Plotly Interactive Guide

import plotly.express as px
import pandas as pd
import numpy as np

# Simulated experimental data
np.random.seed(42)
df = pd.DataFrame({
    'concentration': np.random.uniform(0.1, 10, 200),
    'response': np.random.normal(0, 1, 200),
    'treatment': np.random.choice(['Drug A', 'Drug B', 'Control'], 200),
    'cell_line': np.random.choice(['HeLa', 'MCF7', 'A549'], 200)
})
df['response'] = df['concentration'] * 0.8 + df['response']

fig = px.scatter(
    df,
    x='concentration',
    y='response',
    color='treatment',
    facet_col='cell_line',
    trendline='ols',
    title='Dose-Response Across Cell Lines',
    labels={'concentration': 'Concentration (uM)', 'response': 'Normalized Response'},
    template='plotly_white'
)
fig.update_layout(font=dict(family='Arial', size=12))
fig.show()

fig = px.box(
    df,
    x='treatment',
    y='response',
    color='treatment',
    points='all',
    title='Treatment Response Distribution',
    template='plotly_white'
)
fig.update_traces(quartilemethod='linear')
fig.update_layout(showlegend=False)
fig.show()

fig = px.violin(
    df,
    x='treatment',
    y='response',
    color='treatment',
    box=True,
    points='outliers',
    title='Response Distribution by Treatment Group',
    template='plotly_white'
)
fig.show()

import plotly.graph_objects as go

groups = ['Control', 'Low Dose', 'Medium Dose', 'High Dose']
means = [1.0, 1.8, 3.2, 4.5]
sems = [0.15, 0.22, 0.31, 0.28]

fig = go.Figure()

fig.add_trace(go.Bar(
    x=groups,
    y=means,
    error_y=dict(type='data', array=sems, visible=True),
    marker_color=['#6B7280', '#3B82F6', '#3B82F6', '#3B82F6'],
    text=[f'{m:.2f}' for m in means],
    textposition='outside'
))

fig.update_layout(
    title='Treatment Effect on Biomarker Levels',
    yaxis_title='Relative Expression',
    xaxis_title='Treatment Group',
    template='plotly_white',
    font=dict(family='Arial', size=13),
    bargap=0.3,
    yaxis=dict(range=[0, max(means) * 1.3])
)

# Add significance brackets
fig.add_annotation(
    x=0.5, y=max(means) * 1.15,
    text='*** p < 0.001',
    showarrow=False,
    font=dict(size=12)
)

fig.show()

import plotly.figure_factory as ff

# Compute correlation matrix
corr_matrix = df[['concentration', 'response']].corr()
variables = corr_matrix.columns.tolist()

fig = ff.create_annotated_heatmap(
    z=corr_matrix.values,
    x=variables,
    y=variables,
    colorscale='RdBu_r',
    zmin=-1, zmax=1,
    showscale=True
)

fig.update_layout(
    title='Variable Correlation Matrix',
    template='plotly_white',
    width=600, height=500
)
fig.show()

import plotly.graph_objects as go
import numpy as np

x = np.linspace(-3, 3, 50)
y = np.linspace(-3, 3, 50)
X, Y = np.meshgrid(x, y)
Z = np.sin(np.sqrt(X**2 + Y**2)) * np.exp(-0.1 * (X**2 + Y**2))

fig = go.Figure(data=[go.Surface(
    x=X, y=Y, z=Z,
    colorscale='Viridis',
    contours=dict(
        z=dict(show=True, usecolormap=True, project_z=True)
    )
)])

fig.update_layout(
    title='Response Surface Analysis',
    scene=dict(
        xaxis_title='Factor A',
        yaxis_title='Factor B',
        zaxis_title='Response'
    ),
    width=700, height=600
)
fig.show()

# Create animated scatter showing progression over experimental phases
fig = px.scatter(
    temporal_df,
    x='metric_a',
    y='metric_b',
    animation_frame='time_point',
    animation_group='sample_id',
    size='magnitude',
    color='cluster',
    hover_name='sample_id',
    title='Sample Trajectories Over Time',
    template='plotly_white',
    range_x=[0, 10],
    range_y=[0, 10]
)
fig.layout.updatemenus[0].buttons[0].args[1]['frame']['duration'] = 800
fig.show()

# Static export (requires kaleido)
fig.write_image('figure_1.pdf', width=800, height=500, scale=3)
fig.write_image('figure_1.svg', width=800, height=500)
fig.write_image('figure_1.png', width=800, height=500, scale=3)

# Interactive HTML for supplementary materials
fig.write_html('interactive_figure.html', include_plotlyjs='cdn')

# Save as JSON for reproducibility
fig.write_json('figure_data.json')

from dash import Dash, dcc, html, Input, Output
import plotly.express as px

app = Dash(__name__)

app.layout = html.Div([
    html.H1('Experiment Data Explorer'),
    dcc.Dropdown(
        id='variable-select',
        options=[{'label': v, 'value': v} for v in variables],
        value=variables[0]
    ),
    dcc.Graph(id='main-plot')
])

@app.callback(Output('main-plot', 'figure'), Input('variable-select', 'value'))
def update_plot(selected_var):
    return px.histogram(df, x=selected_var, nbins=30, template='plotly_white')

if __name__ == '__main__':
    app.run(debug=True, port=8050)