Bokeh Visualization Guide

# Install bokeh
# pip install bokeh

from bokeh.plotting import figure, show, output_file, output_notebook
from bokeh.models import ColumnDataSource, HoverTool
import numpy as np
import pandas as pd

# For Jupyter notebooks
output_notebook()

# For standalone HTML files
output_file("research_figure.html")

from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource, HoverTool

# Prepare data
data = pd.DataFrame({
    'sample_id': [f'S{i:03d}' for i in range(100)],
    'measurement_a': np.random.normal(5, 1.5, 100),
    'measurement_b': np.random.normal(10, 2, 100),
    'group': np.random.choice(['Control', 'Treatment A', 'Treatment B'], 100),
    'pvalue': np.random.uniform(0.001, 0.1, 100)
})

source = ColumnDataSource(data)

# Color mapping by group
color_map = {'Control': '#6B7280', 'Treatment A': '#3B82F6', 'Treatment B': '#EF4444'}
data['color'] = data['group'].map(color_map)

p = figure(
    title='Measurement A vs B by Treatment Group',
    x_axis_label='Measurement A (units)',
    y_axis_label='Measurement B (units)',
    width=700, height=500,
    tools='pan,wheel_zoom,box_zoom,reset,save'
)

for group, color in color_map.items():
    subset = data[data['group'] == group]
    p.circle(
        x='measurement_a', y='measurement_b',
        source=ColumnDataSource(subset),
        color=color, size=8, alpha=0.7,
        legend_label=group
    )

# Add hover tooltip
hover = HoverTool(tooltips=[
    ('Sample', '@sample_id'),
    ('Group', '@group'),
    ('Measure A', '@measurement_a{0.3f}'),
    ('Measure B', '@measurement_b{0.3f}'),
    ('p-value', '@pvalue{0.4f}')
])
p.add_tools(hover)
p.legend.location = 'top_left'
p.legend.click_policy = 'hide'

show(p)

from bokeh.layouts import gridplot
from bokeh.models import ColumnDataSource

# Shared data source enables linked selections
source = ColumnDataSource(data=dict(
    x1=np.random.normal(0, 1, 500),
    x2=np.random.normal(0, 1, 500),
    x3=np.random.normal(0, 1, 500),
    cluster=np.random.choice(['A', 'B', 'C'], 500)
))

TOOLS = "pan,wheel_zoom,box_select,lasso_select,reset"

# Create linked scatter plots
p1 = figure(title="PC1 vs PC2", tools=TOOLS, width=400, height=400)
p1.circle('x1', 'x2', source=source, alpha=0.6, size=5, color='#3B82F6',
          selection_color='#EF4444', nonselection_alpha=0.1)

p2 = figure(title="PC1 vs PC3", tools=TOOLS, width=400, height=400,
            x_range=p1.x_range)  # Share x-axis range
p2.circle('x1', 'x3', source=source, alpha=0.6, size=5, color='#3B82F6',
          selection_color='#EF4444', nonselection_alpha=0.1)

p3 = figure(title="PC2 vs PC3", tools=TOOLS, width=400, height=400,
            y_range=p2.y_range)  # Share y-axis range
p3.circle('x2', 'x3', source=source, alpha=0.6, size=5, color='#3B82F6',
          selection_color='#EF4444', nonselection_alpha=0.1)

grid = gridplot([[p1, p2], [p3, None]])
show(grid)

from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource, Whisker
import pandas as pd

groups = ['Control', 'Low', 'Medium', 'High']
q1 = [2.1, 3.5, 5.2, 6.8]
q2 = [3.0, 4.5, 6.5, 8.0]
q3 = [3.8, 5.5, 7.8, 9.2]
lower = [1.2, 2.5, 3.8, 5.5]
upper = [4.5, 6.5, 9.0, 10.5]

source = ColumnDataSource(data=dict(
    groups=groups, q1=q1, q2=q2, q3=q3, lower=lower, upper=upper
))

p = figure(
    x_range=groups,
    title='Biomarker Levels by Dosage Group',
    y_axis_label='Concentration (ng/mL)',
    width=600, height=450
)

# Boxes
p.vbar(x='groups', top='q3', bottom='q2', width=0.5, source=source,
       fill_color='#3B82F6', line_color='black', fill_alpha=0.7)
p.vbar(x='groups', top='q2', bottom='q1', width=0.5, source=source,
       fill_color='#93C5FD', line_color='black', fill_alpha=0.7)

# Whiskers
p.add_layout(Whisker(source=source, base='groups', upper='upper', lower='lower',
                      level='annotation', line_width=2))

# Median line
p.segment(x0='groups', y0='q2', x1='groups', y1='q2', source=source,
          line_color='red', line_width=2)

show(p)

from bokeh.plotting import figure, show
from bokeh.models import LinearColorMapper, ColorBar, BasicTicker
from bokeh.transform import transform

genes = [f'Gene_{i}' for i in range(20)]
samples = [f'Sample_{j}' for j in range(10)]
expression = np.random.randn(20, 10)

# Flatten for Bokeh
x_vals, y_vals, values = [], [], []
for i, gene in enumerate(genes):
    for j, sample in enumerate(samples):
        x_vals.append(sample)
        y_vals.append(gene)
        values.append(expression[i, j])

source = ColumnDataSource(dict(x=x_vals, y=y_vals, values=values))

mapper = LinearColorMapper(palette="RdBu11", low=-3, high=3)

p = figure(
    title="Gene Expression Heatmap",
    x_range=samples, y_range=list(reversed(genes)),
    width=700, height=600,
    toolbar_location='right'
)

p.rect(x='x', y='y', width=1, height=1, source=source,
       fill_color=transform('values', mapper), line_color=None)

color_bar = ColorBar(color_mapper=mapper, ticker=BasicTicker(desired_num_ticks=10),
                     label_standoff=8, width=12, location=(0, 0))
p.add_layout(color_bar, 'right')

p.xaxis.major_label_orientation = 0.8
show(p)

from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.models import Slider
from bokeh.plotting import figure

# Create a plot that updates based on slider input
p = figure(title="Signal with Adjustable Frequency", width=700, height=400)
x = np.linspace(0, 10, 500)
source = ColumnDataSource(data=dict(x=x, y=np.sin(x)))
p.line('x', 'y', source=source, line_width=2)

slider = Slider(start=0.1, end=10, value=1, step=0.1, title="Frequency")

def update(attr, old, new):
    source.data = dict(x=x, y=np.sin(new * x))

slider.on_change('value', update)
curdoc().add_root(column(slider, p))

# Run with: bokeh serve --show script.py

from bokeh.io import export_png, export_svgs

# Export as PNG (requires selenium and a browser driver)
export_png(p, filename="figure.png")

# Export as SVG
p.output_backend = "svg"
export_svgs(p, filename="figure.svg")

# Embed as standalone HTML
from bokeh.embed import file_html
from bokeh.resources import CDN
html = file_html(p, CDN, "Research Figure")
with open("figure.html", "w") as f:
    f.write(html)