Changeset 94d13f1 in sasmodels

Timestamp:

Feb 6, 2017 3:03:18 PM (8 years ago)

Author:

Paul Kienzle <pkienzle@…>

Branches:

master, core_shell_microgels, costrafo411, magnetic_model, ticket-1257-vesicle-product, ticket_1156, ticket_1265_superball, ticket_822_more_unit_tests

Children:

1a580fb

Parents:

bc7ad41

Message:

simplify interface to sesans transform

File:

• sasmodels/sesans.py (modified) (1 diff)

sasmodels/sesans.py

@@ -15 +15 @@
 from numpy import pi, exp  # type: ignore
 from scipy.special import j0
-#from mpmath import j0 as j0
-        
+
 class SesansTransform(object):
-    #: Set of spin-echo lengths in the measured data
-    SE = None  # type: np.ndarray
-    #: Maximum acceptance of scattering vector in the spin echo encoding dimension (for ToF: Q of min(R) and max(lam))
-    zaccept = None # type: float
-    #: Maximum size sensitivity; larger radius requires more computation
-    Rmax = None  # type: float
-    #: q values to calculate when computing transform
+    """
+    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
+
+    *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
-    H = None  # type: np.ndarray
-    H0 = None # type: np.ndarray
+    _H = None  # type: np.ndarray
+    _H0 = None # type: np.ndarray
 
-    def __init__(self, SE, zaccept, Rmax):
-        if self.SE is None or len(SE) != len(self.SE) or np.any(SE != self.SE) or zaccept != self.zaccept or Rmax != self.Rmax:
-            self.SE, self.zaccept, self.Rmax = SE, zaccept, Rmax
-            self._set_q()
-            self._set_hankel()
+    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_hankel(SElength, zaccept, Rmax)
 
     def apply(self, Iq):
-        G0 = np.dot(self.H0, Iq)
-        G = np.dot(self.H.T, Iq)
+        # tye: (np.ndarray) -> np.ndarray
+        G0 = np.dot(self._H0, Iq)
+        G = np.dot(self._H.T, Iq)
         P = G - G0
         return P
 
-    def _set_q(self):
-        #q_min = dq = 0.1 * 2*pi / self.Rmax
+    def _set_hankel(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')
 
-        q_max = 2*pi / (self.SE[1]-self.SE[0])
-        q_min = dq = 0.1 *2*pi / (np.size(self.SE) * self.SE[-1])
+        #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 = np.arange(q_min, q_max, q_min, dtype='float32')
+        dq = q_min
 
-        #q_min = dq = q_max / 100000
-        q=np.arange(q_min, q_max, q_min)
-        self.q = q
-        self.dq = dq
+        H0 = np.float32(dq/(2*pi)) * q
 
-    def _set_hankel(self):
-        #Rmax = #value in text box somewhere in FitPage?
-        q = self.q
-        dq = self.dq
-        SElength = self.SE
+        repq = np.tile(q, (SElength.size, 1)).T
+        repSE = np.tile(SElength, (q.size, 1))
+        H = np.float32(dq/(2*pi)) * j0(repSE*repq) * repq
 
-        H0 = dq / (2 * pi) * q
-        q=np.array(q,dtype='float32')
-        SElength=np.array(SElength,dtype='float32')
-
-        # Using numpy tile, dtype is conserved
-        repq=np.tile(q,(SElength.size,1))
-        repSE=np.tile(SElength,(q.size,1))
-        H = dq / (2 * pi) * j0(repSE*repq.T)*repq.T
-
-        # Using numpy meshgrid - meshgrid produces float64 from float32 inputs! Problem for 32-bit OS: Memerrors!
-        #H0 = dq / (2 * pi) * q
-        #repSE, repq = np.meshgrid(SElength, q)
-        #repq=np.array(repq,dtype='float32')
-        #repSE=np.array(repSE,dtype='float32')
-        #H = dq / (2 * pi) * j0(repSE*repq)*repq
-
-        self.H, self.H0 = H, H0
-
-class SESANS1D(SesansTransform):
-    def __init__(self, data, H0, H, q_calc):
-        # x values of the data (Sasview requires these to be named "q")
-        self.q = data.x
-        self.H0 = H0
-        self.H = H
-        # Pysmear does some checks on the smearer object, these checks require the "data" object...
-        self.data=data
-        # q values of the SAS model
-        self.q_calc = q_calc # These are the MODEL's q values used by the smearer (in this case: the Hankel transform)
-    def apply(self, theory):
-        return SesansTransform.apply(self,theory)
+        self.q_calc = q
+        self._H, self._H0 = H, H0