[chronojump] neuromuscularProfile bars graph done
- From: Xavier de Blas <xaviblas src gnome org>
- To: commits-list gnome org
- Cc:
- Subject: [chronojump] neuromuscularProfile bars graph done
- Date: Sun, 23 Mar 2014 13:37:18 +0000 (UTC)
commit 1aff655ea8af517a34566b6b3681b1fa28d3a12a
Author: Xavier de Blas <xaviblas gmail com>
Date: Sun Mar 23 14:36:02 2014 +0100
neuromuscularProfile bars graph done
encoder/graph.R | 32 +++++++-------
encoder/neuromuscularProfile.R | 96 +++++++++++++++++++++++++++++++++-------
2 files changed, 95 insertions(+), 33 deletions(-)
---
diff --git a/encoder/graph.R b/encoder/graph.R
index ac9218a..e7759b0 100644
--- a/encoder/graph.R
+++ b/encoder/graph.R
@@ -2646,22 +2646,22 @@ doProcess <- function(options) {
quit()
}
npj <- neuromuscularProfileGetData(displacement, curves, (MassBody + MassExtra),
SmoothingOneC)
-
- np.bar.load <- mean(
- npj[[1]]$e1$rfd.avg,
- npj[[2]]$e1$rfd.avg,
- npj[[3]]$e1$rfd.avg
- )
- np.bar.explode <- mean(
- npj[[1]]$c$cl.rfd.avg,
- npj[[2]]$c$cl.rfd.avg,
- npj[[3]]$c$cl.rfd.avg
- )
- np.bar.drive <- mean(
- npj[[1]]$c$cl.i,
- npj[[2]]$c$cl.i,
- npj[[3]]$c$cl.i
- )
+
+ np.bar.load <- mean(c(
+ npj[[1]]$e1$rfd.avg,
+ npj[[2]]$e1$rfd.avg,
+ npj[[3]]$e1$rfd.avg
+ ))
+ np.bar.explode <- mean(c(
+ npj[[1]]$c$cl.rfd.avg,
+ npj[[2]]$c$cl.rfd.avg,
+ npj[[3]]$c$cl.rfd.avg
+ ))
+ np.bar.drive <- mean(c(
+ npj[[1]]$c$cl.i,
+ npj[[2]]$c$cl.i,
+ npj[[3]]$c$cl.i
+ ))
par(mar=c(5,4,4,5))
neuromuscularProfilePlotBars(np.bar.load, np.bar.explode, np.bar.drive)
diff --git a/encoder/neuromuscularProfile.R b/encoder/neuromuscularProfile.R
index 6eca07e..7e789ec 100644
--- a/encoder/neuromuscularProfile.R
+++ b/encoder/neuromuscularProfile.R
@@ -35,6 +35,7 @@ neuromuscularProfileJump <- function(e1, c, e2, mass, smoothingC)
weight <- mass * g
+
#----------------
#1.- e1 variables
#----------------
@@ -46,9 +47,13 @@ neuromuscularProfileJump <- function(e1, c, e2, mass, smoothingC)
#e1f (when force is done)
#from max(abs(speed$y)) at e1, to end of e1
e1.speed <- getSpeed(e1, smoothingC)
- e1f <- e1[max(abs(e1.speed$y)):length(e1)]
+ e1.maxspeed.pos <- mean(which(abs(e1.speed$y) == max(abs(e1.speed$y))))
+ e1f <- e1[e1.maxspeed.pos:length(e1)]
+
+print(c("e max speed.t",e1.maxspeed.pos))
+print(c("length e1",length(e1)))
- #e1f.t duration of e1f
+ #e1f.t duration of e1f (ms)
e1f.t <- length(e1f)
#e1f.fmax <- max Force on e1f
@@ -58,13 +63,16 @@ neuromuscularProfileJump <- function(e1, c, e2, mass, smoothingC)
e1f.force <- mass * (e1f.accel$y + g)
e1f.fmax <- max(e1f.force)
+print(c("e1f.t",e1f.t))
+print(c("mean(e1f.force)",mean(e1f.force)))
+
#e1f.rdf.avg
- #average force on e1f / e1f.t
- e1f.rfd.avg <- mean(e1f.force) / e1f.t #bars LOAD
+ #average force on e1f / e1f.t (s)
+ e1f.rfd.avg <- mean(e1f.force) / (e1f.t / 1000) #bars LOAD
#e1f.i (Impulse)
- #average force on e1f * e1f.t / weight
- e1f.i <- mean(e1f.force) * e1f.t / weight
+ #average force on e1f * e1f.t (s) / mass (Kg)
+ e1f.i <- mean(e1f.force) * (e1f.t / 1000) / mass
e1.list = list(
range = e1.range,
@@ -87,7 +95,9 @@ neuromuscularProfileJump <- function(e1, c, e2, mass, smoothingC)
c.position = cumsum(c)
c.takeoff = min(which(c.force <= weight))
- #c.jumpHeight = (c.position[length(c.position)] - c.position[c.takeoff]) /10
+ c.jumpHeight = (c.position[length(c.position)] - c.position[c.takeoff]) /10
+
+ print(c("jumpHeight", c.jumpHeight))
#cl "land" from bottom to takeoff (force < weight)
#ca "air" from takeoff to max height
@@ -103,20 +113,23 @@ neuromuscularProfileJump <- function(e1, c, e2, mass, smoothingC)
#cl.t = contact time (duration) on cl
cl.t <- length(cl)
- #cl.rfd.avg = average force on cl / cl.t / weight #bars EXPLODE
+ #cl.rfd.avg = average force on cl / cl.t (s) / mass (Kg) #bars EXPLODE
cl.speed <- getSpeed(cl, smoothingC)
cl.accel = getAcceleration(cl.speed)
#speed comes in mm/ms when derivate to accel its mm/ms^2 to convert it to m/s^2 need to *1000 because
it's quadratic
cl.accel$y <- cl.accel$y * 1000
cl.force <- mass * (cl.accel$y + g)
- cl.rfd.avg <- mean(cl.force) / cl.t / weight
+print(c("cl.t",cl.t))
+print(c("mean clforce",mean(cl.force)))
+
+ cl.rfd.avg <- mean(cl.force) / (cl.t / 1000) / mass
- #cl.i = average force on cl * cl.t / weight #impulse #bars DRIVE
- cl.i <- mean(cl.force) * cl.t / weight
+ #cl.i = average force on cl * cl.t (s) / mass (Kg) #impulse #bars DRIVE
+ cl.i <- mean(cl.force) * (cl.t / 1000) / mass
- #cl.f.avg = average force on cl / weight
- cl.f.avg <- mean(cl.force) / weight
+ #cl.f.avg = average force on cl / mass (Kg)
+ cl.f.avg <- mean(cl.force) / mass
#cl.vf (vF -> valley Force)
#minimum force on cl before concentric Speed max
@@ -178,8 +191,8 @@ neuromuscularProfileJump <- function(e1, c, e2, mass, smoothingC)
#e2fFmaxt = duration from land to max force
e2f.f.max.t <- min(which(e2.force == e2f.f.max)) - e2.land.pos
- #e2f.rfd.max = e2f.f.max / e2f.f.max.t
- e2f.rfd.max <- e2f.f.max / e2f.f.max.t
+ #e2f.rfd.max = e2f.f.max / e2f.f.max.t (s)
+ e2f.rfd.max <- e2f.f.max / (e2f.f.max.t / 1000)
e2.list = list(
e2f.t = e2f.t,
@@ -255,10 +268,59 @@ neuromuscularProfileGetData <- function(displacement, curves, mass, smoothingC)
return (npj)
}
+#implement the Excel Forecast in R
+cutreForecastInRDo <- function(table.points, table.values, value)
+{
+ if(value > table.values[11])
+ return (table.points[11])
+ else if(value < table.values[1])
+ return (table.points[1])
+ else {
+ for(i in 1:10) {
+ if(value >= table.values[i] && value < table.values[(i+1)]) {
+ range.values = table.values[(i+1)] - table.values[i]
+ distanceFromLow = value - table.values[i]
+ percentFromLow = distanceFromLow / range.values
+ range.points = table.points[(i+1)] - table.points[i]
+ return (table.points[i] + (range.points * percentFromLow))
+ }
+ }
+ }
+}
+
+cutreForecastInRPrepare <- function(variable, value)
+{
+ table.points <- seq(0,100,length.out=11)
+ table.load <- seq(0,20000,length.out=11)
+ table.explode <- seq(0,250,length.out=11)
+ table.drive <- seq(3,8,length.out=11)
+
+ if(variable == "load")
+ return (cutreForecastInRDo(table.points, table.load, value))
+ else if(variable == "explode")
+ return (cutreForecastInRDo(table.points, table.explode, value))
+ else #(variable == "drive")
+ return (cutreForecastInRDo(table.points, table.drive, value))
+}
+
+
neuromuscularProfilePlotBars <- function(load, explode, drive)
{
- barplot(c(load,explode,drive),col=topo.colors(3),names.arg=c("Load","Explode","Drive"))
print(c("load, explode, drive", load, explode, drive))
+
+ load100 = cutreForecastInRPrepare("load",load)
+ explode100 = cutreForecastInRPrepare("explode",explode)
+ drive100 = cutreForecastInRPrepare("drive",drive)
+ print(c("load100, explode100, drive100", load100, explode100, drive100))
+
+ barplot(c(load100,explode100,drive100),col=topo.colors(3),ylim=c(0,100),
+ names.arg=c(
+ paste("Load\n",round(load,2)," -> ",round(load100,2),"%",sep=""),
+ paste("Explode\n",round(explode,2)," -> ",round(explode100,2),"%",sep=""),
+ paste("Drive\n",round(drive,2)," -> ",round(drive100,2),"%",sep="")
+ ))
+
+
#show small text related to graph result and how to train
}
@@ -282,6 +344,6 @@ neuromuscularProfileWriteData <- function(npj, outputData1)
colnames(df) <- c(paste("e1.",names(npj[[1]]$e1),sep=""), names(npj[[1]]$c), names(npj[[1]]$e2))
print(df)
- write.csv(df, outputData1, quote=FALSE)
+ write.csv2(df, outputData1, quote=FALSE)
}
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