Plot energy spectra below time series

daqviewer/daqviewer.py

@@ -2,24 +2,28 @@ import io
 import os
 import base64
 import datetime
+import numpy as np
 import pandas as pd
 import webbrowser as wb
 
 import dash
-from dash.dependencies import Input, Output
 import dash_core_components as dcc
 import dash_html_components as html
+from dash.dependencies import Input, Output
 
 import plotly.plotly as py
 import plotly.graph_objs as go
 
+import wafo.objects as wo
+
 app = dash.Dash()
 app.title = 'daqviewer'
 app.scripts.config.serve_locally = True
 app.layout = html.Div([
     dcc.Upload(
         id='upload-data',
-        children=html.Div([html.A('Drag and drop csv files, or click to select.')]),
+        children=html.Div(
+            [html.A('Drag and drop csv files, or click to select.')]),
         style={
             'width': '99%',
             'height': '60px',
@@ -45,8 +49,7 @@ def parse_contents(contents, filename, date):
     inst_type = basename.split('_')[-1]
     try:
         if inst_type == 'WP':
-            df = pd.read_csv(
-                io.StringIO(decoded.decode('utf-8')),
+            df = pd.read_csv(io.StringIO(decoded.decode('utf-8')),
                              index_col=0,
                              header=5,
                              skiprows=[6])
@@ -60,8 +63,7 @@ def parse_contents(contents, filename, date):
                         col_names[i + j] = '{}-{}'.format(col, suffix)
             df.columns = col_names
         else:
-            df = pd.read_csv(
-                io.StringIO(decoded.decode('utf-8')),
+            df = pd.read_csv(io.StringIO(decoded.decode('utf-8')),
                              index_col=0,
                              header=3,
                              skiprows=[4])
@@ -73,23 +75,50 @@ def parse_contents(contents, filename, date):
     # Zero time series based on first 5s
     df -= df[:5].mean()
 
-    data = []
+    ts = []
     for col in df.columns:
         trace = go.Scatter(x=df.index, y=df[col], name=col, opacity=0.8)
-        data.append(trace)
+        ts.append(trace)
 
     layout = dict(title=basename, xaxis=dict(rangeslider=dict()))
-    fig = dict(data=data, layout=layout)
+    timeseries = dict(data=ts, layout=layout)
+
+    # Specify wave statistics
+    var = [
+        'Hm0', 'Tm01', 'Tm02', 'Tm24', 'Tp', 'Ss', 'Sp', 'Ka', 'Tp1', 'alpha',
+        'eps2', 'eps4'
+    ]
+
+    spec = []
+    for col in df.columns:
+        t = df.index.values[:, np.newaxis]
+        x = df[[col]].values
 
-    return html.Div([dcc.Graph(id='my-graph', figure=fig)])
+        # Get wave statistics
+        xx = wo.mat2timeseries(np.hstack([t, x]))
+        S = xx.tospecdata()
+        S.freqtype = 'f'
+        values, _, keys = S.characteristic(var)
 
+        # Plot energy spectrum
+        trace = go.Scatter(x=S.args, y=S.data, name=col, opacity=0.8)
+        spec.append(trace)
 
-@app.callback(
-    Output('output-data-upload', 'children'), [
+    energy = dict(data=spec)
+
+    elements = html.Div([
+        dcc.Graph(id='time-series', figure=timeseries),
+        dcc.Graph(id='energy-spectrum', figure=energy)
+    ])
+
+    return elements
+
+
+@app.callback(Output('output-data-upload', 'children'), [
     Input('upload-data', 'contents'),
     Input('upload-data', 'filename'),
     Input('upload-data', 'last_modified')
-    ])
+])
 def update_output(list_of_contents, list_of_names, list_of_dates):
     if list_of_contents is not None:
         children = [
@@ -102,7 +131,7 @@ 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=False)
+    app.run_server(port=port, debug=True)
 
 
 if __name__ == '__main__':