Make plotintervals more rational (dataprep) - continue at cum_flex

rowers/dataprep.py

@@ -0,0 +1,142 @@
+from rowers.models import Workout, User, Rower
+from rowingdata import rowingdata as rrdata
+
+from rowingdata import rower as rrower
+from rowingdata import main as rmain
+
+from pandas import DataFrame,Series
+
+import pandas as pd
+import numpy as np
+
+from scipy.signal import savgol_filter
+
+import datetime
+
+def niceformat(values):
+    out = []
+    for v in values:
+	formattedv = strfdelta(v)
+	out.append(formattedv)
+	
+    return out
+
+def strfdelta(tdelta):
+    try:
+	minutes,seconds = divmod(tdelta.seconds,60)
+	tenths = int(tdelta.microseconds/1e5)
+    except AttributeError:
+	minutes,seconds = divmod(tdelta.view(np.int64),60e9)
+	seconds,rest = divmod(seconds,1e9)
+	tenths = int(rest/1e8)
+    res = "{minutes:0>2}:{seconds:0>2}.{tenths:0>1}".format(
+	minutes=minutes,
+	seconds=seconds,
+	tenths=tenths,
+	)
+    
+    return res
+
+def nicepaceformat(values):
+    out = []
+    for v in values:
+	formattedv = strfdelta(v)
+	out.append(formattedv)
+	
+
+    return out
+
+def timedeltaconv(x):
+    dt = datetime.timedelta(seconds=x)
+
+    return dt
+
+def rdata(file,rower=rrower()):
+    try:
+	res = rrdata(file,rower=rower)
+    except IOError:
+	res = 0
+
+    return res
+
+def getrowdata(id=0):
+
+    # check if valid ID exists (workout exists)
+    row = Workout.objects.get(id=id)
+
+    f1 = row.csvfilename
+	
+    # get user
+
+    r = row.user
+    u = r.user
+
+    rr = rrower(hrmax=r.max,hrut2=r.ut2,
+		hrut1=r.ut1,hrat=r.at,
+		hrtr=r.tr,hran=r.an)
+
+    rowdata = rdata(f1,rower=rr)
+
+    return rowdata,row
+
+def dataprep(rowdatadf):
+    rowdatadf.set_index([range(len(rowdatadf))],inplace=True)
+    t = rowdatadf.ix[:,'TimeStamp (sec)']
+    t = pd.Series(t-rowdatadf.ix[0,'TimeStamp (sec)'])
+
+    row_index = rowdatadf.ix[:,' Stroke500mPace (sec/500m)'] > 3000
+    rowdatadf.loc[row_index,' Stroke500mPace (sec/500m)'] = 3000.
+
+    p = rowdatadf.ix[:,' Stroke500mPace (sec/500m)']
+    hr = rowdatadf.ix[:,' HRCur (bpm)']
+    spm = rowdatadf.ix[:,' Cadence (stokes/min)']
+    cumdist = rowdatadf.ix[:,'cum_dist']
+
+    power = rowdatadf.ix[:,' Power (watts)']
+    averageforce = rowdatadf.ix[:,' AverageDriveForce (lbs)']
+    drivelength = rowdatadf.ix[:,' DriveLength (meters)']
+
+
+    peakforce = rowdatadf.ix[:,' PeakDriveForce (lbs)']
+
+
+    f = rowdatadf['TimeStamp (sec)'].diff().mean()
+    windowsize = 2*(int(10./(f)))+1
+    if windowsize <= 3:
+	windowsize = 5
+
+    if windowsize > 3:
+	spm = savgol_filter(spm,windowsize,3)
+	hr = savgol_filter(hr,windowsize,3)
+	drivelength = savgol_filter(drivelength,windowsize,3)
+
+    t2 = t.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
+
+    
+    p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
+
+
+    drivespeed = drivelength/rowdatadf[' DriveTime (ms)']*1.0e3
+    driveenergy = drivelength*averageforce*4.44822
+    distance = rowdatadf.ix[:,'cum_dist']
+
+    data = DataFrame(
+	dict(
+	    time = t2,
+	    hr = hr,
+	    pace = p2,
+	    spm = spm,
+	    cumdist = cumdist,
+	    ftime = niceformat(t2),
+	    fpace = nicepaceformat(p2),
+	    driveenergy=driveenergy,
+	    power=power,
+	    averageforce=averageforce,
+	    drivelength=drivelength,
+	    peakforce=peakforce,
+	    distance=distance,
+	    drivespeed=drivespeed
+	    )
+	)
+
+    return data