diff --git a/src/rrd_graph.c b/src/rrd_graph.c
index e295397e3caa9458642577e4e7e4b755d26c2c47..fb3c7e9c1381d29f519f90f6c6e3f567a25624f3 100644 (file)
--- a/src/rrd_graph.c
+++ b/src/rrd_graph.c
/****************************************************************************
- * RRDtool 1.2.23 Copyright by Tobi Oetiker, 1997-2007
+ * RRDtool 1.2.99907080300 Copyright by Tobi Oetiker, 1997-2007
****************************************************************************
* rrd__graph.c produce graphs from data in rrdfiles
****************************************************************************/
image_desc_t *im)
{
unsigned long i, ii;
- cairo_status_t status;
+ cairo_status_t status = 0;
if (im == NULL)
return 0;
if (im->font_options)
cairo_font_options_destroy(im->font_options);
- status = cairo_status(im->cr);
-
- if (im->cr)
+ if (im->cr) {
+ status = cairo_status(im->cr);
cairo_destroy(im->cr);
+ }
if (im->surface)
cairo_surface_destroy(im->surface);
if (status)
return 0;
}
+static int AlmostEqual2sComplement(
+ float A,
+ float B,
+ int maxUlps)
+{
+
+ int aInt = *(int *) &A;
+ int bInt = *(int *) &B;
+ int intDiff;
+
+ /* Make sure maxUlps is non-negative and small enough that the
+ default NAN won't compare as equal to anything. */
+
+ /* assert(maxUlps > 0 && maxUlps < 4 * 1024 * 1024); */
+
+ /* Make aInt lexicographically ordered as a twos-complement int */
+
+ if (aInt < 0)
+ aInt = 0x80000000l - aInt;
+
+ /* Make bInt lexicographically ordered as a twos-complement int */
+
+ if (bInt < 0)
+ bInt = 0x80000000l - bInt;
+
+ intDiff = abs(aInt - bInt);
+
+ if (intDiff <= maxUlps)
+ return 1;
+
+ return 0;
+}
+
/* massage data so, that we get one value for each x coordinate in the graph */
int data_proc(
image_desc_t *im)
}
/* adjust min and max values */
+ /* for logscale we add something on top */
if (isnan(im->minval)
- /* don't adjust low-end with log scale *//* why not? */
|| ((!im->rigid) && im->minval > minval)
) {
if (im->logarithmic)
else
im->maxval = maxval;
}
+
/* make sure min is smaller than max */
if (im->minval > im->maxval) {
- im->minval = 0.99 * im->maxval;
+ if (im->minval > 0)
+ im->minval = 0.99 * im->maxval;
+ else
+ im->minval = 1.01 * im->maxval;
}
/* make sure min and max are not equal */
- if (im->minval == im->maxval) {
- im->maxval *= 1.01;
- if (!im->logarithmic) {
- im->minval *= 0.99;
- }
+ if (AlmostEqual2sComplement(im->minval,im->maxval,4)) {
+ if (im->maxval > 0)
+ im->maxval *= 1.01;
+ else
+ im->maxval *= 0.99;
+
/* make sure min and max are not both zero */
- if (im->maxval == 0.0) {
+ if (AlmostEqual2sComplement(im->maxval,0,4)) {
im->maxval = 1.0;
}
}
/* yes we are loosing precision by doing tos with floats instead of doubles
but it seems more stable this way. */
-static int AlmostEqual2sComplement(
- float A,
- float B,
- int maxUlps)
-{
-
- int aInt = *(int *) &A;
- int bInt = *(int *) &B;
- int intDiff;
-
- /* Make sure maxUlps is non-negative and small enough that the
- default NAN won't compare as equal to anything. */
-
- /* assert(maxUlps > 0 && maxUlps < 4 * 1024 * 1024); */
-
- /* Make aInt lexicographically ordered as a twos-complement int */
-
- if (aInt < 0)
- aInt = 0x80000000l - aInt;
-
- /* Make bInt lexicographically ordered as a twos-complement int */
-
- if (bInt < 0)
- bInt = 0x80000000l - bInt;
-
- intDiff = abs(aInt - bInt);
-
- if (intDiff <= maxUlps)
- return 1;
-
- return 0;
-}
/* logaritmic horizontal grid */
int horizontal_log_grid(
gdes->vf.op = VDEF_MAXIMUM;
else if (!strcmp("AVERAGE", func))
gdes->vf.op = VDEF_AVERAGE;
+ else if (!strcmp("STDEV", func))
+ gdes->vf.op = VDEF_STDEV;
else if (!strcmp("MINIMUM", func))
gdes->vf.op = VDEF_MINIMUM;
else if (!strcmp("TOTAL", func))
break;
case VDEF_MAXIMUM:
case VDEF_AVERAGE:
+ case VDEF_STDEV:
case VDEF_MINIMUM:
case VDEF_TOTAL:
case VDEF_FIRST:
}
break;
case VDEF_TOTAL:
+ case VDEF_STDEV:
case VDEF_AVERAGE:{
int cnt = 0;
double sum = 0.0;
+ double average = 0.0;
for (step = 0; step < steps; step++) {
if (finite(data[step * src->ds_cnt])) {
if (dst->vf.op == VDEF_TOTAL) {
dst->vf.val = sum * src->step;
dst->vf.when = 0; /* no time component */
- } else {
+ } else if (dst->vf.op == VDEF_AVERAGE) {
dst->vf.val = sum / cnt;
dst->vf.when = 0; /* no time component */
+ } else {
+ average = sum / cnt;
+ sum = 0.0;
+ for (step = 0; step < steps; step++) {
+ if (finite(data[step * src->ds_cnt])) {
+ sum += pow((data[step * src->ds_cnt] - average), 2.0);
+ };
+ }
+ dst->vf.val = pow(sum / cnt, 0.5);
+ dst->vf.when = 0; /* no time component */
};
} else {
dst->vf.val = DNAN;