2 This file is part of PulseAudio.
4 Copyright 2007 Lennart Poettering
6 PulseAudio is free software; you can redistribute it and/or modify
7 it under the terms of the GNU Lesser General Public License as
8 published by the Free Software Foundation; either version 2.1 of the
9 License, or (at your option) any later version.
11 PulseAudio is distributed in the hope that it will be useful, but
12 WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
14 Lesser General Public License for more details.
16 You should have received a copy of the GNU Lesser General Public
17 License along with PulseAudio; if not, write to the Free Software
18 Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
28 #include <pulse/sample.h>
29 #include <pulse/xmalloc.h>
31 #include <pulsecore/macro.h>
33 #include "time-smoother.h"
35 #define HISTORY_MAX 64
38 * Implementation of a time smoothing algorithm to synchronize remote
39 * clocks to a local one. Evens out noise, adjusts to clock skew and
40 * allows cheap estimations of the remote time while clock updates may
41 * be seldom and recieved in non-equidistant intervals.
43 * Basically, we estimate the gradient of received clock samples in a
44 * certain history window (of size 'history_time') with linear
45 * regression. With that info we estimate the remote time in
46 * 'adjust_time' ahead and smoothen our current estimation function
47 * towards that point with a 3rd order polynomial interpolation with
48 * fitting derivatives. (more or less a b-spline)
50 * The larger 'history_time' is chosen the better we will surpress
51 * noise -- but we'll adjust to clock skew slower..
53 * The larger 'adjust_time' is chosen the smoother our estimation
54 * function will be -- but we'll adjust to clock skew slower, too.
56 * If 'monotonic' is TRUE the resulting estimation function is
57 * guaranteed to be monotonic.
61 pa_usec_t adjust_time, history_time;
63 pa_usec_t time_offset;
65 pa_usec_t px, py; /* Point p, where we want to reach stability */
66 double dp; /* Gradient we want at point p */
68 pa_usec_t ex, ey; /* Point e, which we estimated before and need to smooth to */
69 double de; /* Gradient we estimated for point e */
70 pa_usec_t ry; /* The original y value for ex */
72 /* History of last measurements */
73 pa_usec_t history_x[HISTORY_MAX], history_y[HISTORY_MAX];
74 unsigned history_idx, n_history;
76 /* To even out for monotonicity */
77 pa_usec_t last_y, last_x;
79 /* Cached parameters for our interpolation polynomial y=ax^3+b^2+cx */
81 pa_bool_t abc_valid:1;
83 pa_bool_t monotonic:1;
85 pa_bool_t smoothing:1; /* If FALSE we skip the polonyomial interpolation step */
92 pa_smoother* pa_smoother_new(
93 pa_usec_t adjust_time,
94 pa_usec_t history_time,
98 pa_usec_t time_offset,
103 pa_assert(adjust_time > 0);
104 pa_assert(history_time > 0);
105 pa_assert(min_history >= 2);
106 pa_assert(min_history <= HISTORY_MAX);
108 s = pa_xnew(pa_smoother, 1);
109 s->adjust_time = adjust_time;
110 s->history_time = history_time;
112 s->monotonic = monotonic;
117 s->ex = s->ey = s->ry = 0;
123 s->last_y = s->last_x = 0;
125 s->abc_valid = FALSE;
128 s->smoothing = smoothing;
130 s->min_history = min_history;
133 s->time_offset = s->pause_time = time_offset;
138 void pa_smoother_free(pa_smoother* s) {
146 x = (x) % HISTORY_MAX; \
149 #define REDUCE_INC(x) \
151 x = ((x)+1) % HISTORY_MAX; \
155 static void drop_old(pa_smoother *s, pa_usec_t x) {
157 /* Drop items from history which are too old, but make sure to
158 * always keep min_history in the history */
160 while (s->n_history > s->min_history) {
162 if (s->history_x[s->history_idx] + s->history_time >= x)
163 /* This item is still valid, and thus all following ones
164 * are too, so let's quit this loop */
167 /* Item is too old, let's drop it */
168 REDUCE_INC(s->history_idx);
174 static void add_to_history(pa_smoother *s, pa_usec_t x, pa_usec_t y) {
178 /* First try to update an existing history entry */
180 for (j = s->n_history; j > 0; j--) {
182 if (s->history_x[i] == x) {
190 /* Drop old entries */
193 /* Calculate position for new entry */
194 j = s->history_idx + s->n_history;
204 /* And make sure we don't store more entries than fit in */
205 if (s->n_history > HISTORY_MAX) {
206 s->history_idx += s->n_history - HISTORY_MAX;
207 REDUCE(s->history_idx);
208 s->n_history = HISTORY_MAX;
212 static double avg_gradient(pa_smoother *s, pa_usec_t x) {
213 unsigned i, j, c = 0;
214 int64_t ax = 0, ay = 0, k, t;
217 /* Too few measurements, assume gradient of 1 */
218 if (s->n_history < s->min_history)
221 /* First, calculate average of all measurements */
223 for (j = s->n_history; j > 0; j--) {
225 ax += (int64_t) s->history_x[i];
226 ay += (int64_t) s->history_y[i];
232 pa_assert(c >= s->min_history);
236 /* Now, do linear regression */
240 for (j = s->n_history; j > 0; j--) {
243 dx = (int64_t) s->history_x[i] - ax;
244 dy = (int64_t) s->history_y[i] - ay;
252 r = (double) k / (double) t;
254 return (s->monotonic && r < 0) ? 0 : r;
257 static void calc_abc(pa_smoother *s) {
258 pa_usec_t ex, ey, px, py;
267 /* We have two points: (ex|ey) and (px|py) with two gradients at
268 * these points de and dp. We do a polynomial
269 * interpolation of degree 3 with these 6 values */
271 ex = s->ex; ey = s->ey;
272 px = s->px; py = s->py;
273 de = s->de; dp = s->dp;
277 /* To increase the dynamic range and symplify calculation, we
278 * move these values to the origin */
279 kx = (int64_t) px - (int64_t) ex;
280 ky = (int64_t) py - (int64_t) ey;
282 /* Calculate a, b, c for y=ax^3+bx^2+cx */
284 s->b = (((double) (3*ky)/ (double) kx - dp - (double) (2*de))) / (double) kx;
285 s->a = (dp/(double) kx - 2*s->b - de/(double) kx) / (double) (3*kx);
290 static void estimate(pa_smoother *s, pa_usec_t x, pa_usec_t *y, double *deriv) {
294 if (!s->smoothing || x >= s->px) {
297 /* The requested point is right of the point where we wanted
298 * to be on track again, thus just linearly estimate */
300 t = (int64_t) s->py + (int64_t) llrint(s->dp * (double) (x - s->px));
313 /* Ok, we're not yet on track, thus let's interpolate, and
314 * make sure that the first derivative is smooth */
321 tx -= (double) s->ex;
324 ty = (tx * (s->c + tx * (s->b + tx * s->a)));
326 /* Move back from origin */
327 ty += (double) s->ey;
329 *y = ty >= 0 ? (pa_usec_t) llrint(ty) : 0;
333 *deriv = s->c + (tx * (s->b*2 + tx * s->a*3));
336 /* Guarantee monotonicity */
339 if (deriv && *deriv < 0)
344 void pa_smoother_put(pa_smoother *s, pa_usec_t x, pa_usec_t y) {
355 x = PA_LIKELY(x >= s->time_offset) ? x - s->time_offset : 0;
360 /* First, we calculate the position we'd estimate for x, so that
361 * we can adjust our position smoothly from this one */
362 estimate(s, x, &ney, &nde);
363 s->ex = x; s->ey = ney; s->de = nde;
367 /* Then, we add the new measurement to our history */
368 add_to_history(s, x, y);
370 /* And determine the average gradient of the history */
371 s->dp = avg_gradient(s, x);
373 /* And calculate when we want to be on track again */
375 s->px = s->ex + s->adjust_time;
376 s->py = s->ry + (pa_usec_t) llrint(s->dp * (double) s->adjust_time);
382 s->abc_valid = FALSE;
384 /* pa_log_debug("put(%llu | %llu) = %llu", (unsigned long long) (x + s->time_offset), (unsigned long long) x, (unsigned long long) y); */
387 pa_usec_t pa_smoother_get(pa_smoother *s, pa_usec_t x) {
396 x = PA_LIKELY(x >= s->time_offset) ? x - s->time_offset : 0;
402 estimate(s, x, &y, NULL);
406 /* Make sure the querier doesn't jump forth and back. */
415 /* pa_log_debug("get(%llu | %llu) = %llu", (unsigned long long) (x + s->time_offset), (unsigned long long) x, (unsigned long long) y); */
420 void pa_smoother_set_time_offset(pa_smoother *s, pa_usec_t offset) {
423 s->time_offset = offset;
425 /* pa_log_debug("offset(%llu)", (unsigned long long) offset); */
428 void pa_smoother_pause(pa_smoother *s, pa_usec_t x) {
434 /* pa_log_debug("pause(%llu)", (unsigned long long) x); */
440 void pa_smoother_resume(pa_smoother *s, pa_usec_t x, pa_bool_t fix_now) {
446 if (x < s->pause_time)
449 /* pa_log_debug("resume(%llu)", (unsigned long long) x); */
452 s->time_offset += x - s->pause_time;
455 pa_smoother_fix_now(s);
458 void pa_smoother_fix_now(pa_smoother *s) {
465 pa_usec_t pa_smoother_translate(pa_smoother *s, pa_usec_t x, pa_usec_t y_delay) {
475 x = PA_LIKELY(x >= s->time_offset) ? x - s->time_offset : 0;
477 estimate(s, x, &ney, &nde);
479 /* Play safe and take the larger gradient, so that we wakeup
480 * earlier when this is used for sleeping */
484 /* pa_log_debug("translate(%llu) = %llu (%0.2f)", (unsigned long long) y_delay, (unsigned long long) ((double) y_delay / nde), nde); */
486 return (pa_usec_t) llrint((double) y_delay / nde);
489 void pa_smoother_reset(pa_smoother *s) {
493 s->abc_valid = FALSE;