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more autopep

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This commit is contained in:
Sander Roosendaal
2022-03-10 17:16:31 +01:00
parent 8070a68931
commit a0223bec24
5 changed files with 32 additions and 60 deletions
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28
rowers/datautils.py
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@@ -6,7 +6,6 @@ from scipy import optimize
from rowers.mytypes import otwtypes, otetypes, rowtypes
#p0 = [500,350,10,8000]
p0 = [190, 200, 33, 16000]
# RPE to TSS
@@ -73,10 +72,12 @@ def updatecp(delta, cpvalues, r, workouttype='water'): # pragma: no cover
def cpfit(powerdf, fraclimit=0.0001, nmax=1000):
# Fit the data to thee parameter CP model
def fitfunc(pars, x): return abs(
pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
def fitfunc(pars, x):
return abs(
pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
def errfunc(pars, x, y): return fitfunc(pars, x)-y
def errfunc(pars, x, y):
return fitfunc(pars, x)-y
p1 = p0
@@ -235,8 +236,6 @@ def getcp_new(dfgrouped, logarr): # pragma: no cover
restime = np.array(restime)
power = np.array(power)
#power[0] = power[1]
cpvalues = griddata(restime, power,
logarr, method='linear', fill_value=0)
@@ -264,7 +263,6 @@ def getcp(dfgrouped, logarr):
delta = []
cpvalue = []
avgpower = {}
#avgpower[0] = 0
for id, group in dfgrouped:
tt = group['time'].copy()
@@ -273,7 +271,7 @@ def getcp(dfgrouped, logarr):
# Remove data where PM is repeating final power value
# of an interval during the rest
rolling_std = ww.rolling(window=4).std()
deltas = tt.diff()
# deltas = tt.diff()
mask = rolling_std == 0
ww.loc[mask] = 0
@@ -281,7 +279,7 @@ def getcp(dfgrouped, logarr):
mask = ww > 2000
ww.loc[mask] = 0
tmax = tt.max()
# tmax = tt.max()
try:
avgpower[id] = int(ww.mean())
@@ -389,13 +387,13 @@ def getfastest(df, thevalue, mode='distance'):
dd = pd.Series(dd, dtype='float')
G = pd.concat([pd.Series([0]), dd])
T = pd.concat([pd.Series([0]), dd])
h = np.mgrid[0:len(tt)+1:1, 0:len(tt)+1:1]
distances = pd.DataFrame(h[1]-h[0])
# T = pd.concat([pd.Series([0]), dd])
# h = np.mgrid[0:len(tt)+1:1, 0:len(tt)+1:1]
# distances = pd.DataFrame(h[1]-h[0])
ones = 1+np.zeros(len(G))
Ghor = np.outer(ones, G)
Thor = np.outer(ones, T)
Tver = np.outer(T, ones)
# Thor = np.outer(ones, T)
# Tver = np.outer(T, ones)
Gver = np.outer(G, ones)
Gdif = Ghor-Gver
Gdif = np.tril(Gdif.T).T
@@ -428,8 +426,6 @@ def getfastest(df, thevalue, mode='distance'):
# if restime[i]<thevalue*60*1000:
# print(i,restime[i],distance[i],60*1000*thevalue)
d2 = 0
if mode == 'distance':
duration = griddata(distance, restime, [
thevalue], method='linear', rescale=True)