# %%
# ======================================================================================
# Copyright (©) 2014-2026 Laboratoire Catalyse et Spectrochimie (LCS), Caen, France.
# CeCILL-B FREE SOFTWARE LICENSE AGREEMENT
# See full LICENSE agreement in the root directory.
# ======================================================================================
# ruff: noqa
"""
NDDataset baseline correction
==============================

In this example, we perform a baseline correction of a 2D NDDataset
interactively, using the ``multivariate`` method and a ``pchip``/``polynomial`` interpolation.

For comparison, we also use the `asls`and `snip` models.
"""

# %%
# Import the library
import spectrochempy as scp

# %%
# Load and prepare the dataset
# ----------------------------
datadir = scp.preferences.datadir
nd = scp.read_omnic(datadir / "irdata" / "nh4y-activation.spg")

# %%
# Keep only the spectral region of interest
# (use floats for coordinate-based slicing)
ndp = nd[:, 1291.0:5999.0]

# %%
# Plot the raw dataset
_ = ndp.plot()

# %%
# Quick linear baseline removal with ``basc``:
ndp = ndp.basc()

# %%
# Shift to positive values
offset = ndp.min()
ndp -= offset
_ = ndp.plot()

# %%
# Multivariate baseline correction with pchip interpolation
# -----------------------------------------------------------
# Create a ``Baseline`` object using a multivariate approach with pchip
# (piecewise cubic Hermite) interpolation:
blc = scp.Baseline(
    log_level="WARNING",
    multivariate=True,
    model="polynomial",
    order="pchip",
    n_components=5,
)

# %%
# Define the reference regions for the baseline:
blc.ranges = [
    [1556.30, 1568.26],
    [1795.00, 1956.75],
    [3766.03, 3915.81],
    [4574.26, 4616.04],
    [4980.10, 4998.01],
    [5437.52, 5994.70],
]

# %%
# Fit the model:
_ = blc.fit(ndp)

# %%
# The baseline and corrected datasets are stored in the processor:
baseline = blc.baseline
corrected = blc.corrected

# %%
# Plot the corrected dataset:
_ = corrected.plot()

# %%
# A detailed view with region annotations:
ax = blc.plot(nb_traces=2, offset=50, show_regions=True)

# %%
# Compare individual spectra (corrected, baseline, original):
_ = corrected[0].plot()
_ = baseline[0].plot(clear=False, color="red", ls="-")
_ = ndp[0].plot(clear=False, color="green", ls="--")

# %%
_ = corrected[10].plot()
_ = baseline[10].plot(clear=False, color="red", ls="-")
_ = ndp[10].plot(clear=False, color="green", ls="--")

# %%
# Switch to a polynomial model
# -----------------------------
# The pchip interpolation seems too rigid in some regions.
# We can change the model without redefining the ``Baseline`` object:
blc.model = "polynomial"
blc.order = 5

# %%
# Refit and compare:
_ = blc.fit(ndp)

baseline = blc.baseline
corrected = blc.corrected

_ = corrected[0].plot()
_ = baseline[0].plot(clear=False, color="red", ls="-")
_ = ndp[0].plot(clear=False, color="green", ls="--")

# %%
_ = corrected[10].plot()
_ = baseline[10].plot(clear=False, color="red", ls="-")
_ = ndp[10].plot(clear=False, color="green", ls="--")

# %%
_ = corrected.plot()

# %%
# Try the AsLS model
# -------------------
# The Asymmetric Least Squares model (Eilers and Boelens, 2005) offers
# a different trade-off. The ``mu`` parameter controls smoothness and
# ``asymmetry`` controls the weighting.
blc.multivariate = False
blc.model = "asls"
blc.mu = 10**9
blc.asymmetry = 0.002
_ = blc.fit(ndp)

# %%
baseline = blc.baseline
corrected = blc.corrected

# %%
_ = corrected[0].plot()
_ = baseline[0].plot(clear=False, color="red", ls="-")
_ = ndp[0].plot(clear=False, color="green", ls="--")

# %%
_ = corrected[-1].plot()
_ = baseline[-1].plot(clear=False, color="red", ls="-")
_ = ndp[-1].plot(clear=False, color="green", ls="--")

# %%
_ = corrected.plot()

# %%
# Try the SNIP model
# -------------------
# The Statistics-sensitive Non-linear Iterative Peak-clipping method:
blc.multivariate = False
blc.model = "snip"
blc.snip_width = 200
_ = blc.fit(ndp)

# %%
baseline = blc.baseline
corrected = blc.corrected

# %%
_ = corrected[0].plot()
_ = baseline[0].plot(clear=False, color="red", ls="-")
_ = ndp[0].plot(clear=False, color="green", ls="--")

# %%
_ = corrected[-1].plot()
_ = baseline[-1].plot(clear=False, color="red", ls="-")
_ = ndp[-1].plot(clear=False, color="green", ls="--")

# %%
_ = corrected.plot()

# %%
# This ends the example ! The following line can be uncommented if no plot shows when
# running the .py script with python

# scp.show()