Store both timeseries and energy spectrum in json_storage div

daqviewer/daqviewer.py

@@ -76,7 +76,40 @@ def parse_contents(contents, filename):
     # Round dataframe to save disk space
     df = df.round(1)
 
-    return df.to_json(orient='table')
+    # Create dataframe for statistics
+    stats = pd.DataFrame(columns=range(15))
+
+    # Create datafrae for energy spectrum
+    f = np.arange(0, 2.5, step=0.01)
+    dfs = pd.DataFrame(index=f)
+
+    for col in df.columns:
+        t = df.index.values[:, np.newaxis]
+        x = df[[col]].values
+
+        # Get spectral statistics
+        xx = wo.mat2timeseries(np.hstack([t, x]))
+        S = xx.tospecdata()
+        values, _, keys = S.characteristic(range(15))
+        S.freqtype = 'f'
+
+        # Interpolate
+        dfs[col] = np.interp(f, S.args, S.data)
+
+        # Update column names
+        if stats.columns[0] == 0:
+            stats.columns = keys
+
+        # Add spectral values to dataframe
+        stats.loc[col, keys] = values
+
+
+    json_data = {}
+    json_data['timeseries'] = df.to_json(orient='table')
+    json_data['spectrum'] = dfs.to_json(orient='table')
+    json_data['statistics'] = stats.to_json(orient='table')
+
+    return json.dumps(json_data)
 
 
 @app.callback(Output('json-storage', 'children'), [
@@ -94,7 +127,7 @@ def read_json_data(json_data):
     if json_data is None:
         return html.Div([])
 
-    df = pd.read_json(json_data, orient='table')
+    df = pd.read_json(json.loads(json_data)['timeseries'], orient='table')
     ts = []
     for col in df.columns:
         trace = go.Scatter(x=df.index, y=df[col], name=col, opacity=0.8)
@@ -107,29 +140,12 @@ def read_json_data(json_data):
                                      'layout': layout
                                  })
 
-    # Create spectral dataframe
-    dfs = pd.DataFrame(columns=range(15))
-
+    dfs = pd.read_json(json.loads(json_data)['spectrum'],
+                       orient='table')
     spec = []
-    for col in df.columns:
-        t = df.index.values[:, np.newaxis]
-        x = df[[col]].values
-
-        # Get spectral statistics
-        xx = wo.mat2timeseries(np.hstack([t, x]))
-        S = xx.tospecdata()
-        values, _, keys = S.characteristic(range(15))
-        S.freqtype = 'f'
-
-        # Update column names
-        if dfs.columns[0] == 0:
-            dfs.columns = keys
-
-        # Add spectral values to dataframe
-        dfs.loc[col, keys] = values
-
+    for col in dfs.columns:
         # Plot energy spectrum
-        trace = go.Scatter(x=S.args, y=S.data, name=col, opacity=0.8)
+        trace = go.Scatter(x=dfs.index, y=dfs[col], name=col, opacity=0.8)
         spec.append(trace)
 
     graph_energy = dcc.Graph(id='energy-spectrum',
@@ -145,6 +161,9 @@ def read_json_data(json_data):
     # Add location column
     dfs['location'] = dfs.index
 
+    # Round spectral values
+    dfs = dfs.round(1)
+
     variables = {
         'location': '',
         'Hm0': '◊',