"""

1D Spectra

==========

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

:meth:`~anyplotlib.figure_plots.Axes.plot`.

The spectrum contains a broad background and three Gaussian peaks.

Circle markers highlight the peak positions using

:meth:`~anyplotlib.figure_plots.Plot1D.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 :meth:`~anyplotlib.figure_plots.Plot1D.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