[Dart-dev] [3507] DART/trunk/tutorial/building_blocks/script11.m:
This script was not generating images beyond #13 (should gen up to #26).
nancy at ucar.edu
nancy at ucar.edu
Mon Aug 11 15:03:15 MDT 2008
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-------------- next part --------------
Modified: DART/trunk/tutorial/building_blocks/script11.m
===================================================================
--- DART/trunk/tutorial/building_blocks/script11.m 2008-08-11 20:25:56 UTC (rev 3506)
+++ DART/trunk/tutorial/building_blocks/script11.m 2008-08-11 21:03:15 UTC (rev 3507)
@@ -15,10 +15,12 @@
% initial value that gives nice plots
randn('state', 0);
-
% Printing gums up things here, have to use print_count to get later figures
print_count = 13;
+% set this to 0 if you do not want to pause before each figure
+wait = 1;
+
% Set the size and position of the figure box on the screen
set(gcf, 'Units', 'Inches');
set(gcf, 'position', [1 1 6.5 6.5]);
@@ -81,7 +83,7 @@
set(r2, 'yticklabel', []);
hold on;
-% Compute the regression line,
+% Compute the regression line,
s_correl = corrcoef(rnum(1, :), rnum(2, :));
s_var(1) = var(rnum(1, :));
s_var(2) = var(rnum(2, :));
@@ -113,7 +115,8 @@
set(r1, 'xticklabel', []);
h_y_label = ylabel('Unobserved State Variable');
-pause;
+if (wait > 0), pause, end;
+
% Setup the printing characteristics
set(gcf, 'PaperPositionMode', 'auto');
set(gcf, 'PaperOrientation', 'landscape');
@@ -131,32 +134,32 @@
subplot(r3);
hold on;
% Do a simple update scheme in the marginal window
-% Generate a random sample of the prior
+% Generate a random sample of the prior
x_prior = rnum(1, :);
-
-% Plot a gaussian fit to ensemble
-x = -5:0.01:5;
-prior = normpdf(x, mean(x_prior), std(x_prior));
-obs = normpdf(x, 2.3, 0.6);
-product = prior .* obs;
+
+% Plot a gaussian fit to ensemble
+x = -5:0.01:5;
+prior = normpdf(x, mean(x_prior), std(x_prior));
+obs = normpdf(x, 2.3, 0.6);
+product = prior .* obs;
h_prior = plot(x, prior, 'g', 'linewidth', 3)
set(h_prior, 'color', [0 0.73 0]);
-%text(-3.0, 0.3, 'Prior PDF', 'fontsize', 24);
-pause;
+%text(-3.0, 0.3, 'Prior PDF', 'fontsize', 24);
+if (wait > 0), pause, end;
print -depsc s11f02.eps;
-
-h_obs = plot(x, obs, 'r', 'linewidth', 3)
-%text(1.6, 0.4, 'Obs. Likelihood', 'fontsize', 24);
-pause;
+
+h_obs = plot(x, obs, 'r', 'linewidth', 3)
+%text(1.6, 0.4, 'Obs. Likelihood', 'fontsize', 24);
+if (wait > 0), pause, end;
print -depsc s11f03.eps;
-% Need to get integrated value of the observed value, y2
-pb = (sum(product) * 0.01);
-posterior = product./pb;
-h_posterior = plot(x, posterior, 'b', 'linewidth', 3)
-%text(-1.9, 0.55, 'Posterior PDF', 'fontsize', 24);
+% Need to get integrated value of the observed value, y2
+pb = (sum(product) * 0.01);
+posterior = product./pb;
+h_posterior = plot(x, posterior, 'b', 'linewidth', 3)
+%text(-1.9, 0.55, 'Posterior PDF', 'fontsize', 24);
-pause;
+if (wait > 0), pause, end;
print -depsc s11f04.eps;
% Plot a random sample from the update (cheat and take it from
@@ -167,7 +170,7 @@
set(h_post_plot, 'markersize', 12)
set(h_post_plot, 'linewidth', 2);
-pause
+if (wait > 0), pause, end;
print -depsc s11f05.eps;
% Plot the increment vectors
@@ -177,7 +180,7 @@
arr2 = [x_posterior(i), 0.15 + 0.175 * (i-1)];
h_arrow(i) = arrow(arr1, arr2, 4.0);
set(h_arrow(i), 'linewidth', 3);
- pause
+ if (wait > 0), pause, end;
outfile = ['s11f0', num2str(5 + i), '.eps'];
print (gcf, '-depsc', outfile);
@@ -202,7 +205,7 @@
set(h_posterior(i), 'linewidth', 2);
end
-pause
+if (wait > 0), pause, end;
print -depsc s11f11.eps;
% Position the marginal and joint plot boxes
@@ -221,7 +224,7 @@
%Move the label for the squeeze
set(h_inc, 'position', [-1.5 0.82 0]);
-pause
+if (wait > 0), pause, end;
print -depsc s11f12.eps;
% Plot best fit line through this mess; just uses the standard deviations?
@@ -233,9 +236,10 @@
y_point(2) = s_mean(2) + (x_point(2) - s_mean(1)) * slope;
plot(x_point, y_point, 'r', 'linewidth', 3);
-pause
+if (wait > 0), pause, end;
if(print_count == 13)
print -depsc s11f13.eps;
+ print_count = print_count+1;
end
% Plot the un-normalized increment vectors
@@ -252,10 +256,11 @@
y_delta(i) = (x_posterior(i) - x_prior(i)) * slope
y_inc = [rnum(2, i), rnum(2, i) + (x_posterior(i) - x_prior(i)) * slope];
h_slope(i) = plot(x_inc, y_inc, 'b', 'linewidth', 3);
- pause
+ if (wait > 0), pause, end;
outfile = ['s11f', num2str(13 + i), '.eps'];
if(print_count == 13 + i)
print (gcf, '-depsc', outfile);
+ print_count = print_count+1;
end
subplot(r3)
@@ -286,18 +291,13 @@
yv = [rnum(2, i), rnum(2, i) + y_delta(i)];
subplot(r1);
h_full_dist(i) = plot(xv, yv);
- if(i == 1)
- pause
- if(print_count == 19)
- print -depsc s11f19.eps;
- end
- end
yvc = [rnum(2, i), rnum(2, i) + y_delta(i) * s_correl(1, 2)];
h_part_dist(i) = plot(xv, yvc, 'linewidth', 3);
- pause
+ if (wait > 0), pause, end;
outfile = ['s11f', num2str(18 + i), '.eps'];
- if(print_count == 18 + i)
+ if(print_count == 18 + i)
print (gcf, '-depsc', outfile);
+ print_count = print_count+1;
end
subplot(r3)
@@ -336,9 +336,10 @@
set(h_state_prior(i), 'xdata', [0.05]);
end
-pause;
-if(print_count == 24)
+if (wait > 0), pause, end;
+if(print_count == 24)
print -depsc s11f24.eps;
+ print_count = print_count+1;
end
% Get 1000 points from bottom of axis to top of axis
@@ -350,12 +351,13 @@
x_final_pts = normpdf(y_final_pts, prior_mean, prior_std);
hhh = plot(x_final_pts, y_final_pts, 'g', 'linewidth', 3);
set(hhh, 'color', [0 0.73 0]);
-text(0.4, 3.3, 'Prior State Fit', 'fontsize', 24);
+text(0.4, 3.3, 'Prior State Fit', 'fontsize', 24);
-pause
+if (wait > 0), pause, end;
if(print_count == 25)
print -depsc s11f25.eps;
+ print_count = print_count+1;
end
% Plot the posterior fit
@@ -363,7 +365,7 @@
final_std = std(final_state);
x_final_pts = normpdf(y_final_pts, final_mean, final_std);
plot(x_final_pts, y_final_pts, 'linewidth', 3);
-text(0.55, 4.9, 'Posterior Fit', 'fontsize', 24);
+text(0.55, 4.9, 'Posterior Fit', 'fontsize', 24);
if(print_count == 26)
print -depsc s11f26.eps;
end
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