Can you give me an example for clock synchronization between two Speedgoat target computers using PTP?Can you give me an example for clock synchronization between two Speedgoat target computers using PTP? Can you give me an example for clock synchronization between two Speedgoat target computers using PTP? slrt, ptp, speedgoat, sync MATLAB Answers — New Questions

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I don’t manipulate my figures any, so I don’t need whatever toolbar this is. Also, it’s causing MATLAB to generate strange errors, which are annoying but don’t actually mean anything important for my program. I’m looking for a way to generate static figures (figures that cannot be panned, zoomed, etc) or at least disabling this toolbar when creating your normal bar graphs.I don’t manipulate my figures any, so I don’t need whatever toolbar this is. Also, it’s causing MATLAB to generate strange errors, which are annoying but don’t actually mean anything important for my program. I’m looking for a way to generate static figures (figures that cannot be panned, zoomed, etc) or at least disabling this toolbar when creating your normal bar graphs. I don’t manipulate my figures any, so I don’t need whatever toolbar this is. Also, it’s causing MATLAB to generate strange errors, which are annoying but don’t actually mean anything important for my program. I’m looking for a way to generate static figures (figures that cannot be panned, zoomed, etc) or at least disabling this toolbar when creating your normal bar graphs. matlab, figure MATLAB Answers — New Questions

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Hi all, I have multiple sets of data that are to be fitted by my fitting function. Since these data sets are kind of related, what I did was to use an inital guess to find the parameter values for the first dataset, and then use the parameter values as the guess for the second dataset so on and forth. However, the fitting was really bad. May I ask if there is another way around this kind of problem involving fitting of multiple datasets?

tic
%% Preparation
clear; clc

data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

%% Preamble

% Fundamental constants
h = 4.0135667696*10^-15; % units: eV/ Hz
c = 3*10^8; % SI units
kB = 8.617333268*10^-5; % units: eV/ K

% Data
wavelength = data(1:end, 1); % contains all of the wavelengths
delay_t = data(1, 1:end); % conatains all of the delay times
E = (h*c)./(wavelength*10^-9); % contains all of the probe energies

% Range of data needed
Range_E = E>=1.5 & E<=2.2;
Range_W = wavelength >= (h*c)/(2.2*10^-9) & wavelength <= (h*c)/(1.5*10^-9);
Range_T = delay_t>=0.5 & delay_t<=1000;

E_p = E(Range_E); % probe energies for scatterplot
w_p = wavelength(Range_W);
delay_T = delay_t(Range_T);

Range_Efit = E_p>=1.62 & E_p<=max(E_p);
Range_Wfit = w_p >= (h*c)/(2.2*10^-9) & w_p <= (h*c)/(1.62*10^-9);
E_fit = E_p(Range_Efit); % probe energies for fitting
W_fit = w_p(Range_Wfit);
t_min = min(delay_T);
t_max = max(delay_T);
w_min = min(w_p);
w_max = max(w_p);
[row1, col1] = find(data == w_min);
[row2, col2] = find(data == w_max);
[row3, col3] = find(data == t_min);
[row4, col4] = find(data == t_max);

% New cleaned up data
data_new = data(row1:row2, col3:col4);% new data containing required delta A

% for n = 1:length(delay_T)
% delta_Abs(:,n) = -1*data_new(:,n);
% delta_Abs_norm(:,n) = delta_Abs(:,n)/max(delta_Abs(:,n));
% delta_Abs_fit(:,n) = data_new(Range_Wfit,n);
% delta_Abs_norm_fit(:,n) = delta_Abs_fit(:,n)/max(delta_Abs_fit(:, n));
% % plot command
% plot(E_p, delta_Abs_norm)
% xlabel(‘Probe Energy (eV)’)
% ylabel(‘Normalised Delta A (a.u.)’)
% legend(‘Experimental Data’)
% end

% Fitting function: Maxwell-Boltzmann distribution
function F = MB(y, e_fit)
data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv");
h = 4.0135667696*10^-15;
c = 3*10^8;
kB = 8.617333268*10^-5;
delay_t = data(1, :);
Range_T = delay_t>=0.5 & delay_t<=1000;
delay_T = delay_t(Range_T);
wavelength = data(1:end, 1);
E = (h*c)./(wavelength*10^-9);
Range_E = E>=1.5 & E<=2.2;
E_p = E(Range_E);
Range_Efit = E_p>=1.62 & E_p<=max(E_p);
E_fit = E_p(Range_Efit);
for i = 1:length(E_fit)
E_fit = e_fit(i);
F(i) = y(1).*exp(-(E_fit./(kB.*y(2)))) + y(3);
end
F = F(:);
end

% For loop to create required datasets for plotting

for n = 2:length(delay_T)
delta_Abs(:,1) = -1*data_new(:,1);
delta_Abs_norm(:,1) = delta_Abs(:,1)./max(abs(delta_Abs(:,1)));
delta_Abs_fit(:,1) = data_new(Range_Wfit,1);
delta_Abs_norm_fit(:,1) = delta_Abs_norm(Range_Wfit);

delta_Abs(:,n) = -1*data_new(:,n);
delta_Abs_norm(:,n) = delta_Abs(:,n)./max(abs(delta_Abs(:,n)));
delta_Abs_fit(:,n) = data_new(Range_Wfit,n);
delta_Abs_norm_fit(:,n) = delta_Abs_norm(Range_Wfit);

% lsqcurvefit
optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘levenberg-marquardt’, ‘MaxFunctionEvaluations’,10^20, ‘MaxIterations’, 10^20, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);
lb = [0, 293, -1]; ub = [Inf, 6000, 1];
lb1 = [0, 293, -1]; ub1 = [Inf, 2000, 1];
lb2 = [0, 293, -1]; ub2 = [Inf, 1200, 1];
lb3 = [0, 293, -1]; ub3 = [Inf, 1000, 1];
lb4 = [0, 293, -1]; ub4 = [Inf, 750, 1];
y1 = [2*10^7, 1000, 0.5];
y2 = [2*10^7, 800, 0.5];
y3 = [2*10^10, 800, 0.5];
y4 = [2*10^11, 600, 0.5];
y5 = [2*10^14, 600, 0.5];
y(1,:) = lsqcurvefit(@MB, y1, E_fit, delta_Abs_norm_fit(:, 1), lb1, ub1, optim_lsq);
y(n,:) = lsqcurvefit(@MB, y(n-1, :), E_fit, delta_Abs_norm_fit(:, n), lb, ub, optim_lsq);

% plot command
plot(E_p, delta_Abs_norm)
hold on
plot(E_fit, MB(y(1,:), E_fit), ‘red’)
plot(E_fit, MB(y(n,:), E_fit), ‘red’)
xlabel(‘Probe Energy (eV)’)
ylabel(‘Normalised Delta A (a.u.)’)
legend(‘Fitted curve’)
end
carrier_T = y(:,2);
disp(carrier_T)
toc
%% Miscellaneous

% For a rectangular pic:
% drag horizontally until it just covers the letter ‘c’ in the word ‘col’

% For a square pic:
% drag horizontall until it just covers the letter ‘o’ in the word ‘Contribution’ in the next line)
% legend(‘Experimental Data’, ‘Fitted Curve’, ‘Carrier Contribution’,’Excitonic Contribution’)Hi all, I have multiple sets of data that are to be fitted by my fitting function. Since these data sets are kind of related, what I did was to use an inital guess to find the parameter values for the first dataset, and then use the parameter values as the guess for the second dataset so on and forth. However, the fitting was really bad. May I ask if there is another way around this kind of problem involving fitting of multiple datasets?

tic
%% Preparation
clear; clc

data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

%% Preamble

% Fundamental constants
h = 4.0135667696*10^-15; % units: eV/ Hz
c = 3*10^8; % SI units
kB = 8.617333268*10^-5; % units: eV/ K

% Data
wavelength = data(1:end, 1); % contains all of the wavelengths
delay_t = data(1, 1:end); % conatains all of the delay times
E = (h*c)./(wavelength*10^-9); % contains all of the probe energies

% Range of data needed
Range_E = E>=1.5 & E<=2.2;
Range_W = wavelength >= (h*c)/(2.2*10^-9) & wavelength <= (h*c)/(1.5*10^-9);
Range_T = delay_t>=0.5 & delay_t<=1000;

E_p = E(Range_E); % probe energies for scatterplot
w_p = wavelength(Range_W);
delay_T = delay_t(Range_T);

Range_Efit = E_p>=1.62 & E_p<=max(E_p);
Range_Wfit = w_p >= (h*c)/(2.2*10^-9) & w_p <= (h*c)/(1.62*10^-9);
E_fit = E_p(Range_Efit); % probe energies for fitting
W_fit = w_p(Range_Wfit);
t_min = min(delay_T);
t_max = max(delay_T);
w_min = min(w_p);
w_max = max(w_p);
[row1, col1] = find(data == w_min);
[row2, col2] = find(data == w_max);
[row3, col3] = find(data == t_min);
[row4, col4] = find(data == t_max);

% New cleaned up data
data_new = data(row1:row2, col3:col4);% new data containing required delta A

% for n = 1:length(delay_T)
% delta_Abs(:,n) = -1*data_new(:,n);
% delta_Abs_norm(:,n) = delta_Abs(:,n)/max(delta_Abs(:,n));
% delta_Abs_fit(:,n) = data_new(Range_Wfit,n);
% delta_Abs_norm_fit(:,n) = delta_Abs_fit(:,n)/max(delta_Abs_fit(:, n));
% % plot command
% plot(E_p, delta_Abs_norm)
% xlabel(‘Probe Energy (eV)’)
% ylabel(‘Normalised Delta A (a.u.)’)
% legend(‘Experimental Data’)
% end

% Fitting function: Maxwell-Boltzmann distribution
function F = MB(y, e_fit)
data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv");
h = 4.0135667696*10^-15;
c = 3*10^8;
kB = 8.617333268*10^-5;
delay_t = data(1, :);
Range_T = delay_t>=0.5 & delay_t<=1000;
delay_T = delay_t(Range_T);
wavelength = data(1:end, 1);
E = (h*c)./(wavelength*10^-9);
Range_E = E>=1.5 & E<=2.2;
E_p = E(Range_E);
Range_Efit = E_p>=1.62 & E_p<=max(E_p);
E_fit = E_p(Range_Efit);
for i = 1:length(E_fit)
E_fit = e_fit(i);
F(i) = y(1).*exp(-(E_fit./(kB.*y(2)))) + y(3);
end
F = F(:);
end

% For loop to create required datasets for plotting

for n = 2:length(delay_T)
delta_Abs(:,1) = -1*data_new(:,1);
delta_Abs_norm(:,1) = delta_Abs(:,1)./max(abs(delta_Abs(:,1)));
delta_Abs_fit(:,1) = data_new(Range_Wfit,1);
delta_Abs_norm_fit(:,1) = delta_Abs_norm(Range_Wfit);

delta_Abs(:,n) = -1*data_new(:,n);
delta_Abs_norm(:,n) = delta_Abs(:,n)./max(abs(delta_Abs(:,n)));
delta_Abs_fit(:,n) = data_new(Range_Wfit,n);
delta_Abs_norm_fit(:,n) = delta_Abs_norm(Range_Wfit);

% lsqcurvefit
optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘levenberg-marquardt’, ‘MaxFunctionEvaluations’,10^20, ‘MaxIterations’, 10^20, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);
lb = [0, 293, -1]; ub = [Inf, 6000, 1];
lb1 = [0, 293, -1]; ub1 = [Inf, 2000, 1];
lb2 = [0, 293, -1]; ub2 = [Inf, 1200, 1];
lb3 = [0, 293, -1]; ub3 = [Inf, 1000, 1];
lb4 = [0, 293, -1]; ub4 = [Inf, 750, 1];
y1 = [2*10^7, 1000, 0.5];
y2 = [2*10^7, 800, 0.5];
y3 = [2*10^10, 800, 0.5];
y4 = [2*10^11, 600, 0.5];
y5 = [2*10^14, 600, 0.5];
y(1,:) = lsqcurvefit(@MB, y1, E_fit, delta_Abs_norm_fit(:, 1), lb1, ub1, optim_lsq);
y(n,:) = lsqcurvefit(@MB, y(n-1, :), E_fit, delta_Abs_norm_fit(:, n), lb, ub, optim_lsq);

% plot command
plot(E_p, delta_Abs_norm)
hold on
plot(E_fit, MB(y(1,:), E_fit), ‘red’)
plot(E_fit, MB(y(n,:), E_fit), ‘red’)
xlabel(‘Probe Energy (eV)’)
ylabel(‘Normalised Delta A (a.u.)’)
legend(‘Fitted curve’)
end
carrier_T = y(:,2);
disp(carrier_T)
toc
%% Miscellaneous

% For a rectangular pic:
% drag horizontally until it just covers the letter ‘c’ in the word ‘col’

% For a square pic:
% drag horizontall until it just covers the letter ‘o’ in the word ‘Contribution’ in the next line)
% legend(‘Experimental Data’, ‘Fitted Curve’, ‘Carrier Contribution’,’Excitonic Contribution’) Hi all, I have multiple sets of data that are to be fitted by my fitting function. Since these data sets are kind of related, what I did was to use an inital guess to find the parameter values for the first dataset, and then use the parameter values as the guess for the second dataset so on and forth. However, the fitting was really bad. May I ask if there is another way around this kind of problem involving fitting of multiple datasets?

tic
%% Preparation
clear; clc

data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv"); % insert file path within parenthesis

%% Preamble

% Fundamental constants
h = 4.0135667696*10^-15; % units: eV/ Hz
c = 3*10^8; % SI units
kB = 8.617333268*10^-5; % units: eV/ K

% Data
wavelength = data(1:end, 1); % contains all of the wavelengths
delay_t = data(1, 1:end); % conatains all of the delay times
E = (h*c)./(wavelength*10^-9); % contains all of the probe energies

% Range of data needed
Range_E = E>=1.5 & E<=2.2;
Range_W = wavelength >= (h*c)/(2.2*10^-9) & wavelength <= (h*c)/(1.5*10^-9);
Range_T = delay_t>=0.5 & delay_t<=1000;

E_p = E(Range_E); % probe energies for scatterplot
w_p = wavelength(Range_W);
delay_T = delay_t(Range_T);

Range_Efit = E_p>=1.62 & E_p<=max(E_p);
Range_Wfit = w_p >= (h*c)/(2.2*10^-9) & w_p <= (h*c)/(1.62*10^-9);
E_fit = E_p(Range_Efit); % probe energies for fitting
W_fit = w_p(Range_Wfit);
t_min = min(delay_T);
t_max = max(delay_T);
w_min = min(w_p);
w_max = max(w_p);
[row1, col1] = find(data == w_min);
[row2, col2] = find(data == w_max);
[row3, col3] = find(data == t_min);
[row4, col4] = find(data == t_max);

% New cleaned up data
data_new = data(row1:row2, col3:col4);% new data containing required delta A

% for n = 1:length(delay_T)
% delta_Abs(:,n) = -1*data_new(:,n);
% delta_Abs_norm(:,n) = delta_Abs(:,n)/max(delta_Abs(:,n));
% delta_Abs_fit(:,n) = data_new(Range_Wfit,n);
% delta_Abs_norm_fit(:,n) = delta_Abs_fit(:,n)/max(delta_Abs_fit(:, n));
% % plot command
% plot(E_p, delta_Abs_norm)
% xlabel(‘Probe Energy (eV)’)
% ylabel(‘Normalised Delta A (a.u.)’)
% legend(‘Experimental Data’)
% end

% Fitting function: Maxwell-Boltzmann distribution
function F = MB(y, e_fit)
data = importdata("Experimental dataTransient AbsorptionFCPIB-293K-2.5mW-400nm-Jan072021 -ibg -bg -chirp.csv");
h = 4.0135667696*10^-15;
c = 3*10^8;
kB = 8.617333268*10^-5;
delay_t = data(1, :);
Range_T = delay_t>=0.5 & delay_t<=1000;
delay_T = delay_t(Range_T);
wavelength = data(1:end, 1);
E = (h*c)./(wavelength*10^-9);
Range_E = E>=1.5 & E<=2.2;
E_p = E(Range_E);
Range_Efit = E_p>=1.62 & E_p<=max(E_p);
E_fit = E_p(Range_Efit);
for i = 1:length(E_fit)
E_fit = e_fit(i);
F(i) = y(1).*exp(-(E_fit./(kB.*y(2)))) + y(3);
end
F = F(:);
end

% For loop to create required datasets for plotting

for n = 2:length(delay_T)
delta_Abs(:,1) = -1*data_new(:,1);
delta_Abs_norm(:,1) = delta_Abs(:,1)./max(abs(delta_Abs(:,1)));
delta_Abs_fit(:,1) = data_new(Range_Wfit,1);
delta_Abs_norm_fit(:,1) = delta_Abs_norm(Range_Wfit);

delta_Abs(:,n) = -1*data_new(:,n);
delta_Abs_norm(:,n) = delta_Abs(:,n)./max(abs(delta_Abs(:,n)));
delta_Abs_fit(:,n) = data_new(Range_Wfit,n);
delta_Abs_norm_fit(:,n) = delta_Abs_norm(Range_Wfit);

% lsqcurvefit
optim_lsq = optimoptions(‘lsqcurvefit’, ‘Algorithm’, ‘levenberg-marquardt’, ‘MaxFunctionEvaluations’,10^20, ‘MaxIterations’, 10^20, ‘FunctionTolerance’,10^-20, ‘StepTolerance’, 10^-20);
lb = [0, 293, -1]; ub = [Inf, 6000, 1];
lb1 = [0, 293, -1]; ub1 = [Inf, 2000, 1];
lb2 = [0, 293, -1]; ub2 = [Inf, 1200, 1];
lb3 = [0, 293, -1]; ub3 = [Inf, 1000, 1];
lb4 = [0, 293, -1]; ub4 = [Inf, 750, 1];
y1 = [2*10^7, 1000, 0.5];
y2 = [2*10^7, 800, 0.5];
y3 = [2*10^10, 800, 0.5];
y4 = [2*10^11, 600, 0.5];
y5 = [2*10^14, 600, 0.5];
y(1,:) = lsqcurvefit(@MB, y1, E_fit, delta_Abs_norm_fit(:, 1), lb1, ub1, optim_lsq);
y(n,:) = lsqcurvefit(@MB, y(n-1, :), E_fit, delta_Abs_norm_fit(:, n), lb, ub, optim_lsq);

% plot command
plot(E_p, delta_Abs_norm)
hold on
plot(E_fit, MB(y(1,:), E_fit), ‘red’)
plot(E_fit, MB(y(n,:), E_fit), ‘red’)
xlabel(‘Probe Energy (eV)’)
ylabel(‘Normalised Delta A (a.u.)’)
legend(‘Fitted curve’)
end
carrier_T = y(:,2);
disp(carrier_T)
toc
%% Miscellaneous

% For a rectangular pic:
% drag horizontally until it just covers the letter ‘c’ in the word ‘col’

% For a square pic:
% drag horizontall until it just covers the letter ‘o’ in the word ‘Contribution’ in the next line)
% legend(‘Experimental Data’, ‘Fitted Curve’, ‘Carrier Contribution’,’Excitonic Contribution’) lsqcurvefit MATLAB Answers — New Questions

​

A sequence of functions F0, F1, F2…is defined as follows, for x a positive real number:#
F0 = x^2024
For n>=1, the function Fn is the derivative of F(n-1), with respect to x.
Find the first value of n for which Fn = 0A sequence of functions F0, F1, F2…is defined as follows, for x a positive real number:#
F0 = x^2024
For n>=1, the function Fn is the derivative of F(n-1), with respect to x.
Find the first value of n for which Fn = 0 A sequence of functions F0, F1, F2…is defined as follows, for x a positive real number:#
F0 = x^2024
For n>=1, the function Fn is the derivative of F(n-1), with respect to x.
Find the first value of n for which Fn = 0 loops, differentiation MATLAB Answers — New Questions

​

Hi.
I recently was trying to make a scientific graph, which required the X Axis to be in even intervals of powers of 10 (i.e. 0.1, 10, 100, 1000, etc.) with no in-betweeners. When I tried to use the xtick function, the command worked, but it didn’t create the necessary even spacing required at the intervals. What can I do? (The code and images are below.)
size = particle_size.("Size (d.nm)");
number = particle_size.("Number (Percent)");

plot(normalize(size,’range’,[0.1 10000]),number,"red",LineWidth=0.5)
xlim([0.1 10000])
ylim([0 14])

title("Particle Size Of Carbon Leaves",FontSize=20,FontWeight="bold")
xlabel("Size (d.nm)",FontSize=14,FontWeight="bold")
ylabel("Number (Percent)",FontSize=14,FontWeight="bold")
legend("Carbon Leaves",FontWeight="bold")

xtick = [0.1, 1, 10, 100, 1000, 10000];

box on
ax = gca;
ax.LineWidth = 2;
ax.FontSize = 12;
ax.XTick = xtick;

(The graph required)

(What MATLAB gave me)Hi.
I recently was trying to make a scientific graph, which required the X Axis to be in even intervals of powers of 10 (i.e. 0.1, 10, 100, 1000, etc.) with no in-betweeners. When I tried to use the xtick function, the command worked, but it didn’t create the necessary even spacing required at the intervals. What can I do? (The code and images are below.)
size = particle_size.("Size (d.nm)");
number = particle_size.("Number (Percent)");

plot(normalize(size,’range’,[0.1 10000]),number,"red",LineWidth=0.5)
xlim([0.1 10000])
ylim([0 14])

title("Particle Size Of Carbon Leaves",FontSize=20,FontWeight="bold")
xlabel("Size (d.nm)",FontSize=14,FontWeight="bold")
ylabel("Number (Percent)",FontSize=14,FontWeight="bold")
legend("Carbon Leaves",FontWeight="bold")

xtick = [0.1, 1, 10, 100, 1000, 10000];

box on
ax = gca;
ax.LineWidth = 2;
ax.FontSize = 12;
ax.XTick = xtick;

(The graph required)

(What MATLAB gave me) Hi.
I recently was trying to make a scientific graph, which required the X Axis to be in even intervals of powers of 10 (i.e. 0.1, 10, 100, 1000, etc.) with no in-betweeners. When I tried to use the xtick function, the command worked, but it didn’t create the necessary even spacing required at the intervals. What can I do? (The code and images are below.)
size = particle_size.("Size (d.nm)");
number = particle_size.("Number (Percent)");

plot(normalize(size,’range’,[0.1 10000]),number,"red",LineWidth=0.5)
xlim([0.1 10000])
ylim([0 14])

title("Particle Size Of Carbon Leaves",FontSize=20,FontWeight="bold")
xlabel("Size (d.nm)",FontSize=14,FontWeight="bold")
ylabel("Number (Percent)",FontSize=14,FontWeight="bold")
legend("Carbon Leaves",FontWeight="bold")

xtick = [0.1, 1, 10, 100, 1000, 10000];

box on
ax = gca;
ax.LineWidth = 2;
ax.FontSize = 12;
ax.XTick = xtick;

(The graph required)

(What MATLAB gave me) graphs, axis MATLAB Answers — New Questions

​