merge 'lennart' branch back into trunk.

git-svn-id: file:///home/lennart/svn/public/pulseaudio/trunk@1971 fefdeb5f-60dc-0310-8127-8f9354f1896f

src/pulsecore/time-smoother.c

@@ -0,0 +1,378 @@
+/* $Id$ */
+
+/***
+  This file is part of PulseAudio.
+
+  Copyright 2007 Lennart Poettering
+
+  PulseAudio is free software; you can redistribute it and/or modify
+  it under the terms of the GNU Lesser General Public License as
+  published by the Free Software Foundation; either version 2.1 of the
+  License, or (at your option) any later version.
+
+  PulseAudio is distributed in the hope that it will be useful, but
+  WITHOUT ANY WARRANTY; without even the implied warranty of
+  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+  Lesser General Public License for more details.
+
+  You should have received a copy of the GNU Lesser General Public
+  License along with PulseAudio; if not, write to the Free Software
+  Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
+  USA.
+***/
+
+#ifdef HAVE_CONFIG_H
+#include <config.h>
+#endif
+
+#include <stdio.h>
+
+#include <pulse/sample.h>
+#include <pulse/xmalloc.h>
+
+#include <pulsecore/macro.h>
+
+#include "time-smoother.h"
+
+#define HISTORY_MAX 50
+
+/*
+ * Implementation of a time smoothing algorithm to synchronize remote
+ * clocks to a local one. Evens out noise, adjusts to clock skew and
+ * allows cheap estimations of the remote time while clock updates may
+ * be seldom and recieved in non-equidistant intervals.
+ *
+ * Basically, we estimate the gradient of received clock samples in a
+ * certain history window (of size 'history_time') with linear
+ * regression. With that info we estimate the remote time in
+ * 'adjust_time' ahead and smoothen our current estimation function
+ * towards that point with a 3rd order polynomial interpolation with
+ * fitting derivatives. (more or less a b-spline)
+ *
+ * The larger 'history_time' is chosen the better we will surpress
+ * noise -- but we'll adjust to clock skew slower..
+ *
+ * The larger 'adjust_time' is chosen the smoother our estimation
+ * function will be -- but we'll adjust to clock skew slower, too.
+ *
+ * If 'monotonic' is TRUE the resulting estimation function is
+ * guaranteed to be monotonic.
+ */
+
+struct pa_smoother {
+    pa_usec_t adjust_time, history_time;
+    pa_bool_t monotonic;
+
+    pa_usec_t time_offset;
+
+    pa_usec_t px, py;     /* Point p, where we want to reach stability */
+    double dp;            /* Gradient we want at point p */
+
+    pa_usec_t ex, ey;     /* Point e, which we estimated before and need to smooth to */
+    double de;            /* Gradient we estimated for point e */
+
+                          /* History of last measurements */
+    pa_usec_t history_x[HISTORY_MAX], history_y[HISTORY_MAX];
+    unsigned history_idx, n_history;
+
+    /* To even out for monotonicity */
+    pa_usec_t last_y;
+
+    /* Cached parameters for our interpolation polynomial y=ax^3+b^2+cx */
+    double a, b, c;
+    pa_bool_t abc_valid;
+
+    pa_bool_t paused;
+    pa_usec_t pause_time;
+};
+
+pa_smoother* pa_smoother_new(pa_usec_t adjust_time, pa_usec_t history_time, pa_bool_t monotonic) {
+    pa_smoother *s;
+
+    pa_assert(adjust_time > 0);
+    pa_assert(history_time > 0);
+
+    s = pa_xnew(pa_smoother, 1);
+    s->adjust_time = adjust_time;
+    s->history_time = history_time;
+    s->time_offset = 0;
+    s->monotonic = monotonic;
+
+    s->px = s->py = 0;
+    s->dp = 1;
+
+    s->ex = s->ey = 0;
+    s->de = 1;
+
+    s->history_idx = 0;
+    s->n_history = 0;
+
+    s->last_y = 0;
+
+    s->abc_valid = FALSE;
+
+    s->paused = FALSE;
+
+    return s;
+}
+
+void pa_smoother_free(pa_smoother* s) {
+    pa_assert(s);
+
+    pa_xfree(s);
+}
+
+static void drop_old(pa_smoother *s, pa_usec_t x) {
+    unsigned j;
+
+    /* First drop items from history which are too old, but make sure
+     * to always keep two entries in the history */
+
+    for (j = s->n_history; j > 2; j--) {
+
+        if (s->history_x[s->history_idx] + s->history_time >= x) {
+            /* This item is still valid, and thus all following ones
+             * are too, so let's quit this loop */
+            break;
+        }
+
+        /* Item is too old, let's drop it */
+        s->history_idx ++;
+        while (s->history_idx >= HISTORY_MAX)
+            s->history_idx -= HISTORY_MAX;
+
+        s->n_history --;
+    }
+}
+
+static void add_to_history(pa_smoother *s, pa_usec_t x, pa_usec_t y) {
+    unsigned j;
+    pa_assert(s);
+
+    drop_old(s, x);
+
+    /* Calculate position for new entry */
+    j = s->history_idx + s->n_history;
+    while (j >= HISTORY_MAX)
+        j -= HISTORY_MAX;
+
+    /* Fill in entry */
+    s->history_x[j] = x;
+    s->history_y[j] = y;
+
+    /* Adjust counter */
+    s->n_history ++;
+
+    /* And make sure we don't store more entries than fit in */
+    if (s->n_history >= HISTORY_MAX) {
+        s->history_idx += s->n_history - HISTORY_MAX;
+        s->n_history = HISTORY_MAX;
+    }
+}
+
+static double avg_gradient(pa_smoother *s, pa_usec_t x) {
+    unsigned i, j, c = 0;
+    int64_t ax = 0, ay = 0, k, t;
+    double r;
+
+    drop_old(s, x);
+
+    /* First, calculate average of all measurements */
+    i = s->history_idx;
+    for (j = s->n_history; j > 0; j--) {
+
+        ax += s->history_x[i];
+        ay += s->history_y[i];
+        c++;
+
+        i++;
+        while (i >= HISTORY_MAX)
+            i -= HISTORY_MAX;
+    }
+
+    /* Too few measurements, assume gradient of 1 */
+    if (c < 2)
+        return 1;
+
+    ax /= c;
+    ay /= c;
+
+    /* Now, do linear regression */
+    k = t = 0;
+
+    i = s->history_idx;
+    for (j = s->n_history; j > 0; j--) {
+        int64_t dx, dy;
+
+        dx = (int64_t) s->history_x[i] - ax;
+        dy = (int64_t) s->history_y[i] - ay;
+
+        k += dx*dy;
+        t += dx*dx;
+
+        i++;
+        while (i >= HISTORY_MAX)
+            i -= HISTORY_MAX;
+    }
+
+    r = (double) k / t;
+
+    return s->monotonic && r < 0 ? 0 : r;
+}
+
+static void estimate(pa_smoother *s, pa_usec_t x, pa_usec_t *y, double *deriv) {
+    pa_assert(s);
+    pa_assert(y);
+
+    if (x >= s->px) {
+        int64_t t;
+
+        /* The requested point is right of the point where we wanted
+         * to be on track again, thus just linearly estimate */
+
+        t = (int64_t) s->py + (int64_t) (s->dp * (x - s->px));
+
+        if (t < 0)
+            t = 0;
+
+        *y = (pa_usec_t) t;
+
+        if (deriv)
+            *deriv = s->dp;
+
+    } else {
+
+        if (!s->abc_valid) {
+            pa_usec_t ex, ey, px, py;
+            int64_t kx, ky;
+            double de, dp;
+
+            /* Ok, we're not yet on track, thus let's interpolate, and
+             * make sure that the first derivative is smooth */
+
+            /* We have two points: (ex|ey) and (px|py) with two gradients
+             * at these points de and dp. We do a polynomial interpolation
+             * of degree 3 with these 6 values */
+
+            ex = s->ex; ey = s->ey;
+            px = s->px; py = s->py;
+            de = s->de; dp = s->dp;
+
+            pa_assert(ex < px);
+
+            /* To increase the dynamic range and symplify calculation, we
+             * move these values to the origin */
+            kx = (int64_t) px - (int64_t) ex;
+            ky = (int64_t) py - (int64_t) ey;
+
+            /* Calculate a, b, c for y=ax^3+b^2+cx */
+            s->c = de;
+            s->b = (((double) (3*ky)/kx - dp - 2*de)) / kx;
+            s->a = (dp/kx - 2*s->b - de/kx) / (3*kx);
+
+            s->abc_valid = TRUE;
+        }
+
+        /* Move to origin */
+        x -= s->ex;
+
+        /* Horner scheme */
+        *y = (pa_usec_t) ((double) x * (s->c + (double) x * (s->b + (double) x * s->a)));
+
+        /* Move back from origin */
+        *y += s->ey;
+
+        /* Horner scheme */
+        if (deriv)
+            *deriv = s->c + ((double) x * (s->b*2 + (double) x * s->a*3));
+    }
+
+    /* Guarantee monotonicity */
+    if (s->monotonic) {
+
+        if (*y < s->last_y)
+            *y = s->last_y;
+        else
+            s->last_y = *y;
+
+        if (deriv && *deriv < 0)
+            *deriv = 0;
+    }
+}
+
+void pa_smoother_put(pa_smoother *s, pa_usec_t x, pa_usec_t y) {
+    pa_usec_t ney;
+    double nde;
+
+    pa_assert(s);
+    pa_assert(x >= s->time_offset);
+
+    /* Fix up x value */
+    if (s->paused)
+        x = s->pause_time;
+    else
+        x -= s->time_offset;
+
+    pa_assert(x >= s->ex);
+
+    /* First, we calculate the position we'd estimate for x, so that
+     * we can adjust our position smoothly from this one */
+    estimate(s, x, &ney, &nde);
+    s->ex = x; s->ey = ney; s->de = nde;
+
+    /* Then, we add the new measurement to our history */
+    add_to_history(s, x, y);
+
+    /* And determine the average gradient of the history */
+    s->dp = avg_gradient(s, x);
+
+    /* And calculate when we want to be on track again */
+    s->px = x + s->adjust_time;
+    s->py = y + s->dp *s->adjust_time;
+
+    s->abc_valid = FALSE;
+}
+
+pa_usec_t pa_smoother_get(pa_smoother *s, pa_usec_t x) {
+    pa_usec_t y;
+
+    pa_assert(s);
+    pa_assert(x >= s->time_offset);
+
+    /* Fix up x value */
+    if (s->paused)
+        x = s->pause_time;
+    else
+        x -= s->time_offset;
+
+    pa_assert(x >= s->ex);
+
+    estimate(s, x, &y, NULL);
+    return y;
+}
+
+void pa_smoother_set_time_offset(pa_smoother *s, pa_usec_t offset) {
+    pa_assert(s);
+
+    s->time_offset = offset;
+}
+
+void pa_smoother_pause(pa_smoother *s, pa_usec_t x) {
+    pa_assert(s);
+
+    if (s->paused)
+        return;
+
+    s->paused = TRUE;
+    s->pause_time = x;
+}
+
+void pa_smoother_resume(pa_smoother *s, pa_usec_t x) {
+    pa_assert(s);
+
+    if (!s->paused)
+        return;
+
+    s->paused = FALSE;
+    s->time_offset += x - s->pause_time;
+}