# Difference between revisions of "Bass funct.ck"

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//Bass Funct: a 2D Linear Dynamical System | //Bass Funct: a 2D Linear Dynamical System | ||

− | //watson 2008 | + | //watson 2008 watsonix at gmail |

//please email me with comments/ideas! | //please email me with comments/ideas! | ||

Line 105: | Line 105: | ||

Feel free to add your comments or ideas here | Feel free to add your comments or ideas here | ||

# Note its easy to instead map the two oscillators to mono. Just remove the .left and .right for s1 and s2. | # Note its easy to instead map the two oscillators to mono. Just remove the .left and .right for s1 and s2. | ||

+ | # Q from watson: am I managing memory here correctly? i think its leaking memory all over the place as it goes along. do i need to actively destroy variables? |

## Latest revision as of 04:26, 26 March 2010

## Code

//Bass Funct: a 2D Linear Dynamical System
//watson 2008 watsonix at gmail
//please email me with comments/ideas!
//comment: note this would not be hard at all to extend to higher dimensions.
SinOsc s1 => dac.left;
SinOsc s2 => dac.right;
//initial condition
[1.0,-1.0] @=> float x_this[];
//frequency scaling: scale the state variables by this much before => freq
40 => float freq_scale;
//time step duration (numerical integration)
.001 => float delta_t;
//time dilation (how to comparitively step through real time)
1 => float time_dil;
//extra parameters
[-10.0] @=> float r[]; //static parameter value
//dynamic periodic parameter
SinOsc param_osc => blackhole;
.01 => param_osc.freq;
//A matrix: deterimines the dynamical system behavior
//"Nonlinear Dynamics and Chaos" by Strogatz is a great place to start
[ -0.3 , 0.15 , .4, -.6 ] @=> float A[];
while ((delta_t*time_dil)::second => now){
//<<< x_this[0] >>>;
//<<< x_this[1] >>>;
x_next ( x_this, A, r, delta_t) @=> x_this;
x_this[0] * freq_scale => s1.freq;
x_this[1] * freq_scale => s2.freq;
// <<<r[0]>>> + .003 => r[0];
param_osc.last() => r[0];
}
//next x using runge-kata numerical integration
fun float[] x_next( float x_last[], float A[], float r[], float delta_t ) {
f(x_last, A, r) @=> float test[];
mult_array ( f(x_last, A, r), delta_t) @=> float k1[];
mult_array ( f( add_array(x_last, div_array(k1,2)), A, r), delta_t ) @=> float k2[];
mult_array ( f( add_array(x_last, div_array(k2,2)), A, r), delta_t ) @=> float k3[];
mult_array ( f( add_array(x_last, k3), A, r), delta_t ) @=> float k4[];
return add_array( x_last, div_array( add_array(k1, add_array( mult_array(k2,2), add_array(
mult_array(k3,2), k4) ) ), 6 ) );
}
//dx/dt
//note: currently assumes a 2D vector from x
fun float[] f( float x[], float A[], float r[]) {
float x_new[2];
A[0] * x[0] + A[1] * x[1] + r[0] => x_new[0];
A[2] * x[0] + A[3] * x[1] => x_new[1];
return x_new;
}
//ARRAY UTILITIES *****************
fun float[] add_array( float a1[], float a2[] ) {
float sum[2];
for( 0 => int i; i < a1.cap(); i++ ) {
a1[i] + a2[i] => sum[i];
}
return sum;
}
fun float[] div_array( float a[], float d ) {
float quotient[2];
for( 0 => int i; i < a.cap(); i++ ) {
a[i] / d => quotient[i];
}
return quotient;
}
fun float[] mult_array( float a[], float d ) {
float prod[2];
for( 0 => int i; i < a.cap(); i++ ) {
a[i] * d => prod[i];
}
return prod;
}

```
```

## Comments

Feel free to add your comments or ideas here

- Note its easy to instead map the two oscillators to mono. Just remove the .left and .right for s1 and s2.
- Q from watson: am I managing memory here correctly? i think its leaking memory all over the place as it goes along. do i need to actively destroy variables?