daqviewer/daqviewer/daqviewer.py

import io
import os
import base64
import datetime
import numpy as np
import pandas as pd
import webbrowser as wb

import dash
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.')]),
        style={
            'width': '99%',
            'height': '60px',
            'lineHeight': '60px',
            'borderWidth': '1px',
            'borderStyle': 'dashed',
            'borderRadius': '5px',
            'textAlign': 'center',
            'margin': '10px'
        },
        # Allow multiple files to be uploaded
        multiple=True),
    html.Div(id='output-data-upload'),
])


def parse_contents(contents, filename, date):
    basename, ext = os.path.splitext(filename)
    content_type, content_string = contents.split(',')
    decoded = base64.b64decode(content_string)

    # Check instrument type
    inst_type = basename.split('_')[-1]
    try:
        if inst_type == 'WP':
            df = pd.read_csv(io.StringIO(decoded.decode('utf-8')),
                             index_col=0,
                             header=5,
                             skiprows=[6])

            # Rename columns based on probe locations
            suffixes = ['P1', 'P2', 'P3', 'incident', 'reflected']
            col_names = list(df.columns)
            for i, col in enumerate(col_names[:-4]):
                if ('.' not in col) and (col_names[i + 4] == col + '.4'):
                    for j, suffix in enumerate(suffixes):
                        col_names[i + j] = '{}-{}'.format(col, suffix)
            df.columns = col_names
        else:
            df = pd.read_csv(io.StringIO(decoded.decode('utf-8')),
                             index_col=0,
                             header=3,
                             skiprows=[4])

    except Exception as e:
        print(e)
        return html.Div(['There was an error processing this file.'])

    # Zero time series based on first 5s
    df -= df[:5].mean()

    ts = []
    for col in df.columns:
        trace = go.Scatter(x=df.index, y=df[col], name=col, opacity=0.8)
        ts.append(trace)

    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'
    ]

    spec = []
    for col in df.columns:
        t = df.index.values[:, np.newaxis]
        x = df[[col]].values

        # 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)

    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 = [
            parse_contents(c, n, d)
            for c, n, d in zip(list_of_contents, list_of_names, list_of_dates)
        ]
        return children


def main():
    port = 8050
    wb.open('http://localhost:{}'.format(port))
    app.run_server(port=port, debug=True)


if __name__ == '__main__':
    main()