8: Added code from moodle
marcgauch
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74af4c4018
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import math
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from dash import Dash, dcc, html, Input, Output
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import plotly.express as px
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import plotly.graph_objects as go
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import numpy as np
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import pandas as pd
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import dash_bootstrap_components as dbc
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from sklearn.datasets import make_blobs
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# New: Density heatmap (2 columns) as third plot on tab 2
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# with color and resolution options
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# New: Everything with inline style and bootstrap (no CSS)
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app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
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# generate random normal distributed data for x and y
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# and store it in a Pandas DataFrame (for plot 1,2, and 5)
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np.random.seed(seed=8)
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df = pd.DataFrame({'y': np.random.normal(loc=0, scale=10, size=1000),
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'x': np.random.normal(loc=10, scale=2, size=1000)})
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# define cluster colors
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COLORS = {'0': "red", '1': "blue", '2': "grey"}
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# generic cluster data (for plot 3 and 4)
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X, y = make_blobs(n_samples=7500, centers=3, n_features=2, random_state=0, cluster_std=0.75)
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cluster_df = pd.DataFrame(data=X, columns=["X", "Y"])
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cluster_df['cluster'] = [str(i) for i in y]
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app.layout = html.Div([html.Div([html.H1("Dashboard 6")], style={'margin': '10px 25px 25px 25px'}), html.Div([dcc.Tabs(id="tabs", children=[
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dcc.Tab(label='Tab One', children=[html.Div([
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dbc.Row([dbc.Col([dcc.Dropdown(options=['red', 'green', 'blue'], value='red', id='color', multi=False)], width=6),
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dbc.Col([dcc.Slider(min=math.floor(df['y'].min()), max=math.ceil(df['y'].max()), id="min_value")], width=6)]),
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dbc.Row([dbc.Col([dcc.Graph(id="graph_1")],width=6),
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dbc.Col([dcc.Graph(id="graph_2")],width=6)])], style={"margin": "100px 25px 25px 25px"}),]),
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dcc.Tab(label='Tab Two', id="tab_2_graphs", children=[html.Div([
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dbc.Row([dbc.Col([dcc.Graph(id="graph_3")], width=8),
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dbc.Col([dcc.Graph(id="graph_4")], width=4)]),
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dbc.Row([dbc.Col(html.Div([dbc.Label("Number of bins:", html_for="graph_5_nbins"),
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dcc.Dropdown(options= [str(i) for i in range(5, 100, 5)], value='40', id='graph_5_nbins', multi=False)]),width={"size": 3},),
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dbc.Col(html.Div([dbc.Label("Color:", html_for="graph_5_color"),
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dcc.Dropdown(options=["Viridis", "Magma", "Hot", "GnBu", "Greys"], value='Viridis', id='graph_5_color', multi=False)]),width={"size": 3,"offset": 1},),
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dbc.Col(html.Div([dbc.Label("Separated for Cluster:", html_for="graph_5_separated"),
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dcc.RadioItems(options=["Yes","No"], value='No', id='graph_5_separated')]),width={"size": 3,"offset": 1},)]),
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dbc.Row([dbc.Col([dcc.Graph(id="graph_5")], width=12)])], style={"margin": "10px 25px 25px 25px"})]),])], style={"margin": "10px 25px 25px 25px"})])
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def update_selected_data(selected_data):
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if selected_data is None or
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(isinstance(selected_data, dict) and
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'xaxis.range[0]' not in selected_data):
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cluster_dff = cluster_df
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else:
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cluster_dff =
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cluster_df[
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(cluster_df['X'] >=
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selected_data.get('xaxis.range[0]')) &
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(cluster_df['X'] <=
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selected_data.get('xaxis.range[1]')) &
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(cluster_df['Y'] >=
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selected_data.get('yaxis.range[0]')) &
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(cluster_df['Y'] <=
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selected_data.get('yaxis.range[1]'))]
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return cluster_dff
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@app.callback(Output("graph_1", "figure"), Input("color", "value"))
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def update_graph_1(dropdown_value_color):
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fig = px.histogram(df, x="y", color_discrete_sequence=[dropdown_value_color])
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fig.update_layout(template="plotly_white")
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return fig
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@app.callback(Output("graph_2", "figure"), Input("min_value", "value"))
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def update_graph_2(min_value):
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if min_value:
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dff = df[df['y'] > min_value]
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else:
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dff = df
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fig = px.scatter(dff, x='x', y='y')
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fig.update_layout(template="plotly_white")
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return fig
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@app.callback(Output("graph_3", "figure"), Output("graph_4", "figure"), Input("graph_3", "relayoutData"))
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def update_graph_3_and_4(selected_data):
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PLOT_HEIGHT = 400
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cluster_dff = update_selected_data(selected_data=selected_data)
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fig3 = px.scatter(cluster_dff, x="X", y="Y", color="cluster", color_discrete_map=COLORS, category_orders={"cluster": ["0", "1", "2"]})
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fig3.update_layout(height=PLOT_HEIGHT, template="plotly_white", coloraxis_showscale=False)
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fig3.update_traces(marker=dict(size=8))
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group_counts = cluster_dff[['cluster', 'X']].groupby('cluster').count()
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fig4 = go.Figure(data=[go.Bar(x=group_counts.index, y=group_counts['X'], marker_color=[COLORS.get(i) for i in group_counts.index])])
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fig4.update_layout(height=PLOT_HEIGHT, template="plotly_white", title="<b>Counts per cluster</b>", xaxis_title="cluster", title_font_size=25)
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return fig3, fig4
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@app.callback(Output("graph_5", "figure"), Input("graph_5_nbins", "value"), Input("graph_5_color", "value"), Input("graph_5_separated", "value"), Input("graph_3", "relayoutData"),)
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def update_graph_5(nbins, color, separated, selected_data):
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cluster_dff = update_selected_data(selected_data=selected_data)
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fig = px.density_heatmap(cluster_dff, x="X", y="Y", nbinsx=int(nbins), nbinsy=int(nbins), color_continuous_scale=color, facet_col=None if separated == "No" else "cluster",
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category_orders={"cluster": ["0", "1", "2"]})
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fig.update_layout(template="plotly_white")
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return fig
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if __name__ == '__main__':
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app.run_server(debug=True, port=8014)
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@ -0,0 +1,25 @@
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blinker==1.6.2
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click==8.1.3
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dash==2.9.3
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dash-bootstrap-components==1.4.1
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dash-core-components==2.0.0
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dash-html-components==2.0.0
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dash-table==5.0.0
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Flask==2.3.2
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itsdangerous==2.1.2
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Jinja2==3.1.2
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joblib==1.2.0
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MarkupSafe==2.1.2
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numpy==1.24.3
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packaging==23.1
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pandas==2.0.1
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plotly==5.14.1
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python-dateutil==2.8.2
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pytz==2023.3
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scikit-learn==1.2.2
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scipy==1.10.1
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six==1.16.0
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tenacity==8.2.2
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threadpoolctl==3.1.0
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tzdata==2023.3
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Werkzeug==2.3.4
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