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authorDaniel Baumann <daniel.baumann@progress-linux.org>2024-05-04 14:18:53 +0000
committerDaniel Baumann <daniel.baumann@progress-linux.org>2024-05-04 14:18:53 +0000
commita0e0018c9a7ef5ce7f6d2c3ae16aecbbd16a8f67 (patch)
tree8feaf1a1932871b139b3b30be4c09c66489918be /netem
parentInitial commit. (diff)
downloadiproute2-a0e0018c9a7ef5ce7f6d2c3ae16aecbbd16a8f67.tar.xz
iproute2-a0e0018c9a7ef5ce7f6d2c3ae16aecbbd16a8f67.zip
Adding upstream version 6.1.0.upstream/6.1.0upstream
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'netem')
-rw-r--r--netem/.gitignore5
-rw-r--r--netem/Makefile32
-rw-r--r--netem/README.distribution97
-rw-r--r--netem/experimental.dat13448
-rw-r--r--netem/maketable.c234
-rw-r--r--netem/normal.c51
-rw-r--r--netem/pareto.c41
-rw-r--r--netem/paretonormal.c81
-rw-r--r--netem/stats.c77
9 files changed, 14066 insertions, 0 deletions
diff --git a/netem/.gitignore b/netem/.gitignore
new file mode 100644
index 0000000..e36f396
--- /dev/null
+++ b/netem/.gitignore
@@ -0,0 +1,5 @@
+*.dist
+maketable
+normal
+pareto
+paretonormal
diff --git a/netem/Makefile b/netem/Makefile
new file mode 100644
index 0000000..ba4c5a7
--- /dev/null
+++ b/netem/Makefile
@@ -0,0 +1,32 @@
+# SPDX-License-Identifier: GPL-2.0
+include ../config.mk
+
+DISTGEN = maketable normal pareto paretonormal
+DISTDATA = normal.dist pareto.dist paretonormal.dist experimental.dist
+
+HOSTCC ?= $(CC)
+CCOPTS = $(CBUILD_CFLAGS)
+LDLIBS += -lm
+
+all: $(DISTGEN) $(DISTDATA)
+
+$(DISTGEN):
+ $(HOSTCC) $(CCOPTS) -I../include -o $@ $@.c -lm
+
+%.dist: %
+ ./$* > $@
+
+experimental.dist: maketable experimental.dat
+ ./maketable experimental.dat > experimental.dist
+
+stats: stats.c
+ $(HOSTCC) $(CCOPTS) -I../include -o $@ $@.c -lm
+
+install: all
+ mkdir -p $(DESTDIR)$(LIBDIR)/tc
+ for i in $(DISTDATA); \
+ do install -m 644 $$i $(DESTDIR)$(LIBDIR)/tc; \
+ done
+
+clean:
+ rm -f $(DISTDATA) $(DISTGEN)
diff --git a/netem/README.distribution b/netem/README.distribution
new file mode 100644
index 0000000..6d52785
--- /dev/null
+++ b/netem/README.distribution
@@ -0,0 +1,97 @@
+Notes about distribution tables from Nistnet
+-------------------------------------------------------------------------------
+I. About the distribution tables
+
+The table used for "synthesizing" the distribution is essentially a scaled,
+translated, inverse to the cumulative distribution function.
+
+Here's how to think about it: Let F() be the cumulative distribution
+function for a probability distribution X. We'll assume we've scaled
+things so that X has mean 0 and standard deviation 1, though that's not
+so important here. Then:
+
+ F(x) = P(X <= x) = \int_{-inf}^x f
+
+where f is the probability density function.
+
+F is monotonically increasing, so has an inverse function G, with range
+0 to 1. Here, G(t) = the x such that P(X <= x) = t. (In general, G may
+have singularities if X has point masses, i.e., points x such that
+P(X = x) > 0.)
+
+Now we create a tabular representation of G as follows: Choose some table
+size N, and for the ith entry, put in G(i/N). Let's call this table T.
+
+The claim now is, I can create a (discrete) random variable Y whose
+distribution has the same approximate "shape" as X, simply by letting
+Y = T(U), where U is a discrete uniform random variable with range 1 to N.
+To see this, it's enough to show that Y's cumulative distribution function,
+(let's call it H), is a discrete approximation to F. But
+
+ H(x) = P(Y <= x)
+ = (# of entries in T <= x) / N -- as Y chosen uniformly from T
+ = i/N, where i is the largest integer such that G(i/N) <= x
+ = i/N, where i is the largest integer such that i/N <= F(x)
+ -- since G and F are inverse functions (and F is
+ increasing)
+ = floor(N*F(x))/N
+
+as desired.
+
+II. How to create distribution tables (in theory)
+
+How can we create this table in practice? In some cases, F may have a
+simple expression which allows evaluating its inverse directly. The
+pareto distribution is one example of this. In other cases, and
+especially for matching an experimentally observed distribution, it's
+easiest simply to create a table for F and "invert" it. Here, we give
+a concrete example, namely how the new "experimental" distribution was
+created.
+
+1. Collect enough data points to characterize the distribution. Here, I
+collected 25,000 "ping" roundtrip times to a "distant" point (time.nist.gov).
+That's far more data than is really necessary, but it was fairly painless to
+collect it, so...
+
+2. Normalize the data so that it has mean 0 and standard deviation 1.
+
+3. Determine the cumulative distribution. The code I wrote creates a table
+covering the range -10 to +10, with granularity .00005. Obviously, this
+is absurdly over-precise, but since it's a one-time only computation, I
+figured it hardly mattered.
+
+4. Invert the table: for each table entry F(x) = y, make the y*TABLESIZE
+(here, 4096) entry be x*TABLEFACTOR (here, 8192). This creates a table
+for the ("normalized") inverse of size TABLESIZE, covering its domain 0
+to 1 with granularity 1/TABLESIZE. Note that even with the granularity
+used in creating the table for F, it's possible not all the entries in
+the table for G will be filled in. So, make a pass through the
+inverse's table, filling in any missing entries by linear interpolation.
+
+III. How to create distribution tables (in practice)
+
+If you want to do all this yourself, I've provided several tools to help:
+
+1. maketable does the steps 2-4 above, and then generates the appropriate
+header file. So if you have your own time distribution, you can generate
+the header simply by:
+
+ maketable < time.values > header.h
+
+2. As explained in the other README file, the somewhat sleazy way I have
+of generating correlated values needs correction. You can generate your
+own correction tables by compiling makesigtable and makemutable with
+your header file. Check the Makefile to see how this is done.
+
+3. Warning: maketable, makesigtable and especially makemutable do
+enormous amounts of floating point arithmetic. Don't try running
+these on an old 486. (NIST Net itself will run fine on such a
+system, since in operation, it just needs to do a few simple integral
+calculations. But getting there takes some work.)
+
+4. The tables produced are all normalized for mean 0 and standard
+deviation 1. How do you know what values to use for real? Here, I've
+provided a simple "stats" utility. Give it a series of floating point
+values, and it will return their mean (mu), standard deviation (sigma),
+and correlation coefficient (rho). You can then plug these values
+directly into NIST Net.
diff --git a/netem/experimental.dat b/netem/experimental.dat
new file mode 100644
index 0000000..3663a3e
--- /dev/null
+++ b/netem/experimental.dat
@@ -0,0 +1,13448 @@
+211.6
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+179.8
+179.3
+178.6
+180.4
+174.9
+177.8
+187.4
+183.7
+181.6
+177.7
+185.4
+180.6
+178.5
+182.8
+179.9
+177.8
+178.0
+184.3
+191.0
+181.1
+197.9
+181.1
+197.8
+175.6
+176.9
+177.9
+187.4
+180.8
+180.6
+174.9
+180.8
+175.7
+176.4
+179.9
+178.3
+178.3
+178.4
+196.4
+177.3
+184.3
+177.3
+176.5
+181.0
+175.0
+179.2
+178.1
+183.3
+177.0
+175.2
+177.2
+173.5
+176.5
+177.0
+178.6
+181.3
+189.2
+186.4
+177.4
+181.7
+176.2
+194.1
+177.9
+186.6
+182.6
+176.6
+175.4
+250.3
+178.7
+183.0
+182.7
+183.1
+183.6
+178.8
+178.3
+179.1
+177.7
+177.9
+186.2
+195.4
+179.2
+183.3
+183.8
+186.3
+182.7
+178.9
+196.0
+177.1
+184.5
+188.3
+188.4
+181.5
+190.1
+179.0
+177.2
+189.1
+184.1
+184.2
+178.7
+187.4
+182.5
+189.8
+181.1
+186.0
+178.8
+178.0
+176.9
+180.6
+174.3
+180.4
+176.7
+175.5
+193.7
+189.7
+196.6
+178.9
+186.3
+182.5
+185.7
+175.7
+176.0
+194.1
+181.0
+178.6
+178.4
+186.8
+175.6
+185.4
+180.7
+184.7
+188.1
+180.4
+183.0
+178.4
+185.1
+181.8
+175.8
+178.9
+177.9
+177.9
+176.5
+290.3
+181.8
+180.8
+183.9
+190.8
+179.0
+183.7
+181.9
+177.5
+178.4
+179.4
+179.9
+174.2
+182.7
+182.9
+178.8
+179.2
+183.2
+181.8
+175.6
+179.0
+175.7
+178.1
+179.2
+183.8
+187.8
+187.3
+175.7
+175.0
+185.5
+175.3
+181.9
+176.7
+184.6
+185.8
+181.1
+186.6
+178.8
+183.4
+175.1
+178.4
+179.6
+176.7
+177.1
+176.5
+181.7
+199.1
+194.8
+176.5
+182.2
+189.9
+177.6
+179.3
+184.9
+178.1
+176.4
+184.6
+174.6
+188.6
+183.7
+178.1
+177.7
+186.1
+176.3
+180.9
+180.3
+181.0
+180.0
+180.0
+180.4
+174.8
+179.5
+176.5
+179.9
+175.6
+184.0
+176.4
+191.6
+178.4
+184.8
+186.7
+197.0
diff --git a/netem/maketable.c b/netem/maketable.c
new file mode 100644
index 0000000..ccb8f0c
--- /dev/null
+++ b/netem/maketable.c
@@ -0,0 +1,234 @@
+/*
+ * Experimental data distribution table generator
+ * Taken from the uncopyrighted NISTnet code (public domain).
+ *
+ * Read in a series of "random" data values, either
+ * experimentally or generated from some probability distribution.
+ * From this, create the inverse distribution table used to approximate
+ * the distribution.
+ */
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <malloc.h>
+#include <string.h>
+#include <sys/types.h>
+#include <sys/stat.h>
+
+
+double *
+readdoubles(FILE *fp, int *number)
+{
+ struct stat info;
+ double *x;
+ int limit;
+ int n=0, i;
+
+ if (!fstat(fileno(fp), &info) &&
+ info.st_size > 0) {
+ limit = 2*info.st_size/sizeof(double); /* @@ approximate */
+ } else {
+ limit = 10000;
+ }
+
+ x = calloc(limit, sizeof(double));
+ if (!x) {
+ perror("double alloc");
+ exit(3);
+ }
+
+ for (i=0; i<limit; ++i){
+ if (fscanf(fp, "%lf", &x[i]) != 1 ||
+ feof(fp))
+ break;
+ ++n;
+ }
+ *number = n;
+ return x;
+}
+
+void
+arraystats(double *x, int limit, double *mu, double *sigma, double *rho)
+{
+ int n=0, i;
+ double sumsquare=0.0, sum=0.0, top=0.0;
+ double sigma2=0.0;
+
+ for (i=0; i<limit; ++i){
+ sumsquare += x[i]*x[i];
+ sum += x[i];
+ ++n;
+ }
+ *mu = sum/(double)n;
+ *sigma = sqrt((sumsquare - (double)n*(*mu)*(*mu))/(double)(n-1));
+
+ for (i=1; i < n; ++i){
+ top += ((double)x[i]- *mu)*((double)x[i-1]- *mu);
+ sigma2 += ((double)x[i-1] - *mu)*((double)x[i-1] - *mu);
+
+ }
+ *rho = top/sigma2;
+}
+
+/* Create a (normalized) distribution table from a set of observed
+ * values. The table is fixed to run from (as it happens) -4 to +4,
+ * with granularity .00002.
+ */
+
+#define TABLESIZE 16384/4
+#define TABLEFACTOR 8192
+#ifndef MINSHORT
+#define MINSHORT -32768
+#define MAXSHORT 32767
+#endif
+
+/* Since entries in the inverse are scaled by TABLEFACTOR, and can't be bigger
+ * than MAXSHORT, we don't bother looking at a larger domain than this:
+ */
+#define DISTTABLEDOMAIN ((MAXSHORT/TABLEFACTOR)+1)
+#define DISTTABLEGRANULARITY 50000
+#define DISTTABLESIZE (DISTTABLEDOMAIN*DISTTABLEGRANULARITY*2)
+
+static int *
+makedist(double *x, int limit, double mu, double sigma)
+{
+ int *table;
+ int i, index, first=DISTTABLESIZE, last=0;
+ double input;
+
+ table = calloc(DISTTABLESIZE, sizeof(int));
+ if (!table) {
+ perror("table alloc");
+ exit(3);
+ }
+
+ for (i=0; i < limit; ++i) {
+ /* Normalize value */
+ input = (x[i]-mu)/sigma;
+
+ index = (int)rint((input+DISTTABLEDOMAIN)*DISTTABLEGRANULARITY);
+ if (index < 0) index = 0;
+ if (index >= DISTTABLESIZE) index = DISTTABLESIZE-1;
+ ++table[index];
+ if (index > last)
+ last = index +1;
+ if (index < first)
+ first = index;
+ }
+ return table;
+}
+
+/* replace an array by its cumulative distribution */
+static void
+cumulativedist(int *table, int limit, int *total)
+{
+ int accum=0;
+
+ while (--limit >= 0) {
+ accum += *table;
+ *table++ = accum;
+ }
+ *total = accum;
+}
+
+static short *
+inverttable(int *table, int inversesize, int tablesize, int cumulative)
+{
+ int i, inverseindex, inversevalue;
+ short *inverse;
+ double findex, fvalue;
+
+ inverse = (short *)malloc(inversesize*sizeof(short));
+ for (i=0; i < inversesize; ++i) {
+ inverse[i] = MINSHORT;
+ }
+ for (i=0; i < tablesize; ++i) {
+ findex = ((double)i/(double)DISTTABLEGRANULARITY) - DISTTABLEDOMAIN;
+ fvalue = (double)table[i]/(double)cumulative;
+ inverseindex = (int)rint(fvalue*inversesize);
+ inversevalue = (int)rint(findex*TABLEFACTOR);
+ if (inversevalue <= MINSHORT) inversevalue = MINSHORT+1;
+ if (inversevalue > MAXSHORT) inversevalue = MAXSHORT;
+ if (inverseindex >= inversesize) inverseindex = inversesize- 1;
+
+ inverse[inverseindex] = inversevalue;
+ }
+ return inverse;
+
+}
+
+/* Run simple linear interpolation over the table to fill in missing entries */
+static void
+interpolatetable(short *table, int limit)
+{
+ int i, j, last, lasti = -1;
+
+ last = MINSHORT;
+ for (i=0; i < limit; ++i) {
+ if (table[i] == MINSHORT) {
+ for (j=i; j < limit; ++j)
+ if (table[j] != MINSHORT)
+ break;
+ if (j < limit) {
+ table[i] = last + (i-lasti)*(table[j]-last)/(j-lasti);
+ } else {
+ table[i] = last + (i-lasti)*(MAXSHORT-last)/(limit-lasti);
+ }
+ } else {
+ last = table[i];
+ lasti = i;
+ }
+ }
+}
+
+static void
+printtable(const short *table, int limit)
+{
+ int i;
+
+ printf("# This is the distribution table for the experimental distribution.\n");
+
+ for (i=0 ; i < limit; ++i) {
+ printf("%d%c", table[i],
+ (i % 8) == 7 ? '\n' : ' ');
+ }
+}
+
+int
+main(int argc, char **argv)
+{
+ FILE *fp;
+ double *x;
+ double mu, sigma, rho;
+ int limit;
+ int *table;
+ short *inverse;
+ int total;
+
+ if (argc > 1) {
+ if (!(fp = fopen(argv[1], "r"))) {
+ perror(argv[1]);
+ exit(1);
+ }
+ } else {
+ fp = stdin;
+ }
+ x = readdoubles(fp, &limit);
+ if (limit <= 0) {
+ fprintf(stderr, "Nothing much read!\n");
+ exit(2);
+ }
+ arraystats(x, limit, &mu, &sigma, &rho);
+#ifdef DEBUG
+ fprintf(stderr, "%d values, mu %10.4f, sigma %10.4f, rho %10.4f\n",
+ limit, mu, sigma, rho);
+#endif
+
+ table = makedist(x, limit, mu, sigma);
+ free((void *) x);
+ cumulativedist(table, DISTTABLESIZE, &total);
+ inverse = inverttable(table, TABLESIZE, DISTTABLESIZE, total);
+ interpolatetable(inverse, TABLESIZE);
+ printtable(inverse, TABLESIZE);
+ return 0;
+}
diff --git a/netem/normal.c b/netem/normal.c
new file mode 100644
index 0000000..90963f4
--- /dev/null
+++ b/netem/normal.c
@@ -0,0 +1,51 @@
+/*
+ * Normal distribution table generator
+ * Taken from the uncopyrighted NISTnet code.
+ */
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <string.h>
+#include <limits.h>
+
+#include <linux/types.h>
+#include <linux/pkt_sched.h>
+
+#define TABLESIZE 16384
+#define TABLEFACTOR NETEM_DIST_SCALE
+
+static double
+normal(double x, double mu, double sigma)
+{
+ return .5 + .5*erf((x-mu)/(sqrt(2.0)*sigma));
+}
+
+
+int
+main(int argc, char **argv)
+{
+ int i, n;
+ double x;
+ double table[TABLESIZE+1];
+
+ for (x = -10.0; x < 10.05; x += .00005) {
+ i = rint(TABLESIZE * normal(x, 0.0, 1.0));
+ table[i] = x;
+ }
+
+
+ printf("# This is the distribution table for the normal distribution.\n");
+ for (i = n = 0; i < TABLESIZE; i += 4) {
+ int value = (int) rint(table[i]*TABLEFACTOR);
+ if (value < SHRT_MIN) value = SHRT_MIN;
+ if (value > SHRT_MAX) value = SHRT_MAX;
+
+ printf(" %d", value);
+ if (++n == 8) {
+ putchar('\n');
+ n = 0;
+ }
+ }
+
+ return 0;
+}
diff --git a/netem/pareto.c b/netem/pareto.c
new file mode 100644
index 0000000..51d9437
--- /dev/null
+++ b/netem/pareto.c
@@ -0,0 +1,41 @@
+/*
+ * Pareto distribution table generator
+ * Taken from the uncopyrighted NISTnet code.
+ */
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <limits.h>
+
+#include <linux/types.h>
+#include <linux/pkt_sched.h>
+
+static const double a=3.0;
+#define TABLESIZE 16384
+#define TABLEFACTOR NETEM_DIST_SCALE
+
+int
+main(int argc, char **argv)
+{
+ int i, n;
+ double dvalue;
+
+ printf("# This is the distribution table for the pareto distribution.\n");
+
+ for (i = 65536, n = 0; i > 0; i -= 16) {
+ dvalue = (double)i/(double)65536;
+ dvalue = 1.0/pow(dvalue, 1.0/a);
+ dvalue -= 1.5;
+ dvalue *= (4.0/3.0)*(double)TABLEFACTOR;
+ if (dvalue > 32767)
+ dvalue = 32767;
+
+ printf(" %d", (int)rint(dvalue));
+ if (++n == 8) {
+ putchar('\n');
+ n = 0;
+ }
+ }
+
+ return 0;
+}
diff --git a/netem/paretonormal.c b/netem/paretonormal.c
new file mode 100644
index 0000000..9773e37
--- /dev/null
+++ b/netem/paretonormal.c
@@ -0,0 +1,81 @@
+/*
+ * Paretoormal distribution table generator
+ *
+ * This distribution is simply .25*normal + .75*pareto; a combination
+ * which seems to match experimentally observed distributions reasonably
+ * well, but is computationally easy to handle.
+ * The entries represent a scaled inverse of the cumulative distribution
+ * function.
+ *
+ * Taken from the uncopyrighted NISTnet code.
+ */
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+#include <limits.h>
+#include <malloc.h>
+
+#include <linux/types.h>
+#include <linux/pkt_sched.h>
+
+#define TABLESIZE 16384
+#define TABLEFACTOR NETEM_DIST_SCALE
+
+static double
+normal(double x, double mu, double sigma)
+{
+ return .5 + .5*erf((x-mu)/(sqrt(2.0)*sigma));
+}
+
+static const double a=3.0;
+
+static int
+paretovalue(int i)
+{
+ double dvalue;
+
+ i = 65536-4*i;
+ dvalue = (double)i/(double)65536;
+ dvalue = 1.0/pow(dvalue, 1.0/a);
+ dvalue -= 1.5;
+ dvalue *= (4.0/3.0)*(double)TABLEFACTOR;
+ if (dvalue > 32767)
+ dvalue = 32767;
+ return (int)rint(dvalue);
+}
+
+int
+main(int argc, char **argv)
+{
+ int i,n;
+ double x;
+ double table[TABLESIZE+1];
+
+ for (x = -10.0; x < 10.05; x += .00005) {
+ i = rint(TABLESIZE*normal(x, 0.0, 1.0));
+ table[i] = x;
+ }
+ printf(
+"# This is the distribution table for the paretonormal distribution.\n"
+ );
+
+ for (i = n = 0; i < TABLESIZE; i += 4) {
+ int normvalue, parvalue, value;
+
+ normvalue = (int) rint(table[i]*TABLEFACTOR);
+ parvalue = paretovalue(i);
+
+ value = (normvalue+3*parvalue)/4;
+ if (value < SHRT_MIN) value = SHRT_MIN;
+ if (value > SHRT_MAX) value = SHRT_MAX;
+
+ printf(" %d", value);
+ if (++n == 8) {
+ putchar('\n');
+ n = 0;
+ }
+ }
+
+ return 0;
+}
diff --git a/netem/stats.c b/netem/stats.c
new file mode 100644
index 0000000..ed70f16
--- /dev/null
+++ b/netem/stats.c
@@ -0,0 +1,77 @@
+/*
+ * Experimental data distribution table generator
+ * Taken from the uncopyrighted NISTnet code (public domain).
+ *
+ * Rread in a series of "random" data values, either
+ * experimentally or generated from some probability distribution.
+ * From this, report statistics.
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <math.h>
+#include <malloc.h>
+#include <sys/types.h>
+#include <sys/stat.h>
+
+void
+stats(FILE *fp)
+{
+ struct stat info;
+ double *x;
+ int limit;
+ int n=0, i;
+ double mu=0.0, sigma=0.0, sumsquare=0.0, sum=0.0, top=0.0, rho=0.0;
+ double sigma2=0.0;
+
+ fstat(fileno(fp), &info);
+ if (info.st_size > 0) {
+ limit = 2*info.st_size/sizeof(double); /* @@ approximate */
+ } else {
+ limit = 10000;
+ }
+ x = (double *)malloc(limit*sizeof(double));
+
+ for (i=0; i<limit; ++i){
+ fscanf(fp, "%lf", &x[i]);
+ if (feof(fp))
+ break;
+ sumsquare += x[i]*x[i];
+ sum += x[i];
+ ++n;
+ }
+ mu = sum/(double)n;
+ sigma = sqrt((sumsquare - (double)n*mu*mu)/(double)(n-1));
+
+ for (i=1; i < n; ++i){
+ top += ((double)x[i]-mu)*((double)x[i-1]-mu);
+ sigma2 += ((double)x[i-1] - mu)*((double)x[i-1] - mu);
+
+ }
+ rho = top/sigma2;
+
+ printf("mu = %12.6f\n", mu);
+ printf("sigma = %12.6f\n", sigma);
+ printf("rho = %12.6f\n", rho);
+ /*printf("sigma2 = %10.4f\n", sqrt(sigma2/(double)(n-1)));*/
+ /*printf("correlation rho = %10.6f\n", top/((double)(n-1)*sigma*sigma));*/
+}
+
+
+int
+main(int argc, char **argv)
+{
+ FILE *fp;
+
+ if (argc > 1) {
+ fp = fopen(argv[1], "r");
+ if (!fp) {
+ perror(argv[1]);
+ exit(1);
+ }
+ } else {
+ fp = stdin;
+ }
+ stats(fp);
+ return 0;
+}