Changes in src/sas/sascalc/pr/invertor.py [463e7ffc:9a5097c] in sasview

File:

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

src/sas/sascalc/pr/invertor.py

@@ -7 +7 @@
 """
 
-import numpy
+import numpy as np
 import sys
 import math
@@ -18 +18 @@
 from scipy import optimize
 from sas.sascalc.pr.core.pr_inversion import Cinvertor
-
-logger = logging.getLogger(__name__)
 
 def help():
@@ -191 +189 @@
         #import numpy
         if name == 'x':
-            out = numpy.ones(self.get_nx())
+            out = np.ones(self.get_nx())
             self.get_x(out)
             return out
         elif name == 'y':
-            out = numpy.ones(self.get_ny())
+            out = np.ones(self.get_ny())
             self.get_y(out)
             return out
         elif name == 'err':
-            out = numpy.ones(self.get_nerr())
+            out = np.ones(self.get_nerr())
             self.get_err(out)
             return out
@@ -327 +325 @@
             raise RuntimeError, msg
 
-        p = numpy.ones(nfunc)
+        p = np.ones(nfunc)
         t_0 = time.time()
         out, cov_x, _, _, _ = optimize.leastsq(self.residuals, p, full_output=1)
@@ -343 +341 @@
 
         if cov_x is None:
-            cov_x = numpy.ones([nfunc, nfunc])
+            cov_x = np.ones([nfunc, nfunc])
             cov_x *= math.fabs(chisqr)
         return out, cov_x
@@ -360 +358 @@
             raise RuntimeError, msg
 
-        p = numpy.ones(nfunc)
+        p = np.ones(nfunc)
         t_0 = time.time()
         out, cov_x, _, _, _ = optimize.leastsq(self.pr_residuals, p, full_output=1)
@@ -437 +435 @@
         """
         # Note: To make sure an array is contiguous:
-        # blah = numpy.ascontiguousarray(blah_original)
+        # blah = np.ascontiguousarray(blah_original)
         # ... before passing it to C
 
@@ -458 +456 @@
             nfunc += 1
 
-        a = numpy.zeros([npts + nq, nfunc])
-        b = numpy.zeros(npts + nq)
-        err = numpy.zeros([nfunc, nfunc])
+        a = np.zeros([npts + nq, nfunc])
+        b = np.zeros(npts + nq)
+        err = np.zeros([nfunc, nfunc])
 
         # Construct the a matrix and b vector that represent the problem
@@ -478 +476 @@
         self.chi2 = chi2
 
-        inv_cov = numpy.zeros([nfunc, nfunc])
+        inv_cov = np.zeros([nfunc, nfunc])
         # Get the covariance matrix, defined as inv_cov = a_transposed * a
         self._get_invcov_matrix(nfunc, nr, a, inv_cov)
@@ -492 +490 @@
 
         try:
-            cov = numpy.linalg.pinv(inv_cov)
+            cov = np.linalg.pinv(inv_cov)
             err = math.fabs(chi2 / float(npts - nfunc)) * cov
         except:
             # We were not able to estimate the errors
             # Return an empty error matrix
-            logger.error(sys.exc_value)
+            logging.error(sys.exc_value)
 
         # Keep a copy of the last output
@@ -507 +505 @@
             self.background = c[0]
 
-            err_0 = numpy.zeros([nfunc, nfunc])
-            c_0 = numpy.zeros(nfunc)
+            err_0 = np.zeros([nfunc, nfunc])
+            c_0 = np.zeros(nfunc)
 
             for i in range(nfunc_0):
@@ -543 +541 @@
             # number of terms
             best_alpha, _, _ = self.estimate_alpha(self.nfunc)
-            logger.warning("Invertor.estimate_numterms: %s" % sys.exc_value)
+            logging.warning("Invertor.estimate_numterms: %s" % sys.exc_value)
             return self.nfunc, best_alpha, "Could not estimate number of terms"
 
@@ -664 +662 @@
                                                    str(self.cov[i][i])))
         file.write("<r>  <Pr>  <dPr>\n")
-        r = numpy.arange(0.0, self.d_max, self.d_max / npts)
+        r = np.arange(0.0, self.d_max, self.d_max / npts)
 
         for r_i in r:
@@ -696 +694 @@
                         toks = line.split('=')
                         self.nfunc = int(toks[1])
-                        self.out = numpy.zeros(self.nfunc)
-                        self.cov = numpy.zeros([self.nfunc, self.nfunc])
+                        self.out = np.zeros(self.nfunc)
+                        self.cov = np.zeros([self.nfunc, self.nfunc])
                     elif line.startswith('#alpha='):
                         toks = line.split('=')