isdeployed)
cd(fileparts(which(mfilename)));
end
% Ask user if they want to use a demo image or their own image.
message = sprintf('Do you want use a standard demo image,\nOr pick one of your own?');
reply2 = questdlg(message, 'Which Image?', 'Demo','My Own', 'Demo');
% Open an image.
if strcmpi(reply2, 'Demo')
% Read standard MATLAB demo image.
message = sprintf('Which demo image do you want to use?');
selectedImage = questdlg(message, 'Which Demo Image?', 'Onions', 'Peppers', 'Stained Fabric', 'Onions');
if strcmp(selectedImage, 'Onions')
fullImageFileName = 'onion.png';
elseif strcmp(selectedImage, 'Peppers')
fullImageFileName = 'peppers.png';
else
fullImageFileName = 'fabric.png';
end
else
% They want to pick their own.
% Change default directory to the one containing the standard demo images for the MATLAB Image Processing Toolbox.
originalFolder = pwd;
folder = fullfile(matlabroot, '\toolbox\images\imdemos');
if ~exist(folder, 'dir')
folder = pwd;
end
cd(folder);
% Browse for the image file.
[baseFileName, folder] = uigetfile('*.*', 'Specify an image file');
fullImageFileName = fullfile(folder, baseFileName);
% Set current folder back to the original one.
cd(originalFolder);
selectedImage = 'My own image'; % Need for the if threshold selection statement later.
end
% Check to see that the image exists. (Mainly to check on the demo images.)
if ~exist(fullImageFileName, 'file')
message = sprintf('This file does not exist:\n%s', fullImageFileName);
WarnUser(message);
return;
end
% Read in image into an array.
[rgbImage storedColorMap] = imread(fullImageFileName);
[rows columns numberOfColorBands] = size(rgbImage);
% If it's monochrome (indexed), convert it to color.
% Check to see if it's an 8-bit image needed later for scaling).
if strcmpi(class(rgbImage), 'uint8')
% Flag for 256 gray levels.
eightBit = true;
else
eightBit = false;
end
if numberOfColorBands == 1
if isempty(storedColorMap)
% Just a simple gray level image, not indexed with a stored color map.
% Create a 3D true color image where we copy the monochrome image into all 3 (R, G, & B) color planes.
rgbImage = cat(3, rgbImage, rgbImage, rgbImage);
else
% It's an indexed image.
rgbImage = ind2rgb(rgbImage, storedColorMap);
% ind2rgb() will convert it to double and normalize it to the range 0-1.
% Convert back to uint8 in the range 0-255, if needed.
if eightBit
rgbImage = uint8(255 * rgbImage);
end
end
end
% Display the original image.
h1 = subplot(3, 4, 1);
imshow(rgbImage);
drawnow; % Make it display immediately.
if numberOfColorBands > 1
title('Original Color Image', 'FontSize', fontSize);
else
caption = sprintf('Original Indexed Image\n(converted to true color with its stored colormap)');
title(caption, 'FontSize', fontSize);
end
% Let user outline region over rgb image.
% [xCoords, yCoords, roiPosition] = DrawBoxRegion(h1); % Draw a box.
mask = DrawFreehandRegion(h1, rgbImage); % Draw a freehand, irregularly-shaped region.
% Mask the image.
maskedRgbImage = bsxfun(@times, rgbImage, cast(mask, class(rgbImage)));
% Display it.
subplot(3, 4, 5);
imshow(maskedRgbImage);
title('The Region You Drew', 'FontSize', fontSize);
% Convert image from RGB colorspace to lab color space.
cform = makecform('srgb2lab');
lab_Image = applycform(im2double(rgbImage),cform);
% Extract out the color bands from the original image
% into 3 separate 2D arrays, one for each color component.
LChannel = lab_Image(:, :, 1);
aChannel = lab_Image(:, :, 2);
bChannel = lab_Image(:, :, 3);
% Display the lab images.
subplot(3, 4, 2);
imshow(LChannel, []);
title('L Channel', 'FontSize', fontSize);
subplot(3, 4, 3);
imshow(aChannel, []);
title('a Channel', 'FontSize', fontSize);
subplot(3, 4, 4);
imshow(bChannel, []);
title('b Channel', 'FontSize', fontSize);
% Get the average lab color value.
[LMean, aMean, bMean] = GetMeanLABValues(LChannel, aChannel, bChannel, mask);
% Get box coordinates and get mean within the box.
% x1 = round(roiPosition(1));
% x2 = round(roiPosition(1) + roiPosition(3) - 1);
% y1 = round(roiPosition(2));
% y2 = round(roiPosition(2) + roiPosition(4) - 1);
%
% LMean = mean2(LChannel(y1:y2, x1:x2))
% aMean = mean2(aChannel(y1:y2, x1:x2))
% bMean = mean2(bChannel(y1:y2, x1:x2))
% Make uniform images of only that one single LAB color.
LStandard = LMean * ones(rows, columns);
aStandard = aMean * ones(rows, columns);
bStandard = bMean * ones(rows, columns);
% Create the delta images: delta L, delta A, and delta B.
deltaL = LChannel - LStandard;
deltaa = aChannel - aStandard;
deltab = bChannel - bStandard;
% Create the Delta E image.
% This is an image that represents the color difference.
% Delta E is the square root of the sum of the squares of the delta images.
deltaE = sqrt(deltaL .^ 2 + deltaa .^ 2 + deltab .^ 2);
% Mask it to get the Delta E in the mask region only.
maskedDeltaE = deltaE .* mask;
% Get the mean delta E in the mask region
% Note: deltaE(mask) is a 1D vector of ONLY the pixel values within the masked area.
meanMaskedDeltaE = mean(deltaE(mask));
% Get the standard deviation of the delta E in the mask region
stDevMaskedDeltaE = std(deltaE(mask));
message = sprintf('The mean LAB = (%.2f, %.2f, %.2f).\nThe mean Delta E in the masked region is %.2f +/- %.2f',...
LMean, aMean, bMean, meanMaskedDeltaE, stDevMaskedDeltaE);
% Display the masked Delta E image - the delta E within the masked region only.
subplot(3, 4, 6);
imshow(maskedDeltaE, []);
caption = sprintf('Delta E between image within masked region\nand mean color within masked region.\n(With amplified intensity)');
title(caption, 'FontSize', fontSize);
% Display the Delta E image - the delta E over the entire image.
subplot(3, 4, 7);
imshow(deltaE, []);
caption = sprintf('Delta E Image\n(Darker = Better Match)');
title(caption, 'FontSize', fontSize);
% Plot the histograms of the Delta E color difference image,
% both within the masked region, and for the entire image.
PlotHistogram(deltaE(mask), deltaE, [3 4 8], 'Histograms of the 2 Delta E Images');
message = sprintf('%s\n\nRegions close in color to the color you picked\nwill be dark in the Delta E image.\n', message);
msgboxw(message);
% Find out how close the user wants to match the colors.
prompt = {sprintf('First, examine the histogram.\nThen find pixels within this Delta E (from the average color in the region you drew):')};
dialogTitle = 'Enter Delta E Tolerance';
numberOfLines = 1;
% Set the default tolerance to be the mean delta E in the masked region plus two standard deviations.
strTolerance = sprintf('%.1f', meanMaskedDeltaE + 3 * stDevMaskedDeltaE);
defaultAnswer = {strTolerance}; % Suggest this number to the user.
response = inputdlg(prompt, dialogTitle, numberOfLines, defaultAnswer);
% Update tolerance with user's response.
tolerance = str2double(cell2mat(response));
% Let them interactively select the threshold with the threshold() m-file.
% (Note: This is a separate function in a separate file in my File Exchange.)
% threshold(deltaE);
% Place a vertical bar at the threshold location.
handleToSubPlot8 = subplot(3, 4, 8); % Get the handle to the plot.
PlaceVerticalBarOnPlot(handleToSubPlot8, tolerance, [0 .5 0]); % Put a vertical red line there.
% Find pixels within that delta E.
binaryImage = deltaE <= tolerance;
subplot(3, 4, 9);
imshow(binaryImage, []);
title('Matching Colors Mask', 'FontSize', fontSize);
% Mask the image with the matching colors and extract those pixels.
matchingColors = bsxfun(@times, rgbImage, cast(binaryImage, class(rgbImage)));
subplot(3, 4, 10);
imshow(matchingColors);
caption = sprintf('Matching Colors (Delta E <= %.1f)', tolerance);
title(caption, 'FontSize', fontSize);
% Mask the image with the NON-matching colors and extract those pixels.
nonMatchingColors = bsxfun(@times, rgbImage, cast(~binaryImage, class(rgbImage)));
subplot(3, 4, 11);
imshow(nonMatchingColors);
caption = sprintf('Non-Matching Colors (Delta E > %.1f)', tolerance);
title(caption, 'FontSize', fontSize);
% Display credits: the MATLAB logo and my name.
ShowCredits(); % Display logo in plot position #12.
% Alert user that the demo has finished.
message = sprintf('Done!\n\nThe demo has finished.\nRegions close in color to the color you picked\nwill be dark in the Delta E image.\n');
msgbox(message);
catch ME
errorMessage = sprintf('Error running this m-file:\n%s\n\nThe error message is:\n%s', ...
mfilename('fullpath'), ME.message);
errordlg(errorMessage);
end
return; % from SimpleColorDetection()
% ---------- End of main function ---------------------------------
%----------------------------------------------------------------------------
% Display the MATLAB logo.
function ShowCredits()
try
% xpklein;
% surf(peaks(30));
logoFig = subplot(3, 4, 12);
caption = sprintf('A MATLAB Demo\nby ImageAnalyst');
text(0.5,1.15, caption, 'Color','r', 'FontSize', 18, 'FontWeight','b', 'HorizontalAlignment', 'Center') ;
positionOfLowerRightPlot = get(logoFig, 'position');
L = 40*membrane(1,25);
logoax = axes('CameraPosition', [-193.4013 -265.1546 220.4819],...
'CameraTarget',[26 26 10], ...
'CameraUpVector',[0 0 1], ...
'CameraViewAngle',9.5, ...
'DataAspectRatio', [1 1 .9],...
'Position', positionOfLowerRightPlot, ...
'Visible','off', ...
'XLim',[1 51], ...
'YLim',[1 51], ...
'ZLim',[-13 40], ...
'parent',gcf);
s = surface(L, ...
'EdgeColor','none', ...
'FaceColor',[0.9 0.2 0.2], ...
'FaceLighting','phong', ...
'AmbientStrength',0.3, ...
'DiffuseStrength',0.6, ...
'Clipping','off',...
'BackFaceLighting','lit', ...
'SpecularStrength',1.1, ...
'SpecularColorReflectance',1, ...
'SpecularExponent',7, ...
'Tag','TheMathWorksLogo', ...
'parent',logoax);
l1 = light('Position',[40 100 20], ...
'Style','local', ...
'Color',[0 0.8 0.8], ...
'parent',logoax);
l2 = light('Position',[.5 -1 .4], ...
'Color',[0.8 0.8 0], ...
'parent',logoax);
catch ME
errorMessage = sprintf('Error running ShowCredits().\n\nThe error message is:\n%s', ...
ME.message);
errordlg(errorMessage);
end
return; % from ShowCredits()
%-----------------------------------------------------------------------------
function [xCoords, yCoords, roiPosition] = DrawBoxRegion(handleToImage)
try
% Open a temporary full-screen figure if requested.
enlargeForDrawing = true;
axes(handleToImage);
if enlargeForDrawing
hImage = findobj(gca,'Type','image');
numberOfImagesInside = length(hImage);
if numberOfImagesInside > 1
imageInside = get(hImage(1), 'CData');
else
imageInside = get(hImage, 'CData');
end
hTemp = figure;
hImage2 = imshow(imageInside, []);
[rows columns NumberOfColorBands] = size(imageInside);
set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
end
txtInfo = sprintf('Draw a box over the unstained fabric by clicking and dragging over the image.\nDouble click inside the box to finish drawing.');
text(10, 40, txtInfo, 'color', 'r', 'FontSize', 24);
% Prompt user to draw a region on the image.
msgboxw(txtInfo);
% Erase all previous lines.
if ~enlargeForDrawing
axes(handleToImage);
% ClearLinesFromAxes(handles);
end
hBox = imrect;
roiPosition = wait(hBox);
roiPosition
% Erase all previous lines.
if ~enlargeForDrawing
axes(handleToImage);
% ClearLinesFromAxes(handles);
end
xCoords = [roiPosition(1), roiPosition(1)+roiPosition(3), roiPosition(1)+roiPosition(3), roiPosition(1), roiPosition(1)];
yCoords = [roiPosition(2), roiPosition(2), roiPosition(2)+roiPosition(4), roiPosition(2)+roiPosition(4), roiPosition(2)];
% Plot the mask as an outline over the image.
hold on;
plot(xCoords, yCoords, 'linewidth', 2);
close(hTemp);
catch ME
errorMessage = sprintf('Error running DrawRegion:\n\n\nThe error message is:\n%s', ...
ME.message);
WarnUser(errorMessage);
end
return; % from DrawRegion
%-----------------------------------------------------------------------------
function [mask] = DrawFreehandRegion(handleToImage, rgbImage)
try
fontSize = 14;
% Open a temporary full-screen figure if requested.
enlargeForDrawing = true;
axes(handleToImage);
if enlargeForDrawing
hImage = findobj(gca,'Type','image');
numberOfImagesInside = length(hImage);
if numberOfImagesInside > 1
imageInside = get(hImage(1), 'CData');
else
imageInside = get(hImage, 'CData');
end
hTemp = figure;
hImage2 = imshow(imageInside, []);
[rows columns NumberOfColorBands] = size(imageInside);
set(gcf, 'Position', get(0,'Screensize')); % Maximize figure.
end
message = sprintf('Left click and hold to begin drawing.\nSimply lift the mouse button to finish');
text(10, 40, message, 'color', 'r', 'FontSize', fontSize);
% Prompt user to draw a region on the image.
uiwait(msgbox(message));
% Now, finally, have the user freehand draw the mask in the image.
hFH = imfreehand();
% Once we get here, the user has finished drawing the region.
% Create a binary image ("mask") from the ROI object.
mask = hFH.createMask();
% Close the maximized figure because we're done with it.
close(hTemp);
% Display the freehand mask.
subplot(3, 4, 5);
imshow(mask);
title('Binary mask of the region', 'FontSize', fontSize);
% Mask the image.
maskedRgbImage = bsxfun(@times, rgbImage, cast(mask,class(rgbImage)));
% Display it.
subplot(3, 4, 6);
imshow(maskedRgbImage);
catch ME
errorMessage = sprintf('Error running DrawFreehandRegion:\n\n\nThe error message is:\n%s', ...
ME.message);
WarnUser(errorMessage);
end
return; % from DrawFreehandRegion
%-----------------------------------------------------------------------------
% Get the average lab within the mask region.
function [LMean, aMean, bMean] = GetMeanLABValues(LChannel, aChannel, bChannel, mask)
try
LVector = LChannel(mask); % 1D vector of only the pixels within the masked area.
LMean = mean(LVector);
aVector = aChannel(mask); % 1D vector of only the pixels within the masked area.
aMean = mean(aVector);
bVector = bChannel(mask); % 1D vector of only the pixels within the masked area.
bMean = mean(bVector);
catch ME
errorMessage = sprintf('Error running GetMeanLABValues:\n\n\nThe error message is:\n%s', ...
ME.message);
WarnUser(errorMessage);
end
return; % from GetMeanLABValues
%==========================================================================================================================
function WarnUser(warningMessage)
uiwait(warndlg(warningMessage));
return; % from WarnUser()
%==========================================================================================================================
function msgboxw(message)
uiwait(msgbox(message));
return; % from msgboxw()
%==========================================================================================================================
% Plots the histograms of the pixels in both the masked region and the entire image.
function PlotHistogram(maskedRegion, doubleImage, plotNumber, caption)
try
fontSize = 14;
subplot(plotNumber(1), plotNumber(2), plotNumber(3));
% Find out where the edges of the histogram bins should be.
maxValue1 = max(maskedRegion(:));
maxValue2 = max(doubleImage(:));
maxOverallValue = max([maxValue1 maxValue2]);
edges = linspace(0, maxOverallValue, 100);
% Get the histogram of the masked region into 100 bins.
pixelCount1 = histc(maskedRegion(:), edges);
% Get the histogram of the entire image into 100 bins.
pixelCount2 = histc(doubleImage(:), edges);
% Plot the histogram of the entire image.
plot(edges, pixelCount2, 'b-');
% Now plot the histogram of the masked region.
% However there will likely be so few pixels that this plot will be so low and flat compared to the histogram of the entire
% image that you probably won't be able to see it. To get around this, let's scale it to make it higher so we can see it.
gainFactor = 1.0;
maxValue3 = max(max(pixelCount2));
pixelCount3 = gainFactor * maxValue3 * pixelCount1 / max(pixelCount1);
hold on;
plot(edges, pixelCount3, 'r-');
title(caption, 'FontSize', fontSize);
% Scale x axis manually.
xlim([0 edges(end)]);
legend('Entire', 'Masked');
catch ME
errorMessage = sprintf('Error running PlotHistogram:\n\n\nThe error message is:\n%s', ...
ME.message);
WarnUser(errorMessage);
end
return; % from PlotHistogram
%=====================================================================
% Shows vertical lines going up from the X axis to the curve on the plot.
function lineHandle = PlaceVerticalBarOnPlot(handleToPlot, x, lineColor)
try
% If the plot is visible, plot the line.
if get(handleToPlot, 'visible')
axes(handleToPlot); % makes existing axes handles.axesPlot the current axes.
% Make sure x location is in the valid range along the horizontal X axis.
XRange = get(handleToPlot, 'XLim');
maxXValue = XRange(2);
if x > maxXValue
x = maxXValue;
end
% Erase the old line.
%hOldBar=findobj('type', 'hggroup');
%delete(hOldBar);
% Draw a vertical line at the X location.
hold on;
yLimits = ylim;
lineHandle = line([x x], [yLimits(1) yLimits(2)], 'Color', lineColor, 'LineWidth', 3);
hold off;
end
catch ME
errorMessage = sprintf('Error running PlaceVerticalBarOnPlot:\n\n\nThe error message is:\n%s', ...
ME.message);
WarnUser(errorMessage);
end
return; % End of PlaceVerticalBarOnPlot
|
github
|
kartik-nighania/ardupilot-master
|
RotToQuat.m
|
.m
|
ardupilot-master/libraries/AP_NavEKF/Models/Common/RotToQuat.m
| 288 |
utf_8
|
9239706354267c8f5f2a29f992c07de9
|
% convert froma rotation vector in radians to a quaternion
function quaternion = RotToQuat(rotVec)
vecLength = sqrt(rotVec(1)^2 + rotVec(2)^2 + rotVec(3)^2);
if vecLength < 1e-6
quaternion = [1;0;0;0];
else
quaternion = [cos(0.5*vecLength); rotVec/vecLength*sin(0.5*vecLength)];
end
|
github
|
kartik-nighania/ardupilot-master
|
NormQuat.m
|
.m
|
ardupilot-master/libraries/AP_NavEKF/Models/Common/NormQuat.m
| 198 |
utf_8
|
ed913e87efc9194a2c52b266fced8da7
|
% normalise the quaternion
function quaternion = normQuat(quaternion)
quatMag = sqrt(quaternion(1)^2 + quaternion(2)^2 + quaternion(3)^2 + quaternion(4)^2);
quaternion(1:4) = quaternion / quatMag;
|
github
|
kartik-nighania/ardupilot-master
|
QuatToEul.m
|
.m
|
ardupilot-master/libraries/AP_NavEKF/Models/Common/QuatToEul.m
| 436 |
utf_8
|
c19c9235052d99b8b943a7157e83fc94
|
% Convert from a quaternion to a 321 Euler rotation sequence in radians
function Euler = QuatToEul(quat)
Euler = zeros(3,1);
Euler(1) = atan2(2*(quat(3)*quat(4)+quat(1)*quat(2)), quat(1)*quat(1) - quat(2)*quat(2) - quat(3)*quat(3) + quat(4)*quat(4));
Euler(2) = -asin(2*(quat(2)*quat(4)-quat(1)*quat(3)));
Euler(3) = atan2(2*(quat(2)*quat(3)+quat(1)*quat(4)), quat(1)*quat(1) + quat(2)*quat(2) - quat(3)*quat(3) - quat(4)*quat(4));
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
initParamDist.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/toolbox/initParamDist.m
| 1,393 |
utf_8
|
f5c0d7c880e1e6a811cc0157eb7fd94a
|
function prob_bij = initParamDist(edgeD, edge_pairs, samples)
% Initialize tree parameters using distances
adjmat = logical(edgeD);
Ntotal = size(adjmat,1);
Nobserved = size(samples,1);
Nsamples = size(samples,2);
prob_bi = zeros(Ntotal,2);
prob_bi(1:Nobserved,2) = sum(samples-1,2)/Nsamples;
for i=Nobserved+1:Ntotal
neighbors = find(adjmat(i,1:Nobserved));
if(length(neighbors) > 3)
votes = sum(samples(neighbors,:)-1,1);
prob_bi(i,2) = max(sum((votes > length(neighbors)/2))/Nsamples,0.05);
else
prob_bi(i,2) = rand(1);
end
end
prob_bi(:,1) = 1 - prob_bi(:,2);
prob_bij = sparse(2*Ntotal,2*Ntotal);
for e=1:size(edge_pairs,1)
u = edge_pairs(e,1);
v = edge_pairs(e,2);
prob_bij(2*u-1:2*u,2*v-1:2*v) = findJointProb(edgeD(u,v),prob_bi(u,2),prob_bi(v,2));
end
prob_bi = prob_bi';
prob_bij = prob_bij + prob_bij' + diag(prob_bi(:));
%%%%%%
function jointProb = findJointProb(edge_dist,a,b)
detJoint = exp(-edge_dist + 0.5*sum(log([1-a a 1-b b])));
p11 = detJoint + a*b;
jointProb = [1+p11-a-b, b-p11; a-p11, p11];
if(all(jointProb(:)>=0) && all(jointProb(:) <= 1))
return;
end
p11 = a*b - detJoint;
jointProb = [1+p11-a-b, b-p11; a-p11, p11];
if(all(jointProb(:)>=0) && all(jointProb(:) <= 1))
return;
end
minP = max(0,a+b-1);
maxP = min(a,b);
p11 = (maxP-minP)*rand(1)+minP;
jointProb = [1+p11-a-b, b-p11; a-p11, p11];
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
makeModel.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/toolbox/makeModel.m
| 2,836 |
utf_8
|
577899ddcf8258afdccd0c8bc94a1aca
|
function [adjmat, level_m] = makeModel(graph, m)
% Generate the adjacency matrix for the given graph with m observed
% variables.
level_m = m;
switch graph
case 'star'
M = m+1;
adjmat = sparse(M,M);
adjmat(1:m,end) = 1;
case 'doubleStar'
M = m+2;
adjmat = sparse(M,M);
adjmat(1:ceil(m/2),end-1)=1;
adjmat(ceil(m/2)+1:end-1,end)=1;
case 'hmm'
M = 2*m-2;
adjmat = sparse(M,M);
adjmat(1,m+1) = 1;
adjmat(m,end) = 1;
adjmat(2:m-1,m+1:M) = speye(m-2);
adjmat(m+1:M-1,m+2:M) = speye(m-3);
case 'regular'
adjmat = sparse(m,m);
num_nodes = m;
while(num_nodes > 2)
num_p = floor(num_nodes/3);
new_adjmat = sparse(kron(eye(num_p),[1 1 1]));
res_node = num_nodes - 3*num_p;
if(res_node == 1)
new_adjmat = [new_adjmat, [zeros(num_p-1,1); 1]];
elseif(res_node == 2)
new_adjmat = [new_adjmat, [zeros(num_p-1,2); 1 1]];
end
adjmat = [adjmat; zeros(num_p,size(adjmat,2)-size(new_adjmat,2)), new_adjmat];
adjmat = [adjmat, zeros(size(adjmat,1),num_p)];
num_nodes = num_p;
level_m = [level_m; size(adjmat,1)];
end
if(num_nodes == 2)
adjmat(end-1,end) = 1;
end
M = size(adjmat,1);
case '3cayley'
adjmat = sparse(m,m);
num_nodes = m;
while(num_nodes > 2)
num_p = floor(num_nodes/2);
new_adjmat = sparse(kron(eye(num_p),[1 1]));
res_node = num_nodes - 2*num_p;
if(res_node == 1)
new_adjmat = [new_adjmat, [zeros(num_p-1,1); 1]];
end
adjmat = [adjmat; zeros(num_p,size(adjmat,2)-size(new_adjmat,2)), new_adjmat];
adjmat = [adjmat, zeros(size(adjmat,1),num_p)];
num_nodes = num_p;
level_m = [level_m; size(adjmat,1)];
end
if(num_nodes == 2)
adjmat(end-1,end) = 1;
end
M = size(adjmat,1);
case '5cayley'
[adjmat,level_m] = makeCayleyTree(m,4);
end
adjmat = adjmat + adjmat';
function [adjmat,level_m] = makeCayleyTree(m,d)
level_m = m;
adjmat = sparse(m,m);
num_nodes = m;
while(num_nodes > 2);
num_p = floor(num_nodes/d);
new_adjmat = sparse(kron(eye(num_p),repmat(1,1,d)));
res_node = num_nodes - d*num_p;
if(res_node > 0)
new_adjmat = [new_adjmat, [zeros(num_p-1,res_node); repmat(1,1,res_node)]];
end
adjmat = [adjmat; zeros(num_p,size(adjmat,2)-size(new_adjmat,2)), new_adjmat];
adjmat = [adjmat, zeros(size(adjmat,1),num_p)];
num_nodes = num_p;
level_m = [level_m; size(adjmat,1)];
end
if(num_nodes == 2)
adjmat(end-1,end) = 1;
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
drawWeightedGraph.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/toolbox/drawWeightedGraph.m
| 8,290 |
utf_8
|
136465d2b59b2a3dbd583c773e6f79a7
|
function [x, y, h] = drawWeightedGraph(adj, labels, root, edge_weight, node_t, varargin)
% DRAW_LAYOUT Draws a layout for a graph
%
% [X, Y, H] = DRAW_LAYOUT(ADJ, <LABELS, ISBOX, X, Y>)
%
% Inputs :
% ADJ : Adjacency matrix (source, sink)
% LABELS : Cell array containing labels <Default : '1':'N'>
% ISBOX : 1 if node is a box, 0 if oval <Default : zeros>
% X, Y, : Coordinates of nodes on the unit square <Default : calls make_layout>
%
% Outputs :
% X, Y : Coordinates of nodes on the unit square
% H : Object handles
%
% Usage Example : [x, y] = draw_layout([0 1;0 0], {'Hidden','Visible'}, [1 0]');
%
% h(i,1) is the text handle - color
% h(i,2) is the circle handle - facecolor
%
% See also MAKE_LAYOUT
% Change History :
% Date Time Prog Note
% 13-Apr-2000 9:06 PM ATC Created under MATLAB 5.3.1.29215a (R11.1)
% 16-Sep-2009 Jin Choi Modified to draw a weighted tree
%
% ATC = Ali Taylan Cemgil,
% SNN - University of Nijmegen, Department of Medical Physics and Biophysics
% e-mail : [email protected]
adj = double(adj);
N = size(adj,1);
if nargin<2,
labels = cellstr(int2str((1:N)'));
end
if nargin<3
root = 1;
end
if nargin < 5
node_t = zeros(N,1);
end
%scrsz = get(0,'ScreenSize');
set(gcf,'Position',[1, 100, 1500, 800])
axis([0 1 0 1]);
set(gca,'XTick',[],'YTick',[],'box','on');
%set(gcf,'Position',[1, 100, 1500, 800])
% axis('square');
%colormap(flipud(gray));
[x y] = treeLayout(adj,root,edge_weight);
idx1 = find(node_t==0); h1 = []; wd1=[];
if ~isempty(idx1)
[h1 wd1] = textoval(x(idx1), y(idx1), labels(idx1), varargin{:});
end;
idx2 = find(node_t~=0); h2 = []; wd2 = [];
if ~isempty(idx2)
[h2 wd2] = textbox(x(idx2), y(idx2), labels(idx2), varargin{:});
end;
wd = zeros(size(wd1,1)+size(wd2,1),2);
if ~isempty(idx1), wd(idx1, :) = wd1; end;
if ~isempty(idx2), wd(idx2, :) = wd2; end;
% bug: this code assumes [x y] is the center of each box and oval, which
% isn't exactly true.
%h_edge = [];
for i=1:N,
j = find(adj(i,:)==1);
for k=j,
if x(k)-x(i)==0,
sign = 1;
if y(i)>y(k), alpha = -pi/2; else alpha = pi/2; end;
else
alpha = atan((y(k)-y(i))/(x(k)-x(i)));
if x(i)<x(k), sign = 1; else sign = -1; end;
end;
dy1 = sign.*wd(i,2).*sin(alpha); dx1 = sign.*wd(i,1).*cos(alpha);
dy2 = sign.*wd(k,2).*sin(alpha); dx2 = sign.*wd(k,1).*cos(alpha);
if adj(k,i)==0, % if directed edge
h = arrow([x(i)+dx1 y(i)+dy1],[x(k)-dx2 y(k)-dy2],'BaseAngle',30);
else
h = line([x(i)+dx1 x(k)-dx2],[y(i)+dy1 y(k)-dy2]);
adj(k,i)=-1; % Prevent drawing lines twice
end;
%h_edge = [h_edge h];
weight = 10*edge_weight(i,k);
if(weight > 0)
line_color = 'blue';
else
line_color = 'red';
end
set(h, 'LineWidth', max(0.1,abs(weight)), 'Color',line_color);
end;
end;
color.box = 'black';
color.text = color.box;
%color.edge = [1 1 1]*3/4;
%color.edge = 'green';
if ~isempty(idx1)
set(h1(:,1),'Color',color.text)
set(h1(:,2),'EdgeColor',color.box)
end
if ~isempty(idx2)
set(h2(:,1),'Color',color.text)
set(h2(:,2),'EdgeColor',color.box)
end
%set(h_edge,'Color',color.edge)
if nargout>2,
h = zeros(length(wd),2);
if ~isempty(idx1),
h(idx1,:) = h1;
end;
if ~isempty(idx2),
h(idx2,:) = h2;
end;
end;
%%%%%
function [t, wd] = textoval(x, y, str, varargin)
% TEXTOVAL Draws an oval around text objects
%
% [T, WIDTH] = TEXTOVAL(X, Y, STR)
% [..] = TEXTOVAL(STR) % Interactive
%
% Inputs :
% X, Y : Coordinates
% TXT : Strings
%
% Outputs :
% T : Object Handles
% WIDTH : x and y Width of ovals
%
% Usage Example : [t] = textoval('Visit to Asia?');
%
%
% Note :
% See also TEXTBOX
% Uses :
% Change History :
% Date Time Prog Note
% 15-Jun-1998 10:36 AM ATC Created under MATLAB 5.1.0.421
% 12-Mar-2004 10:00 AM minka Changed placement/sizing.
%
% ATC = Ali Taylan Cemgil,
% SNN - University of Nijmegen, Department of Medical Physics and Biophysics
% e-mail : [email protected]
temp = [];
textProperties = {'BackgroundColor','Color','FontAngle','FontName','FontSize','FontUnits','FontWeight','Rotation'};
varargin = argfilter(varargin,textProperties);
if nargin == 1
str = x;
end
if ~isa(str,'cell') str=cellstr(str); end;
N = length(str);
wd = zeros(N,2);
for i=1:N,
if nargin == 1
[x, y] = ginput(1);
end
tx = text(x(i),y(i),str{i},'HorizontalAlignment','center',varargin{:});
% minka
[ptc wx wy] = draw_oval(tx);
wd(i,:) = [wx wy];
% draw_oval will paint over the text, so need to redraw it
delete(tx);
tx = text(x(i),y(i),str{i},'HorizontalAlignment','center',varargin{:});
temp = [temp; tx ptc];
end
if nargout>0, t = temp; end;
%%%%%%%%%
function [ptc, wx, wy] = draw_oval(tx, x, y)
% Draws an oval box around a tex object
sz = get(tx,'Extent');
% minka
wy = 2/3*sz(4);
wx = 2/3*sz(3);
x = sz(1)+sz(3)/2;
y = sz(2)+sz(4)/2;
ptc = ellipse(x, y, wx, wy);
set(ptc, 'FaceColor','w');
%%%%%%%%%%%%%
function [p] = ellipse(x, y, rx, ry, c)
% ELLIPSE Draws Ellipse shaped patch objects
%
% [<P>] = ELLIPSE(X, Y, Rx, Ry, C)
%
% Inputs :
% X : N x 1 vector of x coordinates
% Y : N x 1 vector of y coordinates
% Rx, Ry : Radii
% C : Color index
%
%
% Outputs :
% P = Handles of Ellipse shaped path objects
%
% Usage Example : [] = ellipse();
%
%
% Note :
% See also
% Uses :
% Change History :
% Date Time Prog Note
% 27-May-1998 9:55 AM ATC Created under MATLAB 5.1.0.421
% ATC = Ali Taylan Cemgil,
% SNN - University of Nijmegen, Department of Medical Physics and Biophysics
% e-mail : [email protected]
if (nargin < 2) error('Usage Example : e = ellipse([0 1],[0 -1],[1 0.5],[2 0.5]); '); end;
if (nargin < 3) rx = 0.1; end;
if (nargin < 4) ry = rx; end;
if (nargin < 5) c = 1; end;
if length(c)==1, c = ones(size(x)).*c; end;
if length(rx)==1, rx = ones(size(x)).*rx; end;
if length(ry)==1, ry = ones(size(x)).*ry; end;
n = length(x);
p = zeros(size(x));
t = 0:pi/30:2*pi;
for i=1:n,
px = rx(i)*cos(t)+x(i);
py = ry(i)*sin(t)+y(i);
p(i) = patch(px,py,c(i));
end;
if nargout>0, pp = p; end;
%%%%%
function [t, wd] = textbox(x,y,str,varargin)
% TEXTBOX Draws A Box around the text
%
% [T, WIDTH] = TEXTBOX(X, Y, STR)
% [..] = TEXTBOX(STR)
%
% Inputs :
% X, Y : Coordinates
% TXT : Strings
%
% Outputs :
% T : Object Handles
% WIDTH : x and y Width of boxes
%%
% Usage Example : t = textbox({'Ali','Veli','49','50'});
%
%
% Note :
% See also TEXTOVAL
% Uses :
% Change History :
% Date Time Prog Note
% 09-Jun-1998 11:43 AM ATC Created under MATLAB 5.1.0.421
% 12-Mar-2004 10:00 AM minka Changed placement/sizing.
%
% ATC = Ali Taylan Cemgil,
% SNN - University of Nijmegen, Department of Medical Physics and Biophysics
% e-mail : [email protected]
temp = [];
textProperties = {'BackgroundColor','Color','FontAngle','FontName','FontSize','FontUnits','FontWeight','Rotation'};
varargin = argfilter(varargin,textProperties);
if nargin == 1
str = x;
end
if ~isa(str,'cell') str=cellstr(str); end;
N = length(str);
wd = zeros(N,2);
for i=1:N,
if nargin == 1
[x, y] = ginput(1);
end
tx = text(x(i),y(i),str{i},'HorizontalAlignment','center',varargin{:});
% minka
[ptc wx wy] = draw_box(tx);
wd(i,:) = [wx wy];
% draw_box will paint over the text, so need to redraw it
delete(tx);
tx = text(x(i),y(i),str{i},'HorizontalAlignment','center',varargin{:});
temp = [temp; tx ptc];
end;
if nargout>0, t = temp; end;
function [ptc, wx, wy] = draw_box(tx)
% Draws a box around a text object
sz = get(tx,'Extent');
% minka
wy = 1/2*sz(4);
wx = 1/2*sz(3);
x = sz(1)+sz(3)/2;
y = sz(2)+sz(4)/2;
ptc = patch([x-wx x+wx x+wx x-wx], [y+wy y+wy y-wy y-wy],'w');
set(ptc, 'FaceColor','w');
function args = argfilter(args,keep)
%ARGFILTER Remove unwanted arguments.
% ARGFILTER(ARGS,KEEP), where ARGS = {'arg1',value1,'arg2',value2,...},
% returns a new argument list where only the arguments named in KEEP are
% retained. KEEP is a character array or cell array of strings.
% Written by Tom Minka
if ischar(keep)
keep = cellstr(keep);
end
i = 1;
while i < length(args)
if ~ismember(args{i},keep)
args = args(setdiff(1:length(args),[i i+1]));
else
i = i + 2;
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
forrest_ll2.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/toolbox/forrest_ll2.m
| 2,850 |
utf_8
|
a284d701978ee08905a2c299a206516a
|
function [l, missed] = forrest_ll2(data, atree, verbose);
% calculuate the log-likelihood of data under a forrest model
%
% Copyright (C) 2006 - 2009 by Stefan Harmeling (2009-06-26).
if ~exist('verbose', 'var') || isempty(verbose)
verbose = 0;
end
if isempty(atree)
error('[%s.m] tree is empty', mfilename);
end
l = 0; % log-likelihood
ignored = 0;
min_ll_datum = inf;
%%%fprintf('[%s.m] calculating the log-likelihood of the data\n');
for i = 1:size(data.x, 2)
if verbose > 1
fprintf('[%s.m] %d/%d\n', mfilename, i, size(data.x, 2));
end
datum = data.x(:, i);
ll_datum = 0;
for j = 1:length(atree.t0) % loop over all roots
msg = gen_message(atree.t0(j), datum, atree);
ll_datum = ll_datum + log(msg * atree.p0{j}); % sum out the root variable
end
if ll_datum == -Inf
ignored = ignored + 1;
else
min_ll_datum = min(min_ll_datum,ll_datum);
l = l + ll_datum;
end
end
if ignored > 0
warning(sprintf('[%s.m] %d of %d data points had zero prob.', mfilename, ...
ignored, size(data.x, 2)));
fprintf('Using the minimum lilkelihood %f instead of -Inf\n',min_ll_datum);
l = l+ignored*min_ll_datum;
end
missed = ignored;
function msg = gen_message(subtree, datum, atree);
% the message is a likelihood of the leaves of the current subtree fixed
% given all values of the root of the current subtree
%
% e.g. for the tree (x1 x2 (x3 x4 x5))
%
% x1
% / \
% x2 x3
% / \
% x4 x5
%
% p(<none> | x5) = gen_message(5, ...);
% = (0 0 1 0 0); % int x5 determining position of 1
% p(<none> | x4) = gen_message(4, ...);
% = (0 0 1 0 0); % int x4 determining position of 1
% p(x4, x5 | x3) = gen_message(3, ...);
% = p(x4|x3) p(x5|x3)
% = (p(x4|x3)*p(<none>|x4)) .* (p(x5|x3)*p(<none>|x5))
% p(<none> | x2) = gen_message(2, ...);
% = (0 0 0 1 0); % int x2 determining position of 1
% p(x2, x4, x5 | x1) = gen_message(1, ...);
% = p(x2|x1)*sum_x3 p(x3|x1) p(x4,x5|x3)
% do we have data at the current node?
if subtree > atree.nobs
% NOT OBSERVED
% create a vector with ones that allows all values
msg = ones(1, atree.nsyms(subtree));
else
% OBSERVED
% create a vector with zeros and a single one, which will pick out the
% correct row from the CPT of the parent
msg = zeros(1, atree.nsyms(subtree));
msg(datum(subtree)) = 1;
end
% does subtree has kids?
nkids = size(atree.t{subtree}, 2);
if nkids > 0
% ask for the messages of the kids
for j = 1:nkids
kid = atree.t{subtree}(j);
kid_msg = gen_message(kid, datum, atree);
cpt = atree.p{subtree}{j};
% the matrix product does sum out the kid
msg = msg .* (kid_msg*cpt);
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
queryFamiliesClustering.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/toolbox/queryFamiliesClustering.m
| 5,005 |
utf_8
|
8df6181dc3a9864c626905100cb4d562
|
function [families, parents, avg_log_ratio] = queryFamiliesClustering(distance,numSamples,verbose)
% Find family groups by adaptive thresholding
if(nargin < 3)
verbose = 0;
end
edgeD_min = -log(0.1);
edgeD_max = -log(0.9);
m = size(distance,1);
%relD_thres = 2*edgeD_min; % For reliable statistics, ignore distances below this threshold
relD_thres = -log(0.05)+0.1*log(numSamples);
diff_log_ratio = inf*ones(m);
avg_log_ratio = sparse(m,m);
for i=1:m
for j=i+1:m
if(distance(i,j) > 2*edgeD_min)
diff_log_ratio(i,j) = 10;
continue;
end
if(m > 5)
other_nodes = (distance(i,:) < relD_thres) & (distance(j,:) < relD_thres);
dt = relD_thres;
while(sum(other_nodes) <= 5) % Need at least 2 other nodes to identify siblings
dt = dt + log(2);
other_nodes = (distance(i,:) < dt) & (distance(j,:) < dt);
end
else
other_nodes = true(1,m);
end
other_nodes([i,j]) = false;
log_ratio = distance(i,other_nodes) - distance(j,other_nodes);
diff_log_ratio(i,j) = max(log_ratio) - min(log_ratio);
avg_log_ratio(i,j) = mean(log_ratio);
end
end
avg_log_ratio = avg_log_ratio - avg_log_ratio';
D = min(diff_log_ratio,diff_log_ratio');
families = kmeansDistance(D,verbose);
% Check whether there exists a parent node for each grouping
parents = zeros(length(families),1);
for f = 1:length(families)
members = families{f};
parent_score = zeros(length(members),1);
for i=1:length(members)
p = members(i);
parent_score(i) = sum(abs(avg_log_ratio(p,members) + distance(p,members)));
end
[min_parent_score, j] = min(parent_score);
if(length(members)==1 || min_parent_score < 2*edgeD_max*(length(members)-1)) % d(j,h) > edgeD_max
parents(f) = members(j);
end
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function best_clusters = kmeansDistance(D,verbose)
m = size(D,1);
minD = min(D);
[foo,sort_ind_minD] = sort(minD,'descend');
for i=1:m
D(i,i) = 0;
end
%max_mean_silh = max(minD)/max(D(:));
max_mean_silh = -log(0.5)/max(D(:));
best_clusters = {1:m};
if(verbose)
fprintf('k = 1, mean silhouette = %f\n',max_mean_silh);
end
for k = 2:m-2
for init_ite=1:4
% Select the initial center points
if (init_ite==1)
centers = sort_ind_minD([1:k-1,end])';
elseif(init_ite==2)
centers = sort_ind_minD(1:k)';
else
randpermm = randperm(m);
centers = randpermm(1:k)';
end
prev_centers = centers;
for ite=1:5
% Assign clusters for each point
clusters = mat2cell(centers,ones(k,1));
noncenters = setdiff(1:m,centers);
for j=1:length(noncenters)
i = noncenters(j);
[foo, assignC] = min(D(i,centers));
clusters{assignC}(end+1) = i;
end
% Pick a new center for each cluster
for c=1:k
minmaxD = inf;
for j=1:length(clusters{c})
i = clusters{c}(j);
maxD = max(D(i,clusters{c}));
if(maxD < minmaxD)
minmaxD = maxD;
center = i;
end
end
centers(c) = center;
end
if(isempty(setdiff(centers,prev_centers)))
break;
else
prev_centers = centers;
end
end
mean_silh = compSilhouette(D, clusters);
if(mean_silh > max_mean_silh)
max_mean_silh = mean_silh;
best_clusters = clusters;
if(verbose)
fprintf('* ');
%fprintf('k = %d, mean silhouette = %f\n',k,mean_silh);
disp(clusters)
end
end
if(verbose)
fprintf('k = %d, mean silhouette = %f\n',k,mean_silh);
end
end
end
%fprintf('*\n');
function mean_silh = compSilhouette(D, clusters)
m = size(D,1);
k = length(clusters);
sumDcluster = zeros(m,k);
%
maxinD = zeros(k,1);
for c=1:k
sumDcluster(:,c) = mean(D(:,clusters{c}),2);
% %disp(clusters{c})
maxinD(c) = max(max(D(clusters{c},clusters{c})));
end
maxa = max(maxinD);
silh = zeros(m,1);
for c=1:k
numMembers = length(clusters{c});
otherClusterMembers = true(1,m);
otherClusterMembers(clusters{c}) = false;
for j=1:numMembers;
i = clusters{c}(j);
if(numMembers > 1)
a = sumDcluster(i,c)*numMembers/(numMembers-1);
%a = max(D(i,clusters{c}));
else
a = maxa;
%silh(i) = 0;
end
b = min(sumDcluster(i,[1:c-1,c+1:end]));
%b = min(D(i,otherClusterMembers));
silh(i) = (b-a)/max(a,b);
end
end
%mean_silh = mean(silh(silh~=0));
mean_silh = mean(silh);
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
treeLayout.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/toolbox/treeLayout.m
| 2,812 |
utf_8
|
a3b707653d47e879ac3e0006f320963f
|
function [x,y] = treeLayout(adj,root,edge_weight)
% Similar to make_layout but specialized for a tree.
if nargin < 2
root = 1;
end
if nargin < 3
edge_weight = adj;
end
N = size(adj,1);
level = poset(adj,root)'-1;
y = (level+1)./(max(level)+2);
y = 1-y;
% neighbors = find(adj(root,:));
% [temp1, sorted_index] = sort(edge_weight(root,neighbors),'descend');
% neighbors = neighbors(sorted_index);
% if(length(neighbors) > 20)
% neighbors1 = neighbors(1:2:end);
% neighbors2 = neighbors(2:2:end);
% y(neighbors1) = y(neighbors1)+0.03;
% y(neighbors2) = y(neighbors2)-0.03;
% end
x = zeros(size(y));
for i=0:max(level),
idx = find(level==i);
if(i<1)
x(idx) = 0.5;
child_order = (1:length(idx));
else
offset=0;
pidx = find(level==i-1);
[v, ind] = sort(child_order);
pidx = pidx(ind);
child_order = zeros(length(idx),1);
for j=1:length(pidx)
[tf,child_nodes] = ismember(find(adj(pidx(j),:)),idx);
child_nodes = child_nodes(tf);
% Sort child with edge weights
%child_edge_weights = edge_weight(pidx(j),idx(child_nodes));
%[temp1, edge_weight_order] = sort(child_edge_weights,'ascend');
%[temp2, siblings_order] = sort(edge_weight_order, 'ascend');
siblings_order = 1:length(child_nodes);
child_order(child_nodes) = siblings_order+offset;
offset = offset + length(child_nodes);
end
x(idx) = child_order./(length(idx)+1);
if(length(idx)>20)
[tmp,co_sorted] = sort(child_order,'ascend');
idx_co = idx(co_sorted);
y(idx_co(1:2:end)) = y(idx_co(1:2:end))+0.03;
y(idx_co(2:2:end)) = y(idx_co(2:2:end))-0.03;
end
end
end;
%%%%%%%
function [depth] = poset(adj, root)
% POSET Identify a partial ordering among the nodes of a graph
%
% [DEPTH] = POSET(ADJ,ROOT)
%
% Inputs :
% ADJ : Adjacency Matrix
% ROOT : Node to start with
%
% Outputs :
% DEPTH : Depth of the Node
%
% Usage Example : [depth] = poset(adj,12);
%
%
% Note : All Nodes must be connected
% See also
% Uses :
% Change History :
% Date Time Prog Note
% 17-Jun-1998 12:01 PM ATC Created under MATLAB 5.1.0.421
% ATC = Ali Taylan Cemgil,
% SNN - University of Nijmegen, Department of Medical Physics and Biophysics
% e-mail : [email protected]
adj = adj+adj';
N = size(adj,1);
depth = zeros(N,1);
depth(root) = 1;
queue = root;
while 1,
if isempty(queue),
if all(depth), break;
else
root = find(depth==0);
root = root(1);
depth(root) = 1;
queue = root;
end;
end;
r = queue(1); queue(1) = [];
idx = find(adj(r,:));
idx2 = find(~depth(idx));
idx = idx(idx2);
queue = [queue idx];
depth(idx) = depth(r)+1;
end;
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
treeMsgOrder.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/toolbox/treeMsgOrder.m
| 1,545 |
utf_8
|
1d7c7a885cd6c01b2ab00e3df5baee6f
|
function msg = treeMsgOrder(adj, root)
%treeMsgOrder Find message scheduling for inference on a tree.
% Determines a sequence of message updates by which BP produces optimal
% smoothed estimates on a tree-structured undirected graph.
%
% msg = treeMsgOrder(adj, root)
%
% PARAMETERS:
% adj = adjacency matrix of tree-structured graph with N nodes
% root = index of root node used to define scheduling (DEFAULT=1)
% OUTPUTS:
% msg = 2(N-1)-by-2 matrix such that row i gives the source and
% destination nodes for the i^th message passing
% Erik Sudderth
% May 16, 2003 - Initial version
% Check and process input arguments
if (nargin < 1)
error('Invalid number of arguments');
end
if (nargin < 2)
root = 1;
end
N = length(adj);
if (root > N | root < 1)
error('Invalid root node');
end
msg = zeros(2*(N-1),2);
% Recurse from root to define outgoing (scale-recursive) message pass
msgIndex = N;
prevNodes = [];
crntNodes = root;
while (msgIndex <= 2*(N-1))
allNextNodes = [];
for (i = 1:length(crntNodes))
nextNodes = setdiff(find(adj(crntNodes(i),:)),prevNodes);
Nnext = length(nextNodes);
msg(msgIndex:msgIndex+Nnext-1,:) = ...
[repmat(crntNodes(i),Nnext,1), nextNodes'];
msgIndex = msgIndex + Nnext;
allNextNodes = [allNextNodes, nextNodes];
end
prevNodes = [prevNodes, crntNodes];
crntNodes = allNextNodes;
end
% Incoming messages are reverse of outgoing
msg(1:N-1,:) = fliplr(flipud(msg(N:end,:)));
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
myProcessOptions.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/misc/myProcessOptions.m
| 674 |
utf_8
|
b94d252a960faa95a3074129247619e6
|
function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
if isempty(v)
varargout{(i+1)/2}=varargin{i+1};
else
varargout{(i+1)/2}=v;
end
else
varargout{(i+1)/2}=varargin{i+1};
end
end
end
function [o] = toUpper(o)
if ~isempty(o)
fn = fieldnames(o);
for i = 1:length(fn)
o = setfield(o,upper(fn{i}),getfield(o,fn{i}));
end
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
myProcessOptions.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/minConf/myProcessOptions.m
| 674 |
utf_8
|
b94d252a960faa95a3074129247619e6
|
function [varargout] = myProcessOptions(options,varargin)
% Similar to processOptions, but case insensitive and
% using a struct instead of a variable length list
options = toUpper(options);
for i = 1:2:length(varargin)
if isfield(options,upper(varargin{i}))
v = getfield(options,upper(varargin{i}));
if isempty(v)
varargout{(i+1)/2}=varargin{i+1};
else
varargout{(i+1)/2}=v;
end
else
varargout{(i+1)/2}=varargin{i+1};
end
end
end
function [o] = toUpper(o)
if ~isempty(o)
fn = fieldnames(o);
for i = 1:length(fn)
o = setfield(o,upper(fn{i}),getfield(o,fn{i}));
end
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
minConf_TMP.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/minConf/minConf/minConf_TMP.m
| 8,550 |
utf_8
|
6983e0de62f07b14b5a7e0f3b9d6b3df
|
function [x,f,funEvals] = minConF_BC(funObj,x,LB,UB,options)
% function [x,f] = minConF_BC(funObj,x,LB,UB,options)
%
% Function for using Two-Metric Projection to solve problems of the form:
% min funObj(x)
% s.t. LB_i <= x_i <= UB_i
%
% @funObj(x): function to minimize (returns gradient as second argument)
%
% options:
% verbose: level of verbosity (0: no output, 1: final, 2: iter (default), 3:
% debug)
% optTol: tolerance used to check for progress (default: 1e-7)
% maxIter: maximum number of calls to funObj (default: 250)
% numDiff: compute derivatives numerically (0: use user-supplied
% derivatives (default), 1: use finite differences, 2: use complex
% differentials)
% method: 'sd', 'lbfgs', 'newton'
nVars = length(x);
% Set Parameters
if nargin < 5
options = [];
end
[verbose,numDiff,optTol,maxIter,suffDec,interp,method,corrections,damped] = ...
myProcessOptions(...
options,'verbose',3,'numDiff',0,'optTol',1e-6,'maxIter',500,'suffDec',1e-4,...
'interp',1,'method','lbfgs','corrections',100,'damped',0);
% Output Log
if verbose >= 3
fprintf('%10s %10s %15s %15s %15s\n','Iteration','FunEvals','Step Length','Function Val','Opt Cond');
end
% Make objective function (if using numerical derivatives)
funEvalMultiplier = 1;
if numDiff
if numDiff == 2
useComplex = 1;
else
useComplex = 0;
end
funObj = @(x)autoGrad(x,useComplex,funObj);
funEvalMultiplier = nVars+1-useComplex;
end
% Evaluate Initial Point
x = projectBounds(x,LB,UB);
if strcmp(method,'newton')
[f,g,H] = funObj(x);
secondOrder = 1;
else
[f,g] = funObj(x);
secondOrder = 0;
end
funEvals = 1;
% Compute Working Set
working = ones(nVars,1);
working((x < LB+optTol*2) & g >= 0) = 0;
working((x > UB-optTol*2) & g <= 0) = 0;
working = find(working);
% Check Optimality
if isempty(working)
if verbose >= 1
fprintf('All variables are at their bound and no further progress is possible at initial point\n');
end
return;
elseif norm(g(working)) <= optTol
if verbose >=1
fprintf('All working variables satisfy optimality condition at initial point\n');
end
return;
end
if verbose >= 3
switch method
case 'sd'
fprintf('Steepest Descent\n');
case 'lbfgs'
fprintf('L-BFGS\n');
case 'bfgs'
fprintf('BFGS\n');
case 'newton'
fprintf('Newton\n');
end
end
i = 1;
while funEvals <= maxIter
% Compute Step Direction
d = zeros(nVars,1);
switch(method)
case 'sd'
d(working) = -g(working);
case 'lbfgs'
if i == 1
d(working) = -g(working);
old_dirs = zeros(nVars,0);
old_stps = zeros(nVars,0);
Hdiag = 1;
else
if damped
[old_dirs,old_stps,Hdiag] = dampedUpdate(g-g_old,x-x_old,corrections,verbose==3,old_dirs,old_stps,Hdiag);
else
[old_dirs,old_stps,Hdiag] = lbfgsUpdate(g-g_old,x-x_old,corrections,verbose==3,old_dirs,old_stps,Hdiag);
end
curvSat = sum(old_dirs(working,:).*old_stps(working,:)) > 1e-10;
d(working) = lbfgs(-g(working),old_dirs(working,curvSat),old_stps(working,curvSat),Hdiag);
end
g_old = g;
x_old = x;
case 'bfgs'
if i == 1
d(working) = -g(working);
B = eye(nVars);
else
y = g-g_old;
s = x-x_old;
ys = y'*s;
if i == 2
if ys > 1e-10
B = ((y'*y)/(y'*s))*eye(nVars);
end
end
if ys > 1e-10
B = B + (y*y')/(y'*s) - (B*s*s'*B)/(s'*B*s);
else
if verbose == 2
fprintf('Skipping Update\n');
end
end
d(working) = -B(working,working)\g(working);
end
g_old = g;
x_old = x;
case 'newton'
[R,posDef] = chol(H(working,working));
if posDef == 0
d(working) = -R\(R'\g(working));
else
if verbose == 3
fprintf('Adjusting Hessian\n');
end
H(working,working) = H(working,working) + eye(length(working)) * max(0,1e-12 - min(real(eig(H(working,working)))));
d(working) = -H(working,working)\g(working);
end
otherwise
fprintf('Unrecognized Method: %s\n',method);
break;
end
% Check that Progress can be made along the direction
f_old = f;
gtd = g'*d;
if gtd > -optTol
if verbose >= 2
fprintf('Directional Derivative below optTol\n');
end
break;
end
% Select Initial Guess to step length
if i == 1 && ~secondOrder
t = min(1,1/sum(abs(g(working))));
else
t = 1;
end
% Evaluate the Objective and Projected Gradient at the Initial Step
x_new = projectBounds(x+t*d,LB,UB);
if secondOrder
[f_new,g_new,H] = funObj(x_new);
else
[f_new,g_new] = funObj(x_new);
end
funEvals = funEvals+1;
% Backtracking Line Search
lineSearchIters = 1;
while f_new > f + suffDec*g'*(x_new-x) || ~isLegal(f_new)
temp = t;
if interp == 0 || ~isLegal(f_new) || ~isLegal(g_new)
if verbose == 3
fprintf('Halving Step Size\n');
end
t = .5*t;
else
if verbose == 3
fprintf('Cubic Backtracking\n');
end
t = polyinterp([0 f gtd; t f_new g_new'*d]);
end
% Adjust if change is too small
if t < temp*1e-3
if verbose == 3
fprintf('Interpolated value too small, Adjusting\n');
end
t = temp*1e-3;
elseif t > temp*0.6
if verbose == 3
fprintf('Interpolated value too large, Adjusting\n');
end
t = temp*0.6;
end
% Check whether step has become too small
if sum(abs(t*d)) < optTol
if verbose == 3
fprintf('Line Search failed\n');
end
t = 0;
f_new = f;
g_new = g;
break;
end
% Evaluate New Point
x_new = projectBounds(x+t*d,LB,UB);
[f_new,g_new] = funObj(x_new);
funEvals = funEvals+1;
lineSearchIters = lineSearchIters+1;
end
% Take Step
x = x_new;
f = f_new;
g = g_new;
% Compute Working Set
working = ones(nVars,1);
working((x < LB+optTol*2) & g >= 0) = 0;
working((x > UB-optTol*2) & g <= 0) = 0;
working = find(working);
% Output Log
if verbose >= 2
fprintf('%10d %10d %15.5e %15.5e %15.5e\n',i,funEvals*funEvalMultiplier,t,f,sum(abs(g(working))));
end
% Check Optimality
if isempty(working)
if verbose >= 1
fprintf('All variables are at their bound and no further progress is possible\n');
end
break;
elseif norm(g(working)) <= optTol
if verbose >=1
fprintf('All working variables satisfy optimality condition\n');
end
break;
end
% Check for lack of progress
if sum(abs(t*d)) < optTol
if verbose >= 1
fprintf('Step size below optTol\n');
end
break;
end
if abs(f-f_old) < optTol
if verbose >= 1
fprintf('Function value changing by less than optTol\n');
end
break;
end
if funEvals*funEvalMultiplier > maxIter
if verbose >= 1
fprintf('Function Evaluations exceeds maxIter\n');
end
break;
end
% If necessary, compute Hessian
if secondOrder && lineSearchIters > 1
[f_new,g_new,H] = funObj(x);
end
i = i + 1;
end
end
function [x] = projectBounds(x,LB,UB)
x(x < LB) = LB(x < LB);
x(x > UB) = UB(x > UB);
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
minConf_PQN.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/minConf/minConf/minConf_PQN.m
| 8,743 |
utf_8
|
833730ebce2f402d389c4ad511129e60
|
function [x,f,funEvals] = minConf_PQN(funObj,x,funProj,options)
% function [x,f] = minConf_PQN(funObj,funProj,x,options)
%
% Function for using a limited-memory projected quasi-Newton to solve problems of the form
% min funObj(x) s.t. x in C
%
% The projected quasi-Newton sub-problems are solved the spectral projected
% gradient algorithm
%
% @funObj(x): function to minimize (returns gradient as second argument)
% @funProj(x): function that returns projection of x onto C
%
% options:
% verbose: level of verbosity (0: no output, 1: final, 2: iter (default), 3:
% debug)
% optTol: tolerance used to check for optimality (default: 1e-5)
% progTol: tolerance used to check for progress (default: 1e-9)
% maxIter: maximum number of calls to funObj (default: 500)
% maxProject: maximum number of calls to funProj (default: 100000)
% numDiff: compute derivatives numerically (0: use user-supplied
% derivatives (default), 1: use finite differences, 2: use complex
% differentials)
% suffDec: sufficient decrease parameter in Armijo condition (default: 1e-4)
% corrections: number of lbfgs corrections to store (default: 10)
% adjustStep: use quadratic initialization of line search (default: 0)
% bbInit: initialize sub-problem with Barzilai-Borwein step (default: 1)
% SPGoptTol: optimality tolerance for SPG direction finding (default: 1e-6)
% SPGiters: maximum number of iterations for SPG direction finding (default:10)
nVars = length(x);
% Set Parameters
if nargin < 4
options = [];
end
[verbose,numDiff,optTol,progTol,maxIter,maxProject,suffDec,corrections,adjustStep,bbInit,...
SPGoptTol,SPGprogTol,SPGiters,SPGtestOpt] = ...
myProcessOptions(...
options,'verbose',2,'numDiff',0,'optTol',1e-5,'progTol',1e-9,'maxIter',500,'maxProject',100000,'suffDec',1e-4,...
'corrections',10,'adjustStep',0,'bbInit',0,'SPGoptTol',1e-6,'SPGprogTol',1e-10,'SPGiters',10,'SPGtestOpt',0);
% Output Parameter Settings
if verbose >= 3
fprintf('Running PQN...\n');
fprintf('Number of L-BFGS Corrections to store: %d\n',corrections);
fprintf('Spectral initialization of SPG: %d\n',bbInit);
fprintf('Maximum number of SPG iterations: %d\n',SPGiters);
fprintf('SPG optimality tolerance: %.2e\n',SPGoptTol);
fprintf('SPG progress tolerance: %.2e\n',SPGprogTol);
fprintf('PQN optimality tolerance: %.2e\n',optTol);
fprintf('PQN progress tolerance: %.2e\n',progTol);
fprintf('Quadratic initialization of line search: %d\n',adjustStep);
fprintf('Maximum number of function evaluations: %d\n',maxIter);
fprintf('Maximum number of projections: %d\n',maxProject);
end
% Output Log
if verbose >= 2
fprintf('%10s %10s %10s %15s %15s %15s\n','Iteration','FunEvals','Projections','Step Length','Function Val','Opt Cond');
end
% Make objective function (if using numerical derivatives)
funEvalMultiplier = 1;
if numDiff
if numDiff == 2
useComplex = 1;
else
useComplex = 0;
end
funObj = @(x)autoGrad(x,useComplex,funObj);
funEvalMultiplier = nVars+1-useComplex;
end
% Project initial parameter vector
x = funProj(x);
projects = 1;
% Evaluate initial parameters
[f,g] = funObj(x);
funEvals = 1;
% Check Optimality of Initial Point
projects = projects+1;
if max(abs(funProj(x-g)-x)) < optTol
if verbose >= 1
fprintf('First-Order Optimality Conditions Below optTol at Initial Point\n');
end
return;
end
i = 1;
while funEvals <= maxIter
% Compute Step Direction
if i == 1
p = funProj(x-g);
projects = projects+1;
S = zeros(nVars,0);
Y = zeros(nVars,0);
Hdiag = 1;
else
y = g-g_old;
s = x-x_old;
[S,Y,Hdiag] = lbfgsUpdate(y,s,corrections,verbose==3,S,Y,Hdiag);
% Make Compact Representation
k = size(Y,2);
L = zeros(k);
for j = 1:k
L(j+1:k,j) = S(:,j+1:k)'*Y(:,j);
end
N = [S/Hdiag Y];
M = [S'*S/Hdiag L;L' -diag(diag(S'*Y))];
HvFunc = @(v)lbfgsHvFunc2(v,Hdiag,N,M);
if bbInit
% Use Barzilai-Borwein step to initialize sub-problem
alpha = (s'*s)/(s'*y);
if alpha <= 1e-10 || alpha > 1e10
alpha = min(1,1/sum(abs(g)));
end
% Solve Sub-problem
xSubInit = x-alpha*g;
feasibleInit = 0;
else
xSubInit = x;
feasibleInit = 1;
end
% Solve Sub-problem
[p,subProjects] = solveSubProblem(x,g,HvFunc,funProj,SPGoptTol,SPGprogTol,SPGiters,SPGtestOpt,feasibleInit,xSubInit);
projects = projects+subProjects;
end
d = p-x;
g_old = g;
x_old = x;
% Check that Progress can be made along the direction
gtd = g'*d;
if gtd > -progTol
if verbose >= 1
fprintf('Directional Derivative below progTol\n');
end
break;
end
% Select Initial Guess to step length
if i == 1 || adjustStep == 0
t = 1;
else
t = min(1,2*(f-f_old)/gtd);
end
% Bound Step length on first iteration
if i == 1
t = min(1,1/sum(abs(g)));
end
% Evaluate the Objective and Gradient at the Initial Step
if t == 1
x_new = p;
else
x_new = x + t*d;
end
[f_new,g_new] = funObj(x_new);
funEvals = funEvals+1;
% Backtracking Line Search
f_old = f;
while f_new > f + suffDec*g'*(x_new-x) || ~isLegal(f_new)
temp = t;
% Backtrack to next trial value
if ~isLegal(f_new) || ~isLegal(g_new)
if verbose == 3
fprintf('Halving Step Size\n');
end
t = t/2;
else
if verbose == 3
fprintf('Cubic Backtracking\n');
end
t = polyinterp([0 f gtd; t f_new g_new'*d]);
end
% Adjust if change is too small/large
if t < temp*1e-3
if verbose == 3
fprintf('Interpolated value too small, Adjusting\n');
end
t = temp*1e-3;
elseif t > temp*0.6
if verbose == 3
fprintf('Interpolated value too large, Adjusting\n');
end
t = temp*0.6;
end
% Check whether step has become too small
if sum(abs(t*d)) < progTol || t == 0
if verbose == 3
fprintf('Line Search failed\n');
end
t = 0;
f_new = f;
g_new = g;
break;
end
% Evaluate New Point
f_prev = f_new;
t_prev = temp;
x_new = x + t*d;
[f_new,g_new] = funObj(x_new);
funEvals = funEvals+1;
end
% Take Step
x = x_new;
f = f_new;
g = g_new;
optCond = max(abs(funProj(x-g)-x));
projects = projects+1;
% Output Log
if verbose >= 2
fprintf('%10d %10d %10d %15.5e %15.5e %15.5e\n',i,funEvals*funEvalMultiplier,projects,t,f,optCond);
end
% Check optimality
if optCond < optTol
fprintf('First-Order Optimality Conditions Below optTol\n');
break;
end
if max(abs(t*d)) < progTol
if verbose >= 1
fprintf('Step size below progTol\n');
end
break;
end
if abs(f-f_old) < progTol
if verbose >= 1
fprintf('Function value changing by less than progTol\n');
end
break;
end
if funEvals*funEvalMultiplier > maxIter
if verbose >= 1
fprintf('Function Evaluations exceeds maxIter\n');
end
break;
end
if projects > maxProject
if verbose >= 1
fprintf('Number of projections exceeds maxProject\n');
end
break;
end
i = i + 1;
% pause
end
end
function [p,subProjects] = solveSubProblem(x,g,H,funProj,optTol,progTol,maxIter,testOpt,feasibleInit,x_init)
% Uses SPG to solve for projected quasi-Newton direction
options.verbose = 0;
options.optTol = optTol;
options.progTol = progTol;
options.maxIter = maxIter;
options.testOpt = testOpt;
options.feasibleInit = feasibleInit;
funObj = @(p)subHv(p,x,g,H);
[p,f,funEvals,subProjects] = minConf_SPG(funObj,x_init,funProj,options);
end
function [f,g] = subHv(p,x,g,HvFunc)
d = p-x;
Hd = HvFunc(d);
f = g'*d + (1/2)*d'*Hd;
g = g + Hd;
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
UGM_TreeBP.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/UGM/misc/UGM_TreeBP.m
| 2,745 |
utf_8
|
baa995f7edc3145b6631c22a4890e471
|
function [messages] = UGM_TreeBP(nodePot,edgePot,edgeStruct,maximize)
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
nStates = edgeStruct.nStates;
V = edgeStruct.V;
E = edgeStruct.E;
nodeDone = zeros(nNodes,1);
sent = zeros(nEdges*2,1);
messages = zeros(maxState,nEdges*2);
waiting = ones(nEdges*2,1);
done = 0;
while ~done
done = 1;
for n = 1:nNodes
if nodeDone(n) == 1
continue;
end
wait = waiting(V(n):V(n+1)-1);
sending = sent(V(n):V(n+1)-1);
nWaiting = sum(wait==1);
if nWaiting == 0
% Send messages
%fprintf('Sending final messages\n');
for sendEdge = [V(n)+find(sending==0)-1]'
%fprintf('Sending\n');
sent(sendEdge) = 1;
[messages,waiting] = send(n,sendEdge,nodePot,edgePot,messages,waiting,edgeStruct,maximize);
done = 0;
end
%fprintf('Node %d is done\n',n);
nodeDone(n) = 1;
elseif nWaiting > 1
%fprintf('Node %d is waiting for more than 1, skipping\n',n);
continue;
else
%fprintf('Node %d is waiting for 1 neighbor, trying to send to this 1\n',n);
remainingEdge = V(n)+find(wait==1)-1;
if ~sent(remainingEdge)
%fprintf('Sending\n');
sent(remainingEdge) = 1;
[messages,waiting] = send(n,remainingEdge,nodePot,edgePot,messages,waiting,edgeStruct,maximize);
done = 0;
end
end
end
end
end
function [messages,waiting] = send(n,e,nodePot,edgePot,messages,waiting,edgeStruct,maximize)
edgeEnds = edgeStruct.edgeEnds;
V = edgeStruct.V;
E = edgeStruct.E;
nStates = edgeStruct.nStates;
nEdges = size(edgeEnds,1);
edge = E(e);
if n == edgeEnds(edge,1)
nei = edgeEnds(edge,2);
else
nei = edgeEnds(edge,1);
end
%fprintf('Sending from %d to %d\n',n,nei);
for tmp = V(nei):V(nei+1)-1
if tmp ~= e && E(tmp) == E(e)
waiting(tmp) = 0;
end
end
e = edge;
% Compute Product of node potential with all incoming messages except
% along e
temp = nodePot(n,1:nStates(n))';
neighbors = E(V(n):V(n+1)-1);
for e2 = neighbors(:)'
if e ~= e2
if n == edgeEnds(e2,2)
temp = temp .* messages(1:nStates(n),e2);
else
temp = temp .* messages(1:nStates(n),e2+nEdges);
end
end
end
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
if n == edgeEnds(e,2)
pot_ij = edgePot(1:nStates(n1),1:nStates(n2),e);
else
pot_ij = edgePot(1:nStates(n1),1:nStates(n2),e)';
end
if maximize
newm = max_mult(pot_ij,temp);
else
newm = pot_ij*temp;
end
if n == edgeEnds(e,2);
messages(1:nStates(n1),e+nEdges) = newm./sum(newm);
else
messages(1:nStates(n2),e) = newm./sum(newm);
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
UGM_Sample_VarMCMC.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/UGM/sample/UGM_Sample_VarMCMC.m
| 2,625 |
utf_8
|
b61803b06589d39b8589ddf7e705bdf6
|
function [samples] = UGM_Sample_VarMCMC(nodePot,edgePot,edgeStruct,burnIn,varProb)
% MCMC sampler that switches between random walk MH and variational MF
% sampling
%
% varProb is the probability of trying the variational move
% (set to 0 for purely variational proposals)
[nNodes,maxStates] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
V = edgeStruct.V;
E = edgeStruct.E;
nStates = edgeStruct.nStates;
maxIter = edgeStruct.maxIter;
% Fit mean-field model
MFnodeBel = UGM_Infer_MeanField(nodePot,edgePot,edgeStruct);
% Initialize
y = meanFieldSample(MFnodeBel);
samples = zeros(nNodes,maxIter);
for i= 1:burnIn+maxIter
if rand < varProb
% Do variational Metropolis-Hastings step
%fprintf('Computing Variational Sample\n');
logPot = UGM_LogConfigurationPotential(y,nodePot,edgePot,edgeEnds);
mfLogPot = 0;
for n = 1:nNodes
mfLogPot = mfLogPot + log(MFnodeBel(n,y(n)));
end
y_new = meanFieldSample(MFnodeBel);
logPot_new = UGM_LogConfigurationPotential(y_new,nodePot,edgePot,edgeEnds);
mfLogPot_new = 0;
for n = 1:nNodes
mfLogPot_new = mfLogPot_new + log(MFnodeBel(n,y_new(n)));
end
%imagesc([reshape(y,32,32) reshape(y_new,32,32)])
%colormap gray
logAcceptance = logPot_new + mfLogPot - logPot - mfLogPot_new;
acceptance = exp(logAcceptance);
if rand < acceptance
y = y_new;
%fprintf('Accepted\n');
else
%fprintf('Rejected\n');
end
%pause
else
% Do Gibbs step
%fprintf('Computing Gibbs Sample\n');
y = gibbsSample(y,nodePot,edgePot,nStates,edgeEnds,V,E);
end
if i > burnIn
samples(:,i-burnIn) = y;
end
end
end
function [y] = meanFieldSample(nodeBel)
[nNodes,maxStates] = size(nodeBel);
y = zeros(nNodes,1);
for n = 1:nNodes
y(n) = sampleDiscrete(nodeBel(n,:));
end
end
function [y] = gibbsSample(y,nodePot,edgePot,nStates,edgeEnds,V,E)
[nNodes,maxState] = size(nodePot);
for n = 1:nNodes
% Compute Node Potential
pot = nodePot(n,1:nStates(n));
% Find Neighbors
edges = E(V(n):V(n+1)-1);
% Multiply Edge Potentials
for e = edges(:)'
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
if n == edgeEnds(e,1)
ep = edgePot(1:nStates(n1),y(n2),e)';
else
ep = edgePot(y(n1),1:nStates(n2),e);
end
pot = pot .* ep;
end
% Sample State;
y(n) = sampleDiscrete(pot./sum(pot));
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
UGM_Sample_Gibbs.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/UGM/sample/UGM_Sample_Gibbs.m
| 1,475 |
utf_8
|
fc98ab2b4b0110d00bef783f438a7cb3
|
function [samples] = UGM_Sample_Gibbs(nodePot,edgePot,edgeStruct,burnIn,y)
% [samples] = UGM_Sample_Gibbs(nodePot,edgePot,edgeStruct,burnIn,y)
% Single Site Gibbs Sampling
if nargin < 5
% Initialize
[junk y] = max(nodePot,[],2);
end
if edgeStruct.useMex
samples = UGM_Sample_GibbsC(nodePot,edgePot,int32(edgeStruct.edgeEnds),int32(edgeStruct.nStates),int32(edgeStruct.V),int32(edgeStruct.E),edgeStruct.maxIter,burnIn,int32(y));
else
samples = Sample_Gibbs(nodePot,edgePot,edgeStruct,burnIn,y);
end
end
function [samples] = Sample_Gibbs(nodePot,edgePot,edgeStruct,burnIn,y)
[nNodes,maxStates] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
V = edgeStruct.V;
E = edgeStruct.E;
nStates = edgeStruct.nStates;
maxIter = edgeStruct.maxIter;
samples = zeros(nNodes,0);
for i = 1:burnIn+maxIter
for n = 1:nNodes
% Compute Node Potential
pot = nodePot(n,1:nStates(n));
% Find Neighbors
edges = E(V(n):V(n+1)-1);
% Multiply Edge Potentials
for e = edges(:)'
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
if n == edgeEnds(e,1)
ep = edgePot(1:nStates(n1),y(n2),e)';
else
ep = edgePot(y(n1),1:nStates(n2),e);
end
pot = pot .* ep;
end
% Sample State;
y(n) = sampleDiscrete(pot./sum(pot));
end
if i > burnIn
samples(:,i-burnIn) = y;
end
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
UGM_Sample_Exact.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/UGM/sample/UGM_Sample_Exact.m
| 2,003 |
utf_8
|
a154b2b69704368d50fb3f106f13340b
|
function [samples] = UGM_Sample_Exact(nodePot,edgePot,edgeStruct)
% Exact sampling
assert(prod(edgeStruct.nStates) < 50000000,'Brute Force Exact Sampling not recommended for models with > 50 000 000 states');
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
nStates = edgeStruct.nStates;
maxIter= edgeStruct.maxIter;
samples = zeros(nNodes,0);
Z = computeZ(nodePot,edgePot,edgeEnds,nStates);
for s = 1:maxIter
samples(:,s) = sampleY(nodePot,edgePot,edgeEnds,nStates,Z);
end
end
function [Z] = computeZ(nodePot,edgePot,edgeEnds,nStates)
nEdges = size(edgePot,3);
[nNodes maxStates] = size(nodePot);
y = ones(1,nNodes);
Z = 0;
while 1
pot = 1;
% Nodes
for n = 1:nNodes
pot = pot*nodePot(n,y(n));
end
% Edges
for e = 1:nEdges
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
pot = pot*edgePot(y(n1),y(n2),e);
end
% Update Z
Z = Z + pot;
% Go to next y
for yInd = 1:nNodes
y(yInd) = y(yInd) + 1;
if y(yInd) <= nStates(yInd)
break;
else
y(yInd) = 1;
end
end
% Stop when we are done all y combinations
if sum(y==1) == nNodes
break;
end
end
end
function [y] = sampleY(nodePot,edgePot,edgeEnds,nStates,Z)
[nNodes,maxStates] = size(nodePot);
nEdges = size(edgePot,3);
y = ones(1,nNodes);
cumulativePot = 0;
U = rand;
while 1
pot = 1;
% Nodes
for n = 1:nNodes
pot = pot*nodePot(n,y(n));
end
% Edges
for e = 1:nEdges
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
pot = pot*edgePot(y(n1),y(n2),e);
end
% Update cumulative potential
cumulativePot = cumulativePot + pot;
if cumulativePot/Z > U
% Take this y
break;
end
% Go to next y
for yInd = 1:nNodes
y(yInd) = y(yInd) + 1;
if y(yInd) <= nStates(yInd)
break;
else
y(yInd) = 1;
end
end
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
UGM_Infer_Exact.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/UGM/infer/UGM_Infer_Exact.m
| 1,876 |
utf_8
|
ee20f10625b7e182499b0d93e3434775
|
function [nodeBel, edgeBel, logZ] = UGM_Infer_Exact(nodePot, edgePot, edgeStruct)
% INPUT
% nodePot(node,class)
% edgePot(class,class,edge) where e is referenced by V,E (must be the same
% between feature engine and inference engine)
%
% OUTPUT
% nodeBel(node,class) - marginal beliefs
% edgeBel(class,class,e) - pairwise beliefs
% logZ - negative of free energy
assert(prod(edgeStruct.nStates) < 50000000,'Brute Force Exact Inference not recommended for models with > 50 000 000 states');
if edgeStruct.useMex
[nodeBel,edgeBel,logZ] = UGM_Infer_ExactC(nodePot,edgePot,int32(edgeStruct.edgeEnds),int32(edgeStruct.nStates));
else
[nodeBel,edgeBel,logZ] = Infer_Exact(nodePot,edgePot,edgeStruct);
end
end
function [nodeBel, edgeBel, logZ] = Infer_Exact(nodePot, edgePot, edgeStruct)
[nNodes,maxStates] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
nStates = edgeStruct.nStates;
% Initialize
nodeBel = zeros(size(nodePot));
edgeBel = zeros(size(edgePot));
y = ones(1,nNodes);
Z = 0;
i = 1;
while 1
pot = UGM_ConfigurationPotential(y,nodePot,edgePot,edgeEnds);
% Update nodeBel
for n = 1:nNodes
nodeBel(n,y(n)) = nodeBel(n,y(n))+pot;
end
% Update edgeBel
for e = 1:nEdges
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
edgeBel(y(n1),y(n2),e) = edgeBel(y(n1),y(n2),e)+pot;
end
% Update Z
Z = Z + pot;
% Go to next y
for yInd = 1:nNodes
y(yInd) = y(yInd) + 1;
if y(yInd) <= nStates(yInd)
break;
else
y(yInd) = 1;
end
end
% Stop when we are done all y combinations
if yInd == nNodes && y(end) == 1
break;
end
end
nodeBel = nodeBel./Z;
edgeBel = edgeBel./Z;
logZ = log(Z);
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
UGM_Infer_TRBP.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/UGM/infer/UGM_Infer_TRBP.m
| 4,237 |
utf_8
|
eee0c6b71628076316fbde2d253256e7
|
function [nodeBel, edgeBel, logZ] = UGM_Infer_TRBP(nodePot,edgePot,edgeStruct)
[nNodes,maxStates] = size(nodePot);
nEdges = size(edgePot,3);
% Compute Edge Appearance Probabilities
if 0 %nEdges == nNodes*(nNodes-1)/2
mu = ((nNodes-1)/nEdges)*ones(nEdges,1);
elseif 1
% Generate Random Spanning Trees until all edges are covered
[nNodes,maxState] = size(nodePot);
edgeEnds = edgeStruct.edgeEnds;
i = 0;
edgeAppears = zeros(nEdges,1);
while 1
i = i+1;
edgeAppears = edgeAppears+minSpan(nNodes,[edgeEnds rand(nEdges,1)]);
if all(edgeAppears > 0)
break;
end
end
mu = edgeAppears/i;
else
mu = ones(nEdges,1); % Ordinary BP
end
[nodeBel, edgeBel, logZ] = Infer_TRBP(nodePot,edgePot,edgeStruct,mu);
end
%%
function [nodeBel, edgeBel, logZ] = Infer_TRBP(nodePot,edgePot,edgeStruct,mu)
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
V = edgeStruct.V;
E = edgeStruct.E;
nStates = edgeStruct.nStates;
maximize = 0;
new_msg = UGM_TRBP(nodePot,edgePot,edgeStruct,maximize,mu);
%% Compute nodeBel
nodeBel = zeros(nNodes,maxState);
for n = 1:nNodes
edges = E(V(n):V(n+1)-1);
prod_of_msgs(1:nStates(n),n) = nodePot(n,1:nStates(n))';
for e = edges(:)'
if n == edgeEnds(e,2)
prod_of_msgs(1:nStates(n),n) = prod_of_msgs(1:nStates(n),n) .* (new_msg(1:nStates(n),e).^mu(e));
else
prod_of_msgs(1:nStates(n),n) = prod_of_msgs(1:nStates(n),n) .* (new_msg(1:nStates(n),e+nEdges).^mu(e));
end
end
nodeBel(n,1:nStates(n)) = prod_of_msgs(1:nStates(n),n)'./sum(prod_of_msgs(1:nStates(n),n));
end
%% Compute edgeBel
if nargout > 1
edgeBel = zeros(maxState,maxState,nEdges);
for e = 1:nEdges
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
% nodePot by all messages to n1 except from n2
edges = E(V(n1):V(n1+1)-1);
temp1 = nodePot(n1,1:nStates(n1))';
for e2 = edges(:)'
if n1 == edgeEnds(e2,2)
incoming = new_msg(1:nStates(n1),e2);
else
incoming = new_msg(1:nStates(n1),e2+nEdges);
end
if e ~= e2
temp1 = temp1 .* incoming.^mu(e2);
else
temp1 = temp1 ./ incoming.^(1-mu(e2));
end
end
% nodePot by all messages to n2 except from n1
edges = E(V(n2):V(n2+1)-1);
temp2 = nodePot(n2,1:nStates(n2))';
for e2 = edges(:)'
if n2 == edgeEnds(e2,2)
incoming = new_msg(1:nStates(n2),e2);
else
incoming = new_msg(1:nStates(n2),e2+nEdges);
end
if e ~= e2
temp2 = temp2 .* incoming.^mu(e2);
else
temp2 = temp2 ./ incoming.^(1-mu(e2));
end
end
eb = repmat(temp1,[1 nStates(n2)]).*repmat(temp2',[nStates(n1) 1]).*(edgePot(1:nStates(n1),1:nStates(n2),e).^(1/mu(e)));
edgeBel(1:nStates(n1),1:nStates(n2),e) = eb./sum(eb(:));
end
end
%% Compute Free Energy
if nargout > 2
Energy1 = 0; Energy2 = 0; Entropy1 = 0; Entropy2 = 0;
nodeBel = nodeBel+eps;
edgeBel = edgeBel+eps;
for n = 1:nNodes
edges = E(V(n):V(n+1)-1);
nNbrs = length(edges);
% Node Entropy (note: different weighting than in Bethe)
Entropy1 = Entropy1 + (sum(mu(edges))-1)*sum(nodeBel(n,1:nStates(n)).*log(nodeBel(n,1:nStates(n))));
% Node Energy
Energy1 = Energy1 - sum(nodeBel(n,1:nStates(n)).*log(nodePot(n,1:nStates(n))));
end
for e = 1:nEdges
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
% Pairwise Entropy (note: different weighting than in Bethe)
eb = edgeBel(1:nStates(n1),1:nStates(n2),e);
Entropy2 = Entropy2 - mu(e)*sum(eb(:).*log(eb(:)));
% Pairwise Energy
ep = edgePot(1:nStates(n1),1:nStates(n2),e);
Energy2 = Energy2 - sum(eb(:).*log(ep(:)));
end
F = (Energy1+Energy2) - (Entropy1+Entropy2);
logZ = -F;
end
end
|
github
|
ColumbiaDVMM/Weak-attributes-for-large-scale-image-retrieval-master
|
UGM_Infer_LBP.m
|
.m
|
Weak-attributes-for-large-scale-image-retrieval-master/UGM/UGM/infer/UGM_Infer_LBP.m
| 2,799 |
utf_8
|
5101f62b8c2760b27424c72e3f8746c5
|
function [nodeBel, edgeBel, logZ] = UGM_Infer_LBP(nodePot,edgePot,edgeStruct)
if edgeStruct.useMex
[nodeBel,edgeBel,logZ] = UGM_Infer_LBPC(nodePot,edgePot,int32(edgeStruct.edgeEnds),int32(edgeStruct.nStates),int32(edgeStruct.V),int32(edgeStruct.E),edgeStruct.maxIter);
else
[nodeBel, edgeBel, logZ] = Infer_LBP(nodePot,edgePot,edgeStruct);
end
end
function [nodeBel, edgeBel, logZ] = Infer_LBP(nodePot,edgePot,edgeStruct)
[nNodes,maxState] = size(nodePot);
nEdges = size(edgePot,3);
edgeEnds = edgeStruct.edgeEnds;
V = edgeStruct.V;
E = edgeStruct.E;
nStates = edgeStruct.nStates;
maximize = 0;
new_msg = UGM_LoopyBP(nodePot,edgePot,edgeStruct,maximize);
% Compute nodeBel
for n = 1:nNodes
edges = E(V(n):V(n+1)-1);
prod_of_msgs(1:nStates(n),n) = nodePot(n,1:nStates(n))';
for e = edges(:)'
if n == edgeEnds(e,2)
prod_of_msgs(1:nStates(n),n) = prod_of_msgs(1:nStates(n),n) .* new_msg(1:nStates(n),e);
else
prod_of_msgs(1:nStates(n),n) = prod_of_msgs(1:nStates(n),n) .* new_msg(1:nStates(n),e+nEdges);
end
end
nodeBel(n,1:nStates(n)) = prod_of_msgs(1:nStates(n),n)'./sum(prod_of_msgs(1:nStates(n),n));
end
if nargout > 1
% Compute edge beliefs
edgeBel = zeros(maxState,maxState,nEdges);
for e = 1:nEdges
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
belN1 = nodeBel(n1,1:nStates(n1))'./new_msg(1:nStates(n1),e+nEdges);
belN2 = nodeBel(n2,1:nStates(n2))'./new_msg(1:nStates(n2),e);
b1=repmatC(belN1,1,nStates(n2));
b2=repmatC(belN2',nStates(n1),1);
eb = b1.*b2.*edgePot(1:nStates(n1),1:nStates(n2),e);
edgeBel(1:nStates(n1),1:nStates(n2),e) = eb./sum(eb(:));
end
end
if nargout > 2
% Compute Bethe free energy
Energy1 = 0; Energy2 = 0; Entropy1 = 0; Entropy2 = 0;
nodeBel = nodeBel+eps;
edgeBel = edgeBel+eps;
for n = 1:nNodes
edges = E(V(n):V(n+1)-1);
nNbrs = length(edges);
% Node Entropy (can get divide by zero if beliefs at 0)
Entropy1 = Entropy1 + (nNbrs-1)*sum(nodeBel(n,1:nStates(n)).*log(nodeBel(n,1:nStates(n))));
% Node Energy
Energy1 = Energy1 - sum(nodeBel(n,1:nStates(n)).*log(nodePot(n,1:nStates(n))));
end
for e = 1:nEdges
n1 = edgeEnds(e,1);
n2 = edgeEnds(e,2);
% Pairwise Entropy (can get divide by zero if beliefs at 0)
eb = edgeBel(1:nStates(n1),1:nStates(n2),e);
Entropy2 = Entropy2 - sum(eb(:).*log(eb(:)));
% Pairwise Energy
ep = edgePot(1:nStates(n1),1:nStates(n2),e);
Energy2 = Energy2 - sum(eb(:).*log(ep(:)));
end
F = (Energy1+Energy2) - (Entropy1+Entropy2);
logZ = -F;
end
end
|
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