-                      r79492222
+                      rc76bdc3
+# 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.
+#project funded by the US National Science Foundation.
 #See the license text in license.txt
 #copyright 2010, University of Tennessee
 …
 """
 import math
+import math
 import numpy
 …
 # The minimum q-value to be used when extrapolating
 Q_MINIMUM  = 1e-5
+Q_MINIMUM = 1e-5
 # The maximum q-value to be used when extrapolating
 Q_MAXIMUM  = 10
+Q_MAXIMUM = 10
 # Number of steps in the extrapolation
 …
     """
     Define interface that need to compute a function or an inverse
     function given some x, y
+    function given some x, y
     """
     def linearize_data(self, data):
         """
         Linearize data so that a linear fit can be performed.
+        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))
+        assert len(data.x) == len(data.y)
         if data.dy is not None:
             assert(len(data.x)==len(data.dy))
+            assert len(data.x) == len(data.dy)
             dy = data.dy
         else:
             dy = numpy.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]
+                          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 = numpy.asarray(x_out)
 …
         dy_out = numpy.asarray(dy_out)
         linear_data = LoaderData1D(x=x_out, y=y_out, dy=dy_out)
         return linear_data
     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)]
+        return [p[0] > 0 and p[1] > 0 and p[2] > 0 for p in zip(data.x, data.y,
+                                                                data.dy)]
     def linearize_q_value(self, value):
         """
 …
         """
         return NotImplemented
     def evaluate_model(self, x):
         """
 …
         """
         return NotImplemented
     def evaluate_model_errors(self, x):
         """
 …
         """
         return NotImplemented
 class Guinier(Transform):
     """
     class of type Transform that performs operations related to guinier
+    class of type Transform that performs operations related to guinier
     function
     """
 …
         self.radius = radius
         ## Uncertainty of scale parameter
         self.dscale  = 0
+        self.dscale = 0
         ## Unvertainty of radius parameter
         self.dradius = 0
     def linearize_q_value(self, value):
         """
         Transform the input q-value for linearization
         :param value: q-value
         :return: q*q
         """
         return value * value
     def extract_model_parameters(self, constant, slope, dconstant=0, dslope=0):
         """
 …
         self.radius = math.sqrt(-3 * slope)
         # Errors
         self.dscale = math.exp(constant)*dconstant
+        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
+            self.dradius = -3.0 / 2.0 / math.sqrt(-3 * n_zero) * dslope
         else:
             self.dradius = -3.0/2.0/math.sqrt(-3 * slope)*dslope
+            self.dradius = -3.0 / 2.0 / math.sqrt(-3 * slope) * dslope
         return [self.radius, self.scale], [self.dradius, self.dscale]
     def evaluate_model(self, x):
         """
 …
         """
         return self._guinier(x)
     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 = numpy.array([self.dscale * math.exp(-((self.radius * q)**2/3)) \
+        p1 = numpy.array([self.dscale * math.exp(-((self.radius * q) ** 2 / 3)) \
                           for q in x])
         p2 = numpy.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 numpy.array( [math.sqrt(err) for err in diq2] )
+        p2 = numpy.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 numpy.array([math.sqrt(err) for err in diq2])
     def _guinier(self, x):
         """
 …
         to x
         Compute a F(x) = scale* e-((radius*x)**2/3).
         :param x: a vector of q values
+        :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
+        """
+        # 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 = numpy.array([math.exp(-((self.radius * i)**2/3)) for i in x ])
+            msg = "Rg expected positive value, but got %s" % self.radius
+            raise ValueError(msg)
+        value = numpy.array([math.exp(-((self.radius * i) ** 2 / 3)) for i in x])
         return self.scale * value
 class PowerLaw(Transform):
     """
     class of type transform that perform operation related to power_law
+    class of type transform that perform operation related to power_law
     function
     """
 …
         self.scale = scale
         self.power = power
+        self.dscale = 0.0
+        self.dpower = 0.0
     def linearize_q_value(self, value):
         """
         Transform the input q-value for linearization
         :param value: q-value
         :return: log(q)
         """
         return math.log(value)
     def extract_model_parameters(self, constant, slope, dconstant=0, dslope=0):
         """
         Assign new value to the scale and the power
+        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.dscale = math.exp(constant) * dconstant
         self.dpower = -dslope
         return [self.power, self.scale], [self.dpower, self.dscale]
     def evaluate_model(self, x):
         """
 …
         """
         return self._power_law(x)
     def evaluate_model_errors(self, x):
         """
 …
         """
         p1 = numpy.array([self.dscale * math.pow(q, -self.power) for q in x])
         p2 = numpy.array([self.scale * self.power * math.pow(q, -self.power-1)\
+        p2 = numpy.array([self.scale * self.power * math.pow(q, -self.power - 1)\
                            * self.dpower for q in x])
         diq2 = p1*p1 + p2*p2
         return numpy.array( [math.sqrt(err) for err in diq2] )
+        diq2 = p1 * p1 + p2 * p2
+        return numpy.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
+            when power= 4. the model is porod
             else power_law
         The model has three parameters: ::
 …
 . power: power of the function
 . 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
+            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 = numpy.array([ math.pow(i, -self.power) for i in x ])
+            msg = "scale expected positive value, but got %s" % self.scale
+            raise ValueError(msg)
+        value = numpy.array([math.pow(i, -self.power) for i in x])
         return self.scale * value
 class Extrapolator:
+class Extrapolator(object):
     """
     Extrapolate I(q) distribution using a given model
 …
         """
         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
+        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
+        :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:
+            if q_value > 0:
                 self.qmin = q_value
                 break
         self.qmax = max(self.data.x)
     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
 …
         if qmax is None:
             qmax = self.qmax
         # Identify the bin range for the fit
         idx = (self.data.x >= qmin) & (self.data.x <= qmax)
         fx = numpy.zeros(len(self.data.x))
         # Uncertainty
         if type(self.data.dy)==numpy.ndarray and \
             len(self.data.dy)==len(self.data.x) and \
             numpy.all(self.data.dy>0):
+        if type(self.data.dy) == numpy.ndarray and \
+            len(self.data.dy) == len(self.data.x) and \
+            numpy.all(self.data.dy > 0):
             sigma = self.data.dy
         else:
 …
         # 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]))
+                                                         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
+                                           dy=sigma[idx])
+        ##power is given only for function = power_law
         if power != None:
             sigma2 = linearized_data.dy * linearized_data.dy
             a = -(power)
             b = (numpy.sum(linearized_data.y/sigma2) \
                  - a*numpy.sum(linearized_data.x/sigma2))/numpy.sum(1.0/sigma2)
             deltas = linearized_data.x*a + \
                     numpy.ones(len(linearized_data.x))*b-linearized_data.y
             residuals = numpy.sum(deltas*deltas/sigma2)
             err = math.fabs(residuals) / numpy.sum(1.0/sigma2)
+            b = (numpy.sum(linearized_data.y / sigma2) \
+                 - a * numpy.sum(linearized_data.x / sigma2)) / numpy.sum(1.0 / sigma2)
+            deltas = linearized_data.x * a + \
+                    numpy.ones(len(linearized_data.x)) * b - linearized_data.y
+            residuals = numpy.sum(deltas * deltas / sigma2)
+            err = math.fabs(residuals) / numpy.sum(1.0 / sigma2)
             return [a, b], [0, math.sqrt(err)]
         else:
+            A = numpy.vstack([ linearized_data.x/linearized_data.dy,
+.0/linearized_data.dy]).T
+            (p, residuals, rank, s) = numpy.linalg.lstsq(A,
+                                        linearized_data.y/linearized_data.dy)
+            A = numpy.vstack([linearized_data.x / linearized_data.dy, 1.0 / linearized_data.dy]).T
+            (p, residuals, _, _) = numpy.linalg.lstsq(A, linearized_data.y / linearized_data.dy)
             # Get the covariance matrix, defined as inv_cov = a_transposed * a
             err = numpy.zeros(2)
 …
             except:
                 err = [-1.0, -1.0]
             return p, err
 class InvariantCalculator(object):
 …
     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.
+    :note: Some computations depends on each others.
     """
     def __init__(self, data, background=0, scale=1 ):
+    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
+        :param background: Background value. The data will be corrected
             before processing
         :param scale: Scaling factor for I(q). The data will be corrected
 …
         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 != None and self._data.dxl.all() >0:
+        if self._data.dxl != 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_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"
+            raise ValueError, "Data must be of type DataLoader.Data1D"
         #from copy import deepcopy
         new_data = (self._scale * data) - self._background
+        new_data = (self._scale * data) - self._background
         # Check that the vector lengths are equal
         assert(len(new_data.x)==len(new_data.y))
+        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 = numpy.ones(len(new_data.x))
+            (min(new_data.dy) == 0 and max(new_data.dy) == 0):
+            new_data.dy = numpy.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
+        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 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
 …
         """
         extrapolator = Extrapolator(data=self._data, model=model)
         p, dp = extrapolator.fit(power=power, qmin=qmin, qmax=qmax)
+        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
+            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
+            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
 …
             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))
+        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:
 …
                 gx = data.x * data.x
             # assumes that len(x) == len(dxl).
             else:
                 gx = data.dxl * data.x
             n = len(data.x)- 1
+            else:
+                gx = data.dxl * data.x
+            n = len(data.x) - 1
             #compute the first delta q
             dx0 = (data.x[1] - data.x[0])/2
+            dx0 = (data.x[1] - data.x[0]) / 2
             #compute the last delta q
             dxn = (data.x[n] - data.x[n-1])/2
             sum = 0
             sum += gx[0] * data.y[0] * dx0
             sum += gx[n] * data.y[n] * dxn
+            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 sum
+                return total
             else:
                 #iterate between for element different
                 #from the first and the last
                 for i in xrange(1, n-1):
                     dxi = (data.x[i+1] - data.x[i-1])/2
                     sum += gx[i] * data.y[i] * dxi
                 return sum
+                for i in xrange(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 ]
 …
         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))
+            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)
+                dy = math.sqrt(data.y)
             else:
                 dy = data.dy
 …
             else:
                 gx = data.dxl * data.x
             n = len(data.x) - 1
             #compute the first delta
             dx0 = (data.x[1] - data.x[0])/2
+            dx0 = (data.x[1] - data.x[0]) / 2
             #compute the last delta
             dxn= (data.x[n] - data.x[n-1])/2
             sum = 0
             sum += (gx[0] * dy[0] * dx0)**2
             sum += (gx[n] * dy[n] * dxn)**2
+            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(sum)
+                return math.sqrt(total)
             else:
                 #iterate between for element different
                 #from the first and the last
                 for i in xrange(1, n-1):
                     dxi = (data.x[i+1] - data.x[i-1])/2
                     sum += (gx[i] * dy[i] * dxi)**2
                 return math.sqrt(sum)
+                for i in xrange(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):
+                               q_start=Q_MINIMUM, q_end=Q_MAXIMUM):
         """
         :return: extrapolate data create from data
 …
         iq = model.evaluate_model(q)
         diq = model.evaluate_model_errors(q)
         result_data = LoaderData1D(x=q, y=iq, dy=diq)
         if self._smeared != None:
             result_data.dxl = self._smeared * numpy.ones(len(q))
         return result_data
     def get_data(self):
         """
 …
         """
         return self._data
     def get_extrapolation_power(self, range='high'):
         """
 …
             return self._low_extrapolation_power_fitted
         return self._high_extrapolation_power_fitted
     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.
         """
 …
         qmin = self._data.x[0]
         qmax = self._data.x[self._low_extrapolation_npts - 1]
         # Extrapolate the low-Q data
         p, dp = self._fit(model=self._low_extrapolation_function,
                               qmin=qmin,
                           qmax=qmax,
                           power=self._low_extrapolation_power)
+        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
+            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)
+                                    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
+        # 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
+        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
     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.
         """
 …
         qmin = self._data.x[x_len - (self._high_extrapolation_npts - 1)]
         qmax = self._data.x[x_len]
         # fit the data with a model to get the appropriate parameters
         p, dp = self._fit(model=self._high_extrapolation_function,
                           qmin=qmin,
                           qmax=qmax,
                           power=self._high_extrapolation_power)
+        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)
+                                    npts=INTEGRATION_NSTEPS,
+                                    q_start=qmax, q_end=Q_MAXIMUM)
         return self._get_qstar(data), self._get_qstar_uncertainty(data)
     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
+        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.
+        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, npts_in-1)]
+        q_end = self._data.x[max(0, npts_in - 1)]
         if q_start >= q_end:
             return numpy.zeros(0), numpy.zeros(0)
 …
         return self._get_extrapolated_data(\
                                     model=self._low_extrapolation_function,
                                            npts=npts,
                                            q_start=q_start, q_end=q_end)
+                                    npts=npts,
+                                    q_start=q_start, q_end=q_end)
     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
+        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.
+        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
 …
             npts_in = self._high_extrapolation_npts
         _npts = len(self._data.x)
         q_start = self._data.x[min(_npts, _npts-npts_in)]
+        q_start = self._data.x[min(_npts, _npts - npts_in)]
         if q_start >= q_end:
             return numpy.zeros(0), numpy.zeros(0)
         return self._get_extrapolated_data(\
                                 model=self._high_extrapolation_function,
                                            npts=npts,
                                            q_start=q_start, q_end=q_end)
+                                npts=npts,
+                                q_start=q_start, q_end=q_end)
     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
 …
             of type string.
         :param power: an power to apply power_low function
         """
         range = range.lower()
 …
             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_npts = npts
             self._high_extrapolation_power = power
             self._high_extrapolation_power_fitted = power
 …
             else:
                 self._low_extrapolation_function = Guinier()
             self._low_extrapolation_npts  = npts
+            self._low_extrapolation_npts = npts
             self._low_extrapolation_power = power
             self._low_extrapolation_power_fitted = power
     def get_qstar(self, extrapolation=None):
         """
         Compute the invariant of the local copy of data.
         :param extrapolation: string to apply optional extrapolation
+        :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()
+        extrapolation = extrapolation.lower()
         if extrapolation == "low":
             qs_low, dqs_low = self.get_qstar_low()
             qs_hi, dqs_hi   = 0, 0
+            qs_hi, dqs_hi = 0, 0
         elif extrapolation == "high":
             qs_low, dqs_low = 0, 0
             qs_hi, dqs_hi   = self.get_qstar_high()
+            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)
+            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
     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 porod_const: Porod constant to compute the surface
         :param extrapolation: string to apply optional extrapolation
         :return: specific surface
+        :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
+        return 2 * math.pi * volume * (1 - volume) * \
+            float(porod_const) / self._qstar
     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)
 …
                  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"
+            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)
+        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:
 …
             raise RuntimeError, msg
         elif discrim == 0:
             return 1/2
+            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
+            elif 0 <= volume2 and volume2 <= 1:
+                return volume2
             msg = "Could not compute the volume fraction: inconsistent results"
             raise RuntimeError, msg
     def get_qstar_with_error(self, extrapolation=None):
         """
 …
         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
     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 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)
+        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
 …
         # Compute uncertainty
         uncertainty = math.fabs((0.5 * 4 * k * \
                         self._qstar_err)/(2 * math.sqrt(1 - k * self._qstar)))
+                        self._qstar_err) / (2 * math.sqrt(1 - k * self._qstar)))
         return volume, uncertainty
     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 porod_const: porod constant value
         :param extrapolation: string to apply optional extrapolation
         :return S, dS: the surface, with its uncertainty
         """
 …
         v, dv = self.get_volume_fraction_with_error(contrast, extrapolation)
         s = self.get_surface(contrast=contrast, porod_const=porod_const,
+        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))
+        ds = porod_const * 2 * math.pi * ((dv - 2 * v * dv) / self._qstar\
+                 + self._qstar_err * (v - v ** 2))
         return s, ds

src/sas/pr/invertor.py

-                      r5f8fc78
+                      rc76bdc3
     slit_height = None
     slit_width = None
+    ## Error
+    err = 0
+    ## Data to invert
+    x = None
+    y = None
     def __init__(self):
         Cinvertor.__init__(self)
+        self.err = None
+        self.d_max = None
+        self.d_max = 200.0
         self.q_min = self.set_qmin(-1.0)
         self.q_max = self.set_qmax(-1.0)
-        self.x = None
-        self.y = None
         self.has_bck = False

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Changeset c76bdc3 in sasview for src

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src/sas/invariant/invariant.py

src/sas/pr/invertor.py

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