PIERWSZE ZAJECIA PPS
Never
>> who >> a=12.99 a = 12.9900 >> who Your variables are: a >> whos Name Size Bytes Class Attributes a 1x1 8 double >> A=[ 1 2 3; 4 5 6; 7 8 9] A = 1 2 3 4 5 6 7 8 9 >> who Your variables are: A a >> whos Name Size Bytes Class Attributes A 3x3 72 double a 1x1 8 double >> B=[2 -1 3.5; -0.13 2.2; 3 0.5 34] Error using vertcat Dimensions of matrices being concatenated are not consistent. >> B=[2 -1 3.5; -0.13 -0.3 2.2; 3 0.5 34] B = 2.0000 -1.0000 3.5000 -0.1300 -0.3000 2.2000 3.0000 0.5000 34.0000 >> >> whos Name Size Bytes Class Attributes A 3x3 72 double B 3x3 72 double a 1x1 8 double >> P=[] P = [] >> whos Name Size Bytes Class Attributes A 3x3 72 double B 3x3 72 double P 0x0 0 double a 1x1 8 double >> Suma = A + B Suma = 3.0000 1.0000 6.5000 3.8700 4.7000 8.2000 10.0000 8.5000 43.0000 >> Suma A+B ; Error: "Suma" was previously used as a variable, conflicting with its use here as the name of a function or command. See "How MATLAB Recognizes Command Syntax" in the MATLAB documentation for details. >> Roz A-B Undefined function 'Roz' for input arguments of type 'char'. >> Roz= A-B Roz = -1.0000 3.0000 -0.5000 4.1300 5.3000 3.8000 4.0000 7.5000 -25.0000 >> A10=10*A A10 = 10 20 30 40 50 60 70 80 90 >> AP=5+A AP = 6 7 8 9 10 11 12 13 14 >> Mnoz=A*B Mnoz = 10.7400 -0.1000 109.9000 25.3500 -2.5000 229.0000 39.9600 -4.9000 348.1000 >> Dziel = A/B Dziel = -0.4315 -4.5199 0.4251 -0.4857 -13.2421 1.0833 -0.5399 -21.9643 1.7415 >> Dziel1 = A*inv(B) Dziel1 = -0.4315 -4.5199 0.4251 -0.4857 -13.2421 1.0833 -0.5399 -21.9643 1.7415 >> T=Dziel - Dziel1 T = 1.0e-14 * 0.0111 -0.0888 -0.0056 0.0111 0 0.0222 0.1221 -0.3553 0 >> Nowa=A.*B Nowa = 2.0000 -2.0000 10.5000 -0.5200 -1.5000 13.2000 21.0000 4.0000 306.0000 >> // powstajaca macierznowa // powstajaca macierznowa | Error: Unexpected MATLAB operator. >> // skladniki iloczynu przechowywane sa w tej samej macierzy // skladniki iloczynu przechowywane sa w tej samej macierzy | Error: Unexpected MATLAB operator. >> pierwszy wyrazA bedzie mial numer 1.1, zawsze peirwszy wiersz druga kolumna Undefined function 'pierwszy' for input arguments of type 'char'. >> POT=A.^B POT = 1.0e+32 * 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 2.7813 >> w podstawie a sa przechowywane wykladniki macierzy B Undefined function 'w' for input arguments of type 'char'. >> teraz manipulacja blokami danych Undefined function 'teraz' for input arguments of type 'char'. >> a a = 12.9900 >> A A = 1 2 3 4 5 6 7 8 9 >> B B = 2.0000 -1.0000 3.5000 -0.1300 -0.3000 2.2000 3.0000 0.5000 34.0000 >> chcemy utworzyc macierz 3x6 zeby A dolaczyc z prawej Undefined function 'chcemy' for input arguments of type 'char'. >> laczymy teraz tablice Undefined function 'laczymy' for input arguments of type 'char'. >> AB = [A, B] AB = 1.0000 2.0000 3.0000 2.0000 -1.0000 3.5000 4.0000 5.0000 6.0000 -0.1300 -0.3000 2.2000 7.0000 8.0000 9.0000 3.0000 0.5000 34.0000 >> utworz nowa macierz w pierwszym wierzu a i drugim wierszu b Undefined function 'utworz' for input arguments of type 'char'. >> AB1 = [A; B] AB1 = 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 2.0000 -1.0000 3.5000 -0.1300 -0.3000 2.2000 3.0000 0.5000 34.0000 >> whos Name Size Bytes Class Attributes A 3x3 72 double A10 3x3 72 double AB 3x6 144 double AB1 6x3 144 double AP 3x3 72 double B 3x3 72 double Dziel 3x3 72 double Dziel1 3x3 72 double Mnoz 3x3 72 double Nowa 3x3 72 double P 0x0 0 double POT 3x3 72 double Roz 3x3 72 double Suma 3x3 72 double T 3x3 72 double a 1x1 8 double >> ABB = [AB, B, A] ABB = 1.0000 2.0000 3.0000 2.0000 -1.0000 3.5000 2.0000 -1.0000 3.5000 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 -0.1300 -0.3000 2.2000 -0.1300 -0.3000 2.2000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 3.0000 0.5000 34.0000 3.0000 0.5000 34.0000 7.0000 8.0000 9.0000 >> whos Name Size Bytes Class Attributes A 3x3 72 double A10 3x3 72 double AB 3x6 144 double AB1 6x3 144 double ABB 3x12 288 double AP 3x3 72 double B 3x3 72 double Dziel 3x3 72 double Dziel1 3x3 72 double Mnoz 3x3 72 double Nowa 3x3 72 double P 0x0 0 double POT 3x3 72 double Roz 3x3 72 double Suma 3x3 72 double T 3x3 72 double a 1x1 8 double >> size(ABB) ans = 3 12 >> polecenie wyzej to sprawdzenie wielkosci tablicy Undefined function 'polecenie' for input arguments of type 'char'. >> AB2= [AB;B,B] AB2 = 1.0000 2.0000 3.0000 2.0000 -1.0000 3.5000 4.0000 5.0000 6.0000 -0.1300 -0.3000 2.2000 7.0000 8.0000 9.0000 3.0000 0.5000 34.0000 2.0000 -1.0000 3.5000 2.0000 -1.0000 3.5000 -0.1300 -0.3000 2.2000 -0.1300 -0.3000 2.2000 3.0000 0.5000 34.0000 3.0000 0.5000 34.0000 >> AB3 = [A,A;B] Error using vertcat Dimensions of matrices being concatenated are not consistent. >> wyzej nie dziala bo niezgodne wymiary Undefined function 'wyzej' for input arguments of type 'char'. >> dana=AB(2,3) dana = 6 >> 2*6+2 ans = 14 >> teraz tworzy sie ans czyli answer, nazwa domyslna zmiennej nieprzypisanej zadnej zmiennej Undefined function 'teraz' for input arguments of type 'char'. >> transponowanie macierzy Undefined function 'transponowanie' for input arguments of type 'char'. >> AT=A' AT = 1 4 7 2 5 8 3 6 9 >> help clear clear Clear variables and functions from memory. clear removes all variables from the workspace. clear VARIABLES does the same thing. clear GLOBAL removes all global variables. clear FUNCTIONS removes all compiled MATLAB and MEX-functions. clear ALL removes all variables, globals, functions and MEX links. clear ALL at the command prompt also clears the base import list. clear IMPORT clears the base import list. It can only be issued at the command prompt. It cannot be used in a function. clear CLASSES is the same as clear ALL except that class definitions are also cleared. If any objects exist outside the workspace (say in userdata or persistent in a locked program file) a warning will be issued and the class definition will not be cleared. clear CLASSES must be used if the number or names of fields in a class are changed. clear JAVA is the same as clear ALL except that java classes on the dynamic java path (defined using JAVACLASSPATH) are also cleared. clear VAR1 VAR2 ... clears the variables specified. The wildcard character '*' can be used to clear variables that match a pattern. For instance, clear X* clears all the variables in the current workspace that start with X. clear -REGEXP PAT1 PAT2 can be used to match all patterns using regular expressions. This option only clears variables. For more information on using regular expressions, type "doc regexp" at the command prompt. If X is global, clear X removes X from the current workspace, but leaves it accessible to any functions declaring it global. clear GLOBAL X completely removes the global variable X. clear GLOBAL -REGEXP PAT removes global variables that match regular expression patterns. Note that to clear specific global variables, the GLOBAL option must come first. Otherwise, all global variables will be cleared. clear FUN clears the function specified. If FUN has been locked by MLOCK it will remain in memory. Use a partial path (see PARTIALPATH) to distinguish between different overloaded versions of FUN. For instance, 'clear inline/display' clears only the INLINE method for DISPLAY, leaving any other implementations in memory. clear ALL, clear FUN, or clear FUNCTIONS also have the side effect of removing debugging breakpoints and reinitializing persistent variables since the breakpoints for a function and persistent variables are cleared whenever the program file changes or is cleared. Use the functional form of clear, such as clear('name'), when the variable name or function name is stored in a string. Examples for pattern matching: clear a* % Clear variables starting with "a" clear -regexp ^b\d{3}$ % Clear variables starting with "b" and % followed by 3 digits clear -regexp \d % Clear variables containing any digits See also clearvars, who, whos, mlock, munlock, persistent, import. Reference page in Help browser doc clear >> diag(A) ans = 1 5 9 >> czyli glowna przekatna Undefined function 'czyli' for input arguments of type 'char'. >> diag(AB) ans = 1 5 9 >> det(A) ans = 6.6613e-16 >> wyznacznik macierzy, proste Undefined function 'wyznacznik' for input arguments of type 'char'. >> poznajemy operator zakresu, czyli range operator Undefined function 'poznajemy' for input arguments of type 'char'. >> kiedy chcemy skopiowac fragment jakiejs wiekszej tablicy Undefined function 'kiedy' for input arguments of type 'char'. >> AB2=[ A A A; B B B] AB2 = 1.0000 2.0000 3.0000 1.0000 2.0000 3.0000 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 4.0000 5.0000 6.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 7.0000 8.0000 9.0000 7.0000 8.0000 9.0000 2.0000 -1.0000 3.5000 2.0000 -1.0000 3.5000 2.0000 -1.0000 3.5000 -0.1300 -0.3000 2.2000 -0.1300 -0.3000 2.2000 -0.1300 -0.3000 2.2000 3.0000 0.5000 34.0000 3.0000 0.5000 34.0000 3.0000 0.5000 34.0000 >> size(AB2) ans = 6 9 >> Anowa = AB(2:5; 2:3) Anowa = AB(2:5; 2:3) | Error: Unbalanced or unexpected parenthesis or bracket. >> Anowa = AB(2:5,2:3) Index exceeds matrix dimensions. >> wiersz4 = AB2(4,1:9) wiersz4 = 2.0000 -1.0000 3.5000 2.0000 -1.0000 3.5000 2.0000 -1.0000 3.5000 >> wiersz41=AB2(4, :) wiersz41 = 2.0000 -1.0000 3.5000 2.0000 -1.0000 3.5000 2.0000 -1.0000 3.5000 >> kol2=AB@( : , 2) kol2=AB@( : , 2) | Error: Unbalanced or unexpected parenthesis or bracket. >> kol2=AB2(:,2) kol2 = 2.0000 5.0000 8.0000 -1.0000 -0.3000 0.5000 >> kol2t=kol2' kol2t = 2.0000 5.0000 8.0000 -1.0000 -0.3000 0.5000 >> A A = 1 2 3 4 5 6 7 8 9 >> sum(A) ans = 12 15 18 >> sum(A') ans = 6 15 24 >> sum(A')' ans = 6 15 24 >> wektory - ciagi liczbowe wektory - ciagi liczbowe | Error: Unexpected MATLAB expression. >> operator zakresu to dwukropek Undefined function 'operator' for input arguments of type 'char'. >> k=0:10 k = 0 1 2 3 4 5 6 7 8 9 10 >> k1=2:0.1:3.2 k1 = Columns 1 through 12 2.0000 2.1000 2.2000 2.3000 2.4000 2.5000 2.6000 2.7000 2.8000 2.9000 3.0000 3.1000 Column 13 3.2000 >> czyli zaczynamy od 2 z krokiem co 0.1 i do 3.2 Undefined function 'czyli' for input arguments of type 'char'. >> ciagx=2:5:49 ciagx = 2 7 12 17 22 27 32 37 42 47 >> % (ogolnie: ciag = start :krok/przyrost: koncowa wartosc/koniec) >> cl=1:0.7:12 cl = Columns 1 through 12 1.0000 1.7000 2.4000 3.1000 3.8000 4.5000 5.2000 5.9000 6.6000 7.3000 8.0000 8.7000 Columns 13 through 16 9.4000 10.1000 10.8000 11.5000 >> size(cl) ans = 1 16 >> r=size(cl) r = 1 16 >> size(r) ans = 1 2 >> w=r(1,1) w = 1 >> kol=r(1,2) kol = 16 >> [w,k]=size(cl) w = 1 k = 16 >> w w = 1 >> k k = 16 >> dl=length(cl) dl = 16 >> c2=1:-0.2:-1 c2 = 1.0000 0.8000 0.6000 0.4000 0.2000 0 -0.2000 -0.4000 -0.6000 -0.8000 -1.0000 >> c3=-1:-0.2:1 c3 = Empty matrix: 1-by-0 >> k=0:20 k = Columns 1 through 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Column 21 20 >> kpol=0.5*k kpol = Columns 1 through 12 0 0.5000 1.0000 1.5000 2.0000 2.5000 3.0000 3.5000 4.0000 4.5000 5.0000 5.5000 Columns 13 through 21 6.0000 6.5000 7.0000 7.5000 8.0000 8.5000 9.0000 9.5000 10.0000 >> kk=5+k kk = Columns 1 through 20 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Column 21 25 >> pi ans = 3.1416 >> i ans = 0.0000 + 1.0000i >> j ans = 0.0000 + 1.0000i >> %pi to taka predefiniowana stala, nie musimy dysponowac jej dokladna wartoscia >> e Undefined function or variable 'e'. >> %kiedy wpisujemy "i" to jednostka urojona, podobnie dziala "j" >> %matlab dziala na liczbach rzeczywistych i zespolonych >> pi=50 pi = 50 >> who Your variables are: A AB2 B Nowa Suma c2 dana kk kpol wiersz4 A10 ABB Dziel P T c3 dl kol pi wiersz41 AB AP Dziel1 POT a ciagx k kol2 r AB1 AT Mnoz Roz ans cl k1 kol2t w >> %po takiej operacji zmienna zablokuje nam dostep do predefiniowanej liczby pi >> %jak odzyskac dostep do liczby pi do jej normalnej wartosci? >> %who maskuje dostep do predefinoiwanej jednostki, jezeli w komendzie, poleceniu pojawi sie nazwa zmiennej to wtedy matlab rozpoczyna przeszukiwanie i jesli jej nie nzajdzie szuka >> mx=max(A) mx = 7 8 9 >> mn=min(A) mn = 1 2 3 >> mmm=max(mx) mmm = 9 >> mmm=max(max(A)) mmm = 9 >> E=eye(4) E = 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 >> %macierz jednostkowa >> E1=eye(4,7) E1 = 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 >> E1=eye(6,3) E1 = 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 >> %magic robi ze wszystkie sumy wyrazow w wierszach i kolumnach sa jednakowe >> M=magic(6) M = 35 1 6 26 19 24 3 32 7 21 23 25 31 9 2 22 27 20 8 28 33 17 10 15 30 5 34 12 14 16 4 36 29 13 18 11 >> w1=sum(M) w1 = 111 111 111 111 111 111 >> %sumowanie wartosci w kolumnach >> w2=sum(M') w2 = 111 111 111 111 111 111 >> %sumowanie wartosci w wierszach >> J=ones(3,6) J = 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 >> Z=zeros(4,5) Z = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 >> % stworzymy sobie kolejno wektor 4 zer a potem z 4 jedynek czy jakos tak >> Z=zeros(1,4) Z = 0 0 0 0 >> J=ones(1,4) J = 1 1 1 1 >> okres=[J,Z] okres = 1 1 1 1 0 0 0 0 >> syg=[okres, okres, okres, okres, okres] syg = Columns 1 through 20 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 Columns 21 through 40 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 >> length(syg) ans = 40 >> %uzywanie petli - macierz pusta >> sygnal =[] sygnal = [] >> for i=1:10 sygnal=[sygnal;okres] end sygnal = 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 sygnal = 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 >> help randn randn Normally distributed pseudorandom numbers. R = randn(N) returns an N-by-N matrix containing pseudorandom values drawn from the standard normal distribution. randn(M,N) or randn([M,N]) returns an M-by-N matrix. randn(M,N,P,...) or randn([M,N,P,...]) returns an M-by-N-by-P-by-... array. randn returns a scalar. randn(SIZE(A)) returns an array the same size as A. Note: The size inputs M, N, P, ... should be nonnegative integers. Negative integers are treated as 0. R = randn(..., 'double') or R = randn(..., 'single') returns an array of normal values of the specified class. The sequence of numbers produced by randn is determined by the settings of the uniform random number generator that underlies RAND, randn, and RANDI. randn uses one or more uniform random values to create each normal random value. Control that shared random number generator using RNG. Examples: Example 1: Generate values from a normal distribution with mean 1 and standard deviation 2. r = 1 + 2.*randn(100,1); Example 2: Generate values from a bivariate normal distribution with specified mean vector and covariance matrix. mu = [1 2]; Sigma = [1 .5; .5 2]; R = chol(Sigma); z = repmat(mu,100,1) + randn(100,2)*R; Example 3: Reset the random number generator used by RAND, RANDI, and randn to its default startup settings, so that randn produces the same random numbers as if you restarted MATLAB. rng('default'); randn(1,5) Example 4: Save the settings for the random number generator used by RAND, RANDI, and randn, generate 5 values from randn, restore the settings, and repeat those values. s = rng z1 = randn(1,5) rng(s); z2 = randn(1,5) % z2 contains exactly the same values as z1 Example 5: Reinitialize the random number generator used by RAND, RANDI, and randn with a seed based on the current time. randn will return different values each time you do this. NOTE: It is usually not necessary to do this more than once per MATLAB session. rng('shuffle'); randn(1,5) See Updating Your Random Number Generator Syntax to use RNG to replace randn with the 'seed' or 'state' inputs. See also rand, randi, rng, RandStream, RandStream/randn Overloaded methods: RandStream/randn distributed/randn codistributor2dbc/randn codistributor1d/randn codistributed/randn gpuArray/randn Reference page in Help browser doc randn >> %funkcja randn zwraca probki o rozkladzie noramlnym, czyli gaussowaskim >> szum=randn(1,10) szum = 0.5377 1.8339 -2.2588 0.8622 0.3188 -1.3077 -0.4336 0.3426 3.5784 2.7694 >> %sygnal losowy o rozkladzie gaussowskim >> szum_rown=rand(2,5) szum_rown = 0.1576 0.9572 0.8003 0.4218 0.7922 0.9706 0.4854 0.1419 0.9157 0.9595 >> % wyzej - sygnal o rozkladzie rownomiernym >> %na przedziale ten sygnal jest 0 , 1 >> srednia = mean(szum) srednia = 0.6243 >> %wartosc srednia >> szum=randn(1,1000); srednia=mean(szum) srednia = -0.0460 >> %cztery funkcje ktore dotycza zaokraglenie >> ceil(5.3) ans = 6 >> %zawsdze w gore >> floor(3.8) ans = 3 >> fix(6.7) ans = 6 >> fix(-4.8) ans = -4 >> %zawsze w kierunku zera - fix >> round(13.4) ans = 13 >> %round zaokraglenie >> fix(2.7) ans = 2 >> round(2.7) ans = 3 >> %TABLICE ZNAKOWE >> txt1='Ulubiona ksiazka' txt1 = Ulubiona ksiazka >> txt2='Ala ma kota' txt2 = Ala ma kota >> tekst = [txt1, ':', txt2] tekst = Ulubiona ksiazka:Ala ma kota >> tekst = [txt1, ' : ', txt2] tekst = Ulubiona ksiazka : Ala ma kota >> %symulacja rzutu kostka >> rzut=rand(1,1) rzut = 0.5301 >> rzut=6*rand(1,1) rzut = 1.6504 >> rzut=floor(6*rand(1,1)) rzut = 1 >> rzut=floor(rand(1,1)) +1 rzut = 1 >> rzut = floor(6*rand(1,5)) +1 rzut = 2 5 6 1 4 >> rzut = floor(6*rand(1,5)) +1 rzut = 1 5 6 1 6 >> rzut = floor(6*rand(1,5)) +1 rzut = 1 5 5 4 6 >> rzut = floor(6*rand(1,5)) +1 rzut = 6 4 1 2 2 >> rzut = floor(6*rand(1,5)) +1 rzut = 1 1 4 6 3 >> rzut = floor(6*rand(1,5)) +1 rzut = 3 6 6 5 6 >> %za kazdym nastepnym razem wywolanai tej funkcja sa inne wartosci >> %FUNKCJE TRYGONOMETRYCZNE >> A=3 %amplituda A = 3 >> f=2 %czestotliwosc f = 2 >> t=0:0.01:1 %wektor czasu t = Columns 1 through 12 0 0.0100 0.0200 0.0300 0.0400 0.0500 0.0600 0.0700 0.0800 0.0900 0.1000 0.1100 Columns 13 through 24 0.1200 0.1300 0.1400 0.1500 0.1600 0.1700 0.1800 0.1900 0.2000 0.2100 0.2200 0.2300 Columns 25 through 36 0.2400 0.2500 0.2600 0.2700 0.2800 0.2900 0.3000 0.3100 0.3200 0.3300 0.3400 0.3500 Columns 37 through 48 0.3600 0.3700 0.3800 0.3900 0.4000 0.4100 0.4200 0.4300 0.4400 0.4500 0.4600 0.4700 Columns 49 through 60 0.4800 0.4900 0.5000 0.5100 0.5200 0.5300 0.5400 0.5500 0.5600 0.5700 0.5800 0.5900 Columns 61 through 72 0.6000 0.6100 0.6200 0.6300 0.6400 0.6500 0.6600 0.6700 0.6800 0.6900 0.7000 0.7100 Columns 73 through 84 0.7200 0.7300 0.7400 0.7500 0.7600 0.7700 0.7800 0.7900 0.8000 0.8100 0.8200 0.8300 Columns 85 through 96 0.8400 0.8500 0.8600 0.8700 0.8800 0.8900 0.9000 0.9100 0.9200 0.9300 0.9400 0.9500 Columns 97 through 101 0.9600 0.9700 0.9800 0.9900 1.0000 >> s=A*sin(2*pi*f*t) % sygnal s = Columns 1 through 12 0 2.7279 -2.2704 -0.8382 2.9681 -1.6321 -1.6097 2.9718 -0.8637 -2.2530 2.7388 -0.0266 Columns 13 through 24 -2.7167 2.2877 0.8127 -2.9641 1.6543 1.5872 -2.9753 0.8891 2.2353 -2.7496 0.0531 2.7054 Columns 25 through 36 -2.3048 -0.7871 2.9599 -1.6764 -1.5647 2.9786 -0.9144 -2.2175 2.7601 -0.0797 -2.6938 2.3217 Columns 37 through 48 0.7615 -2.9554 1.6983 1.5419 -2.9817 0.9397 2.1996 -2.7704 0.1062 2.6820 -2.3384 -0.7358 Columns 49 through 60 2.9508 -1.7201 -1.5191 2.9845 -0.9649 -2.1814 2.7805 -0.1327 -2.6700 2.3549 0.7100 -2.9459 Columns 61 through 72 1.7418 1.4961 -2.9871 0.9900 2.1631 -2.7903 0.1593 2.6578 -2.3713 -0.6842 2.9407 -1.7634 Columns 73 through 84 -1.4731 2.9894 -1.0150 -2.1446 2.8000 -0.1858 -2.6454 2.3875 0.6583 -2.9354 1.7848 1.4499 Columns 85 through 96 -2.9915 1.0399 2.1260 -2.8094 0.2123 2.6327 -2.4035 -0.6323 2.9298 -1.8061 -1.4266 2.9934 Columns 97 through 101 -1.0648 -2.1072 2.8186 -0.2387 -2.6199 >> plot(t,s) >> grid on >> %ZAPIS DO PLIKU
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