Add 'sharing_wp.py'

daqviewer/sharing_wp.py

@@ -0,0 +1,161 @@
+import json
+import numpy as np
+import dash
+import dash_table
+import pandas as pd
+import plotly.graph_objs as go
+import dash_table
+import dash_core_components as dcc
+import dash_html_components as html
+from dash.dependencies import Input, Output, State
+
+app = dash.Dash(__name__)
+
+app.layout = html.Div([
+    html.Div(dcc.Graph(id='graph')),
+    html.Div(id='div-table'),
+    html.Div(id='datatable-row-ids-container'),
+    html.Div([
+        html.Button('Load', id='button'),
+        html.Div(id='storage', style={'display': 'none'})
+    ])
+])
+
+# app.css.append_css('style.css')
+
+
+@app.callback(
+    [Output('storage', 'children'),
+     Output('div-table', 'children')],
+    [Input('button', 'n_clicks')],
+)
+def load_data(n_clicks):
+    csv_name = 'test_20_WP.csv'
+
+    # Check instrument type
+    inst_type = 'WP'
+    if inst_type == 'WP':
+        df = pd.read_csv(csv_name, 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(csv_name, index_col=0, header=3, skiprows=[4])
+
+    # Zero time series based on first 5s
+    df -= df[:5].mean()
+
+    df = df[:100]
+
+    # Round floats
+    df = df.round(2)
+
+    variables = {
+        'location': '',
+        'H_sig': 'o',
+        'H_1%': 'x',
+        'H_max': '□',
+        'Hm0': '◊',
+        'Tp': '',
+        'Tp1': '',
+    }
+
+    stats = df.describe().T
+    s1 = stats.index.to_series()
+    s1.name = 'Name'
+    stats = pd.concat([s1, stats], axis=1)
+
+    datatable = dash_table.DataTable(
+        id='datatable-row-ids',
+        # columns=[{
+        #     'name': [val, key],
+        #     'id': key
+        # } for key, val in variables.items()],
+        columns=[{
+            'name': x,
+            'id': x
+        } for x in stats.columns],
+        data=stats.to_dict('records'),
+        editable=False,
+        sort_action='native',
+        sort_mode='multi',
+        row_selectable='multi',
+        row_deletable=False,
+        selected_rows=np.arange(stats.shape[0]),
+        style_as_list_view=True,
+        style_cell={
+            'minWidth': '100px',
+            'width': '100px',
+            'maxWidth': '100px',
+        },
+    )
+
+    return df.to_json(orient='table'), [datatable]
+
+
+@app.callback(
+    Output('graph', 'figure'),
+    [Input('storage', 'children')],
+)
+def update_graph(json_data):
+
+    if json_data is None:
+        return {'data': []}
+
+    df = pd.read_json(json_data, orient='table')
+
+    ts = []
+    for col in df.columns:
+        trace = go.Scatter(x=df.index, y=df[col], name=col, opacity=0.8)
+        ts.append(trace)
+
+    layout = {'title': 'basename', 'xaxis': {'rangeslider': {}}}
+    timeseries = {'data': ts, 'layout': layout}
+
+    return timeseries
+
+
+# @app.callback(Output('datatable-row-ids-container', 'children'), [
+#     Input('datatable-row-ids', 'derived_virtual_row_ids'),
+#     Input('datatable-row-ids', 'selected_row_ids'),
+#     Input('datatable-row-ids', 'active_cell')
+# ])
+# def update_graphs(row_ids, selected_row_ids, active_cell):
+#     # When the table is first rendered, `derived_virtual_data` and
+#     # `derived_virtual_selected_rows` will be `None`. This is due to an
+#     # idiosyncracy in Dash (unsupplied properties are always None and Dash
+#     # calls the dependent callbacks when the component is first rendered).
+#     # So, if `rows` is `None`, then the component was just rendered
+#     # and its value will be the same as the component's dataframe.
+#     # Instead of setting `None` in here, you could also set
+#     # `derived_virtual_data=df.to_rows('dict')` when you initialize
+#     # the component.
+#     selected_id_set = set(selected_row_ids or [])
+#
+#     if selected_id_set:
+#         dff = df.loc[selected_id_set]
+#     else:
+#         dff = df
+#
+#     spec = []
+#     for i, row in dff.iterrows():
+#         x = np.arange(10)
+#         y = x * row['H_sig']
+#
+#         trace = go.Scatter(x=x, y=y, name=i)
+#         spec.append(trace)
+#
+#         energy = dict(data=spec)
+#
+#         graph = dcc.Graph(id='time-series', figure=energy)
+#
+#     return graph
+
+if __name__ == '__main__':
+    app.run_server(debug=True)