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Datafilter - meanmax(efforts)

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.. meanmax(efforts) - runs the filtering scheme used on the cp plot to
   filter out submaximal data; a mixture of comparison to a LR, maximal
   from same date considered as an averaging tail and use of power
   index.

.. it returns a vector of indexes into the meanmax data for power;
   so meanmax(POWER)[meanmax(efforts)] can be used, but since the
   index starts at 0, need to add 1 to get seconds.
This commit is contained in:
Mark Liversedge
2020-03-22 09:40:41 +00:00
parent d36b39d31e
commit ce89389451
1 changed files with 141 additions and 4 deletions
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145
src/Core/DataFilter.cpp
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@@ -33,6 +33,7 @@
#include <QDebug>
#include <QMutex>
#include "lmcurve.h"
#include "LTMTrend.h" // for LR when copying CP chart filtering mechanism
#ifdef GC_WANT_PYTHON
#include "PythonEmbed.h"
@@ -1543,7 +1544,7 @@ void Leaf::validateFilter(Context *context, DataFilterRuntime *df, Leaf *leaf)
} else {
QString symbol=*(leaf->fparms[0]->lvalue.n);
leaf->seriesType = RideFile::seriesForSymbol(symbol);
if (leaf->seriesType==RideFile::none) {
if (symbol != "efforts" && leaf->seriesType==RideFile::none) {
leaf->inerror = true;
DataFiltererrors << QString(tr("invalid series name '%1'").arg(symbol));
}
@@ -2701,16 +2702,152 @@ Result Leaf::eval(DataFilterRuntime *df, Leaf *leaf, float x, long it, RideItem
Result returning(0);
QString symbol = *(leaf->fparms[0]->lvalue.n);
// go get it for the current date range
bool rangemode = true;
if (d.from==QDate() && d.to==QDate()) {
// not in rangemode
rangemode = false;
// the ride mean max
returning.vector = m->fileCache()->meanMaxArray(leaf->seriesType);
if (symbol == "efforts") {
// keep all from a ride -- XXX TODO averaging tails removal...
returning.vector = m->fileCache()->meanMaxArray(RideFile::watts);
for(int i=0; i<returning.vector.count(); i++) returning.vector[i]=i;
} else returning.vector = m->fileCache()->meanMaxArray(leaf->seriesType);
} else {
// use a season meanmax
RideFileCache bestsCache(m->context, d.from, d.to, false, QStringList(), true, NULL);
returning.vector = bestsCache.meanMaxArray(leaf->seriesType);
}
// get meanmax, unless its efforts, where we do rather more...
if (symbol != "efforts") returning.vector = bestsCache.meanMaxArray(leaf->seriesType);
else {
// get power anyway
returning.vector = bestsCache.meanMaxArray(RideFile::watts);
// need more than 2 entries
if (returning.vector.count() > 3) {
// we need to return an index to use to filter values
// in the meanmax array; remove sub-maximals by
// averaging tails or clearly submaximal using the
// same filter that is used in the CP plot
// get data to filter
QVector<double> t;
t.resize(returning.vector.size());
for (int i=0; i<t.count(); i++) t[i]=i;
QVector<double> p = returning.vector;
QVector<QDate> w = bestsCache.meanMaxDates(leaf->seriesType);
t.remove(0);
p.remove(0);
w.remove(0);
// linear regression of the full data, to help determine
// the maximal point on the MMP curve for each day
// using brace to set scope and descope temporary variables
// as we use a fair few, but not worth making a function
double slope=0, intercept=0;
{
// we want 2m to 20min data (check bounds)
int want = p.count() > 1200 ? 1200-121 : p.count()-121;
QVector<double> j = p.mid(120, want);
QVector<double> ts = t.mid(120, want);
// convert time data to seconds (is in minutes)
// and power to joules (power x time)
for(int i=0; i<j.count(); i++) {
ts[i] = ts[i];
j[i] = (j[i] * ts[i]) ;
}
// LTMTrend does a linear regression for us, lets reuse it
// I know, we see, to have a zillion ways to do this...
LTMTrend regress(ts.data(), j.data(), ts.count());
// save away the slope and intercept
slope = regress.slope();
intercept = regress.intercept();
}
// filter out efforts on same day that are the furthest
// away from a linear regression
// the best we found is stored in here
struct { int i; double p, t, d, pix; } keep;
for(int i=0; i<t.count(); i++) {
// reset our holding variable - it will be updated
// with the maximal point we want to retain for the
// day we are filtering for. Initial means no value
// has been set yet, so the first point will set it.
if (w[i] != QDate()) {
// lets filter all on today, use first one to set the best found so far
keep.d = (p[i] * t[i] ) - ((slope * t[i] ) + intercept);
keep.i=i;
keep.p=p[i];
keep.t=t[i];
keep.pix=powerIndex(keep.p, keep.t);
// but clear since we iterate beyond
if (i>0) { // always keep pmax point
p[i]=0;
t[i]=0;
}
// from here to the end of all the points, lets see if there is one further away?
for(int z=i+1; z<t.count(); z++) {
if (w[z] == w[i]) {
// if its beloe the line multiply distance by -1
double d = (p[z] * t[z] ) - ((slope * t[z]) + intercept);
double pix = powerIndex(p[z],t[z]);
// use the regression for shorter durations and 3p for longer
if ((keep.t < 120 && keep.d < d) || (keep.t >= 120 && keep.pix < pix)) {
keep.d = d;
keep.i = z;
keep.p = p[z];
keep.t = t[z];
}
if (z>0) { // always keep pmax point
w[z] = QDate();
p[z] = 0;
t[z] = 0;
}
}
}
// reinstate best we found
p[keep.i] = keep.p;
t[keep.i] = keep.t;
}
}
// so lets send over the indexes
// we keep t[0] as it gets removed in
// all cases below, saves a bit of
// torturous logic later
returning.vector.clear();
returning.vector << 0;// it gets removed
for(int i=0; i<t.count(); i++) if (t[i] > 0) returning.vector << i;
}
}
}
// really annoying that it starts from 0s not 1s, this is a legacy
// bug that we cannot fix easily, but this is new, so lets not