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Merge branch 'release/v3.10'

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This commit is contained in:
Sander Roosendaal
2017-06-09 02:02:32 +02:00
parent 0bca99da07 d4a9060c36
commit f6b7654014
2 changed files with 36 additions and 22 deletions
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10
rowers/interactiveplots.py
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@@ -645,10 +645,14 @@ def interactive_otwcpchart(powerdf,promember=0):
# message = "CP model fit didn't give correct results"
deltas = fitt.apply(lambda x: timedeltaconv(x))
ftime = niceformat(deltas)
sourcecomplex = ColumnDataSource(
data = dict(
power = fitpower,
duration = fitt
CP = fitpower,
duration = fitt,
ftime = ftime
)
)
@@ -689,7 +693,7 @@ def interactive_otwcpchart(powerdf,promember=0):
hover.mode = 'mouse'
plot.line('duration','power',source=sourcecomplex,legend="CP Model",
plot.line('duration','CP',source=sourcecomplex,legend="CP Model",
color='green')
script, div = components(plot)

48
rowers/views.py
View File

@@ -2897,26 +2897,32 @@ def otwrankings_view(request,theuser=0,
thesecs.append(timesecs)
if len(thesecs) != 0:
maxt = pd.Series(thesecs).max()
maxt = 2*pd.Series(thesecs).max()
else:
maxt = 1000.
maxlog10 = np.log10(maxt)
logarr = np.arange(100)*maxlog10/100.
logarr = [int(10.**(la)) for la in logarr]
logarr = pd.Series(logarr)
logarr.drop_duplicates(keep='first',inplace=True)
logarr = logarr.values
delta = []
cpvalue = []
avgpower = {}
dfgrouped = df.groupby(['workoutid'])
for id,group in dfgrouped:
tt = group['time']
ww = group['power']
tt = group['time'].copy()
ww = group['power'].copy()
try:
avgpower[id] = int(ww.mean())
except ValueError:
@@ -2926,40 +2932,44 @@ def otwrankings_view(request,theuser=0,
dt = []
cpw = []
for i in range(length-2):
w_roll = ww.rolling(i+2,min_periods=2).mean()
# now goes with # data points - should be fixed seconds
indexmax = w_roll.idxmax(axis=1)
try:
t_0 = tt.ix[indexmax]
t_1 = tt.ix[indexmax-i-2]
deltat = 1.0e-3*(t_0-t_1)
wmax = w_roll.ix[indexmax]
dt.append(deltat)
cpw.append(wmax)
except KeyError:
pass
w_roll = ww.rolling(i+2).mean().dropna()
if len(w_roll):
# now goes with # data points - should be fixed seconds
indexmax = w_roll.idxmax(axis=1)
try:
t_0 = tt.ix[indexmax]
t_1 = tt.ix[indexmax-i-2]
deltat = 1.0e-3*(t_0-t_1)
wmax = w_roll.ix[indexmax]
if not np.isnan(deltat) and not np.isnan(wmax):
dt.append(deltat)
cpw.append(wmax)
except KeyError:
pass
dt = pd.Series(dt)
cpw = pd.Series(cpw)
cpvalues = griddata(dt.values,
cpw.values,
logarr,method='linear',fill_value=0)
logarr,method='linear',
rescale=True)
for cpv in cpvalues:
cpvalue.append(cpv)
for d in logarr:
delta.append(d)
print avgpower
dt = pd.Series(delta,name='Delta')
delta = pd.Series(delta,name='Delta')
cpvalue = pd.Series(cpvalue,name='CP')
powerdf = pd.DataFrame({
'Delta':delta,
'CP':cpvalue,
})
powerdf = powerdf[powerdf['CP']>0]
powerdf.dropna(axis=0,inplace=True)
powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)