Fit table width to contents
parent
502769b76a
commit
3571d56c96
@ -1,164 +0,0 @@
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import json
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import numpy as np
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import dash
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import dash_table
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import pandas as pd
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import plotly.graph_objs as go
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import dash_table
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import dash_core_components as dcc
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import dash_html_components as html
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from dash.dependencies import Input, Output, State
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app = dash.Dash(__name__)
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app.layout = html.Div([
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html.Div(dcc.Graph(id='timeseries')),
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html.Div(id='div-table'),
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html.Div(id='datatable-row-ids-container'),
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html.Div(id='storage', style={'display': 'none'}),
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html.Div([
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html.Button('Load', id='button'),
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])
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])
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# app.css.append_css('style.css')
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@app.callback(
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Output('storage', 'children'),
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[Input('button', 'n_clicks')],
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)
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def load_data(n_clicks):
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csv_name = 'test_20_WP.csv'
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# Check instrument type
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inst_type = 'WP'
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if inst_type == 'WP':
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df = pd.read_csv(csv_name, index_col=0, header=5, skiprows=[6])
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# Rename columns based on probe locations
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suffixes = ['P1', 'P2', 'P3', 'incident', 'reflected']
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col_names = list(df.columns)
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for i, col in enumerate(col_names[:-4]):
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if ('.' not in col) and (col_names[i + 4] == col + '.4'):
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for j, suffix in enumerate(suffixes):
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col_names[i + j] = '{}-{}'.format(col, suffix)
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df.columns = col_names
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else:
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df = pd.read_csv(csv_name, index_col=0, header=3, skiprows=[4])
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# Zero time series based on first 5s
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df -= df[:5].mean()
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df = df[:100]
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# Round floats
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df = df.round(2)
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return df.to_json(orient='table')
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@app.callback(
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Output('timeseries', 'figure'),
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[Input('storage', 'children')],
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)
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def update_graph(json_data):
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if json_data is None:
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return {'data': []}
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df = pd.read_json(json_data, orient='table')
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ts = []
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for col in df.columns:
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trace = go.Scatter(x=df.index, y=df[col], name=col, opacity=0.8)
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ts.append(trace)
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layout = {'title': 'basename', 'xaxis': {'rangeslider': {}}}
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timeseries = {'data': ts, 'layout': layout}
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return timeseries
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@app.callback(
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Output('div-table', 'children'),
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[Input('storage', 'children')],
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)
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def update_table(json_data):
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if json_data is None:
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return {'data': []}
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df = pd.read_json(json_data, orient='table')
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stats = df.describe().T
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stats = stats.round(2)
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s1 = stats.index.to_series()
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s1.name = 'Name'
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stats = pd.concat([s1, stats], axis=1)
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datatable = dash_table.DataTable(
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id='datatable-row-ids',
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# columns=[{
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# 'name': [val, key],
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# 'id': key
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# } for key, val in variables.items()],
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columns=[{
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'name': x,
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'id': x
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} for x in stats.columns],
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data=stats.to_dict('records'),
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editable=False,
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sort_action='native',
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sort_mode='multi',
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row_selectable='multi',
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row_deletable=False,
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selected_rows=np.arange(stats.shape[0]),
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style_as_list_view=True,
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style_cell={
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'minWidth': '100px',
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'width': '100px',
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'maxWidth': '100px',
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},
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)
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return [datatable]
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# @app.callback(Output('datatable-row-ids-container', 'children'), [
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# Input('datatable-row-ids', 'derived_virtual_row_ids'),
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# Input('datatable-row-ids', 'selected_row_ids'),
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# Input('datatable-row-ids', 'active_cell')
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# ])
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# def update_graphs(row_ids, selected_row_ids, active_cell):
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# # When the table is first rendered, `derived_virtual_data` and
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# # `derived_virtual_selected_rows` will be `None`. This is due to an
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# # idiosyncracy in Dash (unsupplied properties are always None and Dash
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# # calls the dependent callbacks when the component is first rendered).
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# # So, if `rows` is `None`, then the component was just rendered
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# # and its value will be the same as the component's dataframe.
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# # Instead of setting `None` in here, you could also set
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# # `derived_virtual_data=df.to_rows('dict')` when you initialize
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# # the component.
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# selected_id_set = set(selected_row_ids or [])
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#
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# if selected_id_set:
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# dff = df.loc[selected_id_set]
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# else:
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# dff = df
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#
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# spec = []
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# for i, row in dff.iterrows():
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# x = np.arange(10)
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# y = x * row['H_sig']
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#
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# trace = go.Scatter(x=x, y=y, name=i)
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# spec.append(trace)
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#
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# energy = dict(data=spec)
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#
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# graph = dcc.Graph(id='time-series', figure=energy)
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#
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# return graph
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if __name__ == '__main__':
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app.run_server(debug=True)
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@ -1,101 +0,0 @@
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import os
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import dash
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from dash.dependencies import Input, Output
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import dash_table
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import dash_core_components as dcc
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import dash_html_components as html
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import plotly.graph_objs as go
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import pandas as pd
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import numpy as np
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# __file__ = '.'
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csv_name = os.path.dirname(__file__) + '../statistics.csv'
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df = pd.read_csv(csv_name)
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df_spec = df.copy()
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df_spec.T
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# Round floats
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df = df.round(2)
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variables = {
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'location': '',
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'H_sig': 'o',
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'H_1%': 'x',
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'H_max': '□',
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'Hm0': '◊',
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'Tp': '',
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'Tp1': '',
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}
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df = df[variables.keys()]
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df['id'] = df['location']
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df = df.set_index('id', drop=False)
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app = dash.Dash(__name__)
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app.layout = html.Div([
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dash_table.DataTable(
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id='datatable-row-ids',
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columns=[{
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'name': [val, key],
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'id': key
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} for key, val in variables.items()],
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data=df.to_dict('records'),
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editable=False,
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sort_action='native',
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sort_mode='multi',
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row_selectable='multi',
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row_deletable=False,
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selected_rows=np.arange(df.shape[0]),
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style_as_list_view=True,
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style_cell={
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'minWidth': '100px',
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'width': '100px',
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'maxWidth': '100px',
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},
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),
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html.Div(id='datatable-row-ids-container')
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])
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@app.callback(Output('datatable-row-ids-container', 'children'), [
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Input('datatable-row-ids', 'derived_virtual_row_ids'),
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Input('datatable-row-ids', 'selected_row_ids'),
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Input('datatable-row-ids', 'active_cell')
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])
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def update_graphs(row_ids, selected_row_ids, active_cell):
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# When the table is first rendered, `derived_virtual_data` and
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# `derived_virtual_selected_rows` will be `None`. This is due to an
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# idiosyncracy in Dash (unsupplied properties are always None and Dash
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# calls the dependent callbacks when the component is first rendered).
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# So, if `rows` is `None`, then the component was just rendered
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# and its value will be the same as the component's dataframe.
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# Instead of setting `None` in here, you could also set
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# `derived_virtual_data=df.to_rows('dict')` when you initialize
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# the component.
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selected_id_set = set(selected_row_ids or [])
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if selected_id_set:
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dff = df.loc[selected_id_set]
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else:
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dff = df
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spec = []
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for i, row in dff.iterrows():
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x = np.arange(10)
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y = x * row['H_sig']
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trace = go.Scatter(x=x, y=y, name=i)
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spec.append(trace)
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energy = dict(data=spec)
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graph = dcc.Graph(id='time-series', figure=energy)
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return graph
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if __name__ == '__main__':
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app.run_server(debug=False)
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