From 5149f08de7b93889209436c7b7f6044daf56f7a4 Mon Sep 17 00:00:00 2001 From: Sander Roosendaal Date: Wed, 13 Mar 2024 18:57:18 +0100 Subject: [PATCH] working --- rowers/dataprep.py | 111 +++++++++++----------- rowers/datautils.py | 16 ++-- rowers/models.py | 21 ++-- rowers/rowing-workout-metrics.proto | 49 ---------- rowers/rowing_workout_metrics.proto | 49 ---------- rowers/rowing_workout_metrics_pb2.py | 8 +- rowers/rowing_workout_metrics_pb2_grpc.py | 65 ++++--------- rowers/tests/testdata/testdata.tcx.gz | Bin 4000 -> 4002 bytes rowers/utils.py | 6 +- 9 files changed, 97 insertions(+), 228 deletions(-) delete mode 100644 rowers/rowing-workout-metrics.proto delete mode 100644 rowers/rowing_workout_metrics.proto diff --git a/rowers/dataprep.py b/rowers/dataprep.py index d05ec634..26684810 100644 --- a/rowers/dataprep.py +++ b/rowers/dataprep.py @@ -502,8 +502,28 @@ def calculate_goldmedalstandard(rower, workout, recurrance=True): def setcp(workout, background=False, recurrance=True): - filename = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id) + try: + filename = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id) + df = pd.read_parquet(filename) + if not df.empty: + # check dts + tarr = datautils.getlogarr(4000) + if df['delta'][0] in tarr: + return(df, df['delta'], df['cp']) + except: + pass + + strokesdf = getsmallrowdata_db( + ['power', 'workoutid', 'time'], ids=[workout.id]) + + if strokesdf.empty: + return pd.DataFrame({'delta': [], 'cp': []}), pd.Series(dtype='float'), pd.Series(dtype='float') + + totaltime = strokesdf['time'].max() + maxt = totaltime/1000. + logarr = datautils.getlogarr(maxt) + csvfilename = workout.csvfilename # check what the real file name is if os.path.exists(csvfilename): @@ -514,80 +534,53 @@ def setcp(workout, background=False, recurrance=True): csvfile = csvfilename+'.gz' else: # pragma: no cover return pd.DataFrame({'delta': [], 'cp': []}), pd.Series(dtype='float'), pd.Series(dtype='float') + csvfile = os.path.abspath(csvfile) - strokesdf = getsmallrowdata_db( - ['power', 'workoutid', 'time'], ids=[workout.id]) - protos = grpc.protos("rowing_workout_metrics.proto") - services = grpc.services("rowing_workout_metrics.proto") - req = protos.CPRequest( - filename = csvfile, - filetype = "CSV", - tarr = datautils.getlogarr(1.05*strokesdf['time'].max()) - ) - response = services.GetCP(req, "localhost:50052", insecure=True) - delta = response.delta - cpvalues = response.power + with grpc.insecure_channel( + target='localhost:50052', + options=[('grpc.lb_policy_name', 'pick_first'), + ('grpc.enable_retries', 0), ('grpc.keepalive_timeout_ms', + 10000)] + ) as channel: + try: + grpc.channel_ready_future(channel).result(timeout=10) + except grpc.FutureTimeoutError: # pragma: no cover + dologging('metrics.log','grpc channel time out in setcp') + return pd.DataFrame({'delta': [], 'cp': []}), pd.Series(dtype='float'), pd.Series(dtype='float') + + stub = metrics_pb2_grpc.MetricsStub(channel) + req = metrics_pb2.CPRequest(filename = csvfile, filetype = "CSV", tarr = logarr) + + try: + response = stub.GetCP(req, timeout=60) + except Exception as e: + dologging('metrics.log', traceback.format_exc()) + return pd.DataFrame({'delta': [], 'cp': []}), pd.Series(dtype='float'), pd.Series(dtype='float') + + delta = pd.Series(np.array(response.delta)) + cpvalues = pd.Series(np.array(response.power)) powermean = response.avgpower + + df = pd.DataFrame({ 'delta': delta, 'cp': cpvalues, 'id': workout.id, }) + df.to_parquet(filename, engine='fastparquet', compression='GZIP') + if recurrance: goldmedalstandard, goldmedalduration = calculate_goldmedalstandard( workout.user, workout) workout.goldmedalstandard = goldmedalstandard workout.goldmedalduration = goldmedalduration workout.save() + return df, delta, cpvalues - - - try: - if strokesdf['power'].std() == 0: - return pd.DataFrame(), pd.Series(dtype='float'), pd.Series(dtype='float') - except (KeyError, TypeError): - return pd.DataFrame(), pd.Series(dtype='float'), pd.Series(dtype='float') - - if background: # pragma: no cover - _ = myqueue(queuelow, handle_setcp, strokesdf, filename, workout.id) - return pd.DataFrame({'delta': [], 'cp': []}), pd.Series(dtype='float'), pd.Series(dtype='float') - - if not strokesdf.empty: - totaltime = strokesdf['time'].max() - try: - powermean = strokesdf['power'].mean() - except KeyError: # pragma: no cover - 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) - - df = pd.DataFrame({ - 'delta': delta, - 'cp': cpvalues, - 'id': workout.id, - }) - df.to_parquet(filename, engine='fastparquet', - compression='GZIP') - if recurrance: - goldmedalstandard, goldmedalduration = calculate_goldmedalstandard( - workout.user, workout) - workout.goldmedalstandard = goldmedalstandard - workout.goldmedalduration = goldmedalduration - workout.save() - return df, delta, cpvalues - - return pd.DataFrame({'delta': [], 'cp': []}), pd.Series(dtype='float'), pd.Series(dtype='float') def update_wps(r, types, mode='water', asynchron=True): @@ -723,6 +716,7 @@ def join_workouts(r, ids, title='Joined Workout', def fetchcp_new(rower, workouts): data = [] + for workout in workouts: cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id) try: @@ -743,12 +737,15 @@ def fetchcp_new(rower, workouts): if len(data) > 1: df = pd.concat(data, axis=0) + try: df = df[df['cp'] == df.groupby(['delta'])['cp'].transform('max')] except KeyError: # pragma: no cover return pd.Series(dtype='float'), pd.Series(dtype='float'), 0, pd.Series(dtype='float'), pd.Series(dtype='float') df = df.sort_values(['delta']).reset_index() + df = df[df['cp']>20] + return df['delta'], df['cp'], 0, df['workout'], df['url'] diff --git a/rowers/datautils.py b/rowers/datautils.py index fcebd334..cb17a357 100644 --- a/rowers/datautils.py +++ b/rowers/datautils.py @@ -123,18 +123,18 @@ def cpfit(powerdf, fraclimit=0.0001, nmax=1000): def getlogarr(maxt): - maxlog10 = np.log10(maxt-5) + dtmin = 10 + maxlog10 = np.log10(maxt) # print(maxlog10,round(maxlog10)) - aantal = 10*round(maxlog10) + aantal = 40 logarr = np.arange(aantal+1)/10. + vs = 10.**logarr + res = [] - for la in logarr: - try: - v = 5+int(10.**(la)) - except ValueError: # pragma: no cover - v = 0 - res.append(v) + for v in vs: + if v > dtmin and vuIcmVC4TW=dT7J%RLD~vp}4~tUg!o!Q} zx=7P5HbBxXnxNaa#ne_E?b@;-O)mZSOG=63Bp$TK-XS;x<{_{~bJKit$aCk>w{I`b z4_+@f+tuazn}=xN;la1Z4~|}Jy48BMK6|`e_sh-qUElrIZI=gW=k@lRhcEm7>Tt1m z^X82?>oymwwK=)GSfnqvCoh*5-F9*N;fvSwaDR%!?(JVV%cGNi^?KF+@x$AGxz0Cu z;{Fef{M0m;l{ZAYT!@Kt8m1NX|q@9N}*OhX6;5 zwA1_jpLSOV&--rEzgS%?zj^p)$6jA39s1(3@6PjK&zC2c>(lMAus>RSw)MjS->>#> z(6eRwvDK|7(c=2khaDazQR~i@|5&EG|GDdz-(6koUd{fGtMp5* zH$Pc_x9s}uPs`1-)%sPx{C9ujN2JsI(w)Cr9-~bc+5h?B3X5AVapUV}-P_g0tBe2B 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index bf71456b..34f808ea 100644 --- a/rowers/utils.py +++ b/rowers/utils.py @@ -347,9 +347,9 @@ def isbreakthrough(delta, cpvalues, p0, p1, p2, p3, ratio): pwr *= ratio - delta = delta.values.astype(int) - cpvalues = cpvalues.values.astype(int) - pwr = pwr.astype(int) + delta = delta.astype(int, errors='ignore').values + cpvalues = cpvalues.astype(int, errors='ignore').values + pwr = pwr.astype(int, errors='ignore').values res = np.sum(cpvalues > pwr+1) res2 = np.sum(cpvalues > pwr2+1)