336 lines
8.1 KiB
Python
336 lines
8.1 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import pandas as pd
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import numpy as np
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from scipy.interpolate import griddata
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from scipy import optimize
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#p0 = [500,350,10,8000]
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p0 = [190,200,33,16000]
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def updatecp(delta,cpvalues,r):
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cp2 = r.p0/(1+delta/r.p2)
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cp2 += r.p1/(1+delta/r.p3)
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delta = delta.append(delta)
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cp = cpvalues.append(cp2)
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powerdf = pd.DataFrame({
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'Delta':delta,
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'CP':cp,
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})
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powerdf.dropna(axis=0,inplace=True)
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powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)
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powerdf.drop_duplicates(subset='Delta',keep='first',inplace=True)
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res = cpfit(powerdf)
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p1 = res[0]
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r.p0 = p1[0]
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r.p1 = p1[1]
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r.p2 = p1[2]
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r.p3 = p1[3]
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r.cpratio = res[3]
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r.save()
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return 1
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def cpfit(powerdf):
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# Fit the data to thee parameter CP model
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fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
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errfunc = lambda pars,x,y: fitfunc(pars,x)-y
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p1 = p0
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thesecs = powerdf['Delta']
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theavpower = powerdf['CP']
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if len(thesecs)>=4:
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try:
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p1, success = optimize.leastsq(errfunc, p0[:], args = (thesecs,theavpower))
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except:
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factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
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p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
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else:
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factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
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p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
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p1 = [abs(p) for p in p1]
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fitt = pd.Series(10**(4*np.arange(100)/100.))
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fitpower = fitfunc(p1,fitt)
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fitpoints = fitfunc(p1,thesecs)
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fitpoints0 = fitpoints.copy()
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dd = fitpoints-theavpower
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ddmin = dd.min()
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frac = abs(ddmin)/fitpoints.mean()
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while frac>0.0001:
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fitpoints = fitpoints*(fitpoints.mean()-ddmin)/(fitpoints.mean())
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dd = fitpoints-theavpower
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ddmin = dd.min()
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frac = abs(ddmin)/fitpoints.mean()
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ratio = fitpoints.mean()/fitpoints0.mean()
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return p1,fitt,fitpower,ratio
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def getlogarr(maxt):
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maxlog10 = np.log10(maxt-5)
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logarr = np.arange(50)*maxlog10/50.
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logarr = [5+int(10.**(la)) for la in logarr]
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logarr = pd.Series(logarr)
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logarr.drop_duplicates(keep='first',inplace=True)
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logarr = logarr.values
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return logarr
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def getsinglecp(df):
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thesecs = df['TimeStamp (sec)'].max()-df['TimeStamp (sec)'].min()
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if thesecs != 0:
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maxt = 1.05*thesecs
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else:
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maxt = 1000.
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logarr = getlogarr(maxt)
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dfnew = pd.DataFrame({
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'time':1000*(df['TimeStamp (sec)']-df.loc[:,'TimeStamp (sec)'].iloc[0]),
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'power':df[' Power (watts)']
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})
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dfnew['workoutid'] = 0
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dfgrouped = dfnew.groupby(['workoutid'])
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delta,cpvalue,avgpower = getcp(dfgrouped,logarr)
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return delta,cpvalue,avgpower
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def getcp_new(dfgrouped,logarr):
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delta = []
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cpvalue = []
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avgpower = {}
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for id, group in dfgrouped:
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tt = group['time'].copy()
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ww = group['power'].copy()
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try:
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avgpower[id] = int(ww.mean())
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except ValueError:
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avgpower[id] = '---'
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tmax = tt.max()
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if tmax > 500000:
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newlen = int(tmax/2000.)
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newt = np.arange(newlen)*tmax/float(newlen)
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deltat = newt[1]-newt[0]
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else:
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newt = np.arange(0,tmax,10.)
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deltat = 10.
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ww = griddata(tt.values,
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ww.values,
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newt,method='linear',
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rescale=True)
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tt = pd.Series(newt)
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ww = pd.Series(ww)
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G = pd.Series(ww.cumsum())
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G = pd.concat([pd.Series([0]),G])
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h = np.mgrid[0:len(tt)+1:1,0:len(tt)+1:1]
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distances = pd.DataFrame(h[1]-h[0])
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ones = 1+np.zeros(len(G))
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Ghor = np.outer(ones,G)
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Gver = np.outer(G,ones)
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Gdif = Ghor - Gver
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Gdif = np.tril(Gdif.T).T
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Gdif = pd.DataFrame(Gdif)
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F = Gdif/distances
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F.fillna(inplace=True,method='ffill',axis=1)
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F.fillna(inplace=True,value=0)
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restime = []
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power = []
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for i in np.arange(0,len(tt)+1,1):
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restime.append(deltat*i)
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cp = np.diag(F,i).max()
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power.append(cp)
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power[0] = power[1]
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restime = np.array(restime)
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power = np.array(power)
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#power[0] = power[1]
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cpvalues = griddata(restime,power,
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logarr,method='linear', fill_value=0)
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for cpv in cpvalues:
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cpvalue.append(cpv)
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for d in logarr:
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delta.append(d)
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df = pd.DataFrame({
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'delta':delta,
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'cpvalue':cpvalue
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})
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df.dropna(axis=0, how='any',inplace=True)
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df = df.sort_values(['delta','cp'], ascending=[1, 0])
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df = df.drop_duplicates(subset='Delta', keep='first')
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delta = df['delta']
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cpvalue = df['cpvalue']
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return delta,cpvalue,avgpower
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def getcp(dfgrouped,logarr):
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delta = []
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cpvalue = []
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avgpower = {}
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#avgpower[0] = 0
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for id,group in dfgrouped:
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tt = group['time'].copy()
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ww = group['power'].copy()
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# Remove data where PM is repeating final power value
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# of an interval during the rest
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rolling_std = ww.rolling(window=4).std()
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deltas = tt.diff()
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mask = rolling_std == 0
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ww.loc[mask] = 0
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mask = ww > 2000
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ww.loc[mask] = 0
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tmax = tt.max()
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if tmax > 500000:
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newlen = int(tmax/2000.)
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else:
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newlen = len(tt)
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if newlen < len(tt):
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newt = np.arange(newlen)*tmax/float(newlen)
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ww = griddata(tt.values,
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ww.values,
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newt,method='linear',
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rescale=True)
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tt = pd.Series(newt)
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ww = pd.Series(ww)
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try:
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avgpower[id] = int(ww.mean())
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except ValueError:
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avgpower[id] = '---'
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if not np.isnan(ww.mean()):
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length = len(ww)
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dt = []
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cpw = []
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for i in xrange(length-2):
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deltat,wmax = getmaxwattinterval(tt,ww,i)
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if not np.isnan(deltat) and not np.isnan(wmax):
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dt.append(deltat)
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cpw.append(wmax)
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dt = pd.Series(dt)
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cpw = pd.Series(cpw)
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if len(dt)>2:
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cpvalues = griddata(dt.values,
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cpw.values,
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logarr,method='linear',
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rescale=True)
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for cpv in cpvalues:
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cpvalue.append(cpv)
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for d in logarr:
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delta.append(d)
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delta = pd.Series(delta,name='Delta')
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cpvalue = pd.Series(cpvalue,name='CP')
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cpdf = pd.DataFrame({
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'delta':delta,
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'cpvalue':cpvalue
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})
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cpdf.dropna(axis=0, how='any',inplace=True)
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delta = cpdf['delta']
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cpvalue = cpdf['cpvalue']
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return delta,cpvalue,avgpower
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def getmaxwattinterval(tt,ww,i):
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w_roll = ww.rolling(i+2).mean().dropna()
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if len(w_roll):
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# now goes with # data points - should be fixed seconds
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indexmax = w_roll.idxmax(axis=1)
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# indexmaxpos = indexmax.get_loc(indexmax)
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indexmaxpos = indexmax
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try:
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t_0 = tt.ix[indexmaxpos]
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t_1 = tt.ix[indexmaxpos-i]
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deltas = tt.ix[indexmaxpos-i:indexmaxpos].diff().dropna()
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testres = 1.0e-3*deltas.max() < 30.
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if testres:
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deltat = 1.0e-3*(t_0-t_1)
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wmax = w_roll.ix[indexmaxpos]
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#if wmax > 800 or wmax*5.0e-4*deltat > 800.0:
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# wmax = 0
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else:
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wmax = 0
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deltat = 0
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except KeyError:
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wmax = 0
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deltat = 0
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else:
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wmax = 0
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deltat = 0
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return deltat,wmax
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