Source code for sas.sascalc.invariant.invariant

# pylint: disable=invalid-name
#####################################################################
#This software was developed by the University of Tennessee as part of the
#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
#project funded by the US National Science Foundation.
#See the license text in license.txt
#copyright 2010, University of Tennessee
######################################################################

"""
This module implements invariant and its related computations.

:author: Gervaise B. Alina/UTK
:author: Mathieu Doucet/UTK
:author: Jae Cho/UTK

"""
import math
import numpy as np

from sas.sascalc.dataloader.data_info import Data1D as LoaderData1D

# The minimum q-value to be used when extrapolating
Q_MINIMUM = 1e-5

# The maximum q-value to be used when extrapolating
Q_MAXIMUM = 10

# Number of steps in the extrapolation
INTEGRATION_NSTEPS = 1000

[docs]class Transform(object): """ Define interface that need to compute a function or an inverse function given some x, y """
[docs] def linearize_data(self, data): """ Linearize data so that a linear fit can be performed. Filter out the data that can't be transformed. :param data: LoadData1D instance """ # Check that the vector lengths are equal assert len(data.x) == len(data.y) if data.dy is not None: assert len(data.x) == len(data.dy) dy = data.dy else: dy = np.ones(len(data.y)) # Transform the data data_points = zip(data.x, data.y, dy) output_points = [(self.linearize_q_value(p[0]), math.log(p[1]), p[2] / p[1]) for p in data_points if p[0] > 0 and \ p[1] > 0 and p[2] > 0] x_out, y_out, dy_out = zip(*output_points) # Create Data1D object x_out = np.asarray(x_out) y_out = np.asarray(y_out) dy_out = np.asarray(dy_out) linear_data = LoaderData1D(x=x_out, y=y_out, dy=dy_out) return linear_data
[docs] def get_allowed_bins(self, data): """ Goes through the data points and returns a list of boolean values to indicate whether each points is allowed by the model or not. :param data: Data1D object """ return [p[0] > 0 and p[1] > 0 and p[2] > 0 for p in zip(data.x, data.y, data.dy)]
[docs] def linearize_q_value(self, value): """ Transform the input q-value for linearization """ return NotImplemented
[docs] def extract_model_parameters(self, constant, slope, dconstant=0, dslope=0): """ set private member """ return NotImplemented
[docs] def evaluate_model(self, x): """ Returns an array f(x) values where f is the Transform function. """ return NotImplemented
[docs] def evaluate_model_errors(self, x): """ Returns an array of I(q) errors """ return NotImplemented
[docs]class Guinier(Transform): """ class of type Transform that performs operations related to guinier function """ def __init__(self, scale=1, radius=60): Transform.__init__(self) self.scale = scale self.radius = radius ## Uncertainty of scale parameter self.dscale = 0 ## Unvertainty of radius parameter self.dradius = 0
[docs] def linearize_q_value(self, value): """ Transform the input q-value for linearization :param value: q-value :return: q*q """ return value * value
[docs] def extract_model_parameters(self, constant, slope, dconstant=0, dslope=0): """ assign new value to the scale and the radius """ self.scale = math.exp(constant) if slope > 0: slope = 0.0 self.radius = math.sqrt(-3 * slope) # Errors self.dscale = math.exp(constant) * dconstant if slope == 0.0: n_zero = -1.0e-24 self.dradius = -3.0 / 2.0 / math.sqrt(-3 * n_zero) * dslope else: self.dradius = -3.0 / 2.0 / math.sqrt(-3 * slope) * dslope return [self.radius, self.scale], [self.dradius, self.dscale]
[docs] def evaluate_model(self, x): """ return F(x)= scale* e-((radius*x)**2/3) """ return self._guinier(x)
[docs] def evaluate_model_errors(self, x): """ Returns the error on I(q) for the given array of q-values :param x: array of q-values """ p1 = np.array([self.dscale * math.exp(-((self.radius * q) ** 2 / 3)) \ for q in x]) p2 = np.array([self.scale * math.exp(-((self.radius * q) ** 2 / 3))\ * (-(q ** 2 / 3)) * 2 * self.radius * self.dradius for q in x]) diq2 = p1 * p1 + p2 * p2 return np.array([math.sqrt(err) for err in diq2])
def _guinier(self, x): """ Retrieve the guinier function after apply an inverse guinier function to x Compute a F(x) = scale* e-((radius*x)**2/3). :param x: a vector of q values :param scale: the scale value :param radius: the guinier radius value :return: F(x) """ # transform the radius of coming from the inverse guinier function to a # a radius of a guinier function if self.radius <= 0: msg = "Rg expected positive value, but got %s" % self.radius raise ValueError(msg) value = np.array([math.exp(-((self.radius * i) ** 2 / 3)) for i in x]) return self.scale * value
[docs]class PowerLaw(Transform): """ class of type transform that perform operation related to power_law function """ def __init__(self, scale=1, power=4): Transform.__init__(self) self.scale = scale self.power = power self.dscale = 0.0 self.dpower = 0.0
[docs] def linearize_q_value(self, value): """ Transform the input q-value for linearization :param value: q-value :return: log(q) """ return math.log(value)
[docs] def extract_model_parameters(self, constant, slope, dconstant=0, dslope=0): """ Assign new value to the scale and the power """ self.power = -slope self.scale = math.exp(constant) # Errors self.dscale = math.exp(constant) * dconstant self.dpower = -dslope return [self.power, self.scale], [self.dpower, self.dscale]
[docs] def evaluate_model(self, x): """ given a scale and a radius transform x, y using a power_law function """ return self._power_law(x)
[docs] def evaluate_model_errors(self, x): """ Returns the error on I(q) for the given array of q-values :param x: array of q-values """ p1 = np.array([self.dscale * math.pow(q, -self.power) for q in x]) p2 = np.array([self.scale * self.power * math.pow(q, -self.power - 1)\ * self.dpower for q in x]) diq2 = p1 * p1 + p2 * p2 return np.array([math.sqrt(err) for err in diq2])
def _power_law(self, x): """ F(x) = scale* (x)^(-power) when power= 4. the model is porod else power_law The model has three parameters: :: 1. x: a vector of q values 2. power: power of the function 3. scale : scale factor value :param x: array :return: F(x) """ if self.power <= 0: msg = "Power_law function expected positive power," msg += " but got %s" % self.power raise ValueError(msg) if self.scale <= 0: msg = "scale expected positive value, but got %s" % self.scale raise ValueError(msg) value = np.array([math.pow(i, -self.power) for i in x]) return self.scale * value
[docs]class Extrapolator(object): """ Extrapolate I(q) distribution using a given model """ def __init__(self, data, model=None): """ Determine a and b given a linear equation y = ax + b If a model is given, it will be used to linearize the data before the extrapolation is performed. If None, a simple linear fit will be done. :param data: data containing x and y such as y = ax + b :param model: optional Transform object """ self.data = data self.model = model # Set qmin as the lowest non-zero value self.qmin = Q_MINIMUM for q_value in self.data.x: if q_value > 0: self.qmin = q_value break self.qmax = max(self.data.x)
[docs] def fit(self, power=None, qmin=None, qmax=None): """ Fit data for y = ax + b return a and b :param power: a fixed, otherwise None :param qmin: Minimum Q-value :param qmax: Maximum Q-value """ if qmin is None: qmin = self.qmin if qmax is None: qmax = self.qmax # Identify the bin range for the fit idx = (self.data.x >= qmin) & (self.data.x <= qmax) fx = np.zeros(len(self.data.x)) # Uncertainty if type(self.data.dy) == np.ndarray and \ len(self.data.dy) == len(self.data.x) and \ np.all(self.data.dy > 0): sigma = self.data.dy else: sigma = np.ones(len(self.data.x)) # Compute theory data f(x) fx[idx] = self.data.y[idx] # Linearize the data if self.model is not None: linearized_data = self.model.linearize_data(\ LoaderData1D(self.data.x[idx], fx[idx], dy=sigma[idx])) else: linearized_data = LoaderData1D(self.data.x[idx], fx[idx], dy=sigma[idx]) ##power is given only for function = power_law if power is not None: sigma2 = linearized_data.dy * linearized_data.dy a = -(power) b = (np.sum(linearized_data.y / sigma2) \ - a * np.sum(linearized_data.x / sigma2)) / np.sum(1.0 / sigma2) deltas = linearized_data.x * a + \ np.ones(len(linearized_data.x)) * b - linearized_data.y residuals = np.sum(deltas * deltas / sigma2) err = math.fabs(residuals) / np.sum(1.0 / sigma2) return [a, b], [0, math.sqrt(err)] else: A = np.vstack([linearized_data.x / linearized_data.dy, 1.0 / linearized_data.dy]).T (p, residuals, _, _) = np.linalg.lstsq(A, linearized_data.y / linearized_data.dy) # Get the covariance matrix, defined as inv_cov = a_transposed * a err = np.zeros(2) try: inv_cov = np.dot(A.transpose(), A) cov = np.linalg.pinv(inv_cov) err_matrix = math.fabs(residuals) * cov err = [math.sqrt(err_matrix[0][0]), math.sqrt(err_matrix[1][1])] except: err = [-1.0, -1.0] return p, err
[docs]class InvariantCalculator(object): """ Compute invariant if data is given. Can provide volume fraction and surface area if the user provides Porod constant and contrast values. :precondition: the user must send a data of type DataLoader.Data1D the user provide background and scale values. :note: Some computations depends on each others. """ def __init__(self, data, background=0, scale=1): """ Initialize variables. :param data: data must be of type DataLoader.Data1D :param background: Background value. The data will be corrected before processing :param scale: Scaling factor for I(q). The data will be corrected before processing """ # Background and scale should be private data member if the only way to # change them are by instantiating a new object. self._background = background self._scale = scale # slit height for smeared data self._smeared = None # The data should be private self._data = self._get_data(data) # get the dxl if the data is smeared: This is done only once on init. if self._data.dxl is not None and self._data.dxl.all() > 0: # assumes constant dxl self._smeared = self._data.dxl[0] # Since there are multiple variants of Q*, you should force the # user to use the get method and keep Q* a private data member self._qstar = None # You should keep the error on Q* so you can reuse it without # recomputing the whole thing. self._qstar_err = 0 # Extrapolation parameters self._low_extrapolation_npts = 4 self._low_extrapolation_function = Guinier() self._low_extrapolation_power = None self._low_extrapolation_power_fitted = None self._high_extrapolation_npts = 4 self._high_extrapolation_function = PowerLaw() self._high_extrapolation_power = None self._high_extrapolation_power_fitted = None # Extrapolation range self._low_q_limit = Q_MINIMUM def _get_data(self, data): """ :note: this function must be call before computing any type of invariant :return: new data = self._scale *data - self._background """ if not issubclass(data.__class__, LoaderData1D): #Process only data that inherited from DataLoader.Data_info.Data1D raise ValueError("Data must be of type DataLoader.Data1D") #from copy import deepcopy new_data = (self._scale * data) - self._background # Check that the vector lengths are equal assert len(new_data.x) == len(new_data.y) # Verify that 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)) return new_data def _fit(self, model, qmin=Q_MINIMUM, qmax=Q_MAXIMUM, power=None): """ fit data with function using data = self._get_data() fx = Functor(data , function) y = data.y slope, constant = linalg.lstsq(y,fx) :param qmin: data first q value to consider during the fit :param qmax: data last q value to consider during the fit :param power : power value to consider for power-law :param function: the function to use during the fit :return a: the scale of the function :return b: the other parameter of the function for guinier will be radius for power_law will be the power value """ extrapolator = Extrapolator(data=self._data, model=model) p, dp = extrapolator.fit(power=power, qmin=qmin, qmax=qmax) return model.extract_model_parameters(constant=p[1], slope=p[0], dconstant=dp[1], dslope=dp[0]) def _get_qstar(self, data): """ Compute invariant for pinhole data. This invariant is given by: :: q_star = x0**2 *y0 *dx0 +x1**2 *y1 *dx1 + ..+ xn**2 *yn *dxn for non smeared data q_star = dxl0 *x0 *y0 *dx0 +dxl1 *x1 *y1 *dx1 + ..+ dlxn *xn *yn *dxn for smeared data where n >= len(data.x)-1 dxl = slit height dQl dxi = 1/2*(xi+1 - xi) + (xi - xi-1) dx0 = (x1 - x0)/2 dxn = (xn - xn-1)/2 :param data: the data to use to compute invariant. :return q_star: invariant value for pinhole data. q_star > 0 """ if len(data.x) <= 1 or len(data.y) <= 1 or len(data.x) != len(data.y): msg = "Length x and y must be equal" msg += " and greater than 1; got x=%s, y=%s" % (len(data.x), len(data.y)) raise ValueError(msg) else: # Take care of smeared data if self._smeared is None: gx = data.x * data.x # assumes that len(x) == len(dxl). else: gx = data.dxl * data.x n = len(data.x) - 1 #compute the first delta q dx0 = (data.x[1] - data.x[0]) / 2 #compute the last delta q dxn = (data.x[n] - data.x[n - 1]) / 2 total = 0 total += gx[0] * data.y[0] * dx0 total += gx[n] * data.y[n] * dxn if len(data.x) == 2: return total else: #iterate between for element different #from the first and the last for i in range(1, n - 1): dxi = (data.x[i + 1] - data.x[i - 1]) / 2 total += gx[i] * data.y[i] * dxi return total def _get_qstar_uncertainty(self, data): """ Compute invariant uncertainty with with pinhole data. This uncertainty is given as follow: :: dq_star = math.sqrt[(x0**2*(dy0)*dx0)**2 + (x1**2 *(dy1)*dx1)**2 + ..+ (xn**2 *(dyn)*dxn)**2 ] where n >= len(data.x)-1 dxi = 1/2*(xi+1 - xi) + (xi - xi-1) dx0 = (x1 - x0)/2 dxn = (xn - xn-1)/2 dyn: error on dy :param data: :note: if data doesn't contain dy assume dy= math.sqrt(data.y) """ if len(data.x) <= 1 or len(data.y) <= 1 or \ len(data.x) != len(data.y) or \ (data.dy is not None and (len(data.dy) != len(data.y))): msg = "Length of data.x and data.y must be equal" msg += " and greater than 1; got x=%s, y=%s" % (len(data.x), len(data.y)) raise ValueError(msg) else: #Create error for data without dy error if data.dy is None: dy = math.sqrt(data.y) else: dy = data.dy # Take care of smeared data if self._smeared is None: gx = data.x * data.x # assumes that len(x) == len(dxl). else: gx = data.dxl * data.x n = len(data.x) - 1 #compute the first delta dx0 = (data.x[1] - data.x[0]) / 2 #compute the last delta dxn = (data.x[n] - data.x[n - 1]) / 2 total = 0 total += (gx[0] * dy[0] * dx0) ** 2 total += (gx[n] * dy[n] * dxn) ** 2 if len(data.x) == 2: return math.sqrt(total) else: #iterate between for element different #from the first and the last for i in range(1, n - 1): dxi = (data.x[i + 1] - data.x[i - 1]) / 2 total += (gx[i] * dy[i] * dxi) ** 2 return math.sqrt(total) def _get_extrapolated_data(self, model, npts=INTEGRATION_NSTEPS, q_start=Q_MINIMUM, q_end=Q_MAXIMUM): """ :return: extrapolate data create from data """ #create new Data1D to compute the invariant q = np.linspace(start=q_start, stop=q_end, num=npts, endpoint=True) iq = model.evaluate_model(q) diq = model.evaluate_model_errors(q) result_data = LoaderData1D(x=q, y=iq, dy=diq) if self._smeared is not None: result_data.dxl = self._smeared * np.ones(len(q)) return result_data
[docs] def get_data(self): """ :return: self._data """ return self._data
[docs] def get_extrapolation_power(self, range='high'): """ :return: the fitted power for power law function for a given extrapolation range """ if range == 'low': return self._low_extrapolation_power_fitted return self._high_extrapolation_power_fitted
[docs] def get_qstar_low(self): """ Compute the invariant for extrapolated data at low q range. Implementation: data = self._get_extra_data_low() return self._get_qstar() :return q_star: the invariant for data extrapolated at low q. """ # Data boundaries for fitting qmin = self._data.x[0] qmax = self._data.x[int(self._low_extrapolation_npts - 1)] # Extrapolate the low-Q data p, _ = self._fit(model=self._low_extrapolation_function, qmin=qmin, qmax=qmax, power=self._low_extrapolation_power) self._low_extrapolation_power_fitted = p[0] # Distribution starting point self._low_q_limit = Q_MINIMUM if Q_MINIMUM >= qmin: self._low_q_limit = qmin / 10 data = self._get_extrapolated_data(\ model=self._low_extrapolation_function, npts=INTEGRATION_NSTEPS, q_start=self._low_q_limit, q_end=qmin) # Systematic error # If we have smearing, the shape of the I(q) distribution at low Q will # may not be a Guinier or simple power law. The following is # a conservative estimation for the systematic error. err = qmin * qmin * math.fabs((qmin - self._low_q_limit) * \ (data.y[0] - data.y[INTEGRATION_NSTEPS - 1])) return self._get_qstar(data), self._get_qstar_uncertainty(data) + err
[docs] def get_qstar_high(self): """ Compute the invariant for extrapolated data at high q range. Implementation: data = self._get_extra_data_high() return self._get_qstar() :return q_star: the invariant for data extrapolated at high q. """ # Data boundaries for fitting x_len = len(self._data.x) - 1 qmin = self._data.x[int(x_len - (self._high_extrapolation_npts - 1))] qmax = self._data.x[int(x_len)] # fit the data with a model to get the appropriate parameters p, _ = self._fit(model=self._high_extrapolation_function, qmin=qmin, qmax=qmax, power=self._high_extrapolation_power) self._high_extrapolation_power_fitted = p[0] #create new Data1D to compute the invariant data = self._get_extrapolated_data(\ model=self._high_extrapolation_function, npts=INTEGRATION_NSTEPS, q_start=qmax, q_end=Q_MAXIMUM) return self._get_qstar(data), self._get_qstar_uncertainty(data)
[docs] def get_extra_data_low(self, npts_in=None, q_start=None, npts=20): """ Returns the extrapolated data used for the loew-Q invariant calculation. By default, the distribution will cover the data points used for the extrapolation. The number of overlap points is a parameter (npts_in). By default, the maximum q-value of the distribution will be the minimum q-value used when extrapolating for the purpose of the invariant calculation. :param npts_in: number of data points for which the extrapolated data overlap :param q_start: is the minimum value to uses for extrapolated data :param npts: the number of points in the extrapolated distribution """ # Get extrapolation range if q_start is None: q_start = self._low_q_limit if npts_in is None: npts_in = self._low_extrapolation_npts q_end = self._data.x[max(0, int(npts_in - 1))] if q_start >= q_end: return np.zeros(0), np.zeros(0) return self._get_extrapolated_data(\ model=self._low_extrapolation_function, npts=npts, q_start=q_start, q_end=q_end)
[docs] def get_extra_data_high(self, npts_in=None, q_end=Q_MAXIMUM, npts=20): """ Returns the extrapolated data used for the high-Q invariant calculation. By default, the distribution will cover the data points used for the extrapolation. The number of overlap points is a parameter (npts_in). By default, the maximum q-value of the distribution will be Q_MAXIMUM, the maximum q-value used when extrapolating for the purpose of the invariant calculation. :param npts_in: number of data points for which the extrapolated data overlap :param q_end: is the maximum value to uses for extrapolated data :param npts: the number of points in the extrapolated distribution """ # Get extrapolation range if npts_in is None: npts_in = int(self._high_extrapolation_npts) _npts = len(self._data.x) q_start = self._data.x[min(_npts, int(_npts - npts_in))] if q_start >= q_end: return np.zeros(0), np.zeros(0) return self._get_extrapolated_data(\ model=self._high_extrapolation_function, npts=npts, q_start=q_start, q_end=q_end)
[docs] def set_extrapolation(self, range, npts=4, function=None, power=None): """ Set the extrapolation parameters for the high or low Q-range. Note that this does not turn extrapolation on or off. :param range: a keyword set the type of extrapolation . type string :param npts: the numbers of q points of data to consider for extrapolation :param function: a keyword to select the function to use for extrapolation. of type string. :param power: an power to apply power_low function """ range = range.lower() if range not in ['high', 'low']: raise ValueError("Extrapolation range should be 'high' or 'low'") function = function.lower() if function not in ['power_law', 'guinier']: msg = "Extrapolation function should be 'guinier' or 'power_law'" raise ValueError(msg) if range == 'high': if function != 'power_law': msg = "Extrapolation only allows a power law at high Q" raise ValueError(msg) self._high_extrapolation_npts = npts self._high_extrapolation_power = power self._high_extrapolation_power_fitted = power else: if function == 'power_law': self._low_extrapolation_function = PowerLaw() else: self._low_extrapolation_function = Guinier() self._low_extrapolation_npts = npts self._low_extrapolation_power = power self._low_extrapolation_power_fitted = power
[docs] def get_qstar(self, extrapolation=None): """ Compute the invariant of the local copy of data. :param extrapolation: string to apply optional extrapolation :return q_star: invariant of the data within data's q range :warning: When using setting data to Data1D , the user is responsible of checking that the scale and the background are properly apply to the data """ self._qstar = self._get_qstar(self._data) self._qstar_err = self._get_qstar_uncertainty(self._data) if extrapolation is None: return self._qstar # Compute invariant plus invariant of extrapolated data extrapolation = extrapolation.lower() if extrapolation == "low": qs_low, dqs_low = self.get_qstar_low() qs_hi, dqs_hi = 0, 0 elif extrapolation == "high": qs_low, dqs_low = 0, 0 qs_hi, dqs_hi = self.get_qstar_high() elif extrapolation == "both": qs_low, dqs_low = self.get_qstar_low() qs_hi, dqs_hi = self.get_qstar_high() self._qstar += qs_low + qs_hi self._qstar_err = math.sqrt(self._qstar_err * self._qstar_err \ + dqs_low * dqs_low + dqs_hi * dqs_hi) return self._qstar
[docs] def get_surface(self, contrast, porod_const, extrapolation=None): """ Compute the specific surface from the data. Implementation:: V = self.get_volume_fraction(contrast, extrapolation) Compute the surface given by: surface = (2*pi *V(1- V)*porod_const)/ q_star :param contrast: contrast value to compute the volume :param porod_const: Porod constant to compute the surface :param extrapolation: string to apply optional extrapolation :return: specific surface """ # Compute the volume volume = self.get_volume_fraction(contrast, extrapolation) return 2 * math.pi * volume * (1 - volume) * \ float(porod_const) / self._qstar
[docs] def get_volume_fraction(self, contrast, extrapolation=None): """ Compute volume fraction is deduced as follow: :: q_star = 2*(pi*contrast)**2* volume( 1- volume) for k = 10^(-8)*q_star/(2*(pi*|contrast|)**2) we get 2 values of volume: with 1 - 4 * k >= 0 volume1 = (1- sqrt(1- 4*k))/2 volume2 = (1+ sqrt(1- 4*k))/2 q_star: the invariant value included extrapolation is applied unit 1/A^(3)*1/cm q_star = self.get_qstar() the result returned will be 0 <= volume <= 1 :param contrast: contrast value provides by the user of type float. contrast unit is 1/A^(2)= 10^(16)cm^(2) :param extrapolation: string to apply optional extrapolation :return: volume fraction :note: volume fraction must have no unit """ if contrast <= 0: raise ValueError("The contrast parameter must be greater than zero") # Make sure Q star is up to date self.get_qstar(extrapolation) if self._qstar <= 0: msg = "Invalid invariant: Invariant Q* must be greater than zero" raise RuntimeError(msg) # Compute intermediate constant k = 1.e-8 * self._qstar / (2 * (math.pi * math.fabs(float(contrast))) ** 2) # Check discriminant value discrim = 1 - 4 * k # Compute volume fraction if discrim < 0: msg = "Could not compute the volume fraction: negative discriminant" raise RuntimeError(msg) elif discrim == 0: return 1 / 2 else: volume1 = 0.5 * (1 - math.sqrt(discrim)) volume2 = 0.5 * (1 + math.sqrt(discrim)) if 0 <= volume1 and volume1 <= 1: return volume1 elif 0 <= volume2 and volume2 <= 1: return volume2 msg = "Could not compute the volume fraction: inconsistent results" raise RuntimeError(msg)
[docs] def get_qstar_with_error(self, extrapolation=None): """ Compute the invariant uncertainty. This uncertainty computation depends on whether or not the data is smeared. :param extrapolation: string to apply optional extrapolation :return: invariant, the invariant uncertainty """ self.get_qstar(extrapolation) return self._qstar, self._qstar_err
[docs] def get_volume_fraction_with_error(self, contrast, extrapolation=None): """ Compute uncertainty on volume value as well as the volume fraction This uncertainty is given by the following equation: :: dV = 0.5 * (4*k* dq_star) /(2* math.sqrt(1-k* q_star)) for k = 10^(-8)*q_star/(2*(pi*|contrast|)**2) q_star: the invariant value including extrapolated value if existing dq_star: the invariant uncertainty dV: the volume uncertainty The uncertainty will be set to -1 if it can't be computed. :param contrast: contrast value :param extrapolation: string to apply optional extrapolation :return: V, dV = volume fraction, error on volume fraction """ volume = self.get_volume_fraction(contrast, extrapolation) # Compute error k = 1.e-8 * self._qstar / (2 * (math.pi * math.fabs(float(contrast))) ** 2) # Check value inside the sqrt function value = 1 - k * self._qstar if (value) <= 0: uncertainty = -1 # Compute uncertainty uncertainty = math.fabs((0.5 * 4 * k * \ self._qstar_err) / (2 * math.sqrt(1 - k * self._qstar))) return volume, uncertainty
[docs] def get_surface_with_error(self, contrast, porod_const, extrapolation=None): """ Compute uncertainty of the surface value as well as the surface value. The uncertainty is given as follow: :: dS = porod_const *2*pi[( dV -2*V*dV)/q_star + dq_star(v-v**2) q_star: the invariant value dq_star: the invariant uncertainty V: the volume fraction value dV: the volume uncertainty :param contrast: contrast value :param porod_const: porod constant value :param extrapolation: string to apply optional extrapolation :return S, dS: the surface, with its uncertainty """ # We get the volume fraction, with error # get_volume_fraction_with_error calls get_volume_fraction # get_volume_fraction calls get_qstar # which computes Qstar and dQstar v, dv = self.get_volume_fraction_with_error(contrast, extrapolation) s = self.get_surface(contrast=contrast, porod_const=porod_const, extrapolation=extrapolation) ds = porod_const * 2 * math.pi * ((dv - 2 * v * dv) / self._qstar\ + self._qstar_err * (v - v ** 2)) return s, ds