Changes in / [630156b:2f9f1ec] in sasmodels

Files:

• example/6pc SD.txt (added)
• example/Beise009798_01.ses (added)
• example/Beise009798_02.ses (added)
• example/Beise009798_03.ses (added)
• example/Gab_5pc_deb2p0.ses (added)
• example/Gab_5pc_deb2p0.txt (added)
• example/oriented_sesans.py (added)
• example/unoriented_sesans.py (added)
• sasmodels/direct_model.py (modified) (7 diffs)
• sasmodels/sesans.py (modified) (3 diffs)
• sasmodels/sesanscos.py (added)
• sasmodels/sesanscosine.py (added)

sasmodels/direct_model.py

@@ -202 +202 @@
 
         if self.data_type == 'sesans':
-            q = sesans.make_q(data.sample.zacceptance, data.Rmax)
-            index = slice(None, None)
-            res = None
-            if data.y is not None:
-                Iq, dIq = data.y, data.dy
+            from sas.sascalc.data_util.nxsunit import Converter
+            qmax, qunits = data.sample.zacceptance
+            SElength = Converter(data._xunit)(data.x, "A")
+            zaccept = Converter(qunits)(qmax, "1/A"),
+            Rmax = 10000000
+            index = slice(None, None)  # equivalent to index [:]
+            Iq = data.y[index]
+            dIq = data.dy[index]
+            oriented = getattr(data, 'oriented', False)
+            if oriented:
+                res = sesans.OrientedSesansTransform(data.x[index], SElength, zaccept, Rmax)
+                # Oriented sesans transform produces q_calc = [qx, qy]
+                q_vectors = res.q_calc
             else:
-                Iq, dIq = None, None
-            #self._theory = np.zeros_like(q)
-            q_vectors = [q]
-            q_mono = sesans.make_all_q(data)
+                res = sesans.SesansTransform(data.x[index], SElength, zaccept, Rmax)
+                # Unoriented sesans transform produces q_calc = q
+                q_vectors = [res.q_calc]
         elif self.data_type == 'Iqxy':
             #if not model.info.parameters.has_2d:
@@ -230 +237 @@
             #self._theory = np.zeros_like(self.Iq)
             q_vectors = res.q_calc
-            q_mono = []
         elif self.data_type == 'Iq':
             index = (data.x >= data.qmin) & (data.x <= data.qmax)
@@ -255 +261 @@
             #self._theory = np.zeros_like(self.Iq)
             q_vectors = [res.q_calc]
-            q_mono = []
         elif self.data_type == 'Iq-oriented':
             index = (data.x >= data.qmin) & (data.x <= data.qmax)
@@ -272 +277 @@
                                       qy_width=data.dxl[index])
             q_vectors = res.q_calc
-            q_mono = []
         else:
             raise ValueError("Unknown data type") # never gets here
@@ -279 +283 @@
         # so we can save/restore state
         self._kernel_inputs = q_vectors
-        self._kernel_mono_inputs = q_mono
         self._kernel = None
         self.Iq, self.dIq, self.index = Iq, dIq, index
@@ -306 +309 @@
         if self._kernel is None:
             self._kernel = self._model.make_kernel(self._kernel_inputs)
-            self._kernel_mono = (
-                self._model.make_kernel(self._kernel_mono_inputs)
-                if self._kernel_mono_inputs else None)
 
         Iq_calc = call_kernel(self._kernel, pars, cutoff=cutoff)
@@ -316 +316 @@
         # TODO: refactor so we don't store the result in the model
         self.Iq_calc = None
-        if self.data_type == 'sesans':
-            Iq_mono = (call_kernel(self._kernel_mono, pars, mono=True)
-                       if self._kernel_mono_inputs else None)
-            result = sesans.transform(self._data,
-                                      self._kernel_inputs[0], Iq_calc,
-                                      self._kernel_mono_inputs, Iq_mono)
-        else:
-            result = self.resolution.apply(Iq_calc)
-            if hasattr(self.resolution, 'nx'):
-                self.Iq_calc = (
-                    self.resolution.qx_calc, self.resolution.qy_calc,
-                    np.reshape(Iq_calc, (self.resolution.ny, self.resolution.nx))
-                )
+        result = self.resolution.apply(Iq_calc)
+        if hasattr(self.resolution, 'nx'):
+            self.Iq_calc = (
+                self.resolution.qx_calc, self.resolution.qy_calc,
+                np.reshape(Iq_calc, (self.resolution.ny, self.resolution.nx))
+            )
         return result
 

sasmodels/sesans.py

@@ -20 +20 @@
     Spin-Echo SANS transform calculator.  Similar to a resolution function,
     the SesansTransform object takes I(q) for the set of *q_calc* values and
-    produces a transformed dataset
+    produces a transformed dataset.
 
     *SElength* (A) is the set of spin-echo lengths in the measured data.
@@ -48 +48 @@
 
     def apply(self, Iq):
-        # tye: (np.ndarray) -> np.ndarray
         G0 = np.dot(self._H0, Iq)
         G = np.dot(self._H.T, Iq)
@@ -73 +72 @@
         self.q_calc = q
         self._H, self._H0 = H, H0
+
+class OrientedSesansTransform(object):
+    """
+    Oriented Spin-Echo SANS transform calculator.  Similar to a resolution
+    function, the OrientedSesansTransform object takes I(q) for the set
+    of *q_calc* values and produces a transformed dataset.
+
+    *SElength* (A) is the set of spin-echo lengths in the measured data.
+
+    *zaccept* (1/A) is the maximum acceptance of scattering vector in the spin
+    echo encoding dimension (for ToF: Q of min(R) and max(lam)).
+
+    *Rmax* (A) is the maximum size sensitivity; larger radius requires more
+    computation time.
+    """
+    #: SElength from the data in the original data units; not used by transform
+    #: but the GUI uses it, so make sure that it is present.
+    q = None  # type: np.ndarray
+
+    #: q values to calculate when computing transform
+    q_calc = None  # type: np.ndarray
+
+    # transform arrays
+    _cosmat = None  # type: np.ndarray
+    _cos0 = None # type: np.ndarray
+    _Iq_shape = None # type: Tuple[int, int]
+
+    def __init__(self, z, SElength, zaccept, Rmax):
+        # type: (np.ndarray, float, float) -> None
+        #import logging; logging.info("creating SESANS transform")
+        self.q = z
+        self._set_cosmat(SElength, zaccept, Rmax)
+
+    def apply(self, Iq):
+        dq = self.q_calc[0][0]
+        Iq = np.reshape(Iq, self._Iq_shape)
+        G0 = self._cos0 * np.sum(Iq) * dq
+        G = np.sum(np.dot(Iq, self._cosmat), axis=0) * dq
+        P = G - G0
+        return P
+
+    def _set_cosmat(self, SElength, zaccept, Rmax):
+        # type: (np.ndarray, float, float) -> None
+        # Force float32 arrays, otherwise run into memory problems on some machines
+        SElength = np.asarray(SElength, dtype='float32')
+
+        # Rmax = #value in text box somewhere in FitPage?
+        q_max = 2 * pi / (SElength[1] - SElength[0])
+        q_min = 0.1 * 2 * pi / (np.size(SElength) * SElength[-1])
+        q_min *= 100
+
+        q = np.arange(q_min, q_max, q_min, dtype='float32')
+        dq = q_min
+
+        cos0 = np.float32(dq / (2 * pi))
+        cosmat = np.float32(dq / (2 * pi)) * np.cos(q[:, None] * SElength[None, :])
+
+        qx, qy = np.meshgrid(q, q)
+        self._Iq_shape = qx.shape
+        self.q_calc = qx.flatten(), qy.flatten()
+        self._cosmat, self._cos0 = cosmat, cos0