3085 lines
88 KiB
Python
3085 lines
88 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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# All the data preparation, data cleaning and data mangling should
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# be defined here
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from __future__ import unicode_literals, absolute_import
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from rowers.models import Workout, Team
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import pytz
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import collections
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from rowingdata import rowingdata as rrdata
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from rowingdata import rower as rrower
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import yaml
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import shutil
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from shutil import copyfile
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from rowingdata import (
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get_file_type, get_empower_rigging,get_empower_firmware
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)
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from rowers.tasks import handle_sendemail_unrecognized
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from rowers.tasks import handle_zip_file
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from pandas import DataFrame, Series
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import dask.dataframe as dd
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from dask.delayed import delayed
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import pyarrow.parquet as pq
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import pyarrow as pa
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from pyarrow.lib import ArrowInvalid
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from django.utils import timezone
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from django.utils.timezone import get_current_timezone
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from django_mailbox.models import Message,Mailbox,MessageAttachment
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from django.core.exceptions import ValidationError
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from time import strftime
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import arrow
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thetimezone = get_current_timezone()
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from rowingdata import (
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TCXParser, RowProParser, ErgDataParser,
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CoxMateParser,
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BoatCoachParser, RowPerfectParser, BoatCoachAdvancedParser,
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ETHParser,
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MysteryParser, BoatCoachOTWParser,QuiskeParser,
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painsledDesktopParser, speedcoachParser, ErgStickParser,
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SpeedCoach2Parser, FITParser, fitsummarydata,
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RitmoTimeParser,KinoMapParser,
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make_cumvalues,cumcpdata,ExcelTemplate,
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summarydata, get_file_type,
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)
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from rowingdata.csvparsers import HumonParser
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from rowers.metrics import axes,calc_trimp,rowingmetrics,dtypes,metricsgroups
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from rowers.models import strokedatafields
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#allowedcolumns = [item[0] for item in rowingmetrics]
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allowedcolumns = [key for key,value in strokedatafields.items()]
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#from async_messages import messages as a_messages
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import os
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import zipfile
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import pandas as pd
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import numpy as np
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import itertools
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from fitparse import FitFile
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from fitparse.base import FitHeaderError
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import math
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from rowers.tasks import (
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handle_sendemail_unrecognized, handle_sendemail_breakthrough,
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handle_sendemail_hard, handle_updatecp,handle_updateergcp,
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handle_calctrimp,
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)
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from django.conf import settings
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from sqlalchemy import create_engine
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import sqlalchemy as sa
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import sys
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import rowers.utils as utils
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import rowers.datautils as datautils
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from rowers.utils import lbstoN,myqueue,is_ranking_piece,wavg
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from timezonefinder import TimezoneFinder
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import django_rq
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queue = django_rq.get_queue('default')
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queuelow = django_rq.get_queue('low')
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queuehigh = django_rq.get_queue('default')
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from rowsandall_app.settings import SITE_URL
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from rowers.mytypes import otwtypes,otetypes,rowtypes
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from rowers import mytypes
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from rowers.database import *
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from rowers.opaque import encoder
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# mapping the DB column names to the CSV file column names
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columndict = {
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'time': 'TimeStamp (sec)',
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'hr': ' HRCur (bpm)',
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'velo': ' AverageBoatSpeed (m/s)',
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'pace': ' Stroke500mPace (sec/500m)',
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'spm': ' Cadence (stokes/min)',
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'power': ' Power (watts)',
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'averageforce': ' AverageDriveForce (lbs)',
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'drivelength': ' DriveLength (meters)',
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'peakforce': ' PeakDriveForce (lbs)',
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'distance': ' Horizontal (meters)',
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'catch': 'catch',
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'finish': 'finish',
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'peakforceangle': 'peakforceangle',
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'wash': 'wash',
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'slip': 'slip',
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'workoutstate': ' WorkoutState',
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'cumdist': 'cum_dist',
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}
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from scipy.signal import savgol_filter
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import datetime
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def get_video_data(w,groups=['basic'],mode='water'):
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modes = [mode,'both','basic']
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columns = ['time','velo','spm']
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columns += [name for name,d in rowingmetrics if d['group'] in groups and d['mode'] in modes]
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columns = list(set(columns))
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df = getsmallrowdata_db(columns,ids=[w.id],
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workstrokesonly=False,doclean=False,compute=False)
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df['time'] = (df['time']-df['time'].min())/1000.
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df.sort_values(by='time',inplace=True)
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df.set_index(pd.to_timedelta(df['time'],unit='s'),inplace=True)
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df2 = df.resample('1s').first().fillna(method='ffill')
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df2['time'] = df2.index.total_seconds()
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if 'pace' in columns:
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df2['pace'] = df2['pace']/1000.
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p = df2['pace']
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p = p.apply(lambda x:timedeltaconv(x))
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p = nicepaceformat(p)
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df2['pace'] = p
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#mask = df2['time'] < delay
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#df2 = df2.mask(mask).dropna()
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df2['time'] = (df2['time']-df2['time'].min())
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df2 = df2.round(decimals=2)
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boatspeed = (100*df2['velo']).astype(int)/100.
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try:
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coordinates = get_latlon_time(w.id)
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except KeyError:
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nulseries = df['time']*0
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coordinates = pd.DataFrame({
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'time': df['time'],
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'latitude': nulseries,
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'longitude': nulseries,
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})
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coordinates.set_index(pd.to_timedelta(coordinates['time'],unit='s'),inplace=True)
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coordinates = coordinates.resample('1s').mean().interpolate()
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#mask = coordinates['time'] < delay
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#coordinates = coordinates.mask(mask).dropna()
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coordinates['time'] = coordinates['time']-coordinates['time'].min()
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latitude = coordinates['latitude']
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longitude = coordinates['longitude']
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# bundle data
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data = {
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'boatspeed':boatspeed.values.tolist(),
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'latitude':latitude.values.tolist(),
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'longitude':longitude.values.tolist(),
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}
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# metrics = {
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# 'boatspeed': {
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# 'unit': 'm/s',
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# 'metric': 'boatspeed',
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# 'name': 'Boat Speed'
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# },
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# }
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metrics = {}
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for c in columns:
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if c != 'time':
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try:
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if dict(rowingmetrics)[c]['numtype'] == 'integer':
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data[c] = df2[c].astype(int).tolist()
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else:
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sigfigs = dict(rowingmetrics)[c]['sigfigs']
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if (c != 'pace'):
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da = ((10**sigfigs)*df2[c]).astype(int)/(10**sigfigs)
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else:
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da = df2[c]
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data[c] = da.values.tolist()
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metrics[c] = {
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'name': dict(rowingmetrics)[c]['verbose_name'],
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'metric': c,
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'unit': ''
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}
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except KeyError:
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pass
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metrics['boatspeed'] = metrics.pop('velo')
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# metrics['workperstroke'] = metrics.pop('driveenergy')
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metrics = collections.OrderedDict(sorted(metrics.items()))
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maxtime = coordinates['time'].max()
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return data, metrics, maxtime
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def polarization_index(df,rower):
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df['dt'] = df['time'].diff()/6.e4
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# remove rest (spm<15)
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df.dropna(axis=0,inplace=True)
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df['dt'] = df['dt'].clip(upper=4,lower=0)
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masklow = (df['power']>0) & (df['power']<int(rower.pw_at))
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maskmid = (df['power']>=rower.pw_at) & (df['power']<int(rower.pw_an))
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maskhigh = (df['power']>rower.pw_an)
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time_low_pw = df.loc[masklow,'dt'].sum()
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time_mid_pw = df.loc[maskmid,'dt'].sum()
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time_high_pw = df.loc[maskhigh,'dt'].sum()
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frac_low = time_low_pw/(time_low_pw+time_mid_pw+time_high_pw)
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frac_mid = time_mid_pw/(time_low_pw+time_mid_pw+time_high_pw)
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frac_high = time_high_pw/(time_low_pw+time_mid_pw+time_high_pw)
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index = math.log10(frac_high*100.*frac_low/frac_mid)
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return index
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def get_latlon(id):
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try:
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w = Workout.objects.get(id=id)
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except Workout.DoesNotExist:
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return False
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rowdata = rdata(w.csvfilename)
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if rowdata.df.empty:
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return [pd.Series([]), pd.Series([])]
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try:
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try:
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latitude = rowdata.df.loc[:, ' latitude']
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longitude = rowdata.df.loc[:, ' longitude']
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except KeyError:
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latitude = 0 * rowdata.df.loc[:, 'TimeStamp (sec)']
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longitude = 0 * rowdata.df.loc[:, 'TimeStamp (sec)']
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return [latitude, longitude]
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except AttributeError:
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return [pd.Series([]), pd.Series([])]
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return [pd.Series([]), pd.Series([])]
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def get_latlon_time(id):
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try:
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w = Workout.objects.get(id=id)
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except Workout.DoesNotExist:
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return False
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rowdata = rdata(w.csvfilename)
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if rowdata.df.empty:
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return [pd.Series([]), pd.Series([])]
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try:
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try:
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latitude = rowdata.df.loc[:, ' latitude']
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longitude = rowdata.df.loc[:, ' longitude']
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except KeyError:
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latitude = 0 * rowdata.df.loc[:, 'TimeStamp (sec)']
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longitude = 0 * rowdata.df.loc[:, 'TimeStamp (sec)']
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except AttributeError:
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return pd.DataFrame()
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df = pd.DataFrame({
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'time': rowdata.df['TimeStamp (sec)']-rowdata.df['TimeStamp (sec)'].min(),
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'latitude': rowdata.df[' latitude'],
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'longitude': rowdata.df[' longitude']
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})
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return df
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def workout_summary_to_df(
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rower,
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startdate=datetime.datetime(1970,1,1),
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enddate=timezone.now()+timezone.timedelta(days=1)):
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ws = Workout.objects.filter(user=rower).order_by("startdatetime")
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types = []
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names = []
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startdatetimes = []
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timezones = []
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distances = []
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durations = []
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weightcategories = []
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adaptivetypes = []
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weightvalues = []
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notes = []
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tcx_links = []
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csv_links = []
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rscores = []
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trimps = []
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for w in ws:
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types.append(w.workouttype)
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names.append(w.name)
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startdatetimes.append(w.startdatetime)
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timezones.append(w.timezone)
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distances.append(w.distance)
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durations.append(w.duration)
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weightcategories.append(w.weightcategory)
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adaptivetypes.append(w.adaptiveclass)
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weightvalues.append(w.weightvalue)
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notes.append(w.notes)
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tcx_link = SITE_URL+'/rowers/workout/{id}/emailtcx'.format(
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id=encoder.encode_hex(w.id)
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)
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tcx_links.append(tcx_link)
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csv_link = SITE_URL+'/rowers/workout/{id}/emailcsv'.format(
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id=encoder.encode_hex(w.id)
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)
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csv_links.append(csv_link)
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trimps.append(workout_trimp(w)[0])
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rscore = workout_rscore(w)
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rscores.append(int(rscore[0]))
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df = pd.DataFrame({
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'name':names,
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'date':startdatetimes,
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'timezone':timezones,
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'type':types,
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'distance (m)':distances,
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'duration ':durations,
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'weight category':weightcategories,
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'adaptive classification':adaptivetypes,
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'weight (kg)':weightvalues,
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'notes':notes,
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'Stroke Data TCX':tcx_links,
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'Stroke Data CSV':csv_links,
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'TRIMP Training Load':trimps,
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'TSS Training Load':rscores,
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})
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return df
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def get_workouts(ids, userid):
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goodids = []
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for id in ids:
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w = Workout.objects.get(id=id)
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if int(w.user.user.id) == int(userid):
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goodids.append(id)
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return [Workout.objects.get(id=id) for id in goodids]
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def filter_df(datadf, fieldname, value, largerthan=True):
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try:
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x = datadf[fieldname]
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except KeyError:
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return datadf
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try:
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if largerthan:
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mask = datadf[fieldname] < value
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else:
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mask = datadf[fieldname] >= value
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datadf.loc[mask, fieldname] = np.nan
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except TypeError:
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pass
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return datadf
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# joins workouts
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def join_workouts(r,ids,title='Joined Workout',
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parent=None,
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setprivate=False,
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forceunit='lbs',killparents=False):
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message = None
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summary = ''
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if parent:
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oarlength = parent.oarlength
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inboard = parent.inboard
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workouttype = parent.workouttype
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notes = parent.notes
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summary = parent.summary
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if parent.privacy == 'hidden':
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makeprivate = True
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else:
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makeprivate = False
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startdatetime = parent.startdatetime
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else:
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oarlength = 2.89
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inboard = 0.88
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workouttype = 'rower'
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notes = ''
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summary = ''
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makeprivate = False
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startdatetime = timezone.now()
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if setprivate == True and makeprivate == False:
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makeprivate = True
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elif setprivate == False and makeprivate == True:
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makeprivate = False
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|
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# reorder in chronological order
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ws = Workout.objects.filter(id__in=ids).order_by("startdatetime")
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if not parent:
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parent = ws[0]
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oarlength = parent.oarlength
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inboard = parent.inboard
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workouttype = parent.workouttype
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notes = parent.notes
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summary = parent.summary
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files = [w.csvfilename for w in ws]
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row = rdata(files[0])
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files = files[1:]
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while len(files):
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row2 = rdata(files[0])
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if row2 != 0:
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row = row+row2
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files = files[1:]
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timestr = strftime("%Y%m%d-%H%M%S")
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csvfilename = 'media/df_' + timestr + '.csv'
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row.write_csv(csvfilename,gzip=True)
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id, message = save_workout_database(csvfilename, r,
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workouttype=workouttype,
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title=title,
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notes=notes,
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oarlength=oarlength,
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inboard=inboard,
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makeprivate=makeprivate,
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dosmooth=False,
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consistencychecks=False)
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|
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if killparents:
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for w in ws:
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w.delete()
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|
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w = Workout.objects.get(id=id)
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w.duplicate = False
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w.save()
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if message is not None and "duplicate" in message:
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message = ""
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return (id, message)
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|
|
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def df_resample(datadf):
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# time stamps must be in seconds
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timestamps = datadf['TimeStamp (sec)'].astype('int')
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datadf['timestamps'] = timestamps
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newdf = datadf.groupby(['timestamps']).mean()
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return newdf
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|
|
|
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def clean_df_stats(datadf, workstrokesonly=True, ignorehr=True,
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ignoreadvanced=False):
|
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# clean data remove zeros and negative values
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before = len(datadf)
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|
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data_orig = datadf.copy()
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|
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# bring metrics which have negative values to positive domain
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if len(datadf)==0:
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return datadf
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try:
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datadf['catch'] = -datadf['catch']
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except (KeyError,TypeError):
|
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pass
|
|
|
|
try:
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datadf['peakforceangle'] = datadf['peakforceangle'] + 1000
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|
except (KeyError,TypeError):
|
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pass
|
|
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try:
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datadf['hr'] = datadf['hr'] + 10
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|
except (KeyError,TypeError):
|
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pass
|
|
|
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# protect 0 spm values from being nulled
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try:
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datadf['spm'] = datadf['spm'] + 1.0
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except (KeyError,TypeError) as e:
|
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pass
|
|
|
|
# protect 0 workoutstate values from being nulled
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try:
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datadf['workoutstate'] = datadf['workoutstate'] + 1
|
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except (KeyError,TypeError) as e:
|
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pass
|
|
|
|
|
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try:
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datadf = datadf.clip(lower=0)
|
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except TypeError:
|
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pass
|
|
|
|
# protect advanced metrics columns
|
|
advancedcols = [
|
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'rhythm',
|
|
'power',
|
|
'drivelength',
|
|
'forceratio',
|
|
'drivespeed',
|
|
'driveenergy',
|
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'catch',
|
|
'finish',
|
|
'averageforce',
|
|
'peakforce',
|
|
'slip',
|
|
'wash',
|
|
'peakforceangle',
|
|
'effectiveangle',
|
|
]
|
|
|
|
datadf.replace(to_replace=0, value=np.nan, inplace=True)
|
|
# datadf = datadf.map_partitions(lambda df:df.replace(to_replace=0,value=np.nan))
|
|
|
|
# bring spm back to real values
|
|
try:
|
|
datadf['spm'] = datadf['spm'] - 1
|
|
except (TypeError,KeyError) as e:
|
|
pass
|
|
|
|
# bring workoutstate back to real values
|
|
try:
|
|
datadf['workoutstate'] = datadf['workoutstate'] - 1
|
|
except (TypeError,KeyError) as e:
|
|
pass
|
|
|
|
|
|
# return from positive domain to negative
|
|
try:
|
|
datadf['catch'] = -datadf['catch']
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
datadf['peakforceangle'] = datadf['peakforceangle'] - 1000
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
datadf['hr'] = datadf['hr'] - 10
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
# clean data for useful ranges per column
|
|
if not ignorehr:
|
|
try:
|
|
mask = datadf['hr'] < 30
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['spm'] < 0
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
|
|
try:
|
|
mask = datadf['efficiency'] > 200.
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
|
|
try:
|
|
mask = datadf['spm'] < 10
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
|
|
try:
|
|
mask = datadf['pace'] / 1000. > 300.
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['efficiency'] < 0.
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['pace'] / 1000. < 60.
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['spm'] > 60
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
|
|
try:
|
|
mask = datadf['wash'] < 1
|
|
datadf.loc[mask, 'wash'] = np.nan
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
# try to guess ignoreadvanced
|
|
if not ignoreadvanced:
|
|
for metric in advancedcols:
|
|
try:
|
|
sum = datadf[metric].std()
|
|
if sum == 0 or np.isnan(sum):
|
|
ignoreadvanced = True
|
|
except KeyError:
|
|
pass
|
|
|
|
if not ignoreadvanced:
|
|
try:
|
|
mask = datadf['rhythm'] < 0
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['rhythm'] > 70
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['power'] < 20
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['drivelength'] < 0.5
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['forceratio'] < 0.2
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['forceratio'] > 1.0
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['drivespeed'] < 0.5
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['drivespeed'] > 4
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['driveenergy'] > 2000
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['driveenergy'] < 100
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
mask = datadf['catch'] > -30.
|
|
datadf.mask(mask,inplace=True)
|
|
except (KeyError,TypeError):
|
|
pass
|
|
|
|
|
|
workoutstateswork = [1, 4, 5, 8, 9, 6, 7]
|
|
workoutstatesrest = [3]
|
|
workoutstatetransition = [0, 2, 10, 11, 12, 13]
|
|
|
|
if workstrokesonly == 'True' or workstrokesonly == True:
|
|
try:
|
|
datadf = datadf[~datadf['workoutstate'].isin(workoutstatesrest)]
|
|
except:
|
|
pass
|
|
|
|
after = len(datadf.dropna())
|
|
ratio = float(after)/float(before)
|
|
|
|
if ratio < 0.001 or after < 2:
|
|
return data_orig
|
|
|
|
return datadf
|
|
|
|
def getpartofday(row,r):
|
|
workoutstartdatetime = row.rowdatetime
|
|
try:
|
|
latavg = row.df[' latitude'].mean()
|
|
lonavg = row.df[' longitude'].mean()
|
|
|
|
tf = TimezoneFinder()
|
|
try:
|
|
timezone_str = tf.timezone_at(lng=lonavg, lat=latavg)
|
|
except (ValueError,OverflowError):
|
|
timezone_str = 'UTC'
|
|
if timezone_str == None:
|
|
timezone_str = tf.closest_timezone_at(lng=lonavg,
|
|
lat=latavg)
|
|
if timezone_str == None:
|
|
timezone_str = r.defaulttimezone
|
|
try:
|
|
workoutstartdatetime = pytz.timezone(timezone_str).localize(
|
|
row.rowdatetime
|
|
)
|
|
except ValueError:
|
|
workoutstartdatetime = row.rowdatetime
|
|
except KeyError:
|
|
timezone_str = r.defaulttimezone
|
|
workoutstartdatetime = row.rowdatetime
|
|
|
|
h = workoutstartdatetime.astimezone(pytz.timezone(timezone_str)).hour
|
|
|
|
if h < 12:
|
|
return "Morning"
|
|
elif h < 18:
|
|
return "Afternoon"
|
|
elif h < 22:
|
|
return "Evening"
|
|
else:
|
|
return "Night"
|
|
|
|
return None
|
|
|
|
def getstatsfields():
|
|
fielddict = {name:d['verbose_name'] for name,d in rowingmetrics}
|
|
|
|
# fielddict.pop('ergpace')
|
|
# fielddict.pop('hr_an')
|
|
# fielddict.pop('hr_tr')
|
|
# fielddict.pop('hr_at')
|
|
# fielddict.pop('hr_ut2')
|
|
# fielddict.pop('hr_ut1')
|
|
fielddict.pop('time')
|
|
fielddict.pop('distance')
|
|
# fielddict.pop('nowindpace')
|
|
# fielddict.pop('fnowindpace')
|
|
# fielddict.pop('fergpace')
|
|
# fielddict.pop('equivergpower')
|
|
# fielddict.pop('workoutstate')
|
|
# fielddict.pop('fpace')
|
|
# fielddict.pop('pace')
|
|
# fielddict.pop('id')
|
|
# fielddict.pop('ftime')
|
|
# fielddict.pop('x_right')
|
|
# fielddict.pop('hr_max')
|
|
# fielddict.pop('hr_bottom')
|
|
fielddict.pop('cumdist')
|
|
|
|
try:
|
|
fieldlist = [field for field, value in fielddict.iteritems()]
|
|
except AttributeError:
|
|
fieldlist = [field for field, value in fielddict.items()]
|
|
|
|
return fieldlist, fielddict
|
|
|
|
|
|
|
|
# A string representation for time deltas
|
|
def niceformat(values):
|
|
out = []
|
|
for v in values:
|
|
formattedv = strfdelta(v)
|
|
out.append(formattedv)
|
|
|
|
return out
|
|
|
|
# A nice printable format for time delta values
|
|
|
|
|
|
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 timedelta_to_seconds(tdelta):
|
|
return 60.*tdelta.minute+tdelta.second
|
|
|
|
|
|
# A nice printable format for pace values
|
|
|
|
|
|
def nicepaceformat(values):
|
|
out = []
|
|
for v in values:
|
|
formattedv = strfdelta(v)
|
|
out.append(formattedv)
|
|
|
|
return out
|
|
|
|
# Convert seconds to a Time Delta value, replacing NaN with a 5:50 pace
|
|
|
|
|
|
def timedeltaconv(x):
|
|
if np.isfinite(x) and x != 0 and x > 0 and x < 175000:
|
|
dt = datetime.timedelta(seconds=x)
|
|
else:
|
|
dt = datetime.timedelta(seconds=350.)
|
|
|
|
return dt
|
|
|
|
|
|
def paceformatsecs(values):
|
|
out = []
|
|
for v in values:
|
|
td = timedeltaconv(v)
|
|
formattedv = strfdelta(td)
|
|
out.append(formattedv)
|
|
|
|
return out
|
|
|
|
def update_c2id_sql(id,c2id):
|
|
engine = create_engine(database_url, echo=False)
|
|
table = 'rowers_workout'
|
|
|
|
query = "UPDATE %s SET uploadedtoc2 = %s WHERE `id` = %s;" % (table,c2id,id)
|
|
|
|
with engine.connect() as conn, conn.begin():
|
|
result = conn.execute(query)
|
|
|
|
conn.close()
|
|
engine.dispose()
|
|
|
|
return 1
|
|
|
|
def fitnessmetric_to_sql(m,table='powertimefitnessmetric',debug=False):
|
|
engine = create_engine(database_url, echo=False)
|
|
columns = ', '.join(m.keys())
|
|
placeholders = ", ".join(["?"] * len(m))
|
|
|
|
query = "INSERT into %s ( %s ) Values (%s)" % (table, columns, placeholders)
|
|
|
|
values = tuple(m[key] for key in m.keys())
|
|
with engine.connect() as conn, conn.begin():
|
|
result = conn.execute(query,values)
|
|
|
|
conn.close()
|
|
engine.dispose()
|
|
|
|
return 1
|
|
|
|
|
|
def getcpdata_sql(rower_id,table='cpdata'):
|
|
engine = create_engine(database_url, echo=False)
|
|
query = sa.text('SELECT * from {table} WHERE user={rower_id};'.format(
|
|
rower_id=rower_id,
|
|
table=table,
|
|
))
|
|
connection = engine.raw_connection()
|
|
df = pd.read_sql_query(query, engine)
|
|
|
|
return df
|
|
|
|
def deletecpdata_sql(rower_id,table='cpdata'):
|
|
engine = create_engine(database_url, echo=False)
|
|
query = sa.text('DELETE from {table} WHERE user={rower_id};'.format(
|
|
rower_id=rower_id,
|
|
table=table,
|
|
))
|
|
with engine.connect() as conn, conn.begin():
|
|
try:
|
|
result = conn.execute(query)
|
|
except:
|
|
print("Database locked")
|
|
conn.close()
|
|
engine.dispose()
|
|
|
|
|
|
|
|
def updatecpdata_sql(rower_id,delta,cp,table='cpdata',distance=[]):
|
|
deletecpdata_sql(rower_id)
|
|
df = pd.DataFrame(
|
|
{
|
|
'delta':delta,
|
|
'cp':cp,
|
|
'user':rower_id
|
|
}
|
|
)
|
|
|
|
if not distance.empty:
|
|
df['distance'] = distance
|
|
|
|
engine = create_engine(database_url, echo=False)
|
|
with engine.connect() as conn, conn.begin():
|
|
df.to_sql(table, engine, if_exists='append', index=False)
|
|
conn.close()
|
|
engine.dispose()
|
|
|
|
|
|
def runcpupdate(
|
|
rower,type='water',
|
|
startdate=timezone.now()-datetime.timedelta(days=365),
|
|
enddate=timezone.now()+datetime.timedelta(days=5)
|
|
):
|
|
if type == 'water':
|
|
theworkouts = Workout.objects.filter(
|
|
user=rower,rankingpiece=True,
|
|
workouttype='water',
|
|
startdatetime__gte=startdate,
|
|
startdatetime__lte=enddate
|
|
)
|
|
table = 'cpdata'
|
|
else:
|
|
theworkouts = Workout.objects.filter(
|
|
user=rower,rankingpiece=True,
|
|
workouttype__in=[
|
|
'rower',
|
|
'dynamic',
|
|
'slides'
|
|
],
|
|
startdatetime__gte=startdate,
|
|
startdatetime__lte=enddate
|
|
)
|
|
table = 'cpergdata'
|
|
|
|
theids = [w.id for w in theworkouts]
|
|
|
|
job = myqueue(
|
|
queue,
|
|
handle_updatecp,
|
|
rower.id,
|
|
theids,
|
|
table=table)
|
|
|
|
return job
|
|
|
|
def fetchcperg(rower,theworkouts):
|
|
theids = [int(w.id) for w in theworkouts]
|
|
thefilenames = [w.csvfilename for w in theworkouts]
|
|
cpdf = getcpdata_sql(rower.id,table='ergcpdata')
|
|
|
|
job = myqueue(
|
|
queue,
|
|
handle_updateergcp,
|
|
rower.id,
|
|
thefilenames)
|
|
|
|
return cpdf
|
|
|
|
def fetchcp_new(rower,workouts):
|
|
|
|
data = []
|
|
for workout in workouts:
|
|
cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
|
|
try:
|
|
df = pd.read_parquet(cpfile)
|
|
data.append(df)
|
|
except OSError:
|
|
# CP data file doesn't exist yet. has to be created
|
|
strokesdf = getsmallrowdata_db(['power','workoutid','time'],ids = [workout.id])
|
|
if not strokesdf.empty:
|
|
totaltime = strokesdf['time'].max()
|
|
try:
|
|
powermean = strokesdf['power'].mean()
|
|
except KeyError:
|
|
powermean = 0
|
|
|
|
if powermean != 0:
|
|
thesecs = totaltime
|
|
maxt = 1.05 * thesecs
|
|
|
|
if maxt > 0:
|
|
logarr = datautils.getlogarr(maxt)
|
|
dfgrouped = strokesdf.groupby(['workoutid'])
|
|
delta, cpvalues, avgpower = datautils.getcp(dfgrouped, logarr)
|
|
filename = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
|
|
df = pd.DataFrame({
|
|
'delta':delta,
|
|
'cp':cpvalues,
|
|
'id':workout.id,
|
|
})
|
|
df.to_parquet(filename,engine='fastparquet',compression='GZIP')
|
|
data.append(df)
|
|
|
|
|
|
if len(data) == 0:
|
|
return pd.Series(),pd.Series(),0
|
|
if len(data)>1:
|
|
df = pd.concat(data,axis=0)
|
|
|
|
df = df.groupby(['delta']).max()
|
|
|
|
|
|
df = df.sort_values(['delta']).reset_index()
|
|
#dindex = df['id'].shift(1)-df['id']
|
|
#dpowerplus = df['cp'].shift(1)-df['cp']
|
|
#dpowermin = df['cp'].shift(-1)-df['cp']
|
|
#badrows = []
|
|
#badid = 0
|
|
#for index,row in df.iterrows():
|
|
# if dindex[index] != 0 and dpowermin[index] > 0:
|
|
# badrows.append(index)
|
|
# badid = row['id']
|
|
# elif row['id'] == badid:
|
|
# badrows.append(index)
|
|
# else:
|
|
# badid = 0
|
|
|
|
|
|
#df = df.drop(index = badrows)
|
|
|
|
|
|
return df['delta'],df['cp'],0
|
|
|
|
def fetchcp(rower,theworkouts,table='cpdata'):
|
|
# get all power data from database (plus workoutid)
|
|
theids = [int(w.id) for w in theworkouts]
|
|
columns = ['power','workoutid','time']
|
|
df = getsmallrowdata_db(columns,ids=theids)
|
|
df.dropna(inplace=True,axis=0)
|
|
if df.empty:
|
|
avgpower2 = {}
|
|
for id in theids:
|
|
avgpower2[id] = 0
|
|
return pd.Series([]),pd.Series([]),avgpower2
|
|
|
|
try:
|
|
dfgrouped = df.groupby(['workoutid'])
|
|
except KeyError:
|
|
avgpower2 = {}
|
|
return pd.Series([]),pd.Series([]),avgpower2
|
|
try:
|
|
avgpower2 = dict(dfgrouped.mean()['power'].astype(int))
|
|
except KeyError:
|
|
avgpower2 = {}
|
|
for id in theids:
|
|
avgpower2[id] = 0
|
|
return pd.Series([]),pd.Series([]),avgpower2
|
|
|
|
cpdf = getcpdata_sql(rower.id,table=table)
|
|
|
|
if not cpdf.empty:
|
|
return cpdf['delta'],cpdf['cp'],avgpower2
|
|
else:
|
|
job = myqueue(queue,
|
|
handle_updatecp,
|
|
rower.id,
|
|
theids,
|
|
table=table)
|
|
|
|
return pd.Series([]),pd.Series([]),avgpower2
|
|
|
|
|
|
return pd.Series([]),pd.Series([]),avgpower2
|
|
|
|
|
|
# create a new workout from manually entered data
|
|
def create_row_df(r,distance,duration,startdatetime,workouttype='rower',
|
|
avghr=None,avgpwr=None,avgspm=None,
|
|
rankingpiece = False,
|
|
duplicate=False,
|
|
title='Manual entry',notes='',weightcategory='hwt',
|
|
adaptiveclass='None'):
|
|
|
|
if duration is not None:
|
|
totalseconds = duration.hour*3600.
|
|
totalseconds += duration.minute*60.
|
|
totalseconds += duration.second
|
|
totalseconds += duration.microsecond/1.e6
|
|
else:
|
|
totalseconds = 60.
|
|
|
|
if distance is None:
|
|
distance = 0
|
|
|
|
try:
|
|
nr_strokes = int(distance/10.)
|
|
except TypeError:
|
|
nr_strokes = int(20.*totalseconds)
|
|
|
|
if nr_strokes == 0:
|
|
nr_strokes = 100
|
|
|
|
unixstarttime = arrow.get(startdatetime).timestamp
|
|
|
|
|
|
if not avgspm:
|
|
try:
|
|
spm = 60.*nr_strokes/totalseconds
|
|
except ZeroDivisionError:
|
|
spm = 20.
|
|
else:
|
|
spm = avgspm
|
|
|
|
step = totalseconds/float(nr_strokes)
|
|
|
|
elapsed = np.arange(nr_strokes)*totalseconds/(float(nr_strokes-1))
|
|
|
|
dstep = distance/float(nr_strokes)
|
|
|
|
d = np.arange(nr_strokes)*distance/(float(nr_strokes-1))
|
|
|
|
unixtime = unixstarttime + elapsed
|
|
|
|
try:
|
|
pace = 500.*totalseconds/distance
|
|
except ZeroDivisionError:
|
|
pace = 240.
|
|
|
|
if workouttype in ['rower','slides','dynamic']:
|
|
try:
|
|
velo = distance/totalseconds
|
|
except ZeroDivisionError:
|
|
velo = 2.4
|
|
power = 2.8*velo**3
|
|
elif avgpwr is not None:
|
|
power = avgpwr
|
|
else:
|
|
power = 0
|
|
|
|
if avghr is not None:
|
|
hr = avghr
|
|
else:
|
|
hr = 0
|
|
|
|
df = pd.DataFrame({
|
|
'TimeStamp (sec)': unixtime,
|
|
' Horizontal (meters)': d,
|
|
' Cadence (stokes/min)': spm,
|
|
' Stroke500mPace (sec/500m)':pace,
|
|
' ElapsedTime (sec)':elapsed,
|
|
' Power (watts)':power,
|
|
' HRCur (bpm)':hr,
|
|
})
|
|
|
|
timestr = strftime("%Y%m%d-%H%M%S")
|
|
|
|
csvfilename = 'media/df_' + timestr + '.csv'
|
|
df[' ElapsedTime (sec)'] = df['TimeStamp (sec)']
|
|
|
|
row = rrdata(df=df)
|
|
|
|
row.write_csv(csvfilename, gzip = True)
|
|
|
|
id, message = save_workout_database(csvfilename, r,
|
|
title=title,
|
|
notes=notes,
|
|
rankingpiece=rankingpiece,
|
|
duplicate=duplicate,
|
|
dosmooth=False,
|
|
workouttype=workouttype,
|
|
consistencychecks=False,
|
|
weightcategory=weightcategory,
|
|
adaptiveclass=adaptiveclass,
|
|
totaltime=totalseconds)
|
|
|
|
return (id, message)
|
|
|
|
from rowers.utils import totaltime_sec_to_string
|
|
|
|
# Processes painsled CSV file to database
|
|
def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
|
|
boattype='1x',
|
|
adaptiveclass='None',
|
|
weightcategory='hwt',
|
|
dosummary=True, title='Workout',
|
|
workoutsource='unknown',
|
|
notes='', totaldist=0, totaltime=0,
|
|
rankingpiece=False,
|
|
duplicate=False,
|
|
summary='',
|
|
makeprivate=False,
|
|
oarlength=2.89, inboard=0.88,
|
|
forceunit='lbs',
|
|
consistencychecks=False,
|
|
impeller=False):
|
|
|
|
message = None
|
|
|
|
|
|
powerperc = 100 * np.array([r.pw_ut2,
|
|
r.pw_ut1,
|
|
r.pw_at,
|
|
r.pw_tr, r.pw_an]) / r.ftp
|
|
|
|
# make workout and put in database
|
|
rr = rrower(hrmax=r.max, hrut2=r.ut2,
|
|
hrut1=r.ut1, hrat=r.at,
|
|
hrtr=r.tr, hran=r.an, ftp=r.ftp,
|
|
powerperc=powerperc, powerzones=r.powerzones)
|
|
row = rdata(f2, rower=rr)
|
|
|
|
if title is None or title == '':
|
|
title = 'Workout'
|
|
partofday = getpartofday(row,r)
|
|
|
|
|
|
if partofday is not None:
|
|
title = '{partofday} {workouttype}'.format(
|
|
partofday=partofday,
|
|
workouttype=workouttype,
|
|
)
|
|
|
|
if row.df.empty:
|
|
return (0, 'Error: CSV data file was empty')
|
|
|
|
dtavg = row.df['TimeStamp (sec)'].diff().mean()
|
|
|
|
if dtavg < 1:
|
|
newdf = df_resample(row.df)
|
|
try:
|
|
os.remove(f2)
|
|
except:
|
|
pass
|
|
return new_workout_from_df(r, newdf,
|
|
title=title,boattype=boattype,
|
|
workouttype=workouttype,
|
|
workoutsource=workoutsource)
|
|
try:
|
|
checks = row.check_consistency()
|
|
allchecks = 1
|
|
for key, value in checks.items():
|
|
if not value:
|
|
allchecks = 0
|
|
except ZeroDivisionError:
|
|
pass
|
|
|
|
if not allchecks and consistencychecks:
|
|
# row.repair()
|
|
pass
|
|
|
|
if row == 0:
|
|
return (0, 'Error: CSV data file not found')
|
|
|
|
try:
|
|
lat = row.df[' latitude']
|
|
if lat.mean() != 0 and lat.std() != 0 and workouttype == 'rower':
|
|
workouttype = 'water'
|
|
except KeyError:
|
|
pass
|
|
|
|
if dosmooth:
|
|
# auto smoothing
|
|
pace = row.df[' Stroke500mPace (sec/500m)'].values
|
|
velo = 500. / pace
|
|
|
|
f = row.df['TimeStamp (sec)'].diff().mean()
|
|
if f != 0 and not np.isnan(f):
|
|
windowsize = 2 * (int(10. / (f))) + 1
|
|
else:
|
|
windowsize = 1
|
|
if not 'originalvelo' in row.df:
|
|
row.df['originalvelo'] = velo
|
|
|
|
if windowsize > 3 and windowsize < len(velo):
|
|
velo2 = savgol_filter(velo, windowsize, 3)
|
|
else:
|
|
velo2 = velo
|
|
|
|
velo3 = pd.Series(velo2)
|
|
velo3 = velo3.replace([-np.inf, np.inf], np.nan)
|
|
velo3 = velo3.fillna(method='ffill')
|
|
|
|
pace2 = 500. / abs(velo3)
|
|
|
|
row.df[' Stroke500mPace (sec/500m)'] = pace2
|
|
|
|
row.df = row.df.fillna(0)
|
|
|
|
row.write_csv(f2, gzip=True)
|
|
try:
|
|
os.remove(f2)
|
|
except:
|
|
pass
|
|
|
|
# recalculate power data
|
|
if workouttype == 'rower' or workouttype == 'dynamic' or workouttype == 'slides':
|
|
try:
|
|
row.erg_recalculatepower()
|
|
row.write_csv(f2, gzip=True)
|
|
except:
|
|
pass
|
|
|
|
averagehr = row.df[' HRCur (bpm)'].mean()
|
|
maxhr = row.df[' HRCur (bpm)'].max()
|
|
|
|
if totaldist == 0:
|
|
totaldist = row.df['cum_dist'].max()
|
|
if totaltime == 0:
|
|
totaltime = row.df['TimeStamp (sec)'].max(
|
|
) - row.df['TimeStamp (sec)'].min()
|
|
try:
|
|
totaltime = totaltime + row.df.loc[:, ' ElapsedTime (sec)'].iloc[0]
|
|
except KeyError:
|
|
pass
|
|
|
|
if np.isnan(totaltime):
|
|
totaltime = 0
|
|
|
|
|
|
if dosummary:
|
|
summary = row.allstats()
|
|
|
|
|
|
|
|
timezone_str = 'UTC'
|
|
try:
|
|
workoutstartdatetime = timezone.make_aware(row.rowdatetime)
|
|
except ValueError:
|
|
workoutstartdatetime = row.rowdatetime
|
|
|
|
try:
|
|
latavg = row.df[' latitude'].mean()
|
|
lonavg = row.df[' longitude'].mean()
|
|
|
|
tf = TimezoneFinder()
|
|
try:
|
|
timezone_str = tf.timezone_at(lng=lonavg, lat=latavg)
|
|
except (ValueError,OverflowError):
|
|
timezone_str = 'UTC'
|
|
if timezone_str == None:
|
|
timezone_str = tf.closest_timezone_at(lng=lonavg,
|
|
lat=latavg)
|
|
if timezone_str == None:
|
|
timezone_str = r.defaulttimezone
|
|
try:
|
|
workoutstartdatetime = pytz.timezone(timezone_str).localize(
|
|
row.rowdatetime
|
|
)
|
|
except ValueError:
|
|
workoutstartdatetime = workoutstartdatetime.astimezone(
|
|
pytz.timezone(timezone_str)
|
|
)
|
|
except KeyError:
|
|
timezone_str = r.defaulttimezone
|
|
|
|
duration = totaltime_sec_to_string(totaltime)
|
|
|
|
workoutdate = workoutstartdatetime.astimezone(
|
|
pytz.timezone(timezone_str)
|
|
).strftime('%Y-%m-%d')
|
|
workoutstarttime = workoutstartdatetime.astimezone(
|
|
pytz.timezone(timezone_str)
|
|
).strftime('%H:%M:%S')
|
|
|
|
|
|
if makeprivate:
|
|
privacy = 'hidden'
|
|
else:
|
|
privacy = 'visible'
|
|
|
|
# checking for inf values
|
|
|
|
totaldist = np.nan_to_num(totaldist)
|
|
maxhr = np.nan_to_num(maxhr)
|
|
averagehr = np.nan_to_num(averagehr)
|
|
|
|
dragfactor = 0
|
|
if workouttype in otetypes:
|
|
dragfactor = row.dragfactor
|
|
|
|
t = datetime.datetime.strptime(duration,"%H:%M:%S.%f")
|
|
delta = datetime.timedelta(hours=t.hour, minutes=t.minute, seconds=t.second)
|
|
|
|
workoutenddatetime = workoutstartdatetime+delta
|
|
|
|
# check for duplicate start times and duration
|
|
ws = Workout.objects.filter(user=r,date=workoutdate,duplicate=False).exclude(
|
|
startdatetime__gt=workoutenddatetime
|
|
)
|
|
|
|
ws2 = []
|
|
|
|
for ww in ws:
|
|
t = ww.duration
|
|
delta = datetime.timedelta(hours=t.hour, minutes=t.minute, seconds=t.second)
|
|
enddatetime = ww.startdatetime+delta
|
|
if enddatetime > workoutstartdatetime:
|
|
ws2.append(ww)
|
|
|
|
|
|
if (len(ws2) != 0):
|
|
message = "Warning: This workout overlaps with an existing one and was marked as a duplicate"
|
|
duplicate = True
|
|
|
|
# test title length
|
|
if title is not None and len(title)>140:
|
|
title = title[0:140]
|
|
|
|
w = Workout(user=r, name=title, date=workoutdate,
|
|
workouttype=workouttype,
|
|
boattype=boattype,
|
|
dragfactor=dragfactor,
|
|
duration=duration, distance=totaldist,
|
|
weightcategory=weightcategory,
|
|
adaptiveclass=adaptiveclass,
|
|
starttime=workoutstarttime,
|
|
duplicate=duplicate,
|
|
workoutsource=workoutsource,
|
|
rankingpiece=rankingpiece,
|
|
forceunit=forceunit,
|
|
csvfilename=f2, notes=notes, summary=summary,
|
|
maxhr=maxhr, averagehr=averagehr,
|
|
startdatetime=workoutstartdatetime,
|
|
inboard=inboard, oarlength=oarlength,
|
|
timezone=timezone_str,
|
|
privacy=privacy,
|
|
impeller=impeller)
|
|
try:
|
|
w.save()
|
|
except ValidationError:
|
|
try:
|
|
w.startdatetime = timezone.now()
|
|
w.save()
|
|
except ValidationError:
|
|
return (0,'Unable to create your workout')
|
|
|
|
if privacy == 'visible':
|
|
ts = Team.objects.filter(rower=r)
|
|
for t in ts:
|
|
w.team.add(t)
|
|
|
|
# put stroke data in database
|
|
res = dataprep(row.df, id=w.id, bands=True,
|
|
barchart=True, otwpower=True, empower=True, inboard=inboard)
|
|
|
|
rscore,normp = workout_rscore(w)
|
|
trimp,hrtss = workout_trimp(w)
|
|
|
|
isbreakthrough = False
|
|
ishard = False
|
|
if workouttype in rowtypes:
|
|
df = getsmallrowdata_db(['power', 'workoutid', 'time'], ids=[w.id])
|
|
try:
|
|
powermean = df['power'].mean()
|
|
except KeyError:
|
|
powermean = 0
|
|
|
|
if powermean != 0:
|
|
thesecs = totaltime
|
|
maxt = 1.05 * thesecs
|
|
if maxt > 0:
|
|
logarr = datautils.getlogarr(maxt)
|
|
dfgrouped = df.groupby(['workoutid'])
|
|
delta, cpvalues, avgpower = datautils.getcp(dfgrouped, logarr)
|
|
filename = 'media/cpdata_{id}.parquet.gz'.format(id=w.id)
|
|
cpdf = pd.DataFrame({
|
|
'delta':delta,
|
|
'cp':cpvalues,
|
|
'id':w.id,
|
|
})
|
|
cpdf.to_parquet(filename,engine='fastparquet',compression='GZIP')
|
|
|
|
if workouttype in otwtypes:
|
|
res, btvalues, res2 = utils.isbreakthrough(
|
|
delta, cpvalues, r.p0, r.p1, r.p2, r.p3, r.cpratio)
|
|
elif workouttype in otetypes:
|
|
res, btvalues, res2 = utils.isbreakthrough(
|
|
delta, cpvalues, r.ep0, r.ep1, r.ep2, r.ep3, r.ecpratio)
|
|
else:
|
|
res = 0
|
|
res2 = 0
|
|
if res:
|
|
isbreakthrough = True
|
|
res = datautils.updatecp(delta, cpvalues, r,workouttype=workouttype)
|
|
if res2 and not isbreakthrough:
|
|
ishard = True
|
|
|
|
# submit email task to send email about breakthrough workout
|
|
if isbreakthrough:
|
|
if r.getemailnotifications and not r.emailbounced:
|
|
job = myqueue(queuehigh,handle_sendemail_breakthrough,
|
|
w.id,
|
|
r.user.email,
|
|
r.user.first_name,
|
|
r.user.last_name,
|
|
btvalues=btvalues.to_json())
|
|
|
|
# submit email task to send email about breakthrough workout
|
|
if ishard:
|
|
if r.getemailnotifications and not r.emailbounced:
|
|
job = myqueue(queuehigh,handle_sendemail_hard,
|
|
w.id,
|
|
r.user.email,
|
|
r.user.first_name,
|
|
r.user.last_name,
|
|
btvalues=btvalues.to_json())
|
|
|
|
|
|
return (w.id, message)
|
|
|
|
parsers = {
|
|
'kinomap': KinoMapParser,
|
|
'xls': ExcelTemplate,
|
|
'rp': RowProParser,
|
|
'tcx':TCXParser,
|
|
'mystery':MysteryParser,
|
|
'ritmotime':RitmoTimeParser,
|
|
'quiske': QuiskeParser,
|
|
'rowperfect3': RowPerfectParser,
|
|
'coxmate': CoxMateParser,
|
|
'bcmike': BoatCoachAdvancedParser,
|
|
'boatcoach': BoatCoachParser,
|
|
'boatcoachotw': BoatCoachOTWParser,
|
|
'painsleddesktop': painsledDesktopParser,
|
|
'speedcoach': speedcoachParser,
|
|
'speedcoach2': SpeedCoach2Parser,
|
|
'ergstick': ErgStickParser,
|
|
'fit': FITParser,
|
|
'ergdata': ErgDataParser,
|
|
'humon': HumonParser,
|
|
'eth': ETHParser,
|
|
}
|
|
|
|
def parsenonpainsled(fileformat,f2,summary):
|
|
try:
|
|
row = parsers[fileformat](f2)
|
|
hasrecognized = True
|
|
except (KeyError,IndexError,ValueError):
|
|
hasrecognized = False
|
|
return None, hasrecognized, '', 'unknown'
|
|
|
|
# handle speed coach GPS 2
|
|
if (fileformat == 'speedcoach2'):
|
|
oarlength, inboard = get_empower_rigging(f2)
|
|
empowerfirmware = get_empower_firmware(f2)
|
|
if empowerfirmware != '':
|
|
fileformat = fileformat+'v'+str(empowerfirmware)
|
|
else:
|
|
fileformat = 'speedcoach2v0'
|
|
try:
|
|
summary = row.allstats()
|
|
except ZeroDivisionError:
|
|
summary = ''
|
|
|
|
# handle FIT
|
|
if (fileformat == 'fit'):
|
|
try:
|
|
s = fitsummarydata(f2)
|
|
s.setsummary()
|
|
summary = s.summarytext
|
|
except:
|
|
pass
|
|
hasrecognized = True
|
|
|
|
|
|
return row,hasrecognized,summary,fileformat
|
|
|
|
def handle_nonpainsled(f2, fileformat, summary=''):
|
|
oarlength = 2.89
|
|
inboard = 0.88
|
|
hasrecognized = False
|
|
impeller = False
|
|
|
|
row,hasrecognized,summary,fileformat = parsenonpainsled(fileformat,f2,summary)
|
|
|
|
# Handle c2log
|
|
if (fileformat == 'c2log' or fileformat == 'rowprolog'):
|
|
return (0,'',0,0,'',impeller)
|
|
|
|
if not hasrecognized:
|
|
return (0,'',0,0,'',impeller)
|
|
|
|
f_to_be_deleted = f2
|
|
# should delete file
|
|
f2 = f2[:-4] + 'o.csv'
|
|
row2 = rrdata(df = row.df)
|
|
|
|
if 'speedcoach2' in fileformat:
|
|
# impeller consistency
|
|
impellerdata, consistent, ratio = row.impellerconsistent(threshold=0.3)
|
|
if impellerdata and consistent:
|
|
impeller = True
|
|
if impellerdata and not consistent:
|
|
row2.use_gpsdata()
|
|
|
|
row2.write_csv(f2, gzip=True)
|
|
|
|
|
|
# os.remove(f2)
|
|
try:
|
|
os.remove(f_to_be_deleted)
|
|
except:
|
|
try:
|
|
os.remove(f_to_be_deleted + '.gz')
|
|
except:
|
|
pass
|
|
|
|
return (f2, summary, oarlength, inboard, fileformat, impeller)
|
|
|
|
# Create new workout from file and store it in the database
|
|
# This routine should be used everywhere in views.py and mailprocessing.py
|
|
# Currently there is code duplication
|
|
|
|
def get_workouttype_from_fit(filename,workouttype='water'):
|
|
try:
|
|
fitfile = FitFile(filename,check_crc=False)
|
|
except FitHeaderError:
|
|
return workouttype
|
|
|
|
records = fitfile.messages
|
|
fittype = 'rowing'
|
|
for record in records:
|
|
if record.name in ['sport','lap']:
|
|
try:
|
|
fittype = record.get_values()['sport'].lower()
|
|
except KeyError:
|
|
return 'water'
|
|
try:
|
|
workouttype = mytypes.fitmappinginv[fittype]
|
|
except KeyError:
|
|
return workouttype
|
|
|
|
return workouttype
|
|
|
|
import rowingdata.tcxtools as tcxtools
|
|
|
|
def get_workouttype_from_tcx(filename,workouttype='water'):
|
|
tcxtype = 'rowing'
|
|
if workouttype in mytypes.otwtypes:
|
|
return workouttype
|
|
try:
|
|
d = tcxtools.tcx_getdict(filename)
|
|
try:
|
|
tcxtype = d['Activities']['Activity']['@Sport'].lower()
|
|
if tcxtype == 'other':
|
|
tcxtype = 'rowing'
|
|
except KeyError:
|
|
return workouttype
|
|
|
|
except TypeError:
|
|
pass
|
|
|
|
try:
|
|
workouttype = mytypes.garminmappinginv[tcxtype.upper()]
|
|
except KeyError:
|
|
return workouttype
|
|
|
|
return workouttype
|
|
|
|
def new_workout_from_file(r, f2,
|
|
workouttype='rower',
|
|
workoutsource=None,
|
|
title='Workout',
|
|
boattype='1x',
|
|
makeprivate=False,
|
|
notes='',
|
|
uploadoptions={'boattype':'1x','workouttype':'rower'}):
|
|
message = ""
|
|
impeller = False
|
|
try:
|
|
fileformat = get_file_type(f2)
|
|
except (IOError,UnicodeDecodeError):
|
|
os.remove(f2)
|
|
message = "Rowsandall could not process this file. The extension is supported but the file seems corrupt. Contact info@rowsandall.com if you think this is incorrect."
|
|
return (0, message, f2)
|
|
|
|
summary = ''
|
|
oarlength = 2.89
|
|
inboard = 0.88
|
|
|
|
# Save zip files to email box for further processing
|
|
if len(fileformat) == 3 and fileformat[0] == 'zip':
|
|
uploadoptions['fromuploadform'] = True
|
|
bodyyaml = yaml.safe_dump(uploadoptions,default_flow_style=False)
|
|
f_to_be_deleted = f2
|
|
impeller = False
|
|
workoutsbox = Mailbox.objects.filter(name='workouts')[0]
|
|
msg = Message(mailbox=workoutsbox,
|
|
from_header=r.user.email,
|
|
subject = title,body=bodyyaml)
|
|
msg.save()
|
|
f3 = 'media/mailbox_attachments/'+f2[6:]
|
|
copyfile(f2,f3)
|
|
f3 = f3[6:]
|
|
a = MessageAttachment(message=msg,document=f3)
|
|
a.save()
|
|
message = "Zip file was stored for offline processing"
|
|
|
|
return -1, message, f2
|
|
|
|
# Some people try to upload Concept2 logbook summaries
|
|
if fileformat == 'c2log':
|
|
os.remove(f2)
|
|
message = "This summary does not contain stroke data. Use the files containing stroke by stroke data."
|
|
return (0, message, f2)
|
|
|
|
if fileformat == 'nostrokes':
|
|
os.remove(f2)
|
|
message = "It looks like this file doesn't contain stroke data."
|
|
return (0, message, f2)
|
|
|
|
if fileformat == 'kml':
|
|
os.remove(f2)
|
|
message = "KML files are not supported"
|
|
return (0, message, f2)
|
|
|
|
# Some people upload corrupted zip files
|
|
if fileformat == 'notgzip':
|
|
os.remove(f2)
|
|
message = "Rowsandall could not process this file. The extension is supported but the file seems corrupt. Contact info@rowsandall.com if you think this is incorrect."
|
|
return (0, message, f2)
|
|
|
|
# Some people try to upload RowPro summary logs
|
|
if fileformat == 'rowprolog':
|
|
os.remove(f2)
|
|
message = "This RowPro logbook summary does not contain stroke data. Please use the Stroke Data CSV file for the individual workout in your log."
|
|
return (0, message, f2)
|
|
|
|
# Sometimes people try an unsupported file type.
|
|
# Send an email to info@rowsandall.com with the file attached
|
|
# for me to check if it is a bug, or a new file type
|
|
# worth supporting
|
|
if fileformat == 'gpx':
|
|
print('aap')
|
|
os.remove(f2)
|
|
message = "GPX files support is on our roadmap. Check back soon."
|
|
return (0, message, f2)
|
|
|
|
if fileformat == 'unknown':
|
|
message = "We couldn't recognize the file type"
|
|
extension = os.path.splitext(f2)[1]
|
|
filename = os.path.splitext(f2)[0]
|
|
if extension == '.gz':
|
|
filename = os.path.splitext(filename)[0]
|
|
extension2 = os.path.splitext(filename)[1]+extension
|
|
extension = extension2
|
|
f4 = filename+'a'+extension
|
|
copyfile(f2,f4)
|
|
job = myqueue(queuehigh,
|
|
handle_sendemail_unrecognized,
|
|
f4,
|
|
r.user.email)
|
|
|
|
return (0, message, f2)
|
|
|
|
if fileformat == 'att':
|
|
# email attachment which can safely be ignored
|
|
return (0, '', f2)
|
|
|
|
# Get workout type from fit & tcx
|
|
if (fileformat == 'fit'):
|
|
workouttype = get_workouttype_from_fit(f2,workouttype=workouttype)
|
|
if (fileformat == 'tcx'):
|
|
workouttype = get_workouttype_from_tcx(f2,workouttype=workouttype)
|
|
|
|
# handle non-Painsled by converting it to painsled compatible CSV
|
|
if (fileformat != 'csv'):
|
|
f2, summary, oarlength, inboard, fileformat, impeller = handle_nonpainsled(
|
|
f2,
|
|
fileformat,
|
|
summary=summary
|
|
)
|
|
if not f2:
|
|
message = 'Something went wrong'
|
|
return (0, message, '')
|
|
|
|
|
|
|
|
dosummary = (fileformat != 'fit' and 'speedcoach2' not in fileformat)
|
|
dosummary = dosummary or summary == ''
|
|
|
|
if 'speedcoach2' in fileformat and workouttype == 'rower':
|
|
workouttype = 'water'
|
|
|
|
if workoutsource is None:
|
|
workoutsource = fileformat
|
|
|
|
id, message = save_workout_database(
|
|
f2, r,
|
|
notes=notes,
|
|
workouttype=workouttype,
|
|
weightcategory=r.weightcategory,
|
|
adaptiveclass=r.adaptiveclass,
|
|
boattype=boattype,
|
|
makeprivate=makeprivate,
|
|
dosummary=dosummary,
|
|
workoutsource=workoutsource,
|
|
summary=summary,
|
|
inboard=inboard, oarlength=oarlength,
|
|
title=title,
|
|
forceunit='N',
|
|
impeller=impeller,
|
|
)
|
|
|
|
job = myqueue(queuehigh,handle_calctrimp,id,f2,r.ftp,r.sex,r.hrftp,r.max,r.rest)
|
|
|
|
return (id, message, f2)
|
|
|
|
|
|
def split_workout(r, parent, splitsecond, splitmode):
|
|
data, row = getrowdata_db(id=parent.id)
|
|
latitude, longitude = get_latlon(parent.id)
|
|
if not latitude.empty and not longitude.empty:
|
|
data[' latitude'] = latitude
|
|
data[' longitude'] = longitude
|
|
|
|
data['time'] = data['time'] / 1000.
|
|
|
|
data1 = data[data['time'] <= splitsecond].copy()
|
|
data2 = data[data['time'] > splitsecond].copy()
|
|
|
|
data1 = data1.sort_values(['time'])
|
|
data1 = data1.interpolate(method='linear', axis=0, limit_direction='both',
|
|
limit=10)
|
|
data1.fillna(method='bfill', inplace=True)
|
|
|
|
# Some new stuff to try out
|
|
data1 = data1.groupby('time', axis=0).mean()
|
|
data1['time'] = data1.index
|
|
data1.reset_index(drop=True, inplace=True)
|
|
|
|
data2 = data2.sort_values(['time'])
|
|
data2 = data2.interpolate(method='linear', axis=0, limit_direction='both',
|
|
limit=10)
|
|
data2.fillna(method='bfill', inplace=True)
|
|
|
|
# Some new stuff to try out
|
|
data2 = data2.groupby('time', axis=0).mean()
|
|
data2['time'] = data2.index
|
|
data2.reset_index(drop=True, inplace=True)
|
|
|
|
data1['pace'] = data1['pace'] / 1000.
|
|
data2['pace'] = data2['pace'] / 1000.
|
|
|
|
data1.drop_duplicates(subset='time', inplace=True)
|
|
data2.drop_duplicates(subset='time', inplace=True)
|
|
|
|
messages = []
|
|
ids = []
|
|
|
|
if 'keep first' in splitmode:
|
|
if 'firstprivate' in splitmode:
|
|
setprivate = True
|
|
else:
|
|
setprivate = False
|
|
|
|
id, message = new_workout_from_df(r, data1,
|
|
title=parent.name + ' (1)',
|
|
parent=parent,
|
|
setprivate=setprivate,
|
|
forceunit='N')
|
|
messages.append(message)
|
|
ids.append(encoder.encode_hex(id))
|
|
if 'keep second' in splitmode:
|
|
data2['cumdist'] = data2['cumdist'] - data2.iloc[
|
|
0,
|
|
data2.columns.get_loc('cumdist')
|
|
]
|
|
data2['distance'] = data2['distance'] - data2.iloc[
|
|
0,
|
|
data2.columns.get_loc('distance')
|
|
]
|
|
data2['time'] = data2['time'] - data2.iloc[
|
|
0,
|
|
data2.columns.get_loc('time')
|
|
]
|
|
if 'secondprivate' in splitmode:
|
|
setprivate = True
|
|
else:
|
|
setprivate = False
|
|
|
|
dt = datetime.timedelta(seconds=splitsecond)
|
|
|
|
id, message = new_workout_from_df(r, data2,
|
|
title=parent.name + ' (2)',
|
|
parent=parent,
|
|
setprivate=setprivate,
|
|
dt=dt, forceunit='N')
|
|
messages.append(message)
|
|
ids.append(encoder.encode_hex(id))
|
|
|
|
if not 'keep original' in splitmode:
|
|
if 'keep second' in splitmode or 'keep first' in splitmode:
|
|
parent.delete()
|
|
messages.append('Deleted Workout: ' + parent.name)
|
|
else:
|
|
messages.append('That would delete your workout')
|
|
ids.append(encoder.encode_hex(parent.id))
|
|
elif 'originalprivate' in splitmode:
|
|
parent.privacy = 'hidden'
|
|
parent.save()
|
|
|
|
return ids, messages
|
|
|
|
# Create new workout from data frame and store it in the database
|
|
# This routine should be used everywhere in views.py and mailprocessing.py
|
|
# Currently there is code duplication
|
|
|
|
|
|
def new_workout_from_df(r, df,
|
|
title='New Workout',
|
|
workoutsource='unknown',
|
|
boattype='1x',
|
|
workouttype='rower',
|
|
parent=None,
|
|
setprivate=False,
|
|
forceunit='lbs',
|
|
dt=datetime.timedelta()):
|
|
|
|
message = None
|
|
|
|
summary = ''
|
|
if parent:
|
|
oarlength = parent.oarlength
|
|
inboard = parent.inboard
|
|
|
|
workoutsource = parent.workoutsource
|
|
workouttype = parent.workouttype
|
|
boattype = parent.boattype
|
|
notes = parent.notes
|
|
summary = parent.summary
|
|
if parent.privacy == 'hidden':
|
|
makeprivate = True
|
|
else:
|
|
makeprivate = False
|
|
|
|
startdatetime = parent.startdatetime + dt
|
|
else:
|
|
oarlength = 2.89
|
|
inboard = 0.88
|
|
notes = ''
|
|
summary = ''
|
|
makeprivate = False
|
|
startdatetime = timezone.now()
|
|
|
|
if setprivate:
|
|
makeprivate = True
|
|
|
|
timestr = strftime("%Y%m%d-%H%M%S")
|
|
|
|
csvfilename = 'media/df_' + timestr + '.csv'
|
|
if forceunit == 'N':
|
|
# change to lbs for now
|
|
df['peakforce'] /= lbstoN
|
|
df['averageforce'] /= lbstoN
|
|
|
|
df.rename(columns=columndict, inplace=True)
|
|
|
|
#starttimeunix = mktime(startdatetime.utctimetuple())
|
|
starttimeunix = arrow.get(startdatetime).timestamp
|
|
df[' ElapsedTime (sec)'] = df['TimeStamp (sec)']
|
|
|
|
df['TimeStamp (sec)'] = df['TimeStamp (sec)'] + starttimeunix
|
|
|
|
row = rrdata(df=df)
|
|
row.write_csv(csvfilename, gzip=True)
|
|
|
|
# res = df.to_csv(csvfilename+'.gz',index_label='index',
|
|
# compression='gzip')
|
|
|
|
id, message = save_workout_database(csvfilename, r,
|
|
workouttype=workouttype,
|
|
boattype=boattype,
|
|
title=title,
|
|
workoutsource=workoutsource,
|
|
notes=notes,
|
|
oarlength=oarlength,
|
|
inboard=inboard,
|
|
makeprivate=makeprivate,
|
|
dosmooth=False,
|
|
consistencychecks=False)
|
|
|
|
return (id, message)
|
|
|
|
|
|
|
|
|
|
# A wrapper around the rowingdata class, with some error catching
|
|
|
|
|
|
def rdata(file, rower=rrower()):
|
|
try:
|
|
res = rrdata(csvfile=file, rower=rower)
|
|
except (IOError, IndexError):
|
|
try:
|
|
res = rrdata(csvfile=file + '.gz', rower=rower)
|
|
except (IOError, IndexError):
|
|
res = rrdata()
|
|
except:
|
|
res = rrdata()
|
|
except EOFError:
|
|
res = rrdata()
|
|
except:
|
|
res = rrdata()
|
|
|
|
return res
|
|
|
|
# Remove all stroke data for workout ID from database
|
|
|
|
|
|
def delete_strokedata(id):
|
|
dirname = 'media/strokedata_{id}.parquet.gz'.format(id=id)
|
|
try:
|
|
shutil.rmtree(dirname)
|
|
except OSError:
|
|
try:
|
|
os.remove(dirname)
|
|
except FileNotFoundError:
|
|
pass
|
|
except FileNotFoundError:
|
|
pass
|
|
|
|
# Replace stroke data in DB with data from CSV file
|
|
|
|
|
|
def update_strokedata(id, df):
|
|
delete_strokedata(id)
|
|
rowdata = dataprep(df, id=id, bands=True, barchart=True, otwpower=True)
|
|
|
|
# Test that all data are of a numerical time
|
|
|
|
|
|
def testdata(time, distance, pace, spm):
|
|
t1 = np.issubdtype(time, np.number)
|
|
t2 = np.issubdtype(distance, np.number)
|
|
t3 = np.issubdtype(pace, np.number)
|
|
t4 = np.issubdtype(spm, np.number)
|
|
|
|
return t1 and t2 and t3 and t4
|
|
|
|
# Get data from DB for one workout (fetches all data). If data
|
|
# is not in DB, read from CSV file (and create DB entry)
|
|
|
|
|
|
def getrowdata_db(id=0, doclean=False, convertnewtons=True,
|
|
checkefficiency=True):
|
|
data = read_df_sql(id)
|
|
try:
|
|
data['deltat'] = data['time'].diff()
|
|
except KeyError:
|
|
data = pd.DataFrame()
|
|
|
|
if data.empty:
|
|
rowdata, row = getrowdata(id=id)
|
|
if not rowdata.empty:
|
|
data = dataprep(rowdata.df, id=id, bands=True,
|
|
barchart=True, otwpower=True)
|
|
else:
|
|
data = pd.DataFrame() # returning empty dataframe
|
|
else:
|
|
row = Workout.objects.get(id=id)
|
|
|
|
|
|
|
|
if checkefficiency==True and not data.empty:
|
|
try:
|
|
if data['efficiency'].mean() == 0 and data['power'].mean() != 0:
|
|
data = add_efficiency(id=id)
|
|
except KeyError:
|
|
data = add_efficiency(id=id)
|
|
|
|
if doclean:
|
|
data = clean_df_stats(data, ignorehr=True)
|
|
|
|
|
|
return data, row
|
|
|
|
# Fetch a subset of the data from the DB
|
|
|
|
def getsmallrowdata_db(columns, ids=[], doclean=True,workstrokesonly=True,compute=True):
|
|
# prepmultipledata(ids)
|
|
|
|
|
|
if ids:
|
|
csvfilenames = ['media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
|
|
else:
|
|
return pd.DataFrame()
|
|
|
|
data = []
|
|
columns = [c for c in columns if c != 'None']
|
|
columns = list(set(columns))
|
|
|
|
|
|
if len(ids)>1:
|
|
for id,f in zip(ids,csvfilenames):
|
|
try:
|
|
#df = dd.read_parquet(f,columns=columns,engine='pyarrow')
|
|
df = pd.read_parquet(f,columns=columns)
|
|
data.append(df)
|
|
except OSError:
|
|
rowdata, row = getrowdata(id=id)
|
|
if rowdata and len(rowdata.df):
|
|
datadf = dataprep(rowdata.df,id=id,bands=True,otwpower=True,barchart=True)
|
|
# df = dd.read_parquet(f,columns=columns,engine='pyarrow')
|
|
df = pd.read_parquet(f,columns=columns)
|
|
data.append(df)
|
|
|
|
df = pd.concat(data,axis=0)
|
|
# df = dd.concat(data,axis=0)
|
|
|
|
else:
|
|
try:
|
|
df = pd.read_parquet(csvfilenames[0],columns=columns)
|
|
except (OSError,ArrowInvalid):
|
|
rowdata,row = getrowdata(id=ids[0])
|
|
if rowdata and len(rowdata.df):
|
|
data = dataprep(rowdata.df,id=ids[0],bands=True,otwpower=True,barchart=True)
|
|
df = pd.read_parquet(csvfilenames[0],columns=columns)
|
|
# df = dd.read_parquet(csvfilenames[0],
|
|
# column=columns,engine='pyarrow',
|
|
# )
|
|
|
|
# df = df.loc[:,~df.columns.duplicated()]
|
|
else:
|
|
df = pd.DataFrame()
|
|
|
|
|
|
if compute and len(df):
|
|
data = df.copy()
|
|
if doclean:
|
|
data = clean_df_stats(data, ignorehr=True,
|
|
workstrokesonly=workstrokesonly)
|
|
data.dropna(axis=1,how='all',inplace=True)
|
|
data.dropna(axis=0,how='any',inplace=True)
|
|
return data
|
|
|
|
return df
|
|
|
|
def getsmallrowdata_db_dask(columns, ids=[], doclean=True,workstrokesonly=True,compute=True):
|
|
# prepmultipledata(ids)
|
|
|
|
csvfilenames = ['media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
|
|
data = []
|
|
columns = [c for c in columns if c != 'None']
|
|
columns = list(set(columns))
|
|
|
|
if len(ids)>1:
|
|
for id,f in zip(ids,csvfilenames):
|
|
try:
|
|
#df = dd.read_parquet(f,columns=columns,engine='pyarrow')
|
|
df = dd.read_parquet(f,columns=columns)
|
|
data.append(df)
|
|
except OSError:
|
|
rowdata, row = getrowdata(id=id)
|
|
if rowdata and len(rowdata.df):
|
|
datadf = dataprep(rowdata.df,id=id,bands=True,otwpower=True,barchart=True)
|
|
# df = dd.read_parquet(f,columns=columns,engine='pyarrow')
|
|
df = dd.read_parquet(f,columns=columns)
|
|
data.append(df)
|
|
|
|
df = dd.concat(data,axis=0)
|
|
# df = dd.concat(data,axis=0)
|
|
|
|
else:
|
|
try:
|
|
df = dd.read_parquet(csvfilenames[0],columns=columns)
|
|
except OSError:
|
|
rowdata,row = getrowdata(id=ids[0])
|
|
if rowdata and len(rowdata.df):
|
|
data = dataprep(rowdata.df,id=ids[0],bands=True,otwpower=True,barchart=True)
|
|
df = dd.read_parquet(csvfilenames[0],columns=columns)
|
|
# df = dd.read_parquet(csvfilenames[0],
|
|
# column=columns,engine='pyarrow',
|
|
# )
|
|
|
|
# df = df.loc[:,~df.columns.duplicated()]
|
|
|
|
|
|
|
|
if compute:
|
|
data = df.compute()
|
|
if doclean:
|
|
data = clean_df_stats(data, ignorehr=True,
|
|
workstrokesonly=workstrokesonly)
|
|
data.dropna(axis=1,how='all',inplace=True)
|
|
data.dropna(axis=0,how='any',inplace=True)
|
|
return data
|
|
|
|
return df
|
|
|
|
def getsmallrowdata_db_old(columns, ids=[], doclean=True, workstrokesonly=True):
|
|
prepmultipledata(ids)
|
|
data,extracols = read_cols_df_sql(ids, columns)
|
|
if extracols and len(ids)==1:
|
|
w = Workout.objects.get(id=ids[0])
|
|
row = rdata(w.csvfilename)
|
|
try:
|
|
row.set_instroke_metrics()
|
|
except (AttributeError,TypeError):
|
|
pass
|
|
|
|
try:
|
|
f = row.df['TimeStamp (sec)'].diff().mean()
|
|
except (AttributeError,KeyError) as e:
|
|
f = 0
|
|
|
|
if f != 0 and not np.isnan(f):
|
|
windowsize = 2 * (int(10. / (f))) + 1
|
|
else:
|
|
windowsize = 1
|
|
for c in extracols:
|
|
try:
|
|
cdata = row.df[c]
|
|
cdata.fillna(inplace=True,method='bfill')
|
|
# This doesn't work because sometimes data are duplicated at save
|
|
try:
|
|
cdata2 = savgol_filter(cdata.values,windowsize,3)
|
|
data[c] = cdata2
|
|
except ValueError:
|
|
data[c] = cdata
|
|
|
|
|
|
|
|
except (KeyError, AttributeError):
|
|
data[c] = 0
|
|
|
|
|
|
# convert newtons
|
|
|
|
if doclean:
|
|
data = clean_df_stats(data, ignorehr=True,
|
|
workstrokesonly=workstrokesonly)
|
|
data.dropna(axis=1,how='all',inplace=True)
|
|
data.dropna(axis=0,how='any',inplace=True)
|
|
|
|
|
|
return data
|
|
|
|
# Fetch both the workout and the workout stroke data (from CSV file)
|
|
|
|
|
|
def getrowdata(id=0):
|
|
|
|
# check if valid ID exists (workout exists)
|
|
try:
|
|
row = Workout.objects.get(id=id)
|
|
except Workout.DoesNotExist:
|
|
return rrdata(),None
|
|
|
|
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, ftp=r.ftp)
|
|
|
|
rowdata = rdata(f1, rower=rr)
|
|
|
|
return rowdata, row
|
|
|
|
# Checks if all rows for a list of workout IDs have entries in the
|
|
# stroke_data table. If this is not the case, it creates the stroke
|
|
# data
|
|
# In theory, this should never yield any work, but it's a good
|
|
# safety net for programming errors elsewhere in the app
|
|
# Also used heavily when I moved from CSV file only to CSV+Stroke data
|
|
|
|
import glob
|
|
|
|
def prepmultipledata(ids, verbose=False):
|
|
filenames = glob.glob('media/*.parquet')
|
|
ids = [id for id in ids if 'media/strokedata_{id}.parquet.gz'.format(id=id) not in filenames]
|
|
|
|
for id in ids:
|
|
rowdata, row = getrowdata(id=id)
|
|
if verbose:
|
|
print(id)
|
|
if rowdata and len(rowdata.df):
|
|
data = dataprep(rowdata.df, id=id, bands=True,
|
|
barchart=True, otwpower=True)
|
|
return ids
|
|
|
|
# Read a set of columns for a set of workout ids, returns data as a
|
|
# pandas dataframe
|
|
|
|
|
|
def read_cols_df_sql(ids, columns, convertnewtons=True):
|
|
# drop columns that are not in offical list
|
|
# axx = [ax[0] for ax in axes]
|
|
|
|
extracols = []
|
|
|
|
columns = list(columns) + ['distance', 'spm', 'workoutid']
|
|
columns = [x for x in columns if x != 'None']
|
|
columns = list(set(columns))
|
|
ids = [int(id) for id in ids]
|
|
|
|
|
|
if len(ids) == 0:
|
|
return pd.DataFrame(),extracols
|
|
elif len(ids) == 1:
|
|
try:
|
|
filename = 'media/strokedata_{id}.parquet.gz'.format(id=ids[0])
|
|
df = pd.read_parquet(filename,columns=columns)
|
|
except OSError:
|
|
rowdata,row = getrowdata(id=ids[0])
|
|
if rowdata and len(rowdata.df):
|
|
datadf = dataprep(rowdata.df,id=ids[0],bands=True,otwpower=True,barchart=True)
|
|
df = pd.read_parquet(filename,columns=columns)
|
|
else:
|
|
data = []
|
|
filenames = ['media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
|
|
for id,f in zip(ids,filenames):
|
|
try:
|
|
df = pd.read_parquet(f,columns=columns)
|
|
data.append(df)
|
|
except (OSError,IndexError):
|
|
rowdata,row = getrowdata(id=id)
|
|
if rowdata and len(rowdata.df):
|
|
datadf = dataprep(rowdata.df,id=id,bands=True,otwpower=True,barchart=True)
|
|
df = pd.read_parquet(f,columns=columns)
|
|
data.append(df)
|
|
|
|
df = pd.concat(data,axis=0)
|
|
|
|
|
|
df = df.fillna(value=0)
|
|
|
|
if 'peakforce' in columns:
|
|
funits = ((w.id, w.forceunit)
|
|
for w in Workout.objects.filter(id__in=ids))
|
|
for id, u in funits:
|
|
if u == 'lbs':
|
|
mask = df['workoutid'] == id
|
|
df.loc[mask, 'peakforce'] = df.loc[mask, 'peakforce'] * lbstoN
|
|
if 'averageforce' in columns:
|
|
funits = ((w.id, w.forceunit)
|
|
for w in Workout.objects.filter(id__in=ids))
|
|
for id, u in funits:
|
|
if u == 'lbs':
|
|
mask = df['workoutid'] == id
|
|
df.loc[mask, 'averageforce'] = df.loc[mask,
|
|
'averageforce'] * lbstoN
|
|
|
|
return df,extracols
|
|
|
|
def read_cols_df_sql_old(ids, columns, convertnewtons=True):
|
|
# drop columns that are not in offical list
|
|
# axx = [ax[0] for ax in axes]
|
|
prepmultipledata(ids)
|
|
axx = [f.name for f in StrokeData._meta.get_fields()]
|
|
|
|
extracols = []
|
|
|
|
columns2 = list(columns)
|
|
|
|
for c in columns:
|
|
if not c in axx:
|
|
columns2.remove(c)
|
|
extracols.append(c)
|
|
|
|
columns = list(columns2) + ['distance', 'spm', 'workoutid']
|
|
columns = [x for x in columns if x != 'None']
|
|
columns = list(set(columns))
|
|
cls = ''
|
|
ids = [int(id) for id in ids]
|
|
engine = create_engine(database_url, echo=False)
|
|
|
|
for column in columns:
|
|
cls += column + ', '
|
|
cls = cls[:-2]
|
|
if len(ids) == 0:
|
|
return pd.DataFrame(),extracols
|
|
# query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid=0'.format(
|
|
# columns=cls,
|
|
# ))
|
|
elif len(ids) == 1:
|
|
query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid={id} ORDER BY time ASC'.format(
|
|
id=ids[0],
|
|
columns=cls,
|
|
))
|
|
else:
|
|
query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid IN {ids} ORDER BY time ASC'.format(
|
|
columns=cls,
|
|
ids=tuple(ids),
|
|
))
|
|
|
|
|
|
connection = engine.raw_connection()
|
|
df = pd.read_sql_query(query, engine)
|
|
|
|
|
|
df = df.fillna(value=0)
|
|
|
|
if 'peakforce' in columns:
|
|
funits = ((w.id, w.forceunit)
|
|
for w in Workout.objects.filter(id__in=ids))
|
|
for id, u in funits:
|
|
if u == 'lbs':
|
|
mask = df['workoutid'] == id
|
|
df.loc[mask, 'peakforce'] = df.loc[mask, 'peakforce'] * lbstoN
|
|
if 'averageforce' in columns:
|
|
funits = ((w.id, w.forceunit)
|
|
for w in Workout.objects.filter(id__in=ids))
|
|
for id, u in funits:
|
|
if u == 'lbs':
|
|
mask = df['workoutid'] == id
|
|
df.loc[mask, 'averageforce'] = df.loc[mask,
|
|
'averageforce'] * lbstoN
|
|
|
|
engine.dispose()
|
|
return df,extracols
|
|
|
|
# Read stroke data from the DB for a Workout ID. Returns a pandas dataframe
|
|
|
|
def read_df_sql(id):
|
|
try:
|
|
f = 'media/strokedata_{id}.parquet.gz'.format(id=id)
|
|
df = pd.read_parquet(f)
|
|
except (OSError,ArrowInvalid):
|
|
rowdata,row = getrowdata(id=id)
|
|
if rowdata and len(rowdata.df):
|
|
data = dataprep(rowdata.df,id=id,bands=True,otwpower=True,barchart=True)
|
|
df = pd.read_parquet(f)
|
|
else:
|
|
df = pd.DataFrame()
|
|
|
|
df = df.fillna(value=0)
|
|
|
|
return df
|
|
|
|
def read_df_sql_old(id):
|
|
engine = create_engine(database_url, echo=False)
|
|
|
|
df = pd.read_sql_query(sa.text('SELECT * FROM strokedata WHERE workoutid={id} ORDER BY time ASC'.format(
|
|
id=id)), engine)
|
|
|
|
engine.dispose()
|
|
df = df.fillna(value=0)
|
|
|
|
funit = Workout.objects.get(id=id).forceunit
|
|
|
|
if funit == 'lbs':
|
|
try:
|
|
df['peakforce'] = df['peakforce'] * lbstoN
|
|
except KeyError:
|
|
pass
|
|
|
|
try:
|
|
df['averageforce'] = df['averageforce'] * lbstoN
|
|
except KeyError:
|
|
pass
|
|
|
|
return df
|
|
|
|
# Get the necessary data from the strokedata table in the DB.
|
|
# For the flex plot
|
|
|
|
|
|
def smalldataprep(therows, xparam, yparam1, yparam2):
|
|
df = pd.DataFrame()
|
|
if yparam2 == 'None':
|
|
yparam2 = 'power'
|
|
df[xparam] = []
|
|
df[yparam1] = []
|
|
df[yparam2] = []
|
|
df['distance'] = []
|
|
df['spm'] = []
|
|
for workout in therows:
|
|
f1 = workout.csvfilename
|
|
|
|
try:
|
|
rowdata = dataprep(rrdata(csvfile=f1).df)
|
|
|
|
rowdata = pd.DataFrame({xparam: rowdata[xparam],
|
|
yparam1: rowdata[yparam1],
|
|
yparam2: rowdata[yparam2],
|
|
'distance': rowdata['distance'],
|
|
'spm': rowdata['spm'],
|
|
}
|
|
)
|
|
if workout.forceunit == 'lbs':
|
|
try:
|
|
rowdata['peakforce'] *= lbstoN
|
|
except KeyError:
|
|
pass
|
|
|
|
try:
|
|
rowdata['averageforce'] *= lbstoN
|
|
except KeyError:
|
|
pass
|
|
|
|
df = pd.concat([df, rowdata], ignore_index=True)
|
|
except IOError:
|
|
try:
|
|
rowdata = dataprep(rrdata(csvfile=f1 + '.gz').df)
|
|
rowdata = pd.DataFrame({xparam: rowdata[xparam],
|
|
yparam1: rowdata[yparam1],
|
|
yparam2: rowdata[yparam2],
|
|
'distance': rowdata['distance'],
|
|
'spm': rowdata['spm'],
|
|
}
|
|
)
|
|
if workout.forceunit == 'lbs':
|
|
try:
|
|
rowdata['peakforce'] *= lbstoN
|
|
except KeyError:
|
|
pass
|
|
|
|
try:
|
|
rowdata['averageforce'] *= lbstoN
|
|
except KeyError:
|
|
pass
|
|
df = pd.concat([df, rowdata], ignore_index=True)
|
|
except IOError:
|
|
pass
|
|
|
|
return df
|
|
|
|
# data fusion
|
|
|
|
|
|
def datafusion(id1, id2, columns, offset):
|
|
workout1 = Workout.objects.get(id=id1)
|
|
workout2 = Workout.objects.get(id=id2)
|
|
|
|
df1, w1 = getrowdata_db(id=id1)
|
|
df1 = df1.drop([ # 'cumdist',
|
|
'hr_ut2',
|
|
'hr_ut1',
|
|
'hr_at',
|
|
'hr_tr',
|
|
'hr_an',
|
|
'hr_max',
|
|
'ftime',
|
|
'fpace',
|
|
'workoutid',
|
|
'id'],
|
|
1, errors='ignore')
|
|
|
|
# Add coordinates to DataFrame
|
|
latitude, longitude = get_latlon(id1)
|
|
|
|
df1[' latitude'] = latitude
|
|
df1[' longitude'] = longitude
|
|
|
|
df2 = getsmallrowdata_db(['time'] + columns, ids=[id2], doclean=False)
|
|
|
|
forceunit = 'N'
|
|
|
|
offsetmillisecs = offset.seconds * 1000 + offset.microseconds / 1000.
|
|
offsetmillisecs += offset.days * (3600 * 24 * 1000)
|
|
df2['time'] = df2['time'] + offsetmillisecs
|
|
|
|
keep1 = {c: c for c in set(df1.columns)}
|
|
|
|
for c in columns:
|
|
keep1.pop(c)
|
|
|
|
for c in df1.columns:
|
|
if not c in keep1:
|
|
df1 = df1.drop(c, 1, errors='ignore')
|
|
|
|
df = pd.concat([df1, df2], ignore_index=True)
|
|
df = df.sort_values(['time'])
|
|
df = df.interpolate(method='linear', axis=0, limit_direction='both',
|
|
limit=10)
|
|
df.fillna(method='bfill', inplace=True)
|
|
|
|
# Some new stuff to try out
|
|
df = df.groupby('time', axis=0).mean()
|
|
df['time'] = df.index
|
|
df.reset_index(drop=True, inplace=True)
|
|
|
|
df['time'] = df['time'] / 1000.
|
|
df['pace'] = df['pace'] / 1000.
|
|
df['cum_dist'] = df['cumdist']
|
|
|
|
return df, forceunit
|
|
|
|
|
|
def fix_newtons(id=0, limit=3000):
|
|
# rowdata,row = getrowdata_db(id=id,doclean=False,convertnewtons=False)
|
|
rowdata = getsmallrowdata_db(['peakforce'], ids=[id], doclean=False)
|
|
try:
|
|
#avgforce = rowdata['averageforce']
|
|
peakforce = rowdata['peakforce']
|
|
if peakforce.mean() > limit:
|
|
w = Workout.objects.get(id=id)
|
|
print("fixing ", id)
|
|
rowdata = rdata(w.csvfilename)
|
|
if rowdata and len(rowdata.df):
|
|
update_strokedata(w.id, rowdata.df)
|
|
except KeyError:
|
|
pass
|
|
|
|
def remove_invalid_columns(df):
|
|
for c in df.columns:
|
|
if not c in allowedcolumns:
|
|
df.drop(labels=c,axis=1,inplace=True)
|
|
|
|
return df
|
|
|
|
def add_efficiency(id=0):
|
|
rowdata, row = getrowdata_db(id=id,
|
|
doclean=False,
|
|
convertnewtons=False,
|
|
checkefficiency=False)
|
|
power = rowdata['power']
|
|
pace = rowdata['pace'] / 1.0e3
|
|
velo = 500. / pace
|
|
ergpw = 2.8 * velo**3
|
|
efficiency = 100. * ergpw / power
|
|
|
|
efficiency = efficiency.replace([-np.inf, np.inf], np.nan)
|
|
efficiency.fillna(method='ffill')
|
|
rowdata['efficiency'] = efficiency
|
|
|
|
rowdata = remove_invalid_columns(rowdata)
|
|
rowdata = rowdata.replace([-np.inf, np.inf], np.nan)
|
|
rowdata = rowdata.fillna(method='ffill')
|
|
|
|
delete_strokedata(id)
|
|
|
|
if id != 0:
|
|
rowdata['workoutid'] = id
|
|
filename = 'media/strokedata_{id}.parquet.gz'.format(id=id)
|
|
df = dd.from_pandas(rowdata,npartitions=1)
|
|
df.to_parquet(filename,engine='fastparquet',compression='GZIP')
|
|
|
|
return rowdata
|
|
|
|
# This is the main routine.
|
|
# it reindexes, sorts, filters, and smooths the data, then
|
|
# saves it to the stroke_data table in the database
|
|
# Takes a rowingdata object's DataFrame as input
|
|
|
|
|
|
def dataprep(rowdatadf, id=0, bands=True, barchart=True, otwpower=True,
|
|
empower=True, inboard=0.88, forceunit='lbs'):
|
|
|
|
if rowdatadf.empty:
|
|
return 0
|
|
|
|
#rowdatadf.set_index([range(len(rowdatadf))], inplace=True)
|
|
t = rowdatadf.loc[:, 'TimeStamp (sec)']
|
|
t = pd.Series(t - rowdatadf.loc[:, 'TimeStamp (sec)'].iloc[0])
|
|
|
|
row_index = rowdatadf.loc[:, ' Stroke500mPace (sec/500m)'] > 3000
|
|
rowdatadf.loc[row_index, ' Stroke500mPace (sec/500m)'] = 3000.
|
|
|
|
p = rowdatadf.loc[:, ' Stroke500mPace (sec/500m)']
|
|
try:
|
|
velo = rowdatadf.loc[:,' AverageBoatSpeed (m/s)']
|
|
except KeyError:
|
|
velo = 500./p
|
|
|
|
hr = rowdatadf.loc[:, ' HRCur (bpm)']
|
|
spm = rowdatadf.loc[:, ' Cadence (stokes/min)']
|
|
cumdist = rowdatadf.loc[:, 'cum_dist']
|
|
power = rowdatadf.loc[:, ' Power (watts)']
|
|
averageforce = rowdatadf.loc[:, ' AverageDriveForce (lbs)']
|
|
drivelength = rowdatadf.loc[:, ' DriveLength (meters)']
|
|
try:
|
|
workoutstate = rowdatadf.loc[:, ' WorkoutState']
|
|
except KeyError:
|
|
workoutstate = 0 * hr
|
|
|
|
peakforce = rowdatadf.loc[:, ' PeakDriveForce (lbs)']
|
|
|
|
forceratio = averageforce / peakforce
|
|
forceratio = forceratio.fillna(value=0)
|
|
|
|
try:
|
|
drivetime = rowdatadf.loc[:, ' DriveTime (ms)']
|
|
recoverytime = rowdatadf.loc[:, ' StrokeRecoveryTime (ms)']
|
|
rhythm = 100. * drivetime / (recoverytime + drivetime)
|
|
rhythm = rhythm.fillna(value=0)
|
|
except:
|
|
rhythm = 0.0 * forceratio
|
|
|
|
f = rowdatadf['TimeStamp (sec)'].diff().mean()
|
|
if f != 0 and not np.isinf(f):
|
|
try:
|
|
windowsize = 2 * (int(10. / (f))) + 1
|
|
except ValueError:
|
|
windowsize = 1
|
|
else:
|
|
windowsize = 1
|
|
if windowsize <= 3:
|
|
windowsize = 5
|
|
|
|
if windowsize > 3 and windowsize < len(hr):
|
|
spm = savgol_filter(spm, windowsize, 3)
|
|
hr = savgol_filter(hr, windowsize, 3)
|
|
drivelength = savgol_filter(drivelength, windowsize, 3)
|
|
forceratio = savgol_filter(forceratio, windowsize, 3)
|
|
|
|
try:
|
|
t2 = t.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
|
|
except TypeError:
|
|
t2 = 0 * t
|
|
|
|
p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
|
|
|
|
try:
|
|
drivespeed = drivelength / rowdatadf[' DriveTime (ms)'] * 1.0e3
|
|
except TypeError:
|
|
drivespeed = 0.0 * rowdatadf['TimeStamp (sec)']
|
|
|
|
drivespeed = drivespeed.fillna(value=0)
|
|
|
|
try:
|
|
driveenergy = rowdatadf['driveenergy']
|
|
except KeyError:
|
|
if forceunit == 'lbs':
|
|
driveenergy = drivelength * averageforce * lbstoN
|
|
else:
|
|
drivenergy = drivelength * averageforce
|
|
|
|
if forceunit == 'lbs':
|
|
averageforce *= lbstoN
|
|
peakforce *= lbstoN
|
|
|
|
powerhr = 60.*power/hr
|
|
powerhr = powerhr.fillna(value=0)
|
|
|
|
if driveenergy.mean() == 0 and driveenergy.std() == 0:
|
|
driveenergy = 0*driveenergy+100
|
|
|
|
distance = rowdatadf.loc[:, 'cum_dist']
|
|
velo = 500. / p
|
|
|
|
distanceperstroke = 60. * velo / spm
|
|
|
|
|
|
data = DataFrame(
|
|
dict(
|
|
time=t * 1e3,
|
|
hr=hr,
|
|
pace=p * 1e3,
|
|
spm=spm,
|
|
velo=velo,
|
|
cumdist=cumdist,
|
|
ftime=niceformat(t2),
|
|
fpace=nicepaceformat(p2),
|
|
driveenergy=driveenergy,
|
|
power=power,
|
|
workoutstate=workoutstate,
|
|
averageforce=averageforce,
|
|
drivelength=drivelength,
|
|
peakforce=peakforce,
|
|
forceratio=forceratio,
|
|
distance=distance,
|
|
drivespeed=drivespeed,
|
|
rhythm=rhythm,
|
|
distanceperstroke=distanceperstroke,
|
|
# powerhr=powerhr,
|
|
)
|
|
)
|
|
|
|
|
|
if bands:
|
|
# HR bands
|
|
data['hr_ut2'] = rowdatadf.loc[:, 'hr_ut2']
|
|
data['hr_ut1'] = rowdatadf.loc[:, 'hr_ut1']
|
|
data['hr_at'] = rowdatadf.loc[:, 'hr_at']
|
|
data['hr_tr'] = rowdatadf.loc[:, 'hr_tr']
|
|
data['hr_an'] = rowdatadf.loc[:, 'hr_an']
|
|
data['hr_max'] = rowdatadf.loc[:, 'hr_max']
|
|
data['hr_bottom'] = 0.0 * data['hr']
|
|
|
|
try:
|
|
tel = rowdatadf.loc[:, ' ElapsedTime (sec)']
|
|
except KeyError:
|
|
rowdatadf[' ElapsedTime (sec)'] = rowdatadf['TimeStamp (sec)']
|
|
|
|
if empower:
|
|
try:
|
|
wash = rowdatadf.loc[:, 'wash']
|
|
except KeyError:
|
|
wash = 0 * power
|
|
|
|
try:
|
|
catch = rowdatadf.loc[:, 'catch']
|
|
except KeyError:
|
|
catch = 0 * power
|
|
|
|
try:
|
|
finish = rowdatadf.loc[:, 'finish']
|
|
except KeyError:
|
|
finish = 0 * power
|
|
|
|
try:
|
|
peakforceangle = rowdatadf.loc[:, 'peakforceangle']
|
|
except KeyError:
|
|
peakforceangle = 0 * power
|
|
|
|
if data['driveenergy'].mean() == 0:
|
|
try:
|
|
driveenergy = rowdatadf.loc[:, 'driveenergy']
|
|
except KeyError:
|
|
driveenergy = power * 60 / spm
|
|
else:
|
|
driveenergy = data['driveenergy']
|
|
|
|
arclength = (inboard - 0.05) * (np.radians(finish) - np.radians(catch))
|
|
if arclength.mean() > 0:
|
|
drivelength = arclength
|
|
elif drivelength.mean() == 0:
|
|
drivelength = driveenergy / (averageforce * 4.44822)
|
|
|
|
try:
|
|
slip = rowdatadf.loc[:, 'slip']
|
|
except KeyError:
|
|
slip = 0 * power
|
|
|
|
try:
|
|
totalangle = finish - catch
|
|
effectiveangle = finish - wash - catch - slip
|
|
except ValueError:
|
|
totalangle = 0 * power
|
|
effectiveangle = 0 * power
|
|
|
|
if windowsize > 3 and windowsize < len(slip):
|
|
try:
|
|
wash = savgol_filter(wash, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
try:
|
|
slip = savgol_filter(slip, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
try:
|
|
catch = savgol_filter(catch, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
try:
|
|
finish = savgol_filter(finish, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
try:
|
|
peakforceangle = savgol_filter(peakforceangle, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
try:
|
|
driveenergy = savgol_filter(driveenergy, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
try:
|
|
drivelength = savgol_filter(drivelength, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
try:
|
|
totalangle = savgol_filter(totalangle, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
try:
|
|
effectiveangle = savgol_filter(effectiveangle, windowsize, 3)
|
|
except TypeError:
|
|
pass
|
|
|
|
velo = 500. / p
|
|
|
|
ergpw = 2.8 * velo**3
|
|
efficiency = 100. * ergpw / power
|
|
|
|
efficiency = efficiency.replace([-np.inf, np.inf], np.nan)
|
|
efficiency.fillna(method='ffill')
|
|
|
|
try:
|
|
data['wash'] = wash
|
|
data['catch'] = catch
|
|
data['slip'] = slip
|
|
data['finish'] = finish
|
|
data['peakforceangle'] = peakforceangle
|
|
data['driveenergy'] = driveenergy
|
|
data['drivelength'] = drivelength
|
|
data['totalangle'] = totalangle
|
|
data['effectiveangle'] = effectiveangle
|
|
data['efficiency'] = efficiency
|
|
except ValueError:
|
|
pass
|
|
|
|
if otwpower:
|
|
try:
|
|
nowindpace = rowdatadf.loc[:, 'nowindpace']
|
|
except KeyError:
|
|
nowindpace = p
|
|
try:
|
|
equivergpower = rowdatadf.loc[:, 'equivergpower']
|
|
except KeyError:
|
|
equivergpower = 0 * p + 50.
|
|
|
|
nowindpace2 = nowindpace.apply(lambda x: timedeltaconv(x))
|
|
ergvelo = (equivergpower / 2.8)**(1. / 3.)
|
|
|
|
ergpace = 500. / ergvelo
|
|
ergpace[ergpace == np.inf] = 240.
|
|
ergpace2 = ergpace.apply(lambda x: timedeltaconv(x))
|
|
|
|
data['ergpace'] = ergpace * 1e3
|
|
data['nowindpace'] = nowindpace * 1e3
|
|
data['equivergpower'] = equivergpower
|
|
data['fergpace'] = nicepaceformat(ergpace2)
|
|
data['fnowindpace'] = nicepaceformat(nowindpace2)
|
|
|
|
data = data.replace([-np.inf, np.inf], np.nan)
|
|
data = data.fillna(method='ffill')
|
|
|
|
# write data if id given
|
|
if id != 0:
|
|
data['workoutid'] = id
|
|
data.fillna(0,inplace=True)
|
|
for k, v in dtypes.items():
|
|
try:
|
|
data[k] = data[k].astype(v)
|
|
except KeyError:
|
|
pass
|
|
|
|
|
|
filename = 'media/strokedata_{id}.parquet.gz'.format(id=id)
|
|
df = dd.from_pandas(data,npartitions=1)
|
|
df.to_parquet(filename,engine='fastparquet',compression='GZIP')
|
|
|
|
|
|
return data
|
|
|
|
|
|
def workout_trimp(w):
|
|
r = w.user
|
|
|
|
if w.trimp > 0:
|
|
return w.trimp,w.hrtss
|
|
|
|
r = w.user
|
|
ftp = float(r.ftp)
|
|
if w.workouttype in otwtypes:
|
|
ftp = ftp*(100.-r.otwslack)/100.
|
|
|
|
if r.hrftp == 0:
|
|
hrftp = (r.an+r.tr)/2.
|
|
r.hrftp = int(hrftp)
|
|
r.save()
|
|
|
|
|
|
if w.averagehr is None:
|
|
rowdata = rdata(w.csvfilename)
|
|
|
|
try:
|
|
avghr = rowdata.df[' HRCur (bpm)'].mean()
|
|
maxhr = rowdata.df[' HRCur (bpm)'].max()
|
|
except KeyError:
|
|
avghr = None
|
|
maxhr = None
|
|
|
|
w.averagehr = avghr
|
|
w.maxhr = maxhr
|
|
w.save()
|
|
|
|
job = myqueue(
|
|
queuehigh,
|
|
handle_calctrimp,
|
|
w.id,
|
|
w.csvfilename,
|
|
ftp,
|
|
r.sex,
|
|
r.hrftp,
|
|
r.max,
|
|
r.rest)
|
|
|
|
return 0,0
|
|
|
|
def workout_rscore(w):
|
|
if w.rscore > 0:
|
|
return w.rscore,w.normp
|
|
|
|
r = w.user
|
|
ftp = float(r.ftp)
|
|
if w.workouttype in otwtypes:
|
|
ftp = ftp*(100.-r.otwslack)/100.
|
|
|
|
if r.hrftp == 0:
|
|
hrftp = (r.an+r.tr)/2.
|
|
r.hrftp = int(hrftp)
|
|
r.save()
|
|
|
|
|
|
|
|
job = myqueue(
|
|
queuehigh,
|
|
handle_calctrimp,
|
|
w.id,
|
|
w.csvfilename,
|
|
ftp,
|
|
r.sex,
|
|
r.hrftp,
|
|
r.max,
|
|
r.rest)
|
|
|
|
return 0,0
|
|
|
|
def workout_normv(w,pp=4.0):
|
|
if w.normv > 0:
|
|
return w.normv,w.normw
|
|
|
|
r = w.user
|
|
ftp = float(r.ftp)
|
|
if w.workouttype in otwtypes:
|
|
ftp = ftp*(100.-r.otwslack)/100.
|
|
|
|
if r.hrftp == 0:
|
|
hrftp = (r.an+r.tr)/2.
|
|
r.hrftp = int(hrftp)
|
|
r.save()
|
|
|
|
|
|
|
|
job = myqueue(
|
|
queuehigh,
|
|
handle_calctrimp,
|
|
w.id,
|
|
w.csvfilename,
|
|
ftp,
|
|
r.sex,
|
|
r.hrftp,
|
|
r.max,
|
|
r.rest)
|
|
|
|
return 0,0
|