Ciao e grazie ancora. Il file è un semplice file in cui c'è tutta una serie di numeri (un vettore riga per intenderci) ...ma nel programma originale (Quello con load) funziona...
Comunque questo è il programma .m come l'ho modificato (e mi da quell'errore che ti ho detto)
% in questo programma si cerca di esprimere il generico spettro di carbonio
% amorfo ad esempio e pirovalvole come combinazione lineare degli spettri
% standardizzati di grafite e diamante. Gli spettri sono stati acquisiti
%in diffrazione
% usando condizioni il più possibile standard.
%%% Convoluzione su eVc riga 768 graconv2
%%% convoluzione su eVl (riga 184) è graconv_n (da riga 624)
clear all
close all
global ckgran ckdian c60n flat flat2 b60 x a1 a2 a3 s1 s2 s3 Rgra2 Rmat2 flatc
global A lambda sse R2 a1 mu1 eps1 a2 mu1 mu2 eps2 Rgra Rmat x sig eps
global xdata ydata intspx sp2e5n resid range Kfit gra2_T ckgraconv_n
global gra_T pi_gau sig_gau y36nn eps3 ygra2n graconv2 ckgra_o xdatas ydatas
%%%%%%%%%%%%%%%%%%%START MIE MODIFICHE%%%%%%%%%%%%%%%%%%%%
global diamdiff
%%%%%%%%%%%%%%%%%%%END MIE MODIFICHE%%%%%%%%%%%%%%%%%%%%
%%% Spettri in imaging
%%%%%%%%%%%%%%% START MIE MODIFICHE%%%%%%%%%%%%%%%%%%%%%%%%%
%load 'dia_221_2_0_2.txt'; %spettro diamante in diffrazione
diamdiff=input('Nome spettro diamante in diffrazione= ');
[fid msg]=fopen(diamdiff,'r');
%%%%%%%%%%%%%END MIE MODIFICHE%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
load 'hopg_220_4_0_2.txt'; %spettro grafite in diffrazione campione 2 ex 'hopg_220_4_0_2.txt'
load 'c60_217_0_0_2.txt';
load 'piro_216_0_0_2.txt'; % spettro PIROVALVOLA in diffrazione
%% seleziono i files acquisiti per 40.96 s OVVERO GLI SPETTRI CK SENZA
%% NOISE
df =0.2;%% risoluzione spettrale
N = 1024;%% sample length
T = 40.96;%% acquisition time ck
eVstart = 216; % start scala energia %%% da inserire per ogni campione !!!!!!!
eV0=220; % scala basi
eVel_start = -25; %da inserire per ogni campione
%eVel_start = -25.2
for j=1: N
eV_el(j) = -18.6 + df*j; %%% scala di energia picco elastico
eV(j) = eV0+ df*j;
jmin =round((282.5-eV0)/df); % indice che corrisponde ad Emin 282.5 eV
% jmin =round((280-eV0)/df); % indice che corrisponde ad Emin 280 eV
j280 = round((282.5-eV0)/df);
j292 = round((292-eV0)/df);
j293 =round((293-eV0)/df);
j310=round((310-eV0)/df);
j350=round((350-eV0)/df);
jlim = j310; %%% FISSA LA WINDOW DI ENERGIA CHE PARTE DA Emin [Emax di Titantah (291,292,293)]
%jlim = j295;
j288 = round((288-eV0)/df);
% jlim = j300;
range = jlim -jmin; %%% WINDOW DI ENERGIA SU CUI EFFETTUARE IL FIT
rangel = j350-jmin;%% range su cui effettuare la convoluzione
%%% costruire il vettore con spettro ck e spettro zl
ck(j) =piro_216_0_0_2(j);% sample 9 ora 7 (il più grafitico) O
%% BASI DI TITANTAH
%%% Spettri che costituiscono le basi
%%%%%%%%%%%%%%%%%%START MIE MODIFICHE%%%%%%%%%%%
%ckdiadiff(j) =dia_221_2_0_2(j);%% spettro del diamante in diffrazione
ckdiadiff(j) =diamdiff(j);%% spettro del diamante in diffrazione
%%%%%%%%%%%%% END MIE MODIFICHE%%%%%%%%%%%%%%%%%%%%
ckgradiff(j) = hopg_220_4_0_2(j);%% spettro della grafite in diffrazione
c60diff(j)= c60_217_0_0_2(j); %%% spettro c60 in diffrazione
%c60diff(j) = c60_270314_holey_c_12__1350mm__nd_ck_06_02_1024_1nn_spot5_en_cor_edge_217_0_0_2(j); %
end
%% TRASPONGO GLI SPETTRI PER FARNE DEI VETTORI COLONNA
ckdiadiff = ckdiadiff';
ckgradiff = ckgradiff';
c60diff = c60diff';
ck = ck';
ns = round((eV0-eVstart)/df); % numero punti di cui traslare lo spettro
%%% shifto opportunamente gli spettri %%%%%%%%%%%%%%%%%%
for k=ns+2+6+15:N
ck_gra(k) = ckgradiff(k-2);
ck_dia(k) = ckdiadiff(k-6);
c60(k-15) = c60diff(k);
ck_s(k-ns) = ck(k);
end
%%% per normalizzare gli spettri li divido per l'area ck totale
%
intdiack =0;
intgrack =0;
intc60=0;
intck = 0;
%%% integrale spettro ck 282 - 380 eV Ick (lo spettro ck si deve
%%% estendere su un range di circa 100 eV
for j= jmin:jlim%%%CAMBIARE IL RANGE PER MODIFICARE LA NORMALIZZAZIONE!!!!!
intdiack =intdiack + df*ck_dia(j);
intgrack = intgrack +df*ck_gra (j);
intc60 = intc60 +df*c60(j);
intck = intck +df*ck_s (j);
end
%%%%%%%%%%%%%%%%%%%% spettri normalizzati%%%%%%%%%%%%%%%%%%%%%%
for k=1:range %%%%%%%%%%%%% spettri delle basi normalizzati nel range 280 - 300 eV
ckdian(k) = ck_dia(k+jmin-1)./intdiack;
ckgran (k) =ck_gra(k+jmin-1)./intgrack;
c60n (k) = c60(k+jmin-1)./intc60; %%% fullerene normalizzato
%%% spettro normalizzato del campione incognito
ckxfit(k) = ck_s(k+jmin-1)/intck;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
eVc(k) = eV(k+jmin-1);
end
%%%%%%%%%%%%%%%%%%%%%%%%% metodo di marco %%%%%%%%%%%%%%
graconv=0;
sig = 0.2;%% da inserire il valore giusto per ogni campione
basis = (1./(sig*(2*pi)^0.5).*exp(-((eVc-284.7).^2)./(2*sig^2)));
graconv = conv(basis,ckgran); %% convoluzione fra lo spettro di grafite e un picco gaussiano centrato a 284.7 eV
granconv2 =0;
%%%% ora lo spettro originario e quello convoluto devono essere allineati
%%%% %%%%%
shift =11;
for j=1:numel(graconv)-shift
graconv2(j)=graconv(j+shift);
end
%%% normalizzazione dello spettro convoluto
intgraconv=0;
for j= 1:range%%%CAMBIARE IL RANGE PER MODIFICARE LA NORMALIZZAZIONE!!!!!
intgraconv =intgraconv + df*graconv2(j);
end
ckgraconv_n=graconv2(1:range)./intgraconv;
figure (1)
plot(eVc, ckxfit,'m*'); hold on
plot(eVc, ckgran,'r'); hold on
plot(eVc, ckgraconv_n,'b');
%
%%%%% programma per fitting %%%%%%%%%%%%%%%%%%%
Kfit = 1;
xdata=eVc;%
ydata = ckxfit ; %%% campione amorfo da fittare
%%%%%%%%%%%% FITTING PER DEFINIRE SIGMA %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%% della grafite convoluta
[estimates, model] = fitcurvedemo(xdata,ydata);
% if ((ydata - (estimates(1)*ckgran + estimates(2)*ckdian)) >0)
[sse, FittedCurve] = model(estimates);
figure (2)
plot(xdata, ydata, 'k*'); hold on %%% grafico del campione da fittare
% plot(xdata, FittedCurve, 'b');
plot(xdata,ydata - A.*ckgraconv_n-lambda.*ckdian,'r'); %residuo (differenza tra campione e campione fittato)
plot(xdata, A.*ckgraconv_n+lambda.*ckdian,'k'); %% funzione di fitting
axis([ 282 300 0 0.06])
xlabel('energy (eV)');
ylabel('relative counts');
legend (' Sample 6' )
questo è il file fitcurvedemo.m che serve per il fitting
function [estimates, model] = fitcurvedemo(xdata, ydata)
% Call fminsearch with a random starting point.
global ckgraconv_n ckdian
global A lambda sse R2
start_point = rand(1,2);%% scegli un valore casuale per tutti i parametri
%start_point = 0.72;
% start_point = rand(1,2,3);%% scegli un valore casuale per tutti i parametri
%start_point = 3(1,3,5);
model = @fun;
estimates = fminsearch(model, start_point);
% expfun accepts curve parameters as inputs, and outputs sse,
% the sum of squares error for A*exp(-lambda*xdata)-ydata,
% and the FittedCurve. FMINSEARCH only needs sse, but we want
% to plot the FittedCurve at the end.
function [sse, FittedCurve] = fun(params)
A = params(1);
lambda = params(2);
% sig_1p = params(1);
% % m_1p = params(2);
% sig_2p = params(2);
% % m_2p = params(4);
% % sig_3p = params(5);
% % m_3p = params(6);
% A = params(3);
% % B = params(8);
% % C = params(9);
% eps = params(3);
% peak = params(2);
%FittedCurve = A .* exp(-((xdata-292)/lambda).^2);
%FittedCurve = A*ckgrasc+lambda.*ckdiasc;
%FittedCurve = A*ckgran+lambda.*ckdian;
%FittedCurve = A.*ckgran+lambda.*ckdian;
% FittedCurve = A.*ckgran+(1-A).*ckdian;
FittedCurve =( A.*ckgraconv_n + lambda.*ckdian); %% picco gaussiano allargato
%%%%%%%%%%%%% fitting agli spettri espressi come comb lin di
%%%%%%%%%%%%% funzioni gaussiane
% FittedCurve = (0.72*((1/(2*3.1416*sig_1p)^0.5)*exp(-(xdata-m_1p)^2/(2*sig_1p))+0.2*(1/(2*3.1416*sig_2p)^0.5)*exp(-(xdata-m_2p)^2/(2*sig_2p))))+0.22*(1/(2*3.1416*sig_3p)^0.5)*exp(-(xdata-m_3p)^2/(2*sig_3p));
% FittedCurve =((1/(2*3.1416*sig_1p)^0.5)*exp(-(xdata-285.5).^2/(2*sig_1p))+A*(1/(2*3.1416*sig_2p).^0.5)*exp(-(xdata-292).^2/(2*sig_2p)));
% a1d = 0.0616 ;% (-0.01512, 0.1383)
% b1d = 289.1 ; % (289, 289.2)
% c1d = 0.5628 ; % (0.2931, 0.8325)
% a2d = 0.07788; % (0.03446, 0.1213)
% b2d = 290; % (289.5, 290.6)
% c2d = 1.003; % (0.284, 1.723)
% a3d = 0.2585 ; % (0.2517, 0.2653)
% b3d = 292.2; % (291.8, 292.6)
% c3d = 2.72; % (2.491, 2.948)
%
% a1g2 = 0.117; % (0.09806, 0.136)
% b1g2 = 284.7;% (284.6, 284.7)
% c1g2 = 0.9052 ; % (0.7591, 1.051)
% a2g2 = 0.0599 ;% (0.05511, 0.06468)
% b2g2 = 287.4; % (286.9, 287.9)
% c2g2 = 2.969; % (1.851, 4.087)
% a3g2 = 0.214; % (0.2035, 0.2246)
% b3g2 = 291.7; % (291.7, 291.8)
% c3g2 = 1.472; % (1.341, 1.603)
%
% % a1 = 0.1474 ;% (0.1377, 0.157)
% % b1 = 285.6 ;% (285.5, 285.6)
% % c1 = 0.8875 ;% (0.8172, 0.9578)
% % a2 = 0.08283;% (0.07901, 0.08665)
% % b2 = 288.6;% (288.2, 288.9)
% % c2 = 3.466 ;% (2.932, 4)
% % a3 = 0.1832;% (0.1628, 0.2035)
% % b3 = 292.5 ;% (292.3, 292.7)
% % c3 = 1.379 ;% (1.103, 1.655)
%
% %Coefficients (with 95% confidence bounds):
% a1 = 0.1559; % (0.1547, 0.1571)
% b1 = 292.4; % (292.3, 292.5)
% c1 = 4.494; % (4.223, 4.766)
% a2 = 0.05103; % (0.04697, 0.05508)
% b2 = 286; % (fixed at bound)
% c2 = 1.98; % (1.826, 2.134)
% a3 = 0.05235; % (0.04955, 0.05516)
% b3 = 284.6; % (284.6, 284.6)
% c3 = 0.972; % (0.931, 1.013)
% neral model Gauss3:
% f(x) =
% a1*exp(-((x-b1)/c1)^2) + a2*exp(-((x-b2)/c2)^2) +
% a3*exp(-((x-b3)/c3)^2)
% Coefficients (with 95% confidence bounds):
a1 = 0.1767;% (0.173, 0.1804)
b1 = 292;% (291.9, 292.1)
c1 = 1.879;% (1.755, 2.004)
a2 = 0.1361;% (0.1281, 0.1441)
b2 = 284.7;% (284.7, 284.7)
c2 = 0.8192;% (0.764, 0.8743)
a3 = 0.06714;% (0.06387, 0.07042)
b3 = 287;% (286.8, 287.2)
c3 = 2.76;% (2.451, 3.069)
% Goodness of fit:
% SSE: 0.001257
% R-square: 0.9943
% Adjusted R-square: 0.9935
% RMSE: 0.004738
%
%
%FittedCurve = A.*(a1.*exp(-((xdata-b1)/c1).^2) + a2.*exp(-((xdata-b2)/c2).^2) + a3.*exp(-((xdata-b3)/c3).^2));
% FittedCurve = A.*(a1g2.*exp(-((xdata-b1g2)/c1g2).^2) + a2g2.*exp(-((xdata-b2g2)./c2g2).^2) + a3g2.*exp(-((xdata-b3g2)/c3g2).^2))
% +lambda.*(a1d.*exp(-((xdata-b1d)/c1d).^2) + a2d.*exp(-((xdata-b2d)/c2d).^2)+a3d.*exp(-((xdata-b3d)/c3d).^2));
%FittedCurve = A.*(a1.*exp(-((xdata-b1)/c1).^2))+a2.*exp(-((xdata-b2)./c2).^2) + a3.*exp(-((xdata-b3)/c3).^2)+ lambda.*ckdian;
% +lambda.*(a1d.*exp(-((xdata-b1d)/c1d).^2) + a2d.*exp(-((xdata-b2d)/c2d).^2)+a3d.*exp(-((xdata-b3d)/c3d).^2));
ymean = mean(ydata);
SS = ydata-ymean;
SSt =sum(SS.^2);
ErrorVector = FittedCurve - ydata;
sse = sum(ErrorVector .^ 2);
R2 = 1 - sse/SSt;
end
end
questo è il file dati che voglio chiamare
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questi sono gli altri 3 file che per ora stanno ancora con la funzione load, ma che dovrò fare digitando il nome anche quelli
c60_217_0_0_2.
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hopg_220_4_0_2.txt
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piro_216_0_0_2.txt
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Quindi io quello che devo fare, mentre il programma originale (che sta nel primo messaggio) carica i 4 files dati tramite la funzione load, io devo fare in modo che eseguendo il programma, chiede all'utente i nomi dei 4 files .txt