debugged plot and calculation of Weibull cdf

DavidMercier · Sep 2, 2015 · c35c806 · c35c806
1 parent be67ca9
commit c35c806
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Showing 6 changed files with 38 additions and 35 deletions.
diff --git a/matlab_code/get_and_plot.m b/matlab_code/get_and_plot.m
@@ -56,6 +56,8 @@
 
                 [maxpeak, minpeak] = peakdet(gui.data(ii_sheet).data_ddh_cleaned(:), delta_data);
 
+                % Attribution of pop-in indice(s) for each load-disp curve
+                % (for each Excel sheet)...
                 gui.data(ii_sheet).num_popin_int = maxpeak(:, 1);
 
                 if gui.data(ii_sheet).num_popin_int == -1;
@@ -101,19 +103,16 @@
 
             end
 
+            % Extraction of values of load or disp at the pop-in
+            % indice obtained from peak detection (see above)
             if gui.settings.value_crit_param == 1
                 gui.data(ii_sheet).sum_L = ...
                     gui.data(ii_sheet).data_L_cleaned(ind_popin);
 
-                gui.data(ii_sheet).mean_sum_L = ...
-                    mean(gui.data(ii_sheet).sum_L);
-
             elseif gui.settings.value_crit_param == 2
                 gui.data(ii_sheet).sum_h = ...
                     gui.data(ii_sheet).data_h_cleaned(ind_popin);
 
-                gui.data(ii_sheet).mean_sum_h = ...
-                    mean(gui.data(ii_sheet).sum_h);
             end
             guidata(gcf, gui);
         end
@@ -123,38 +122,41 @@
         %% Set the data to plot
         if gui.settings.value_crit_param == 1
             for ii = 1:1:length(gui.data)
-                gui.results(ii).binCtrs = gui.data(ii).sum_L / mean([gui.data.mean_sum_L]);
-                gui.results(ii).max_binCtrs = max([gui.data(ii).sum_L]);
+                gui.results(ii).binCtrs_init = gui.data(ii).sum_L;
+                gui.results(ii).binCtrs = gui.data(ii).sum_L / mean([gui.data.sum_L]);
             end
+            gui.resultsMax_binCtrs = max([gui.data.sum_L]);
         elseif gui.settings.value_crit_param  == 2
             for ii = 1:1:length(gui.data)
-                gui.results(ii).binCtrs = gui.data(ii).sum_h / mean([gui.data.mean_sum_h]);
-                gui.results(ii).max_binCtrs = max([gui.data(ii).sum_h]);
+                gui.results(ii).binCtrs_init = gui.data(ii).sum_h;
+                gui.results(ii).binCtrs = gui.data(ii).sum_h / mean([gui.data.sum_h]);
             end
+            gui.resultsMax_binCtrs = max([gui.data.sum_h]);
         end
 
         if strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(1,:)) || ...
                 strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(3,:))
+            xdata_init = sort([gui.results(:).binCtrs_init]);
             xdata = sort([gui.results(:).binCtrs]);
-            for ii = 1:1:length(gui.data)
-                gui.results(ii).xdata = xdata(ii);
-            end
 
         elseif strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(2,:)) || ...
                 strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(4,:))
+            xdata_init = sort([gui.results(:).binCtrs_init],'descend');
             xdata = sort([gui.results(:).binCtrs],'descend');
-            for ii = 1:1:length(gui.data)
-                gui.results(ii).xdata = xdata(ii);
-            end
 
         else
             xdata = sort([gui.data(:).sum_L]);
+            xdata_init = xdata;
             for ii = 1:1:length(gui.data)
                 if xdata(ii) < 0
                     xdata(ii) = 0;
                 end
-                gui.results(ii).xdata = xdata(ii);
             end
+
+        end
+        for ii = 1:1:length(gui.data)
+            gui.results(ii).xdata_init = xdata_init(ii);
+            gui.results(ii).xdata = xdata(ii);
         end
 
         for ii = 1:1:length(gui.data)
@@ -170,13 +172,13 @@
 
         % Calculations of the cumulative function
         if strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(1,:))
-            weibull_cdf(OPTIONS, [gui.results.xdata],[gui.results.ydata]);
+            weibull_cdf(OPTIONS, [gui.results.xdata_init],[gui.results.ydata]);
         elseif strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(2,:))
-            weibull_cdf_survival(OPTIONS, [gui.results.xdata],[gui.results.ydata]);
+            weibull_cdf_survival(OPTIONS, [gui.results.xdata_init],[gui.results.ydata]);
         elseif strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(3,:))
-            weibull_modified_cdf(OPTIONS, [gui.results.xdata],[gui.results.ydata]);
+            weibull_modified_cdf(OPTIONS, [gui.results.xdata_init],[gui.results.ydata]);
         elseif strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(4,:))
-            weibull_modified_cdf_survival(OPTIONS, [gui.results.xdata],[gui.results.ydata]);
+            weibull_modified_cdf_survival(OPTIONS, [gui.results.xdata_init],[gui.results.ydata]);
         elseif strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(5,:))
             mason_cdf(OPTIONS, [gui.results.xdata],[gui.results.ydata]);
         end

diff --git a/matlab_code/plot/plot_cdf.m b/matlab_code/plot/plot_cdf.m
@@ -42,10 +42,10 @@
             strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(3,:)) || ...
             strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(4,:))
         str_title = ['Mean critical parameter=', ...
-            num2str(coefEsts(2) * mean([gui.data(:).mean_sum_L])), ...
+            num2str(coefEsts(2)), ...
             ' & Weibull modulus=', num2str(coefEsts(1))];
         %str_xlabel = 'Critical load (mN)';
-        str_xlabel = 'Ratio of the critical load to the average critical load)';
+        str_xlabel = 'Ratio of the critical load to the average critical load';
     else
         str_title = strcat('{\eta}=', num2str(coefEsts(1)), ...
             ' & {\epsilon}=', num2str(coefEsts(2)), ...
@@ -59,10 +59,10 @@
             strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(3,:)) || ...
             strcmp(gui.settings.cumulFunction, gui.settings.cumulFunctionList(4,:))
         str_title = strcat('Mean critical parameter=', ...
-            num2str(coefEsts(2) * mean([gui.data(:).mean_sum_h])), ...
+            num2str(coefEsts(2)), ...
             ' & Weibull modulus=', num2str(coefEsts(1)));
         %str_xlabel = 'Critical displacement (nm)';
-        str_xlabel = 'Ratio of the critical displacement to the average critical displacement)';
+        str_xlabel = 'Ratio of the critical displacement to the average critical displacement';
     else
         str_title = strcat('{\eta}=', num2str(coefEsts(1)), ...
             ' & {\epsilon}=', num2str(coefEsts(2)), ...
@@ -71,7 +71,8 @@
     end
 end
 
-line(gui.cumulativeFunction.xdata_cdf, gui.cumulativeFunction.ydata_cdf, ...
+line(gui.cumulativeFunction.xdata_cdf./gui.cumulativeFunction.coefEsts(2), ...
+    gui.cumulativeFunction.ydata_cdf, ...
     'Color', 'r', 'LineWidth', 2);
 
 legend({'Row data' 'Weibull model'}, 'Location', 'NorthWest');

diff --git a/matlab_code/statistics/weibull/weibull_cdf.m b/matlab_code/statistics/weibull/weibull_cdf.m
@@ -14,7 +14,7 @@ function weibull_cdf(OPTIONS, xdata_fit, ydata_fit)
 % Make a starting guess of coefficients p(1) and p(2)
 % p(1) = Weibull modulus --> 10 when good homogeneity in size defect distribution
 % p(2) = Mean critical value
-gui.cumulativeFunction.p0 = [1 ; mean(ydata_fit)];
+gui.cumulativeFunction.p0 = [1 ; mean(xdata_fit)];
 
 [gui.cumulativeFunction.coefEsts, ...
     gui.cumulativeFunction.resnorm, ...
@@ -26,7 +26,7 @@ function weibull_cdf(OPTIONS, xdata_fit, ydata_fit)
     lsqcurvefit(weibull_cdf_m, gui.cumulativeFunction.p0, ...
     gui.cumulativeFunction.xdata_cdf, ydata_fit, ...
     [gui.config.numerics.Min_mWeibull ; 0], ...
-    [gui.config.numerics.Max_mWeibull ; max(ydata_fit)], ...
+    [gui.config.numerics.Max_mWeibull ; max(xdata_fit)], ...
     OPTIONS);
 
 gui.cumulativeFunction.coefEsts(1) = ...

diff --git a/matlab_code/statistics/weibull/weibull_cdf_survival.m b/matlab_code/statistics/weibull/weibull_cdf_survival.m
@@ -14,7 +14,7 @@ function weibull_cdf_survival(OPTIONS, xdata_fit, ydata_fit)
 % Make a starting guess of coefficients p(1) and p(2)
 % p(1) = Weibull modulus --> 10 when good homogeneity in size defect distribution
 % p(2) = Mean critical value
-gui.cumulativeFunction.p0 = [1 ; mean(ydata_fit)];
+gui.cumulativeFunction.p0 = [1 ; mean(xdata_fit)];
 
 [gui.cumulativeFunction.coefEsts, ...
     gui.cumulativeFunction.resnorm, ...
@@ -26,7 +26,7 @@ function weibull_cdf_survival(OPTIONS, xdata_fit, ydata_fit)
     lsqcurvefit(weibull_cdf_s, gui.cumulativeFunction.p0, ...
     gui.cumulativeFunction.xdata_cdf, ydata_fit, ...
     [gui.config.numerics.Min_mWeibull ; 0], ...
-    [gui.config.numerics.Max_mWeibull ; max(ydata_fit)], ...
+    [gui.config.numerics.Max_mWeibull ; max(xdata_fit)], ...
     OPTIONS);
 
 gui.cumulativeFunction.coefEsts(1) = ...

diff --git a/matlab_code/statistics/weibull/weibull_modified_cdf.m b/matlab_code/statistics/weibull/weibull_modified_cdf.m
@@ -13,7 +13,7 @@ function weibull_modified_cdf(OPTIONS, xdata_fit, ydata_fit)
 % p(1) = Weibull modulus --> 10 when good homogeneity in size defect distribution
 % p(2) = Mean critical value
 gui.cumulativeFunction.p0 = ...
-    [1 ; mean(ydata_fit)];
+    [1 ; mean(xdata_fit)];
 
 [gui.cumulativeFunction.coefEsts, ...
     gui.cumulativeFunction.resnorm, ...
@@ -25,7 +25,7 @@ function weibull_modified_cdf(OPTIONS, xdata_fit, ydata_fit)
     lsqcurvefit(weibull_cdf_m, gui.cumulativeFunction.p0, ...
     gui.cumulativeFunction.xdata_cdf, ydata_fit, ...
     [gui.config.numerics.Min_mChechenin ; 0], ...
-    [gui.config.numerics.Max_mChechenin ; max(ydata_fit)], ...
+    [gui.config.numerics.Max_mChechenin ; max(xdata_fit)], ...
     OPTIONS);
 
 gui.cumulativeFunction.coefEsts(1) = ...
@@ -34,7 +34,7 @@ function weibull_modified_cdf(OPTIONS, xdata_fit, ydata_fit)
     real(gui.cumulativeFunction.coefEsts(2));
 
 gui.cumulativeFunction.ydata_cdf = ...
-    (1 - exp(-(gui.cumulativeFunction.xdata_cdf./gui.cumulativeFunction.coefEsts(2)) ...
+    (1 - exp(-log(2) .* (gui.cumulativeFunction.xdata_cdf./gui.cumulativeFunction.coefEsts(2)) ...
     .^ gui.cumulativeFunction.coefEsts(1)));
 
 guidata(gcf, gui);

diff --git a/matlab_code/statistics/weibull/weibull_modified_cdf_survival.m b/matlab_code/statistics/weibull/weibull_modified_cdf_survival.m
@@ -13,7 +13,7 @@ function weibull_modified_cdf_survival(OPTIONS, xdata_fit, ydata_fit)
 % p(1) = Weibull modulus --> 10 when good homogeneity in size defect distribution
 % p(2) = Mean critical value
 gui.cumulativeFunction.p0 = ...
-    [1 ; mean(ydata_fit)];
+    [1 ; mean(xdata_fit)];
 
 [gui.cumulativeFunction.coefEsts, ...
     gui.cumulativeFunction.resnorm, ...
@@ -25,7 +25,7 @@ function weibull_modified_cdf_survival(OPTIONS, xdata_fit, ydata_fit)
     lsqcurvefit(weibull_cdf_s, gui.cumulativeFunction.p0, ...
     gui.cumulativeFunction.xdata_cdf, ydata_fit, ...
     [gui.config.numerics.Min_mChechenin ; 0], ...
-    [gui.config.numerics.Max_mChechenin ; max(ydata_fit)], ...
+    [gui.config.numerics.Max_mChechenin ; max(xdata_fit)], ...
     OPTIONS);
 
 gui.cumulativeFunction.coefEsts(1) = ...
@@ -34,7 +34,7 @@ function weibull_modified_cdf_survival(OPTIONS, xdata_fit, ydata_fit)
     real(gui.cumulativeFunction.coefEsts(2));
 
 gui.cumulativeFunction.ydata_cdf = ...
-    exp(-(gui.cumulativeFunction.xdata_cdf./gui.cumulativeFunction.coefEsts(2)) ...
+    exp(-log(2) .* (gui.cumulativeFunction.xdata_cdf./gui.cumulativeFunction.coefEsts(2)) ...
     .^ gui.cumulativeFunction.coefEsts(1));
 
 guidata(gcf, gui);