261 lines
7.3 KiB
Python
261 lines
7.3 KiB
Python
import pandas as pd
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import datetime
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from datetime import timedelta
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from uuid import uuid4
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from rowsandall_app.settings import UPLOAD_SERVICE_SECRET, UPLOAD_SERVICE_URL
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from rowsandall_app.settings import NK_API_LOCATION
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import requests
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def strfdelta(tdelta):
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try:
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minutes, seconds = divmod(tdelta.seconds, 60)
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tenths = int(tdelta.microseconds / 1e5)
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except AttributeError: # pragma: no cover
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minutes, seconds = divmod(tdelta.view(np.int64), 60e9)
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seconds, rest = divmod(seconds, 1e9)
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tenths = int(rest / 1e8)
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res = "{minutes:0>2}:{seconds:0>2}.{tenths:0>1}".format(
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minutes=minutes,
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seconds=seconds,
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tenths=tenths,
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)
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return res
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def add_workout_from_data(userid,nkid,data,strokedata,source='nk',splitdata=None,
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workoutsource='nklinklogbook'):
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csvfilename = 'media/{code}_{nkid}.csv.gz'.format(
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nkid=nkid,
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code = uuid4().hex[:16]
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)
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try:
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userid=int(userid)
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except TypeError:
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userid = userid.id
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strokedata.to_csv(csvfilename, index_label='index', compression='gzip')
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title = data["name"]
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speedInput = data["speedInput"]
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elapsedTime = data["elapsedTime"]
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totalDistanceGps = data["totalDistanceGps"]
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totalDistanceImp = data["totalDistanceImp"]
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intervals = data["intervals"] # add intervals
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oarlockSessions = data["oarlockSessions"]
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deviceId = data["deviceId"] # you could get the firmware version
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totalDistance = totalDistanceGps
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useImpeller = False
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if speedInput: # pragma: no cover
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totdalDistance = totalDistanceImp
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useImpeller = True
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summary = get_nk_allstats(data,strokedata)
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speedInput = data['speedInput'] # 0 = GPS; 1 = Impeller
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# oarlock inboard, length, boat name
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if oarlockSessions:
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oarlocksession = oarlockSessions[0] # should take seatIndex
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boatName = oarlocksession["boatName"]
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oarLength = oarlocksession["oarLength"] # cm
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oarInboardLength = oarlocksession["oarInboardLength"] # cm
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seatNumber = oarlocksession["seatNumber"]
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try:
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oarlockfirmware = oarlocksession["firmwareVersion"]
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except KeyError:
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oarlockfirmware = ''
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else: # pragma: no cover
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boatName = ''
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oarLength = 289
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oarInboardLength = 88
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seatNumber = 1
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oarlockfirmware = ''
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workouttype = "water"
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boattype = "1x"
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uploadoptions = {
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'secret': UPLOAD_SERVICE_SECRET,
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'user':userid,
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'file': csvfilename,
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'title': title,
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'workouttype': workouttype,
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'boattype': boattype,
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'nkid':nkid,
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'inboard': oarInboardLength/100.,
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'oarlength': oarLength/100.,
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'summary':summary,
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'oarlockfirmware':oarlockfirmware,
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'elapsedTime': elapsedTime/1000., # in seconds
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'totalDistance': totalDistance,
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'useImpeller': useImpeller
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}
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session = requests.session()
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newHeaders = {'Content-type': 'application/json', 'Accept': 'text/plain'}
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session.headers.update(newHeaders)
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response = session.post(UPLOAD_SERVICE_URL,json=uploadoptions)
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if response.status_code != 200: # pragma: no cover
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return 0,response.text
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try:
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workoutid = response.json()['id']
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except KeyError:
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workoutid = 1
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# evt update workout summary
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# return
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return workoutid,""
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def get_nk_intervalstats(workoutdata,strokedata):
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intervals = workoutdata['intervals']
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separator = "|"
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stri = "Workout Details\n"
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stri += "#-{sep}SDist{sep}-Split-{sep}-SPace-{sep}-Pwr-{sep}-SPM--{sep}-AvgHR-{sep}DPS-\n".format(
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sep=separator)
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i = 0
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for interval in intervals:
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id = interval['id']
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sdist = interval['totalDistanceGps']
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avgpace = interval['avgPaceGps']/1000.
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avgpacetd = timedelta(seconds=avgpace)
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newpacestring = strfdelta(avgpacetd)
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elapsedSeconds = interval['elapsedTime']/1000. # secs
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elapsedTime = datetime.datetime.utcfromtimestamp(elapsedSeconds)
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newsplitstring = "%s.%i" % (elapsedTime.strftime("%M:%S"), elapsedTime.microsecond/100000)
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pwr = interval['avgPower']
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avghr = interval['avgHeartRate']
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spm = interval['avgStrokeRate']
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dps = interval['distStrokeGps']
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stri += "{i:0>2}{sep}{sdist:0>5}{sep}{split}{sep}{space}{sep} {pwr:0>3} {sep}".format(
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i=i + 1,
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sdist=int(float(sdist)),
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split=newsplitstring,
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space=newpacestring,
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pwr=int(pwr),
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sep=separator,
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)
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stri += " {spm} {sep} {avghr:0>3} {sep}{dps:0>4.1f}\n".format(
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sep=separator,
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avghr=avghr,
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spm=spm,
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dps=dps,
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)
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i += 1
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return stri
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def get_nk_summary(workoutdata,strokedata):
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name = workoutdata['name']
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avgpace = workoutdata['avgPaceGps']/1000. # secs
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avgpacetd = timedelta(seconds=avgpace)
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avgpacestring = strfdelta(avgpacetd)
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elapsedSeconds = workoutdata['elapsedTime']/1000. # secs
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elapsedTime = datetime.datetime.utcfromtimestamp(elapsedSeconds)
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timestring = "%s.%i" % (elapsedTime.strftime("%H:%M:%S"), elapsedTime.microsecond/100000)
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dist = workoutdata['totalDistanceGps']
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spm = workoutdata['avgStrokeRate']
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avghr = workoutdata['avgHeartRate']
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avgdps = workoutdata['distStrokeGps']
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maxhr = strokedata['heartRate'].max()
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pwr = workoutdata['avgPower']
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sep = "|"
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stri1 = "Workout Summary - " + name + "\n"
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stri1 += "--{sep}Total{sep}--Total---{sep}--Avg--{sep}-Avg-{sep}-Avg--{sep}-Avg-{sep}-Max-{sep}-Avg\n".format(
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sep=sep)
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stri1 += "--{sep}Dist-{sep}--Time----{sep}-Pace--{sep}-Pwr-{sep}-SPM--{sep}-HR--{sep}-HR--{sep}-DPS\n".format(
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sep=sep)
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stri1 += "--{sep}{dist:0>5.0f}{sep}".format(
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sep=sep,
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dist=dist,
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)
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stri1 += timestring + sep + avgpacestring
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stri1 += "{sep}{avgpower:0>5.1f}".format(
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sep=sep,
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avgpower=pwr,
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)
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stri1 += "{sep} {avgsr:2.1f} {sep}{avghr:0>5.1f}{sep}".format(
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avgsr=spm,
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sep=sep,
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avghr=avghr
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)
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stri1 += "{maxhr:0>5.1f}{sep}{avgdps:0>4.1f}\n".format(
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sep=sep,
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maxhr=maxhr,
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avgdps=avgdps
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)
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return stri1
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def get_nk_allstats(data,workoutdata):
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stri = get_nk_summary(data, workoutdata) + \
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get_nk_intervalstats(data, workoutdata)
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return stri
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#def get_workout(user,nkid):
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def getdict(x, seatIndex=1):
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seatStrokes = pd.DataFrame(x)
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try:
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seatStrokes = seatStrokes.set_index('seatIndex')
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return dict(seatStrokes.loc[seatIndex])
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except KeyError:
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pass
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return {}
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def strokeDataToDf(strokeData):
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df = pd.DataFrame.from_dict(strokeData)
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oarlockData = df['oarlockStrokes']
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oarlockData = oarlockData.apply(lambda x:getdict(x, seatIndex=1))
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df2 = pd.DataFrame.from_records(oarlockData.values)
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#df.set_index('timestamp',inplace=True)
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if not df2.empty:
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#df2.set_index('timestamp',inplace=True)
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df = df.merge(df2,left_index=True,right_index=True)
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df = df.rename(columns={"timestamp_x":"timestamp"})
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df = df.drop('oarlockStrokes',axis=1)
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df.sort_values(by='timestamp',ascending=True,inplace=True)
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df.fillna(inplace=True,method='ffill')
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return df
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