From cc6738bdf5d8660065a626d6cc3ebbb5a4b88cbe Mon Sep 17 00:00:00 2001
From: Alejandro Martinez <amtriathlon@gmail.com>
Date: Mon, 5 Sep 2022 11:51:44 -0300
Subject: [PATCH] Remove R charts from default layouts

SS-RR and Tri Mix replaced by Python versions
contributed by Fe at the users forum.
The intention is to avoid triggering #4267
---
 src/Resources/xml/analysis-perspectives.xml |  8 ++--
 src/Resources/xml/home-perspectives.xml     | 44 ++-------------------
 2 files changed, 8 insertions(+), 44 deletions(-)

diff --git a/src/Resources/xml/analysis-perspectives.xml b/src/Resources/xml/analysis-perspectives.xml
index b3c6ccfa3..1b872e933 100644
--- a/src/Resources/xml/analysis-perspectives.xml
+++ b/src/Resources/xml/analysis-perspectives.xml
@@ -855,17 +855,17 @@
 		<property name="bin" type="double" value="1" />
 		<property name="useSelected" type="int" value="0" />
 	</chart>
-	<chart id="38" name="" title="SS-SR (Requires R)" >
-		<property name="title" type="QString" value="SS-SR (Requires R)" />
+	<chart id="43" name="" title="SS-SR" >
+		<property name="title" type="QString" value="SS-SR" />
 		<property name="subtitle" type="QString" value="" />
 		<property name="widthFactor" type="double" value="2" />
 		<property name="heightFactor" type="double" value="2" />
 		<property name="style" type="int" value="0" />
 		<property name="resizable" type="bool" value="0" />
-		<property name="script" type="QString" value="## R-chart script.\n## For use with GoldenCheetah.\n## tested on GC.version() is &quot;DEV-V3.5 1705&quot;\n##\nxtext&lt;-&quot;Pace/100m (min:sec)&quot;\nytext&lt;-&quot;Stroke Rate/min (srm)&quot;\n\npushoff&lt;-3 ## push-off compensation (m)\nstrokestyle&lt;-1## 1=freestyle, 2=backstroke, 3=breaststroke, 4=butterfly\n## plot colors\nregionlowfill&lt;-adjustcolor(&quot;#5076a3&quot;,alpha.f=.6)\nregionlowborder&lt;-&quot;#5076a3&quot;\nregionhighfill&lt;-adjustcolor(&quot;#a34b49&quot;,alpha.f=.6)\nregionhighborder&lt;-&quot;#a34b49&quot;\nsb_size_min&lt;-1\nsb_size_max&lt;-15\nsb_alpha&lt;-0.5\n## plot symbols\npl25&lt;-21\npl50&lt;-22\nplx&lt;-23\n#plot limits\nxmin&lt;-130\nxmax&lt;-60\nymin&lt;-30\nymax&lt;-100\n\ndarken &lt;- function(color, factor=1.4){\n    col &lt;- col2rgb(color)\n    col &lt;- col/factor\n    col &lt;- rgb(t(col), maxColorValue=255)\n}\n\nstroketext&lt;-list(&quot;Freestyle&quot;, &quot;Backstroke&quot;, &quot;Breaststroke&quot;, &quot;Butterfly&quot;)\nheader&lt;-paste(stroketext[strokestyle],&quot; stroke rate chart&quot;)\n\n## plot\npar(mar=c(6,6,3,0))\nplot(NULL,NULL, xlim=c(xmin,xmax),ylim=c(ymin,ymax),  type=&quot;n&quot;,xlab=&quot;&quot;,ylab=&quot;&quot;, xaxt=&quot;n&quot;, yaxt=&quot;n&quot;)\n## ticks\nat=seq(xmin,xmax,-10)\naxis(1, at=at, tcl=-0.8, labels=sprintf(&quot;%d:%02d&quot;,at%/%60,at%%60))##major x-axis\naxis(1, at=seq(xmin,xmax,-1), labels=NA)##minor x-axis\naxis(2, at=seq(ymin,ymax,10), tcl=-0.8, las=2)##major y-axis\naxis(2, at=seq(ymin,ymax,1), labels=NA)##minor y-axis\n\n## region low\n##lines(x_region1,region_low,type=&quot;l&quot;, col=&quot;blue&quot;)\nx_region1&lt;- c(130 ,120 ,110 ,100 ,90  ,80  ,70  ,60)\nregion_low&lt;-c(50.0,50.8,51.6,53.0,54.5,57.0,61.0,67.0)\npolygon(c(xmin,x_region1,xmax),c(ymin,region_low,ymin),col=regionlowfill,border=regionlowborder)\n##text(127,31,&quot;Low Stroke rate&quot;, col=&quot;#0000FF&quot;, cex=1)\nmtext(&quot;Low Stroke rate&quot;,1, line=-3.5, col=&quot;#0000FF&quot;, cex=1)\n\n## region high\n##lines(x_region2,region_high,type=&quot;l&quot;, col=&quot;red&quot;)\nx_region2&lt;-  c(130 ,120 ,110 ,100 ,95.5,90  ,80  ,70  ,67)\nregion_high&lt;-c(61.8,63.4,65.2,68.0,70.0,73.1,83.1,96.0,100.0)\npolygon(c(xmin,x_region2),c(ymax,region_high),col=regionhighfill,border=regionhighborder)\n##text(126.9,99,&quot;High Stroke rate&quot;, col=&quot;#ff0000&quot;, cex=1)\nmtext(&quot;High Stroke rate&quot;,3, line=-3.5, col=&quot;#ff0000&quot;, cex=1)\n\n## region OK\n##polygon(c(x_region1,rev(c(x_region2,xmax))),c(region_low,rev(c(region_high,ymax))),col=&quot;gray&quot;,border=&quot;gray&quot;)\n\n## title\ntitle(main=header,cex.main=2.0,xlab=xtext,ylab=ytext)\n\n## grid lines\ngrid(col=&quot;#606060&quot;, lty=&quot;solid&quot;, lwd=0.5)\n\n## plot values, use available swim data(XDATA)\nmetrics&lt;-GC.activity.metrics(compare=TRUE)\nactivities&lt;-GC.activity(compare=TRUE)\nfor(a in 1:length(activities)){\n	if (&quot;SWIM_TYPE&quot; %in% names(activities[[a]]$activity))##swim activity?\n	{\n	swim&lt;-activities[[a]]$activity[which(as.numeric(activities[[a]]$activity$SWIM_TYPE)==strokestyle),]\n	if (&quot;Pool_Length&quot; %in% names(metrics[[a]]$metrics)){Pool_Length&lt;-as.numeric(metrics[[a]]$metrics$Pool_Length)}else{Pool_Length&lt;-0}\n	pointfill&lt;-activities[[a]]$color\n	pointfill&lt;-adjustcolor(activities[[a]]$color,alpha.f=sb_alpha)\n	pointborder&lt;-darken(activities[[a]]$color)\n\n	if (Pool_Length==25){sb1&lt;-pl25}\n	else {if (Pool_Length==50){sb1&lt;-pl50}\n	else {sb1&lt;-plx}}\n\n	pace&lt;-swim$SWIM_DURATION*100/Pool_Length\n	sr&lt;-(60*2*swim$SWIM_STROKES/swim$SWIM_DURATION)/(1-pushoff/Pool_Length)## 2arms per minute, incl pushoff compensation\n	##lines(pace, sr, type=&quot;p&quot;, col=pointborder, bg=pointfill, pch=sb1, cex=pointsize)\n	pl&lt;-lowess(round(pace),sr)\n	xn&lt;-table(pl$x)\n	f&lt;-xn/max(xn)\n	lines(pl$x[!duplicated(pl$x)],pl$y[!duplicated(pl$x)], type=&quot;o&quot;, col=pointborder, bg=pointfill, pch=sb1, cex=sb_size_min+(sb_size_max-sb_size_min)*f)\n	}\n}\nlegend(&quot;top&quot;, legend=c(&quot;25m&quot;,&quot;50m&quot;,&quot;custom&quot;), pch=c(pl25,pl50,plx),  horiz=TRUE, bty=&quot;n&quot;, xpd=TRUE, inset=c(0), pt.cex=2)\n" />
+		<property name="script" type="QString" value="## R (Daimon) -&gt; Python (Fe)\n##.....................................................\n##.....................................................\n##\n## R script\n## Chart: SS-SR\n## Creator: Daimon\n##\n## For use with GoldenCheetah\n## tested on GC.version() is &quot;DEV-V3.5 1705&quot;\n##\n\n## SS-SR\n## Last Edited At: 2017-09-26 - Creator: Daimon\n## This stroke rate chart is combined with the SwimSmooth preferde (optimal) zones for freestyle swimming.\n\n## This chart displays the (combined lowess) strokerate on the x-axis versus the pace on the y-axis.\n\n## The script expects Garmin values for the Strokestyle. (1=freestyle, 2=backstroke, 3=breaststroke, 4=butterfly)\n\n## It takes into account a 3m push-off per length/lane.\n## You can change this value in the script at &quot;pushoff&lt;-3 ## push-off compensation (m)&quot;\n## The type of stroke can be change in the script at &quot;strokestyle&lt;-1## 1=freestyle, 2=backstroke, 3=breaststroke, 4=butterfly&quot; but the defined strokerate areas are SwimSmooth Freestyle zones!\n\n## There are simulair R-chart scripts available in the repository - this script is my interpretation and uses the available swim metrics - it&apos;s a matter of preference which script to use.\n\n## Since no default value is defined for Pool Length there is a prerequisite.\n## (If Pool Length is not available no strokerate nor pace will be calculated)\n\n## Prerequisite:\n## check in the menu under Tools-&gt;Options select the Datafields tab\n## for the presence/definition of the DataField &quot;Pool Length&quot; .\n\n## if not: in the Menu under Tools-&gt;Options select the Datafields tab\n## select &quot;+&quot; and edit the created row to &quot; Workout&quot;, &quot;Pool Length&quot; ,&quot;Double&quot;\n\n## In the legenda &quot;custom&quot; indicates a pool length not exactly 25m or 50m\n\n## http://www.swimsmooth.com/strokerate.html\n\n##.....................................................\n##\n## Python script\n## Chart: Py_SS-SR_04\n## Creator: Fe \n##\n## GC.version() &quot;V3.6-RC1&quot; (Build id: 4010 2022/07/04)\n##\n## 08&apos;2022\n## Python Script \n##\n## Python: 3.7.9 (Aug 17 2020)\n## Plotly: 4.6.0 --&gt; 5.2.1\n## Test: GarminSwimExample_2015_11_05_18_45_42.fit\n##\n## Golden Cheetah: &quot;V3.6-RC1&quot; (Build id: 4010 2022/07/04)\n## Activities\n##\n## Note: add marker symbol (25 circle, 50 square, custom diamond -&gt; sb1) (trace_sm_lowess)\n##\n##.....................................................\n\n## packages\n#import pandas as pd\nimport plotly as ply\nimport plotly.express as px\nimport plotly.graph_objs as go\nimport numpy as np\nimport datetime\nimport tempfile\nimport pathlib\n\n#......................................................\n\n#&quot;&quot;&quot;\n#This module implements the Lowess function for nonparametric regression.\n#Functions:\n#lowess Fit a smooth nonparametric regression curve to a scatterplot.\n#For more information, see\n#William S. Cleveland: &quot;Robust locally weighted regression and smoothing\n#scatterplots&quot;, Journal of the American Statistical Association, December 1979,\n#volume 74, number 368, pp. 829-836.\n#William S. Cleveland and Susan J. Devlin: &quot;Locally weighted regression: An\n#approach to regression analysis by local fitting&quot;, Journal of the American\n#Statistical Association, September 1988, volume 83, number 403, pp. 596-610.\n#&quot;&quot;&quot;\n# Authors: Alexandre Gramfort &lt;alexandre.gramfort@telecom-paristech.fr&gt;\n#\n# License: BSD (3-clause)\n\n#https://gist.github.com/agramfort/850437\n\n#from scipy import linalg \nfrom numpy import linalg\n\ndef gramfort_lowess(x, y, f=2. / 3., iter=3):\n\n    #&quot;&quot;&quot;lowess(x, y, f=2./3., iter=3) -&gt; yest\n    #Lowess smoother: Robust locally weighted regression.\n    #The lowess function fits a nonparametric regression curve to a scatterplot.\n    #The arrays x and y contain an equal number of elements; each pair\n    #(x[i], y[i]) defines a data point in the scatterplot. The function returns\n    #the estimated (smooth) values of y.\n    #The smoothing span is given by f. A larger value for f will result in a\n    #smoother curve. The number of robustifying iterations is given by iter. The\n    #function will run faster with a smaller number of iterations.\n    #&quot;&quot;&quot;\n\n    n = len(x)\n    #r = int(ceil(f * n)) #update fe\n    r = int(np.ceil(f * n)) #update fe   \n    h = [np.sort(np.abs(x - x[i]))[r] for i in range(n)]\n    w = np.clip(np.abs((x[:, None] - x[None, :]) / h), 0.0, 1.0)\n    w = (1 - w ** 3) ** 3\n    yest = np.zeros(n)\n    delta = np.ones(n)\n    for iteration in range(iter):\n        for i in range(n):\n            weights = delta * w[:, i]\n            b = np.array([np.sum(weights * y), np.sum(weights * y * x)])\n            A = np.array([[np.sum(weights), np.sum(weights * x)],\n                          [np.sum(weights * x), np.sum(weights * x * x)]])\n            beta = linalg.solve(A, b)\n            yest[i] = beta[0] + beta[1] * x[i]\n\n        residuals = y - yest\n        s = np.median(np.abs(residuals))\n        delta = np.clip(residuals / (6.0 * s), -1, 1)\n        delta = (1 - delta ** 2) ** 2\n\n    return yest\n\n\n#......................................................\n\ntemp_file = tempfile.NamedTemporaryFile(mode=&quot;w+t&quot;, prefix=&quot;GC_&quot;, suffix=&quot;.html&quot;, delete=False)\n\n#......................................................\nxtext=&quot;Pace/100m (min:sec)&quot;\nytext=&quot;Stroke Rate/min (srm)&quot;\n\npushoff=3  ## push-off compensation (m)\nstrokestyle=1  ## 1=freestyle, 2=backstroke, 3=breaststroke, 4=butterfly\n\n## plot colors\nregionlowfill=&apos;rgb(80,118,163)&apos;\n# regionlowborder&lt;-&quot;#5076a3&quot;\nregionhighfill=&apos;rgb(163,75,73)&apos;\n# regionhighborder&lt;-&quot;#a34b49&quot;\nplot_bgc=&apos;rgb(25,25,25)&apos;\npaper_bgc=&apos;rgb(20,20,20)&apos;\n\nsb_size_min=1\nsb_size_max=70\nsb_alpha=0.5\n\n## plot symbols\npl25=&apos;circle&apos;#21\npl50=&apos;square&apos;#22\nplx=&apos;diamond&apos;#23\n\n#plot limits\nxmin=130\nxmax=60\nymin=30\nymax=100\n\nstroketext=[&quot;Freestyle&quot;, &quot;Backstroke&quot;, &quot;Breaststroke&quot;, &quot;Butterfly&quot;]\nheader=(str(stroketext[strokestyle-1])+&quot; stroke rate chart&quot;)\n\n## ticks\nat=np.arange(xmin,xmax-10,-10)\nat_ms=[&apos;%d:%02d&apos;%(x/60,x%60) for x in at]\n\n## region low\nx_region1=at #[130 ,120 ,110 ,100 ,90  ,80  ,70  ,60]\nregion_low=[50.0,50.8,51.6,53.0,54.5,57.0,61.0,67.0] \n\n## region high\nx_region2=[130 ,120 ,110 ,100 ,95.5,90  ,80  ,70  ,67]\nregion_high=[61.8,63.4,65.2,68.0,70.0,73.1,83.1,96.0,100.0]\n\n#......................................................\n\n## data    \n\nswim_field=[&apos;SWIM_TYPE&apos;,&apos;SWIM_DURATION&apos;,&apos;SWIM_STROKES&apos;]\n\nmetrics=GC.activityMetrics(compare=True)\n\nact_datetime=[[metrics[i][0][y] for y in[&apos;date&apos;,&apos;time&apos;]] for i in range(0,len(metrics))]\n\nact_list=[GC.activity(activity=datetime.datetime.combine(act_datetime[i][0],act_datetime[i][1])) for i in range(0,len(act_datetime))]\n\n#......................................................\n\ntrace=[]\nlayout=dict(paper_bgcolor=paper_bgc,plot_bgcolor=plot_bgc)\n\n##\nfor i in range(0,len(act_list)):\n\n    if &quot;SWIM_TYPE&quot; in act_list[i]:\n\n        swim=[];pace=[];sr=[];pace_sr=[];pl=[];f=[];pl_x_unique=[]\n        pl_y_unique=[];trace_sm_lowess=[];trace_pace_sr=[]      \n\n        swim=np.array_split([np.array(list(act_list[i][y]))[np.array(list(act_list[i][&apos;SWIM_TYPE&apos;]))==strokestyle] for y in swim_field],1)\n\n        if &quot;Pool_Length&quot; in metrics[i][0]:	\n            Pool_Length=int(metrics[i][0][&apos;Pool_Length&apos;])\n        else:\n            Pool_Length=0\n\n        pointfill=metrics[i][1]\n\n        if Pool_Length==25: sb1=pl25 #25 #circle\n        elif Pool_Length==50: sb1=pl50  #50 #square               \n        else: sb1=plx #custom #diamond\n\n        pace=swim[0][1]*100/Pool_Length\n\n        sr=(60*2*swim[0][2]/swim[0][1])/(1-pushoff/Pool_Length)   \n\n        pace_sr=np.array([pace,sr])\n\n\n        #........................................................\n\n        # License: BSD (3-clause)\n        pl=gramfort_lowess(pace, sr, f=2./3.) # return y\n        # ......................\n      \n        pl_x_unique=np.unique(np.round(pace),return_index=True,return_counts=True) \n        pl_y_unique=np.round(pl[pl_x_unique[1]],2) \n        f=pl_x_unique[2]/max(pl_x_unique[2]) \n\n        #........................................................\n\n        trace_sm_lowess=dict(\n             x=pl_x_unique[0],y=pl_y_unique,\n             mode=&apos;lines+markers&apos;, \n             name=metrics[i][0][&apos;date&apos;].strftime(&apos;%A %y-%m-%d&apos;),#&apos;sm_lowess &apos;+str(i+1),\n             marker=dict(size=(sb_size_min+(sb_size_max-sb_size_min)*f),opacity=0.4,size_max=70,symbol=sb1),\n             line = dict(color=pointfill,width=1,opacity=1))\n        trace.append(trace_sm_lowess)\n\n#......................................................\n\n##\nif &apos;SWIM_TYPE&apos; in act_list[0]: \n\n    trace_region_low=dict(x=x_region1,y=region_low,\n         mode=&apos;lines&apos;,hoverinfo=&apos;skip&apos;,showlegend=False,\n         fill=&apos;tozeroy&apos;,\n         line = dict(color=regionlowfill,width=0.1,opacity=0.6))\n    trace.append(trace_region_low)\n\n    trace_region=dict(x=x_region2,y=region_high,\n         mode=&apos;lines&apos;,hoverinfo=&apos;skip&apos;,showlegend=False,\n         line = dict(color=regionhighfill,width=0.1,opacity=0.6))\n    trace.append(trace_region)\n\n    trace_region_high=dict(x=x_region2,y=[100*len(region_high)],\n         mode=&apos;lines&apos;,hoverinfo=&apos;skip&apos;,showlegend=False, \n         fill=&apos;tonexty&apos;,\n         line = dict(color=regionhighfill,width=0.1,opacity=0.6))\n    trace.append(trace_region_high)\n\n    minor_xaxis=dict(overinfo=&apos;skip&apos;,showlegend=False,xaxis=&apos;x2&apos;)\n    trace.append(minor_xaxis)\n    minor_yaxis=dict(overinfo=&apos;skip&apos;,showlegend=False,yaxis=&apos;y2&apos;)\n    trace.append(minor_yaxis)\n\n##\n    text=[]\n    text.append(dict(x=min(at)+(max(at)-min(at))/2,y=32,text=&apos;Low Stroke rate&apos;,font=dict(size=12,color=&apos;rgb(0,0,255)&apos;),showarrow=False))\n    text.append(dict(x=min(at)+(max(at)-min(at))/2,y=96.5,text=&apos;High Stroke rate&apos;,font=dict(size=12,color=&apos;rgb(255,0,0)&apos;),showarrow=False))\n    text.append(dict(x=min(at)+(max(at)-min(at))/2,y=99,text=u&apos;\u25ef&apos;+&apos; 25   &apos;+u&apos;\u20de&apos;+&apos; 50   &apos;+u&apos;\u20df&apos;+&apos;  custom&apos;,font=dict(size=11,color=&apos;white&apos;),showarrow=False))\n\n##\n    layout=dict(\n        paper_bgcolor=paper_bgc,\n        plot_bgcolor=plot_bgc,\n        title=header,\n        titlefont=dict(color=&apos;white&apos;,size=18),\n        hovermode=&apos;x&apos;,\n        margin=dict(pad=20), \n        xaxis=\n(dict(range=[xmin,xmax],titlefont=dict(size=13,color=&apos;rgb(132,159,190)&apos;),linewidth=1,ticks=&quot;outside&quot;,color=&apos;white&apos;, tickwidth=0.5,ticklen=8,title=xtext,tickfont=dict(size=12,color=&apos;rgb(132,159,190)&apos;),tickvals=at,ticktext=at_ms,mirror=True)),\n        yaxis = dict(range=[ymin,ymax],titlefont=dict(size=13,color=&apos;rgb(132,159,190) &apos;),linewidth=1,ticks=&quot;outside&quot;,color=&apos;white&apos;,tickwidth=0.5,ticklen=8,title=ytext,tickfont=dict(size=12,color=&apos;rgb(132,159,190)&apos;),mirror=True),\n        xaxis2 = dict(range=[xmax,xmin],showgrid=False,ticks=&quot;outside&quot;,color=&apos;white&apos;,ticklen=4,tick0 = xmax, showticklabels=False,overlaying = &apos;x&apos;, dtick = 1),\n        yaxis2 = dict(range=[ymax,ymin],showgrid=False,ticks=&quot;outside&quot;,color=&apos;white&apos;,ticklen=4,tick0 = xmax, showticklabels=False,overlaying = &apos;y&apos;, dtick = 1),\n    annotations=text,legend=dict(font=dict(size=11,color=&apos;lightgreen&apos;),orientation=&quot;h&quot;,y=-0.24,xanchor=&quot;left&quot;,x=-0.03,yanchor=&quot;bottom&quot;))\n\n\n#......................................................\n\n##\nfig=dict(data=trace,layout=layout)\n\n\nply.offline.plot(fig,auto_open = False, filename=temp_file.name, validate=False)\n\nGC.webpage(pathlib.Path(temp_file.name).as_uri())\n" />
 		<property name="state" type="QString" value="" />
-		<property name="plotOnChart" type="bool" value="0" />
 		<property name="showConsole" type="bool" value="0" />
+		<property name="asWeb" type="bool" value="1" />
 	</chart>
 	<chart id="37" name="" title="Map" >
 		<property name="title" type="QString" value="Map" />
diff --git a/src/Resources/xml/home-perspectives.xml b/src/Resources/xml/home-perspectives.xml
index c7211be6a..137eb5d13 100644
--- a/src/Resources/xml/home-perspectives.xml
+++ b/src/Resources/xml/home-perspectives.xml
@@ -333,30 +333,6 @@
 		<property name="resizable" type="bool" value="0" />
 		<property name="settings" type="QString" value="{ &quot;title&quot;: &quot; &quot;,\n&quot;description&quot;: &quot;Peronnet Thibault Model, WKO4 Model is basically an unattributed rip-off &quot;,\n&quot;type&quot;: 1,\n&quot;animate&quot;: false,\n&quot;intervalrefresh&quot;: false,\n&quot;legendpos&quot;: 2,\n&quot;stack&quot;: false,\n&quot;orientation&quot;: 1,\n&quot;bgcolor&quot;: &quot;#010102&quot;, \n&quot;scale&quot;: 1,\n&quot;SERIES&quot;: [\n{ &quot;name&quot;: &quot;Model &quot;, &quot;group&quot;: &quot; &quot;, &quot;xname&quot;: &quot;seconds &quot;, &quot;yname&quot;: &quot;watts &quot;, &quot;program&quot;: &quot;{:sl:n:sl:n    finalise {:sl:n        # raw data to fit with, just first 2hr:sl:n        mmp &lt;- head(meanmax(POWER),10000);:sl:n        secs &lt;- seq(1,length(mmp),1);:sl:n        multiuniq(mmp,secs);:sl:n:sl:n        # fit starting values:sl:n        ftp &lt;- 200; frc&lt;-11000; pmax &lt;- 1000; a&lt;-40;:sl:n        tau2&lt;-50; tte&lt;-1800;:sl:n:sl:n        fit &lt;- lm((frc:sl:/x*(1-exp(-x:sl:/(frc:sl:/pmax)))) + ((ftp*(1-exp(-x:sl:/tau2))) - (bool(x&gt;tte)*(a*log(x:sl:/tte)))), secs,mmp);:sl:n:sl:n        # success, generate prediction data:sl:n        if (fit[0] &lt;&gt; 0) { :sl:n            annotate(label, :qu:Pmax:qu:, round(pmax));:sl:n            annotate(label, :qu:FTP:qu:, round(ftp));:sl:n            annotate(label, :qu:FRC:qu:, round(frc));:sl:n            annotate(label, :qu:TTE:qu:, round(tte)); :sl:n :sl:n            # cache the paramters so we can use them:sl:n            store(:qu:ftp:qu:,ftp);:sl:n            store(:qu:frc:qu:,frc);:sl:n            store(:qu:pmax:qu:,pmax);:sl:n            store(:qu:tte:qu:,tte);:sl:n            store(:qu:a:qu:,a);:sl:n            store(:qu:tau2:qu:,tau2);:sl:n            store(:qu:secs:qu:, secs);:sl:n:sl:n            # generate the model curve:sl:n            yy &lt;- sapply(secs, (frc:sl:/x*(1-exp(-x:sl:/(frc:sl:/pmax)))) + ((ftp*(1-exp(-x:sl:/tau2))) - (bool(x&gt;tte)*(a*log(x:sl:/tte))))); :sl:n:sl:n        }:sl:n:sl:n    }:sl:n:sl:n    x { round(secs); }:sl:n    y { round(yy); }:sl:n}:sl:n &quot;, &quot;line&quot;: 1, &quot;symbol&quot;: 0, &quot;size&quot;: 2, &quot;color&quot;: &quot;#ff0004&quot;, &quot;opacity&quot;: 100, &quot;legend&quot;: true, &quot;opengl&quot;: true, &quot;datalabels&quot;: false, &quot;aggregate&quot;: 1, &quot;fill&quot;: false},\n{ &quot;name&quot;: &quot;Bests &quot;, &quot;group&quot;: &quot; &quot;, &quot;xname&quot;: &quot;seconds &quot;, &quot;yname&quot;: &quot;watts &quot;, &quot;program&quot;: &quot;{:sl:n:sl:n    finalise {:sl:n        # raw data to fit with, just first 20 minutes:sl:n        mmp &lt;- head(meanmax(POWER),10000);:sl:n        secs &lt;- seq(1,length(mmp),1);:sl:n    }:sl:n:sl:n    x { round(secs); }:sl:n    y { round(mmp); }:sl:n}:sl:n &quot;, &quot;line&quot;: 3, &quot;symbol&quot;: 0, &quot;size&quot;: 3, &quot;color&quot;: &quot;#ffa71a&quot;, &quot;opacity&quot;: 100, &quot;legend&quot;: true, &quot;opengl&quot;: false, &quot;datalabels&quot;: false, &quot;aggregate&quot;: 1, &quot;fill&quot;: false},\n{ &quot;name&quot;: &quot;pFRC &quot;, &quot;group&quot;: &quot; &quot;, &quot;xname&quot;: &quot;seconds &quot;, &quot;yname&quot;: &quot;watts &quot;, &quot;program&quot;: &quot;{:sl:n:sl:n    finalise {:sl:n            # cache the paramters so we can use them:sl:n            ftp &lt;- fetch(:qu:ftp:qu:);:sl:n            frc &lt;- fetch(:qu:frc:qu:);:sl:n            pmax&lt;- fetch(:qu:pmax:qu:);:sl:n            tte&lt;- fetch(:qu:tte:qu:);:sl:n            a&lt;- fetch(:qu:a:qu:);:sl:n            tau2&lt;- fetch(:qu:tau2:qu:);:sl:n            secs&lt;- fetch(:qu:secs:qu:);:sl:n:sl:n            # generate the model curve:sl:n            yy &lt;- sapply(secs, frc:sl:/x*(1-exp(-x:sl:/(frc:sl:/pmax)))); :sl:n:sl:n    }:sl:n:sl:n    x { round(secs); }:sl:n    y { round(yy); }:sl:n}:sl:n &quot;, &quot;line&quot;: 1, &quot;symbol&quot;: 0, &quot;size&quot;: 2, &quot;color&quot;: &quot;#113dff&quot;, &quot;opacity&quot;: 100, &quot;legend&quot;: true, &quot;opengl&quot;: false, &quot;datalabels&quot;: false, &quot;aggregate&quot;: 1, &quot;fill&quot;: false},\n{ &quot;name&quot;: &quot;pFTP &quot;, &quot;group&quot;: &quot; &quot;, &quot;xname&quot;: &quot;seconds &quot;, &quot;yname&quot;: &quot;watts &quot;, &quot;program&quot;: &quot;{:sl:n:sl:n    finalise {:sl:n            # cache the paramters so we can use them:sl:n            ftp &lt;- fetch(:qu:ftp:qu:);:sl:n            frc &lt;- fetch(:qu:frc:qu:);:sl:n            pmax&lt;- fetch(:qu:pmax:qu:);:sl:n            tte&lt;- fetch(:qu:tte:qu:);:sl:n            a&lt;- fetch(:qu:a:qu:);:sl:n            tau2&lt;- fetch(:qu:tau2:qu:);:sl:n            secs&lt;- fetch(:qu:secs:qu:);:sl:n:sl:n            # generate the model curve:sl:n            yy &lt;- sapply(secs, ((ftp*(1-exp(-x:sl:/tau2))) - (bool(x&gt;tte)*(a*log(x:sl:/tte))))); :sl:n:sl:n    }:sl:n:sl:n    x { round(secs); }:sl:n    y { round(yy); }:sl:n}:sl:n &quot;, &quot;line&quot;: 1, &quot;symbol&quot;: 0, &quot;size&quot;: 2, &quot;color&quot;: &quot;#4bc045&quot;, &quot;opacity&quot;: 100, &quot;legend&quot;: true, &quot;opengl&quot;: true, &quot;datalabels&quot;: false, &quot;aggregate&quot;: 1, &quot;fill&quot;: false} ]\n,\n&quot;AXES&quot;: [\n{ &quot;name&quot;: &quot;seconds &quot;, &quot;type&quot;: 0, &quot;orientation&quot;: 1, &quot;align&quot;: 1, &quot;minx&quot;: 1, &quot;maxx&quot;: 10000, &quot;miny&quot;: 0, &quot;maxy&quot;: 0, &quot;smooth&quot;: 0, &quot;groupby&quot;: 0, &quot;visible&quot;: true, &quot;fixed&quot;: true, &quot;log&quot;: true, &quot;minorgrid&quot;: false, &quot;majorgrid&quot;: true, &quot;labelcolor&quot;: &quot;#2684f6&quot;, &quot;axiscolor&quot;: &quot;#2684f6&quot;},\n{ &quot;name&quot;: &quot;watts &quot;, &quot;type&quot;: 0, &quot;orientation&quot;: 2, &quot;align&quot;: 1, &quot;minx&quot;: 0, &quot;maxx&quot;: 0, &quot;miny&quot;: 0, &quot;maxy&quot;: 950, &quot;smooth&quot;: 0, &quot;groupby&quot;: 0, &quot;visible&quot;: true, &quot;fixed&quot;: true, &quot;log&quot;: false, &quot;minorgrid&quot;: false, &quot;majorgrid&quot;: true, &quot;labelcolor&quot;: &quot;#ff0004&quot;, &quot;axiscolor&quot;: &quot;#ff0004&quot;} ]\n}" />
 	</chart>
-	<chart id="39" name="" title="Power Profile (R)" >
-		<property name="title" type="QString" value="Power Profile (R)" />
-		<property name="subtitle" type="QString" value="" />
-		<property name="widthFactor" type="double" value="2" />
-		<property name="heightFactor" type="double" value="2" />
-		<property name="style" type="int" value="0" />
-		<property name="resizable" type="bool" value="0" />
-		<property name="script" type="QString" value="library(fmsb)\nWeight=as.numeric(GC.athlete()[4])\nmmp &lt;- GC.season.meanmax(compare=FALSE)\nmm &lt;- mmp$wpk\n\ntime &lt;- seq(5, 3600, 1)\n\n\nlbltime&lt;-c(30,60,240,300,600,1200,3600,7200,5)\n\nlab&lt;-c(&quot;30s&quot;,&quot;1m&quot;,&quot;4m&quot;,&quot;5m&quot;,&quot;10m&quot;,&quot;20m&quot;,&quot;1h&quot;,&quot;2h&quot;,&quot;5s&quot;)\n\np_max &lt;- round(mmp$power[lbltime])\nppr&lt;-as.data.frame(t(p_max))\ncolnames(ppr)=lab\nppr=round((ppr/Weight),1)\n\nmax=c(15.1,11.5,7.4,7.2,6.8,6.2,5.6,4.7,23.1)\n\nppr=rev(ppr/max)\n\nppr &lt;- rbind(rep(1,10) , rep(0.4,10) , ppr)\n \nradarchart( ppr  , axistype=1 , \n     #custom polygon\n    pcol=&quot;cyan&quot; , pfcol=rgb(0.2,0.5,0.5,0.5) , plwd=2 , \n \n    #custom the grid\n    cglcol=&quot;red&quot;, cglty=1, axislabcol=&quot;green3&quot;, cglwd=1,caxislabels=seq(40,100,10),seg=6,\n title=paste(&quot;Cyclist profile:&quot;,GC.athlete()[1]),\n\nvlabels=rev(c(&quot;&quot;,&quot;&quot;,&quot;&quot;,&quot;&quot;,&quot;&quot;,&quot;&quot;,&quot;&quot;,&quot;&quot;,&quot;&quot;)),\n \n    #custom labels\n    vlcex=1\n    )\n\ntext(0.22,1.07,paste(&quot;P :&quot;,p_max[9],&quot;W\n\nPI : &quot;,round(ppr[3,1],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(0,1.2,paste(&quot;5 sec.&quot;),cex=1.25,col=&quot;green3&quot;)\n\ntext(0.95,0.78,paste(&quot;P :&quot;,p_max[1],&quot;W\n\nPI : &quot;,round(ppr[3,9],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(0.77,0.92,paste(&quot;30 sec.&quot;),cex=1.25,col=&quot;green3&quot;)\n\ntext(1.2,0.07,paste(&quot;P :&quot;,p_max[2],&quot;W\n\nPI : &quot;,round(ppr[3,8],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(1.18,0.21,paste(&quot;1 min&quot;),cex=1.25,col=&quot;green3&quot;)\n\ntext(1,-0.75,paste(&quot;P :&quot;,p_max[3],&quot;W\n\nPI : &quot;,round(ppr[3,7],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(1.07,-0.6,paste(&quot;4 min&quot;),cex=1.25,col=&quot;green3&quot;)\n\ntext(0.4,-1.18,paste(&quot;P :&quot;,p_max[4],&quot;W\n\nPI : &quot;,round(ppr[3,6],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(0.4,-1.05,paste(&quot;5 min&quot;),cex=1.25,col=&quot;green3&quot;)\n\ntext(-0.4,-1.18,paste(&quot;P :&quot;,p_max[5],&quot;W\n\nPI : &quot;,round(ppr[3,5],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(-0.4,-1.05,paste(&quot;10 min&quot;),cex=1.25,col=&quot;green3&quot;)\n\ntext(-1,-0.75,paste(&quot;P :&quot;,p_max[6],&quot;W\n\nPI : &quot;,round(ppr[3,4],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(-1,-0.6,paste(&quot;20 min&quot;),cex=1.25,col=&quot;green3&quot;)\n\ntext(-1.2,0.08,paste(&quot;P :&quot;,p_max[7],&quot;W\n\nPI : &quot;,round(ppr[3,3],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(-1.19,0.21,paste(&quot;60 min&quot;),cex=1.25,col=&quot;green3&quot;)\n\ntext(-0.9,0.78,paste(&quot;P :&quot;,p_max[8],&quot;W\n\nPI : &quot;,round(ppr[3,2],3)*100),cex=1,col=&quot;dodgerblue1&quot;)\ntext(-0.78,0.92,paste(&quot;2 H&quot;),cex=1.25,col=&quot;green3&quot;)" />
-		<property name="state" type="QString" value="" />
-		<property name="plotOnChart" type="bool" value="0" />
-		<property name="showConsole" type="bool" value="0" />
-	</chart>
-	<chart id="39" name="" title="Fatigue Profile (R)" >
-		<property name="title" type="QString" value="Fatigue Profile (R)" />
-		<property name="subtitle" type="QString" value="" />
-		<property name="widthFactor" type="double" value="2" />
-		<property name="heightFactor" type="double" value="2" />
-		<property name="style" type="int" value="0" />
-		<property name="resizable" type="bool" value="0" />
-		<property name="script" type="QString" value="##############################################################################\n## PULL THE ACTIVITY METRICS\n#GC.page(1024,1000)\nathlete = GC.athlete()\nmmp &lt;- GC.season.meanmax(compare=FALSE)\nint_duration &lt;- c(5,10,20, 30,60,120, 180,300,480, 1200,3600,5400)\nint_dur_str &lt;- c(&quot;NP-5s&quot;,&quot;NP-10s&quot;,&quot;NP-20s&quot;,&quot;AC-30s&quot;,&quot;AC-1m&quot;,&quot;AC-2m&quot;,&quot;VO2-3m&quot;,&quot;VO2-5m&quot;,&quot;VO2-8min&quot;,&quot;LT-20m&quot;,&quot;LT-1h&quot;,&quot;LT-1h30&quot;)\np_max &lt;- mmp$power[int_duration]			# max power data\np_max[c(11,12)] = mmp$IsoPower[int_duration[c(11,12)]]		# NP for 1h and 1h30\np_max_wpk &lt;- mmp$wpk[c(5,60,300,2500)]			# W/k data\n\n#p_max[1] = 1200\n#p_max[5] = 2000\n#p_max[8] = 2000\n#p_max[11] = 2000\n\n\nif (athlete$gender==&apos;male&apos;) \n{\n limits_5s &lt;- c(12.15,13.93,15.70,17.48,19.55,21.33,23.11)\n limits_1m &lt;- c(6.44, 7.13, 7.82, 8.51 , 9.32 , 10.1 , 10.7)\n limits_5m &lt;- c(3.05, 3.67, 4.29, 4.91 , 5.64 , 6.26 , 6.88)\n limits_1h &lt;- c(2.49, 3.02, 3.55, 4.09 , 4.71 , 5.24 , 5.78)\n}\n\ncategory_string &lt;- c(&quot;untrained&quot;,&quot;fair&quot;,&quot;moderate&quot;,&quot;good&quot;,&quot;very good&quot;,&quot;excellent&quot;,&quot;exceptional&quot;,&quot;world class&quot;)\ncategory_col &lt;- c(&quot;darkred&quot;  ,&quot;red&quot; ,&quot;orange&quot;,&quot;yellow&quot;,&quot;lightgreen&quot;,&quot;green4&quot;,&quot;darkgreen&quot;,&quot;purple&quot;)\n#c_border = c(&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;,&quot;white&quot;)\n\np_max_5s = limits_5s*athlete$weight\np_max_1m = limits_1m*athlete$weight\np_max_5m = limits_5m*athlete$weight\np_max_1h = limits_1h*athlete$weight\n\n\nif (athlete$gender==&apos;male&apos;) \n{\n w_above_avg &lt;- c(0,0.15,0.20 ,0,0.10,0.15, 0,0.04,0.10, 0,0.02,0.05)\n above_avg   &lt;- c(0,0.22,0.35, 0,0.21,0.23, 0,0.08,0.18, 0,0.04,0.08)\n at_average  &lt;- c(0,0.31,0.47, 0,0.25,0.36, 0,0.15,0.24, 0,0.07,0.15)\n below_avg   &lt;- c(0,0.41,0.61, 0,0.31,0.50, 0,0.20,0.30, 0,0.11,0.25)\n}\np_ref = c(p_max[1],p_max[1],p_max[1],p_max[4],p_max[4],p_max[4],p_max[7],p_max[7],p_max[7],p_max[10],p_max[10],p_max[10])\np_w_above = p_ref*(1-w_above_avg)\np_above 	= p_ref*(1-above_avg)\np_at 	= p_ref*(1-at_average)\np_below 	= p_ref*(1-below_avg)\n\nc_plot = c(&quot;grey&quot;,&quot;white&quot;,&quot;white&quot;,&quot;grey&quot;,&quot;white&quot;,&quot;white&quot;,&quot;grey&quot;,&quot;white&quot;,&quot;white&quot;,&quot;grey&quot;,&quot;white&quot;,&quot;white&quot;)\nzone_nam &lt;-c(&quot;well above average&quot;,&quot;above average&quot;,&quot;average&quot;,&quot;below average&quot;,&quot;well below average&quot;)\nzone_col &lt;-c(&quot;green4&quot;,&quot;lightgreen&quot;,&quot;yellow&quot;,&quot;red&quot;,&quot;darkred&quot;)\nc_plot[p_max&gt;p_w_above] = zone_col[1]			# well above\nc_plot[p_max&lt;p_w_above&amp;p_max&gt;p_above] = zone_col[2]	# above\nc_plot[p_max&lt;p_above&amp;p_max&gt;p_at] = zone_col[3]		# average\nc_plot[p_max&lt;p_at&amp;p_max&gt;p_below]= zone_col[4]		# below \nc_plot[p_max&lt;p_below] = zone_col[5]			# well below\n\nc_border = c_plot\nc_border[1] =category_col[findInterval(p_max[1],p_max_5s)+1]\nc_border[5] =category_col[findInterval(p_max[5],p_max_1m)+1]\nc_border[8] =category_col[findInterval(p_max[8],p_max_5m)+1]\nc_border[11]=category_col[findInterval(p_max[11],p_max_1h)+1]\n\n\ntxt_sz =1.3\nopar &lt;- par(lwd = 5)\nx &lt;- barplot(p_max,names=int_dur_str, col=c_plot, ylim=c(0, max(p_max)*1.3),cex.axis=txt_sz, cex.names=txt_sz, border=c_border) \nopar &lt;- par(lwd = 1)\n\n#abline(h=p_ref,slope=0,col=&quot;white&quot;,lty=2)\ntitle(main=&quot;Power and Fatigue Profile&quot;,xlab=&quot;Interval Time&quot;,ylab=&quot;MaxPower(W)&quot;,cex.main=txt_sz*txt_sz,cex.lab=txt_sz)\n#legend(&quot;topright&quot;,title=&quot;sdf&quot;, col=&quot;white&quot;,c(&quot;reference int. time&quot;,zone_nam),fill=c(&quot;grey&quot;,zone_col))\n#legend(&quot;right&quot;,c(&quot;reference int. time&quot;,zone_nam),fill=c(&quot;grey&quot;,zone_col))\n\nlegend(&quot;topright&quot;, inset=.02, title=&quot;Bar Color: Fatigue Profile&quot;, c(&quot;fatigue reference interval&quot;,zone_nam), fill=c(&quot;grey&quot;,zone_col), horiz=FALSE, cex=txt_sz)\nlegend(&quot;top&quot;, inset=.02, title=&quot;Border Color: Power Profile&quot; ,category_string, fill=category_col, horiz=FALSE, cex=txt_sz)\n\nlines(x,p_w_above	,type=&quot;b&quot;,col=zone_col[1],lwd=2)\nlines(x,p_above	,type=&quot;b&quot;,col=zone_col[2],lwd=2)\nlines(x,p_at	,type=&quot;l&quot;,col=zone_col[3],lty=&quot;dotted&quot;,lwd=2)\nlines(x,p_below	,type=&quot;b&quot;,col=zone_col[4],lwd=2)\ntext(x,(max(p_max)-min(p_max))/7,paste0(round(p_max),&quot;W&quot;), pos=1, col=&quot;black&quot;,cex=txt_sz)#opar &lt;- par(lwd = 1)\n\nfor (cat in 1:7)\n{	\n lines(c(x[1]-0.5,x[1]+0.5),c(p_max_5s[cat],p_max_5s[cat]), col=&quot;black&quot;)\n text(x[1],p_max_5s[cat]-(p_max_5s[7]-p_max_5s[1])/14, category_string[cat], cex=txt_sz*1., col=&quot;black&quot;)\n lines(c(x[5]-0.5,x[5]+0.5),c(p_max_1m[cat],p_max_1m[cat]), col=&quot;black&quot;)\n text(x[5],p_max_1m[cat]-(p_max_1m[7]-p_max_1m[1])/14, category_string[cat], cex=txt_sz*1., col=&quot;black&quot;)\n lines(c(x[8]-0.5,x[8]+0.5),c(p_max_5m[cat],p_max_5m[cat]), col=&quot;black&quot;)\n text(x[8],p_max_5m[cat]-(p_max_5m[7]-p_max_5m[1])/14, category_string[cat], cex=txt_sz*1., col=&quot;black&quot;)\n lines(c(x[11]-0.5,x[11]+0.5),c(p_max_1h[cat],p_max_1h[cat]), col=&quot;black&quot;)\n text(x[11],p_max_1h[cat]-(p_max_1h[7]-p_max_1h[1])/14, category_string[cat], cex=txt_sz*1., col=&quot;black&quot;)\n}\ntext(x[1],p_max_5s[7]+(p_max_5s[7]-p_max_5s[1])/14, category_string[8], cex=txt_sz*1., col=&quot;black&quot;)\ntext(x[5],p_max_1m[7]+(p_max_1m[7]-p_max_1m[1])/14, category_string[8], cex=txt_sz*1., col=&quot;black&quot;)\ntext(x[8],p_max_5m[7]+(p_max_5m[7]-p_max_5m[1])/14, category_string[8], cex=txt_sz*1., col=&quot;black&quot;)\ntext(x[11],p_max_1h[7]+(p_max_1h[7]-p_max_1h[1])/14, category_string[8], cex=txt_sz*1.,col=&quot;black&quot;)\n\n\n## Summary Box Labels\ntext(x[1],(max(p_max)-min(p_max))/20, paste0(round(p_max[1]/athlete$weight,2),&quot;W/kg&quot; ),cex=txt_sz*0.8,col=&quot;darkblue&quot;)\ntext(x[5],(max(p_max)-min(p_max))/20, paste0(round(p_max[5]/athlete$weight,2),&quot;W/kg&quot; ),cex=txt_sz*0.8,col=&quot;darkblue&quot;)\ntext(x[8],(max(p_max)-min(p_max))/20, paste0(round(p_max[8]/athlete$weight,2),&quot;W/kg&quot; ),cex=txt_sz*0.8,col=&quot;darkblue&quot;)\ntext(x[11],(max(p_max)-min(p_max))/20,paste0(round(p_max[11]/athlete$weight,2),&quot;W/kg(NP)&quot; ),cex=txt_sz*0.8,col=&quot;darkblue&quot;)\ntext(x[4],p_max[1]*1.13,paste0(&quot;Name:   &quot;, athlete$name)			,cex=txt_sz*1.,col=&quot;yellow&quot;)\ntext(x[4],p_max[1]*1.10,paste0(&quot;Gender: &quot;, athlete$gender)			,cex=txt_sz*1.,col=&quot;yellow&quot;)\ntext(x[4],p_max[1]*1.07,paste0(&quot;Weight: &quot;, athlete$weight, &quot;kg&quot;)		,cex=txt_sz*1.,col=&quot;yellow&quot;)\n#text(x[6],p_max[1]*1.04,	,cex=txt_sz*1.,col=&quot;yellow&quot;)\n#text(x[6],p_max[1]*1.01,	,cex=txt_sz*1.,col=&quot;yellow&quot;)\n#text(x[6],p_max[1]*0.98,	,cex=txt_sz*1.,col=&quot;yellow&quot;)\n#text(x[6],p_max[1]*0.95,	,cex=txt_sz*1.,col=&quot;yellow&quot;)\n\n\n" />
-		<property name="state" type="QString" value="" />
-		<property name="plotOnChart" type="bool" value="0" />
-		<property name="showConsole" type="bool" value="0" />
-	</chart>
 </layout>
 <layout name="Triathlon" style="0" type="8" expression="" trainswitch="0">
 	<chart id="47" name="" title="Overview" >
@@ -537,29 +513,17 @@
 		<property name="settings" type="LTMSettings" value="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" />
 		<property name="useSelected" type="int" value="0" />
 	</chart>
-	<chart id="39" name="" title="Run Profile (R)" >
-		<property name="title" type="QString" value="Run Profile (R)" />
+	<chart id="44" name="" title="Tri Mix" >
+		<property name="title" type="QString" value="Tri Mix" />
 		<property name="subtitle" type="QString" value="" />
 		<property name="widthFactor" type="double" value="2" />
 		<property name="heightFactor" type="double" value="2" />
 		<property name="style" type="int" value="0" />
 		<property name="resizable" type="bool" value="0" />
-		<property name="script" type="QString" value="## CHART SIZING\nGC.page(width=800, height=600)\n\n## PULL THE ACTIVITY METRICS FROM GC RECORDS\nmm &lt;- GC.season.meanmax(all=FALSE, filter=&quot;isRun&lt;&gt;0&quot;, compare=FALSE)\nathlete &lt;- GC.athlete()\nseasons &lt;- GC.season(compare=TRUE)\n\n## MANIPULATE THE DATA\n# mean maximal speed in m/s from 1s to 1h30&apos; (OJO con el ultimo ****)\nspeed &lt;- mm$speed[2:12000]/3.6\n# pace from speed in min/km\npace &lt;- (1000/60)/speed\n# distance from mean maximal speed\ndistance &lt;- round(speed*(1:length(pace))/1000,1)\n\ndistance.value &lt;- c(0, 0.4, 0.8, 1.5, 5, 10, 21.1, 42.2)\ndistance.name &lt;-c (&quot;&quot;, &quot;400m&quot;, &quot;800m&quot;, &quot;1500m&quot;, &quot;5k&quot;, &quot;10k&quot;, &quot;1/2 Mar&quot;, &quot;Marathon&quot;)\n\ndf.working &lt;- data.frame(distance, pace, speed)\ndf.working &lt;- subset(df.working, distance==distance.value)\ndf.working &lt;- aggregate(.~distance, df.working, mean)\nnames(df.working)[names(df.working)==&quot;distance&quot;] &lt;- &quot;distance.value&quot;\n\n## Account for Different Genders\nifelse (athlete$gender==&apos;male&apos;, index.TOP&lt;-c(NA, 9.26, 7.93, 7.28, 6.6, 6.34, 6.02, 5.72), index.TOP&lt;-c(NA, 8.4, 7.06, 6.52, 5.87, 5.4, 5.19))\nifelse (athlete$gender==&apos;male&apos;, index.BOT&lt;-c(NA, 3.27, 2.8, 2.57, 2.33, 2.24, 2.13, 2.02), index.BOT&lt;-c(NA, 2.97, 2.5, 2.3, 2.08, 1.91, 1.84))\n\n## Build the DataFrame\ndf.final &lt;- data.frame(distance.value, distance.name)\ndf.final &lt;- merge(df.final, df.working, all.x=TRUE)\nindex &lt;-((df.final$speed-index.BOT)/(index.TOP-index.BOT))*10\ndf.final &lt;- cbind(df.final, index)\n\n## Round the Numbers to make the frame more readable\ndf.final$pace &lt;- round(df.final$pace, 2)\ndf.final$speed &lt;- round(df.final$speed, 2)\ndf.final$index &lt;- round(df.final$index, 2)\n\n## Remove Wacky Values off the Scale\ndf.final$index &lt;- ifelse(df.final$index&lt;0, NA, df.final$index)\ndf.final$index &lt;- ifelse(df.final$index&gt;10, NA, df.final$index)\n\n## Athlete Phenotype\npheno.lbl &lt;- c(NA, &quot;1. T-MD&quot;, &quot;1. T-MD&quot;, &quot;2. T-LD&quot;, &quot;2. T-LD&quot;, &quot;2. T-LD&quot;, &quot;3. Mar&quot;, &quot;3. Mar&quot;)\ndf.pheno &lt;- data.frame(pheno.lbl, df.final$index)\ndf.pheno &lt;- aggregate(.~pheno.lbl, df.pheno, max, na.rm=TRUE, na.action=&quot;na.pass&quot;)\n# df.pheno[ is.na(df.pheno) ] &lt;- NA \n# df.pheno[which(!is.finite(df.pheno))] &lt;- NA\nis.na(df.pheno)&lt;-sapply(df.pheno, is.infinite)\ndf.pheno[is.na(df.pheno)]&lt;-NA\n\npheno.allrounderband = 1\npheno.best &lt;- which(df.pheno$frame.index==max(df.pheno$frame.index, na.rm=TRUE))\n\nif (sum(is.na(df.pheno$frame.index))&gt;0) {\n  phenotype=&quot;Not Enough Datapoints&quot;\n} else if (max(df.pheno$frame.index, na.rm=TRUE)-min(df.pheno$frame.index, na.rm=TRUE)&lt;pheno.allrounderband) {\n  phenotype=&quot;All-Rounder&quot;\n} else if (pheno.best==1) {\n  phenotype&lt;-&quot;Track - Middle Distance&quot;\n} else if (pheno.best==2) {\n  phenotype&lt;-&quot;Track - Long Distance&quot;\n} else\n  phenotype&lt;-&quot;Marathoner&quot;\n\n## CHART INPUTS &amp; SETTINGS\nchart.lbl.yaxis&lt;-c(&quot;Untrained&quot;, &quot;Novice&quot;, &quot;Fair&quot;, &quot;Moderate&quot;, &quot;Good&quot;, &quot;Very Good&quot;, &quot;NCAA Div.3&quot;, &quot;NCAA Div.1&quot;, &quot;World Class&quot;)\nchart.lbl.sub = &quot;Run Distance&quot;\nchart.lbl.title = paste0(&quot;Runner Pace Profile &quot;, &quot; (&quot;, seasons$name, &quot;)&quot;)\nchart.in.textsize=1\nchart.in.chart.color1=&quot;Red&quot;\nchart.in.chart.color2=&quot;White&quot;\nchart.indices.posy&lt;-c(0, 0.87, 1.9, 3.08, 4.39, 5.74, 7.01, 7.98, 10)\n\n## Bar Value Labels\nchart.lbl.posx&lt;-c(0.8, 2, 3.2, 4.4, 5.6, 6.8, 8, 9.2)-0.15\nchart.lbl.posy&lt;-c(df.final$index-0.3)\nchart.in.textscale = 0.9\n\n## Make the M:SS Labels\nchart.lbl.val.minute &lt;- as.integer(df.final$pace)\nchart.lbl.val.second &lt;- round((df.final$pace-as.integer(df.final$pace))*60, 0)\nchart.lbl.val.second &lt;- sprintf(&quot;%02i&quot;, chart.lbl.val.second)\nchart.lbl.val.pace &lt;- paste0(chart.lbl.val.minute, &quot;:&quot;, chart.lbl.val.second, &quot;/km&quot;)\n\n## Total Event Time Labels\nchart.lbl.val.totaltime &lt;- (df.final$pace*df.final$distance.value)/1440\nchart.lbl.val.totaltime &lt;- format(as.POSIXct(Sys.Date() + chart.lbl.val.totaltime), &quot;%H:%M:%S&quot;, tz=&quot;UTC&quot;)\n\n## Summary Box Labels\nchart.summarybox.text &lt;- c(paste0(&quot;Athlete: &quot;, athlete$name), paste0(&quot;Gender: &quot;, athlete$gender), paste0(&quot;Weight: &quot;, athlete$weight, &quot;kg&quot;), paste0(&quot;Phenotype: &quot;, phenotype))\nchart.summarybox.posy &lt;- c(9.6, 9.2, 8.8, 8.4)\n\n## PLOT THE CHART\n## BarPlot\nbarplot(rep(NA, length(df.final$index)),\n        xlim=c(0, 9.6),\n        ylim=c(0, 10),\n        main=chart.lbl.title,\n        cex.main=1.5,\n        font.main=2,\n        sub=chart.lbl.sub,\n        font.xlab=2,\n        col=heat.colors(9),\n        names.arg=df.final$distance.name,\n        yaxt=&apos;n&apos;,\n        space=0.2,\n        col.axis=chart.in.chart.color2,\n        col.sub=chart.in.chart.color2)\n\n## Reference Lines\nabline(h=chart.indices.posy, slope=0, col=&quot;gray50&quot;, lty=3)\ntext(0.42, chart.indices.posy+(0.2), chart.lbl.yaxis, cex=chart.in.textsize, col=chart.in.chart.color2)\nabline(h=10, slope=0, col=&quot;gray50&quot;, lty=3)\ntext(0.42, 10-(0.3), &quot;World Record&quot;, cex=chart.in.textsize, col=chart.in.chart.color2)\n\n## Plot the Barplot over the Reference Lines\nbarplot(df.final$index,\n        add=T,\n        col=heat.colors(9),\n        yaxt=&apos;n&apos;,\n        border=FALSE)\n\n## Bar Value Labels\ntext(chart.lbl.posx, chart.lbl.posy, chart.lbl.val.pace, col=&quot;black&quot;, cex=chart.in.textsize*chart.in.textscale)\ntext(chart.lbl.posx, chart.lbl.posy-0.4, chart.lbl.val.totaltime, col=&quot;black&quot;, cex=chart.in.textsize*chart.in.textscale)\n\n## Summary Box Labels\ntext(8.5, chart.summarybox.posy, chart.summarybox.text, cex=chart.in.textsize*chart.in.textscale, col=chart.in.chart.color1)\n\nprint(df.pheno)\nprint(df.final)\nprint(df.working)" />
+		<property name="script" type="QString" value="\n\n## R (Daimon) -&gt; Python (Fe)\n##.....................................................\n##.....................................................\n##\n## R-chart script.\n## Chart: SS-SR\n## Creator: Daimon\n##\n## For use with GoldenCheetah.\n## tested on GC.version() is &quot;DEV-V3.5 1705&quot;\n##\n## .................................................\n##\n## Tri Mix\n## Last Edited At: 2017-09-26 - Creator: Daimon\n## Triathlon\n## Chart to display the relation between Swim-Bike-Run in the selected Data Range as\n## function of activities / duration / distance.\n\n## Default graph is pie chart.\n## (in script &quot;pie=TRUE&quot; displays pie charts, &quot;pie=FALSE&quot; displays bar graph)\n\n## Remark:\n## if the triathlon sports are not defined as the default English &quot;Swim&quot;,&quot;Bike&quot;,&quot;Run&quot; then change the names accoordingly in the script &quot;sportlist=c(&quot;Swim&quot;,&quot;Bike&quot;,&quot;Run&quot;)&quot;\n## \n##.....................................................\n##\n## Python script\n## Chart: Py_tri_mix_01   (cover R tri mix)\n## Creator: Fe \n##\n## GC.version() &quot;V3.6-RC1&quot; (Build id: 4010 2022/07/04)\n##\n## 08&apos;2022\n## Python Script \n##\n## Python1: pre-installed (Build id: 4010 2022/07/04)\n## Plotly1: pre-installed (Build id: 4010 2022/07/04)\n##\n## Python2: 3.7.9 (Aug 17 2020)\n## Plotly2: 4.6.0 --&gt; 5.2.1\n##\n## Golden Cheetah: &quot;V3.6-RC1&quot; (Build id: 4010 2022/07/04)\n## Trends\n##\n##.....................................................\n\n\n## packages\nimport numpy as np\nimport plotly as ply\nimport datetime\nimport tempfile\nimport pathlib\nfrom plotly import tools\nimport plotly.graph_objs as go\n\n#......................................................\n\ntemp_file = tempfile.NamedTemporaryFile(mode=&quot;w+t&quot;, prefix=&quot;GC_&quot;, suffix=&quot;.html&quot;, delete=False)\n\n#......................................................\n\npie=True## False\n\nsportlist=[&quot;Swim&quot;,&quot;Bike&quot;,&quot;Run&quot;]\n\ncolor=[&apos;rgb(87,140,181)&apos;,&apos;rgb(255, 153, 0)&apos;,&apos;rgb(133,166,122)&apos;]\n#&apos;rgb(255,214,77)&apos;\n\nheader1=&apos;Activities&apos; #&quot;\nheader2=&apos;Duration  (h:mm:ss)&apos;\nheader3=&apos;Distance (km)&apos;\nheader4=&apos;Normalized effort (%)&apos; ##???\npaper_bgcolor=&apos;rgb(20,20,20)&apos;\nplot_bgcolor=&apos;rgb(20,20,20)&apos;\n\nmetrics=GC.seasonMetrics(all=False, filter=&apos;&apos;, compare=False)\ntrg=[];time=[];distance=[]\n\nfor sport_name in sportlist:\n    filter=np.array(metrics[&apos;Sport&apos;])==[sport_name]\n    trg.append(len(np.array(metrics[&apos;Duration&apos;])[filter]))\n    time.append(np.sum(np.array(metrics[&apos;Duration&apos;])[filter]))\n    distance.append(np.sum(np.array(metrics[&apos;Distance&apos;])[filter]))\n\n\n## trg=[50,70,35] ## random values, only for plot jpg\n## time=[70000,158529,45000] ## random values, only for plot jpg\n## distance=[100,1000,450] ## random values, only for plot jpg\n\n\ndef hms(seconds):\n    h = int(seconds // 3600)\n    m = int(seconds % 3600 // 60)\n    s = int(seconds % 3600 % 60)\n    return &apos;{:02d}:{:02d}:{:02d}&apos;.format(h, m, s)\n\ntime_hms=[hms(sec) for sec in time]\n\ntrace=[];layout=[]\n\nif pie:\n    trace1 = dict(\n      values=trg,\n      marker=dict(colors=color),labels=sportlist,textfont=dict(size=13,color=&apos;white&apos;),\n      type=&apos;pie&apos;,\n      hoverinfo=&apos;label+percent&apos;,textinfo=&apos;value+label&apos;,textposition=&apos;outside&apos;,\n      #domain=dict(column=0,row=0),\n      domain=dict(x=[0,0.49],y=[0.55,1])\n      )\n    trace.append(trace1)\n\n    trace2 = dict(\n      values=time,\n      marker=dict(colors=color),labels=sportlist,textfont=dict(size=13,color=&apos;white&apos;),\n      type=&apos;pie&apos;,\n      text=time_hms,#[hms(sec) for sec in time],\n      hoverinfo=&apos;percent+label&apos;,textinfo=&apos;text+label&apos;,textposition=&apos;outside&apos;,\n      #domain=dict(column=1,row=0),\n      domain=dict(x=[0.51,1.0],y=[0.55,1])\n      )\n    trace.append(trace2)\n\n    trace3 = dict(\n      values=np.round(distance,2),\n      marker=dict(colors=color),labels=sportlist,textfont=dict(size=13,color=&apos;white&apos;),\n      type=&apos;pie&apos;,\n      hoverinfo=&apos;label+percent&apos;,textinfo=&apos;value+label&apos;,textposition=&apos;outside&apos;,\n      #domain=dict(column=0,row=1)\n      domain=dict(x=[0,0.49],y=[0,0.45])\n      )\n    trace.append(trace3)\n\n    grid_data=[[0.25,1.05,header1,&apos;orange&apos;],[0.8,1.05,header2,&apos;orange&apos;],[0.25,0.5,header3,&apos;orange&apos;],[0.8,0.4,&apos;Sport&apos;,&apos;lightgreen&apos;]]\n\n    grid_title=[dict(x=a,y=b,text=c,showarrow=False,xanchor=&apos;center&apos;,font=dict(color=d,size=15)) for a,b,c,d in grid_data]\n\n    layout=dict(title=&apos;Tri mix&apos;,titlefont=dict(size=18,color=&apos;rgb(243,218,11)&apos;),paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,grid=dict(rows=2,columns=2),#columns=2),\n    legend=dict(x=0.8,y=0.25,xanchor=&quot;center&quot;,font=dict(color=&apos;white&apos;)),\n    annotations=grid_title\n    )\n\nelse:\n\n    trace1 = dict(\n      x=sportlist,y=trg,\n      name=header1,\n      text=trg,marker=dict(color=color),textfont=dict(size=14,color=&apos;white&apos;),\n      type=&apos;bar&apos;,\n      xaxis=&apos;x1&apos;,yaxis=&apos;y1&apos;,\n      )\n    trace.append(trace1)\n\n    trace2 = dict(\n      x=sportlist,y=time,\n      name=header2,\n      text=time_hms,marker=dict(color=color),textfont=dict(size=14,color=&apos;white&apos;),\n      type=&apos;bar&apos;,\n      xaxis=&apos;x2&apos;,yaxis=&apos;y2&apos;,\n      )\n    trace.append(trace2)\n\n    trace3 = dict(\n      x=sportlist,y=np.round(distance,2),\n      name=header3,\n      text=np.round(distance,2),marker=dict(color=color),textfont=dict(size=14,color=&apos;white&apos;),\n      type=&apos;bar&apos;,\n      xaxis=&apos;x3&apos;,yaxis=&apos;y3&apos;,\n      )\n    trace.append(trace3)\n\n    trace4 = dict(\n      x=[0,1],y=[0,1],mode=&quot;lines&quot;,line=dict(color=plot_bgcolor),\n      xaxis=&apos;x4&apos;,yaxis=&apos;y4&apos;,\n      )\n    trace.append(trace4)\n\n\n    axis=dict(\n        showline=False,zeroline=False,showgrid=False,mirror=False,tickfont=dict(size=11,color=&apos;lightgrey&apos;))\n        \n    grid_data=[[len(trg)/3,max(trg)*1.08,header1,&apos;orange&apos;,&apos;x1&apos;,&apos;y1&apos;],[len(time)/3,max(time)*1.08,header2,&apos;orange&apos;,&apos;x2&apos;,&apos;y2&apos;],[len(distance)/3,max(distance)*1.08,header3,&apos;orange&apos;,&apos;x3&apos;,&apos;y3&apos;]]#,[1/2,1/2,&apos;Sport&apos;,&apos;lightgreen&apos;,&apos;x4&apos;,&apos;y4&apos;]]\n\n    grid_title=[dict(x=a,y=b,text=c,xref=e,yref=f,showarrow=False,font=dict(color=d,size=14)) for a,b,c,d,e,f in grid_data]\n\n    grid_data_4=[[1/2,1/1.2,&apos;Sport :&apos;,&apos;white&apos;,&apos;x4&apos;,&apos;y4&apos;,17,&apos;left&apos;],[1/2,1/1.4,&apos;- &apos;+str(sportlist[0]),color[0],&apos;x4&apos;,&apos;y4&apos;,15,&apos;left&apos;],[1/2,1/1.6,&apos;- &apos;+str(sportlist[1]),color[1],&apos;x4&apos;,&apos;y4&apos;,15,&apos;left&apos;],[1/2,1/1.9,&apos;- &apos;+str(sportlist[2]),color[2],&apos;x4&apos;,&apos;y4&apos;,15,&apos;left&apos;]]\n\n    grid_title_4=[dict(x=a,y=b,text=c,xref=e,yref=f,showarrow=False,font=dict(color=d,size=g),xanchor=h) for a,b,c,d,e,f,g,h in grid_data_4]\n                \n\n    layout=dict(\n      title=&apos;Tri mix&apos;,titlefont=dict(size=18,color=&apos;rgb(243,218,11)&apos;),\n      paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,bargap=0,showlegend=False,\n      xaxis1=dict(axis,**dict(domain=[0.0,0.45], anchor=&apos;y1&apos;)),\n      xaxis2=dict(axis,**dict(domain=[0.55, 1.0], anchor=&apos;y2&apos;)),\n      xaxis3=dict(axis,**dict(domain=[0.0, 0.45],anchor=&apos;y3&apos;)),\n      xaxis4=dict(axis,**dict(domain=[0.55, 1.0],anchor=&apos;y4&apos;,showticklabels=False)),  \n      yaxis1=dict(axis,**dict(domain=[0.55, 1.0], anchor=&apos;x1&apos;,showticklabels=False)),\n      yaxis2=dict(axis,**dict(domain=[0.55,1.0], anchor=&apos;x2&apos;,showticklabels=False)),\n      yaxis3=dict(axis,**dict(domain=[0.0, 0.45], anchor=&apos;x3&apos;,showticklabels=False)),\n      yaxis4=dict(axis,**dict(domain=[0.0, 0.45], anchor=&apos;x4&apos;,showticklabels=False)),\n      #legend=dict(x=0.8,y=0.15,xanchor=&quot;center&quot;,font=dict(color=&apos;white&apos;)),\n      annotations=grid_title+grid_title_4\n    )\n\n\n\nfig = dict(data=trace, layout=layout)\n\nply.offline.plot(fig,auto_open = False, filename=temp_file.name, validate=False)\n\nGC.webpage(pathlib.Path(temp_file.name).as_uri())\n\n\n\n" />
 		<property name="state" type="QString" value="" />
-		<property name="plotOnChart" type="bool" value="0" />
-		<property name="showConsole" type="bool" value="0" />
-	</chart>
-	<chart id="39" name="" title="Tri Mix (R)" >
-		<property name="title" type="QString" value="Tri Mix (R)" />
-		<property name="subtitle" type="QString" value="" />
-		<property name="widthFactor" type="double" value="2" />
-		<property name="heightFactor" type="double" value="2" />
-		<property name="style" type="int" value="0" />
-		<property name="resizable" type="bool" value="0" />
-		<property name="script" type="QString" value="## R-chart script.\n## For use with GoldenCheetah.\n## tested on GC.version() is &quot;DEV-V3.5 1705&quot;\n##\n\npie=TRUE ##FALSE\nsportlist=c(&quot;Swim&quot;,&quot;Bike&quot;, &quot;Run&quot;, &quot;Strength&quot;)\neco=c(3, 1/5, 1, 0)\ncolor=c(&quot;#578cb5&quot;,&quot;#85a67a&quot;, &quot;#ffaa7f&quot;,&quot;#ffd64d&quot;)\n\npar(mfrow=c(2,2), mar=c(3,3,3,3))\n##layout(matrix(c(1,2,3,4), 2, 2, byrow = TRUE), widths=c(1,1), heights=c(1,1))\n\n##Get metrics\nmetrics&lt;- GC.season.metrics(all=FALSE, filter=&quot;&quot;, compare=FALSE)\n\n##Filter Walks\n#metrics&lt;-metrics[metrics$Workout_Code!=&quot;Walk&quot;,]\n\n##Count Weeks\nweeks &lt;- 1\nif (length(metrics$date)&gt;0) {\n    weeks &lt;- max( (as.numeric(metrics$date[length(metrics$date)]-metrics$date[1])+1)/7, 1)\n}\n\nmetriclist&lt;- c()\ntrg&lt;- c();time&lt;-c();distance&lt;-c()\nfor(a in 1:length(sportlist)) {\n    metriclist[[a]]&lt;-metrics[which(metrics$Sport==sportlist[a]),]\n    trg[a]&lt;-length(metriclist[[a]]$Sport)\n    if (trg[a]&gt;0) {\n        time[a]&lt;-Reduce(&quot;+&quot;,metriclist[[a]]$Duration)\n        distance[a]&lt;-Reduce(&quot;+&quot;,metriclist[[a]]$Distance)\n    } else {\n        time[a]&lt;-0\n        distance[a]&lt;-0\n    }\n}\n\ntrg&lt;-trg/weeks\ntime[is.na(time)]&lt;-0\ntime&lt;-time%/%weeks\ndistance[is.na(distance)]&lt;-0\ndistance&lt;-distance/weeks\n\nheader1&lt;-sprintf(&quot;Activities (%.1f/wk)&quot;, sum(trg))\nheader2&lt;-sprintf(&quot;Duration (%d:%02d:%02d/wk)&quot;, sum(time)%/%3600, sum(time)%/%60%%60, sum(time)%%60)\nheader3&lt;-sprintf(&quot;Distance (ECO %.1f/wk)&quot;, sum(distance*eco))\n\nif (pie==TRUE){\n	pie(trg, labels=sprintf(&quot;%s %.1f&quot;,sportlist,trg), main=header1, col=color)\n	pie(time, labels=sprintf(&quot;%s %d:%02d:%02d&quot;,sportlist,time%/%3600,time%/%60%%60,time%%60), main=header2, col=color)\n	pie(distance*eco, labels=sprintf(&quot;%s %.1f km (%.1f)&quot;,sportlist,distance,distance*eco), main=header3, col=color)\n	plot.new()\n	}else{\n	barplot(trg, names.arg=sprintf(&quot;%s %.1f&quot;,sportlist,trg), main=header1, col=color, space=0, axes=FALSE)\n	barplot(time, names.arg=sprintf(&quot;%s %d:%02d:%02d&quot;,sportlist,time%/%3600,time%/%60%%60,time%%60), main=header2, col=color, space=0, axes=FALSE)\n	barplot(distance*eco, names.arg=sprintf(&quot;%s %.1f&quot;,sportlist,distance), main=header3, col=color, space=0, axes=FALSE)\n	plot.new()\n	}\n\ntitle&lt;-sprintf(&quot;%s (%.1f weeks)&quot;, GC.season()$name, weeks)\nlegend(x=&quot;center&quot;, title=title, legend=sportlist, fill=color, border=&quot;white&quot;,  cex=2, bty=&quot;n&quot;)\n##plot.new()\n" />
-		<property name="state" type="QString" value="" />
-		<property name="plotOnChart" type="bool" value="0" />
 		<property name="showConsole" type="bool" value="0" />
+		<property name="asWeb" type="bool" value="1" />
 	</chart>
 </layout>
 <layout name="Measures" style="0" type="8" expression="" trainswitch="0">