I am using tfestimate and tfest to experimentally derive the frequency response function of a 2 dof system with 1 input and 2 output.
The problem is that tfest seems to cancel or does not recognize a complex pole pair (resonance) near a complex zero pair (antiresonance) around 6 Hz in the tf from input 1 to output 1 and consequently simplifies the frequency response of the system (see image, correct plot in blue vs simplified plot in red):

I tried different types of windowing in tfestimate but the only one that allows me to obtain the correct frequency response after tfest is rectwin without overlap: this works only with mathematical model I/O data, the blue plot shown above, but with the real model data the problem persists. Other windows like hann (which would be more suitable) also leads to a pole-zero simplification after tfest, with mathematical model I/O data too.
Increasing the number of zeros-poles from 2-4 to 6-8 tfest gives me the correct transfer functions, but I absolutely need the model with 2 zeros and 4 poles for a correct dynamic of the system to control by using the identification of its FRF.
To load I/O data see attachment .mat file
The code is the following:
% Upload I/O data
load ‘test_identificazione_chirp_tauGain_0,1.mat’

% Data from real model
th1abs = theta1_rad_MEAS.signals.values;
th2rel = theta2_rad_MEAS.signals.values;
th2abs = th1abs+th2rel;
tauIn = tauInput.signals.values;

nfs = 4096*4; % Number of samples for FFT
Fs = 1000; % Sampling frequency

% Transfer functions estimate from input 1 (tauIn) to outputs 1 and 2 (th1abs and th2abs)
[H_11, f_H_11] = tfestimate(squeeze(tauIn), squeeze(th1abs), rectwin(floor(size(squeeze(tauIn), 1))), 0, nfs*4, Fs);
[H_21, f_H_21] = tfestimate(squeeze(tauIn), squeeze(th2abs), rectwin(floor(size(squeeze(tauIn), 1))), 0, nfs*4, Fs);
% Try also hann window with overlap to get a better curve: hann(floor(size(squeeze(tauIn), 1) / 15)), 3000

% Estimated tf smoothing, by movmean with [x,x] values
H_11 = movmean(H_11,[2,2]);
H_21 = movmean(H_21,[2,2]);

% Estimated tf plot
figure(1)
subplot(1,2,1);
semilogx(f_H_11, mag2db(abs(H_11)), ‘b’, ‘LineWidth’, 0.7);
hold on;
subplot(1,2,2);
semilogx(f_H_21, mag2db(abs(H_21)), ‘b’, ‘LineWidth’, 0.7);
hold on;

% Frequency range to keep only relevant part of tf by tfestimate
fmin = 0.1; % min freq (Hz)
fmax = 15; % max freq (Hz)
idx_11 = (f_H_11 >= fmin) & (f_H_11 <= fmax); % frequency index in the selected range
idx_21 = (f_H_21 >= fmin) & (f_H_21 <= fmax); % frequency index in the selected range

H_11_filtered = H_11(idx_11);
f_11_filtered = f_H_11(idx_11); % corresponding frequencies
H_21_filtered = H_21(idx_21);
f_21_filtered = f_H_21(idx_21); % corresponding frequencies

% rad/s conversion to use in `idfrd`
f_11_rad = f_11_filtered * 2 * pi;
f_21_rad = f_21_filtered * 2 * pi;

% Filtered tf plot
subplot(1,2,1);
semilogx(f_11_filtered, mag2db(abs(H_11_filtered)), ‘r’, ‘LineWidth’, 0.7);
subplot(1,2,2);
semilogx(f_21_filtered, mag2db(abs(H_21_filtered)), ‘r’, ‘LineWidth’, 0.7);

% Creating an identified model based on frequency data
H_11_idfrd = idfrd(H_11_filtered, f_11_rad, ‘Ts’, 0); % continuous time
H_21_idfrd = idfrd(H_21_filtered, f_21_rad, ‘Ts’, 0); % continuous time

% tf estimation in "tf" variable type
np = 4; % system pole number
nz = 2; % system zero number

sys_est_11 = tfest(H_11_idfrd, np, nz);
sys_est_21 = tfest(H_21_idfrd, np, nz);

sys_est = [sys_est_11; sys_est_21]; % [2×1] dimension

% Estimated FRF plot
figure(2)
P = bodeoptions;
P.FreqUnits = ‘Hz’; % Frequency axys in Hz
bodeplot(sys_est, P);
grid on;
hold on;

% Print estimated tf in the Command Window
sys_est

Any help? Thank you in advance!I am using tfestimate and tfest to experimentally derive the frequency response function of a 2 dof system with 1 input and 2 output.
The problem is that tfest seems to cancel or does not recognize a complex pole pair (resonance) near a complex zero pair (antiresonance) around 6 Hz in the tf from input 1 to output 1 and consequently simplifies the frequency response of the system (see image, correct plot in blue vs simplified plot in red):

I tried different types of windowing in tfestimate but the only one that allows me to obtain the correct frequency response after tfest is rectwin without overlap: this works only with mathematical model I/O data, the blue plot shown above, but with the real model data the problem persists. Other windows like hann (which would be more suitable) also leads to a pole-zero simplification after tfest, with mathematical model I/O data too.
Increasing the number of zeros-poles from 2-4 to 6-8 tfest gives me the correct transfer functions, but I absolutely need the model with 2 zeros and 4 poles for a correct dynamic of the system to control by using the identification of its FRF.
To load I/O data see attachment .mat file
The code is the following:
% Upload I/O data
load ‘test_identificazione_chirp_tauGain_0,1.mat’

% Data from real model
th1abs = theta1_rad_MEAS.signals.values;
th2rel = theta2_rad_MEAS.signals.values;
th2abs = th1abs+th2rel;
tauIn = tauInput.signals.values;

nfs = 4096*4; % Number of samples for FFT
Fs = 1000; % Sampling frequency

% Transfer functions estimate from input 1 (tauIn) to outputs 1 and 2 (th1abs and th2abs)
[H_11, f_H_11] = tfestimate(squeeze(tauIn), squeeze(th1abs), rectwin(floor(size(squeeze(tauIn), 1))), 0, nfs*4, Fs);
[H_21, f_H_21] = tfestimate(squeeze(tauIn), squeeze(th2abs), rectwin(floor(size(squeeze(tauIn), 1))), 0, nfs*4, Fs);
% Try also hann window with overlap to get a better curve: hann(floor(size(squeeze(tauIn), 1) / 15)), 3000

% Estimated tf smoothing, by movmean with [x,x] values
H_11 = movmean(H_11,[2,2]);
H_21 = movmean(H_21,[2,2]);

% Estimated tf plot
figure(1)
subplot(1,2,1);
semilogx(f_H_11, mag2db(abs(H_11)), ‘b’, ‘LineWidth’, 0.7);
hold on;
subplot(1,2,2);
semilogx(f_H_21, mag2db(abs(H_21)), ‘b’, ‘LineWidth’, 0.7);
hold on;

% Frequency range to keep only relevant part of tf by tfestimate
fmin = 0.1; % min freq (Hz)
fmax = 15; % max freq (Hz)
idx_11 = (f_H_11 >= fmin) & (f_H_11 <= fmax); % frequency index in the selected range
idx_21 = (f_H_21 >= fmin) & (f_H_21 <= fmax); % frequency index in the selected range

H_11_filtered = H_11(idx_11);
f_11_filtered = f_H_11(idx_11); % corresponding frequencies
H_21_filtered = H_21(idx_21);
f_21_filtered = f_H_21(idx_21); % corresponding frequencies

% rad/s conversion to use in `idfrd`
f_11_rad = f_11_filtered * 2 * pi;
f_21_rad = f_21_filtered * 2 * pi;

% Filtered tf plot
subplot(1,2,1);
semilogx(f_11_filtered, mag2db(abs(H_11_filtered)), ‘r’, ‘LineWidth’, 0.7);
subplot(1,2,2);
semilogx(f_21_filtered, mag2db(abs(H_21_filtered)), ‘r’, ‘LineWidth’, 0.7);

% Creating an identified model based on frequency data
H_11_idfrd = idfrd(H_11_filtered, f_11_rad, ‘Ts’, 0); % continuous time
H_21_idfrd = idfrd(H_21_filtered, f_21_rad, ‘Ts’, 0); % continuous time

% tf estimation in "tf" variable type
np = 4; % system pole number
nz = 2; % system zero number

sys_est_11 = tfest(H_11_idfrd, np, nz);
sys_est_21 = tfest(H_21_idfrd, np, nz);

sys_est = [sys_est_11; sys_est_21]; % [2×1] dimension

% Estimated FRF plot
figure(2)
P = bodeoptions;
P.FreqUnits = ‘Hz’; % Frequency axys in Hz
bodeplot(sys_est, P);
grid on;
hold on;

% Print estimated tf in the Command Window
sys_est

Any help? Thank you in advance! I am using tfestimate and tfest to experimentally derive the frequency response function of a 2 dof system with 1 input and 2 output.
The problem is that tfest seems to cancel or does not recognize a complex pole pair (resonance) near a complex zero pair (antiresonance) around 6 Hz in the tf from input 1 to output 1 and consequently simplifies the frequency response of the system (see image, correct plot in blue vs simplified plot in red):

I tried different types of windowing in tfestimate but the only one that allows me to obtain the correct frequency response after tfest is rectwin without overlap: this works only with mathematical model I/O data, the blue plot shown above, but with the real model data the problem persists. Other windows like hann (which would be more suitable) also leads to a pole-zero simplification after tfest, with mathematical model I/O data too.
Increasing the number of zeros-poles from 2-4 to 6-8 tfest gives me the correct transfer functions, but I absolutely need the model with 2 zeros and 4 poles for a correct dynamic of the system to control by using the identification of its FRF.
To load I/O data see attachment .mat file
The code is the following:
% Upload I/O data
load ‘test_identificazione_chirp_tauGain_0,1.mat’

% Data from real model
th1abs = theta1_rad_MEAS.signals.values;
th2rel = theta2_rad_MEAS.signals.values;
th2abs = th1abs+th2rel;
tauIn = tauInput.signals.values;

nfs = 4096*4; % Number of samples for FFT
Fs = 1000; % Sampling frequency

% Transfer functions estimate from input 1 (tauIn) to outputs 1 and 2 (th1abs and th2abs)
[H_11, f_H_11] = tfestimate(squeeze(tauIn), squeeze(th1abs), rectwin(floor(size(squeeze(tauIn), 1))), 0, nfs*4, Fs);
[H_21, f_H_21] = tfestimate(squeeze(tauIn), squeeze(th2abs), rectwin(floor(size(squeeze(tauIn), 1))), 0, nfs*4, Fs);
% Try also hann window with overlap to get a better curve: hann(floor(size(squeeze(tauIn), 1) / 15)), 3000

% Estimated tf smoothing, by movmean with [x,x] values
H_11 = movmean(H_11,[2,2]);
H_21 = movmean(H_21,[2,2]);

% Estimated tf plot
figure(1)
subplot(1,2,1);
semilogx(f_H_11, mag2db(abs(H_11)), ‘b’, ‘LineWidth’, 0.7);
hold on;
subplot(1,2,2);
semilogx(f_H_21, mag2db(abs(H_21)), ‘b’, ‘LineWidth’, 0.7);
hold on;

% Frequency range to keep only relevant part of tf by tfestimate
fmin = 0.1; % min freq (Hz)
fmax = 15; % max freq (Hz)
idx_11 = (f_H_11 >= fmin) & (f_H_11 <= fmax); % frequency index in the selected range
idx_21 = (f_H_21 >= fmin) & (f_H_21 <= fmax); % frequency index in the selected range

H_11_filtered = H_11(idx_11);
f_11_filtered = f_H_11(idx_11); % corresponding frequencies
H_21_filtered = H_21(idx_21);
f_21_filtered = f_H_21(idx_21); % corresponding frequencies

% rad/s conversion to use in `idfrd`
f_11_rad = f_11_filtered * 2 * pi;
f_21_rad = f_21_filtered * 2 * pi;

% Filtered tf plot
subplot(1,2,1);
semilogx(f_11_filtered, mag2db(abs(H_11_filtered)), ‘r’, ‘LineWidth’, 0.7);
subplot(1,2,2);
semilogx(f_21_filtered, mag2db(abs(H_21_filtered)), ‘r’, ‘LineWidth’, 0.7);

% Creating an identified model based on frequency data
H_11_idfrd = idfrd(H_11_filtered, f_11_rad, ‘Ts’, 0); % continuous time
H_21_idfrd = idfrd(H_21_filtered, f_21_rad, ‘Ts’, 0); % continuous time

% tf estimation in "tf" variable type
np = 4; % system pole number
nz = 2; % system zero number

sys_est_11 = tfest(H_11_idfrd, np, nz);
sys_est_21 = tfest(H_21_idfrd, np, nz);

sys_est = [sys_est_11; sys_est_21]; % [2×1] dimension

% Estimated FRF plot
figure(2)
P = bodeoptions;
P.FreqUnits = ‘Hz’; % Frequency axys in Hz
bodeplot(sys_est, P);
grid on;
hold on;

% Print estimated tf in the Command Window
sys_est

Any help? Thank you in advance! tfest, system identification, frequency response MATLAB Answers — New Questions

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I have completed my direct power control for my wind energy conversion system, this is going to be implemented in hardware for this i want to generated Verilog or VHDL code of Direct Power Control to produce pulses to the Back to back converter, how to do thisI have completed my direct power control for my wind energy conversion system, this is going to be implemented in hardware for this i want to generated Verilog or VHDL code of Direct Power Control to produce pulses to the Back to back converter, how to do this I have completed my direct power control for my wind energy conversion system, this is going to be implemented in hardware for this i want to generated Verilog or VHDL code of Direct Power Control to produce pulses to the Back to back converter, how to do this fpga, verilog, vhdl MATLAB Answers — New Questions

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% Value changing function: STARTKnob_2
function STARTKnob_2ValueChanging(app, event)
arguments
app
event.Value(1,1) {mustBeNumeric}=0
end
app.StartYear = event.Value;
if app.StartYear>app.StopYear-1 %checks the increasing-xrule
app.StopYear=min([app.StartYear+1,app.STOPKnob.Limits(2)]);
app.STOPKnob.Value=app.StopYear; %rotate the stop knob
app.StartYear=app.StopYear-1;
app.STARTKnob_2.Value=app.StartYear;
end
app.plotData();

end
% Value changing function: STOPKnob
function STOPKnobValueChanging(app, event)
arguments
app
event.Value(1,1) {mustBeNumeric}=0
end
app.StopYear = event.Value;
if app.StopYear<app.StartYear+1
app.StartYear=max([app.StopYear-1 app.STARTKnob_2.Limits(1)]);
app.STARTKnob_2.Value=app.StartYear;
app.StopYear=app.StartYear+1;
app.STOPKnob.Value=app.StopYear;
end
app.plotData();% Value changing function: STARTKnob_2
function STARTKnob_2ValueChanging(app, event)
arguments
app
event.Value(1,1) {mustBeNumeric}=0
end
app.StartYear = event.Value;
if app.StartYear>app.StopYear-1 %checks the increasing-xrule
app.StopYear=min([app.StartYear+1,app.STOPKnob.Limits(2)]);
app.STOPKnob.Value=app.StopYear; %rotate the stop knob
app.StartYear=app.StopYear-1;
app.STARTKnob_2.Value=app.StartYear;
end
app.plotData();

end
% Value changing function: STOPKnob
function STOPKnobValueChanging(app, event)
arguments
app
event.Value(1,1) {mustBeNumeric}=0
end
app.StopYear = event.Value;
if app.StopYear<app.StartYear+1
app.StartYear=max([app.StopYear-1 app.STARTKnob_2.Limits(1)]);
app.STARTKnob_2.Value=app.StartYear;
app.StopYear=app.StartYear+1;
app.STOPKnob.Value=app.StopYear;
end
app.plotData(); % Value changing function: STARTKnob_2
function STARTKnob_2ValueChanging(app, event)
arguments
app
event.Value(1,1) {mustBeNumeric}=0
end
app.StartYear = event.Value;
if app.StartYear>app.StopYear-1 %checks the increasing-xrule
app.StopYear=min([app.StartYear+1,app.STOPKnob.Limits(2)]);
app.STOPKnob.Value=app.StopYear; %rotate the stop knob
app.StartYear=app.StopYear-1;
app.STARTKnob_2.Value=app.StartYear;
end
app.plotData();

end
% Value changing function: STOPKnob
function STOPKnobValueChanging(app, event)
arguments
app
event.Value(1,1) {mustBeNumeric}=0
end
app.StopYear = event.Value;
if app.StopYear<app.StartYear+1
app.StartYear=max([app.StopYear-1 app.STARTKnob_2.Limits(1)]);
app.STARTKnob_2.Value=app.StartYear;
app.StopYear=app.StartYear+1;
app.STOPKnob.Value=app.StopYear;
end
app.plotData(); xlim error in app designer MATLAB Answers — New Questions

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I need to simulate an article using MATLAB based on customer satisfaction using the Monte Carlo method
using by simulink or matlab code.
I know that this article doesn’t have an accurate circuit or block diagram and it should be optimized from previous sources. Assuming that a part is disconnected or connected randomly
You can see the original article here
here

anyone can help me on this ?!
best regards.I need to simulate an article using MATLAB based on customer satisfaction using the Monte Carlo method
using by simulink or matlab code.
I know that this article doesn’t have an accurate circuit or block diagram and it should be optimized from previous sources. Assuming that a part is disconnected or connected randomly
You can see the original article here
here

anyone can help me on this ?!
best regards. I need to simulate an article using MATLAB based on customer satisfaction using the Monte Carlo method
using by simulink or matlab code.
I know that this article doesn’t have an accurate circuit or block diagram and it should be optimized from previous sources. Assuming that a part is disconnected or connected randomly
You can see the original article here
here

anyone can help me on this ?!
best regards. simulink, monte carlo, active distribution networks, customer satisfaction MATLAB Answers — New Questions

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My aim is to plot 2d the function ph (xx,yy) in the plane (xx,yy).
x-axis is xx, y-axis is yy, and the function ph(xx,yy)
=====================================================
q=1.0;
v=(1.0-sqrt(1.0+4.0*q))/(2.0*q);
p=4*atan(1.0);
for j=0:100
a=0.0+j.*p/100;
fori=0:100
b=0.0+i.*p/100;
x=cos(a);
y=sin(b);
z=x.*x+y.*y;
if z<1.0
xx=x;
yy=y;
zz=z;
D=1.0+q.*(v.*v)*(zz.*zz);
N=1.0-v.*zz-q.*(v.*v)*(zz.*zz);
ph=D/N;

hold on
plot(xx,yy,ph)
end
end
end
=====================================My aim is to plot 2d the function ph (xx,yy) in the plane (xx,yy).
x-axis is xx, y-axis is yy, and the function ph(xx,yy)
=====================================================
q=1.0;
v=(1.0-sqrt(1.0+4.0*q))/(2.0*q);
p=4*atan(1.0);
for j=0:100
a=0.0+j.*p/100;
fori=0:100
b=0.0+i.*p/100;
x=cos(a);
y=sin(b);
z=x.*x+y.*y;
if z<1.0
xx=x;
yy=y;
zz=z;
D=1.0+q.*(v.*v)*(zz.*zz);
N=1.0-v.*zz-q.*(v.*v)*(zz.*zz);
ph=D/N;

hold on
plot(xx,yy,ph)
end
end
end
===================================== My aim is to plot 2d the function ph (xx,yy) in the plane (xx,yy).
x-axis is xx, y-axis is yy, and the function ph(xx,yy)
=====================================================
q=1.0;
v=(1.0-sqrt(1.0+4.0*q))/(2.0*q);
p=4*atan(1.0);
for j=0:100
a=0.0+j.*p/100;
fori=0:100
b=0.0+i.*p/100;
x=cos(a);
y=sin(b);
z=x.*x+y.*y;
if z<1.0
xx=x;
yy=y;
zz=z;
D=1.0+q.*(v.*v)*(zz.*zz);
N=1.0-v.*zz-q.*(v.*v)*(zz.*zz);
ph=D/N;

hold on
plot(xx,yy,ph)
end
end
end
===================================== error in plot MATLAB Answers — New Questions

​

I created 7 waypoints using inverse kinematics designer app and exported the configuration into a matrix (7×6 matrix) containg xyz position and respective euler angles. From this matrix i input only the xyz position data(3×7 matrix) as waypoints to a polynomial trajectory block with time. But when i run the simulink an error is showing up, saying that polynomial trajectory output should be 4×4 homogeneous matrix inorder to connect to the inverse kinematics block input (pose). How could I resolve this issueI created 7 waypoints using inverse kinematics designer app and exported the configuration into a matrix (7×6 matrix) containg xyz position and respective euler angles. From this matrix i input only the xyz position data(3×7 matrix) as waypoints to a polynomial trajectory block with time. But when i run the simulink an error is showing up, saying that polynomial trajectory output should be 4×4 homogeneous matrix inorder to connect to the inverse kinematics block input (pose). How could I resolve this issue I created 7 waypoints using inverse kinematics designer app and exported the configuration into a matrix (7×6 matrix) containg xyz position and respective euler angles. From this matrix i input only the xyz position data(3×7 matrix) as waypoints to a polynomial trajectory block with time. But when i run the simulink an error is showing up, saying that polynomial trajectory output should be 4×4 homogeneous matrix inorder to connect to the inverse kinematics block input (pose). How could I resolve this issue polynomial trajectory, inverse kinematics, simulink MATLAB Answers — New Questions

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Hi y’all

I am trying to save multiple tables I’ve edited in matlab, but I get this error:

Error using save

Too many output arguments.

I’m having trouble figuring out how to fix this. The code it’s probably referring to is:

function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

from the full code here:

% Pull data from files
function [output] = each_day_table(month, day)
fileNames = sprintf(‘D2024%02d%02d*.csv’, month, day) ;
datastore_result = datastore(fileNames) ;
original_data = readall(datastore_result) ;
output = original_data ;
end

% Find Where Equivilant Diamiter > 150 And Remove (ICB measures b/w 2 – 150 μm)
function [output] = remove_150(original)
new = original ;
Greater150 = original.EquivDiameter > 150 ;
new(Greater150, 🙂 = [] ;
output = new ;
end

% To Save Each Newly Made File Onto Computer
function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

function [output] = do_everything(year, month, day)
original_data = each_day_table(month, day) ;
data_lessthan_150 = remove_150( original_data) ;
save_to_folder = save_on_Computer(data_without_zeroes, month, day) ;
output = save_on_Computer ; % change data_with_surface_area with data_with_ratio
end

OL_06_03 = do_everything (2024,06,03) ;
OL_06_04 = do_everything (2024,06,04) ;
OL_06_05 = do_everything (2024,06,05) ;
OL_06_06 = do_everything (2024,06,06) ;Hi y’all

I am trying to save multiple tables I’ve edited in matlab, but I get this error:

Error using save

Too many output arguments.

I’m having trouble figuring out how to fix this. The code it’s probably referring to is:

function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

from the full code here:

% Pull data from files
function [output] = each_day_table(month, day)
fileNames = sprintf(‘D2024%02d%02d*.csv’, month, day) ;
datastore_result = datastore(fileNames) ;
original_data = readall(datastore_result) ;
output = original_data ;
end

% Find Where Equivilant Diamiter > 150 And Remove (ICB measures b/w 2 – 150 μm)
function [output] = remove_150(original)
new = original ;
Greater150 = original.EquivDiameter > 150 ;
new(Greater150, 🙂 = [] ;
output = new ;
end

% To Save Each Newly Made File Onto Computer
function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

function [output] = do_everything(year, month, day)
original_data = each_day_table(month, day) ;
data_lessthan_150 = remove_150( original_data) ;
save_to_folder = save_on_Computer(data_without_zeroes, month, day) ;
output = save_on_Computer ; % change data_with_surface_area with data_with_ratio
end

OL_06_03 = do_everything (2024,06,03) ;
OL_06_04 = do_everything (2024,06,04) ;
OL_06_05 = do_everything (2024,06,05) ;
OL_06_06 = do_everything (2024,06,06) ; Hi y’all

I am trying to save multiple tables I’ve edited in matlab, but I get this error:

Error using save

Too many output arguments.

I’m having trouble figuring out how to fix this. The code it’s probably referring to is:

function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

from the full code here:

% Pull data from files
function [output] = each_day_table(month, day)
fileNames = sprintf(‘D2024%02d%02d*.csv’, month, day) ;
datastore_result = datastore(fileNames) ;
original_data = readall(datastore_result) ;
output = original_data ;
end

% Find Where Equivilant Diamiter > 150 And Remove (ICB measures b/w 2 – 150 μm)
function [output] = remove_150(original)
new = original ;
Greater150 = original.EquivDiameter > 150 ;
new(Greater150, 🙂 = [] ;
output = new ;
end

% To Save Each Newly Made File Onto Computer
function [output] = save_on_Computer(ReferenceData, month, day)
file_name = "OL_"+month+"_"+day+".csv" ;
savefile = save(file_name) ;
output = savefile ;
end

function [output] = do_everything(year, month, day)
original_data = each_day_table(month, day) ;
data_lessthan_150 = remove_150( original_data) ;
save_to_folder = save_on_Computer(data_without_zeroes, month, day) ;
output = save_on_Computer ; % change data_with_surface_area with data_with_ratio
end

OL_06_03 = do_everything (2024,06,03) ;
OL_06_04 = do_everything (2024,06,04) ;
OL_06_05 = do_everything (2024,06,05) ;
OL_06_06 = do_everything (2024,06,06) ; save, error MATLAB Answers — New Questions

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I am trying to compare my c code output with matlab output. My results are matching up to 18th decimal points. When both matlab and c compiler uses ieee754 floating point format. Then why am I observing the difference. As matlab by default uses double precision, my c code is also using double data type.I am trying to compare my c code output with matlab output. My results are matching up to 18th decimal points. When both matlab and c compiler uses ieee754 floating point format. Then why am I observing the difference. As matlab by default uses double precision, my c code is also using double data type. I am trying to compare my c code output with matlab output. My results are matching up to 18th decimal points. When both matlab and c compiler uses ieee754 floating point format. Then why am I observing the difference. As matlab by default uses double precision, my c code is also using double data type. ieee754, precision, accuracy MATLAB Answers — New Questions

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I recently notice that there is an extreme slowdown in my version of Matlab while computing an SVD when the size of the matrix crosses some threshold. I came up with the following example that demonstrates my issue:
N = [10000 11000 12000 13000];
for i = 1:4
A = randn(N(i),3);
tic;
[U,S,V] = svd(A,0);
toc;
end
When I run this in Matlab R2024b (macOS Apple silicon), the output is:
Elapsed time is 0.000396 seconds.
Elapsed time is 0.000275 seconds.
Elapsed time is 0.000264 seconds.
Elapsed time is 0.083150 seconds.
Of course the exact numbers vary trial to trial, but the speed for the last run (where N = 13000) is consistently orders of magnitude slower.
When I run this same code on Matlab R2024b (Intel processor) on the same computer, this slow down does not happen. I was able to replicate this issue across two different Macs (one with M1 and another with M3) and different versions of Matlab (going back to R2023b).
Any idea why this might be happening in the silicon version?
Edit: I’m running macOS 15.1.1I recently notice that there is an extreme slowdown in my version of Matlab while computing an SVD when the size of the matrix crosses some threshold. I came up with the following example that demonstrates my issue:
N = [10000 11000 12000 13000];
for i = 1:4
A = randn(N(i),3);
tic;
[U,S,V] = svd(A,0);
toc;
end
When I run this in Matlab R2024b (macOS Apple silicon), the output is:
Elapsed time is 0.000396 seconds.
Elapsed time is 0.000275 seconds.
Elapsed time is 0.000264 seconds.
Elapsed time is 0.083150 seconds.
Of course the exact numbers vary trial to trial, but the speed for the last run (where N = 13000) is consistently orders of magnitude slower.
When I run this same code on Matlab R2024b (Intel processor) on the same computer, this slow down does not happen. I was able to replicate this issue across two different Macs (one with M1 and another with M3) and different versions of Matlab (going back to R2023b).
Any idea why this might be happening in the silicon version?
Edit: I’m running macOS 15.1.1 I recently notice that there is an extreme slowdown in my version of Matlab while computing an SVD when the size of the matrix crosses some threshold. I came up with the following example that demonstrates my issue:
N = [10000 11000 12000 13000];
for i = 1:4
A = randn(N(i),3);
tic;
[U,S,V] = svd(A,0);
toc;
end
When I run this in Matlab R2024b (macOS Apple silicon), the output is:
Elapsed time is 0.000396 seconds.
Elapsed time is 0.000275 seconds.
Elapsed time is 0.000264 seconds.
Elapsed time is 0.083150 seconds.
Of course the exact numbers vary trial to trial, but the speed for the last run (where N = 13000) is consistently orders of magnitude slower.
When I run this same code on Matlab R2024b (Intel processor) on the same computer, this slow down does not happen. I was able to replicate this issue across two different Macs (one with M1 and another with M3) and different versions of Matlab (going back to R2023b).
Any idea why this might be happening in the silicon version?
Edit: I’m running macOS 15.1.1 svd, mac, memory, speed MATLAB Answers — New Questions

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