Merge branch 'feature/spmtss' into develop

rowers/dataprep.py

@@ -458,7 +458,7 @@ def calculate_goldmedalstandard(rower, workout, recurrance=True):
         if settings.TESTING:
             background = False
         if recurrance:
-            df, delta, cpvalues = setcp(workout, background=background)
+            df, delta, cpvalues, cpvalues_spm = setcp(workout, background=background)
             if df.is_empty():
                 return 0, 0
         else:
@@ -466,7 +466,7 @@ def calculate_goldmedalstandard(rower, workout, recurrance=True):
 
 
     if df.is_empty() and recurrance:  # pragma: no cover
-        df, delta, cpvalues = setcp(workout, recurrance=False, background=True)
+        df, delta, cpvalues, cpvalues_spm = setcp(workout, recurrance=False, background=True)
         if df.is_empty():
             return 0, 0
 
@@ -553,7 +553,10 @@ def setcp(workout, background=False, recurrance=True):
             # check dts
             tarr = datautils.getlogarr(4000)
             if df['delta'][0] in tarr:
-                return(df, df['delta'], df['cp'])
+                try:
+                    return(df, df['delta'], df['cp'], df['cr'])
+                except KeyError:
+                    return(df, df['delta'], df['cp'], 0*df['cp'])
     except Exception as e:
         try:
             os.remove(filename)
@@ -565,7 +568,7 @@ def setcp(workout, background=False, recurrance=True):
     strokesdf = remove_nulls_pl(strokesdf)
 
     if strokesdf.is_empty():
-        return pl.DataFrame({'delta': [], 'cp': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
+        return pl.DataFrame({'delta': [], 'cp': [], 'cr': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
     
     totaltime = strokesdf['time'].max()
     maxt = totaltime/1000.
@@ -580,7 +583,7 @@ def setcp(workout, background=False, recurrance=True):
     elif os.path.exists(csvfilename+'.gz'):  # pragma: no cover
         csvfile = csvfilename+'.gz'
     else:  # pragma: no cover
-        return pl.DataFrame({'delta': [], 'cp': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
+        return pl.DataFrame({'delta': [], 'cp': [], 'cr': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
     csvfile = os.path.abspath(csvfile)
 
     with grpc.insecure_channel(
@@ -593,7 +596,7 @@ def setcp(workout, background=False, recurrance=True):
             grpc.channel_ready_future(channel).result(timeout=10)
         except grpc.FutureTimeoutError:  # pragma: no cover
             dologging('metrics.log','grpc channel time out in setcp')
-            return pl.DataFrame({'delta': [], 'cp': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
+            return pl.DataFrame({'delta': [], 'cp': [], 'cr':[]}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
 
         stub = metrics_pb2_grpc.MetricsStub(channel)
         req = metrics_pb2.CPRequest(filename = csvfile, filetype = "CSV", tarr = logarr)
@@ -602,16 +605,18 @@ def setcp(workout, background=False, recurrance=True):
             response = stub.GetCP(req, timeout=60)
         except Exception as e:
             dologging('metrics.log', traceback.format_exc())
-            return pl.DataFrame({'delta': [], 'cp': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
+            return pl.DataFrame({'delta': [], 'cp': [], 'cr': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
 
     delta = pl.Series(np.array(response.delta))
     cpvalues = pl.Series(np.array(response.power))
     powermean = response.avgpower
+    spmvalues = pl.Series(np.array(response.spm))
 
     try:
         df = pl.DataFrame({
             'delta': delta,
             'cp': cpvalues,
+            'cr': spmvalues,
             'id': workout.id,
         })
         
@@ -620,7 +625,7 @@ def setcp(workout, background=False, recurrance=True):
 
     except Exception as e:
         dologging("metrics.log", "setcp: "+ str(e))
-        return pl.DataFrame({'delta': [], 'cp': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
+        return pl.DataFrame({'delta': [], 'cp': [], 'cr': []}), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
 
     #df.to_parquet(filename, engine='fastparquet', compression='GZIP')
 
@@ -631,7 +636,7 @@ def setcp(workout, background=False, recurrance=True):
         workout.goldmedalduration = goldmedalduration
         workout.save()
 
-    return df, delta, cpvalues
+    return df, delta, cpvalues, spmvalues # pragma: no cover
     
 
 
@@ -772,7 +777,7 @@ def fetchcp_new(rower, workouts):
     data = []
 
     for workout in workouts:
-        df, delta, cpvalues = setcp(workout)
+        df, delta, cpvalues, cpvalues_spm = setcp(workout)
         df = df.drop('id')
         df = df.with_columns((pl.lit(str(workout))).alias("workout"))
         df = df.with_columns((pl.lit(workout.url())).alias("url"))
@@ -780,7 +785,7 @@ def fetchcp_new(rower, workouts):
             data.append(df)
 
     if len(data) == 0:
-        return pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), 0, pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
+        return pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), 0, pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
     if len(data) > 1:
         df = pl.concat(data)
 
@@ -791,16 +796,21 @@ def fetchcp_new(rower, workouts):
             pl.all().sort_by('cp').last(),
         ])
     except (KeyError, ColumnNotFoundError):  # pragma: no cover
-        return pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), 0, pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
+        return pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64), 0, pl.Series(dtype=pl.Float64), pl.Series(dtype=pl.Float64)
 
     df = df.filter(pl.col("cp")>20)
 
-    return df['delta'], df['cp'], 0, df['workout'], df['url']
+    try:
+        testje = df['cr']
+    except KeyError:
+        return df['delta'], df['cp'], 0*df['cp'], 0, df['workout'], df['url']
+
+    return df['delta'], df['cp'], df['cr'], 0, df['workout'], df['url']
 
 
 
 
-def update_rolling_cp(r, types, mode='water', dosend=False):
+def update_rolling_cp(r, types, mode='water', metric='power', dosend=False):
     firstdate = tz.now()-datetime.timedelta(days=r.cprange)
     workouts = Workout.objects.filter(
         date__gte=firstdate,
@@ -808,12 +818,19 @@ def update_rolling_cp(r, types, mode='water', dosend=False):
         user=r
     )
 
-    delta, cp, avgpower, workoutnames, urls = fetchcp_new(r, workouts)
+    delta, cp, cr, avgpower, workoutnames, urls = fetchcp_new(r, workouts)
+
+    if metric == 'power':
+        powerdf = pl.DataFrame({
+            'Delta': delta,
+            'CP': cp,
+        })
+    else:
+        powerdf = pl.DataFrame({
+            'Delta': delta,
+            'CP': cr,
+        })
 
-    powerdf = pl.DataFrame({
-        'Delta': delta,
-        'CP': cp,
-    })
 
     powerdf = powerdf.filter(pl.col("CP")>0)
     powerdf = powerdf.fill_nan(None).drop_nulls().sort(["Delta", "CP"])
@@ -821,6 +838,8 @@ def update_rolling_cp(r, types, mode='water', dosend=False):
 
     if powerdf.is_empty():
         return False
+
+
     
     res2 = datautils.cpfit(powerdf)
     p1 = res2[0]
@@ -830,7 +849,7 @@ def update_rolling_cp(r, types, mode='water', dosend=False):
     pwr += p1[1]/(1+hourseconds/p1[3])
 
     if len(powerdf) != 0:
-        if mode == 'water':
+        if mode == 'water' and metric == 'power':
             r.p0 = p1[0]
             r.p1 = p1[1]
             r.p2 = p1[2]
@@ -839,8 +858,14 @@ def update_rolling_cp(r, types, mode='water', dosend=False):
             r.save()
             if dosend and pwr-5 > r.ftp*(100.-r.otwslack)/100. and r.getemailnotifications and not r.emailbounced:
                 _ = myqueue(queuehigh, handle_sendemail_newftp,r,pwr,'water')
-            
+        elif mode == 'water' and metric == 'spm':
-        else:
+            r.r0 = p1[0]
+            r.r1 = p1[1]
+            r.r2 = p1[2]
+            r.r3 = p1[3]
+            r.crratio = res2[3]
+            r.save()
+        elif mode == 'erg' and metric == 'power':            
             r.ep0 = p1[0]
             r.ep1 = p1[1]
             r.ep2 = p1[2]
@@ -849,14 +874,23 @@ def update_rolling_cp(r, types, mode='water', dosend=False):
             r.save()
             if dosend and pwr-5 > r.ftp and r.getemailnotifications and not r.emailbounced:
                 _ = myqueue(queuehigh, handle_sendemail_newftp,r,pwr,'water')
+        elif mode == 'erg' and metric == 'spm':
+            r.er0 = p1[0]
+            r.er1 = p1[1]
+            r.er2 = p1[2]
+            r.er3 = p1[3]
+            r.ecrratio = res2[3]
+            r.save()
 
 
         return True
     return False
 
 def initiate_cp(r):
-    _ = update_rolling_cp(r, otwtypes, 'water')
+    _ = update_rolling_cp(r, otwtypes, mode='water', metric='power')
-    _ = update_rolling_cp(r, otetypes, 'erg')
+    _ = update_rolling_cp(r, otetypes, mode='erg', metric='power')
+    _ = update_rolling_cp(r, otwtypes, mode='water', metric='spm')
+    _ = update_rolling_cp(r, otetypes, mode='erg', metric='spm')
 
 def split_workout(r, parent, splitsecond, splitmode):
     data, row = getrowdata_db(id=parent.id)
@@ -1061,13 +1095,14 @@ def checkbreakthrough(w, r):
     ishard = False
     workouttype = w.workouttype
     if workouttype in rowtypes:
-        cpdf, delta, cpvalues = setcp(w)
+        cpdf, delta, cpvalues, cpvalues_spm = setcp(w)
         if not cpdf.is_empty():
             if workouttype in otwtypes:
                 try:
                     res, btvalues, res2 = utils.isbreakthrough(
                         delta, cpvalues, r.p0, r.p1, r.p2, r.p3, r.cpratio)
-                    _ = update_rolling_cp(r, otwtypes, 'water')
+                    _ = update_rolling_cp(r, otwtypes, mode='water')
+                    _ = update_rolling_cp(r, otwtypes, mode='water', metric='spm')
                 except ValueError:
                     res = 0
                     res2 = 0
@@ -1076,7 +1111,8 @@ def checkbreakthrough(w, r):
                 try:
                     res, btvalues, res2 = utils.isbreakthrough(
                         delta, cpvalues, r.ep0, r.ep1, r.ep2, r.ep3, r.ecpratio)
-                    _ = update_rolling_cp(r, otetypes, 'erg')
+                    _ = update_rolling_cp(r, otetypes, mode='erg')
+                    _ = update_rolling_cp(r, otetypes, mode='erg', metric='spm')
                 except ValueError:
                     res = 0
                     res2 = 0
@@ -1480,9 +1516,15 @@ def save_workout_database(f2, r, dosmooth=True, workouttype='rower',
             r.running_wps = 400.
         r.save()
         
-
+    if w.workouttype in otwtypes:
+        wps_avg = r.median_wps
+    elif w.workouttype in otetypes:
+        wps_avg = r.median_wps_erg
+    else:
+        wps_avg = 0
+        
     _ = myqueue(queuehigh, handle_calctrimp, w.id, f2,
-                r.ftp, r.sex, r.hrftp, r.max, r.rest)
+                r.ftp, r.sex, r.hrftp, r.max, r.rest, wps_avg)
 
     return (w.id, message)
 
@@ -1771,8 +1813,15 @@ def new_workout_from_df(r, df,
                                         rpe=rpe,
                                         consistencychecks=False)
 
+    if workouttype in otwtypes:
+        wps_avg = r.median_wps
+    elif workouttype in otetypes:
+        wps_avg = r.median_wps_erg
+    else:
+        wps_avg = 0
+    
     _ = myqueue(queuehigh, handle_calctrimp, id, csvfilename,
-                r.ftp, r.sex, r.hrftp, r.max, r.rest)
+                r.ftp, r.sex, r.hrftp, r.max, r.rest, wps_avg)
 
     return (id, message)
 
@@ -1814,7 +1863,14 @@ def workout_trimp(w, reset=False):
                 return 0, 100.*(w.averagehr/r.hrftp)*(w.duration.hour*60 + w.duration.minute)/60.
             except ZeroDivisionError:
                 return 0, 0
-                
+
+        if w.workouttype in otwtypes:
+            wps_avg = r.median_wps
+        elif w.workouttype in otetypes:
+            wps_avg = r.median_wps_erg
+        else:
+            wps_avg = 0
+            
         ftp = float(r.ftp)
         _ = myqueue(
             queuehigh,
@@ -1825,7 +1881,9 @@ def workout_trimp(w, reset=False):
             r.sex,
             r.hrftp,
             r.max,
-            r.rest)
+            r.rest,
+            wps_avg,
+        )
         return w.trimp, w.hrtss
     elif w.trimp > -1 and not reset:
         return w.trimp, w.hrtss
@@ -1857,6 +1915,13 @@ def workout_trimp(w, reset=False):
         w.maxhr = maxhr
         w.save()
 
+    if w.workouttype in otwtypes:
+        wps_avg = r.median_wps
+    elif w.workouttype in otetypes:
+        wps_avg = r.median_wps_erg
+    else:
+        wps_avg = 0
+        
     _ = myqueue(
         queuehigh,
         handle_calctrimp,
@@ -1866,7 +1931,8 @@ def workout_trimp(w, reset=False):
         r.sex,
         r.hrftp,
         r.max,
-        r.rest)
+        r.rest,
+        wps_avg,)
 
     trimp = 0
     averagehr = 0
@@ -1877,6 +1943,45 @@ def workout_trimp(w, reset=False):
 
     return trimp, averagehr
 
+def workout_spmtss(w, reset=False):
+    if w.spmtss > -1 and not reset:
+        return w.spmtss
+
+    if get_existing_job(w):
+        return 0, 0
+
+    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.workouttype in otwtypes:
+        wps_avg = r.median_wps
+    elif w.workouttype in otetypes:
+        wps_avg = r.median_wps_erg
+    else:
+        wps_avg = 0
+
+    _ = myqueue(
+        queuehigh,
+        handle_calctrimp,
+        w.id,
+        w.csvfilename,
+        ftp,
+        r.sex,
+        r.hrftp,
+        r.max,
+        r.rest,
+        wps_avg,)
+
+    return w.spmtss 
+        
+
 
 def workout_rscore(w, reset=False):
     dologging('metrics.log','Workout_rscore for {w} {id}, {reset}'.format(
@@ -1906,6 +2011,13 @@ def workout_rscore(w, reset=False):
         r.save()
 
     dologging('metrics.log','Queueing an asynchronous task')
+
+    if w.workouttype in otwtypes:
+        wps_avg = r.median_wps
+    elif w.workouttype in otetypes:
+        wps_avg = r.median_wps_erg
+    else:
+        wps_avg = 0
         
     _ = myqueue(
         queuehigh,
@@ -1916,7 +2028,8 @@ def workout_rscore(w, reset=False):
         r.sex,
         r.hrftp,
         r.max,
-        r.rest)
+        r.rest,
+        wps_avg,)
 
     return w.rscore, w.normp
 
@@ -1938,6 +2051,13 @@ def workout_normv(w, pp=4.0):
         r.hrftp = int(hrftp)
         r.save()
 
+    if w.workouttype in otwtypes:
+        wps_avg = r.median_wps
+    elif w.workouttype in otetypes:
+        wps_avg = r.median_wps_erg
+    else:
+        wps_avg = 0
+
     _ = myqueue(
         queuehigh,
         handle_calctrimp,
@@ -1947,6 +2067,6 @@ def workout_normv(w, pp=4.0):
         r.sex,
         r.hrftp,
         r.max,
-        r.rest)
+        r.rest, wps_avg)
 
     return 0, 0

rowers/datautils.py

@@ -84,7 +84,10 @@ def cpfit(powerdf, fraclimit=0.0001, nmax=1000):
     p1 = p0
 
     thesecs = powerdf['Delta'].to_numpy()
-    theavpower = powerdf['CP'].to_numpy()
+    try:
+        theavpower = powerdf['CP'].to_numpy()
+    except:  # pragma: no cover
+        theavpower = powerdf['CR'].to_numpy()
         
 
     if len(thesecs) >= 4:

rowers/forms.py

@@ -1299,6 +1299,7 @@ analysischoices = (
     ('stats', 'Statistics'),
     ('compare', 'Compare'),
     ('cp', 'CP chart'),
+    ('cr', 'CR chart'),
 )
 
 
@@ -1370,9 +1371,17 @@ class AnalysisChoiceForm(forms.Form):
         ('automatic', 'Critical Power Rolling Data')
     )
 
+    crfitchoices = (
+        ('data', 'Fit to Selected Workouts'),
+        ('automatic', 'Critical Stroke Rate Rolling Data')
+    )
+
     cpfit = forms.ChoiceField(choices=cpfitchoices,
                               label='Model Fit', initial='data', required=False)
 
+    crfit = forms.ChoiceField(choices=crfitchoices,
+                              label='Model Fit', initial='data', required=False)
+
     cpoverlay = forms.BooleanField(initial=False,
                                    label='Overlay Gold Medal Performance',
                                    required=False)

rowers/interactiveplots.py

@@ -1113,6 +1113,75 @@ def interactive_otwcpchart(powerdf, promember=0, rowername="", r=None,
     return [script, div, p1, ratio, message]
 
 
+def interactive_otwcrchart(powerdf, promember=0, rowername="", r=None,
+                           cpfit='data',
+                           title='', type='water'):
+
+    powerdf2 = powerdf.filter((pl.col("Delta") > 0) & (pl.col("CR") > 0))
+
+    # plot tools
+    if (promember == 1):  # pragma: no cover
+        TOOLS = 'save,pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
+    else:
+        TOOLS = 'pan,box_zoom,wheel_zoom,reset,tap,hover,crosshair'
+
+    x_axis_type = 'log'
+
+    deltas = powerdf2['Delta'].apply(lambda x: timedeltaconv(x))
+    powerdf2 = powerdf2.with_columns(
+        ftime = deltas.apply(lambda x: strfdelta(x)),
+        Deltaminutes = pl.col("Delta")/60.
+    )
+
+    # there is no Paul's law for OTW
+
+    thesecs = powerdf2['Delta']
+    theavpower = powerdf2['CR']
+
+    p1, fitt, fitpower, ratio = datautils.cpfit(powerdf2)
+    if cpfit == 'automatic' and r is not None:
+        if type == 'water':
+            p1 = [r.r0, r.r1, r.r2, r.r3]
+            ratio = r.cpratio
+        elif type == 'erg':  # pragma: no cover
+            p1 = [r.er0, r.er1, r.er2, r.er3]
+            ratio = r.ecrratio
+
+        def fitfunc(pars, x):
+            return abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
+
+        fitpower = fitfunc(p1, fitt)
+
+    message = ""
+    # if len(fitpower[fitpower<0]) > 0:
+    # message = "CP model fit didn't give correct results"
+
+    deltas = fitt.apply(lambda x: timedeltaconv(x))
+    ftime = niceformat(deltas)
+
+
+
+    fit_data =  pl.DataFrame(dict(
+            CR=fitpower,
+            CRmax=ratio*fitpower,
+            duration=fitt/60.,
+            ftime=ftime,
+        ))
+
+    if not title:
+        title = "Critical StrokeRate for "+rowername
+    
+    chart_dict = {
+        'data': powerdf2.to_dicts(),
+        'fitdata': fit_data.to_dicts(),
+        'title': title,
+    }
+
+    script, div = get_chart("/cr", chart_dict)
+
+    return [script, div, p1, ratio, message]
+
+
 def interactive_agegroup_plot(df, distance=2000, duration=None,
                               sex='male', weightcategory='hwt'):
 

rowers/models.py

@@ -1104,6 +1104,18 @@ class Rower(models.Model):
     ep3 = models.FloatField(default=1.0, verbose_name="erg CP p4")
     ecpratio = models.FloatField(default=1.0, verbose_name="erg CP fit ratio")
 
+    r0 = models.FloatField(default=1.0, verbose_name="CR r1")
+    r1 = models.FloatField(default=1.0, verbose_name="CR r2")
+    r2 = models.FloatField(default=1.0, verbose_name="CR r3")
+    r3 = models.FloatField(default=1.0, verbose_name="CR r4")
+    crratio = models.FloatField(default=1.0, verbose_name="CR fit ratio")
+
+    er0 = models.FloatField(default=1.0, verbose_name="erg CR r1")
+    er1 = models.FloatField(default=1.0, verbose_name="erg CR r2")
+    er2 = models.FloatField(default=1.0, verbose_name="erg CR r3")
+    er3 = models.FloatField(default=1.0, verbose_name="erg CR r4")
+    ecrratio = models.FloatField(default=1.0, verbose_name="erg CR fit ratio")
+
     cprange = models.IntegerField(default=42, verbose_name="Range for calculation of breakthrough workouts and fitness (CP)",
                                   choices=cppresets)
 
@@ -3746,6 +3758,7 @@ class Workout(models.Model):
     trimp = models.IntegerField(default=-1, blank=True)
     rscore = models.IntegerField(default=-1, blank=True)
     hrtss = models.IntegerField(default=-1, blank=True)
+    spmtss = models.IntegerField(default=-1, blank=True)
     normp = models.IntegerField(default=-1, blank=True)
     normv = models.FloatField(default=-1, blank=True)
     normw = models.FloatField(default=-1, blank=True)

rowers/rowing_workout_metrics_pb2.py

@@ -1,12 +1,22 @@
 # -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
+# NO CHECKED-IN PROTOBUF GENCODE
 # source: rowing-workout-metrics.proto
-# Protobuf Python Version: 4.25.1
+# Protobuf Python Version: 5.29.0
 """Generated protocol buffer code."""
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import runtime_version as _runtime_version
 from google.protobuf import symbol_database as _symbol_database
 from google.protobuf.internal import builder as _builder
+_runtime_version.ValidateProtobufRuntimeVersion(
+    _runtime_version.Domain.PUBLIC,
+    5,
+    29,
+    0,
+    '',
+    'rowing-workout-metrics.proto'
+)
 # @@protoc_insertion_point(imports)
 
 _sym_db = _symbol_database.Default()
@@ -14,22 +24,22 @@ _sym_db = _symbol_database.Default()
 
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1crowing-workout-metrics.proto\x12\x16rowing_workout_metrics\"p\n\x15WorkoutMetricsRequest\x12\x10\n\x08\x66ilename\x18\x01 \x01(\t\x12\x0b\n\x03sex\x18\x02 \x01(\t\x12\x0b\n\x03\x66tp\x18\x03 \x01(\x01\x12\r\n\x05hrftp\x18\x04 \x01(\x01\x12\r\n\x05hrmax\x18\x05 \x01(\x01\x12\r\n\x05hrmin\x18\x06 \x01(\x01\"p\n\x16WorkoutMetricsResponse\x12\x0b\n\x03tss\x18\x01 \x01(\x01\x12\r\n\x05normp\x18\x02 \x01(\x01\x12\r\n\x05trimp\x18\x03 \x01(\x01\x12\r\n\x05hrtss\x18\x04 \x01(\x01\x12\r\n\x05normv\x18\x05 \x01(\x01\x12\r\n\x05normw\x18\x06 \x01(\x01\"=\n\tCPRequest\x12\x10\n\x08\x66ilename\x18\x01 \x01(\t\x12\x10\n\x08\x66iletype\x18\x02 \x01(\t\x12\x0c\n\x04tarr\x18\x03 \x03(\x01\"<\n\nCPResponse\x12\r\n\x05\x64\x65lta\x18\x01 \x03(\x01\x12\r\n\x05power\x18\x02 \x03(\x01\x12\x10\n\x08\x61vgpower\x18\x03 \x01(\x01\x32\xc7\x01\n\x07Metrics\x12l\n\x0b\x43\x61lcMetrics\x12-.rowing_workout_metrics.WorkoutMetricsRequest\x1a..rowing_workout_metrics.WorkoutMetricsResponse\x12N\n\x05GetCP\x12!.rowing_workout_metrics.CPRequest\x1a\".rowing_workout_metrics.CPResponseB\x1aZ\x18./rowing-workout-metricsb\x06proto3')
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1crowing-workout-metrics.proto\x12\x16rowing_workout_metrics\"\x80\x01\n\x15WorkoutMetricsRequest\x12\x10\n\x08\x66ilename\x18\x01 \x01(\t\x12\x0b\n\x03sex\x18\x02 \x01(\t\x12\x0b\n\x03\x66tp\x18\x03 \x01(\x01\x12\r\n\x05hrftp\x18\x04 \x01(\x01\x12\r\n\x05hrmax\x18\x05 \x01(\x01\x12\r\n\x05hrmin\x18\x06 \x01(\x01\x12\x0e\n\x06wpsavg\x18\x07 \x01(\x01\"\x80\x01\n\x16WorkoutMetricsResponse\x12\x0b\n\x03tss\x18\x01 \x01(\x01\x12\r\n\x05normp\x18\x02 \x01(\x01\x12\r\n\x05trimp\x18\x03 \x01(\x01\x12\r\n\x05hrtss\x18\x04 \x01(\x01\x12\r\n\x05normv\x18\x05 \x01(\x01\x12\r\n\x05normw\x18\x06 \x01(\x01\x12\x0e\n\x06spmtss\x18\x07 \x01(\x01\"=\n\tCPRequest\x12\x10\n\x08\x66ilename\x18\x01 \x01(\t\x12\x10\n\x08\x66iletype\x18\x02 \x01(\t\x12\x0c\n\x04tarr\x18\x03 \x03(\x01\"I\n\nCPResponse\x12\r\n\x05\x64\x65lta\x18\x01 \x03(\x01\x12\r\n\x05power\x18\x02 \x03(\x01\x12\x10\n\x08\x61vgpower\x18\x03 \x01(\x01\x12\x0b\n\x03spm\x18\x04 \x03(\x01\x32\xc7\x01\n\x07Metrics\x12l\n\x0b\x43\x61lcMetrics\x12-.rowing_workout_metrics.WorkoutMetricsRequest\x1a..rowing_workout_metrics.WorkoutMetricsResponse\x12N\n\x05GetCP\x12!.rowing_workout_metrics.CPRequest\x1a\".rowing_workout_metrics.CPResponseB\x1aZ\x18./rowing-workout-metricsb\x06proto3')
 
 _globals = globals()
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
 _builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'rowing_workout_metrics_pb2', _globals)
-if _descriptor._USE_C_DESCRIPTORS == False:
+if not _descriptor._USE_C_DESCRIPTORS:
-  _globals['DESCRIPTOR']._options = None
+  _globals['DESCRIPTOR']._loaded_options = None
   _globals['DESCRIPTOR']._serialized_options = b'Z\030./rowing-workout-metrics'
-  _globals['_WORKOUTMETRICSREQUEST']._serialized_start=56
+  _globals['_WORKOUTMETRICSREQUEST']._serialized_start=57
-  _globals['_WORKOUTMETRICSREQUEST']._serialized_end=168
+  _globals['_WORKOUTMETRICSREQUEST']._serialized_end=185
-  _globals['_WORKOUTMETRICSRESPONSE']._serialized_start=170
+  _globals['_WORKOUTMETRICSRESPONSE']._serialized_start=188
-  _globals['_WORKOUTMETRICSRESPONSE']._serialized_end=282
+  _globals['_WORKOUTMETRICSRESPONSE']._serialized_end=316
-  _globals['_CPREQUEST']._serialized_start=284
+  _globals['_CPREQUEST']._serialized_start=318
-  _globals['_CPREQUEST']._serialized_end=345
+  _globals['_CPREQUEST']._serialized_end=379
-  _globals['_CPRESPONSE']._serialized_start=347
+  _globals['_CPRESPONSE']._serialized_start=381
-  _globals['_CPRESPONSE']._serialized_end=407
+  _globals['_CPRESPONSE']._serialized_end=454
-  _globals['_METRICS']._serialized_start=410
+  _globals['_METRICS']._serialized_start=457
-  _globals['_METRICS']._serialized_end=609
+  _globals['_METRICS']._serialized_end=656
 # @@protoc_insertion_point(module_scope)

rowers/rowing_workout_metrics_pb2_grpc.py

@@ -1,9 +1,29 @@
 # Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
 """Client and server classes corresponding to protobuf-defined services."""
 import grpc
+import warnings
 
 import rowers.rowing_workout_metrics_pb2 as rowing__workout__metrics__pb2
 
+GRPC_GENERATED_VERSION = '1.70.0'
+GRPC_VERSION = grpc.__version__
+_version_not_supported = False
+
+try:
+    from grpc._utilities import first_version_is_lower
+    _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION)
+except ImportError:
+    _version_not_supported = True
+
+if _version_not_supported:
+    raise RuntimeError(
+        f'The grpc package installed is at version {GRPC_VERSION},'
+        + f' but the generated code in rowing_workout_metrics_pb2_grpc.py depends on'
+        + f' grpcio>={GRPC_GENERATED_VERSION}.'
+        + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}'
+        + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.'
+    )
+
 
 class MetricsStub(object):
     """OTW-metrics service definition
@@ -19,12 +39,12 @@ class MetricsStub(object):
                 '/rowing_workout_metrics.Metrics/CalcMetrics',
                 request_serializer=rowing__workout__metrics__pb2.WorkoutMetricsRequest.SerializeToString,
                 response_deserializer=rowing__workout__metrics__pb2.WorkoutMetricsResponse.FromString,
-                )
+                _registered_method=True)
         self.GetCP = channel.unary_unary(
                 '/rowing_workout_metrics.Metrics/GetCP',
                 request_serializer=rowing__workout__metrics__pb2.CPRequest.SerializeToString,
                 response_deserializer=rowing__workout__metrics__pb2.CPResponse.FromString,
-                )
+                _registered_method=True)
 
 
 class MetricsServicer(object):
@@ -60,6 +80,7 @@ def add_MetricsServicer_to_server(servicer, server):
     generic_handler = grpc.method_handlers_generic_handler(
             'rowing_workout_metrics.Metrics', rpc_method_handlers)
     server.add_generic_rpc_handlers((generic_handler,))
+    server.add_registered_method_handlers('rowing_workout_metrics.Metrics', rpc_method_handlers)
 
 
  # This class is part of an EXPERIMENTAL API.
@@ -78,11 +99,21 @@ class Metrics(object):
             wait_for_ready=None,
             timeout=None,
             metadata=None):
-        return grpc.experimental.unary_unary(request, target, '/rowing_workout_metrics.Metrics/CalcMetrics',
+        return grpc.experimental.unary_unary(
+            request,
+            target,
+            '/rowing_workout_metrics.Metrics/CalcMetrics',
             rowing__workout__metrics__pb2.WorkoutMetricsRequest.SerializeToString,
             rowing__workout__metrics__pb2.WorkoutMetricsResponse.FromString,
-            options, channel_credentials,
+            options,
-            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)
 
     @staticmethod
     def GetCP(request,
@@ -95,8 +126,18 @@ class Metrics(object):
             wait_for_ready=None,
             timeout=None,
             metadata=None):
-        return grpc.experimental.unary_unary(request, target, '/rowing_workout_metrics.Metrics/GetCP',
+        return grpc.experimental.unary_unary(
+            request,
+            target,
+            '/rowing_workout_metrics.Metrics/GetCP',
             rowing__workout__metrics__pb2.CPRequest.SerializeToString,
             rowing__workout__metrics__pb2.CPResponse.FromString,
-            options, channel_credentials,
+            options,
-            insecure, call_credentials, compression, wait_for_ready, timeout, metadata)
+            channel_credentials,
+            insecure,
+            call_credentials,
+            compression,
+            wait_for_ready,
+            timeout,
+            metadata,
+            _registered_method=True)

rowers/tasks.py

@@ -1639,6 +1639,7 @@ def handle_calctrimp(id,
                      hrftp,
                      hrmax,
                      hrmin,
+                     wps_avg,
                      debug=False, **kwargs):
 
 
@@ -1648,6 +1649,7 @@ def handle_calctrimp(id,
     hrtss = 0
     normv = 0
     normw = 0
+    spmtss = 0
 
     # check what the real file name is
     if os.path.exists(csvfilename):
@@ -1680,6 +1682,7 @@ def handle_calctrimp(id,
             hrftp=hrftp,
             hrmax=hrmax,
             hrmin=hrmin,
+            wpsavg=wps_avg,
         )
         try:
             response = stub.CalcMetrics(req, timeout=60)
@@ -1693,13 +1696,15 @@ def handle_calctrimp(id,
         normv = response.normv
         normw = response.normw
         hrtss = response.hrtss
-        dologging('metrics.log','File {csvfile}. Got tss {tss}, normp {normp} trimp {trimp} normv {normv} normw {normw} hrtss {hrtss}'.format(
+        spmtss = response.spmtss
+        dologging('metrics.log','File {csvfile}. Got tss {tss}, normp {normp} trimp {trimp} normv {normv} normw {normw} hrtss {hrtss} spmtss {spmtss}'.format(
             tss = tss,
             normp = normp,
             trimp = trimp,
             normv = normv,
             normw = normw,
             hrtss = hrtss,
+            spmtss = spmtss,
             csvfile=csvfile,
         ))
         
@@ -1740,6 +1745,9 @@ def handle_calctrimp(id,
         if hrtss > 1000:  # pragma: no cover
             hrtss = 0
 
+        if spmtss > 1000:  # pragma: no cover
+            spmtss = 0
+
     try:
         workout = Workout.objects.get(id=id)
     except Workout.DoesNotExist: # pragma: no cover
@@ -1752,6 +1760,7 @@ def handle_calctrimp(id,
     workout.hrtss = int(hrtss)
     workout.normv = normv
     workout.normw = normw
+    workout.spmtss = int(spmtss)
     workout.save()
     dologging('metrics.log','Saving to workout {id} {obscure}'.format(
         id = id,

rowers/templates/otwcr.html

@@ -0,0 +1,20 @@
+
+  {{ the_div|safe }}
+  <p>
+    <table width="100%" class="listtable">
+      <thead>
+        <tr>
+	  <th> Duration</th>
+	  <th> Stroke Estimate 1</th>
+	  <th> Stroke Estimate 2</th>
+        <tr>
+      </thead>
+      <tbody>
+				<tr>
+					<td>{{ duration }}</td>
+					<td>{{ power }}</td>
+					<td>{{ upper }}</td>
+					</tr>
+      </tbody>
+    </table>
+    </p>

rowers/templates/user_analysis_select.html

@@ -124,13 +124,13 @@
 <script>
   // script for chart options form
   $(function() {
-
+      
       // Get the form fields and hidden div
       var functionfield = $("#id_function");
       var plotfield = $("#id_plotfield").parent().parent();
       var x_param = $("#id_xparam").parent().parent();
       var y_param = $("#id_yparam").parent().parent();
-
+      
       var groupby = $("#id_groupby").parent().parent();
       var binsize = $("#id_binsize").parent().parent();
       var errorbars = $("#id_ploterrorbars").parent().parent();
@@ -139,17 +139,18 @@
       var spmmax = $("#id_spmmax").parent().parent();
       var workmin = $("#id_workmin").parent().parent();
       var workmax = $("#id_workmax").parent().parent();
-
+      
       var xaxis = $("#id_xaxis").parent().parent();
       var yaxis1 = $("#id_yaxis1").parent().parent();
       var yaxis2 = $("#id_yaxis2").parent().parent();
       var plottype = $("#id_plottype").parent().parent();
-
+      
-			var reststrokes = $("#id_includereststrokes").parent().parent();
+      var reststrokes = $("#id_includereststrokes").parent().parent();
-			var trendline = $("#id_trendline").parent().parent();
+      var trendline = $("#id_trendline").parent().parent();
-			var piece = $("#id_piece").parent().parent();
+      var piece = $("#id_piece").parent().parent();
-			var cpfit = $("#id_cpfit").parent().parent();
+      var cpfit = $("#id_cpfit").parent().parent();
-			var cpoverlay = $("#id_cpoverlay").parent().parent();
+      var crfit = $("#id_crfit").parent().parent();
+      var cpoverlay = $("#id_cpoverlay").parent().parent();
 
 
       // Hide the fields.
@@ -166,15 +167,16 @@
       yaxis1.hide();
       yaxis2.hide();
       plottype.hide();
-			// reststrokes.hide();
+      // reststrokes.hide();
-			workmin.hide();
+      workmin.hide();
-			workmax.hide();
+      workmax.hide();
-			spmmin.hide();
+      spmmin.hide();
-			spmmax.hide();
+      spmmax.hide();
-			cpfit.hide();
+  cpfit.hide();
-			cpoverlay.hide();
+      crfit.hide();
-			piece.hide();
+      cpoverlay.hide();
-			trendline.hide();
+      piece.hide();
+      trendline.hide();
 
       if (functionfield.val() == 'boxplot') {
 	  plotfield.show();
@@ -214,11 +216,16 @@
 		reststrokes.show();
       }
 
-			if (functionfield.val() == 'cp') {
+      if (functionfield.val() == 'cp') {
-				cpfit.show();
+	  cpfit.show();
-				piece.show();
+	  piece.show();
-				cpoverlay.show();
+	  cpoverlay.show();
-			}
+      }
+
+      if (functionfield.val() == 'cr') {
+	  crfit.show();
+	  piece.show();
+      }
 
 
       // Setup an event listener for when the state of the
@@ -241,15 +248,16 @@
 	      palette.hide();
 	      binsize.hide();
 	      errorbars.hide();
-				trendline.hide();
+	      trendline.hide();
 	      xaxis.hide();
 	      yaxis1.hide();
 	      yaxis2.hide();
 	      plottype.hide();
-				reststrokes.show();
+	      reststrokes.show();
-				cpfit.hide();
+	      cpfit.hide();
-				cpoverlay.hide();
+	      crfit.hide();
-				piece.hide();
+	      cpoverlay.hide();
+	      piece.hide();
 	  }
 	  else if (Value=='histo') {
 	      plotfield.show();
@@ -262,16 +270,17 @@
 	      groupby.hide();
 	      palette.hide();
 	      binsize.hide();
-				trendline.hide();
+	      trendline.hide();
 	      errorbars.hide();
 	      xaxis.hide();
 	      yaxis1.hide();
 	      yaxis2.hide();
 	      plottype.hide();
-				reststrokes.show();
+	      reststrokes.show();
-				cpfit.hide();
+	      cpfit.hide();
-				cpoverlay.hide();
+	      crfit.hide();
-				piece.hide();
+	      cpoverlay.hide();
+	      piece.hide();
 
 	  }
 	  else if (Value=='trendflex') {
@@ -284,17 +293,18 @@
 	      spmmin.show();
 	      spmmax.show();
 	      workmin.show();
-				trendline.hide();
+	      trendline.hide();
 	      workmax.show();
 	      plotfield.hide();
 	      xaxis.hide();
 	      yaxis1.hide();
 	      yaxis2.hide();
 	      plottype.hide();
-				reststrokes.show();
+	      reststrokes.show();
-				cpfit.hide();
+	      cpfit.hide();
-				cpoverlay.hide();
+	      crfit.hide();
-				piece.hide();
+	      cpoverlay.hide();
+	      piece.hide();
 
 	  }
 	  else if (Value=='flexall') {
@@ -305,7 +315,7 @@
 	      y_param.hide();
 	      groupby.hide();
 	      spmmin.hide();
-				trendline.show();
+	      trendline.show();
 	      spmmax.hide();
 	      workmin.hide();
 	      workmax.hide();
@@ -314,10 +324,11 @@
 	      binsize.hide();
 	      plottype.hide();
 	      errorbars.hide();
-				reststrokes.show();
+	      reststrokes.show();
-				cpfit.hide();
+	      cpfit.hide();
-				cpoverlay.hide();
+	      crfit.hide();
-				piece.hide();
+	      cpoverlay.hide();
+	      piece.hide();
 	  }
 	  else if (Value=='stats') {
 	      xaxis.hide();
@@ -331,11 +342,12 @@
 	      binsize.hide();
 	      errorbars.hide();
 	      plottype.hide();
-				trendline.hide();
+	      trendline.hide();
-				reststrokes.show();
+	      reststrokes.show();
-				cpfit.hide();
+	      cpfit.hide();
-				cpoverlay.hide();
+	      crfit.hide();
-				piece.hide();
+	      cpoverlay.hide();
+	      piece.hide();
 	  }
 	  else if (Value=='compare') {
 	      xaxis.show();
@@ -350,18 +362,19 @@
 	      workmax.hide();
 	      plotfield.hide();
 	      palette.hide();
-				trendline.hide();
+	      trendline.hide();
 	      binsize.hide();
 	      plottype.show();
 	      errorbars.hide();
-				piece.hide();
+	      piece.hide();
-				cpfit.hide();
+	      cpfit.hide();
-				cpoverlay.hide();
+	      crfit.hide();
-				reststrokes.show();
+	      cpoverlay.hide();
+	      reststrokes.show();
 
 
 	  }
-		else if (Value=='cp') {
+	  else if (Value=='cp') {
 	      plotfield.hide();
 	      spmmin.hide();
 	      spmmax.hide();
@@ -369,7 +382,7 @@
 	      workmax.hide();
 	      x_param.hide();
 	      y_param.hide();
-				trendline.hide();
+	      trendline.hide();
 	      groupby.hide();
 	      palette.hide();
 	      binsize.hide();
@@ -378,10 +391,34 @@
 	      yaxis1.hide();
 	      yaxis2.hide();
 	      plottype.hide();
-				reststrokes.hide();
+	      reststrokes.hide();
-				cpfit.show();
+	      cpfit.show();
-				cpoverlay.hide();
+	      crfit.hide();
-				piece.show();
+	      cpoverlay.hide();
+	      piece.show();
+	  }
+	  else if (Value=='cr') {
+	      plotfield.hide();
+	      spmmin.hide();
+	      spmmax.hide();
+	      workmin.hide();
+	      workmax.hide();
+	      x_param.hide();
+	      y_param.hide();
+	      trendline.hide();
+	      groupby.hide();
+	      palette.hide();
+	      binsize.hide();
+	      errorbars.hide();
+	      xaxis.hide();
+	      yaxis1.hide();
+	      yaxis2.hide();
+	      plottype.hide();
+	      reststrokes.hide();
+	      cpfit.hide();
+	      crfit.show();
+	      cpoverlay.hide();
+	      piece.show();
 	  }
       });
   });

rowers/templates/workoutstats.html

@@ -61,6 +61,7 @@
     <p>TRIMP: TRaining IMPact. A way to combine duration and heart rate into a single number.</p>
     <p>rScore: Score based on rPower and workout duration to estimate training effect</p>
     <p>rScore (HR): Score based on heart rate, designed to give values comparable to rScore. Used instead of rScore for workouts without power data.</p>
+    <p>rScore (SPM): Score based on stroke rate, designed to give values comparable to rScore. Used instead of rScore for workouts without power or heart rate data.</p>
   </li>
   {% endif %}
   <li>

rowers/tests/test_async_tasks.py

@@ -205,7 +205,7 @@ class AsyncTaskTests(TestCase):
     @patch('rowers.dataprep.create_engine')
     def test_handle_calctrimp(self, mocked_sqlalchemy):
         result = get_random_file()
-        res = tasks.handle_calctrimp(1,result['filename'],200,'male',160,90,52)
+        res = tasks.handle_calctrimp(1,result['filename'],200,'male',160,90,52,400)
         self.assertEqual(res,1)
 
     @patch('rowers.tasks.EmailMessage',side_effect=MockEmailMessage)

rowers/views/analysisviews.py

@@ -294,6 +294,8 @@ def analysis_new(request,
             df = comparisondata(tw, options)
         elif function == 'cp':  # pragma: no cover
             df = cpdata(tw, options)
+        elif function == 'cr':  # pragma: no cover
+            df = crdata(tw, options)
         options['savedata'] = False
         request.session['options'] = options
         try:
@@ -661,7 +663,7 @@ def cpdata(workouts, options):
     r = u.rower
     
     
-    delta, cpvalue, avgpower, workoutnames, urls = dataprep.fetchcp_new(
+    delta, cpvalue, crvalue, avgpower, workoutnames, urls = dataprep.fetchcp_new(
         r, workouts)
 
     powerdf = pl.DataFrame({
@@ -811,6 +813,139 @@ def cpdata(workouts, options):
 
     return (script, html_content)
 
+def crdata(workouts, options):
+    start = timezone.now()
+    userid = options['userid']
+    cpfit = options['crfit']
+    cpoverlay = options['cpoverlay']
+
+    u = User.objects.get(id=userid)
+    r = u.rower
+    
+    
+    delta, cpvalue, crvalue, avgpower, workoutnames, urls = dataprep.fetchcp_new(
+        r, workouts)
+
+    powerdf = pl.DataFrame({
+        'Delta': delta,
+        'CR': crvalue,
+        'workout': workoutnames,
+        'url': urls,
+    })
+
+    savedata = options.get('savedata',False)
+    if savedata: # pragma: no cover
+        return powerdf.to_pandas()
+
+    if powerdf.is_empty():  # pragma: no cover
+        return('', '<p>No valid data found</p>')
+
+
+    powerdf = powerdf.lazy().filter(pl.col("CR")>0)
+    powerdf = powerdf.sort(["Delta", "CR"], descending=[False, True])
+    powerdf = powerdf.unique(subset="Delta", keep="first")
+    powerdf = powerdf.fill_nan(None).drop_nulls()
+    powerdf = powerdf.collect()
+
+    rowername = r.user.first_name+" "+r.user.last_name
+
+    wcdurations = []
+    wcpower = []
+
+    if len(powerdf) != 0:
+        datefirst = pd.Series(w.date for w in workouts).min()
+        datelast = pd.Series(w.date for w in workouts).max()
+        title = 'Critical Stroke Rate chart for {name}, from {d1} to {d2}'.format(
+            name=rowername,
+            d1=datefirst,
+            d2=datelast,
+        )
+        wtype = 'water'
+        if workouts[0].workouttype in mytypes.otetypes:  # pragma: no cover
+            wtype = 'erg'
+        if workouts[0].workouttype == 'bikeerg':  # pragma: no cover
+            # for Mike
+            wtype = 'erg'
+
+
+        res = interactive_otwcrchart(powerdf, promember=True, rowername=rowername, r=r,
+                                     cpfit=cpfit, title=title, type=wtype,)
+
+        script = res[0]
+        div = res[1]
+        p1 = res[2]
+        ratio = res[3]
+    else:  # pragma: no cover
+        script = ''
+        div = '<p>No ranking pieces found.</p>'
+        p1 = [1, 1, 1, 1]
+        ratio = 1
+
+    
+
+
+    minutes = options['piece']
+    if minutes != 0:
+        # minutes = 77
+        try:
+            hourvalue, tvalue = divmod(minutes, 60)
+        except:  # pragma: no cover
+            hourvalue = 0
+            tvalue = minutes
+        # hourvalue = 1, tvalue = 17
+        try:
+            hourvalue = int(hourvalue)
+        except TypeError:  # pragma: no cover
+            hourvalue = 0
+        try:
+            minutevalue = int(tvalue)
+        except TypeError:  # pragma: no cover
+            minutevalue = 0
+
+        tvalue = int(60*(tvalue-minutevalue))
+
+        if hourvalue >= 24:  # pragma: no cover
+            hourvalue = 23
+        pieceduration = datetime.time(
+            minute=minutevalue,
+            hour=hourvalue,
+            second=tvalue,
+        )
+
+        pieceseconds = 3600.*pieceduration.hour+60. * \
+            pieceduration.minute+pieceduration.second
+        # CP model
+        pwr = p1[0]/(1+pieceseconds/p1[2])
+        pwr += p1[1]/(1+pieceseconds/p1[3])
+
+        if pwr <= 0:  # pragma: no cover
+            pwr = 50.
+
+        if not np.isnan(pwr):
+            try:
+                pwr2 = pwr*ratio
+            except:  # pragma: no cover
+                pwr2 = pwr
+
+        duration = timedeltaconv(pieceseconds)
+        power = int(pwr)
+        upper = int(pwr2)
+    else:  # pragma: no cover
+        duration = timedeltaconv(0)
+        power = 0
+        upper = 0
+
+    htmly = env.get_template('otwcr.html')
+    html_content = htmly.render({
+        'script': script,
+        'the_div': div,
+        'duration': duration,
+        'power': power,
+        'upper': upper,
+    })
+
+    return (script, html_content)
+
 
 def statsdata(workouts, options):
     #try:
@@ -1063,6 +1198,8 @@ def analysis_view_data(request, userid=0):
         script, div = comparisondata(workouts, options)
     elif function == 'cp':  # pragma: no cover
         script, div = cpdata(workouts, options)
+    elif function == 'cr':  # pragma: no cover
+        script, div = crdata(workouts, options)
     else:  # pragma: no cover
         script = ''
         div = 'Unknown analysis functions'

rowers/views/apiviews.py

@@ -1035,8 +1035,15 @@ def strokedatajson_v2(request, id):
         datadf = dataprep.dataplep(
             rowdata, id=row.id, bands=True, barchart=True, otwpower=True, empower=True)
 
+        if row.workouttype in mytypes.otwtypes:
+            wps_avg = r.median_wps
+        elif row.workouttype in mytypes.ergtypes:
+            wps_avg = r.median_wps_erg
+        else:
+            wps_avg = 0
+        
         _ = myqueue(queuehigh, handle_calctrimp, row.id,
-                    row.csvfilename, r.ftp, r.sex, r.hrftp, r.max, r.rest)
+                    row.csvfilename, r.ftp, r.sex, r.hrftp, r.max, r.rest, wps_avg)
 
         isbreakthrough, ishard = dataprep.checkbreakthrough(row, r)
 

rowers/views/workoutviews.py

@@ -3758,6 +3758,9 @@ def workout_stats_view(request, id=0, message="", successmessage=""):
     # TRIMP
     trimp, hrtss = dataprep.workout_trimp(w)
 
+    # SPMTSS
+    spmtss = dataprep.workout_spmtss(w)
+
     otherstats['trimp'] = {
         'verbose_name': 'TRIMP',
         'value': int(trimp),
@@ -3770,6 +3773,12 @@ def workout_stats_view(request, id=0, message="", successmessage=""):
         'unit': ''
     }
 
+    otherstats['spmScore'] = {
+        'verbose_name': 'rScore (SPM)',
+        'value': int(spmtss),
+        'unit': ''
+    }
+
     return render(request,
                   'workoutstats.html',
                   {