373 lines
9.7 KiB
Python
373 lines
9.7 KiB
Python
from rowers.models import Workout, User, Rower
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from rowingdata import rowingdata as rrdata
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from rowingdata import rower as rrower
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from rowingdata import main as rmain
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from pandas import DataFrame,Series
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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 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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user = settings.DATABASES['default']['USER']
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password = settings.DATABASES['default']['PASSWORD']
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database_name = settings.DATABASES['default']['NAME']
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host = settings.DATABASES['default']['HOST']
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port = settings.DATABASES['default']['PORT']
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database_url = 'mysql://{user}:{password}@{host}:{port}/{database_name}'.format(
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user=user,
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password=password,
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database_name=database_name,
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host=host,
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port=port,
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)
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if settings.DEBUG or user=='':
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# database_url = 'sqlite:///db.sqlite3'
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database_url = 'sqlite:///'+database_name
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from scipy.signal import savgol_filter
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import datetime
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def niceformat(values):
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out = []
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for v in values:
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formattedv = strfdelta(v)
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out.append(formattedv)
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return out
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def strfdelta(tdelta):
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try:
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minutes,seconds = divmod(tdelta.seconds,60)
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tenths = int(tdelta.microseconds/1e5)
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except AttributeError:
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minutes,seconds = divmod(tdelta.view(np.int64),60e9)
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seconds,rest = divmod(seconds,1e9)
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tenths = int(rest/1e8)
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res = "{minutes:0>2}:{seconds:0>2}.{tenths:0>1}".format(
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minutes=minutes,
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seconds=seconds,
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tenths=tenths,
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)
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return res
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def nicepaceformat(values):
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out = []
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for v in values:
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formattedv = strfdelta(v)
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out.append(formattedv)
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return out
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def timedeltaconv(x):
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dt = datetime.timedelta(seconds=x)
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return dt
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def rdata(file,rower=rrower()):
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try:
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res = rrdata(file,rower=rower)
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except IOError:
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res = 0
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return res
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def delete_strokedata(id):
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engine = create_engine(database_url, echo=False)
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query = sa.text('DELETE FROM strokedata * WHERE workoutid={id};'.format(
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id=id,
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))
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with engine.connect() as conn, conn.begin():
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try:
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result = conn.execute(query)
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except:
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print "Database Locked"
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conn.close()
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engine.dispose()
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def testdata(time,distance,pace,spm):
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t1 = np.issubdtype(time,np.number)
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t2 = np.issubdtype(distance,np.number)
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t3 = np.issubdtype(pace,np.number)
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t4 = np.issubdtype(spm,np.number)
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return t1 and t2 and t3 and t4
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def getrowdata_db(id=0):
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data = read_df_sql(id)
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# data['pace'] = data['pace']/1.0e6
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# data['ergpace'] = data['ergpace']/1.0e6
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# data['nowindpace'] = data['nowindpace']/1.0e6
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# data['time'] = data['time']/1.0e6
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data['x_right'] = data['x_right']/1.0e6
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if data.empty:
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rowdata,row = getrowdata(id=id)
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if rowdata:
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data = dataprep(rowdata.df,id=id,bands=True,barchart=True,otwpower=True)
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else:
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data = pd.DataFrame() # returning empty dataframe
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else:
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row = Workout.objects.get(id=id)
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return data,row
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def getsmallrowdata_db(columns,ids=[]):
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prepmultipledata(ids)
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data = read_cols_df_sql(ids,columns)
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print columns
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return data
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def getrowdata(id=0):
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# check if valid ID exists (workout exists)
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row = Workout.objects.get(id=id)
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f1 = row.csvfilename
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# get user
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r = row.user
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u = r.user
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rr = rrower(hrmax=r.max,hrut2=r.ut2,
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hrut1=r.ut1,hrat=r.at,
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hrtr=r.tr,hran=r.an,ftp=r.ftp)
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rowdata = rdata(f1,rower=rr)
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return rowdata,row
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def prepmultipledata(ids,verbose=False):
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query = sa.text('SELECT DISTINCT workoutid FROM strokedata')
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engine = create_engine(database_url, echo=False)
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with engine.connect() as conn, conn.begin():
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res = conn.execute(query)
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res = list(itertools.chain.from_iterable(res.fetchall()))
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conn.close()
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engine.dispose()
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res = list(set(ids)-set(res))
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for id in res:
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rowdata,row = getrowdata(id=id)
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if verbose:
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print id
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if rowdata:
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data = dataprep(rowdata.df,id=id,bands=True,barchart=True,otwpower=True)
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return res
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def read_cols_df_sql(ids,columns):
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columns = list(columns)+['distance','spm']
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columns = [x for x in columns if x != 'None']
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columns = list(set(columns))
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cls = ''
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engine = create_engine(database_url, echo=False)
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for column in columns:
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cls += column+', '
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cls = cls[:-2]
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if len(ids) == 0:
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query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid=0'.format(
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columns = cls,
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))
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elif len(ids) == 1:
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query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid={id}'.format(
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id = ids[0],
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columns = cls,
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))
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else:
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query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid IN {ids}'.format(
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columns = cls,
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ids = tuple(ids),
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))
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df = pd.read_sql_query(query,engine)
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engine.dispose()
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return df
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def read_df_sql(id):
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engine = create_engine(database_url, echo=False)
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df = pd.read_sql_query(sa.text('SELECT * FROM strokedata WHERE workoutid={id}'.format(
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id=id)), engine)
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engine.dispose()
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return df
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def smalldataprep(therows,xparam,yparam1,yparam2):
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df = pd.DataFrame()
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if yparam2 == 'None':
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yparam2 = 'power'
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df[xparam] = []
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df[yparam1] = []
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df[yparam2] = []
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df['distance'] = []
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df['spm'] = []
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for workout in therows:
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f1 = workout.csvfilename
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try:
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rowdata = dataprep(rrdata(f1).df)
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rowdata = pd.DataFrame({xparam: rowdata[xparam],
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yparam1: rowdata[yparam1],
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yparam2: rowdata[yparam2],
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'distance': rowdata['distance'],
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'spm': rowdata['spm'],
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}
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)
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df = pd.concat([df,rowdata],ignore_index=True)
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except IOError:
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pass
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return df
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def dataprep(rowdatadf,id=0,bands=False,barchart=False,otwpower=False):
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rowdatadf.set_index([range(len(rowdatadf))],inplace=True)
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t = rowdatadf.ix[:,'TimeStamp (sec)']
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t = pd.Series(t-rowdatadf.ix[0,'TimeStamp (sec)'])
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row_index = rowdatadf.ix[:,' Stroke500mPace (sec/500m)'] > 3000
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rowdatadf.loc[row_index,' Stroke500mPace (sec/500m)'] = 3000.
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p = rowdatadf.ix[:,' Stroke500mPace (sec/500m)']
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hr = rowdatadf.ix[:,' HRCur (bpm)']
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spm = rowdatadf.ix[:,' Cadence (stokes/min)']
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cumdist = rowdatadf.ix[:,'cum_dist']
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power = rowdatadf.ix[:,' Power (watts)']
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averageforce = rowdatadf.ix[:,' AverageDriveForce (lbs)']
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drivelength = rowdatadf.ix[:,' DriveLength (meters)']
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try:
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workoutstate = rowdatadf.ix[:,' WorkoutState']
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except KeyError:
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workoutstate = 0*hr
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peakforce = rowdatadf.ix[:,' PeakDriveForce (lbs)']
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forceratio = averageforce/peakforce
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forceratio = forceratio.fillna(value=0)
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f = rowdatadf['TimeStamp (sec)'].diff().mean()
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windowsize = 2*(int(10./(f)))+1
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if windowsize <= 3:
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windowsize = 5
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if windowsize > 3:
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spm = savgol_filter(spm,windowsize,3)
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hr = savgol_filter(hr,windowsize,3)
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drivelength = savgol_filter(drivelength,windowsize,3)
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forceratio = savgol_filter(forceratio,windowsize,3)
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try:
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t2 = t.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
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except TypeError:
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t2 = 0*t
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p2 = p.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
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drivespeed = drivelength/rowdatadf[' DriveTime (ms)']*1.0e3
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drivespeed = drivespeed.fillna(value=0)
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driveenergy = drivelength*averageforce*4.44822
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distance = rowdatadf.ix[:,'cum_dist']
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data = DataFrame(
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dict(
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time = t*1e3,
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hr = hr,
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pace = p*1e3,
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spm = spm,
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cumdist = cumdist,
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ftime = niceformat(t2),
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fpace = nicepaceformat(p2),
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driveenergy=driveenergy,
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power=power,
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workoutstate=workoutstate,
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averageforce=averageforce,
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drivelength=drivelength,
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peakforce=peakforce,
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forceratio=forceratio,
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distance=distance,
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drivespeed=drivespeed,
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)
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)
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if bands:
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# HR bands
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data['hr_ut2'] = rowdatadf.ix[:,'hr_ut2']
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data['hr_ut1'] = rowdatadf.ix[:,'hr_ut1']
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data['hr_at'] = rowdatadf.ix[:,'hr_at']
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data['hr_tr'] = rowdatadf.ix[:,'hr_tr']
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data['hr_an'] = rowdatadf.ix[:,'hr_an']
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data['hr_max'] = rowdatadf.ix[:,'hr_max']
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data['hr_bottom'] = 0.0*data['hr']
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if barchart:
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# time increments for bar chart
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time_increments = rowdatadf.ix[:,' ElapsedTime (sec)'].diff()
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time_increments[0] = time_increments[1]
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time_increments = 0.5*time_increments+0.5*np.abs(time_increments)
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x_right = (t2+time_increments.apply(lambda x:timedeltaconv(x)))
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data['x_right'] = x_right
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if otwpower:
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try:
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nowindpace = rowdatadf.ix[:,'nowindpace']
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except KeyError:
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nowindpace = p
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try:
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equivergpower = rowdatadf.ix[:,'equivergpower']
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except KeyError:
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equivergpower = 0*p+50.
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nowindpace = nowindpace.apply(lambda x: timedeltaconv(x))
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ergvelo = (equivergpower/2.8)**(1./3.)
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ergpace = 500./ergvelo
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ergpace[ergpace == np.inf] = 240.
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ergpace = ergpace.apply(lambda x: timedeltaconv(x))
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data['ergpace'] = ergpace
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data['nowindpace'] = nowindpace
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data['equivergpower'] = equivergpower
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data['fergpace'] = nicepaceformat(ergpace)
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data['fnowindpace'] = nicepaceformat(nowindpace)
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data = data.replace([-np.inf,np.inf],np.nan)
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data = data.fillna(method='ffill')
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# write data if id given
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if id != 0:
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data['workoutid'] = id
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engine = create_engine(database_url, echo=False)
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with engine.connect() as conn, conn.begin():
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data.to_sql('strokedata',engine,if_exists='append',index=False)
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conn.close()
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engine.dispose()
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return data
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