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+/*
+ chronyd/chronyc - Programs for keeping computer clocks accurate.
+
+ **********************************************************************
+ * Copyright (C) Miroslav Lichvar 2009-2011, 2014, 2016, 2018
+ *
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of version 2 of the GNU General Public License as
+ * published by the Free Software Foundation.
+ *
+ * This program 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
+ * General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License along
+ * with this program; if not, write to the Free Software Foundation, Inc.,
+ * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
+ *
+ **********************************************************************
+
+ =======================================================================
+
+ Routines implementing a median sample filter.
+
+ */
+
+#include "config.h"
+
+#include "local.h"
+#include "logging.h"
+#include "memory.h"
+#include "regress.h"
+#include "samplefilt.h"
+#include "util.h"
+
+#define MIN_SAMPLES 1
+#define MAX_SAMPLES 256
+
+struct SPF_Instance_Record {
+ int min_samples;
+ int max_samples;
+ int index;
+ int used;
+ int last;
+ int avg_var_n;
+ double avg_var;
+ double max_var;
+ double combine_ratio;
+ NTP_Sample *samples;
+ int *selected;
+ double *x_data;
+ double *y_data;
+ double *w_data;
+};
+
+/* ================================================== */
+
+SPF_Instance
+SPF_CreateInstance(int min_samples, int max_samples, double max_dispersion, double combine_ratio)
+{
+ SPF_Instance filter;
+
+ filter = MallocNew(struct SPF_Instance_Record);
+
+ min_samples = CLAMP(MIN_SAMPLES, min_samples, MAX_SAMPLES);
+ max_samples = CLAMP(MIN_SAMPLES, max_samples, MAX_SAMPLES);
+ max_samples = MAX(min_samples, max_samples);
+ combine_ratio = CLAMP(0.0, combine_ratio, 1.0);
+
+ filter->min_samples = min_samples;
+ filter->max_samples = max_samples;
+ filter->index = -1;
+ filter->used = 0;
+ filter->last = -1;
+ /* Set the first estimate to the system precision */
+ filter->avg_var_n = 0;
+ filter->avg_var = SQUARE(LCL_GetSysPrecisionAsQuantum());
+ filter->max_var = SQUARE(max_dispersion);
+ filter->combine_ratio = combine_ratio;
+ filter->samples = MallocArray(NTP_Sample, filter->max_samples);
+ filter->selected = MallocArray(int, filter->max_samples);
+ filter->x_data = MallocArray(double, filter->max_samples);
+ filter->y_data = MallocArray(double, filter->max_samples);
+ filter->w_data = MallocArray(double, filter->max_samples);
+
+ return filter;
+}
+
+/* ================================================== */
+
+void
+SPF_DestroyInstance(SPF_Instance filter)
+{
+ Free(filter->samples);
+ Free(filter->selected);
+ Free(filter->x_data);
+ Free(filter->y_data);
+ Free(filter->w_data);
+ Free(filter);
+}
+
+/* ================================================== */
+
+/* Check that samples times are strictly increasing */
+
+static int
+check_sample(SPF_Instance filter, NTP_Sample *sample)
+{
+ if (filter->used <= 0)
+ return 1;
+
+ if (UTI_CompareTimespecs(&filter->samples[filter->last].time, &sample->time) >= 0) {
+ DEBUG_LOG("filter non-increasing sample time %s", UTI_TimespecToString(&sample->time));
+ return 0;
+ }
+
+ return 1;
+}
+
+/* ================================================== */
+
+int
+SPF_AccumulateSample(SPF_Instance filter, NTP_Sample *sample)
+{
+ if (!check_sample(filter, sample))
+ return 0;
+
+ filter->index++;
+ filter->index %= filter->max_samples;
+ filter->last = filter->index;
+ if (filter->used < filter->max_samples)
+ filter->used++;
+
+ filter->samples[filter->index] = *sample;
+
+ DEBUG_LOG("filter sample %d t=%s offset=%.9f peer_disp=%.9f",
+ filter->index, UTI_TimespecToString(&sample->time),
+ sample->offset, sample->peer_dispersion);
+ return 1;
+}
+
+/* ================================================== */
+
+int
+SPF_GetLastSample(SPF_Instance filter, NTP_Sample *sample)
+{
+ if (filter->last < 0)
+ return 0;
+
+ *sample = filter->samples[filter->last];
+ return 1;
+}
+
+/* ================================================== */
+
+int
+SPF_GetNumberOfSamples(SPF_Instance filter)
+{
+ return filter->used;
+}
+
+/* ================================================== */
+
+double
+SPF_GetAvgSampleDispersion(SPF_Instance filter)
+{
+ return sqrt(filter->avg_var);
+}
+
+/* ================================================== */
+
+void
+SPF_DropSamples(SPF_Instance filter)
+{
+ filter->index = -1;
+ filter->used = 0;
+}
+
+/* ================================================== */
+
+static const NTP_Sample *tmp_sort_samples;
+
+static int
+compare_samples(const void *a, const void *b)
+{
+ const NTP_Sample *s1, *s2;
+
+ s1 = &tmp_sort_samples[*(int *)a];
+ s2 = &tmp_sort_samples[*(int *)b];
+
+ if (s1->offset < s2->offset)
+ return -1;
+ else if (s1->offset > s2->offset)
+ return 1;
+ return 0;
+}
+
+/* ================================================== */
+
+static int
+select_samples(SPF_Instance filter)
+{
+ int i, j, k, o, from, to, *selected;
+ double min_dispersion;
+
+ if (filter->used < filter->min_samples)
+ return 0;
+
+ selected = filter->selected;
+
+ /* With 4 or more samples, select those that have peer dispersion smaller
+ than 1.5x of the minimum dispersion */
+ if (filter->used > 4) {
+ for (i = 1, min_dispersion = filter->samples[0].peer_dispersion; i < filter->used; i++) {
+ if (min_dispersion > filter->samples[i].peer_dispersion)
+ min_dispersion = filter->samples[i].peer_dispersion;
+ }
+
+ for (i = j = 0; i < filter->used; i++) {
+ if (filter->samples[i].peer_dispersion <= 1.5 * min_dispersion)
+ selected[j++] = i;
+ }
+ } else {
+ j = 0;
+ }
+
+ if (j < 4) {
+ /* Select all samples */
+
+ for (j = 0; j < filter->used; j++)
+ selected[j] = j;
+ }
+
+ /* And sort their indices by offset */
+ tmp_sort_samples = filter->samples;
+ qsort(selected, j, sizeof (int), compare_samples);
+
+ /* Select samples closest to the median */
+ if (j > 2) {
+ from = j * (1.0 - filter->combine_ratio) / 2.0;
+ from = CLAMP(1, from, (j - 1) / 2);
+ } else {
+ from = 0;
+ }
+
+ to = j - from;
+
+ /* Mark unused samples and sort the rest by their time */
+
+ o = filter->used - filter->index - 1;
+
+ for (i = 0; i < from; i++)
+ selected[i] = -1;
+ for (; i < to; i++)
+ selected[i] = (selected[i] + o) % filter->used;
+ for (; i < filter->used; i++)
+ selected[i] = -1;
+
+ for (i = from; i < to; i++) {
+ j = selected[i];
+ selected[i] = -1;
+ while (j != -1 && selected[j] != j) {
+ k = selected[j];
+ selected[j] = j;
+ j = k;
+ }
+ }
+
+ for (i = j = 0, k = -1; i < filter->used; i++) {
+ if (selected[i] != -1)
+ selected[j++] = (selected[i] + filter->used - o) % filter->used;
+ }
+
+ assert(j > 0 && j <= filter->max_samples);
+
+ return j;
+}
+
+/* ================================================== */
+
+static int
+combine_selected_samples(SPF_Instance filter, int n, NTP_Sample *result)
+{
+ double mean_peer_dispersion, mean_root_dispersion, mean_peer_delay, mean_root_delay;
+ double mean_x, mean_y, disp, var, prev_avg_var;
+ NTP_Sample *sample, *last_sample;
+ int i, dof;
+
+ last_sample = &filter->samples[filter->selected[n - 1]];
+
+ /* Prepare data */
+ for (i = 0; i < n; i++) {
+ sample = &filter->samples[filter->selected[i]];
+
+ filter->x_data[i] = UTI_DiffTimespecsToDouble(&sample->time, &last_sample->time);
+ filter->y_data[i] = sample->offset;
+ filter->w_data[i] = sample->peer_dispersion;
+ }
+
+ /* Calculate mean offset and interval since the last sample */
+ for (i = 0, mean_x = mean_y = 0.0; i < n; i++) {
+ mean_x += filter->x_data[i];
+ mean_y += filter->y_data[i];
+ }
+ mean_x /= n;
+ mean_y /= n;
+
+ if (n >= 4) {
+ double b0, b1, s2, sb0, sb1;
+
+ /* Set y axis to the mean sample time */
+ for (i = 0; i < n; i++)
+ filter->x_data[i] -= mean_x;
+
+ /* Make a linear fit and use the estimated standard deviation of the
+ intercept as dispersion */
+ RGR_WeightedRegression(filter->x_data, filter->y_data, filter->w_data, n,
+ &b0, &b1, &s2, &sb0, &sb1);
+ var = s2;
+ disp = sb0;
+ dof = n - 2;
+ } else if (n >= 2) {
+ for (i = 0, disp = 0.0; i < n; i++)
+ disp += (filter->y_data[i] - mean_y) * (filter->y_data[i] - mean_y);
+ var = disp / (n - 1);
+ disp = sqrt(var);
+ dof = n - 1;
+ } else {
+ var = filter->avg_var;
+ disp = sqrt(var);
+ dof = 1;
+ }
+
+ /* Avoid working with zero dispersion */
+ if (var < 1e-20) {
+ var = 1e-20;
+ disp = sqrt(var);
+ }
+
+ /* Drop the sample if the variance is larger than the maximum */
+ if (filter->max_var > 0.0 && var > filter->max_var) {
+ DEBUG_LOG("filter dispersion too large disp=%.9f max=%.9f",
+ sqrt(var), sqrt(filter->max_var));
+ return 0;
+ }
+
+ prev_avg_var = filter->avg_var;
+
+ /* Update the exponential moving average of the variance */
+ if (filter->avg_var_n > 50) {
+ filter->avg_var += dof / (dof + 50.0) * (var - filter->avg_var);
+ } else {
+ filter->avg_var = (filter->avg_var * filter->avg_var_n + var * dof) /
+ (dof + filter->avg_var_n);
+ if (filter->avg_var_n == 0)
+ prev_avg_var = filter->avg_var;
+ filter->avg_var_n += dof;
+ }
+
+ /* Use the long-term average of variance instead of the estimated value
+ unless it is significantly smaller in order to reduce the noise in
+ sourcestats weights */
+ if (var * dof / RGR_GetChi2Coef(dof) < prev_avg_var)
+ disp = sqrt(filter->avg_var) * disp / sqrt(var);
+
+ mean_peer_dispersion = mean_root_dispersion = mean_peer_delay = mean_root_delay = 0.0;
+
+ for (i = 0; i < n; i++) {
+ sample = &filter->samples[filter->selected[i]];
+
+ mean_peer_dispersion += sample->peer_dispersion;
+ mean_root_dispersion += sample->root_dispersion;
+ mean_peer_delay += sample->peer_delay;
+ mean_root_delay += sample->root_delay;
+ }
+
+ mean_peer_dispersion /= n;
+ mean_root_dispersion /= n;
+ mean_peer_delay /= n;
+ mean_root_delay /= n;
+
+ UTI_AddDoubleToTimespec(&last_sample->time, mean_x, &result->time);
+ result->offset = mean_y;
+ result->peer_dispersion = MAX(disp, mean_peer_dispersion);
+ result->root_dispersion = MAX(disp, mean_root_dispersion);
+ result->peer_delay = mean_peer_delay;
+ result->root_delay = mean_root_delay;
+ result->stratum = last_sample->stratum;
+ result->leap = last_sample->leap;
+
+ return 1;
+}
+
+/* ================================================== */
+
+int
+SPF_GetFilteredSample(SPF_Instance filter, NTP_Sample *sample)
+{
+ int n;
+
+ n = select_samples(filter);
+
+ if (n < 1)
+ return 0;
+
+ if (!combine_selected_samples(filter, n, sample))
+ return 0;
+
+ SPF_DropSamples(filter);
+
+ return 1;
+}
+
+/* ================================================== */
+
+void
+SPF_SlewSamples(SPF_Instance filter, struct timespec *when, double dfreq, double doffset)
+{
+ int i, first, last;
+ double delta_time;
+
+ if (filter->last < 0)
+ return;
+
+ /* Always slew the last sample as it may be returned even if no new
+ samples were accumulated */
+ if (filter->used > 0) {
+ first = 0;
+ last = filter->used - 1;
+ } else {
+ first = last = filter->last;
+ }
+
+ for (i = first; i <= last; i++) {
+ UTI_AdjustTimespec(&filter->samples[i].time, when, &filter->samples[i].time,
+ &delta_time, dfreq, doffset);
+ filter->samples[i].offset -= delta_time;
+ }
+}
+
+/* ================================================== */
+
+void
+SPF_AddDispersion(SPF_Instance filter, double dispersion)
+{
+ int i;
+
+ for (i = 0; i < filter->used; i++) {
+ filter->samples[i].peer_dispersion += dispersion;
+ filter->samples[i].root_dispersion += dispersion;
+ }
+}
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