8: Formatted
marcgauch
parent
43f5277b75
commit
db9ea8197f
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@ -19,104 +19,293 @@ app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
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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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df = pd.DataFrame(
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{
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"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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}
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)
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# define cluster colors
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COLORS = {'0': "red", '1': "blue", '2': "grey"}
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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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X, y = make_blobs(
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n_samples=7500, centers=3, n_features=2, random_state=0, cluster_std=0.75
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)
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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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cluster_df["cluster"] = [str(i) for i in y]
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app.layout = html.Div(
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[
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html.Div([html.H1("Dashboard 6")], style={"margin": "10px 25px 25px 25px"}),
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html.Div(
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[
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dcc.Tabs(
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id="tabs",
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children=[
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dcc.Tab(
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label="Tab One",
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children=[
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html.Div(
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[
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dbc.Row(
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[
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dbc.Col(
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[
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dcc.Dropdown(
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options=[
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"red",
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"green",
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"blue",
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],
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value="red",
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id="color",
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multi=False,
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)
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],
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width=6,
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),
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dbc.Col(
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[
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dcc.Slider(
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min=math.floor(
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df["y"].min()
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),
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max=math.ceil(
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df["y"].max()
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),
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id="min_value",
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)
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],
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width=6,
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),
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]
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),
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dbc.Row(
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[
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dbc.Col(
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[dcc.Graph(id="graph_1")], width=6
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),
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dbc.Col(
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[dcc.Graph(id="graph_2")], width=6
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),
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]
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),
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],
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style={"margin": "100px 25px 25px 25px"},
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),
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],
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),
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dcc.Tab(
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label="Tab Two",
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id="tab_2_graphs",
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children=[
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html.Div(
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[
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dbc.Row(
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[
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dbc.Col(
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[dcc.Graph(id="graph_3")], width=8
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),
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dbc.Col(
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[dcc.Graph(id="graph_4")], width=4
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),
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]
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),
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dbc.Row(
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[
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dbc.Col(
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html.Div(
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[
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dbc.Label(
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"Number of bins:",
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html_for="graph_5_nbins",
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),
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dcc.Dropdown(
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options=[
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str(i)
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for i in range(
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5, 100, 5
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)
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],
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value="40",
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id="graph_5_nbins",
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multi=False,
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),
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]
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),
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width={"size": 3},
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),
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dbc.Col(
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html.Div(
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[
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dbc.Label(
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"Color:",
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html_for="graph_5_color",
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),
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dcc.Dropdown(
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options=[
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"Viridis",
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"Magma",
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"Hot",
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"GnBu",
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"Greys",
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],
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value="Viridis",
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id="graph_5_color",
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multi=False,
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),
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]
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),
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width={"size": 3, "offset": 1},
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),
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dbc.Col(
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html.Div(
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[
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dbc.Label(
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"Separated for Cluster:",
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html_for="graph_5_separated",
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),
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dcc.RadioItems(
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options=["Yes", "No"],
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value="No",
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id="graph_5_separated",
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),
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]
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),
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width={"size": 3, "offset": 1},
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),
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]
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),
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dbc.Row(
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[
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dbc.Col(
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[dcc.Graph(id="graph_5")], width=12
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)
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]
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),
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],
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style={"margin": "10px 25px 25px 25px"},
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)
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],
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),
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],
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)
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],
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style={"margin": "10px 25px 25px 25px"},
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),
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]
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)
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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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if selected_data is None or (
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isinstance(selected_data, dict) and "xaxis.range[0]" not in selected_data
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):
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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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cluster_dff = cluster_df[
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(cluster_df["X"] >= selected_data.get("xaxis.range[0]"))
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& (cluster_df["X"] <= selected_data.get("xaxis.range[1]"))
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& (cluster_df["Y"] >= selected_data.get("yaxis.range[0]"))
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& (cluster_df["Y"] <= selected_data.get("yaxis.range[1]"))
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]
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return cluster_dff
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@app.callback(Output("graph_1", "figure"), Input("color", "value"))
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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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@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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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 = 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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@app.callback(
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Output("graph_3", "figure"),
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Output("graph_4", "figure"),
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Input("graph_3", "relayoutData"),
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)
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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 = px.scatter(
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cluster_dff,
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x="X",
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y="Y",
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color="cluster",
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color_discrete_map=COLORS,
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category_orders={"cluster": ["0", "1", "2"]},
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)
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fig3.update_layout(height=PLOT_HEIGHT, template="plotly_white", coloraxis_showscale=False)
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fig3.update_layout(
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height=PLOT_HEIGHT, template="plotly_white", coloraxis_showscale=False
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)
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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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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 = go.Figure(
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data=[
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go.Bar(
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x=group_counts.index,
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y=group_counts["X"],
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marker_color=[COLORS.get(i) for i in group_counts.index],
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)
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]
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)
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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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fig4.update_layout(
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height=PLOT_HEIGHT,
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template="plotly_white",
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title="<b>Counts per cluster</b>",
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xaxis_title="cluster",
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title_font_size=25,
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)
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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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@app.callback(
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Output("graph_5", "figure"),
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Input("graph_5_nbins", "value"),
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Input("graph_5_color", "value"),
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Input("graph_5_separated", "value"),
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Input("graph_3", "relayoutData"),
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)
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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 = px.density_heatmap(
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cluster_dff,
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x="X",
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y="Y",
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nbinsx=int(nbins),
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nbinsy=int(nbins),
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color_continuous_scale=color,
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facet_col=None if separated == "No" else "cluster",
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category_orders={"cluster": ["0", "1", "2"]},
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)
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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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if __name__ == "__main__":
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app.run_server(debug=True, port=8014)
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