# ======================================================================================
# 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
"""
Processing RAMAN spectra
========================
Various examples of processing RAMAN spectra
"""

# %%
# Import API
import spectrochempy as scp

# %%
# Importing a 1D spectra
# ----------------------
# Define the folder where are the spectra
datadir = scp.preferences.datadir
ramandir = datadir / "ramandata/labspec"

# %%
# Read a single spectrum
A = scp.read_labspec("SMC1-Initial_RT.txt", directory=ramandir)

# %%
# Plot the spectrum
A.plot()
# %%
# Crop the spectrum to a useful region
B = A[60.0:]
B.plot()
# %%
# Baseline correction
# -------------------
# Let's try to remove the baseline using different methods
# For this we use the `Baseline` processor
#
# First, we define the baseline processor
blc = scp.Baseline(log_level="INFO")

# %%
# Now we can try the various baseline methods.

# %%
# Detrending
# ~~~~~~~~~~
# the `detrend` method is not strictly speaking a method to calculate a bottom line,
# but it can be useful as a preprocessing to remove a trend.
# Let's define the model to be used for detrending
blc.model = "detrend"

# %%
# Now we need to define the order of the detrending either as an integer giving the
# degree of the polynomial trend or a string among { `constant` , `linear` ,
# `quadratic` , `cubic` }
blc.order = "linear"

# %%
# Now we can fit the model to the data
blc.fit(B)
# %%
# The baseline is now stored in the `baseline` attribute of the processor
corr = blc.transform()
baseline = blc.baseline


# %%
# Let's plot the result of the correction
#
# As we will use this type of plot several times,
# we define a function for it
def plot_result(X, Xc, bas):
    Xcm = Xc.min()
    Xcp = Xc.ptp()
    offset = Xcm + Xcp
    (Xc - Xcm).plot()
    (X + offset).plot(clear=False, color="g", linestyle="-")
    (bas + offset).plot(clear=False, color="r", linestyle="--")


plot_result(B, corr, baseline)

# %%
# Let's try with a polynomial detrend of order 2
blc.order = 2  # quadratic detrending
blc.fit(B)
corr = blc.transform()
baseline = blc.baseline
plot_result(B, corr, baseline)

# %%
# Ok this is a good start.
# But we can do better with more specific baseline correction methods.
# Let's try the asymmetric least squares smoothing model ( `asls` ), on this detrended
# spectrum:
Bd = blc.corrected

# %%
# Asymmetric Least Squares smoothing
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
blc.model = "asls"

# %%
# We need to define the smoothness and asymmetry parameters. The smoothness parameter is
# a positive number that controls the smoothness of the baseline. The larger this number
# is, the smoother the resulting baseline. The asymmetry parameter controls the
# asymmetry for the AsLS resolution.
blc.lamb = 10**8  # smoothness
blc.asymmetry = 0.01

# %%
# Now we can fit the model to the data
blc.fit(Bd)
corr = blc.transform()
baseline = blc.baseline
plot_result(Bd, corr, baseline)

# %%
# The correction appears to be good, but let's see if we can do better by using the
# `snip` method. This method requires to adjust the width of a window (usually set to
# the FWHM of the characteristic peaks).
blc.model = "snip"
blc.snip_width = 55  # estimated FWHM of the peaks (expressed in point. TODO: alternatively use true coordinates)
Bs = A[55.0:]
blc.fit(Bs)
corr = blc.transform()
baseline = blc.baseline
plot_result(Bs, corr, baseline)

# %%
# Baseline correction 2D spectra (series of spectra)
# --------------------------------------------------
# First, we read the series of spectra

C = scp.read_labspec("Activation.txt", directory=ramandir)
# C = C[20:]  # discard the first 20 spectra
C.plot()
# %%
# Now we apply the AsLS method on the series of spectra
#
# We keep the same parameters as before and fit the new dataset
# The baseline is calculated for each spectrum of the series. So the process is
# very slow!  For the demonstration we will the limit the series to 1 spectrum
# over 10.

blc.model = "asls"
blc.log_level = (
    "WARNING"  # supress output of asls (to long for the moment:  TODO optimize this)
)
blc.fit(C[::10])
corr = blc.transform()
baseline = blc.baseline
corr.plot()
# %%
# or the `snip` method (which is much faster)
blc.model = "snip"
blc.fit(C)
corr = blc.transform()
baseline = blc.baseline
corr[::10].plot()
# %%
# Denoising
# ---------
D = corr.copy()
G = scp.denoise(D, ratio=98)

G[::10].plot()

# %%
# This ends the example ! The following line can be removed or commented
# when the example is run as a notebook (`.ipynb`).

# scp.show()