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306-sigmoi
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master
Author | SHA1 | Date | |
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5975c08fb2 | |||
47feebe22f | |||
11fe47bef1 |
16
src/nmreval/clib/diffusion.c
Normal file
16
src/nmreval/clib/diffusion.c
Normal file
@ -0,0 +1,16 @@
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/* integrands used in quadrature integration with scipy's LowLevelCallables */
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#include <math.h>
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double anistropicDiffusion(double x, void *user_data) {
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double *c = (double *)user_data;
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double q = c[0];
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double t = c[1];
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double d_perp = c[2];
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double d_par = c[3];
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double cos_theta = cos(x);
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double sin_theta = sin(x);
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return exp(-q * q * t * (d_par * cos_theta * cos_theta + d_perp * sin_theta * sin_theta)) * sin_theta;
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}
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BIN
src/nmreval/clib/diffusion.so
Executable file
BIN
src/nmreval/clib/diffusion.so
Executable file
Binary file not shown.
@ -5,6 +5,17 @@ from ctypes import CDLL, c_double, c_void_p
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from ..lib.logger import logger
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diffusion_lib = None
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try:
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diffusion_lib = CDLL(str(Path(__file__).parents[1] / 'clib' / 'diffusion.so'))
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diffusion_lib.anistropicDiffusion.restype = c_double
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diffusion_lib.anistropicDiffusion.argtypes = (c_double, c_void_p)
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HAS_C_FUNCS = True
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except OSError:
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HAS_C_FUNCS = False
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lib = None
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try:
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lib = CDLL(str(Path(__file__).parents[1] / 'clib' / 'integrate.so'))
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@ -39,10 +50,8 @@ try:
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lib.energyDistSuscImag.restype = c_double
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lib.energyDistSuscImag.argtypes = (c_double, c_void_p)
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HAS_C_FUNCS = True
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logger.info('Use C functions')
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except OSError:
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HAS_C_FUNCS = False
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logger.info('Use python functions')
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@ -302,15 +302,26 @@ class HdfReader(HdfNode):
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def make_signal(self, node, flag: str = 'fid', value: str = None, group: str = None):
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if value is None:
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data_name = node.name
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value = self._get_parameter_values(node, node.parameter)
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else:
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try:
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data_name = f"{value}={node.parameter[value]}"
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value = node.parameter[value]
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except KeyError:
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print(node.title_parameter)
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try:
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value = node.title_parameter[1][value]
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temp = node
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while value != temp.title_parameter[0][0]:
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if temp.parent is None:
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break
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temp = temp.parent
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value = temp.title_parameter[0][1]
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data_name = temp.name
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except KeyError:
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print(f'{value} is not a valid key for {node.name}')
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data_name = node.name
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value = None
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if group is None:
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@ -343,11 +354,11 @@ class HdfReader(HdfNode):
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dw = float(index['dwelltime'])
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if flag == 'fid':
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x = np.arange(len(y)) * dw
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ret = FID(x, y, name=node.name, value=value, group=group, filename=self.file.filename)
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ret = FID(x, y, name=data_name, value=value, group=group, filename=self.file.filename)
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elif flag == 'spectrum':
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x = np.linspace(-1/dw, 1/dw, num=len(y))
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ret = Spectrum(x, y, name=node.name, value=value, group=group, filename=self.file.filename)
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ret = Spectrum(x, y, name=data_name, value=value, group=group, filename=self.file.filename)
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else:
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raise ValueError(f'{flag} unknown, use `fid` or `spectrum`.')
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@ -203,6 +203,31 @@ class Sinc:
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return c * np.sinc(((x-x0)/w)/np.pi)
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class Sigmoid:
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type = 'Basic'
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name = 'Sigmoid'
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equation = 'C / [1 + exp(-a * (x - x_{0})] + y_{0}'
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params = ['C', 'a', 'x_{0}', 'y_{0}']
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@staticmethod
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def func(x, c, a, x0, y0):
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"""
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Sigmoid function
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.. math::
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y = C / [1 + exp(-a * (x - x_0))] + y_0
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Args:
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x (array_like): Input values
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c (float): Prefactor
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a (float): Steepness of the sigmoid
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x0 (float): x position of the sigmoid's midpoint
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y0 (float): y position of the sigmoid's midpoint
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"""
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return c / (1 + np.exp(-a * (x - x0))) + y0
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class Sine:
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"""
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Wavy sine function
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@ -1,7 +1,11 @@
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from ctypes import c_double, cast, c_void_p, pointer
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import numpy as np
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from scipy import special as special
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from scipy import special as special, LowLevelCallable
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from scipy.integrate import quad
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from ..utils import gamma
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from nmreval.distributions.helper import HAS_C_FUNCS, diffusion_lib
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class Diffusion:
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@ -103,13 +107,29 @@ class AnisotropicDiffusion(object):
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tp = x
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relax = np.exp(-(tp/trel)**brel)*np.exp(-(tp/trel)**brel)
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q_squared = np.power(g * nucleus * tp, 2)
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q = g * nucleus * tp
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t = 2 * tp / 3 + tm
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z = np.sqrt(q_squared * (d_par - d_perp) * t)
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# Callaghan eq (6.89)
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diffs = np.exp(-q_squared*t*d_perp) * special.erf(z) / z
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if HAS_C_FUNCS:
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# divide by 2 to normalize by integral sin(x), x=0..pi
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diffusion_decay = AnisotropicDiffusion._integrate_c(q, t, d_perp, d_par) / 2
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else:
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z = np.sqrt(q**2 * (d_par - d_perp) * t)
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diffusion_decay = np.exp(-q**2 * t * d_perp) * special.erf(z) / z
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return m0 * diffs * relax
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return m0 * diffusion_decay * relax
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@staticmethod
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def _integrate_c(q, t, d_perp, d_par) -> np.ndarray:
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diffusion_decay = np.zeros_like(t)
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for (i, t_i) in enumerate(t):
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c = (c_double * 4)(q, t_i, d_perp, d_par)
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user_data = cast(pointer(c), c_void_p)
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diffusion_decay[i] = quad(LowLevelCallable(diffusion_lib.anistropicDiffusion, user_data), 0, np.pi, epsabs=1e-13)[0]
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return diffusion_decay
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class Peschier:
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