restore

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):