Changes in src/sas/sascalc/dataloader/data_info.py [7988501:1b1a1c1] in sasview

File:

• src/sas/sascalc/dataloader/data_info.py (modified) (12 diffs)

src/sas/sascalc/dataloader/data_info.py

@@ -25 +25 @@
 import numpy
 import math
+
+class plottable_sesans1D(object):
+    """
+    SESANS is a place holder for 1D SESANS plottables.
+
+    #TODO: This was directly copied from the plottables_1D. Modified Somewhat.
+    #Class has been updated.
+    """
+    # The presence of these should be mutually
+    # exclusive with the presence of Qdev (dx)
+    x = None
+    y = None
+    lam = None
+    dx = None
+    dy = None
+    dlam = None
+    ## Slit smearing length
+    dxl = None
+    ## Slit smearing width
+    dxw = None
+
+    # Units
+    _xaxis = ''
+    _xunit = ''
+    _yaxis = ''
+    _yunit = ''
+
+    def __init__(self, x, y, lam, dx=None, dy=None, dlam=None):
+#        print "SESANS plottable working"
+        self.x = numpy.asarray(x)
+        self.y = numpy.asarray(y)
+        self.lam = numpy.asarray(lam)
+        if dx is not None:
+            self.dx = numpy.asarray(dx)
+        if dy is not None:
+            self.dy = numpy.asarray(dy)
+        if dlam is not None:
+            self.dlam = numpy.asarray(dlam)
+
+    def xaxis(self, label, unit):
+        """
+        set the x axis label and unit
+        """
+        self._xaxis = label
+        self._xunit = unit
+
+    def yaxis(self, label, unit):
+        """
+        set the y axis label and unit
+        """
+        self._yaxis = label
+        self._yunit = unit
+
 
 class plottable_1D(object):
@@ -41 +94 @@
     dxw = None
 
-    ## SESANS specific params (wavelengths for spin echo length calculation)
-
-    lam = None
-    dlam = None
-
     # Units
     _xaxis = ''
@@ -52 +100 @@
     _yunit = ''
 
-    def __init__(self, x, y, dx=None, dy=None, dxl=None, dxw=None, lam=None, dlam=None):
+    def __init__(self, x, y, dx=None, dy=None, dxl=None, dxw=None):
         self.x = numpy.asarray(x)
         self.y = numpy.asarray(y)
@@ -63 +111 @@
         if dxw is not None:
             self.dxw = numpy.asarray(dxw)
-        if lam is not None:
-            self.lam = numpy.asarray(lam)
-        if dlam is not None:
-            self.dlam = numpy.asarray(dlam)
 
     def xaxis(self, label, unit):
@@ -692 +736 @@
         return self._perform_union(other)
 
-class Data1D(plottable_1D, DataInfo):
-    """
-    1D data class
-    """
-    if plottable_1D.lam is None: # This means it's SANS data!
-        x_unit = '1/A'
-        y_unit = '1/cm'
-    elif plottable_1D.lam is not None: # This means it's SESANS data!
-        x_unit = 'A'
-        y_unit = 'pol'
-    else: # and if it's neither, you get punished!
-        raise(TypeError,'This is neither SANS nor SESANS data, what the hell are you doing??')
-
-    def __init__(self, x=None, y=None, dx=None, dy=None, lam=None, dlam=None):
+class SESANSData1D(plottable_sesans1D, DataInfo):
+    """
+    SESANS 1D data class
+    """
+    x_unit = 'nm'
+    y_unit = 'pol'
+
+    def __init__(self, x=None, y=None, lam=None, dx=None, dy=None, dlam=None):
         DataInfo.__init__(self)
-        plottable_1D.__init__(self, x, y, dx, dy,None, None, lam, dlam)
-        if self.lam is None: # This means the lam param was not detected in the data: it's SANS data!
-            x_unit = '1/A'
-            y_unit = '1/cm'
-        elif self.lam is not None: # This means lam was detected (should be an empty ndarray): it's SESANS data!
-            x_unit = 'A'
-            y_unit = 'pol'
-        else: # and if it's neither, you get punished!
-            raise(TypeError,'This is neither SANS nor SESANS data, what the hell are you doing??')
+        plottable_sesans1D.__init__(self, x, y, lam, dx, dy, dlam)
 
     def __str__(self):
@@ -729 +759 @@
         return _str
 
-    def is_slit_smeared(self):
-        """
-        Check whether the data has slit smearing information
-        :return: True is slit smearing info is present, False otherwise
-        """
-        def _check(v):
-            if (v.__class__ == list or v.__class__ == numpy.ndarray) \
-                and len(v) > 0 and min(v) > 0:
-                return True
-            return False
-        return _check(self.dxl) or _check(self.dxw)
-
     def clone_without_data(self, length=0, clone=None):
         """
@@ -751 +769 @@
         """
         from copy import deepcopy
-
         if clone is None or not issubclass(clone.__class__, Data1D):
             x = numpy.zeros(length)
@@ -757 +774 @@
             y = numpy.zeros(length)
             dy = numpy.zeros(length)
-            lam = numpy.zeros(length)
-            dlam = numpy.zeros(length)
-            clone = Data1D(x, y, lam=lam, dx=dx, dy=dy, dlam=dlam )
+            clone = Data1D(x, y, dx=dx, dy=dy)
 
         clone.title = self.title
@@ -777 +792 @@
         return clone
 
+class Data1D(plottable_1D, DataInfo):
+    """
+    1D data class
+    """
+    x_unit = '1/A'
+    y_unit = '1/cm'
+
+    def __init__(self, x, y, dx=None, dy=None):
+        DataInfo.__init__(self)
+        plottable_1D.__init__(self, x, y, dx, dy)
+
+    def __str__(self):
+        """
+        Nice printout
+        """
+        _str = "%s\n" % DataInfo.__str__(self)
+        _str += "Data:\n"
+        _str += "   Type:         %s\n" % self.__class__.__name__
+        _str += "   X-axis:       %s\t[%s]\n" % (self._xaxis, self._xunit)
+        _str += "   Y-axis:       %s\t[%s]\n" % (self._yaxis, self._yunit)
+        _str += "   Length:       %g\n" % len(self.x)
+        return _str
+
+    def is_slit_smeared(self):
+        """
+        Check whether the data has slit smearing information
+        :return: True is slit smearing info is present, False otherwise
+        """
+        def _check(v):
+            if (v.__class__ == list or v.__class__ == numpy.ndarray) \
+                and len(v) > 0 and min(v) > 0:
+                return True
+            return False
+        return _check(self.dxl) or _check(self.dxw)
+
+    def clone_without_data(self, length=0, clone=None):
+        """
+        Clone the current object, without copying the data (which
+        will be filled out by a subsequent operation).
+        The data arrays will be initialized to zero.
+
+        :param length: length of the data array to be initialized
+        :param clone: if provided, the data will be copied to clone
+        """
+        from copy import deepcopy
+
+        if clone is None or not issubclass(clone.__class__, Data1D):
+            x = numpy.zeros(length)
+            dx = numpy.zeros(length)
+            y = numpy.zeros(length)
+            dy = numpy.zeros(length)
+            clone = Data1D(x, y, dx=dx, dy=dy)
+
+        clone.title = self.title
+        clone.run = self.run
+        clone.filename = self.filename
+        clone.instrument = self.instrument
+        clone.notes = deepcopy(self.notes)
+        clone.process = deepcopy(self.process)
+        clone.detector = deepcopy(self.detector)
+        clone.sample = deepcopy(self.sample)
+        clone.source = deepcopy(self.source)
+        clone.collimation = deepcopy(self.collimation)
+        clone.trans_spectrum = deepcopy(self.trans_spectrum)
+        clone.meta_data = deepcopy(self.meta_data)
+        clone.errors = deepcopy(self.errors)
+
+        return clone
+
     def _validity_check(self, other):
         """
@@ -797 +881 @@
                 raise ValueError, msg
             # Here we could also extrapolate between data points
-            ZERO = 1.0e-12
+            TOLERANCE = 0.01
             for i in range(len(self.x)):
-                if math.fabs(self.x[i] - other.x[i]) > ZERO:
+                if math.fabs((self.x[i] - other.x[i])/self.x[i]) > TOLERANCE:
                     msg = "Incompatible data sets: x-values do not match"
                     raise ValueError, msg
@@ -1009 +1093 @@
         """
         err_other = None
+        TOLERANCE = 0.01
         if isinstance(other, Data2D):
             # Check that data lengths are the same
@@ -1017 +1102 @@
                 raise ValueError, msg
             for ind in range(len(self.data)):
-                if self.qx_data[ind] != other.qx_data[ind]:
-                    msg = "Incompatible data sets: qx-values do not match"
+                if math.fabs((self.qx_data[ind] - other.qx_data[ind])/self.qx_data[ind]) > TOLERANCE:
+                    msg = "Incompatible data sets: qx-values do not match: %s %s" % (self.qx_data[ind], other.qx_data[ind])
                     raise ValueError, msg
-                if self.qy_data[ind] != other.qy_data[ind]:
-                    msg = "Incompatible data sets: qy-values do not match"
+                if math.fabs((self.qy_data[ind] - other.qy_data[ind])/self.qy_data[ind]) > TOLERANCE:
+                    msg = "Incompatible data sets: qy-values do not match: %s %s" % (self.qy_data[ind], other.qy_data[ind])
                     raise ValueError, msg