8: Added code from moodle
marcgauch
parent
74af4c4018
commit
43f5277b75
|
|
@ -0,0 +1,122 @@
|
|||
import math
|
||||
|
||||
from dash import Dash, dcc, html, Input, Output
|
||||
import plotly.express as px
|
||||
import plotly.graph_objects as go
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import dash_bootstrap_components as dbc
|
||||
from sklearn.datasets import make_blobs
|
||||
|
||||
# New: Density heatmap (2 columns) as third plot on tab 2
|
||||
# with color and resolution options
|
||||
# New: Everything with inline style and bootstrap (no CSS)
|
||||
|
||||
app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
|
||||
|
||||
# generate random normal distributed data for x and y
|
||||
# and store it in a Pandas DataFrame (for plot 1,2, and 5)
|
||||
|
||||
np.random.seed(seed=8)
|
||||
|
||||
df = pd.DataFrame({'y': np.random.normal(loc=0, scale=10, size=1000),
|
||||
'x': np.random.normal(loc=10, scale=2, size=1000)})
|
||||
|
||||
# define cluster colors
|
||||
|
||||
COLORS = {'0': "red", '1': "blue", '2': "grey"}
|
||||
|
||||
# generic cluster data (for plot 3 and 4)
|
||||
|
||||
X, y = make_blobs(n_samples=7500, centers=3, n_features=2, random_state=0, cluster_std=0.75)
|
||||
|
||||
cluster_df = pd.DataFrame(data=X, columns=["X", "Y"])
|
||||
cluster_df['cluster'] = [str(i) for i in y]
|
||||
|
||||
app.layout = html.Div([html.Div([html.H1("Dashboard 6")], style={'margin': '10px 25px 25px 25px'}), html.Div([dcc.Tabs(id="tabs", children=[
|
||||
dcc.Tab(label='Tab One', children=[html.Div([
|
||||
dbc.Row([dbc.Col([dcc.Dropdown(options=['red', 'green', 'blue'], value='red', id='color', multi=False)], width=6),
|
||||
dbc.Col([dcc.Slider(min=math.floor(df['y'].min()), max=math.ceil(df['y'].max()), id="min_value")], width=6)]),
|
||||
dbc.Row([dbc.Col([dcc.Graph(id="graph_1")],width=6),
|
||||
dbc.Col([dcc.Graph(id="graph_2")],width=6)])], style={"margin": "100px 25px 25px 25px"}),]),
|
||||
dcc.Tab(label='Tab Two', id="tab_2_graphs", children=[html.Div([
|
||||
dbc.Row([dbc.Col([dcc.Graph(id="graph_3")], width=8),
|
||||
dbc.Col([dcc.Graph(id="graph_4")], width=4)]),
|
||||
dbc.Row([dbc.Col(html.Div([dbc.Label("Number of bins:", html_for="graph_5_nbins"),
|
||||
dcc.Dropdown(options= [str(i) for i in range(5, 100, 5)], value='40', id='graph_5_nbins', multi=False)]),width={"size": 3},),
|
||||
dbc.Col(html.Div([dbc.Label("Color:", html_for="graph_5_color"),
|
||||
dcc.Dropdown(options=["Viridis", "Magma", "Hot", "GnBu", "Greys"], value='Viridis', id='graph_5_color', multi=False)]),width={"size": 3,"offset": 1},),
|
||||
dbc.Col(html.Div([dbc.Label("Separated for Cluster:", html_for="graph_5_separated"),
|
||||
dcc.RadioItems(options=["Yes","No"], value='No', id='graph_5_separated')]),width={"size": 3,"offset": 1},)]),
|
||||
dbc.Row([dbc.Col([dcc.Graph(id="graph_5")], width=12)])], style={"margin": "10px 25px 25px 25px"})]),])], style={"margin": "10px 25px 25px 25px"})])
|
||||
|
||||
def update_selected_data(selected_data):
|
||||
if selected_data is None or
|
||||
(isinstance(selected_data, dict) and
|
||||
'xaxis.range[0]' not in selected_data):
|
||||
cluster_dff = cluster_df
|
||||
else:
|
||||
cluster_dff =
|
||||
cluster_df[
|
||||
(cluster_df['X'] >=
|
||||
selected_data.get('xaxis.range[0]')) &
|
||||
(cluster_df['X'] <=
|
||||
selected_data.get('xaxis.range[1]')) &
|
||||
(cluster_df['Y'] >=
|
||||
selected_data.get('yaxis.range[0]')) &
|
||||
(cluster_df['Y'] <=
|
||||
selected_data.get('yaxis.range[1]'))]
|
||||
return cluster_dff
|
||||
|
||||
@app.callback(Output("graph_1", "figure"), Input("color", "value"))
|
||||
|
||||
def update_graph_1(dropdown_value_color):
|
||||
fig = px.histogram(df, x="y", color_discrete_sequence=[dropdown_value_color])
|
||||
fig.update_layout(template="plotly_white")
|
||||
return fig
|
||||
|
||||
@app.callback(Output("graph_2", "figure"), Input("min_value", "value"))
|
||||
|
||||
def update_graph_2(min_value):
|
||||
if min_value:
|
||||
dff = df[df['y'] > min_value]
|
||||
else:
|
||||
dff = df
|
||||
|
||||
fig = px.scatter(dff, x='x', y='y')
|
||||
fig.update_layout(template="plotly_white")
|
||||
return fig
|
||||
|
||||
@app.callback(Output("graph_3", "figure"), Output("graph_4", "figure"), Input("graph_3", "relayoutData"))
|
||||
|
||||
def update_graph_3_and_4(selected_data):
|
||||
|
||||
PLOT_HEIGHT = 400
|
||||
|
||||
cluster_dff = update_selected_data(selected_data=selected_data)
|
||||
|
||||
fig3 = px.scatter(cluster_dff, x="X", y="Y", color="cluster", color_discrete_map=COLORS, category_orders={"cluster": ["0", "1", "2"]})
|
||||
|
||||
fig3.update_layout(height=PLOT_HEIGHT, template="plotly_white", coloraxis_showscale=False)
|
||||
fig3.update_traces(marker=dict(size=8))
|
||||
|
||||
group_counts = cluster_dff[['cluster', 'X']].groupby('cluster').count()
|
||||
|
||||
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])])
|
||||
|
||||
fig4.update_layout(height=PLOT_HEIGHT, template="plotly_white", title="<b>Counts per cluster</b>", xaxis_title="cluster", title_font_size=25)
|
||||
|
||||
return fig3, fig4
|
||||
|
||||
@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"),)
|
||||
|
||||
def update_graph_5(nbins, color, separated, selected_data):
|
||||
cluster_dff = update_selected_data(selected_data=selected_data)
|
||||
|
||||
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",
|
||||
category_orders={"cluster": ["0", "1", "2"]})
|
||||
fig.update_layout(template="plotly_white")
|
||||
return fig
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run_server(debug=True, port=8014)
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
blinker==1.6.2
|
||||
click==8.1.3
|
||||
dash==2.9.3
|
||||
dash-bootstrap-components==1.4.1
|
||||
dash-core-components==2.0.0
|
||||
dash-html-components==2.0.0
|
||||
dash-table==5.0.0
|
||||
Flask==2.3.2
|
||||
itsdangerous==2.1.2
|
||||
Jinja2==3.1.2
|
||||
joblib==1.2.0
|
||||
MarkupSafe==2.1.2
|
||||
numpy==1.24.3
|
||||
packaging==23.1
|
||||
pandas==2.0.1
|
||||
plotly==5.14.1
|
||||
python-dateutil==2.8.2
|
||||
pytz==2023.3
|
||||
scikit-learn==1.2.2
|
||||
scipy==1.10.1
|
||||
six==1.16.0
|
||||
tenacity==8.2.2
|
||||
threadpoolctl==3.1.0
|
||||
tzdata==2023.3
|
||||
Werkzeug==2.3.4
|
||||
Loading…
Reference in New Issue