Datafilter vectors - stddev() and variance()

.. both take a single parameter (a vector) and compute the standard
   deviation and variance accordingly. sigma and sigma^2.

src/Core/DataFilter.cpp

@@ -17,6 +17,7 @@
  */
 
 #include "Utils.h"
+#include "Statistic.h"
 #include "DataFilter.h"
 #include "Context.h"
 #include "Athlete.h"
@@ -200,6 +201,9 @@ static struct {
                        // not need the data to be sorted, as it uses argsort internally. As
                        // you can pass multiple vectors they are uniqued in sync with the first list.
 
+    { "variance", 1 }, // variance(v) - calculates the variance for the elements in the vector.
+    { "stddev", 1 },   // stddev(v) - calculates the standard deviation for elements in the vector.
+
 
     // add new ones above this line
     { "", -1 }
@@ -2948,6 +2952,7 @@ Result Leaf::eval(DataFilterRuntime *df, Leaf *leaf, float x, long it, RideItem
             return returning;
         }
 
+        // uniq
         if (leaf->function == "uniq") {
 
             // evaluate all the lists
@@ -2968,7 +2973,7 @@ Result Leaf::eval(DataFilterRuntime *df, Leaf *leaf, float x, long it, RideItem
 
                 // diff length?
                 if (current.vector.count() != len) {
-                    fprintf(stderr, "sort list '%s': not the same length, ignored\n", symbol.toStdString().c_str()); fflush(stderr);
+                    fprintf(stderr, "uniq list '%s': not the same length, ignored\n", symbol.toStdString().c_str()); fflush(stderr);
                     continue;
                 }
 
@@ -3211,6 +3216,21 @@ Result Leaf::eval(DataFilterRuntime *df, Leaf *leaf, float x, long it, RideItem
             return returning;
         }
 
+        // stddev
+        if (leaf->function == "variance") {
+            // array
+            Result v = eval(df,leaf->fparms[0],x, it, m, p, c, s, d);
+            Statistic calc;
+            return calc.variance(v.vector, v.vector.count());
+        }
+
+        if (leaf->function == "stddev") {
+            // array
+            Result v = eval(df,leaf->fparms[0],x, it, m, p, c, s, d);
+            Statistic calc;
+            return calc.standarddeviation(v.vector, v.vector.count());
+        }
+
         // pmc
         if (leaf->function == "pmc") {
 

src/Metrics/Statistic.h

@@ -40,15 +40,6 @@ class Statistic
         double corr(QVector<double> &Xi, QVector<double> &Yi,int n);
         double average(QVector<double> &Xi,int n);
 
-    protected:
-        long points;
-        double sumX, sumY;
-        double sumXsquared,
-               sumYsquared;
-        double sumXY;
-        double a, b, c;   // a = intercept, b = slope, c = r2
-
-    private:
         // Maths functions used by the plots
         QVector<double> array_temp; //Déclaration d'un tableau temporaire
 
@@ -70,6 +61,14 @@ class Statistic
         double variance(QVector<double> &val,int n);
         double standarddeviation(QVector<double> &val,int n);
 
+    protected:
+        long points;
+        double sumX, sumY;
+        double sumXsquared,
+               sumYsquared;
+        double sumXY;
+        double a, b, c;   // a = intercept, b = slope, c = r2
+
 };
 
 #endif