-                      r2cce133
+                      ref9ed58
 import math
 import numpy.linalg.lstsq
+import numpy
 from DataLoader.data_info import Data1D as LoaderData1D
+#from DataLoader.data_info import Data1D as LoaderData1D
 from DataLoader.qsmearing import smear_selection
 …
 INTEGRATION_NSTEPS = 1000
-class FitFunctor:
-    """
-        compute f(x)
-    """
-    def __init__(self,data , function):
-        """
-            @param function :the function used for computing residuals
-            @param Data: data used for computing residuals
-        """
-        self.function = function
-        self.data  = data
-        x_len = len(self.data.x) -1
-        #fitting range
-        self.qmin =  self.data[0]
-        if self.qmin == 0:
-            self.qmin = Q_MINIMUM
-        self.qmax = self.data[x_len]
-        #Unsmeared q range
-        self._qmin_unsmeared = 0
-        self._qmax_unsmeared = self.data[x_len]
-        #bin for smear data
-        self._first_unsmeared_bin = 0
-        self._last_unsmeared_bin  = x_len
-        # Identify the bin range for the unsmeared and smeared spaces
-        self.idx = (self.x>=self.qmin) & (self.x <= self.qmax)
-        self.idx_unsmeared = (self.x>=self._qmin_unsmeared) & (self.x <= self._qmax_unsmeared)
-        #get the smear object of data
-        self.smearer = smear_selection( self.data )
-        self.func_name = "Residuals"
-    def setFitRange(self):
-        """ to set the fit range"""
-        self.qmin =  self.data[0]
-        if self.qmin== 0:
-            self.qmin= MINIMUM
-        x_len= len(self.data.x) -1
-        self.qmax= self.data[x_len]
-        # Determine the range needed in unsmeared-Q to cover
-        # the smeared Q range
-        self._qmin_unsmeared = self.qmin
-        self._qmax_unsmeared = self.qmax
-        self._first_unsmeared_bin = 0
-        self._last_unsmeared_bin  = len(self.data.x)-1
-        if self.smearer!=None:
-            self._first_unsmeared_bin, self._last_unsmeared_bin = self.smearer.get_bin_range(self.qmin, self.qmax)
-            self._qmin_unsmeared = self.data.x[self._first_unsmeared_bin]
-            self._qmax_unsmeared = self.data.x[self._last_unsmeared_bin]
-        # Identify the bin range for the unsmeared and smeared spaces
-        self.idx = (self.data.x>=self.qmin) & (self.data.x <= self.qmax)
-        self.idx_unsmeared = (self.data.x>=self._qmin_unsmeared) & (self.data.x <= self._qmax_unsmeared)
-    def _get_residuals(self, params):
-        """
-            Compute residuals
-            @return res: numpy array of float of size.self.data.x
-        """
-         # Compute theory data f(x)
-        fx = numpy.zeros(len(self.data.x))
-        fx[self.idx_unsmeared] = self.funtion(self.data.x[self.idx_unsmeared])
-        ## Smear theory data
-        if self.smearer is not None:
-            fx = self.smearer(fx, self._first_unsmeared_bin, self._last_unsmeared_bin)
-        ## Sanity check
-        if numpy.size(self.data.dy)!= numpy.size(fx):
-            raise RuntimeError, "Residuals: invalid error array %d <> %d" % (numpy.shape(self.data.dy),
-                                                                              numpy.size(fx))
-        return (self.data.y[self.idx]-fx[self.idx])/self.data.dy[self.idx]
-    def __call__(self,params):
-        """
-            Compute residuals
-            @param params: value of parameters to fit
-        """
-        return self._get_residuals(params)
 def guinier(x, scale=1, radius=0.1):
     """
         Compute a F(x) = scale* e-((radius*x)**2/3).
         @param x: a vector of q values or one q value
+        @param x: a vector of q values
         @param scale: the scale value
         @param radius: the guinier radius value
         @return F(x)
+    """
+    """
+    value = numpy.array([math.exp(-((radius * i)**2/3)) for i in x ])
+    return scale * value
 def power_law(x, scale=1, power=4):
     """
 …
             else power_law
         The model has three parameters:
+        @param x: a vector of q values
         @param power: power of the function
         @param scale : scale factor value
         @param F(x)
+    """
+    """
+    value = numpy.array([ math.pow(i, -power) for i in x ])
+    return scale * value
+class FitFunctor:
+    """
+        compute f(x)
+    """
+    def __init__(self,data , function):
+        """
+            @param function :the function used for computing residuals
+            @param Data: data used for computing residuals
+        """
+        self.function = function
+        self.data  = data
+        x_len = len(self.data.x) -1
+        #fitting range
+        self.qmin =  self.data.x[0]
+        if self.qmin == 0:
+            self.qmin = Q_MINIMUM
+        self.qmax = self.data.x[x_len]
+        #Unsmeared q range
+        self._qmin_unsmeared = 0
+        self._qmax_unsmeared = self.data.x[x_len]
+        #bin for smear data
+        self._first_unsmeared_bin = 0
+        self._last_unsmeared_bin  = x_len
+        # Identify the bin range for the unsmeared and smeared spaces
+        self.idx = (self.data.x >= self.qmin) & (self.data.x <= self.qmax)
+        self.idx_unsmeared = (self.data.x >= self._qmin_unsmeared) \
+                            & (self.data.x <= self._qmax_unsmeared)
+        #get the smear object of data
+        self.smearer = smear_selection( self.data )
+    def set_fit_range(self ,qmin=None, qmax=None):
+        """ to set the fit range"""
+        if qmin is not None:
+            self.qmin = qmin
+        if qmax is not None:
+            self.qmax = qmax
+        # Determine the range needed in unsmeared-Q to cover
+        # the smeared Q range
+        self._qmin_unsmeared = self.qmin
+        self._qmax_unsmeared = self.qmax
+        self._first_unsmeared_bin = 0
+        self._last_unsmeared_bin  = len(self.data.x)-1
+        if self.smearer!=None:
+            self._first_unsmeared_bin, self._last_unsmeared_bin = self.smearer.get_bin_range(self.qmin, self.qmax)
+            self._qmin_unsmeared = self.data.x[self._first_unsmeared_bin]
+            self._qmax_unsmeared = self.data.x[self._last_unsmeared_bin]
+        # Identify the bin range for the unsmeared and smeared spaces
+        self.idx = (self.data.x >= self.qmin) & (self.data.x <= self.qmax)
+        self.idx_unsmeared = (self.data.x >= self._qmin_unsmeared) \
+                                & (self.data.x <= self._qmax_unsmeared)
+    def fit(self):
+        """
+           Fit data for y = ax + b  return a and b
+        """
+        fx = self.data.y
+        ## Smear theory data
+        if self.smearer is not None:
+            fx = self.smearer(fx, self._first_unsmeared_bin,
+                               self._last_unsmeared_bin)
+        A = numpy.vstack([ self.data.x, numpy.ones(len(self.data.x))]).T
+        a, b = numpy.linalg.lstsq(A, fx)[0]
+        return a, b
 class InvariantCalculator(object):
     """
 …
         @note: Some computations depends on each others.
     """
     def __init__(self, data, background=0, scale=1 ):
         """
 …
         self._low_extrapolation_function = guinier
         self._low_extrapolation_power = 4
         self._high_extrapolation_npts = 4
         self._high_extrapolation_function = power_law
         self._high_extrapolation_power = 4
     def set_extrapolation(self, range, npts=4, function=None, power=4):
         """
             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()
 …
             raise ValueError, "Extrapolation function should be 'guinier' or 'power_law'"
         if range=='high':
+        if range == 'high':
             if function != 'power_law':
                 raise ValueError, "Extrapolation only allows a power law at high Q"
 …
             #Process only data that inherited from DataLoader.Data_info.Data1D
             raise ValueError,"Data must be of type DataLoader.Data1D"
     def _fit(self, function, params=[]):
+        return self._scale * data - self._background
+    def _fit(self, function, qmin=Q_MINIMUM, qmax=Q_MAXIMUM):
         """
             fit data with function using
 …
             y = data.y
             out, cov_x = 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 function: the function to use during the fit
+            @param params: the list of parameters values 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
+        """
+        if function.__name__ == "guinier":
+            fit_x = self._data.x * self._data.x
+            qmin = qmin**2
+            qmax = qmax**2
+            fit_y = [math.log(y) for y in self._data.y]
+        elif function.__name__ == "power_law":
+            fit_x = [math.log(x) for x in self._data.x]
+            qmin = math.log(qmin)
+            qmax = math.log(qmax)
+            fit_y = [math.log(y) for y in self._data.y]
+        else:
+            raise ValueError("Unknown function used to fit %s"%function.__name__)
+        fit_data = LoaderData1D(x=fit_x, y=fit_y)
+        fit_data.dxl = self._data.dxl
+        fit_data.dxw = self._data.dxw
+        functor = FitFunctor(data=fit_data, function= function)
+        functor.set_fit_range(qmin=qmin, qmax=qmax)
+        a, b = functor.fit()
+        if function.__name__ == "guinier":
+            # b is the radius value of the guinier function
+            print "b",b
+            b = math.sqrt(-3 * b)
+        # a is the scale of the guinier function
+        a = math.exp(a)
+        return a, math.fabs(b)
+    def _get_qstar(self, data):
+        """
+            Compute invariant for data
+            @param data: data to use to compute invariant.
+        """
+        if data is None:
+            return 0
+        if data.is_slit_smeared():
+            return self._get_qstar_smear(data)
+        else:
+            return self._get_qstar_unsmear(data)
     def get_qstar(self, extrapolation=None):
         """
 …
                 else:
                     qstar_0 = self._get_qstar_unsmear()
+                if extrapolation is None:
+                    return qstar_0
                 if extrapolation==low:
                     return qstar_0 + self._get_qstar_low()
 …
                 elif extrapolation==both:
                     return qstar_0 + self._get_qstar_low() + self._get_qstar_high()
+            @param extrapolation: string to apply optional extrapolation
             @return q_star: invariant of the data within data's q range
+        """
+    def _get_qstar_unsmear(self):
+            @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
+            @warning: if error occur self._get_qstar_low() or self._get_qstar_low()
+            their returned value will be ignored
+        """
+        qstar_0 = self._get_qstar(data=self._data)
+        if extrapolation is None:
+            self._qstar = qstar_0
+            return self._qstar
+        # Compute invariant plus invaraint of extrapolated data
+        extrapolation = extrapolation.lower()
+        if extrapolation == "low":
+            self._qstar = qstar_0 + self._get_qstar_low()
+            return self._qstar
+        elif extrapolation == "high":
+            self._qstar = qstar_0 + self._get_qstar_high()
+            return self._qstar
+        elif extrapolation == "both":
+            self._qstar = qstar_0 + self._get_qstar_low() + self._get_qstar_high()
+            return self._qstar
+    def _get_qstar_unsmear(self, data):
         """
             Compute invariant for pinhole data.
 …
             dx0 = (x1 - x0)/2
             dxn = xn - xn-1
+            @return q_star: invariant value for pinhole data.
+        """
+    def _get_qstar_smear(self):
+            @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:
+            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]
+            sum = 0
+            sum += data.x[0] * data.x[0] * data.y[0] * dx0
+            sum += data.x[n] * data.x[n] * data.y[n] * dxn
+            if len(data.x) == 2:
+                return sum
+            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 += data.x[i] * data.x[i] * data.y[i] * dxi
+                return sum
+    def _get_qstar_smear(self, data):
         """
             Compute invariant for slit-smeared data.
 …
             @return q_star: invariant value for slit smeared data.
         """
+    def _get_qstar_unsmear_uncertainty(self):
+        if not data.is_slit_smeared():
+            msg = "_get_qstar_smear need slit smear data "
+            msg += "Hint :dxl= %s , dxw= %s"%(str(data.dxl), str(data.dxw))
+            raise ValueError, msg
+        if len(data.x) <= 1 or len(data.y) <= 1 or len(data.x) != len(data.y)\
+              or len(data.x)!= len(data.dxl):
+            msg = "x, dxl, and y must be have the same length and greater than 1"
+            raise ValueError, msg
+        else:
+            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]
+            sum = 0
+            sum += data.x[0] * data.dxl[0] * data.y[0] * dx0
+            sum += data.x[n] * data.dxl[n] * data.y[n] * dxn
+            if len(data.x)==2:
+                return sum
+            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 += data.x[i] * data.dxl[i] * data.y[i] * dxi
+                return sum
+    def _get_qstar_uncertainty(self, data=None):
+        """
+            Compute uncertainty of invariant value
+            Implementation:
+                if data is None:
+                    data = self.data
+                if data.slit smear:
+                        return self._get_qstar_smear_uncertainty(data)
+                    else:
+                        return self._get_qstar_unsmear_uncertainty(data)
+            @param: data use to compute the invariant which allow uncertainty
+            computation.
+            @return: uncertainty
+        """
+        if data is None:
+            data = self.data
+        if data.is_slit_smeared():
+            return self._get_qstar_smear_uncertainty(data)
+        else:
+            return self._get_qstar_unsmear_uncertainty(data)
+    def _get_qstar_unsmear_uncertainty(self, data=None):
         """
             Compute invariant uncertainty with with pinhole data.
 …
             dxn = xn - xn-1
             dyn: error on dy
             @param data: data of type Data1D where the scale is applied
                         and the background is subtracted.
+            @param data:
             note: if data doesn't contain dy assume dy= math.sqrt(data.y)
         """
+        if data is None:
+            data = self.data
+        if len(data.x) <= 1 or len(data.y) <= 1 or \
+            len(self.data.x) != len(self.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) or (not data.dy):
+                dy = math.sqrt(y)
+            else:
+                dy = data.dy
+            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]
+            sum = 0
+            sum += (data.x[0] * data.x[0] * dy[0] * dx0)**2
+            sum += (data.x[n] * data.x[n] * dy[n] * dxn)**2
+            if len(data.x) == 2:
+                return math.sqrt(sum)
+            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 += (data.x[i] * data.x[i] * dy[i] * dxi)**2
+                return math.sqrt(sum)
     def _get_qstar_smear_uncertainty(self):
 …
             note: if data doesn't contain dy assume dy= math.sqrt(data.y)
         """
+        if data is None:
+            data = self._data
+        if not data.is_slit_smeared():
+            msg = "_get_qstar_smear_uncertainty need slit smear data "
+            msg += "Hint :dxl= %s , dxw= %s"%(str(data.dxl), str(data.dxw))
+            raise ValueError, msg
+        if len(data.x) <= 1 or len(data.y) <= 1 or len(data.x) != len(data.y)\
+                or len(data.x) != len(data.dxl):
+            msg = "x, dxl, and y must be have the same length and greater than 1"
+            raise ValueError, msg
+        else:
+            #Create error for data without dy error
+            if (data.dy is None) or (not data.dy):
+                dy = math.sqrt(y)
+            else:
+                dy = data.dy
+            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]
+            sum = 0
+            sum += (data.x[0] * data.dxl[0] * dy[0] * dx0)**2
+            sum += (data.x[n] * data.dxl[n] * dy[n] * dxn)**2
+            if len(data.x) == 2:
+                return math.sqrt(sum)
+            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 += (data.x[i] * data.dxl[i] * dy[i] * dxi)**2
+                return math.sqrt(sum)
     def get_surface(self,contrast, porod_const):
 …
             @return: specific surface
         """
+        #Check whether we have Q star
+        if self._qstar is None:
+            self._qstar = self.get_star()
+        if self._qstar == 0:
+            raise RuntimeError("Cannot compute surface, invariant value is zero")
+        # Compute the volume
+        volume = self.get_volume_fraction(contrast)
+        return 2 * math.pi * volume *(1 - volume) * float(porod_const)/self._qstar
     def get_volume_fraction(self, contrast):
         """
 …
             q_star = 2*(pi*contrast)**2* volume( 1- volume)
             for k = 10^(8)*q_star/(2*(pi*|contrast|)**2)
+            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._qstar
+                    q_star = self.get_qstar()
             the result returned will be 0<= volume <= 1
 …
             @note: volume fraction must have no unit
         """
+        if contrast < 0:
+            raise ValueError, "contrast must be greater than zero"
+        if self._qstar is None:
+            self._qstar = self.get_qstar()
+        if self._qstar < 0:
+            raise RuntimeError, "invariant must be greater than zero"
+        print "self._qstar",self._qstar
+        # Compute intermediate constant
+        k =  1.e-8 * self._qstar/(2 * (math.pi * math.fabs(float(contrast)))**2)
+        #Check discriminant value
+        discrim = 1 - 4 * k
+        print "discrim",k,discrim
+        # Compute volume fraction
+        if discrim < 0:
+            raise RuntimeError, "could not compute the volume fraction: negative discriminant"
+        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
+            raise RuntimeError, "could not compute the volume fraction: inconsistent results"
     def _get_qstar_low(self):
         """
 …
             @return q_star: the invariant for data extrapolated at low q.
         """
+        data = self._get_extra_data_low()
+        return self._get_qstar(data=data)
     def _get_qstar_high(self):
 …
             @return q_star: the invariant for data extrapolated at high q.
         """
+        data = self._get_extra_data_high()
+        return self._get_qstar( data=data)
     def _get_extra_data_low(self):
 …
             @return: a new data of type Data1D
         """
+        # Data boundaries for fiiting
+        qmin = self._data.x[0]
+        qmax = self._data.x[self._low_extrapolation_npts]
+        try:
+            # fit the data with a model to get the appropriate parameters
+            a, b = self._fit(function=self._low_extrapolation_function,
+                                   qmin=qmin, qmax=qmax)
+        except:
+            raise
+            return None
+        #create new Data1D to compute the invariant
+        new_x = numpy.linspace(start=Q_MINIMUM,
+                               stop=qmin,
+                               num=INTEGRATION_NSTEPS,
+                               endpoint=True)
+        new_y = self._low_extrapolation_function(x=new_x,
+                                                          scale=a,
+                                                          radius=b)
+        dxl = None
+        dxw = None
+        if self._data.dxl is not None:
+            dxl = numpy.ones(1, INTEGRATION_NSTEPS)
+            dxl = dxl * self._data.dxl[0]
+        if self._data.dxw is not None:
+            dwl = numpy.ones(1, INTEGRATION_NSTEPS)
+            dwl = dwl * self._data.dwl[0]
+        data_min = LoaderData1D(x=new_x, y=new_y)
+        data_min.dxl = dxl
+        data_min.dxw = dxw
+        self._data.clone_without_data( clone= data_min)
+        return data_min
     def _get_extra_data_high(self):
         """
 …
             @return: a new data of type Data1D
         """
+    def get_qstar_with_error(self):
+        # Data boundaries for fiiting
+        x_len = len(self._data.x) - 1
+        qmin = self._data.x[x_len - self._high_extrapolation_npts]
+        qmax = self._data.x[x_len]
+        try:
+            # fit the data with a model to get the appropriate parameters
+            a, b = self._fit(function=self._high_extrapolation_function,
+                                   qmin=qmin, qmax=qmax)
+        except:
+            raise
+            return None
+        #create new Data1D to compute the invariant
+        new_x = numpy.linspace(start=qmax,
+                               stop=Q_MAXIMUM,
+                               num=INTEGRATION_NSTEPS,
+                               endpoint=True)
+        new_y = self._high_extrapolation_function(x=new_x,
+                                                  scale=a,
+                                                  power=b)
+        dxl = None
+        dxw = None
+        if self._data.dxl is not None:
+            dxl = numpy.ones(1, INTEGRATION_NSTEPS)
+            dxl = dxl * self._data.dxl[0]
+        if self._data.dxw is not None:
+            dwl = numpy.ones(1, INTEGRATION_NSTEPS)
+            dwl = dwl * self._data.dwl[0]
+        data_max = LoaderData1D(x=new_x, y=new_y)
+        data_max.dxl = dxl
+        data_max.dxw = dxw
+        self._data.clone_without_data( clone=data_max)
+        return data_max
+    def get_qstar_with_error(self, extrapolation=None):
         """
             Compute the invariant uncertainty.
             This uncertainty computation depends on whether or not the data is
             smeared.
+            @return dq_star:
+                if slit smear:
+                    return self._get_qstar(), self._get_qstar_smear_uncertainty()
+                else:
+                    return self._get_qstar(), self._get_qstar_unsmear_uncertainty()
+        """
+            @return: invariant,  the invariant uncertainty
+                return self._get_qstar(), self._get_qstar_smear_uncertainty()
+        """
+        if self._qstar is None:
+            self._qstar = self.get_qstar(extrapolation=extrapolation)
+        if self._qstar_err is None:
+            self._qstar_err = self._get_qstar_smear_uncertainty()
+        return self._qstar, self._qstar_err
     def get_volume_fraction_with_error(self, contrast):
         """
 …
             @return: V, dV = self.get_volume_fraction_with_error(contrast), dV
         """
+        self._qstar, self._qstar_err = self.get_qstar_with_error()
+        volume = self.get_volume_fraction(contrast)
+        if self._qstar < 0:
+            raise ValueError, "invariant must be greater than zero"
+        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 (1 - k * self._qstar) <= 0:
+            raise ValueError, "Cannot compute incertainty on volume"
+        # Compute uncertainty
+        uncertainty = (0.5 * 4 * k * self._qstar_err)/(2 * math.sqrt(1- k * self._qstar))
+        return volume, math.fabs(uncertainty)
     def get_surface_with_error(self, contrast, porod_const):
         """
 …
             @return S, dS: the surface, with its uncertainty
         """
+        v, dv = self.get_volume_fraction_with_error(contrast)
+        self._qstar, self._qstar_err = self.get_qstar_with_error()
+        if self._qstar <= 0:
+            raise ValueError, "invariant must be greater than zero"
+        ds = porod_const * 2 * math.pi * (( dv - 2 * v * dv)/ self._qstar\
+                 + self._qstar_err * ( v - v**2))
+        s = self.get_surface(contrast=contrast, porod_const=porod_const)
+        return s, ds

Invariant/test/utest_use_cases.py

-                      rb6666d4
+                      ref9ed58
 """
 import unittest
+import numpy
 from DataLoader.loader import  Loader
 from sans.invariant import invariant
+class Data1D:
+    print "I am not of type Dataloader.Data1D"
 class TestInvPolySphere(unittest.TestCase):
     """
 …
     def setUp(self):
         self.data = Loader().load("PolySpheres.txt")
+    def test_wrong_data(self):
+        """ test receiving Data1D not of type loader"""
+        wrong_data= Data1D()
+        try:
+            self.assertRaises(ValueError,invariant.InvariantCalculator(wrong_data))
+        except ValueError, msg:
+            print "test pass "+ str(msg)
+        else: raise ValueError, "fail to raise exception when expected"
     def test_use_case_1(self):
 …
         # methods should check that Q* has been computed. If not, it should
         # compute it by calling get_qstare(), leaving the parameters as default.
+        volume_fraction = inv.get_volume_fraction(contrast=1.0)
+        surface         = inv.get_surface(contrast=1.0, porod_const=1.0)
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 7.48959e-5,2)
+        self.assertAlmostEquals(v, 0.005644689)
+        self.assertAlmostEquals(s, 9.42e+2,2)
     def test_use_case_2(self):
 …
         # Get the invariant with errors
         qstar, qstar_err = inv.get_qstar_with_error()
+        v, dv            = inv.get_volume_fraction_with_error(contrast=1.0)
+        s, ds            = inv.get_surface_with_error(contrast=1.0, porod_const=1.0)
+        # The volume fraction and surface use Q*. That means that the following
+        # methods should check that Q* has been computed. If not, it should
+        # compute it by calling get_qstare(), leaving the parameters as default.
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 7.48959e-5,2)
+        self.assertAlmostEquals(v, 0.005644689)
+        self.assertAlmostEquals(s, 9.42e+2,2)
     def test_use_case_3(self):
 …
         # The function parameter should default to None. If it is None,
         #    the method should pick a good default (Guinier at low-Q and 1/q^4 at high-Q).
         #    The method should also check for consistency of the extrapolate and function
+        #    The method should also check for consistency of the extrapolation and function
         #    parameters. For instance, you might not want to allow 'high' and 'guinier'.
         # The power parameter (not shown below) should default to 4.
         inv.set_extraplotation(range='low', npts=5, function='guinier')
+        inv.set_extrapolation(range='low', npts=20, function='guinier')
         # The version of the call without error
         # At this point, we could still compute Q* without extrapolation by calling
+        # get_qstar with arguments, or with extrapolate=None.
+        qstar = inv.get_qstar(extrapolate='low')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolate='low')
+        # Get the volume fraction and surface
+        v, dv            = inv.get_volume_fraction_with_error(contrast=1.0)
+        s, ds            = inv.get_surface_with_error(contrast=1.0, porod_const=1.0)
+        # get_qstar with arguments, or with extrapolation=None.
+        qstar = inv.get_qstar(extrapolation='low')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 7.49e-5,2)
+        self.assertAlmostEquals(v, 0.005648401)
+        self.assertAlmostEquals(s, 9.42e+2,2)
     def test_use_case_4(self):
         """
 …
         inv = invariant.InvariantCalculator(data=self.data)
         # Set the extrapolation parameters for the high-Q range
+        # The npts parameter should have a good default.
+        # The range parameter should be 'high' or 'low'. Those should be stored separately.
+        # The function parameter should default to None. If it is None,
+        #    the method should pick a good default (Guinier at low-Q and 1/q^4 at high-Q).
+        #    The method should also check for consistency of the extrapolate and function
+        #    parameters. For instance, you might not want to allow 'high' and 'guinier'.
+        # The power parameter should default to 4.
+        inv.set_extraplotation(range='high', npts=5, function='power_law', power=4)
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolate='high'
+        qstar = inv.get_qstar(extrapolate='high')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolate='high')
+        # Get the volume fraction and surface
+        v, dv            = inv.get_volume_fraction_with_error(contrast=1.0)
+        s, ds            = inv.get_surface_with_error(contrast=1.0, porod_const=1.0)
+        inv.set_extrapolation(range='high', npts=20, function='power_law', power=4)
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='high')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='high')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 7.49e-5,2)
+        self.assertAlmostEquals(v, 0.005952674)
+        self.assertAlmostEquals(s, 9.42e+2,2)
     def test_use_case_5(self):
 …
         # Set the extrapolation parameters for the low- and high-Q ranges
+        inv.set_extraplotation(range='low', npts=5, function='guinier')
+        inv.set_extraplotation(range='high', npts=5, function='power_law', power=4)
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolate='high'
+        qstar = inv.get_qstar(extrapolate='both')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolate='both')
+        # Get the volume fraction and surface
+        v, dv            = inv.get_volume_fraction_with_error(contrast=1.0)
+        s, ds            = inv.get_surface_with_error(contrast=1.0, porod_const=1.0)
+        inv.set_extrapolation(range='low', npts=5, function='guinier')
+        inv.set_extrapolation(range='high', npts=5, function='power_law', power=4)
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='both')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='both')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 7.90069e-5,2)
+        self.assertAlmostEquals(v, 0.005952674)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+class TestInvSlitSmear(unittest.TestCase):
+    """
+        Test slit smeared data for invariant computation
+    """
+    def setUp(self):
+        # Data with slit smear
+        list = Loader().load("latex_smeared.xml")
+        self.data_slit_smear = list[1]
+    def test_use_case_1(self):
+        """
+            Invariant without extrapolation
+        """
+        inv = invariant.InvariantCalculator(data=self.data_slit_smear)
+        # get invariant
+        qstar = inv.get_qstar()
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 4.1539e-4)
+        self.assertAlmostEquals(v, 0.032164596)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+    def test_use_case_2(self):
+        """
+            Invariant without extrapolation. Invariant, volume fraction and surface
+            are given with errors.
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_slit_smear)
+        # Get the invariant with errors
+        qstar, qstar_err = inv.get_qstar_with_error()
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 4.1539e-4)
+        self.assertAlmostEquals(v, 0.032164596)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+    def test_use_case_3(self):
+        """
+            Invariant with low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_slit_smear)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=20, function='guinier')
+        # The version of the call without error
+        # At this point, we could still compute Q* without extrapolation by calling
+        # get_qstar with arguments, or with extrapolation=None.
+        qstar = inv.get_qstar(extrapolation='low')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar,4.1534e-4,3)
+        self.assertAlmostEquals(v, 0.032164596)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+    def test_use_case_4(self):
+        """
+            Invariant with high-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_slit_smear)
+        # Set the extrapolation parameters for the high-Q range
+        inv.set_extrapolation(range='high', npts=20, function='power_law', power=4)
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='high')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='high')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 4.1539e-4)
+        self.assertAlmostEquals(v, 0.032164596)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+    def test_use_case_5(self):
+        """
+            Invariant with both high- and low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_slit_smear)
+        # Set the extrapolation parameters for the low- and high-Q ranges
+        inv.set_extrapolation(range='low', npts=20, function='guinier')
+        inv.set_extrapolation(range='high', npts=20, function='power_law', power=4)
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='both')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='both')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 4.1534e-4,3)
+        self.assertAlmostEquals(v, 0.032164596)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+class TestInvPinholeSmear(unittest.TestCase):
+    """
+        Test pinhole smeared data for invariant computation
+    """
+    def setUp(self):
+        # data with smear info
+        list = Loader().load("latex_smeared.xml")
+        self.data_q_smear = list[0]
+    def test_use_case_1(self):
+        """
+            Invariant without extrapolation
+        """
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        qstar = inv.get_qstar()
+        volume_fraction = inv.get_volume_fraction(contrast=2.6e-6)
+        surface = inv.get_surface(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 1.361677e-3)
+        self.assertAlmostEquals(v, 0.115352622)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+    def test_use_case_2(self):
+        """
+            Invariant without extrapolation. Invariant, volume fraction and surface
+            are given with errors.
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        # Get the invariant with errors
+        qstar, qstar_err = inv.get_qstar_with_error()
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 1.361677e-3)
+        self.assertAlmostEquals(v, 0.115352622)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+    def test_use_case_3(self):
+        """
+            Invariant with low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        # Set the extrapolation parameters for the low-Q range
+        inv.set_extrapolation(range='low', npts=20, function='guinier')
+        # The version of the call without error
+        qstar = inv.get_qstar(extrapolation='low')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='low')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=1.0, porod_const=0.08)
+        # Test results
+        self.assertAlmostEquals(qstar, 0.002037677)
+        self.assertAlmostEquals(v, 0.115352622)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+    def test_use_case_4(self):
+        """
+            Invariant with high-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        # Set the extrapolation parameters for the high-Q range
+        inv.set_extrapolation(range='high', npts=20, function='power_law', power=4)
+        # The version of the call without error
+        qstar = inv.get_qstar(extrapolation='high')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='high')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 1.481677e-3)
+        self.assertAlmostEquals(v, 0.127225804)
+        self.assertAlmostEquals(s, 9.42e+2,2)
+    def test_use_case_5(self):
+        """
+            Invariant with both high- and low-Q extrapolation
+        """
+        # Create invariant object. Background and scale left as defaults.
+        inv = invariant.InvariantCalculator(data=self.data_q_smear)
+        # Set the extrapolation parameters for the low- and high-Q ranges
+        inv.set_extrapolation(range='low', npts=20, function='guinier')
+        inv.set_extrapolation(range='high', npts=20, function='power_law', power=4)
+        # The version of the call without error
+        # The function parameter defaults to None, then is picked to be 'power_law' for extrapolation='high'
+        qstar = inv.get_qstar(extrapolation='both')
+        # The version of the call with error
+        qstar, qstar_err = inv.get_qstar_with_error(extrapolation='both')
+        # Get the volume fraction and surface
+        v, dv = inv.get_volume_fraction_with_error(contrast=2.6e-6)
+        s, ds = inv.get_surface_with_error(contrast=2.6e-6, porod_const=2)
+        # Test results
+        self.assertAlmostEquals(qstar, 0.002157677)
+        self.assertAlmostEquals(v, 0.202846825)
+        self.assertAlmostEquals(s, 9.42e+2,2)

Note: See TracChangeset for help on using the changeset viewer.

SasView

Changeset ef9ed58 in sasview

Legend:

Invariant/invariant.py

Invariant/test/utest_use_cases.py

Download in other formats: