Add table for spectral values

daqviewer/daqviewer.py

@@ -7,6 +7,7 @@ import pandas as pd
 import webbrowser as wb
 
 import dash
+import dash_table
 import dash_core_components as dcc
 import dash_html_components as html
 from dash.dependencies import Input, Output
@@ -83,22 +84,26 @@ def parse_contents(contents, filename, date):
     layout = dict(title=basename, xaxis=dict(rangeslider=dict()))
     timeseries = dict(data=ts, layout=layout)
 
-    # Specify wave statistics
-    var = [
-        'Hm0', 'Tm01', 'Tm02', 'Tm24', 'Tp', 'Ss', 'Sp', 'Ka', 'Tp1', 'alpha',
-        'eps2', 'eps4'
-    ]
+    # Create spectral dataframe
+    dfs = pd.DataFrame(columns=range(15))
 
     spec = []
     for col in df.columns:
         t = df.index.values[:, np.newaxis]
         x = df[[col]].values
 
-        # Get wave statistics
+        # Get spectral statistics
         xx = wo.mat2timeseries(np.hstack([t, x]))
         S = xx.tospecdata()
         S.freqtype = 'f'
-        values, _, keys = S.characteristic(var)
+        values, _, keys = S.characteristic(range(15))
+
+        # Update column names
+        if dfs.columns[0] == 0:
+            dfs.columns = keys
+
+        # Add spectral values to dataframe
+        dfs.loc[col, keys] = values
 
         # Plot energy spectrum
         trace = go.Scatter(x=S.args, y=S.data, name=col, opacity=0.8)
@@ -106,9 +111,42 @@ def parse_contents(contents, filename, date):
 
     energy = dict(data=spec)
 
+    # Add location column
+    dfs['location'] = dfs.index
+
+    variables = {
+        'location': '',
+        'Hm0': '◊',
+        'Tp': '□',
+        'Tp1': 'o',
+    }
+
+    # Add table
+    table = dash_table.DataTable(
+        id='datatable-row-ids',
+        columns=[{
+            'name': [val, key],
+            'id': key
+        } for key, val in variables.items()],
+        data=dfs.to_dict('records'),
+        editable=False,
+        sort_action='native',
+        sort_mode='multi',
+        row_selectable='multi',
+        row_deletable=False,
+        selected_rows=np.arange(dfs.shape[0]),
+        style_as_list_view=True,
+        style_cell={
+            'minWidth': '100px',
+            'width': '100px',
+            'maxWidth': '100px',
+        },
+    )
+
     elements = html.Div([
         dcc.Graph(id='time-series', figure=timeseries),
-        dcc.Graph(id='energy-spectrum', figure=energy)
+        dcc.Graph(id='energy-spectrum', figure=energy), table,
+        html.Div(id='datatable-row-ids-container')
     ])
 
     return elements
@@ -131,7 +169,7 @@ def update_output(list_of_contents, list_of_names, list_of_dates):
 def main():
     port = 8050
     wb.open('http://localhost:{}'.format(port))
-    app.run_server(port=port, debug=True)
+    app.run_server(port=port, debug=False)
 
 
 if __name__ == '__main__':

daqviewer/tables.py

@@ -30,10 +30,6 @@ variables = {
 
 df = df[variables.keys()]
 
-# add an id column and set it as the index
-# in this case the unique ID is just the country name, so we could have just
-# renamed 'country' to 'id' (but given it the display name 'country'), but
-# here it's duplicated just to show the more general pattern.
 df['id'] = df['location']
 df = df.set_index('id', drop=False)