1D Spectra

Plot a 1-D spectrum with a physical x-axis (energy in eV) using plot().

The spectrum contains a broad background and three Gaussian peaks. Circle markers highlight the peak positions using add_points(), and a range widget selects a region of interest. A model fit is overlaid with a dashed line, and the background component is shown as a semi-transparent dotted curve with diamond markers.

Pan and zoom with the mouse; press R to reset the view.

import numpy as np
import anyplotlib as vw

rng = np.random.default_rng(0)

# ── Synthetic XPS-style spectrum ──────────────────────────────────────────────
energy = np.linspace(280, 295, 512)          # binding energy axis (eV)

def gaussian(x, mu, sigma, amp):
    return amp * np.exp(-0.5 * ((x - mu) / sigma) ** 2)

background = 0.4 * np.exp(-0.08 * (energy - 280))

# Background + three peaks (C 1s region)
spectrum = (
    background
    + gaussian(energy, 284.8, 0.4, 1.0)      # C–C / C–H
    + gaussian(energy, 286.2, 0.4, 0.35)     # C–O
    + gaussian(energy, 288.0, 0.4, 0.18)     # C=O
    + rng.normal(scale=0.015, size=len(energy))
)

# ── Plot ──────────────────────────────────────────────────────────────────────
fig, ax = vw.subplots(1, 1, figsize=(620, 340))
v = ax.plot(spectrum, axes=[energy], units="eV", y_units="Intensity (a.u.)",
            color="#4fc3f7", linewidth=1.5)

# ── Peak markers (add_points collection) ──────────────────────────────────────
peak_energies = np.array([284.8, 286.2, 288.0])
peak_offsets  = np.column_stack([
    peak_energies,
    np.interp(peak_energies, energy, spectrum),
])
v.add_points(peak_offsets, name="peaks",
             sizes=7, color="#ff1744", facecolors="#ff174433",
             labels=["C\u2013C", "C\u2013O", "C=O"])

# ── Region-of-interest widget ─────────────────────────────────────────────────
v.add_range_widget(x0=285.8, x1=288.8, color="#00e5ff")

fig

Overlay a model fit — linestyle and alpha

Use add_line() to overlay additional curves. Here the noiseless model fit is drawn as a dashed line so it is visually distinct from the noisy measured spectrum. The alpha parameter makes the fit semi-transparent so the data underneath remains readable.

The y-axis range is expanded automatically to accommodate any overlay line whose values fall outside the current bounds.

fit = (
    background
    + gaussian(energy, 284.8, 0.4, 1.0)
    + gaussian(energy, 286.2, 0.4, 0.35)
    + gaussian(energy, 288.0, 0.4, 0.18)
)
v.add_line(fit, x_axis=energy,
           color="#ffcc00", linewidth=2.0,
           linestyle="dashed", alpha=0.85,
           label="fit")

fig

Background component — dotted line with markers

Draw the exponential background component as a dotted curve. Passing marker="D" places a diamond at every data point (useful when the line is sparse or when you want to emphasise individual sample positions). markersize controls the half-size of the symbol in pixels.

# Sub-sample to keep the marker plot readable
step = 32
v.add_line(background[::step], x_axis=energy[::step],
           color="#ce93d8", linewidth=1.2,
           linestyle="dotted", alpha=0.9,
           marker="D", markersize=3,
           label="background")

fig

Post-construction setters

All primary-line style properties can be changed after the panel is created without rebuilding it. This is useful in interactive notebooks where you want to tweak the appearance of the main trace.

v.set_alpha(0.9)          # slightly reduce primary-line opacity
v.set_linewidth(2.0)      # thicker stroke for the main spectrum

fig