I’m part of a group trying to speed up our code. We send out inputs to the simulink model with
simIn(i) = simIn(i).setVariable(‘Amplitude’, pops(i, 1));
simIn(i) = simIn(i).setVariable(‘Freq’, pops(i, 2));
simIn(i) = simIn(i).setVariable(‘PW’, pops(i, 3));
simIn(i) = simIn(i).setVariable(‘Ramp_Toggle’, pops(i, 4));
simIn(i) = simIn(i).setVariable(‘Ramp_Slope’, pops(i, 5));
simIn(i) = simIn(i).setVariable(‘Ramp_Flat’, pops(i, 6));
simIn(i) = simIn(i).setVariable(‘Intraburst_Toggle’, pops(i, 7));
simIn(i) = simIn(i).setVariable(‘Intraburst_Period’, pops(i, 8));
simIn(i) = simIn(i).setVariable(‘Intraburst_Packet’, pops(i, 9));
simIn(i) = simIn(i).setVariable(‘Phasic’, pops(i, 10));

and take back outputs with
simOut = parsim(simIn, ‘ShowSimulationManager’, ‘off’);

Using tic toc it seems that the simulink model takes about 90% of our runtime. Is there a way to run this on the GPU or other ways to speed it up? Any help will be greatly appreciated.
Thank youI’m part of a group trying to speed up our code. We send out inputs to the simulink model with
simIn(i) = simIn(i).setVariable(‘Amplitude’, pops(i, 1));
simIn(i) = simIn(i).setVariable(‘Freq’, pops(i, 2));
simIn(i) = simIn(i).setVariable(‘PW’, pops(i, 3));
simIn(i) = simIn(i).setVariable(‘Ramp_Toggle’, pops(i, 4));
simIn(i) = simIn(i).setVariable(‘Ramp_Slope’, pops(i, 5));
simIn(i) = simIn(i).setVariable(‘Ramp_Flat’, pops(i, 6));
simIn(i) = simIn(i).setVariable(‘Intraburst_Toggle’, pops(i, 7));
simIn(i) = simIn(i).setVariable(‘Intraburst_Period’, pops(i, 8));
simIn(i) = simIn(i).setVariable(‘Intraburst_Packet’, pops(i, 9));
simIn(i) = simIn(i).setVariable(‘Phasic’, pops(i, 10));

and take back outputs with
simOut = parsim(simIn, ‘ShowSimulationManager’, ‘off’);

Using tic toc it seems that the simulink model takes about 90% of our runtime. Is there a way to run this on the GPU or other ways to speed it up? Any help will be greatly appreciated.
Thank you I’m part of a group trying to speed up our code. We send out inputs to the simulink model with
simIn(i) = simIn(i).setVariable(‘Amplitude’, pops(i, 1));
simIn(i) = simIn(i).setVariable(‘Freq’, pops(i, 2));
simIn(i) = simIn(i).setVariable(‘PW’, pops(i, 3));
simIn(i) = simIn(i).setVariable(‘Ramp_Toggle’, pops(i, 4));
simIn(i) = simIn(i).setVariable(‘Ramp_Slope’, pops(i, 5));
simIn(i) = simIn(i).setVariable(‘Ramp_Flat’, pops(i, 6));
simIn(i) = simIn(i).setVariable(‘Intraburst_Toggle’, pops(i, 7));
simIn(i) = simIn(i).setVariable(‘Intraburst_Period’, pops(i, 8));
simIn(i) = simIn(i).setVariable(‘Intraburst_Packet’, pops(i, 9));
simIn(i) = simIn(i).setVariable(‘Phasic’, pops(i, 10));

and take back outputs with
simOut = parsim(simIn, ‘ShowSimulationManager’, ‘off’);

Using tic toc it seems that the simulink model takes about 90% of our runtime. Is there a way to run this on the GPU or other ways to speed it up? Any help will be greatly appreciated.
Thank you simulink, gpu MATLAB Answers — New Questions

​

I am working on the temperature control of an electric oven. I had the idea of obtaining the temperature-time curve when the oven is fed with the alternating wave at maximum power (220 V 60 Hz). According to the Ziegler-Nichols tuning method, my curve would be the response of the system (oven) to a "unit step". But here I have a doubt. To control the power, I am using a BT136 triac, which receives a 5 volt PWM wave at its gate. When this 5 volt PWM signal is at 100% duty cycle, could I consider it as a unit step?
Thanks in advance for your attentionI am working on the temperature control of an electric oven. I had the idea of obtaining the temperature-time curve when the oven is fed with the alternating wave at maximum power (220 V 60 Hz). According to the Ziegler-Nichols tuning method, my curve would be the response of the system (oven) to a "unit step". But here I have a doubt. To control the power, I am using a BT136 triac, which receives a 5 volt PWM wave at its gate. When this 5 volt PWM signal is at 100% duty cycle, could I consider it as a unit step?
Thanks in advance for your attention I am working on the temperature control of an electric oven. I had the idea of obtaining the temperature-time curve when the oven is fed with the alternating wave at maximum power (220 V 60 Hz). According to the Ziegler-Nichols tuning method, my curve would be the response of the system (oven) to a "unit step". But here I have a doubt. To control the power, I am using a BT136 triac, which receives a 5 volt PWM wave at its gate. When this 5 volt PWM signal is at 100% duty cycle, could I consider it as a unit step?
Thanks in advance for your attention control, unit step, pwm MATLAB Answers — New Questions

​

Using Simulink for circuit simulation of a PMSM system, with a focus on MOSFET losses in the inverter circuit.
MOSFET (Ideal, Switching), PS signal controlled. The thermal port is connected to a 25°C constant temperature source.
According to the simulation results, three-phase currents, motor torque and speed are consistent with the expected values.
Q1. The MOSFET switching losses exist only during positive current, and they disappear during negative current. Should switching losses exist only during positive current?
Q2. The fixed switch loss value set in the “MOSFET (Ideal, Switching)” module is 3e-5 J. However, the simulation results show fluctuations around this value with respect to the Id current. What is the specific relationship between the fixed switch loss value and the fluctuating simulation resultsUsing Simulink for circuit simulation of a PMSM system, with a focus on MOSFET losses in the inverter circuit.
MOSFET (Ideal, Switching), PS signal controlled. The thermal port is connected to a 25°C constant temperature source.
According to the simulation results, three-phase currents, motor torque and speed are consistent with the expected values.
Q1. The MOSFET switching losses exist only during positive current, and they disappear during negative current. Should switching losses exist only during positive current?
Q2. The fixed switch loss value set in the “MOSFET (Ideal, Switching)” module is 3e-5 J. However, the simulation results show fluctuations around this value with respect to the Id current. What is the specific relationship between the fixed switch loss value and the fluctuating simulation results Using Simulink for circuit simulation of a PMSM system, with a focus on MOSFET losses in the inverter circuit.
MOSFET (Ideal, Switching), PS signal controlled. The thermal port is connected to a 25°C constant temperature source.
According to the simulation results, three-phase currents, motor torque and speed are consistent with the expected values.
Q1. The MOSFET switching losses exist only during positive current, and they disappear during negative current. Should switching losses exist only during positive current?
Q2. The fixed switch loss value set in the “MOSFET (Ideal, Switching)” module is 3e-5 J. However, the simulation results show fluctuations around this value with respect to the Id current. What is the specific relationship between the fixed switch loss value and the fluctuating simulation results simscape, simulink, mosfet, pmsm MATLAB Answers — New Questions

​

Hi all,

In Simulink, I’m using String Signals to represent internal states for my systems. For example:

mySignal is a string signal, and not a numeric one. I log it into the base workspace inside the simout variable, and I also send it to the Simulation Data Inspector.

Inside the Simulation Data Inspector, this string signal is neatly displayed, like this :

The string values are displayed as colored patches, evolving with the simulation time.

This visualization method for string signals is great, but I can’t find a way to do something similar in MATLAB. Once my string signal is inside a base workspace variable, I try to plot it just like a regular signal, but it doesn’t work:
>> simout

simout =

struct with fields:

time: [1001×1 double]
signals: [1×1 struct]
blockName: ‘plot_string_signals/To Workspace’

>> simout.signals

ans =

struct with fields:

values: [1001×1 string]
dimensions: 1
label: ‘mySignal’

>> plot(simout.time, simout.signals.values)
Error using plot
Invalid data argument.
So my question is: how to plot a string datatype signal/array in MATLAB? Is there a dedicated plotting function for this use case that I don’t know ? I expected the plot function to provide the same kind of graph as the Simulation Data Inspector, but was disappointed.
(I’m using MATLAB 22b Update 8)

Thanks,
SeiganHi all,

In Simulink, I’m using String Signals to represent internal states for my systems. For example:

mySignal is a string signal, and not a numeric one. I log it into the base workspace inside the simout variable, and I also send it to the Simulation Data Inspector.

Inside the Simulation Data Inspector, this string signal is neatly displayed, like this :

The string values are displayed as colored patches, evolving with the simulation time.

This visualization method for string signals is great, but I can’t find a way to do something similar in MATLAB. Once my string signal is inside a base workspace variable, I try to plot it just like a regular signal, but it doesn’t work:
>> simout

simout =

struct with fields:

time: [1001×1 double]
signals: [1×1 struct]
blockName: ‘plot_string_signals/To Workspace’

>> simout.signals

ans =

struct with fields:

values: [1001×1 string]
dimensions: 1
label: ‘mySignal’

>> plot(simout.time, simout.signals.values)
Error using plot
Invalid data argument.
So my question is: how to plot a string datatype signal/array in MATLAB? Is there a dedicated plotting function for this use case that I don’t know ? I expected the plot function to provide the same kind of graph as the Simulation Data Inspector, but was disappointed.
(I’m using MATLAB 22b Update 8)

Thanks,
Seigan Hi all,

In Simulink, I’m using String Signals to represent internal states for my systems. For example:

mySignal is a string signal, and not a numeric one. I log it into the base workspace inside the simout variable, and I also send it to the Simulation Data Inspector.

Inside the Simulation Data Inspector, this string signal is neatly displayed, like this :

The string values are displayed as colored patches, evolving with the simulation time.

This visualization method for string signals is great, but I can’t find a way to do something similar in MATLAB. Once my string signal is inside a base workspace variable, I try to plot it just like a regular signal, but it doesn’t work:
>> simout

simout =

struct with fields:

time: [1001×1 double]
signals: [1×1 struct]
blockName: ‘plot_string_signals/To Workspace’

>> simout.signals

ans =

struct with fields:

values: [1001×1 string]
dimensions: 1
label: ‘mySignal’

>> plot(simout.time, simout.signals.values)
Error using plot
Invalid data argument.
So my question is: how to plot a string datatype signal/array in MATLAB? Is there a dedicated plotting function for this use case that I don’t know ? I expected the plot function to provide the same kind of graph as the Simulation Data Inspector, but was disappointed.
(I’m using MATLAB 22b Update 8)

Thanks,
Seigan plot, string MATLAB Answers — New Questions

​

Hello,
I wish to create a harness of a simulink state present in a stateflow.I am not getting the option of ‘Test Harness’,when right clicked on state

Also i tried to create a harness of a subsystem present in that simulink state but got the following error

So is there any other way to create test harness of such simulink state or subsystem present in that simulink state?
If not then can you suggest how to test such state by creating test hanress?Hello,
I wish to create a harness of a simulink state present in a stateflow.I am not getting the option of ‘Test Harness’,when right clicked on state

Also i tried to create a harness of a subsystem present in that simulink state but got the following error

So is there any other way to create test harness of such simulink state or subsystem present in that simulink state?
If not then can you suggest how to test such state by creating test hanress? Hello,
I wish to create a harness of a simulink state present in a stateflow.I am not getting the option of ‘Test Harness’,when right clicked on state

Also i tried to create a harness of a subsystem present in that simulink state but got the following error

So is there any other way to create test harness of such simulink state or subsystem present in that simulink state?
If not then can you suggest how to test such state by creating test hanress? simulink, stateflow, simulinkstate, modelinloop, testharness, harness, error, states MATLAB Answers — New Questions

​

I’m doing a simulation using the pmsm library and switching elements. I want to know why 0 comes out in the trq scope. 1000 rpm is inputted at a fixed rate using an internal velocity source, and the three-phase current from the switching is outputted normally. The control part id iq is indicated as 0 100, and the id iq calculated from the three phases is also 0 100. How can I solve the problem that the torque converges to zero.I’m doing a simulation using the pmsm library and switching elements. I want to know why 0 comes out in the trq scope. 1000 rpm is inputted at a fixed rate using an internal velocity source, and the three-phase current from the switching is outputted normally. The control part id iq is indicated as 0 100, and the id iq calculated from the three phases is also 0 100. How can I solve the problem that the torque converges to zero. I’m doing a simulation using the pmsm library and switching elements. I want to know why 0 comes out in the trq scope. 1000 rpm is inputted at a fixed rate using an internal velocity source, and the three-phase current from the switching is outputted normally. The control part id iq is indicated as 0 100, and the id iq calculated from the three phases is also 0 100. How can I solve the problem that the torque converges to zero. pmsm MATLAB Answers — New Questions

​

I am simulating for on grid three-phase inverter using PI and PR(seperately control) using SVPWM, this figure aboce is the SVPWM reference signal, which are completely wrong. Somehow the alpha beta component diverge so much from what is intended. what could be the main problem of this? The carrier frequency is 10kHz, and i cant figure out the amplitude because the values kept on diverging and it doesnt show any steadiness

I also tried to switch my own SVPWM design with matlab’s pwm generator and it worked perfectly, I know using the pwm generator is the easier choice, but i want to implement my own SVPWM approach.

This is the DQ component from the PI controller, just before entering into the PWM block, likewise the alpha beta component is also affected by the divergence.
Below is the intended shape of the reference signal for SVPWM, i obtained this from using pure sine waves as the input for the SVPWM, however for an actually inverter, i must use the vref from the PI controller as the input of SVPWMI am simulating for on grid three-phase inverter using PI and PR(seperately control) using SVPWM, this figure aboce is the SVPWM reference signal, which are completely wrong. Somehow the alpha beta component diverge so much from what is intended. what could be the main problem of this? The carrier frequency is 10kHz, and i cant figure out the amplitude because the values kept on diverging and it doesnt show any steadiness

I also tried to switch my own SVPWM design with matlab’s pwm generator and it worked perfectly, I know using the pwm generator is the easier choice, but i want to implement my own SVPWM approach.

This is the DQ component from the PI controller, just before entering into the PWM block, likewise the alpha beta component is also affected by the divergence.
Below is the intended shape of the reference signal for SVPWM, i obtained this from using pure sine waves as the input for the SVPWM, however for an actually inverter, i must use the vref from the PI controller as the input of SVPWM I am simulating for on grid three-phase inverter using PI and PR(seperately control) using SVPWM, this figure aboce is the SVPWM reference signal, which are completely wrong. Somehow the alpha beta component diverge so much from what is intended. what could be the main problem of this? The carrier frequency is 10kHz, and i cant figure out the amplitude because the values kept on diverging and it doesnt show any steadiness

I also tried to switch my own SVPWM design with matlab’s pwm generator and it worked perfectly, I know using the pwm generator is the easier choice, but i want to implement my own SVPWM approach.

This is the DQ component from the PI controller, just before entering into the PWM block, likewise the alpha beta component is also affected by the divergence.
Below is the intended shape of the reference signal for SVPWM, i obtained this from using pure sine waves as the input for the SVPWM, however for an actually inverter, i must use the vref from the PI controller as the input of SVPWM pwm, pulse width modulation, svpwm, inverter, on-grid inverter, simulink, signal, signal processing MATLAB Answers — New Questions

​

Hello,
I am trying to covert .w to .mat file
and I should to using it in my program
Help me plezzzzzzz :(Hello,
I am trying to covert .w to .mat file
and I should to using it in my program
Help me plezzzzzzz  Hello,
I am trying to covert .w to .mat file
and I should to using it in my program
Help me plezzzzzzz  how can convert .wav file to .mat file ? MATLAB Answers — New Questions

​

I am plottting a simple sst difference plot. My lon data is in the range -180 to 180. I have converted it to 0 to 360. After plotting I am geeting these black patches (attached).
clear
clc

% Define the file paths for WeakTEJYears
WeakTEJYears = {
‘F:SST_MA1979.nc’
‘F:SST_MA1983.nc’
‘F:SST_MA1987.nc’
‘F:SST_MA1997.nc’
‘F:SST_MA2002.nc’
‘F:SST_MA2015.nc’
‘F:SST_MA2023.nc’
};

% Define the file paths for StrongTEJYears
StrongTEJYears = {
‘F:SST_MA1985.nc’
‘F:SST_MA1988.nc’
‘F:SST_MA1999.nc’
‘F:SST_MA2009.nc’
‘F:SST_MA2013.nc’
};

% Initialize variables to store the sum of SST data
sst_sum_weak = 0;
sst_sum_strong = 0;

num_weak_files = length(WeakTEJYears);
num_strong_files = length(StrongTEJYears);

lat_min = -25;
lat_max = 70;
lon_min = 0;
lon_max = 360;

for i = 1:num_weak_files
ncfile = WeakTEJYears{i};

latitude = ncread(ncfile, ‘latitude’);
longitude = ncread(ncfile, ‘longitude’);

longitude(longitude < 0) = longitude(longitude < 0) + 360;

[longitude, sort_idx] = sort(longitude);

sst = ncread(ncfile, ‘sst’);
sst_sorted = sst(sort_idx, :, :); % Re-sort SST data according to longitude sorting

lat_idx = find(latitude >= lat_min & latitude <= lat_max);
lon_idx = find(longitude >= lon_min & longitude <= lon_max);

sst_region = sst_sorted(lon_idx, lat_idx, :);

sst_sum_weak = sst_sum_weak + mean(sst_region, 3); % Mean over time
end

for i = 1:num_strong_files
ncfile = StrongTEJYears{i};

latitude = ncread(ncfile, ‘latitude’);
longitude = ncread(ncfile, ‘longitude’);

longitude(longitude < 0) = longitude(longitude < 0) + 360;

[longitude, sort_idx] = sort(longitude);

sst = ncread(ncfile, ‘sst’);
sst_sorted = sst(sort_idx, :, :);

lat_idx = find(latitude >= lat_min & latitude <= lat_max);
lon_idx = find(longitude >= lon_min & longitude <= lon_max);

sst_region = sst_sorted(lon_idx, lat_idx, :);

sst_sum_strong = sst_sum_strong + mean(sst_region, 3); % Mean over time
end

sst_avg_weak = sst_sum_weak / num_weak_files;
sst_avg_strong = sst_sum_strong / num_strong_files;

sst_diff = sst_avg_strong – sst_avg_weak;

figure;
[lon_mesh, lat_mesh] = meshgrid(longitude(lon_idx), latitude(lat_idx));

contourf(lon_mesh, lat_mesh, sst_diff’, 20, ‘EdgeColor’, ‘none’);
colormap jet;
colorbar;
caxis([-2, 2]);
c.Label.String = ‘Temperature (K)’;
c.Label.FontSize = 18;
c.Label.FontWeight = ‘bold’;

coast = load(‘coastlines’);

coastlon = coast.coastlon;
coastlat = coast.coastlat;
coastlon(coastlon < 0) = coastlon(coastlon < 0) + 360;

hold on;
plot(coastlon, coastlat, ‘k’, ‘LineWidth’, 1.5);

xlim([lon_min, lon_max]);
ylim([lat_min, lat_max]);

title(‘Difference: Strong – Weak TEJ Average SST’, ‘FontSize’, 14);I am plottting a simple sst difference plot. My lon data is in the range -180 to 180. I have converted it to 0 to 360. After plotting I am geeting these black patches (attached).
clear
clc

% Define the file paths for WeakTEJYears
WeakTEJYears = {
‘F:SST_MA1979.nc’
‘F:SST_MA1983.nc’
‘F:SST_MA1987.nc’
‘F:SST_MA1997.nc’
‘F:SST_MA2002.nc’
‘F:SST_MA2015.nc’
‘F:SST_MA2023.nc’
};

% Define the file paths for StrongTEJYears
StrongTEJYears = {
‘F:SST_MA1985.nc’
‘F:SST_MA1988.nc’
‘F:SST_MA1999.nc’
‘F:SST_MA2009.nc’
‘F:SST_MA2013.nc’
};

% Initialize variables to store the sum of SST data
sst_sum_weak = 0;
sst_sum_strong = 0;

num_weak_files = length(WeakTEJYears);
num_strong_files = length(StrongTEJYears);

lat_min = -25;
lat_max = 70;
lon_min = 0;
lon_max = 360;

for i = 1:num_weak_files
ncfile = WeakTEJYears{i};

latitude = ncread(ncfile, ‘latitude’);
longitude = ncread(ncfile, ‘longitude’);

longitude(longitude < 0) = longitude(longitude < 0) + 360;

[longitude, sort_idx] = sort(longitude);

sst = ncread(ncfile, ‘sst’);
sst_sorted = sst(sort_idx, :, :); % Re-sort SST data according to longitude sorting

lat_idx = find(latitude >= lat_min & latitude <= lat_max);
lon_idx = find(longitude >= lon_min & longitude <= lon_max);

sst_region = sst_sorted(lon_idx, lat_idx, :);

sst_sum_weak = sst_sum_weak + mean(sst_region, 3); % Mean over time
end

for i = 1:num_strong_files
ncfile = StrongTEJYears{i};

latitude = ncread(ncfile, ‘latitude’);
longitude = ncread(ncfile, ‘longitude’);

longitude(longitude < 0) = longitude(longitude < 0) + 360;

[longitude, sort_idx] = sort(longitude);

sst = ncread(ncfile, ‘sst’);
sst_sorted = sst(sort_idx, :, :);

lat_idx = find(latitude >= lat_min & latitude <= lat_max);
lon_idx = find(longitude >= lon_min & longitude <= lon_max);

sst_region = sst_sorted(lon_idx, lat_idx, :);

sst_sum_strong = sst_sum_strong + mean(sst_region, 3); % Mean over time
end

sst_avg_weak = sst_sum_weak / num_weak_files;
sst_avg_strong = sst_sum_strong / num_strong_files;

sst_diff = sst_avg_strong – sst_avg_weak;

figure;
[lon_mesh, lat_mesh] = meshgrid(longitude(lon_idx), latitude(lat_idx));

contourf(lon_mesh, lat_mesh, sst_diff’, 20, ‘EdgeColor’, ‘none’);
colormap jet;
colorbar;
caxis([-2, 2]);
c.Label.String = ‘Temperature (K)’;
c.Label.FontSize = 18;
c.Label.FontWeight = ‘bold’;

coast = load(‘coastlines’);

coastlon = coast.coastlon;
coastlat = coast.coastlat;
coastlon(coastlon < 0) = coastlon(coastlon < 0) + 360;

hold on;
plot(coastlon, coastlat, ‘k’, ‘LineWidth’, 1.5);

xlim([lon_min, lon_max]);
ylim([lat_min, lat_max]);

title(‘Difference: Strong – Weak TEJ Average SST’, ‘FontSize’, 14); I am plottting a simple sst difference plot. My lon data is in the range -180 to 180. I have converted it to 0 to 360. After plotting I am geeting these black patches (attached).
clear
clc

% Define the file paths for WeakTEJYears
WeakTEJYears = {
‘F:SST_MA1979.nc’
‘F:SST_MA1983.nc’
‘F:SST_MA1987.nc’
‘F:SST_MA1997.nc’
‘F:SST_MA2002.nc’
‘F:SST_MA2015.nc’
‘F:SST_MA2023.nc’
};

% Define the file paths for StrongTEJYears
StrongTEJYears = {
‘F:SST_MA1985.nc’
‘F:SST_MA1988.nc’
‘F:SST_MA1999.nc’
‘F:SST_MA2009.nc’
‘F:SST_MA2013.nc’
};

% Initialize variables to store the sum of SST data
sst_sum_weak = 0;
sst_sum_strong = 0;

num_weak_files = length(WeakTEJYears);
num_strong_files = length(StrongTEJYears);

lat_min = -25;
lat_max = 70;
lon_min = 0;
lon_max = 360;

for i = 1:num_weak_files
ncfile = WeakTEJYears{i};

latitude = ncread(ncfile, ‘latitude’);
longitude = ncread(ncfile, ‘longitude’);

longitude(longitude < 0) = longitude(longitude < 0) + 360;

[longitude, sort_idx] = sort(longitude);

sst = ncread(ncfile, ‘sst’);
sst_sorted = sst(sort_idx, :, :); % Re-sort SST data according to longitude sorting

lat_idx = find(latitude >= lat_min & latitude <= lat_max);
lon_idx = find(longitude >= lon_min & longitude <= lon_max);

sst_region = sst_sorted(lon_idx, lat_idx, :);

sst_sum_weak = sst_sum_weak + mean(sst_region, 3); % Mean over time
end

for i = 1:num_strong_files
ncfile = StrongTEJYears{i};

latitude = ncread(ncfile, ‘latitude’);
longitude = ncread(ncfile, ‘longitude’);

longitude(longitude < 0) = longitude(longitude < 0) + 360;

[longitude, sort_idx] = sort(longitude);

sst = ncread(ncfile, ‘sst’);
sst_sorted = sst(sort_idx, :, :);

lat_idx = find(latitude >= lat_min & latitude <= lat_max);
lon_idx = find(longitude >= lon_min & longitude <= lon_max);

sst_region = sst_sorted(lon_idx, lat_idx, :);

sst_sum_strong = sst_sum_strong + mean(sst_region, 3); % Mean over time
end

sst_avg_weak = sst_sum_weak / num_weak_files;
sst_avg_strong = sst_sum_strong / num_strong_files;

sst_diff = sst_avg_strong – sst_avg_weak;

figure;
[lon_mesh, lat_mesh] = meshgrid(longitude(lon_idx), latitude(lat_idx));

contourf(lon_mesh, lat_mesh, sst_diff’, 20, ‘EdgeColor’, ‘none’);
colormap jet;
colorbar;
caxis([-2, 2]);
c.Label.String = ‘Temperature (K)’;
c.Label.FontSize = 18;
c.Label.FontWeight = ‘bold’;

coast = load(‘coastlines’);

coastlon = coast.coastlon;
coastlat = coast.coastlat;
coastlon(coastlon < 0) = coastlon(coastlon < 0) + 360;

hold on;
plot(coastlon, coastlat, ‘k’, ‘LineWidth’, 1.5);

xlim([lon_min, lon_max]);
ylim([lat_min, lat_max]);

title(‘Difference: Strong – Weak TEJ Average SST’, ‘FontSize’, 14); spatial map, image analysis, matrix manipulation MATLAB Answers — New Questions

​

Hi, All —
I would like to compute the average curvature of a closed 3D surface for which I know the inside-outside function, F(x,y,z)=0. (FWIW, centered on the origin and symmetric about all three Cartesian axes.)
I have implemented a couple of different approaches for Gaussian and mean curvature using divergence, the Hessian, the first and second fundamental forms, and other approaches of which I have a similarly weak grasp. I am generally able to get things to work for degenrate cases (e.g., a sphere), but if I increase the complexity of my object, I am tending to get answers that imply I am flirting with the precipice of numerical instability (e.g., imaginary results where Re(x)/Im(x)~10^8).
I like the idea of this approach because it "feels clean": (1) use continuous function; (2) apply magic; (3) integrate in spherical coordinates; and (4) profit. However, I am beginning to think that I need to tune out the sires sweetly singing and go for a more practical (i.e., tractable) approach.
My other idea is to start by using the inside-outside function to generate [X,Y,Z] similar to what is used by surf(). I can then use surfature() from the FEX to get the curvatures at every point on my [X,Y,Z] mesh. Finally, I could convert everything to spherical coordinates and integrate the curvature arrays to get average values.
My concern here is that reregularly-spaced [X,Y,Z] does not translate to regularly-spaced [r,theta,phi], so the calculation is somehow pathologically biased. I am also unsure about how to do the actual integration after converting my array to spherical coordinates.
Apologies for such a long text-dense question. I thought about trying to illustrate this better with a couple of code snippets, but the algebra is really messy and not all that informative.
Thanks, in advance.Hi, All —
I would like to compute the average curvature of a closed 3D surface for which I know the inside-outside function, F(x,y,z)=0. (FWIW, centered on the origin and symmetric about all three Cartesian axes.)
I have implemented a couple of different approaches for Gaussian and mean curvature using divergence, the Hessian, the first and second fundamental forms, and other approaches of which I have a similarly weak grasp. I am generally able to get things to work for degenrate cases (e.g., a sphere), but if I increase the complexity of my object, I am tending to get answers that imply I am flirting with the precipice of numerical instability (e.g., imaginary results where Re(x)/Im(x)~10^8).
I like the idea of this approach because it "feels clean": (1) use continuous function; (2) apply magic; (3) integrate in spherical coordinates; and (4) profit. However, I am beginning to think that I need to tune out the sires sweetly singing and go for a more practical (i.e., tractable) approach.
My other idea is to start by using the inside-outside function to generate [X,Y,Z] similar to what is used by surf(). I can then use surfature() from the FEX to get the curvatures at every point on my [X,Y,Z] mesh. Finally, I could convert everything to spherical coordinates and integrate the curvature arrays to get average values.
My concern here is that reregularly-spaced [X,Y,Z] does not translate to regularly-spaced [r,theta,phi], so the calculation is somehow pathologically biased. I am also unsure about how to do the actual integration after converting my array to spherical coordinates.
Apologies for such a long text-dense question. I thought about trying to illustrate this better with a couple of code snippets, but the algebra is really messy and not all that informative.
Thanks, in advance. Hi, All —
I would like to compute the average curvature of a closed 3D surface for which I know the inside-outside function, F(x,y,z)=0. (FWIW, centered on the origin and symmetric about all three Cartesian axes.)
I have implemented a couple of different approaches for Gaussian and mean curvature using divergence, the Hessian, the first and second fundamental forms, and other approaches of which I have a similarly weak grasp. I am generally able to get things to work for degenrate cases (e.g., a sphere), but if I increase the complexity of my object, I am tending to get answers that imply I am flirting with the precipice of numerical instability (e.g., imaginary results where Re(x)/Im(x)~10^8).
I like the idea of this approach because it "feels clean": (1) use continuous function; (2) apply magic; (3) integrate in spherical coordinates; and (4) profit. However, I am beginning to think that I need to tune out the sires sweetly singing and go for a more practical (i.e., tractable) approach.
My other idea is to start by using the inside-outside function to generate [X,Y,Z] similar to what is used by surf(). I can then use surfature() from the FEX to get the curvatures at every point on my [X,Y,Z] mesh. Finally, I could convert everything to spherical coordinates and integrate the curvature arrays to get average values.
My concern here is that reregularly-spaced [X,Y,Z] does not translate to regularly-spaced [r,theta,phi], so the calculation is somehow pathologically biased. I am also unsure about how to do the actual integration after converting my array to spherical coordinates.
Apologies for such a long text-dense question. I thought about trying to illustrate this better with a couple of code snippets, but the algebra is really messy and not all that informative.
Thanks, in advance. differential geometry, curvature, spherical averaging MATLAB Answers — New Questions

​