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Commit 3ed94395
authored
2019-03-31 21:29:17 +0200
by
Chiara Antonini
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CRA_Updated_Version/CRA_Toolbox_GUI/compute_MIRI.m
CRA_Updated_Version/CRA_Toolbox_GUI/compute_MIRI.m
0 → 100644
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3ed9439
%Function executed when the button COMPUTE MIRI in the second GUI (gui_MIRI) is
%pushed. It takes in input the handles of both GUIs and it computes MIRI of
%the selected variable. It plots boxplot of MIRI values and the pdf of the
%evaluation function of the chosen variable. It saves the following
%variables:
% 1) MIRIT is the array containing MIRI values of all realizations
% 2) mean_CloudMax is the array containing the mean of values in
% CloudCondMaxT. CloudCondMaxT is the array of the mode of parameter values
% that give rise to the upper tail of the evaluation function
% 3) mean_CloudMin is the array containing the mean of values in
% CloudCondMinT. CloudCondMinT is the array of the mode of parameter values
% that give rise to the lower tail of the evaluation function
% 4) pdf_eval_func contains values of the evaluation function in all
% realizations
% 5) pdf_param contains values of the conditional pdf of parameters in all
% realizations
function
compute_MIRI
(
handles
,
hObject
,
handles1
)
MIRIT
=
zeros
(
handles1
.
Nr
,
handles1
.
num_param
);
CloudCondMaxT
=
[];
CloudCondMinT
=
[];
xbinT
=
[];
ks_y_T
=
[];
try
%create a directory to save all results
mkdir
(
handles1
.
folder
,
handles1
.
chosen_variable
);
xbin_param
=
cell
(
2
,
handles1
.
Nr
);
ks_param
=
cell
(
2
,
handles1
.
Nr
);
h
=
waitbar
(
0
,
'Please wait...'
);
for
k
=
1
:
handles1
.
Nr
waitbar
(
k
/
handles1
.
Nr
,
h
,[
'Realization number '
,
num2str
(
k
)]);
Results_reshape
=
zeros
(
handles1
.
Nsample
,
length
(
handles1
.
time_results
));
for
j
=
1
:
handles1
.
Nsample
Results_reshape
(
j
,:)
=
reshape
(
handles1
.
AllResults
{
k
,
handles1
.
num_variable
}{
j
,
1
},[
1
length
(
handles1
.
time_results
)]);
end
%creation of an object TimeBehavior where results of the chosen
%variable are stored
handles
.
Results
=
TimeBehavior
(
handles1
.
time_results
,
Results_reshape
);
%values of the evaluation function are computed and stored in the
%TimeBehavior object Results
handles
.
Results
.
currentEvalFunc
=
handles
.
current_func
;
handles
.
Results
.
computeEvalFunc
();
guidata
(
hObject
,
handles
);
samples
=
handles
.
Results
.
evalFuncValues
;
% an evaluation function may have identical values when parameters
% do not affect its value. For example, if a variable has a
% descending temporal behavior, the maximum and time of maximum
% will always be the same, independently of parameter perturbation
if
(
length
(
unique
(
samples
))
==
1
)
ME
=
MException
(
'Matlab:EvalFuncValuesNull'
,
'Array of evaluation function contains identical values. Change evaluation function or variable.'
);
throw
(
ME
)
close
(
h
)
end
%creation of an object pdfEstimator for estimating the pdf of the
%evaluation function
pdf_obj
=
pdfEstimator
();
BinEdges
=
[
min
(
samples
):(
max
(
samples
)
-
min
(
samples
))/
length
(
samples
):
max
(
samples
)];
[
ks_y
,
xbin
]
=
pdf_obj
.
evaluate_pdf
(
samples
,
BinEdges
);
xbinT
=
[
xbinT
;
xbin
];
ks_y_T
=
[
ks_y_T
;
ks_y
];
%in addition to the pdf it is possible to plot the histogram of the
%evaluation function
fh
=
figure
(
'Visible'
,
'off'
);
hist
(
samples
);
xlabel
(
handles1
.
chosen_variable
);
set
(
fh
,
'CreateFcn'
,
'set(fh,
''
Visible
''
,
''
on
''
)'
)
saveas
(
fh
,
fullfile
(
handles1
.
folder
,
handles1
.
chosen_variable
,
strcat
(
'hist_'
,
handles1
.
chosen_variable
,
'_Nr'
,
num2str
(
k
))),
'jpeg'
);
close
(
fh
)
%method for extracting the upper and lower tail of the evaluation
%function
handles
.
Results
.
currentTailMethod
=
handles
.
current_tm
;
[
XiMax
,
XiMin
]
=
handles
.
Results
.
compute_tail
(
handles1
.
AllPerturbations
{
k
,
1
},
handles
.
tail_size
);
%[XiMax,XiMin]=handles.Results.compute_tail(handles1.perturbation,handles.tail_size);
handles
.
XiMax
=
XiMax
;
handles
.
XiMin
=
XiMin
;
guidata
(
hObject
,
handles
);
NcloudMax
=
size
(
handles
.
XiMax
,
1
);
NcloudMin
=
size
(
handles
.
XiMin
,
1
);
Ncloud
=
min
([
NcloudMax
NcloudMin
]);
%creation of an object MIRI for computing MIRI and conditional pdfs
%of parameters
obj_MIRI
=
MIRI
();
[
all_xbin_max
,
all_xbin_min
,
all_ks_max
,
all_ks_min
]
=
obj_MIRI
.
estimate_parampdfs
(
handles1
.
nominal_p
,
handles1
.
AllPerturbations
{
k
,
1
},
handles
.
XiMax
,
handles
.
XiMin
);
%[all_xbin_max,all_xbin_min,all_ks_max,all_ks_min]=obj_MIRI.estimate_parampdfs(handles1.nominal_p, handles1.perturbation, Ncloud, handles.XiMax, handles.XiMin);
[
MIRI_param
,
CloudCondMin
,
CloudCondMax
]
=
obj_MIRI
.
MIRI_computation
(
all_ks_max
,
all_ks_min
,
all_xbin_max
,
all_xbin_min
);
MIRIT
(
k
,:)
=
MIRI_param
;
CloudCondMaxT
=
[
CloudCondMaxT
;
CloudCondMax
];
CloudCondMinT
=
[
CloudCondMinT
;
CloudCondMin
];
mean_CloudMax
=
mean
(
CloudCondMaxT
);
mean_CloudMin
=
mean
(
CloudCondMinT
);
for
ip
=
1
:
length
(
handles1
.
nominal_p
)
xbin_param
{
1
,
k
}(
ip
,:)
=
all_xbin_max
(
ip
,:);
xbin_param
{
2
,
k
}(
ip
,:)
=
all_xbin_min
(
ip
,:);
ks_param
{
1
,
k
}(
ip
,:)
=
all_ks_max
(
ip
,:);
ks_param
{
2
,
k
}(
ip
,:)
=
all_ks_min
(
ip
,:);
end
end
close
(
h
)
%MIRI boxplot (or bar if the realization is only one) are displayed in gui_MIRI
if
handles1
.
Nr
==
1
bar
(
handles
.
axes1
,
MIRIT
);
%set(handles.axes1,'XTickLabel',handles1.param_name);
% xticks=get(handles.axes1,'XTickLabel');
% xticklabel_rotate(xticks,90);
ylabel
(
'MIRI'
);
else
boxplot
(
handles
.
axes1
,
MIRIT
,
'labels'
,
handles1
.
param_name
,
'labelorientation'
,
'inline'
);
ylabel
(
'MIRI'
);
labelSize
=
13
;
%size of the label
set
(
findobj
(
handles
.
axes1
,
'Type'
,
'text'
),
'FontSize'
,
labelSize
);
end
fh2
=
figure
(
'Visible'
,
'off'
);
h_new2
=
copyobj
(
handles
.
axes1
,
fh2
);
set
(
h_new2
,
'Units'
,
'Normalized'
);
set
(
h_new2
,
'OuterPosition'
,[
.
1
,
.
1
,
.
85
,
.
85
]);
set
(
gcf
,
'Visible'
,
'off'
,
'CreateFcn'
,
'set(gcf,
''
Visible
''
,
''
on
''
)'
)
saveas
(
fh2
,
fullfile
(
handles1
.
folder
,
handles1
.
chosen_variable
,
'MIRI'
),
'jpeg'
);
close
(
fh2
);
%saving results
disp
(
'saving results...'
);
save
(
fullfile
(
handles1
.
folder
,
handles1
.
chosen_variable
,
'MIRIT.mat'
),
'MIRIT'
);
disp
(
strcat
(
'Array of MIRI has size'
,{
' '
},
sprintf
(
'%.0f'
,
size
(
MIRIT
,
1
)),
'x'
,
sprintf
(
'%.0f'
,
size
(
MIRIT
,
2
))));
disp
(
'saving mode of the conditional upper pdf of the parameter vector'
);
save
(
fullfile
(
handles1
.
folder
,
handles1
.
chosen_variable
,
'meanCloudCondMax.mat'
),
'mean_CloudMax'
);
disp
(
strcat
(
'The mode vector of the upper pdf has size'
,{
' '
},
sprintf
(
'%.0f'
,
size
(
mean_CloudMax
,
1
)),
'x'
,
sprintf
(
'%.0f'
,
size
(
mean_CloudMax
,
2
))));
disp
(
'saving mode of the conditional lower pdf of the parameter vector'
);
save
(
fullfile
(
handles1
.
folder
,
handles1
.
chosen_variable
,
'meanCloudCondMin.mat'
),
'mean_CloudMin'
);
disp
(
strcat
(
'The mode vector of the upper pdf has size'
,{
' '
},
sprintf
(
'%.0f'
,
size
(
mean_CloudMin
,
1
)),
'x'
,
sprintf
(
'%.0f'
,
size
(
mean_CloudMin
,
2
))));
disp
(
'saving the probability density function of the evaluation function'
)
save
(
fullfile
(
handles1
.
folder
,
handles1
.
chosen_variable
,
'pdf_eval_func.mat'
),
'xbinT'
,
'ks_y_T'
);
disp
(
'saving probability density functions of all parameters'
)
save
(
fullfile
(
handles1
.
folder
,
handles1
.
chosen_variable
,
'pdf_param.mat'
),
'xbin_param'
,
'ks_param'
);
%the evaluation function pdf is displayed in gui_MIRI
plotpdf_evalfunc
(
handles
,
hObject
,
handles1
);
catch
ME
if
(
strcmp
(
ME
.
identifier
,
'MATLAB:badsubscript'
))
msg
=
'The pdf is a Dirac delta function: change evaluation function or variable'
;
errordlg
(
msg
);
close
(
h
)
else
errordlg
(
ME
.
message
);
close
(
h
)
end
end
end
\ No newline at end of file
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