# -*- coding: utf-8 -*-
"""
Export isoenergy at the Fermi level
====================================

Simple workflow for exporting the isoenergy at the Fermi level.
The data are a deflector scan on graphene as simulated from a third nearest
neighbor tight binding model. The same workflow can be applied to any
tilt-, polar-, deflector- or hv-scan."""


# %%
# Import the "fundamental" python libraries for a generic data analysis:

import numpy as np

# %%
# Instead of loading the file as for example:

# from navarp.utils import navfile
# file_name = r"nxarpes_simulated_cone.nxs"
# entry = navfile.load(file_name)

##############################################################################
# Here we build the simulated graphene signal with a dedicated function defined
# just for this purpose:
from navarp.extras.simulation import get_tbgraphene_deflector

entry = get_tbgraphene_deflector(
    scans=np.linspace(-0.1, 0.1, 3),
    angles=np.linspace(-25, 6, 400),
    ebins=np.linspace(-13, 0.4, 700),
    tht_an=-18,
    phi_an=0,
    hv=120,
    gamma=0.05
)

# %%
# Fermi level autoset
# ^^^^^^^^^^^^^^^^^^^^^^^^^

entry.autoset_efermi(scan_range=[-2, 2], energy_range=[115.2, 115.8])
print("Energy of the Fermi level = {:.0f} eV".format(entry.efermi))
print("Energy resolution = {:.0f} meV".format(entry.efermi_fwhm*1000))

entry.plt_efermi_fit()

# %%
# Set the k-space for the transformation
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

entry.set_kspace(
    tht_p=0.1,
    k_along_slit_p=1.7,
    scan_p=0,
    ks_p=0,
    e_kin_p=114.3,
)

# %%
# Post processing on the isoscan:
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# First of all let's show it:

entry.isoscan(0).show()

# %%
# The second derivative can be obtained using sigma in the definition, which
# define the extension in points of the Gaussian filter used to then get the
# second derivative. In this case the sigma is different from zero only on the
# second element, meaning that the derivative will be performed only along the
# energy axis:
# sphinx_gallery_thumbnail_number = 3

entry.isoscan(0, sigma=[0, 5]).show()

# %%
# Only the Gaussian filtered image can be obtained using again sigma but also
# specifying the order=0, which by default is equal to 2 giving the second
# derivative as before.:

entry.isoscan(0, sigma=[3, 5], order=0).show()

# %%
# To export it as NXdata class of the nexus format uncomment this line:

# entry.isoscan(0, 0, sigma=[3, 5], order=0).export_as_nxs('fermimap.nxs')

# %%
# To export it as igor-pro text file (itx) uncomment this line:

# entry.isoscan(0, 0, sigma=[3, 5], order=0).export_as_itx('fermimap.itx')