some fixes, after manual testing

rowers/dataroutines.py

@@ -186,10 +186,12 @@ def remove_nulls_pl(data):
     data = data.select(pl.all().forward_fill())
     data = data.select(pl.all().backward_fill())
     data = data.fill_nan(None)
+
     data = data.select(cs.by_dtype(pl.NUMERIC_DTYPES)).collect()
     data = data[[s.name for s in data if not s.is_infinite().sum()]]
     data = data[[s.name for s in data if not (s.null_count() == data.height)]]
 
+
     if not data.is_empty():
         try:
             data = data.drop_nulls()
@@ -207,6 +209,8 @@ def get_video_data(w, groups=['basic'], mode='water'):
     columns = list(set(columns))
     df = getsmallrowdata_pd(columns, ids=[w.id],
                             workstrokesonly=False, doclean=False, compute=False)
+    df.dropna(axis=0, how='all', inplace=True)
+    df.dropna(axis=1, how='all', inplace=True)
 
     df['time'] = (df['time']-df['time'].min())/1000.
 
@@ -245,7 +249,6 @@ def get_video_data(w, groups=['basic'], mode='water'):
     coordinates['time'] = coordinates['time']-coordinates['time'].min()
     latitude = coordinates['latitude']
     longitude = coordinates['longitude']
-
     # bundle data
     data = {
         'boatspeed': boatspeed.values.tolist(),
@@ -263,7 +266,10 @@ def get_video_data(w, groups=['basic'], mode='water'):
                 else:
                     sigfigs = dict(rowingmetrics)[c]['sigfigs']
                     if (c != 'pace'):
-                        da = ((10**sigfigs)*df2[c]).astype(int)/(10**sigfigs)
+                        try:
+                            da = ((10**sigfigs)*df2[c]).astype(int)/(10**sigfigs)
+                        except:
+                            da = df2[c]
                     else:
                         da = df2[c]
                     data[c] = da.values.tolist()
@@ -281,6 +287,14 @@ def get_video_data(w, groups=['basic'], mode='water'):
 
     maxtime = coordinates['time'].max()
 
+    data = pd.DataFrame(data)
+    data.replace([np.inf, -np.inf], np.nan, inplace=True)
+    data.dropna(inplace=True)
+
+    data = pl.from_pandas(data)
+    
+    data = data.to_dict(as_series=False)
+
     return data, metrics, maxtime
 
 
@@ -1490,7 +1504,8 @@ def getrowdata_pl(id=0, doclean=False, convertnewtons=True,
 
 
     
-def read_data(columns, ids=[], doclean=True, workstrokesonly=True, debug=False, for_chart=False, compute=True):
+def read_data(columns, ids=[], doclean=True, workstrokesonly=True, debug=False, for_chart=False, compute=True,
+              startenddict={}):
     if ids:
         csvfilenames = [
             'media/strokedata_{id}.parquet.gz'.format(id=id) for id in ids]
@@ -1504,6 +1519,17 @@ def read_data(columns, ids=[], doclean=True, workstrokesonly=True, debug=False,
     for id, f in zip(ids, csvfilenames):
         if os.path.isfile(f):
             df = pl.scan_parquet(f)
+            if startenddict:
+                try:
+                    startsecond, endsecond = startenddict[id]
+                    df = df.filter(pl.col("time") >= 1.0e3*startsecond,
+                                   pl.col("time") <= 1.0e3*endsecond)
+                    df = df.with_columns(time = pl.col("time")-1.0e3*startsecond)
+                    if 'cumdist' in columns:
+                        df = df.collect()
+                        df = df.with_columns(cumdist = pl.col("cumdist")-df[0, "cumdist"]).lazy()
+                except KeyError:
+                    pass
             data.append(df)
         else:
             rowdata, row = getrowdata(id=id)
@@ -1516,6 +1542,17 @@ def read_data(columns, ids=[], doclean=True, workstrokesonly=True, debug=False,
                                  bands=True, otwpower=True, barchart=True,
                                  polars=True)
                 df = pl.scan_parquet(f)
+                if startenddict:
+                    try:
+                        startsecond, endsecond = startenddict[id]
+                        df = df.filter(pl.col("time") >= 1.0e3*startsecond,
+                                       pl.col("time") <= 1.0e3*endsecond)
+                        df = df.with_columns(time = pl.col("time")-1.0e3*startsecond)
+                        if 'cumdist' in columns:
+                            df = df.collect()
+                            df = df.with_columns(cumdist = pl.col("cumdist")-df[0, "cumdist"]).lazy()
+                    except KeyError:
+                        pass
                 data.append(df)
 
     data = pl.collect_all(data)

rowers/interactiveplots.py

@@ -2099,11 +2099,12 @@ def interactive_multiple_compare_chart(ids, xparam, yparam, plottype='line',
                                        promember=0, workstrokesonly=True,
                                        labeldict=None, startenddict={}):
 
+    print(startenddict)
     columns = [xparam,yparam]
     columns_basic = [xparam,yparam]
     add_columns = [
         'ftime', 'distance', 'fpace',
-        'spm',
+        'spm','cumdist',
         'time', 'pace', 'workoutstate',
         'workoutid'
         ]
@@ -2117,15 +2118,17 @@ def interactive_multiple_compare_chart(ids, xparam, yparam, plottype='line',
         doclean = True
 
 
-    datadf = pd.DataFrame()
+    datadf = pl.DataFrame()
     if promember:
         datadf = dataprep.read_data(columns, ids=ids, doclean=doclean,
                                     compute=compute,
-                                    workstrokesonly=workstrokesonly, for_chart=True)
+                                    workstrokesonly=workstrokesonly, for_chart=True,
+                                    startenddict=startenddict)
     else:
         datadf = dataprep.read_data(columns_basic, ids=ids, doclean=doclean,
                                     compute=compute,
-                                    workstrokesonly=workstrokesonly, for_chart=True)
+                                    workstrokesonly=workstrokesonly, for_chart=True,
+                                    startenddict=startenddict)
 
 
     datadf = dataprep.remove_nulls_pl(datadf)
@@ -2135,6 +2138,8 @@ def interactive_multiple_compare_chart(ids, xparam, yparam, plottype='line',
 
     datadf = datadf.with_columns(pl.col("workoutid").cast(pl.UInt32).keep_name())
 
+    # filter for start end dict
+
 
     nrworkouts = len(ids)
 

rowers/templates/embedded_video.html

@@ -116,7 +116,7 @@ function copyText() {
   </li>
   {% if user.is_authenticated and user == workout.user.user and not locked %}
   <li class="grid_2">
-    <p>Paste link to you tube video below</p>
+    <p>Paste link to Youtube video below</p>
     <p>Use the slider to locate start point for video on workout map</p>
     <p>Playing the video will advance the data in synchonization.  Use the regular YouTube
       controls
@@ -169,9 +169,8 @@ function copyText() {
   </li>
   <li class="grid_2">
     <div id="player"></div>
-    <script>
-      // 1. Code for the map
       {{ mapscript | safe }}
+    <script>
 
 
       // 2. This code loads the IFrame Player API code asynchronously.
@@ -188,7 +187,8 @@ function copyText() {
       var dataplaying = false;
 
       var videotime = 0;
-      var data = JSON.parse('{{ data|safe }}');
+    var data = JSON.parse('{{ data|safe }}');
+
       {% for id, metric in metrics.items %}
       var {{ id }}_values = data["{{ id }}"];
       // console.log("{{ id }}_values",{{ id }}_values);

rowers/templates/multicompare.html

@@ -6,14 +6,9 @@
 
 {% block main %}
 
-<script src="https://cdn.pydata.org/bokeh/release/bokeh-3.1.1.min.js"></script>
-<script async="true" type="text/javascript">
-  Bokeh.set_log_level("info");
-</script>
+<script src="https://d3js.org/d3.v6.js"></script>
+
 
-<script>
-{{ interactiveplot |safe }}
-</script>
   
 <h1>Interactive Comparison</h1>
 
@@ -22,6 +17,7 @@
   <li class="grid_4">
     <div>
       {{ the_div|safe }}
+      {{ interactiveplot |safe }}
     </div>
 
   </li>

rowers/views/analysisviews.py

@@ -387,6 +387,9 @@ def trendflexdata(workouts, options, userid=0):
     datadf = dataprep.filter_df(datadf, 'driveneergy', workmax,
                                 largerthan=False)
 
+    if yparam == 'power':
+        datadf = dataprep.filter_df(datadf, 'power', 1, largerthan=True)
+
     datadf.dropna(axis=0, how='any', inplace=True)
 
     datemapping = {

rowers/views/workoutviews.py

@@ -347,6 +347,7 @@ def workout_video_create_view(request, id=0):
     # get data
     data, metrics, maxtime = dataprep.get_video_data(
         w, groups=metricsgroups, mode=mode)
+
     hascoordinates = pd.Series(data['latitude']).std() > 0
 
     # create map
@@ -377,6 +378,7 @@ def workout_video_create_view(request, id=0):
 
     template = 'embedded_video.html'
 
+
     return render(request,
                   template,
                   {