Update of /cvsroot/pure-data/externals/cxc In directory sc8-pr-cvs1.sourceforge.net:/tmp/cvs-serv4744

Added Files: mean~.c Removed Files: mean.c Log Message: cleaned up cxc so that it passes the automated test in scripts/load_every_help.sh: renamed help files to standard name; made each file named after the class; removed non-functional aliases in flatspace

--- mean.c DELETED ---

--- NEW FILE: mean~.c --- /* jdl@xdv.org, 200203 calculate mean of buffer standard deviation + histogram */

#include "m_pd.h" #include <math.h> //#include <stdlib.h>

/* cx.mean: calculate the mean from a table */ /* as defined by: add all samples together and divide by number of samples */ t_class *cxmean_class;

typedef struct _cxmean { t_object x_obj; t_symbol *x_arrayname; t_float x_mean; float *x_vec; t_float f; int x_nsampsintab; } t_cxmean;

void *cxmean_new(t_symbol *s) { t_cxmean *x = (t_cxmean *)pd_new(cxmean_class); x->x_arrayname = s; x->x_mean = 0; outlet_new(&x->x_obj, &s_float); return (x); }

static void cxmean_set(t_cxmean *x, t_symbol *s) { t_garray *a;

x->x_arrayname = s; if (!(a = (t_garray *)pd_findbyclass(x->x_arrayname, garray_class))) { if (*s->s_name) pd_error(x, "mean~: %s: no such array", x->x_arrayname->s_name); x->x_vec = 0; } else if (!garray_getfloatarray(a, &x->x_nsampsintab, &x->x_vec)) { error("%s: bad template for mean~", x->x_arrayname->s_name); x->x_vec = 0; } else garray_usedindsp(a); }

void cxmean_bang(t_cxmean *x) { outlet_float(x->x_obj.ob_outlet,x->x_mean); }

static void cxmean_mean(t_cxmean *x) { // t_float *bl; t_garray *a; int cnt; t_float *fp; t_float xz = 0.;

cnt = 0; if(!(a = (t_garray *)pd_findbyclass(x->x_arrayname,garray_class))) { pd_error(x, "%s: no such table", x->x_arrayname->s_name); } garray_getfloatarray(a,&x->x_nsampsintab,&x->x_vec);

fp = x->x_vec;

while(cnt < x->x_nsampsintab) { //post("cxc/mean.c: %f",*fp++); xz += *fp++; cnt++; } #ifdef DEBUG post("cxc/mean.c: sampsum: %f",xz); #endif x->x_mean = xz / x->x_nsampsintab;

outlet_float(x->x_obj.ob_outlet, x->x_mean); }

void cxmean_setup(void) { cxmean_class = class_new(gensym("cxmean"), (t_newmethod)cxmean_new, 0, sizeof(t_cxmean), CLASS_DEFAULT, A_DEFSYM, 0); class_addcreator((t_newmethod)cxmean_new,gensym("cx.mean"),A_DEFSYM, 0);

class_addmethod(cxmean_class, (t_method)cxmean_set, gensym("set"), A_DEFSYM, 0); class_addmethod(cxmean_class, (t_method)cxmean_mean, gensym("mean"), A_DEFSYM, 0); class_addbang(cxmean_class, cxmean_bang); class_sethelpsymbol(cxmean_class, gensym("statistics.pd")); }

/* cx.avgdev: calculate the average deviation of an array from mean as input and array. takes mean as input, sum of abs values of sample-val - mean divided by number of samples */

t_class *cxavgdev_class;

typedef struct _cxavgdev { t_object x_obj; t_symbol *x_arrayname; t_float x_avgdev; float *x_vec; t_float f; int x_nsampsintab; } t_cxavgdev;

void *cxavgdev_new(t_symbol *s) { t_cxavgdev *x = (t_cxavgdev *)pd_new(cxavgdev_class); x->x_arrayname = s; x->x_avgdev = 0; outlet_new(&x->x_obj, &s_float); return (x); }

static void cxavgdev_set(t_cxavgdev *x, t_symbol *s) { t_garray *a;

x->x_arrayname = s; if (!(a = (t_garray *)pd_findbyclass(x->x_arrayname, garray_class))) { if (*s->s_name) pd_error(x, "mean~: %s: no such array", x->x_arrayname->s_name); x->x_vec = 0; } else if (!garray_getfloatarray(a, &x->x_nsampsintab, &x->x_vec)) { error("%s: bad template for mean~", x->x_arrayname->s_name); x->x_vec = 0; } else garray_usedindsp(a); }

void cxavgdev_bang(t_cxavgdev *x) { outlet_float(x->x_obj.ob_outlet,x->x_avgdev); }

static void cxavgdev_float(t_cxavgdev *x, t_float f) { // t_float *bl; t_garray *a; int cnt; t_float *fp; t_float xz = 0.; t_float tz = 0.;

cnt = 0; if(!(a = (t_garray *)pd_findbyclass(x->x_arrayname,garray_class))) { pd_error(x, "%s: no such table", x->x_arrayname->s_name); } garray_getfloatarray(a,&x->x_nsampsintab,&x->x_vec);

fp = x->x_vec;

while(cnt < x->x_nsampsintab) { tz = *fp++; tz = fabs(tz - f); xz += tz; #ifdef DEBUG //post("cxc/mean.c: sampdeviation: %f",tz); #endif cnt++; } #ifdef DEBUG post("cxc/mean.c: avgsum: %f",xz); #endif x->x_avgdev = xz / x->x_nsampsintab;

outlet_float(x->x_obj.ob_outlet, x->x_avgdev); }

void cxavgdev_setup(void) { cxavgdev_class = class_new(gensym("cxavgdev"), (t_newmethod)cxavgdev_new, 0, sizeof(t_cxavgdev), CLASS_DEFAULT, A_DEFSYM, 0);

class_addcreator((t_newmethod)cxavgdev_new,gensym("cx.avgdev"),A_DEFSYM, 0); class_addmethod(cxavgdev_class, (t_method)cxavgdev_set, gensym("set"), A_DEFSYM, 0); /* class_addmethod(cxavgdev_class, (t_method)cxavgdev_mean, */ /* gensym("mean"), A_DEFSYM, 0); */ class_addfloat(cxavgdev_class, (t_method)cxavgdev_float); class_addbang(cxavgdev_class, cxavgdev_bang); class_sethelpsymbol(cxavgdev_class, gensym("statistics.pd")); }

/* cx.stddev: calculate the standard deviation of an array from mean as input and array. square root of sum of power of sample - mean divided by num of samps - 1 */

t_class *cxstddev_class;

typedef struct _cxstddev { t_object x_obj; t_symbol *x_arrayname; t_float x_stddev; float *x_vec; t_float f; int x_nsampsintab; } t_cxstddev;

void *cxstddev_new(t_symbol *s) { t_cxstddev *x = (t_cxstddev *)pd_new(cxstddev_class); x->x_arrayname = s; x->x_stddev = 0; outlet_new(&x->x_obj, &s_float); return (x); }

static void cxstddev_set(t_cxstddev *x, t_symbol *s) { t_garray *a;

x->x_arrayname = s; if (!(a = (t_garray *)pd_findbyclass(x->x_arrayname, garray_class))) { if (*s->s_name) pd_error(x, "mean~: %s: no such array", x->x_arrayname->s_name); x->x_vec = 0; } else if (!garray_getfloatarray(a, &x->x_nsampsintab, &x->x_vec)) { error("%s: bad template for mean~", x->x_arrayname->s_name); x->x_vec = 0; } else garray_usedindsp(a); }

void cxstddev_bang(t_cxstddev *x) { outlet_float(x->x_obj.ob_outlet,x->x_stddev); }

static void cxstddev_float(t_cxstddev *x, t_float f) { // t_float *bl; t_garray *a; int cnt; t_float *fp; t_float xz = 0.; t_float tz = 0.;

cnt = 0; if(!(a = (t_garray *)pd_findbyclass(x->x_arrayname,garray_class))) { pd_error(x, "%s: no such table", x->x_arrayname->s_name); } garray_getfloatarray(a,&x->x_nsampsintab,&x->x_vec);

fp = x->x_vec;

while(cnt < x->x_nsampsintab) { tz = *fp++; tz = pow(tz - f,2); // power of 2 xz += tz; #ifdef DEBUG //post("cxc/mean.c: sampdeviation: %f",tz); #endif cnt++; } #ifdef DEBUG post("cxc/mean.c: avgsum: %f",xz); #endif x->x_stddev = sqrt(xz / (x->x_nsampsintab - 1));

outlet_float(x->x_obj.ob_outlet, x->x_stddev); }

void cxstddev_setup(void) { cxstddev_class = class_new(gensym("cxstddev"), (t_newmethod)cxstddev_new, 0, sizeof(t_cxstddev), CLASS_DEFAULT, A_DEFSYM, 0);

class_addcreator((t_newmethod)cxstddev_new,gensym("cx.stddev"),A_DEFSYM, 0); class_addmethod(cxstddev_class, (t_method)cxstddev_set, gensym("set"), A_DEFSYM, 0); /* class_addmethod(cxstddev_class, (t_method)cxstddev_mean, */ /* gensym("mean"), A_DEFSYM, 0); */ class_addfloat(cxstddev_class, (t_method)cxstddev_float); class_addbang(cxstddev_class, cxstddev_bang); class_sethelpsymbol(cxstddev_class, gensym("statistics.pd")); }

/* ---------- mean~ ---------- */ /* output the mean as a signal */

t_class *mean_tilde_class;

typedef struct _mean_tilde { t_object x_obj; t_symbol *x_arrayname; t_float x_mean; float *x_vec; t_float f; int x_nsampsintab; } t_mean_tilde;

void *mean_tilde_new(t_symbol *s) { t_mean_tilde *x = (t_mean_tilde *)pd_new(mean_tilde_class); x->x_arrayname = s; x->x_mean = 0; outlet_new(&x->x_obj, &s_float); return (x); }

static t_int *mean_tilde_perform(t_int *w) { t_mean_tilde *x = (t_mean_tilde *)(w[1]); //t_float *out = (t_float *)(w[3]), t_float *fp; //// t_float *in = (t_float *)(w[2]); //// *out = *in; int n = (int)(w[2]); t_float xz = 0.; fp = x->x_vec; while(n--) { xz += abs(*fp++); //post("cxc/mean.c: %d : %f : %f",n,xz,fp); } x->x_mean = (t_float)(xz / n); //post("cxc/mean.c: %f",xz); return (w+3); //return 0; }

static void mean_tilde_set(t_mean_tilde *x, t_symbol *s) { t_garray *a;

x->x_arrayname = s; if (!(a = (t_garray *)pd_findbyclass(x->x_arrayname, garray_class))) { if (*s->s_name) pd_error(x, "mean~: %s: no such array", x->x_arrayname->s_name); x->x_vec = 0; } else if (!garray_getfloatarray(a, &x->x_nsampsintab, &x->x_vec)) { error("%s: bad template for mean~", x->x_arrayname->s_name); x->x_vec = 0; } else garray_usedindsp(a); }

static void mean_tilde_dsp(t_mean_tilde *x, t_signal **sp) { mean_tilde_set(x, x->x_arrayname); //dsp_add(mean_tilde_perform, 2, x, sp[0]->s_vec, sp[0]->s_n); //dsp_add(mean_tilde_perform, 3, x, sp[0]->s_vec, sp[0]->s_n); dsp_add(mean_tilde_perform, 2, x, sp[0]->s_n); }

void mean_tilde_bang(t_mean_tilde *x) { outlet_float(x->x_obj.ob_outlet,x->x_mean); }

static void mean_tilde_mean(t_mean_tilde *x) { // t_float *bl; t_garray *a; int cnt; t_float *fp; t_float xz = 0.;

cnt = 0; if(!(a = (t_garray *)pd_findbyclass(x->x_arrayname,garray_class))) { pd_error(x, "%s: no such table", x->x_arrayname->s_name); } garray_getfloatarray(a,&x->x_nsampsintab,&x->x_vec);

fp = x->x_vec;

while(cnt < x->x_nsampsintab) { //post("cxc/mean.c: %f",*fp++); xz += *fp++; cnt++; } #ifdef DEBUG post("cxc/mean.c: sampsum: %f",xz); #endif x->x_mean = xz / x->x_nsampsintab;

outlet_float(x->x_obj.ob_outlet, x->x_mean); }

void mean_tilde_setup(void) { //post("mean~ setup"); mean_tilde_class = class_new(gensym("mean~"), (t_newmethod)mean_tilde_new, 0, sizeof(t_mean_tilde), CLASS_DEFAULT, A_DEFSYM, 0); //CLASS_MAINSIGNALIN(mean_tilde_class, t_mean_tilde, f); class_addmethod(mean_tilde_class, nullfn, gensym("signal"), 0); class_addmethod(mean_tilde_class, (t_method)mean_tilde_dsp, gensym("dsp"), 0); class_addmethod(mean_tilde_class, (t_method)mean_tilde_set, gensym("set"), A_DEFSYM, 0); class_addmethod(mean_tilde_class, (t_method)mean_tilde_mean, gensym("mean"), A_DEFSYM, 0); class_addbang(mean_tilde_class, mean_tilde_bang); }