Spectrum of Extracted DIA Data ms_bokeh

This example shows a spectrum from extracted data. No binning is applied.

figure(

id = 'p1740', …)

above = [],

align = 'auto',

aspect_ratio = None,

aspect_scale = 1,

attribution = [],

background_fill_alpha = 1.0,

background_fill_color = '#ffffff',

below = [LinearAxis(id='p1752', ...)],

border_fill_alpha = 1.0,

border_fill_color = '#ffffff',

center = [Grid(id='p1756', ...), Grid(id='p1761', ...), Label(id='p1788', ...), Label(id='p1789', ...), Label(id='p1790', ...), Label(id='p1791', ...), Label(id='p1792', ...), Span(id='p1793', ...)],

context_menu = None,

css_classes = [],

css_variables = {},

disabled = False,

elements = [],

extra_x_ranges = {},

extra_x_scales = {},

extra_y_ranges = {},

extra_y_scales = {},

flow_mode = 'block',

frame_align = True,

frame_height = None,

frame_width = None,

height = 500,

height_policy = 'auto',

hidpi = True,

hold_render = False,

js_event_callbacks = {},

js_property_callbacks = {},

left = [LinearAxis(id='p1757', ...)],

lod_factor = 10,

lod_interval = 300,

lod_threshold = 2000,

lod_timeout = 500,

margin = None,

match_aspect = False,

max_height = None,

max_width = None,

min_border = 0,

min_border_bottom = None,

min_border_left = None,

min_border_right = None,

min_border_top = None,

min_height = None,

min_width = None,

name = None,

outline_line_alpha = 1.0,

outline_line_cap = 'butt',

outline_line_color = '#e5e5e5',

outline_line_dash = [],

outline_line_dash_offset = 0,

outline_line_join = 'bevel',

outline_line_width = 1,

output_backend = 'canvas',

renderers = [GlyphRenderer(id='p1784', ...)],

reset_policy = 'standard',

resizable = False,

right = [],

sizing_mode = None,

styles = {},

stylesheets = [],

subscribed_events = PropertyValueSet(),

syncable = True,

tags = [],

title = Title(id='p1743', ...),

title_location = 'above',

toolbar = Toolbar(id='p1749', ...),

toolbar_inner = False,

toolbar_location = 'above',

toolbar_sticky = True,

visible = True,

width = 500,

width_policy = 'auto',

x_range = Range1d(id='p1794', ...),

x_scale = LinearScale(id='p1750', ...),

y_range = Range1d(id='p1795', ...),

y_scale = LinearScale(id='p1751', ...))

import pandas as pd
from pyopenms_viz.util import download_file

pd.options.plotting.backend = "ms_bokeh"

# GitHub raw URL (primary) with Zenodo as backup
url = "https://raw.githubusercontent.com/OpenMS/pyopenms_viz/main/test/test_data/ionMobilityTestFeatureDf.tsv"
backup_url = (
    "https://zenodo.org/records/17904352/files/ionMobilityTestFeatureDf.tsv?download=1"
)
local_path = "ionMobilityTestFeatureDf.tsv"
download_file(url, local_path, backup_url=backup_url)
df = pd.read_csv(local_path, sep="\t")

df.plot(
    kind="spectrum",
    x="mz",
    y="int",
    custom_annotation="Annotation",
    annotate_mz=True,
    bin_method="none",
    annotate_top_n_peaks=5,
    aggregate_duplicates=True,
)