Commit 224da70b authored by Turnhout, M.C. van's avatar Turnhout, M.C. van
Browse files

fix analysis

parent ee7f2a4d
......@@ -88,7 +88,7 @@ X = sp(:, 2:4); %[mu sigma P]
% % svgprint(get(gca, 'parent'), 'pics/pspacefailed')
% maximum sigma reached
Y = maxsig(gidx);
Y = maxsig;
figure
[c, bp] = hist(Y, 25);
h = bar(bp, c);
......@@ -96,10 +96,10 @@ set(h, 'facecolor', [.8 .8 .8], 'linestyle', 'none')
xlabel('$\max \sigma_x$\,[MPa]')
ylabel('count\,[-]')
% svgprint(get(gca, 'parent'), 'pics/anispace_msig_fig')
pspaceani_eqstabs(X(gidx, :), Y, '\max \sigma_x', 'anispace_msig');
pspaceani_eqstabs(X, Y, '\max \sigma_x', 'anispace_msig');
% depth of minimum
Y = dminl(gidx);
Y = dminl;
figure
[c, bp] = hist(Y, 25);
h = bar(bp, c);
......@@ -107,7 +107,7 @@ set(h, 'facecolor', [.8 .8 .8], 'linestyle', 'none')
xlabel('$\overline{\min l(\sigma)}\,[-]')
ylabel('count\,[-]')
% svgprint(get(gca, 'parent'), 'pics/anispace_md_fig')
pspaceani_eqstabs(X(gidx, :), Y, '\overline{\min l(\sigma)}', 'anispace_md');
pspaceani_eqstabs(X, Y, '\overline{\min l(\sigma)}', 'anispace_md');
......@@ -143,7 +143,7 @@ for y = 2:size(l, 2)
end
% histogram counts for overview
htd(y, :) = hist(Y, range);
if y == yoi, Yoi = y; end
if y == yoi, Yoi = Y; end
end
no(1) = numel(gidx);
figure
......@@ -179,7 +179,7 @@ colormap(mjet); caxis([0 max(htd(:))]); colorbar
% end
%%% analysis for l/L: sigma_x = 0.015
pspaceani_eqstabs(X(gidx, :), Yoi, '\frac{l}{L}(\sigma_x = 0.015\,\text{MPa})', 'anispace_lL');
pspaceani_eqstabs(X, Yoi, '\frac{l}{L}(\sigma_x = 0.015\,\text{MPa})', 'anispace_lL');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% analyse delta kappa
......@@ -204,7 +204,7 @@ for y = 2:size(l, 2)
end
% histogram counts for overview
htd(y, :) = hist(Y, range);
if y == yoi, Yoi = y; end
if y == yoi, Yoi = Y; end
end
figure
......@@ -228,17 +228,17 @@ colormap(mjet); caxis([0 max(htd(:))]); colorbar
% end
%%% analysis for delta kappa: sigma_x = 0.015
pspaceani_eqstabs(X(gidx, :), Yoi, '\Deltaup \kappa (\sigma_x = 0.015\,\text{MPa})', 'anispace_dk');
pspaceani_eqstabs(X, Yoi, '\Deltaup \kappa (\sigma_x = 0.015\,\text{MPa})', 'anispace_dk');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% analyse lambda_L (left / right)
range = linspace(0.995, 1.5, 25);
range = linspace(0.995, 1.005, 25);
htd = zeros(numel(sigma), numel(range));
for y = 2:size(l, 2)
Y = dLl(gidx, y)./4;
Y = dLl(:, y)./4;
if sum(~isnan(Y)) > 7
mdl = fitlm(X(gidx, :), Y);
mdl = fitlm(X, Y);
% 95% confidence intervals for the coefficients
cfci = mdl.coefCI; % intercept + coefficients
cfl(:, y) = cfci(:, 1); % intercept + coefficients
......@@ -254,7 +254,7 @@ for y = 2:size(l, 2)
end
% histogram counts for overview
htd(y, :) = hist(Y, range);
if y == yoi, Yoi = y; end
if y == yoi, Yoi = Y; end
end
figure
imagesc(range, -sigma, htd)
......@@ -277,14 +277,14 @@ colormap(mjet); caxis([0 max(htd(:))]); colorbar
% end
%%% analysis for lambda_L (left): sigma_x = 0.015
pspaceani_eqstabs(X(gidx, :), Yoi, '\lambda_L^\text{l}(\sigma_x = 0.015\,\text{MPa})', 'anispace_dLl');
pspaceani_eqstabs(X, Yoi, '\lambda_L^\text{l}(\sigma_x = 0.015\,\text{MPa})', 'anispace_dLl');
%%% and right
htd = zeros(numel(sigma), numel(range));
for y = 2:size(l, 2)
Y = dLr(gidx, y)/4;
Y = dLr(:, y)/4;
if sum(~isnan(Y)) > 7
mdl = fitlm(X(gidx, :), Y);
mdl = fitlm(X, Y);
% 95% confidence intervals for the coefficients
cfci = mdl.coefCI; % intercept + coefficients
cfl(:, y) = cfci(:, 1); % intercept + coefficients
......@@ -300,7 +300,7 @@ for y = 2:size(l, 2)
end
% histogram counts for overview
htd(y, :) = hist(Y, range);
if y == yoi, Yoi = y; end
if y == yoi, Yoi = Y; end
end
figure
imagesc(range, -sigma, htd)
......@@ -323,16 +323,16 @@ colormap(mjet); caxis([0 max(htd(:))]); colorbar
% end
%%% analysis for lambda_L (right): sigma_x = 0.015
pspaceani_eqstabs(X(gidx, :), Yoi, '\lambda_L^\text{r}(\sigma_x = 0.015\,\text{MPa})', 'anispace_dLr');
pspaceani_eqstabs(X, Yoi, '\lambda_L^\text{r}(\sigma_x = 0.015\,\text{MPa})', 'anispace_dLr');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% analyse kappa_e (left / right)
range = linspace(0.9, 1.1, 50);
range = linspace(0, 1.5, 50);
htd = zeros(numel(sigma), numel(range));
for y = 2:size(l, 2)
Y = skapl(gidx, y)./mkapl(gidx, y);
Y = skapl(:, y)./mkapl(:, y);
if sum(~isnan(Y)) > 7
mdl = fitlm(X(gidx, :), Y);
mdl = fitlm(X, Y);
% 95% confidence intervals for the coefficients
cfci = mdl.coefCI; % intercept + coefficients
cfl(:, y) = cfci(:, 1); % intercept + coefficients
......@@ -348,7 +348,7 @@ for y = 2:size(l, 2)
end
% histogram counts for overview
htd(y, :) = hist(Y, range);
if y == yoi, Yoi = y; end
if y == yoi, Yoi = Y; end
end
figure
imagesc(range, -sigma, htd)
......@@ -371,14 +371,14 @@ colormap(mjet); caxis([0 max(htd(:))]); colorbar
% end
%%% analysis for kappa_e (left): sigma_x = 0.015
pspaceani_eqstabs(X(gidx, :), Yoi, '\kappa_\varepsilon^\text{l}(\sigma_x = 0.015\,\text{MPa})', 'anispace_kel');
pspaceani_eqstabs(X, Yoi, '\kappa_\varepsilon^\text{l}(\sigma_x = 0.015\,\text{MPa})', 'anispace_kel');
%%% and right
htd = zeros(numel(sigma), numel(range));
for y = 2:size(l, 2)
Y = skapr(gidx, y)./mkapr(gidx, y);
Y = skapr(:, y)./mkapr(:, y);
if sum(~isnan(Y)) > 7
mdl = fitlm(X(gidx, :), Y);
mdl = fitlm(X, Y);
% 95% confidence intervals for the coefficients
cfci = mdl.coefCI; % intercept + coefficients
cfl(:, y) = cfci(:, 1); % intercept + coefficients
......@@ -394,7 +394,7 @@ for y = 2:size(l, 2)
end
% histogram counts for overview
htd(y, :) = hist(Y, range);
if y == yoi, Yoi = y; end
if y == yoi, Yoi = Y; end
end
figure
imagesc(range, -sigma, htd)
......@@ -417,7 +417,7 @@ colormap(mjet); caxis([0 max(htd(:))]); colorbar
% end
%%% analysis for kappa_e (right): sigma_x = 0.015
pspaceani_eqstabs(X(gidx, :), Yoi, '\kappa_\varapsilon^\text{r}(\sigma_x = 0.015\,\text{MPa})', 'anispace_ker');
pspaceani_eqstabs(X, Yoi, '\kappa_\varapsilon^\text{r}(\sigma_x = 0.015\,\text{MPa})', 'anispace_ker');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
......@@ -425,9 +425,9 @@ pspaceani_eqstabs(X(gidx, :), Yoi, '\kappa_\varapsilon^\text{r}(\sigma_x = 0.015
range = linspace(0.5, 2.5, 50);
htd = zeros(numel(sigma), numel(range));
for y = 2:size(l, 2)
Y = R(gidx, y).*mkapl(gidx, y);
Y = R(:, y).*mkapl(:, y);
if sum(~isnan(Y)) > 7
mdl = fitlm(X(gidx, :), Y);
mdl = fitlm(X, Y);
% 95% confidence intervals for the coefficients
cfci = mdl.coefCI; % intercept + coefficients
cfl(:, y) = cfci(:, 1); % intercept + coefficients
......@@ -443,7 +443,7 @@ for y = 2:size(l, 2)
end
% histogram counts for overview
htd(y, :) = hist(Y, range);
if y == yoi, Yoi = y; end
if y == yoi, Yoi = Y; end
end
figure
imagesc(range, -sigma, htd)
......@@ -466,14 +466,14 @@ colormap(mjet); caxis([0 max(htd(:))]); colorbar
% end
%%% analysis for kappa_e (left): sigma_x = 0.015
pspaceani_eqstabs(X(gidx, :), Yoi, 'R_\varepsilon^\text{l}(\sigma_x = 0.015\,\text{MPa})', 'anispace_Rel');
pspaceani_eqstabs(X, Yoi, 'R_\varepsilon^\text{l}(\sigma_x = 0.015\,\text{MPa})', 'anispace_Rel');
%%% and right
htd = zeros(numel(sigma), numel(range));
for y = 2:size(l, 2)
Y = R(gidx, y).*mkapr(gidx, y);
Y = R(:, y).*mkapr(:, y);
if sum(~isnan(Y)) > 7
mdl = fitlm(X(gidx, :), Y);
mdl = fitlm(X, Y);
% 95% confidence intervals for the coefficients
cfci = mdl.coefCI; % intercept + coefficients
cfl(:, y) = cfci(:, 1); % intercept + coefficients
......@@ -489,7 +489,7 @@ for y = 2:size(l, 2)
end
% histogram counts for overview
htd(y, :) = hist(Y, range);
if y == yoi, Yoi = y; end
if y == yoi, Yoi = Y; end
end
figure
imagesc(range, -sigma, htd)
......@@ -512,4 +512,4 @@ colormap(mjet); caxis([0 max(htd(:))]); colorbar
% end
%%% analysis for kappa_e (right): sigma_x = 0.015
pspaceani_eqstabs(X(gidx, :), Yoi, 'R_\varapsilon^\text{r}(\sigma_x = 0.015\,\text{MPa})', 'anispace_Rer');
pspaceani_eqstabs(X, Yoi, 'R_\varapsilon^\text{r}(\sigma_x = 0.015\,\text{MPa})', 'anispace_Rer');
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