From a0223bec24a08a200f5b4eef12fb46a574c7fa11 Mon Sep 17 00:00:00 2001 From: Sander Roosendaal Date: Thu, 10 Mar 2022 17:16:31 +0100 Subject: [PATCH] more autopep --- rowers/courses.py | 10 +--------- rowers/datautils.py | 28 ++++++++++++---------------- rowers/imports.py | 24 +++++++----------------- rowers/tpstuff.py | 7 +------ rowers/uploads.py | 23 +++++++++++------------ 5 files changed, 32 insertions(+), 60 deletions(-) diff --git a/rowers/courses.py b/rowers/courses.py index 8b4661dd..7fa3d8f9 100644 --- a/rowers/courses.py +++ b/rowers/courses.py @@ -25,7 +25,7 @@ from django.conf import settings import geocoder -from matplotlib import path +# from matplotlib import path import xml.etree.ElementTree as et from xml.etree.ElementTree import Element, SubElement, Comment, tostring @@ -42,10 +42,6 @@ def howfaris(lat_lon, course): return distance -#whatisnear = 150 - -# get nearest races - def getnearestraces(lat_lon, races, whatisnear=150): newlist = [] @@ -55,7 +51,6 @@ def getnearestraces(lat_lon, races, whatisnear=150): newlist.append(race) else: c = race.course - coords = c.coord distance = howfaris(lat_lon, c) if distance < whatisnear: newlist.append(race) @@ -84,7 +79,6 @@ def getnearestcourses(lat_lon, courses, whatisnear=150, strict=False): newlist = [] counter = 0 for c in courses: - coords = c.coord distance = howfaris(lat_lon, c) if distance < whatisnear: @@ -199,8 +193,6 @@ def coursetokml(course): polygons = GeoPolygon.objects.filter( course=course).order_by("order_in_course") - polygonsxml = [] - for polygon in polygons: placemark = SubElement(folder2, 'Placemark') polygonname = SubElement(placemark, 'name') diff --git a/rowers/datautils.py b/rowers/datautils.py index b54b87a8..47581604 100644 --- a/rowers/datautils.py +++ b/rowers/datautils.py @@ -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]