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doc added; reading of isochronal bds

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This commit is contained in:
dominik
2022-03-22 20:07:59 +01:00
parent a222072b28
commit 6cd630245c
89 changed files with 41178 additions and 973 deletions
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17
nmreval/data/__init__.py
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@ -1,3 +1,20 @@
"""
====================================
Data container (:mod:`nmreval.data`)
====================================
.. autosummary::
:toctree: generated/
:nosignatures:
Points
Signal
FID
Spectrum
BDS
"""
from .points import Points
from .signals import Signal
from .bds import BDS

13
nmreval/data/bds.py
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@ -15,10 +15,11 @@ class BDS(Signal):
def __init__(self, x, y, **kwargs: Any):
super().__init__(x, y, **kwargs)
if np.all(self._x > 0) and not np.allclose(np.diff(self._x), self._x[1]-self._x[0]):
self.dx = self.x[1] / self.x[0]
else:
self.dx = self._x[1] - self._x[0]
if len(self._x) > 1:
if np.all(self._x > 0) and np.allclose(self._x[1:]/self._x[:-1], self._x[1]/self._x[0]):
self.dx = self.x[1] / self.x[0]
else:
self.dx = self._x[1] - self._x[0]
def __repr__(self) -> str:
return f"{self.meta['mode']}: {self.name}"
@ -32,10 +33,10 @@ class BDS(Signal):
window_length (int)
Returns:
Points
Points:
New Points instance with
References:
Reference:
Wübbenhorst, M.; van Turnhout, J.: Analysis of complex dielectric spectra.
I. One-dimensional derivative techniques and three-dimensional modelling.
J. Non-Cryst. Solid. 305 (2002) https://doi.org/10.1016/s0022-3093(02)01086-4

43
nmreval/data/nmr.py
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@ -7,6 +7,9 @@ from .signals import Signal
class FID(Signal):
"""
Extensions if :class:`Signal` for NMR timesignals
"""
def __init__(self, x, y, **kwargs):
super().__init__(x, y, **kwargs)
@ -16,7 +19,16 @@ class FID(Signal):
def __repr__(self):
return f"FID: {self.name}"
def baseline(self, percentage=0.12):
def baseline(self, percentage: float = 0.12):
"""
Substract
Args:
percentage:
Returns:
"""
self._y -= self._y[int(-percentage * self.length):].mean()
return self
@ -51,7 +63,7 @@ class FID(Signal):
return self
def fourier(self):
def fourier(self) -> 'Spectrum':
ft = np.fft.fftshift(np.fft.fft(self._y)) / self.dx
freq = np.fft.fftshift(np.fft.fftfreq(self.length, self.dx))
@ -63,9 +75,15 @@ class FID(Signal):
def fft_depake(self, scale: bool = False):
"""
Calculate deconvoluted Pake spectra
M.A. McCabe, S.R. Wassail:
Rapid deconvolution of NMR powder spectra by weighted fast Fourier transformation,
SSNMR (1997), Vol.10, Nr.1-2, pp. 53-61,
Args:
scale (bool):
Reference:
M.A. McCabe, S.R. Wassail:
Rapid deconvolution of NMR powder spectra by weighted fast Fourier transformation,
SSNMR (1997), Vol.10, Nr.1-2, pp. 53-61
"""
_y = self._y + 0
# Perform FFT depaking
@ -102,8 +120,9 @@ class FID(Signal):
self._y_err = np.std(self._y[-int(0.1*self.length):])/1.41
return self._y_err * np.ones(self.length)
def shift(self, points: Union[str, float], mode: str = 'pts'):
def shift(self, points: int, mode: str = 'pts') -> None:
"""
Shift y values to new x indexes
Args:
points : shift value,
@ -113,13 +132,17 @@ class FID(Signal):
"""
if mode == 'pts':
super().shift(points)
super().shift(int(points))
else:
pts = int(points//self.dx)
super().shift(pts)
class Spectrum(Signal):
"""
Extension of :class:`Signal` for NMR spectra
"""
def __init__(self, x, y, dx=None, **kwargs):
super().__init__(x, y, **kwargs)
if dx is None:
@ -139,7 +162,7 @@ class Spectrum(Signal):
return self
def fourier(self):
def fourier(self) -> FID:
ft = np.fft.ifft(np.fft.ifftshift(self._y * self.dx))
t = np.arange(len(ft))*self.dx
shifted = np.fft.ifftshift(self._x)[0]
@ -172,8 +195,8 @@ class Spectrum(Signal):
return self._y_err * np.ones(self.length)
def shift(self, points, mode='pts'):
def shift(self, points: int, mode: str = 'pts'):
if mode == 'pts':
super().shift(points)
super().shift(int(points))
else:
return self

28
nmreval/data/points.py
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@ -412,11 +412,15 @@ class Points:
def diff(self, log: bool = False, limits: Optional[Tuple[float, float]] = None) -> PointLike:
"""
Calculate first derivate :math:`dy/dx` or :math:`dy/d(\ln x)`.
Args:
log:
limits:
log (bool) : Flag if derivative is with respect to ln(x) instead of x. Default is `False`.
limits (tuple, optional): Range `(xmin, xmax)` which is differentiated.
Returns:
Copy of set with new y values.
"""
@ -430,6 +434,8 @@ class Points:
else:
new_data.y = np.gradient(new_data.y, new_data.x)
new_data.remove(np.argwhere(np.isnan(new_data.y)))
return new_data
def magnitude(self) -> PointLike:
@ -530,6 +536,8 @@ class Points:
def shift(self, points: int) -> PointLike:
"""
Shift indexes of y values.
Move y values `points` indexes, remove invalid indexes and fill remaining with zeros
Negative `points` shift to the left, positive `points` shift to the right
@ -558,11 +566,21 @@ class Points:
return self
def sort(self, axis=0):
if axis == 0:
def sort(self, axis: str = 'x') -> PointLike:
"""
Sort data in increased order.
Args:
axis (str, {`x`, `y`}) : Axis that determines new order. Default is `x`
"""
if axis == 'x':
sort_order = np.argsort(self._x)
else:
elif axis == 'y':
sort_order = np.argsort(self._y)
else:
raise ValueError('Unknown sort axis, use `x` or `y`.')
self._x = self._x[sort_order]
self._y = self._y[sort_order]
self._y_err = self._y_err[sort_order]

7
nmreval/data/signals.py
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@ -6,7 +6,7 @@ from .points import Points
class Signal(Points):
"""
Extension of Points for complex y values.
Extension of :class:`Points` for complex y values.
"""
def __init__(self, x, y, **kwargs):
@ -18,7 +18,10 @@ class Signal(Points):
super().__init__(x, y, **kwargs)
self.dx = kwargs.get('dx', abs(self._x[1] - self._x[0]))
if len(self._x) > 1:
self.dx = kwargs.get('dx', abs(self._x[1] - self._x[0]))
else:
self.dx = 0
self.meta.update({'phase': [], 'shift': 0})
def __repr__(self):