From 8c2c995aab912c81f91407be9162f2c611bb8a02 Mon Sep 17 00:00:00 2001
From: Sander Roosendaal <roosendaalsander@gmail.com>
Date: Mon, 25 Jun 2018 22:08:47 +0200
Subject: [PATCH] seems to auto import well

---
 rowers/stravastuff.py |  1 +
 rowers/tasks.py       | 38 +++++++++++++++++++++++++++++---------
 2 files changed, 30 insertions(+), 9 deletions(-)

diff --git a/rowers/stravastuff.py b/rowers/stravastuff.py
index 8674dc4f..b1ee7d3c 100644
--- a/rowers/stravastuff.py
+++ b/rowers/stravastuff.py
@@ -150,6 +150,7 @@ def add_stroke_data(user,stravaid,workoutid,startdatetime,csvfilename):
 
     starttimeunix = arrow.get(startdatetime).timestamp
 
+    
     job = myqueue(queue,
                   handle_strava_import_stroke_data,
                   r.stravatoken,
diff --git a/rowers/tasks.py b/rowers/tasks.py
index bbbcdd04..1cad32e8 100644
--- a/rowers/tasks.py
+++ b/rowers/tasks.py
@@ -8,6 +8,7 @@ import numpy as np
 import re
 
 from scipy import optimize
+from scipy.signal import savgol_filter
 
 import rowingdata
 
@@ -20,6 +21,7 @@ import datetime
 import pytz
 import iso8601
 
+
 from matplotlib.backends.backend_agg import FigureCanvas
 #from matplotlib.backends.backend_cairo import FigureCanvasCairo as FigureCanvas
 import matplotlib.pyplot as plt
@@ -108,25 +110,31 @@ def handle_strava_import_stroke_data(stravatoken,
     distancejson = requests.get(url,headers=headers)
     url = "https://www.strava.com/api/v3/activities/"+str(stravaid)+"/streams/latlng?resolution="+fetchresolution+"&series_type="+series_type
     latlongjson = requests.get(url,headers=headers)
-
+    url = "https://www.strava.com/api/v3/activities/"+str(stravaid)+"/streams/watts?resolution="+fetchresolution+"&series_type="+series_type
+    wattsjson = requests.get(url,headers=headers)
 
     try:
 	t = np.array(timejson.json()[0]['data'])
 	nr_rows = len(t)
 	d =  np.array(distancejson.json()[1]['data'])
         if nr_rows == 0:
-            return (0,"Error: Time data had zero length")
+            return 0
     except IndexError:
         d = 0*t
         # return (0,"Error: No Distance information in the Strava data")
     except KeyError:
-        return (0,"something went wrong with the Strava import")
+        return 0
 
     try:
 	spm = np.array(spmjson.json()[1]['data'])
     except:
 	spm = np.zeros(nr_rows)
 
+    try:
+        watts = np.array(wattsjson.json()[1]['data'])
+    except:
+        watts = np.zeros(nr_rows)
+
     try:
 	hr = np.array(hrjson.json()[1]['data'])
     except IndexError:
@@ -141,10 +149,17 @@ def handle_strava_import_stroke_data(stravatoken,
     except KeyError:
         velo = np.zeros(nr_rows)
 
-    dt = np.diff(t).mean()
-    wsize = round(5./dt)
+    f = np.diff(t).mean()
+    if f != 0:
+        windowsize = 2*(int(10./(f)))+1
+    else:
+        windowsize = 1
 
-    velo2 = ewmovingaverage(velo,wsize)
+    if windowsize > 3 and windowsize < len(velo):
+        velo2 = savgol_filter(velo,windowsize,3)
+    else:
+        velo2 = velo
+        
     coords = np.array(latlongjson.json()[0]['data'])
     try:
 	lat = coords[:,0]
@@ -162,8 +177,10 @@ def handle_strava_import_stroke_data(stravatoken,
     pace = 500./(1.0*velo2)
     pace[np.isinf(pace)] = 0.0
 
-    unixtime = starttimeunix+10*t
+    unixtime = starttimeunix+t
 
+    strokedistance = 60.*velo2/spm
+    
     nr_strokes = len(t)
 
     df = pd.DataFrame({'TimeStamp (sec)':unixtime,
@@ -178,15 +195,18 @@ def handle_strava_import_stroke_data(stravatoken,
                        'cum_dist':d,
 	               ' DragFactor':np.zeros(nr_strokes),
 	               ' DriveLength (meters)':np.zeros(nr_strokes),
-	               ' StrokeDistance (meters)':np.zeros(nr_strokes),
+	               ' StrokeDistance (meters)':strokedistance,
 	               ' DriveTime (ms)':np.zeros(nr_strokes),
 	               ' StrokeRecoveryTime (ms)':np.zeros(nr_strokes),
 	               ' AverageDriveForce (lbs)':np.zeros(nr_strokes),
 	               ' PeakDriveForce (lbs)':np.zeros(nr_strokes),
 	               ' lapIdx':np.zeros(nr_strokes),
-                       ' Power (watts)':0*d,
+                       ' Power (watts)':watts,
     })
 
+
+    df.sort_values(by='TimeStamp (sec)',ascending=True)
+
     res = df.to_csv(csvfilename+'.gz',index_label='index',compression='gzip')
 
     data = dataprep(df,id=workoutid,bands=False,debug=debug)