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-rw-r--r--mean.c434
1 files changed, 434 insertions, 0 deletions
diff --git a/mean.c b/mean.c
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index 0000000..3781f3e
--- /dev/null
+++ b/mean.c
@@ -0,0 +1,434 @@
+/*
+ jdl@xdv.org, 200203
+ calculate mean of buffer
+ standard deviation + histogram
+ */
+
+#include "m_imp.h"
+//#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("cx.mean"),
+ (t_newmethod)cxmean_new,
+ 0,
+ sizeof(t_cxmean),
+ CLASS_DEFAULT,
+ 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("cxc/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("cx.avgdev"),
+ (t_newmethod)cxavgdev_new,
+ 0,
+ sizeof(t_cxavgdev),
+ CLASS_DEFAULT,
+ 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("cxc/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("cx.stddev"),
+ (t_newmethod)cxstddev_new,
+ 0,
+ sizeof(t_cxstddev),
+ CLASS_DEFAULT,
+ 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("cxc/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);
+}
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