"""
Calculates Raman matrices from Raman tensor
"""
import numpy as np
from ase.units import invcm
from ase.utils.filecache import MultiFileJSONCache
from gpaw.typing import ArrayND
def gaussian(w, sigma):
g = 1.0 / (np.sqrt(2.0 * np.pi) * sigma) * np.exp(-(w**2) / (2 * sigma**2))
return g
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class RamanData:
def __init__(self, raman_name="Rlab", gridspacing=1.0) -> None:
"""Raman Spectroscopy data.
Parameters
----------
raman_name: str
Name of Rlab file cache. Default 'Rlab'
gridspacing: float
grid spacing in cm^-1, default 1 rcm
"""
# JSON cache
self.raman_cache = MultiFileJSONCache(raman_name)
self.wph_w = self.raman_cache["phonon_frequencies"] # eV
self.wrl_w = self.raman_cache["frequency_grid"] # eV
gridmax = self.wph_w[-1] + 6.3e-3 # highest freq + 50rcm
self.gridev_w = np.linspace(
0.0,
gridmax,
num=int(gridmax / (gridspacing * invcm) + 1),
) # eV
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def calculate_raman_intensity(self, d_i, d_o, T=300, sigma=5.0):
"""
Calculates Raman intensities from Raman tensor.
Returns bare `|R|^2` and Bose occupation weighted values
Parameters
----------
d_i: int
Incoming polarization
d_o: int
Outgoing polarization
T: float
Temperature in Kelvin
sigma: float
Gaussian line width in rcm, default 5rcm
"""
KtoeV = 8.617278e-5
sigmaev = sigma * invcm # 1eV = 8.065544E3 rcm
# grid for spectrum with extra space and sigma/5 spacing
if self.gridev_w is None:
self.gridev_w = np.linspace(
0.0,
self.wph_w[-1] * 1.1,
num=int(self.wph_w[-1] / (sigmaev / 5) + 100),
)
int_bare = np.zeros_like(self.gridev_w, dtype=float)
int_occ = np.zeros_like(self.gridev_w, dtype=float)
# Note: Resonant Raman does not have a frequency grid
R_lw = self.raman_cache[f"{'xyz'[d_i]}{'xyz'[d_o]}"]
for l in range(len(self.wph_w)):
occ = 1.0 / (np.exp(self.wph_w[l] / (KtoeV * T)) - 1.0) + 1.0
delta_w = gaussian((self.gridev_w - self.wph_w[l]), sigmaev)
R2_w = np.abs(R_lw[l])**2
R2d_w = R2_w * delta_w
int_bare += R2d_w
int_occ += occ / self.wph_w[l] * R2d_w
return int_bare, int_occ
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def calculate_raman_spectrum(self,
entries, T=300, sigma=5.0
) -> tuple[ArrayND, ArrayND]:
"""
Calculates Raman intensities from Raman tensor.
Returns Raman shift in eV, bare `|R|^2`
and Bose occupation weighted `|R|^2` values
Parameters
----------
entries: str, list
Sting or list of strings with desired polarizations
For example: ["xx", "yy", "xy", "yx"]
T: float
Temperature in Kelvin
sigma: float
Gaussian line width in rcm, default 5rcm
"""
if isinstance(entries, str):
entries = [
entries,
]
spectrum_w = np.zeros_like(self.gridev_w)
polnum = {"x": 0, "y": 1, "z": 2}
for entry in entries:
d_i = polnum[entry[0]]
d_o = polnum[entry[1]]
(int_bare, _) = self.calculate_raman_intensity(d_i, d_o, T, sigma)
spectrum_w += int_bare
return self.gridev_w, spectrum_w
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@classmethod
def plot_raman(
cls,
figname,
grid_w,
spectra_nw,
labels_n=None,
relative=False,
):
"""Plots a given Raman spectrum.
Parameters
----------
figname: str
Filename for figure.
grid_w:
Frequency grid of spectrum in eV
spectra_nw: np.ndarray
Raman spectra to plot
labels_n: list
Labels for the legend
relative: bool
If true, normalize each spectrum to 1
Default is False
"""
import matplotlib.pyplot as plt
from scipy import signal
if not isinstance(spectra_nw, np.ndarray):
spectra_nw = np.asarray(spectra_nw)
ylabel = "Intensity (arb. units)"
if relative:
ylabel = "I/I_max"
spectra_nw /= np.max(spectra_nw, axis=1)[:, np.newaxis]
else:
spectra_nw /= np.max(spectra_nw)
nspec = spectra_nw.shape[0]
if labels_n is None:
labels_n = nspec * [None]
grid_w = cls.eVtorcm(grid_w)
for i in range(nspec):
peaks = signal.find_peaks(spectra_nw[i])[0]
locations = np.take(grid_w, peaks)
intensities = np.take(spectra_nw[i], peaks)
plt.plot(grid_w, spectra_nw[i], label=labels_n[i])
for j, loc in enumerate(locations):
if intensities[j] / np.max(intensities) > 0.05:
plt.axvline(x=loc, color="grey", linestyle="--")
plt.minorticks_on()
if labels_n[0] is not None:
plt.legend()
plt.title("Raman intensity")
plt.xlabel("Raman shift (cm$^{-1}$)")
plt.ylabel(ylabel)
if relative:
plt.yticks([])
plt.savefig(figname, dpi=300)
plt.clf()
@classmethod
def eVtorcm(cls, energy):
return energy / invcm