Merge pull request #155 from pycroscopy/dev_rama

Dev rama

sidpy/sid/dimension.py

@@ -31,6 +31,7 @@ class DimensionType(Enum):
     RECIPROCAL = 2
     SPECTRAL = 3
     TEMPORAL = 4
+    CHANNEL = 5
 
 
 class Dimension(np.ndarray):
@@ -53,7 +54,7 @@ def __new__(cls, values, name='none', quantity='generic', units='generic',
             set of values will be generated if an integer is provided instead
             of an array.
         dimension_type : str or sidpy.sid.dimension.DimensionType
-            For example: 'spectral', 'spatial', 'reciprocal', or 'UNKNOWN'
+            For example: 'spectral', 'spatial', 'reciprocal', 'channel', or 'UNKNOWN',
             'time', 'frame', 'reciprocal'
             This will determine how the data are visualized. 'spatial' are
             image dimensions. 'spectral' indicate spectroscopy data dimensions.

sidpy/viz/dataset_viz.py

@@ -459,6 +459,9 @@ class SpectralImageVisualizer(object):
     """
     ### Interactive spectrum imaging plot
 
+    If there is a 4D dataset, and one of them is named 'channel', 
+    then you can plot the channel spectra too
+
     """
 
     def __init__(self, dset, figure=None, horizontal=True, **kwargs):
@@ -485,26 +488,42 @@ def __init__(self, dset, figure=None, horizontal=True, **kwargs):
 
         if len(dset.shape) < 3:
             raise TypeError('dataset must have at least three dimensions')
+        if len(dset.shape)>4:
+            raise TypeError('dataset must have at most four dimensions')
 
         # We need one stack dim and two image dimes as lists in dictionary
 
         selection = []
         image_dims = []
-        spectral_dims = []
+        spectral_dim = []
+        channel_dim = []
         for dim, axis in dset._axes.items():
             if axis.dimension_type in [DimensionType.SPATIAL, DimensionType.RECIPROCAL]:
                 selection.append(slice(None))
                 image_dims.append(dim)
             elif axis.dimension_type == DimensionType.SPECTRAL:
                 selection.append(slice(0, 1))
-                spectral_dims.append(dim)
+                spectral_dim.append(dim)
+            elif axis.dimension_type == DimensionType.CHANNEL:
+                channel_dim.append(dim)
             else:
                 selection.append(slice(0, 1))
+
         if len(image_dims) != 2:
-            raise TypeError('We need two dimensions with dimension_type SPATIA: to plot an image')
-
-        if len(spectral_dims) != 1:
-            raise TypeError('We need one dimension with dimension_type SPECTRAL for a spectral image plot')
+            raise TypeError('We need two dimensions with dimension_type SPATIAL: to plot an image')
+        if len(channel_dim) >1:
+            raise ValueError("We have more than one Channel Dimension, this won't work for the visualizer")
+        if len(spectral_dim)>1:
+            raise ValueError("We have more than one Spectral Dimension, this won't work for the visualizer...")
+
+        if len(dset.shape)==4:
+            if len(channel_dim)!=1:
+                raise TypeError("We need one dimension with type CHANNEL \
+                    for a spectral image plot for a 4D dataset")
+        elif len(dset.shape)==3:
+            if len(spectral_dim) != 1:
+                raise TypeError("We need one dimension with dimension_type SPECTRAL \
+                 to plot a spectra for a 3D dataset")
 
         self.horizontal = horizontal
         self.x = 0
@@ -516,8 +535,9 @@ def __init__(self, dset, figure=None, horizontal=True, **kwargs):
         size_y = dset.shape[image_dims[1]]
 
         self.dset = dset
-        self.energy_scale = dset._axes[spectral_dims[0]].values
-
+        self.energy_axis = spectral_dim[0]
+        if len(channel_dim)>0: self.channel_axis = channel_dim
+        self.energy_scale = dset._axes[self.energy_axis].values
         self.extent = [0, size_x, size_y, 0]
         self.rectangle = [0, size_x, 0, size_y]
         self.scaleX = 1.0
@@ -526,7 +546,7 @@ def __init__(self, dset, figure=None, horizontal=True, **kwargs):
         self.plot_legend = False
 
         self.image_dims = image_dims
-        self.spec_dim = spectral_dims[0]
+        self.spec_dim = spectral_dim[0]
 
         if horizontal:
             self.axes = self.fig.subplots(ncols=2)
@@ -535,7 +555,12 @@ def __init__(self, dset, figure=None, horizontal=True, **kwargs):
 
         if self.set_title:
             self.fig.canvas.manager.set_window_title(self.dset.title)
-        self.image = dset.mean(axis=spectral_dims[0])
+
+        if len(channel_dim)>0:
+            self.image = dset.mean(axis=(spectral_dim[0],channel_dim[0]))
+        else:
+            self.image = dset.mean(axis=(spectral_dim[0]))
+
         if 1 in self.dset.shape:
             self.image = dset.squeeze()
             # self.extent[2]=self.image.shape[1]
@@ -563,7 +588,8 @@ def __init__(self, dset, figure=None, horizontal=True, **kwargs):
         self.axes[0].add_patch(self.rect)
         self.intensity_scale = 1.
         self.spectrum = self.get_spectrum()
-
+        if len(self.energy_scale)!=self.spectrum.shape[0]:
+            self.spectrum = self.spectrum.T
         self.axes[1].plot(self.energy_scale, self.spectrum)
         self.axes[1].set_title('spectrum {}, {}'.format(self.x, self.y))
         self.xlabel = self.dset.labels[self.spec_dim]
@@ -615,7 +641,7 @@ def get_spectrum(self):
         selection = []
 
         for dim, axis in self.dset._axes.items():
-            # print(dim, axis.dimension_type)
+            print(dim, axis.dimension_type)
             if axis.dimension_type == DimensionType.SPATIAL:
                 if dim == self.image_dims[0]:
                     selection.append(slice(self.x, self.x + self.bin_x))
@@ -624,10 +650,13 @@ def get_spectrum(self):
 
             elif axis.dimension_type == DimensionType.SPECTRAL:
                 selection.append(slice(None))
+            elif axis.dimension_type == DimensionType.CHANNEL:
+                selection.append(slice(None))
             else:
                 selection.append(slice(0, 1))
-
+        
         self.spectrum = self.dset[tuple(selection)].mean(axis=tuple(self.image_dims))
+
         # * self.intensity_scale[self.x,self.y]
         return self.spectrum.squeeze()
 
@@ -674,7 +703,8 @@ def _update(self, ev=None):
         ylim = self.axes[1].get_ylim()
         self.axes[1].clear()
         self.get_spectrum()
-
+        if len(self.energy_scale)!=self.spectrum.shape[0]:
+            self.spectrum = self.spectrum.T
         self.axes[1].plot(self.energy_scale, self.spectrum, label='experiment')
 
         if self.set_title:
@@ -964,6 +994,8 @@ def get_xy(self):
         return [self.x, self.y]
 
 
+#Let's make a 
+
 #Let's make a curve fit visualizer
 
 class SpectralImageFitVisualizer(SpectralImageVisualizer):

sidpy/viz/plot_utils/curve.py

@@ -461,7 +461,7 @@ def plot_complex_spectra(map_stack, x_vec=None, num_comps=4, title=None, x_label
     fig, axes = plt.subplots(nrows * 2, ncols, figsize=figsize)
     fig.subplots_adjust(hspace=0.1, wspace=0.4)
     if title is not None:
-        fig.canvas.set_window_title(title)
+        fig.canvas.manager.set_window_title(title)
         fig.suptitle(title, y=1.025)
 
     title_prefix = ''
@@ -528,6 +528,6 @@ def plot_scree(scree, title='Scree', **kwargs):
     axis.set_title(title)
     axis.set_xlim(left=1, right=len(scree))
     axis.set_ylim(bottom=np.min(scree), top=np.max(scree))
-    fig.canvas.set_window_title(title)
+    fig.canvas.manager.set_window_title(title)
 
     return fig, axis

tests/viz/test_plot_utils.py

@@ -516,7 +516,7 @@ def test_scree_wrong(self):
 
     def test_scree_h5py_dataset(self):
         h5_f = h5py.File('test12.h5', 'a')
-        scree = h5_f.create_dataset("test", data=np.arange(1,25))
+        scree = h5_f.create_dataset("test12", data=np.arange(1,25))
         plot_utils.plot_scree(scree)
 
     """