{
finalise {
# we just fetch samples at end
sp <- samples(SPEED);
sl <- samples(SLOPE);
yy <- 100 * c(
sum(sp[sl[i]<=-15.0]),
sum(sp[sl[i]>-15.0 && sl[i]< -10.0]),
sum(sp[sl[i]>-10.0 && sl[i]< -5.0]),
sum(sp[sl[i]>-5.0 && sl[i]< -0.5]),
sum(sp[sl[i]>-0.5 && sl[i]<0.5]),
sum(sp[sl[i]>0.5 && sl[i]<5.0]),
sum(sp[sl[i]>5.0 && sl[i]<10.0]),
sum(sp[sl[i]>10.0 && sl[i]<15.0]),
sum(sp[sl[i]>=15.0])
)/sum(sp);
xx <- c("down lt -15","down -15~-10","down -10~-5","down -5~-0.5","flat","up 0.5~5","up 5~10","up 10~15","up>15");
}
x { xx; }
y { yy; }
}
Added this as an example chart since I think it can be illustrative of GC formulas expressiveness (great job, Mark!): https://github.com/GoldenCheetah/GoldenCheetah/wiki/UG_Special-Topics_Working-with-User-Charts#slope-histogram-example-chart --
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That is very elegant and shows what can be done with good understanding (and reading the Wiki).
A few years ago I was interested in analysing upcoming rides (from others ride data posted on RWGPS) for % of distance within slope bands to target my amount of hill training so created a basic less elegant bar chart with specific bands of interest. Not what the OP wanted (% of time at slope) but possibly of interest.