From b03d88ba64c9513daf3bc3f30e9e8da81e9d2f91 Mon Sep 17 00:00:00 2001
From: Sander Roosendaal <roosendaalsander@gmail.com>
Date: Sat, 17 Jun 2017 20:27:18 +0200
Subject: [PATCH] restore

---
 rowers/datautils.py        | 64 +++++++++++++++++++++++++++++++++-----
 rowers/interactiveplots.py | 30 ++++++------------
 rowers/views.py            | 10 +-----
 3 files changed, 66 insertions(+), 38 deletions(-)

diff --git a/rowers/datautils.py b/rowers/datautils.py
index 9118dc2c..902d2a26 100644
--- a/rowers/datautils.py
+++ b/rowers/datautils.py
@@ -1,6 +1,60 @@
 import pandas as pd
 import numpy as np
 from scipy.interpolate import griddata
+from scipy import optimize
+
+def cpfit(powerdf):
+    # Fit the data to thee parameter CP model
+    fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
+    errfunc = lambda pars,x,y: fitfunc(pars,x)-y
+    
+    p0 = [500,350,10,8000]
+
+    p1 = p0
+
+    thesecs = powerdf['Delta']
+    theavpower = powerdf['CP']
+
+    if len(thesecs)>=4:
+	p1, success = optimize.leastsq(errfunc, p0[:], args = (thesecs,theavpower))
+    else:
+	factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
+	p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
+
+
+    p1 = [abs(p) for p in p1]
+    fitt = pd.Series(10**(4*np.arange(100)/100.))
+
+    fitpower = fitfunc(p1,fitt)
+
+    fitpoints = fitfunc(p1,thesecs)
+    fitpoints0 = fitpoints.copy()
+    dd = fitpoints-theavpower
+
+    ddmin = dd.min()
+
+    frac = abs(ddmin)/fitpoints.mean()
+
+    while frac>0.0001:
+        fitpoints = fitpoints*(fitpoints.mean()-ddmin)/(fitpoints.mean())
+        dd = fitpoints-theavpower
+        ddmin = dd.min()
+        frac = abs(ddmin)/fitpoints.mean()
+        print ddmin,frac
+    
+    ratio = fitpoints.mean()/fitpoints0.mean()
+        
+    return p1,fitt,fitpower,ratio
+
+def getlogarr(maxt):
+    maxlog10 = np.log10(maxt-5)
+    logarr = np.log10(5.)+np.arange(50)*maxlog10/50.
+    logarr = [int(10.**(la)) for la in logarr]
+    logarr = pd.Series(logarr)
+    logarr.drop_duplicates(keep='first',inplace=True)
+
+    logarr = logarr.values
+    return logarr
 
 def getsinglecp(df):
     thesecs = df['TimeStamp (sec)'].max()-df['TimeStamp (sec)'].min()
@@ -9,13 +63,7 @@ def getsinglecp(df):
     else:
         maxt = 1000.
 
-    maxlog10 = np.log10(maxt)
-    logarr = np.arange(50)*maxlog10/50.
-    logarr = [int(10.**(la)) for la in logarr]
-    logarr = pd.Series(logarr)
-    logarr.drop_duplicates(keep='first',inplace=True)
-
-    logarr = logarr.values
+    logarr = getlogarr(maxt)
 
 
     dfnew = pd.DataFrame({
@@ -42,7 +90,7 @@ def getcp(dfgrouped,logarr):
 
         tmax = tt.max()
         if tmax > 500000:
-            newlen = int(tmax/5000.)
+            newlen = int(tmax/2000.)
         else:
             newlen = len(tt)
         if newlen < len(tt):
diff --git a/rowers/interactiveplots.py b/rowers/interactiveplots.py
index 7bc73bf4..69717915 100644
--- a/rowers/interactiveplots.py
+++ b/rowers/interactiveplots.py
@@ -55,6 +55,7 @@ import rowers.dataprep as dataprep
 from rowers.metrics import axes,axlabels,yaxminima,yaxmaxima
 
 from utils import lbstoN
+import datautils
 
 watermarkurl = "/static/img/logo7.png"
 watermarksource = ColumnDataSource(dict(
@@ -618,29 +619,11 @@ def interactive_otwcpchart(powerdf,promember=0):
 
     # there is no Paul's law for OTW
 
-    # Fit the data to thee parameter CP model
-    fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
-    errfunc = lambda pars,x,y: fitfunc(pars,x)-y
-    
-    p0 = [500,350,10,8000]
-
-    p1 = p0
-
     thesecs = powerdf['Delta']
     theavpower = powerdf['CP']
-
-    if len(thesecs)>=4:
-	p1, success = optimize.leastsq(errfunc, p0[:], args = (thesecs,theavpower))
-    else:
-	factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
-	p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
-
-
-    p1 = [abs(p) for p in p1]
-    fitt = pd.Series(10**(4*np.arange(100)/100.))
-
-    fitpower = fitfunc(p1,fitt)
-
+    
+    p1,fitt,fitpower,ratio = datautils.cpfit(powerdf)
+    
     message = ""
     #if len(fitpower[fitpower<0]) > 0:
 	# message = "CP model fit didn't give correct results"
@@ -652,6 +635,7 @@ def interactive_otwcpchart(powerdf,promember=0):
     sourcecomplex = ColumnDataSource(
         data = dict(
             CP = fitpower,
+            CPmax = ratio*fitpower,
             duration = fitt,
             ftime = ftime
             )
@@ -690,6 +674,7 @@ def interactive_otwcpchart(powerdf,promember=0):
     hover.tooltips = OrderedDict([
 	('Duration ','@ftime'),
 	('Power (W)','@CP{int}'),
+	('Power (W) upper','@CPmax{int}'),
 	])
  
     hover.mode = 'mouse'
@@ -697,6 +682,9 @@ def interactive_otwcpchart(powerdf,promember=0):
     plot.line('duration','CP',source=sourcecomplex,legend="CP Model",
 	      color='green')
     
+    plot.line('duration','CPmax',source=sourcecomplex,legend="CP Model",
+	      color='red')
+    
     script, div = components(plot)
 
     return [script,div,p1,message]
diff --git a/rowers/views.py b/rowers/views.py
index 3ca3cffb..f4238fee 100644
--- a/rowers/views.py
+++ b/rowers/views.py
@@ -2931,15 +2931,7 @@ def otwrankings_view(request,theuser=0,
         maxt = 1000.
 
 
-    maxlog10 = np.log10(maxt)
-    logarr = np.arange(50)*maxlog10/50.
-    logarr = [int(10.**(la)) for la in logarr]
-    logarr = pd.Series(logarr)
-    logarr.drop_duplicates(keep='first',inplace=True)
-
-    logarr = logarr.values
-
-    
+    logarr = datautils.getlogarr(maxt)
     
     dfgrouped = df.groupby(['workoutid'])
     delta,cpvalue,avgpower = datautils.getcp(dfgrouped,logarr)