Changeset e090ba90 in sasview for src/sas/sascalc/corfunc

Timestamp:

Oct 11, 2018 11:59:57 AM (6 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, magnetic_scatt, release-4.2.2, ticket-1009, ticket-1094-headless, ticket-1242-2d-resolution, ticket-1249

Children:

88d2e70

Parents:

67ed543

Message:

remove errors and warnings from py37 tests of sascalc

File:

• src/sas/sascalc/corfunc/corfunc_calculator.py (modified) (4 diffs)

src/sas/sascalc/corfunc/corfunc_calculator.py

@@ -30 +30 @@
             self.start = start
             self.stop = stop
-            self._lastx = []
-            self._lasty = []
+            self._lastx = np.empty(0, dtype='d')
+            self._lasty = None
 
         def __call__(self, x):
             # If input is a single number, evaluate the function at that number
             # and return a single number
-            if type(x) == float or type(x) == int:
+            if isinstance(x, (float, int)):
                 return self._smoothed_function(np.array([x]))[0]
             # If input is a list, and is different to the last input, evaluate
@@ -42 +42 @@
             # the function was called, return the result that was calculated
             # last time instead of explicity evaluating the function again.
-            elif self._lastx == [] or x.tolist() != self._lastx.tolist():
-                self._lasty = self._smoothed_function(x)
-                self._lastx = x
+            if not np.array_equal(x, self._lastx):
+                self._lastx, self._lasty = x, self._smoothed_function(x)
             return self._lasty
 
@@ -88 +87 @@
         # Only process data of the class Data1D
         if not issubclass(data.__class__, Data1D):
-            raise ValueError("Data must be of the type DataLoader.Data1D")
+            raise ValueError("Correlation function cannot be computed with 2D data.")
 
         # Prepare the data
@@ -246 +245 @@
         """Fit the Guinier region of the curve"""
         A = np.vstack([q**2, np.ones(q.shape)]).T
-        return lstsq(A, np.log(iq))
+        return lstsq(A, np.log(iq), rcond=None)
 
     def _fit_porod(self, q, iq):