Merge branch 'develop' into feature/restapi

rowers/dataprep.py

@@ -8,6 +8,7 @@ from pandas import DataFrame,Series
 
 import pandas as pd
 import numpy as np
+import itertools
 
 from django.conf import settings
 from sqlalchemy import create_engine
@@ -84,9 +85,33 @@ def rdata(file,rower=rrower()):
 
 def getrowdata_db(id=0):
     data = read_df_sql(id)
+    data['pace'] = data['pace']/1.0e6
+    data['ergpace'] = data['ergpace']/1.0e6
+    data['nowindpace'] = data['nowindpace']/1.0e6
+    data['time'] = data['time']/1.0e6
+    data['x_right'] = data['x_right']/1.0e6
     if data.empty:
         rowdata,row = getrowdata(id=id)
-        data = dataprep(rowdata.df,id=id,bands=True,barchart=True,otwpower=True)
+        if rowdata:
+            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)
+
+    return data,row
+
+def getsmallrowdata_db(xparam,yparam1,yparam2,ids=[]):
+    if yparam2 == 'None':
+        yparam2 = yparam1
+    prepmultipledata(ids)
+    data = read_cols_df_sql(ids,xparam,yparam1,yparam2)
+    if xparam == 'time':
+        data['time'] = data['time']/1.0e6
+    if yparam1 == 'pace':
+        data['pace'] = data['pace']/1.0e6
+    if yparam2 == 'pace':
+        data['pace'] = data['pace']/1.0e6
 
     return data
         
@@ -110,7 +135,34 @@ def getrowdata(id=0):
 
     return rowdata,row
 
-# temporary
+def prepmultipledata(ids):
+    query = sa.text('SELECT DISTINCT workoutid FROM strokedata')
+    with engine.connect() as conn, conn.begin():
+        res = conn.execute(query)
+        res = list(itertools.chain.from_iterable(res.fetchall()))
+
+    res = list(set(ids)-set(res))
+
+    for id in res:
+        rowdata,row = getrowdata(id=id)
+        if rowdata:
+            data = dataprep(rowdata.df,id=id,bands=True,barchart=True,otwpower=True)    
+    return res
+
+def read_cols_df_sql(ids,col1,col2,col3):
+    columns = list(set((col1,col2,col3,'distance','spm')))
+    cls = ''
+    for column in columns:
+        cls += column+', '
+    cls = cls[:-2]
+    query = sa.text('SELECT {columns} FROM strokedata WHERE workoutid IN {ids}'.format(
+        columns = cls,
+        ids = tuple(ids),
+        ))
+    df = pd.read_sql_query(query,engine)
+    return df
+        
+
 def read_df_sql(id):
     df = pd.read_sql_query(sa.text('SELECT * FROM strokedata WHERE workoutid={id}'.format(
         id=id)), engine)
@@ -118,6 +170,37 @@ def read_df_sql(id):
 
 
 
+
+
+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(f1).df)
+        
+            rowdata = pd.DataFrame({xparam: rowdata[xparam],
+                                    yparam1: rowdata[yparam1],
+                                    yparam2: rowdata[yparam2],
+                                    'distance': rowdata['distance'],
+                                    'spm': rowdata['spm'],
+            }
+            )
+            df = pd.concat([df,rowdata],ignore_index=True)
+        except IOError:
+            pass
+
+    return df
+        
+        
 def dataprep(rowdatadf,id=0,bands=False,barchart=False,otwpower=False):
     rowdatadf.set_index([range(len(rowdatadf))],inplace=True)
     t = rowdatadf.ix[:,'TimeStamp (sec)']
@@ -134,8 +217,11 @@ def dataprep(rowdatadf,id=0,bands=False,barchart=False,otwpower=False):
     power = rowdatadf.ix[:,' Power (watts)']
     averageforce = rowdatadf.ix[:,' AverageDriveForce (lbs)']
     drivelength = rowdatadf.ix[:,' DriveLength (meters)']
-
-
+    try:
+        workoutstate = rowdatadf.ix[:,' WorkoutState']
+    except KeyError:
+        workoutstate = 0*hr
+        
     peakforce = rowdatadf.ix[:,' PeakDriveForce (lbs)']
 
     forceratio = averageforce/peakforce
@@ -152,7 +238,10 @@ def dataprep(rowdatadf,id=0,bands=False,barchart=False,otwpower=False):
 	drivelength = savgol_filter(drivelength,windowsize,3)
         forceratio = savgol_filter(forceratio,windowsize,3)
 
-    t2 = t.fillna(method='ffill').apply(lambda x: timedeltaconv(x))
+    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))
@@ -179,6 +268,7 @@ def dataprep(rowdatadf,id=0,bands=False,barchart=False,otwpower=False):
 	    fpace = nicepaceformat(p2),
 	    driveenergy=driveenergy,
 	    power=power,
+            workoutstate=workoutstate,
 	    averageforce=averageforce,
 	    drivelength=drivelength,
 	    peakforce=peakforce,