# %%
# ======================================================================================
# 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 creation and plotting example
=======================================
In this example, we create a 3D NDDataset from scratch,
and then we plot one section (a 2D plane)
"""

# %%
# Creation
# --------
# Now we will create a 3D NDDataset from scratch
import numpy as np

# %%
# As usual, we start by loading the spectrochempy library
import spectrochempy as scp


# %%
# Coordinates
# ~~~~~~~~~~~
# The `Coord` object allows creating an array of coordinates directly with
# ``Coord.linspace``, attaching metadata (units, labels, title) in a single
# step — no separate numpy array needed.
coord0 = scp.Coord.linspace(
    200.0,
    300.0,
    3,
    labels=["cold", "normal", "hot"],
    units="K",
    title="temperature",
)
coord1 = scp.Coord.linspace(0.0, 60.0, 100, units="minutes", title="time-on-stream")
coord2 = scp.Coord.linspace(4000.0, 1000.0, 100, units="cm^-1", title="wavenumber")

# %%
# Labels can be useful for instance for indexing
a = coord0["normal"]
print(a)


# %%
# Data and nd-Dataset
# ~~~~~~~~~~~~~~~~~~~
# ``scp.fromfunction`` builds an NDDataset directly from a Python function.
# The function receives the coordinate arrays and returns the intensity values.
def synth_func(temperature, time, wavenumber):
    return np.sin(2.0 * np.pi * wavenumber / 4000.0) * np.exp(-time / 60) * temperature


mydataset = scp.fromfunction(
    synth_func,
    coordset=[coord0, coord1, coord2],
    title="Absorbance",
    units="absorbance",
)
mydataset.description = """Dataset example created for this tutorial.
It's a 3-D dataset (with dimensionless intensity: absorbance )"""
mydataset.name = "An example from scratch"
mydataset.author = "Blake and Mortimer"
print(mydataset)

# %%
# We want to plot a section of this 3D NDDataset:
#
# NDDataset can be sliced like conventional numpy-array...
new = mydataset[..., 0]

# %%
# or maybe more conveniently in this case, using an axis labels:
new = mydataset["hot"]

# %%
# To plot a dataset, use the `plot` command (generic plot).
# As the section NDDataset is 2D, a stack plot is displayed by default. As you can see, the x-axis is in wavenumber
# and the ordinate axis is in absorbance units (au). The y dimension of the dataset is the time-on-stream (in minutes).
# Because the time-on-stream values are floats, this triggers the default sequential colormap ('viridis'). The
# corresponding values can be seen if `colorbar` is passed as `True`:
_ = new.plot(colorbar=True)

# %%
# It is also possible to display this dataset as an image (actually a filled contour plot).
# The x is the same as before, but the ordinates are now the time-on-stream values. The color of the pixels is now
# related to the value of the absorbance. As the dataset contains both negative and positive values, the default
# colormap is diverging (`RdBu`).
#
# sphinx_gallery_thumbnail_number = 2
_ = new.plot(method="image", colorbar=True)

# %%
# If a dataset contains only positive values, the default colormap is
# sequential (`viridis`):

_ = scp.abs(new).plot(method="image", colorbar=True)

# %% Contour plots are also available, with the same default colormap as for the image method:
_ = new.plot(method="map")
_ = scp.abs(new).plot(method="map")

# %%
# Note that the scp allows one to use this syntax too:
_ = scp.plot_map(new)

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

# scp.show()