Changeset 9a5097c in sasview for src/sas/sascalc/invariant/invariant.py

Timestamp:

Mar 26, 2017 11:33:16 PM (7 years ago)

Author:

andyfaff

Branches:

master, ESS_GUI, ESS_GUI_Docs, ESS_GUI_batch_fitting, ESS_GUI_bumps_abstraction, ESS_GUI_iss1116, ESS_GUI_iss879, ESS_GUI_iss959, ESS_GUI_opencl, ESS_GUI_ordering, ESS_GUI_sync_sascalc, costrafo411, magnetic_scatt, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1243, ticket-1249, ticket885, unittest-saveload

Children:

ed2276f

Parents:

9146ed9

Message:

MAINT: import numpy as np

File:

• src/sas/sascalc/invariant/invariant.py (modified) (14 diffs)

src/sas/sascalc/invariant/invariant.py

@@ -17 +17 @@
 """
 import math
-import numpy
+import numpy as np
 
 from sas.sascalc.dataloader.data_info import Data1D as LoaderData1D
@@ -50 +50 @@
             dy = data.dy
         else:
-            dy = numpy.ones(len(data.y))
+            dy = np.ones(len(data.y))
 
         # Transform the data
@@ -63 +63 @@
 
         # Create Data1D object
-        x_out = numpy.asarray(x_out)
-        y_out = numpy.asarray(y_out)
-        dy_out = numpy.asarray(dy_out)
+        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)
 
@@ -158 +158 @@
         :param x: array of q-values
         """
-        p1 = numpy.array([self.dscale * math.exp(-((self.radius * q) ** 2 / 3)) \
+        p1 = np.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))\
+        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 numpy.array([math.sqrt(err) for err in diq2])
+        return np.array([math.sqrt(err) for err in diq2])
 
     def _guinier(self, x):
@@ -182 +182 @@
             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])
+        value = np.array([math.exp(-((self.radius * i) ** 2 / 3)) for i in x])
         return self.scale * value
 
@@ -232 +232 @@
         :param x: array of q-values
         """
-        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)\
+        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 numpy.array([math.sqrt(err) for err in diq2])
+        return np.array([math.sqrt(err) for err in diq2])
 
     def _power_law(self, x):
@@ -259 +259 @@
             raise ValueError(msg)
 
-        value = numpy.array([math.pow(i, -self.power) for i in x])
+        value = np.array([math.pow(i, -self.power) for i in x])
         return self.scale * value
 
@@ -304 +304 @@
         idx = (self.data.x >= qmin) & (self.data.x <= qmax)
 
-        fx = numpy.zeros(len(self.data.x))
+        fx = np.zeros(len(self.data.x))
 
         # Uncertainty
-        if type(self.data.dy) == numpy.ndarray and \
+        if type(self.data.dy) == np.ndarray and \
             len(self.data.dy) == len(self.data.x) and \
-            numpy.all(self.data.dy > 0):
+                np.all(self.data.dy > 0):
             sigma = self.data.dy
         else:
-            sigma = numpy.ones(len(self.data.x))
+            sigma = np.ones(len(self.data.x))
 
         # Compute theory data f(x)
@@ -332 +332 @@
             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)
+            b = (np.sum(linearized_data.y / sigma2) \
+                 - a * np.sum(linearized_data.x / sigma2)) / np.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)
+                     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 = 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)
+            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 = numpy.zeros(2)
+            err = np.zeros(2)
             try:
-                inv_cov = numpy.dot(A.transpose(), A)
-                cov = numpy.linalg.pinv(inv_cov)
+                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])]
@@ -434 +434 @@
         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))
+            new_data.dy = np.ones(len(new_data.x))
         return  new_data
 
@@ -571 +571 @@
         """
         #create new Data1D to compute the invariant
-        q = numpy.linspace(start=q_start,
+        q = np.linspace(start=q_start,
                            stop=q_end,
                            num=npts,
@@ -580 +580 @@
         result_data = LoaderData1D(x=q, y=iq, dy=diq)
         if self._smeared != None:
-            result_data.dxl = self._smeared * numpy.ones(len(q))
+            result_data.dxl = self._smeared * np.ones(len(q))
         return result_data
 
@@ -691 +691 @@
 
         if q_start >= q_end:
-            return numpy.zeros(0), numpy.zeros(0)
+            return np.zeros(0), np.zeros(0)
 
         return self._get_extrapolated_data(\
@@ -719 +719 @@
 
         if q_start >= q_end:
-            return numpy.zeros(0), numpy.zeros(0)
+            return np.zeros(0), np.zeros(0)
 
         return self._get_extrapolated_data(\