Esempio n. 1
0
    def reset(self, **traits):
        """Creates the dataset afresh or resets existing data source."""

        # First set the attributes without really doing anything since
        # the notification handlers are not called.
        self.set(trait_change_notify=False, **traits)

        vectors = self.vectors
        scalars = self.scalars
        x, y, z = [np.atleast_3d(a) for a in self.x, self.y, self.z]
    
        u, v, w = self.u, self.v, self.w
        if 'vectors' in traits:
            u=vectors[:,0].ravel()
            v=vectors[:,1].ravel()
            w=vectors[:,2].ravel()
            self.set(u=u,v=v,w=w,trait_change_notify=False)

        else:
            if u is not None and len(u) > 0:
                #vectors = np.concatenate([u[..., np.newaxis],
                #                             v[..., np.newaxis],
                #                             w[..., np.newaxis] ],
                #                axis=3)
                vectors = np.c_[u.ravel(), v.ravel(),
                                   w.ravel()].ravel()
                vectors.shape = (u.shape[0] , u.shape[1], w.shape[2], 3)
                self.set(vectors=vectors, trait_change_notify=False)

        if vectors is not None and len(vectors) > 0 and scalars is not None:
            assert len(scalars) == len(vectors)

        if x.shape[0] <= 1:
            dx = 1
        else:
            dx = x[1, 0, 0] - x[0, 0, 0]
        if y.shape[1] <= 1:
            dy = 1
        else:
            dy = y[0, 1, 0] - y[0, 0, 0]
        if z.shape[2] <= 1:
            dz = 1
        else:
            dz = z[0, 0, 1] - z[0, 0, 0]
        
        if self.m_data is None:
            ds = ArraySource(transpose_input_array=True)
        else:
            ds = self.m_data
        old_scalar = ds.scalar_data
        ds.set(vector_data=vectors,
               origin=[x.min(), y.min(), z.min()],
               spacing=[dx, dy, dz],
               scalar_data=scalars)
        if scalars is old_scalar:
            ds._scalar_data_changed(scalars)

        self.dataset = ds.image_data
        self.m_data = ds 
Esempio n. 2
0
    def reset(self, **traits):
        """Creates the dataset afresh or resets existing data source."""

        # First set the attributes without really doing anything since
        # the notification handlers are not called.
        self.set(trait_change_notify=False, **traits)    
        x, y, mask = self.x, self.y, self.mask
        scalars = self.scalars 
        
        # We may have used this without specifying x and y at all in
        # which case we set them from the shape of scalars.
        nx, ny = scalars.shape       
        
        #Build X and Y from shape of Scalars if they are none
        if x is None and y is None:           
            x, y = np.mgrid[-nx/2.:nx/2, -ny/2.:ny/2]           

        if mask is not None and len(mask) > 0:
            scalars[mask.astype('bool')] = np.nan
            # The NaN trick only works with floats.
            scalars = scalars.astype('float')
            self.set(scalars=scalars, trait_change_notify=False)

        z = np.array([0])     
              
        self.set(x=x, y=y, z=z, trait_change_notify=False)
        # Do some magic to extract the first row/column, independently of
        # the shape of x and y

        x = np.atleast_2d(x.squeeze().T)[0, :].squeeze()
        y = np.atleast_2d(y.squeeze())[0, :].squeeze()    
        
        if x.ndim == 0:
            dx = 1
        else:
            dx = x[1] - x[0]
        if y.ndim == 0:
            dy = 1
        else:
            dy = y[1] - y[0]
        if self.m_data is None:
            ds = ArraySource(transpose_input_array=True)
        else:
            ds = self.m_data
        old_scalar = ds.scalar_data
        ds.set(origin=[x.min(), y.min(), 0],
               spacing=[dx, dy, 1],
               scalar_data=scalars)
        if old_scalar is scalars:
            ds._scalar_data_changed(scalars)
        
        self.dataset = ds.image_data
        self.m_data = ds