IPKM/nmreval
14
2
Fork 1
Code Issues 40 Pull Requests Actions Packages Projects Releases Wiki Activity

wide-line spectra handle missing x values better

Browse Source
This commit is contained in:
Dominik Demuth 2024-12-09 12:28:38 +01:00
parent 90084e3481
commit e19795b51c
2 changed files with 81 additions and 43 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
src/gui_qt/main/management.py
View File

@ -542,7 +542,9 @@ class UpperManagement(QtCore.QObject):
elif fit_limits[0] == 'in':
inside = np.where((_x >= fit_limits[1][0]) & (_x <= fit_limits[1][1]))
else:
inside = np.where((_x < fit_limits[1][0]) | (_x > fit_limits[1][1]))
x_lim, _ = self.graphs[self.current_graph].ranges
inside_graph = (_x >= x_lim[0]) & (_x <= x_lim[1])
inside = np.where(((_x < fit_limits[1][0]) | (_x > fit_limits[1][1])) & inside_graph)
try:
if isinstance(we, str):

120
src/nmreval/models/wideline.py
View File

@ -3,11 +3,42 @@ try:
from scipy.integrate import simpson
except ImportError:
from scipy.integrate import simps as simpson
from numpy import pi
from ..math.orientations import zcw_spherical as crystallites
__all__ = ['CSA', 'Pake', 'SecCentralLine']
def _make_broadening(x: np.ndarray, sigma: float, mode: str):
dx = x[1] - x[0]
_x = np.arange(len(x)) * dx
_x -= 0.5 * _x[-1]
if mode == 'l':
apd = 2 * sigma / (4*_x**2 + sigma**2) / np.pi
else:
ln2 = np.log(2)
apd = np.exp(-4*ln2 * (_x/sigma)**2) * 2 * np.sqrt(ln2/np.pi) / sigma
return apd
def _make_bins(x: np.ndarray) -> np.ndarray:
bins = 0.5 * (x[1:] + x[:-1])
return np.r_[0.5 * (-x[1] + 3 * x[0]), bins, 0.5 * (3 * x[-1] - x[-2])]
def _make_x(x: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
_x = x
dx = x[1:] - x[:-1]
dx = np.min(dx)
width = x[-1] - x[0]
_x = np.arange(width/dx - 1) * dx + x[0]
bins = (_x[1:] + _x[:-1]) / 2
bins = np.r_[_x[0]-dx/2, bins, _x[-1] + dx/2]
return _x, bins
class Pake:
type = 'Spectrum'
name = 'Pake'
@ -17,38 +48,39 @@ class Pake:
choices = [('Broadening', 'broad', {'Gaussian': 'g', 'Lorentzian': 'l'})]
@staticmethod
def func(x, c, delta, eta, sigma, t_pulse, broad='g'):
def func(
x: np.ndarray,
c: float,
delta: float,
eta: float,
sigma: float,
t_pulse: float,
broad: str = 'g',
) -> np.ndarray:
a, b, _ = crystallites(100000)
bins = 0.5 * (x[1:] + x[:-1])
bins = np.r_[0.5*(3*x[0]-x[1]), bins, 0.5*(3*x[-1]-x[-2])]
omega = delta * 0.5 * (3*np.cos(b)**2 - 1 - eta * np.sin(b)**2 * np.cos(2*a))
x_used, bins = _make_x(x)
s_left = np.histogram(omega, bins=bins)[0]
s_right = np.histogram(-omega, bins=bins)[0]
s = s_left + s_right
if sigma != 0:
_x = np.arange(len(x))*(x[1]-x[0])
_x -= 0.5*_x[-1]
if broad == 'l':
apd = 2 * sigma / (4 * _x**2 + sigma**2) / pi
else:
apd = np.exp(-4 * np.log(2) * (_x/sigma)**2) * 2 * np.sqrt(np.log(2) / pi) / sigma
apd = _make_broadening(x_used, sigma, broad)
ret_val = np.convolve(s, apd, mode='same')
else:
ret_val = s
omega_1 = pi/2/t_pulse
attn = omega_1 * np.sin(t_pulse*np.sqrt(omega_1**2+0.5*(2*pi*x)**2)) / \
np.sqrt(omega_1**2+(np.pi*x)**2)
omega_1 = np.pi/2/t_pulse
attn = omega_1 * np.sin(t_pulse*np.sqrt(omega_1**2 + 0.5*(2*np.pi*x_used)**2)) / np.sqrt(omega_1**2 + (np.pi*x_used)**2)
ret_val *= attn
ret_val /= simpson(y=ret_val, x=x_used)
return c * ret_val / simpson(ret_val, x)
if x_used.size == x.size:
return c * ret_val
else:
return c * np.interp(x=x, xp=x_used, fp=ret_val)
class CSA:
@ -60,28 +92,29 @@ class CSA:
choices = [('Broadening', 'broad', {'Gaussian': 'g', 'Lorentzian': 'l'})]
@staticmethod
def func(x, c, delta, eta, w_iso, sigma, broad='g'):
def func(
x: np.ndarray,
c: float,
delta: float,
eta: float,
w_iso: float,
sigma: float,
broad: str = 'g',
) -> np.ndarray:
a, b, _ = crystallites(100000)
bins = 0.5 * (x[1:] + x[:-1])
bins = np.r_[0.5*(-x[1] + 3*x[0]), bins, 0.5*(3*x[-1] - x[-2])]
omega = w_iso + delta * 0.5 * (3*np.cos(b)**2 - 1 - eta * np.sin(b)**2 * np.cos(2*a))
s_left = np.histogram(omega, bins=bins)[0]
s = s_left
s = np.histogram(omega, bins=_make_bins(x))[0]
if sigma != 0:
_x = np.arange(len(x)) * (x[1] - x[0])
_x -= 0.5 * _x[-1]
if broad == 'l':
apd = 2 * sigma / (4*_x**2 + sigma**2) / pi
else:
apd = np.exp(-4 * np.log(2) * (_x / sigma) ** 2) * 2 * np.sqrt(np.log(2) / pi) / sigma
print(len(s))
apd = _make_broadening(x, sigma, broad)
ret_val = np.convolve(s, apd, mode='same')
else:
ret_val = s
return c * ret_val / simpson(ret_val, x)
return c * ret_val / simpson(y=ret_val, x=x)
class SecCentralLine:
@ -94,10 +127,18 @@ class SecCentralLine:
('Broadening', 'broad', {'Gaussian': 'g', 'Lorentzian': 'l'})]
@staticmethod
def func(x, c, cq, eta, f_iso, gb, f_l, spin=2.5, broad='g'):
def func(
x: np.ndarray,
c: float,
cq: float,
eta: float,
f_iso: float,
gb: float,
f_l: float,
spin: float = 2.5,
broad: str = 'g',
) -> np.ndarray:
a, b, _ = crystallites(200000)
bins = 0.5 * (x[1:] + x[:-1])
bins = np.r_[0.5*(-x[1] + 3*x[0]), bins, 0.5*(3*x[-1] - x[-2])]
# coupling constant
omega_q = 2 * np.pi * cq / (2*spin*(2*spin-1))
@ -116,17 +157,12 @@ class SecCentralLine:
orient += prefactor_c
omega = 2*np.pi*f_iso + coupling * orient
s = np.histogram(omega / (2*np.pi), bins=bins)[0]
s = np.histogram(omega / (2*np.pi), bins=_make_bins(x))[0]
if gb != 0:
_x = np.arange(len(x)) * (x[1]-x[0])
_x -= 0.5*_x[-1]
if broad == 'l':
apd = 2*gb / (4*_x**2 + gb**2) / np.pi
else:
apd = np.exp(-4*np.log(2) * (_x/gb)**2) * 2 * np.sqrt(np.log(2)/np.pi) / gb
apd = _make_broadening(x, gb, broad)
ret_val = np.convolve(s, apd, mode='same')
else:
ret_val = s
return c * ret_val / simpson(ret_val, x)
return c * ret_val / simpson(y=ret_val, x=x)