Source code for sas.sascalc.corfunc.corfunc_calculator

"""
This module implements corfunc
"""
import warnings
import numpy as np
from scipy.optimize import curve_fit
from scipy.interpolate import interp1d
from scipy.fftpack import dct
from scipy.signal import argrelextrema
from numpy.linalg import lstsq
from sas.sascalc.dataloader.data_info import Data1D
from sas.sascalc.corfunc.transform_thread import FourierThread
from sas.sascalc.corfunc.transform_thread import HilbertThread

[docs]class CorfuncCalculator(object):
[docs] class _Interpolator(object): """ Interpolates between curve f and curve g over the range start:stop and caches the result of the function when it's called :param f: The first curve to interpolate :param g: The second curve to interpolate :param start: The value at which to start the interpolation :param stop: The value at which to stop the interpolation """
[docs] def __init__(self, f, g, start, stop): self.f = f self.g = g self.start = start self.stop = stop self._lastx = np.empty(0, dtype='d') self._lasty = None
[docs] def __call__(self, x): # If input is a single number, evaluate the function at that number # and return a single number if isinstance(x, (float, int)): return self._smoothed_function(np.array([x]))[0] # If input is a list, and is different to the last input, evaluate # the function at each point. If the input is the same as last time # the function was called, return the result that was calculated # last time instead of explicity evaluating the function again. if not np.array_equal(x, self._lastx): self._lastx, self._lasty = x, self._smoothed_function(x) return self._lasty
[docs] def _smoothed_function(self,x): ys = np.zeros(x.shape) ys[x <= self.start] = self.f(x[x <= self.start]) ys[x >= self.stop] = self.g(x[x >= self.stop]) with warnings.catch_warnings(): # Ignore divide by zero error warnings.simplefilter('ignore') h = 1/(1+(x-self.stop)**2/(self.start-x)**2) mask = np.logical_and(x > self.start, x < self.stop) ys[mask] = h[mask]*self.g(x[mask])+(1-h[mask])*self.f(x[mask]) return ys
[docs] def __init__(self, data=None, lowerq=None, upperq=None, scale=1): """ Initialize the class. :param data: Data of the type DataLoader.Data1D :param lowerq: The Q value to use as the boundary for Guinier extrapolation :param upperq: A tuple of the form (lower, upper). Values between lower and upper will be used for Porod extrapolation :param scale: Scaling factor for I(q) """ self._data = None self.set_data(data, scale) self.lowerq = lowerq self.upperq = upperq self.background = self.compute_background() self._transform_thread = None
[docs] def set_data(self, data, scale=1): """ Prepares the data for analysis :return: new_data = data * scale - background """ if data is None: return # Only process data of the class Data1D if not issubclass(data.__class__, Data1D): raise ValueError("Correlation function cannot be computed with 2D data.") # Prepare the data new_data = Data1D(x=data.x, y=data.y) new_data *= scale # Ensure the errors are set correctly if new_data.dy is None or len(new_data.x) != len(new_data.dy) or \ (min(new_data.dy) == 0 and max(new_data.dy) == 0): new_data.dy = np.ones(len(new_data.x)) self._data = new_data
[docs] def compute_background(self, upperq=None): """ Compute the background level from the Porod region of the data """ if self._data is None: return 0 elif upperq is None and self.upperq is not None: upperq = self.upperq elif upperq is None and self.upperq is None: return 0 q = self._data.x mask = np.logical_and(q > upperq[0], q < upperq[1]) _, _, bg = self._fit_porod(q[mask], self._data.y[mask]) return bg
[docs] def compute_extrapolation(self): """ Extrapolate and interpolate scattering data :return: The extrapolated data """ q = self._data.x iq = self._data.y params, s2 = self._fit_data(q, iq) # Extrapolate to 100*Qmax in experimental data qs = np.arange(0, q[-1]*100, (q[1]-q[0])) iqs = s2(qs) extrapolation = Data1D(qs, iqs) return params, extrapolation, s2
[docs] def compute_transform(self, extrapolation, trans_type, background=None, completefn=None, updatefn=None): """ Transform an extrapolated scattering curve into a correlation function. :param extrapolation: The extrapolated data :param background: The background value (if not provided, previously calculated value will be used) :param extrap_fn: A callable function representing the extraoplated data :param completefn: The function to call when the transform calculation is complete :param updatefn: The function to call to update the GUI with the status of the transform calculation :return: The transformed data """ if self._transform_thread is not None: if self._transform_thread.isrunning(): return if background is None: background = self.background if trans_type == 'fourier': self._transform_thread = FourierThread(self._data, extrapolation, background, completefn=completefn, updatefn=updatefn) elif trans_type == 'hilbert': self._transform_thread = HilbertThread(self._data, extrapolation, background, completefn=completefn, updatefn=updatefn) else: err = ("Incorrect transform type supplied, must be 'fourier'", " or 'hilbert'") raise ValueError(err) self._transform_thread.queue()
[docs] def transform_isrunning(self): if self._transform_thread is None: return False return self._transform_thread.isrunning()
[docs] def stop_transform(self): if self._transform_thread.isrunning(): self._transform_thread.stop()
[docs] def extract_parameters(self, transformed_data): """ Extract the interesting measurements from a correlation function :param transformed_data: Fourier transformation of the extrapolated data """ # Calculate indexes of maxima and minima x = transformed_data.x y = transformed_data.y maxs = argrelextrema(y, np.greater)[0] mins = argrelextrema(y, np.less)[0] # If there are no maxima, return None if len(maxs) == 0: return None GammaMin = y[mins[0]] # The value at the first minimum ddy = (y[:-2]+y[2:]-2*y[1:-1])/(x[2:]-x[:-2])**2 # 2nd derivative of y dy = (y[2:]-y[:-2])/(x[2:]-x[:-2]) # 1st derivative of y # Find where the second derivative goes to zero zeros = argrelextrema(np.abs(ddy), np.less)[0] # locate the first inflection point linear_point = zeros[0] # Try to calculate slope around linear_point using 80 data points lower = linear_point - 40 upper = linear_point + 40 # If too few data points to the left, use linear_point*2 data points if lower < 0: lower = 0 upper = linear_point * 2 # If too few to right, use 2*(dy.size - linear_point) data points elif upper > len(dy): upper = len(dy) width = len(dy) - linear_point lower = 2*linear_point - dy.size m = np.mean(dy[lower:upper]) # Linear slope b = y[1:-1][linear_point]-m*x[1:-1][linear_point] # Linear intercept Lc = (GammaMin-b)/m # Hard block thickness # Find the data points where the graph is linear to within 1% mask = np.where(np.abs((y-(m*x+b))/y) < 0.01)[0] if len(mask) == 0: # Return garbage for bad fits return { 'max': self._round_sig_figs(x[maxs[0]], 6) } dtr = x[mask[0]] # Beginning of Linear Section d0 = x[mask[-1]] # End of Linear Section GammaMax = y[mask[-1]] A = np.abs(GammaMin/GammaMax) # Normalized depth of minimum params = { 'max': x[maxs[0]], 'dtr': dtr, 'Lc': Lc, 'd0': d0, 'A': A, 'fill': Lc/x[maxs[0]] } return params
[docs] def _porod(self, q, K, sigma, bg): """Equation for the Porod region of the data""" return bg + (K*q**(-4))*np.exp(-q**2*sigma**2)
[docs] def _fit_guinier(self, q, iq): """Fit the Guinier region of the curve""" A = np.vstack([q**2, np.ones(q.shape)]).T # CRUFT: numpy>=1.14.0 allows rcond=None for the following default rcond = np.finfo(float).eps * max(A.shape) return lstsq(A, np.log(iq), rcond=rcond)
[docs] def _fit_porod(self, q, iq): """Fit the Porod region of the curve""" fitp = curve_fit(lambda q, k, sig, bg: self._porod(q, k, sig, bg)*q**2, q, iq*q**2, bounds=([-np.inf, 0, -np.inf], [np.inf, np.inf, np.inf]))[0] k, sigma, bg = fitp return k, sigma, bg
[docs] def _fit_data(self, q, iq): """ Given a data set, extrapolate out to large q with Porod and to q=0 with Guinier """ mask = np.logical_and(q > self.upperq[0], q < self.upperq[1]) # Returns an array where the 1st and 2nd elements are the values of k # and sigma for the best-fit Porod function k, sigma, _ = self._fit_porod(q[mask], iq[mask]) bg = self.background # Smooths between the best-fit porod function and the data to produce a # better fitting curve data = interp1d(q, iq) s1 = self._Interpolator(data, lambda x: self._porod(x, k, sigma, bg), self.upperq[0], q[-1]) mask = np.logical_and(q < self.lowerq, 0 < q) # Returns parameters for the best-fit Guinier function g = self._fit_guinier(q[mask], iq[mask])[0] # Smooths between the best-fit Guinier function and the Porod curve s2 = self._Interpolator((lambda x: (np.exp(g[1]+g[0]*x**2))), s1, q[0], self.lowerq) params = {'A': g[1], 'B': g[0], 'K': k, 'sigma': sigma} return params, s2