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Renamed the noise folder to noise 0.2.0 for the first release.
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Jun 13, 2014
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18 changes: 9 additions & 9 deletions
18
Noise/PSD/Kernels/package.mo → Noise 0.2.0/PSD/Kernels/package.mo
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,9 +1,9 @@ | ||
| within Noise.PSD; | ||
| package Kernels "A collection of useful kernels for the convolution filter" | ||
| extends Modelica.Icons.Package; | ||
|
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| annotation (Documentation(revisions="<html> | ||
| <p><img src=\"modelica://Noise/Resources/Images/dlr_logo.png\"/> <b>Developed 2014 at the DLR Institute of System Dynamics and Control</b> </p> | ||
| </html>")); | ||
| end Kernels; | ||
| within Noise.PSD; | ||
| package Kernels "A collection of useful kernels for the convolution filter" | ||
| extends Modelica.Icons.Package; | ||
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| annotation (Documentation(revisions="<html> | ||
| <p><img src=\"modelica://Noise/Resources/Images/dlr_logo.png\"/> <b>Developed 2014 at the DLR Institute of System Dynamics and Control</b> </p> | ||
| </html>")); | ||
| end Kernels; |
8 changes: 4 additions & 4 deletions
8
Noise/PSD/Kernels/package.order → Noise 0.2.0/PSD/Kernels/package.order
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,4 +1,4 @@ | ||
| IdealLowPass | ||
| Linear | ||
| Gaussian | ||
| FirstOrder | ||
| IdealLowPass | ||
| Linear | ||
| Gaussian | ||
| FirstOrder |
224 changes: 112 additions & 112 deletions
224
Noise/PSD/PSD_Convolution.mo → Noise 0.2.0/PSD/PSD_Convolution.mo
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,112 +1,112 @@ | ||
| within Noise.PSD; | ||
| function PSD_Convolution | ||
| "Apply an arbitrary filter by convolution with its impulse response" | ||
| extends Noise.Utilities.Interfaces.PSD; | ||
| replaceable function Kernel = Noise.PSD.Kernels.IdealLowPass constrainedby | ||
| Utilities.Interfaces.Kernel | ||
| annotation(choicesAllMatching=true, Documentation(revisions="<html> | ||
| <p><img src=\"modelica://Noise/Resources/Images/dlr_logo.png\"/> <b>Developed 2014 at the DLR Institute of System Dynamics and Control</b> </p> | ||
| </html>")); | ||
|
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| input Integer n = 5 "Number of support points for convolution" annotation(Dialog); | ||
| input Integer max_n = n "Maximum nummber of support points for comparability" | ||
| annotation(Dialog); | ||
|
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| protected | ||
| Real raw "The raw random numbers from the PDF function"; | ||
| Real coefficient "The convolution coefficients"; | ||
| Real coefficient_l "The convolution coefficients"; | ||
| Real coefficient_r "The convolution coefficients"; | ||
| Real scaling1 "The scaling to make sure, a constant signal remains constant"; | ||
| Real scaling2 "The scaling to make sure, the variance remains"; | ||
| Integer states_temp[size(states_in,1)] | ||
| "Intermediate states for recurrent RNGs"; | ||
| Integer j; | ||
| algorithm | ||
|
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| // Initialize the convolution algorithm | ||
| rand := 0; | ||
| scaling1 := 0; | ||
| scaling2 := 0; | ||
| states_temp := states_in; | ||
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| // Make sure, the recurrent generators are centered | ||
| for i in (-max_n):(-n) loop | ||
| (raw,states_temp) := PDF(instance=(floor(t/dt) + i)*dt, states_in=states_temp); | ||
| end for; | ||
|
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| // What is convolution?! | ||
| // -2dt t +2dt | ||
| // + - + - + - + - + - + - + - + - + -> simulation time | ||
| // -3dt -1dt +1dt +3dt | ||
| // | | ||
| // | ||
| // 1 -| ^ Kernel(delta_t) | ||
| // | / \ | ||
| // | _ / \ _ | ||
| // | -3/ \-2 -1/ 0 \1 2/ \3 | ||
| // 0 -+ - + - + - + - + - + - + - + - + -> phase | ||
| // | | | \ / | | \ / | | ||
| // delta_t/dt <> V V | ||
| // | | | | | | | | dt | ||
| // | / | ||
| // + + + + + + + +<->+ sample | ||
| // -3 -2 -1 0 1 2 3 0 at floor(time/dt) = instance | ||
| // | ||
| // states_in _________________^___^___^___^___^___^___^ | ||
| // iterations | ||
| // until -n +1 +1 +1 +1 +1 +1 | ||
| // | ||
| // Convolution: filter = sum( signal(time) * Kernel(phase*pi) ) | ||
| // time = sample + instance | ||
| // phase = sample - delta_t/dt | ||
| // sample = -2 .. 3 | ||
| // | ||
| // Loop over 2n support points for the convolution = sum( random(t_i)*kernel(t-t_i) ) | ||
| // The random number is for time = (floor(t/dt) * dt + i * dt) | ||
| // The kernel result is for time = t - (floor(t/dt) * dt + i * dt) | ||
| // or, if sampled: time = t - ( t_last + i * dt) | ||
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| coefficient_l := if t_last <= t then Kernel(t= - n*dt, dt=dt) else | ||
| Kernel(t= - n*dt, dt=dt); | ||
| coefficient_r := if t_last <= t then Kernel(t= t - ( t_last-n *dt)+dt, dt=dt) else | ||
| Kernel(t= t - (floor(t/dt-n)*dt)+dt, dt=dt); | ||
| // rand := rand + raw*(coefficient_r - coefficient_l); | ||
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| coefficient_l := if t_last <= t then Kernel(t= t - ( t_last+j *dt)-dt, dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+j)*dt)-dt, dt=dt); | ||
| coefficient_r := if t_last <= t then Kernel(t= t - ( t_last+j *dt), dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+j)*dt), dt=dt); | ||
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| for i in (-n+1):(n) loop | ||
| (raw, states_temp) := PDF(states_in=states_temp, instance=(floor(t/dt+i)*dt)); | ||
| coefficient := if t_last <= t then Kernel(t= t - ( t_last+i *dt), dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+i)*dt), dt=dt); | ||
| coefficient_l := if t_last <= t then Kernel(t= t - ( t_last+i *dt), dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+i)*dt), dt=dt); | ||
| coefficient_r := if t_last <= t then Kernel(t= t - ( t_last+i *dt)+dt, dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+i)*dt)+dt, dt=dt); | ||
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| // Modelica.Utilities.Streams.print("i=" + String(i) + ", raw=" + String(raw)); | ||
| // rand := rand + raw*coefficient; | ||
| rand := rand + raw*(coefficient_r-coefficient_l); | ||
| scaling1 := scaling1 + coefficient^1; | ||
| scaling2 := scaling2 + coefficient^2; | ||
| end for; | ||
|
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| // Scale according to dt and n | ||
| //rand := rand / 1;//dt;//sqrt((2*n+1) *dt^2); | ||
|
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| // Scale the result in order to smooth higher harmonics | ||
| //rand := rand / scaling1; | ||
|
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| // Scale the result in order to match the variance | ||
| //rand := rand * sqrt(abs(scaling1)) / sqrt(scaling2); | ||
|
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| // Make sure, the output states_out for recurrent generators are senseful | ||
| (rand_hold, states_out) := PDF(states_in=states_in, instance=floor(t/dt)*dt+dt); | ||
|
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||
| annotation (Icon(graphics), Documentation(revisions="<html> | ||
| <p><img src=\"modelica://Noise/Resources/Images/dlr_logo.png\"/> <b>Developed 2014 at the DLR Institute of System Dynamics and Control</b> </p> | ||
| </html>")); | ||
| end PSD_Convolution; | ||
| within Noise.PSD; | ||
| function PSD_Convolution | ||
| "Apply an arbitrary filter by convolution with its impulse response" | ||
| extends Noise.Utilities.Interfaces.PSD; | ||
| replaceable function Kernel = Noise.PSD.Kernels.IdealLowPass constrainedby | ||
| Utilities.Interfaces.Kernel | ||
| annotation(choicesAllMatching=true, Documentation(revisions="<html> | ||
| <p><img src=\"modelica://Noise/Resources/Images/dlr_logo.png\"/> <b>Developed 2014 at the DLR Institute of System Dynamics and Control</b> </p> | ||
| </html>")); | ||
|
|
||
| input Integer n = 5 "Number of support points for convolution" annotation(Dialog); | ||
| input Integer max_n = n "Maximum nummber of support points for comparability" | ||
| annotation(Dialog); | ||
|
|
||
| protected | ||
| Real raw "The raw random numbers from the PDF function"; | ||
| Real coefficient "The convolution coefficients"; | ||
| Real coefficient_l "The convolution coefficients"; | ||
| Real coefficient_r "The convolution coefficients"; | ||
| Real scaling1 "The scaling to make sure, a constant signal remains constant"; | ||
| Real scaling2 "The scaling to make sure, the variance remains"; | ||
| Integer states_temp[size(states_in,1)] | ||
| "Intermediate states for recurrent RNGs"; | ||
| Integer j; | ||
| algorithm | ||
|
|
||
| // Initialize the convolution algorithm | ||
| rand := 0; | ||
| scaling1 := 0; | ||
| scaling2 := 0; | ||
| states_temp := states_in; | ||
|
|
||
| // Make sure, the recurrent generators are centered | ||
| for i in (-max_n):(-n) loop | ||
| (raw,states_temp) := PDF(instance=(floor(t/dt) + i)*dt, states_in=states_temp); | ||
| end for; | ||
|
|
||
| // What is convolution?! | ||
| // -2dt t +2dt | ||
| // + - + - + - + - + - + - + - + - + -> simulation time | ||
| // -3dt -1dt +1dt +3dt | ||
| // | | ||
| // | ||
| // 1 -| ^ Kernel(delta_t) | ||
| // | / \ | ||
| // | _ / \ _ | ||
| // | -3/ \-2 -1/ 0 \1 2/ \3 | ||
| // 0 -+ - + - + - + - + - + - + - + - + -> phase | ||
| // | | | \ / | | \ / | | ||
| // delta_t/dt <> V V | ||
| // | | | | | | | | dt | ||
| // | / | ||
| // + + + + + + + +<->+ sample | ||
| // -3 -2 -1 0 1 2 3 0 at floor(time/dt) = instance | ||
| // | ||
| // states_in _________________^___^___^___^___^___^___^ | ||
| // iterations | ||
| // until -n +1 +1 +1 +1 +1 +1 | ||
| // | ||
| // Convolution: filter = sum( signal(time) * Kernel(phase*pi) ) | ||
| // time = sample + instance | ||
| // phase = sample - delta_t/dt | ||
| // sample = -2 .. 3 | ||
| // | ||
| // Loop over 2n support points for the convolution = sum( random(t_i)*kernel(t-t_i) ) | ||
| // The random number is for time = (floor(t/dt) * dt + i * dt) | ||
| // The kernel result is for time = t - (floor(t/dt) * dt + i * dt) | ||
| // or, if sampled: time = t - ( t_last + i * dt) | ||
|
|
||
| coefficient_l := if t_last <= t then Kernel(t= - n*dt, dt=dt) else | ||
| Kernel(t= - n*dt, dt=dt); | ||
| coefficient_r := if t_last <= t then Kernel(t= t - ( t_last-n *dt)+dt, dt=dt) else | ||
| Kernel(t= t - (floor(t/dt-n)*dt)+dt, dt=dt); | ||
| // rand := rand + raw*(coefficient_r - coefficient_l); | ||
|
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| coefficient_l := if t_last <= t then Kernel(t= t - ( t_last+j *dt)-dt, dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+j)*dt)-dt, dt=dt); | ||
| coefficient_r := if t_last <= t then Kernel(t= t - ( t_last+j *dt), dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+j)*dt), dt=dt); | ||
|
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| for i in (-n+1):(n) loop | ||
| (raw, states_temp) := PDF(states_in=states_temp, instance=(floor(t/dt+i)*dt)); | ||
| coefficient := if t_last <= t then Kernel(t= t - ( t_last+i *dt), dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+i)*dt), dt=dt); | ||
| coefficient_l := if t_last <= t then Kernel(t= t - ( t_last+i *dt), dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+i)*dt), dt=dt); | ||
| coefficient_r := if t_last <= t then Kernel(t= t - ( t_last+i *dt)+dt, dt=dt) else | ||
| Kernel(t= t - (floor(t/dt+i)*dt)+dt, dt=dt); | ||
|
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| // Modelica.Utilities.Streams.print("i=" + String(i) + ", raw=" + String(raw)); | ||
| // rand := rand + raw*coefficient; | ||
| rand := rand + raw*(coefficient_r-coefficient_l); | ||
| scaling1 := scaling1 + coefficient^1; | ||
| scaling2 := scaling2 + coefficient^2; | ||
| end for; | ||
|
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| // Scale according to dt and n | ||
| //rand := rand / 1;//dt;//sqrt((2*n+1) *dt^2); | ||
|
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| // Scale the result in order to smooth higher harmonics | ||
| //rand := rand / scaling1; | ||
|
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||
| // Scale the result in order to match the variance | ||
| //rand := rand * sqrt(abs(scaling1)) / sqrt(scaling2); | ||
|
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| // Make sure, the output states_out for recurrent generators are senseful | ||
| (rand_hold, states_out) := PDF(states_in=states_in, instance=floor(t/dt)*dt+dt); | ||
|
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| annotation (Icon(graphics), Documentation(revisions="<html> | ||
| <p><img src=\"modelica://Noise/Resources/Images/dlr_logo.png\"/> <b>Developed 2014 at the DLR Institute of System Dynamics and Control</b> </p> | ||
| </html>")); | ||
| end PSD_Convolution; |
22 changes: 11 additions & 11 deletions
22
Noise/PSD/PSD_IdealLowPass.mo → Noise 0.2.0/PSD/PSD_IdealLowPass.mo
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,11 +1,11 @@ | ||
| within Noise.PSD; | ||
| function PSD_IdealLowPass | ||
| "An ideal low-pass filter based on the convolution with the sinc function" | ||
| extends PSD_Interpolation(redeclare function Kernel = Kernels.IdealLowPass); | ||
| annotation (Icon(graphics={Line( | ||
| points={{-80,60},{60,60},{60,-80}}, | ||
| color={255,0,0}, | ||
| smooth=Smooth.None)}), Documentation(revisions="<html> | ||
| <p><img src=\"modelica://Noise/Resources/Images/dlr_logo.png\"/> <b>Developed 2014 at the DLR Institute of System Dynamics and Control</b> </p> | ||
| </html>")); | ||
| end PSD_IdealLowPass; | ||
| within Noise.PSD; | ||
| function PSD_IdealLowPass | ||
| "An ideal low-pass filter based on the convolution with the sinc function" | ||
| extends PSD_Interpolation(redeclare function Kernel = Kernels.IdealLowPass); | ||
| annotation (Icon(graphics={Line( | ||
| points={{-80,60},{60,60},{60,-80}}, | ||
| color={255,0,0}, | ||
| smooth=Smooth.None)}), Documentation(revisions="<html> | ||
| <p><img src=\"modelica://Noise/Resources/Images/dlr_logo.png\"/> <b>Developed 2014 at the DLR Institute of System Dynamics and Control</b> </p> | ||
| </html>")); | ||
| end PSD_IdealLowPass; |
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