Direct call to visualizer.

pycroscopy/io/pycro_data.py

@@ -13,6 +13,7 @@ import numpy as np
 from .hdf_utils import checkIfMain, get_attr, get_data_descriptor, get_formatted_labels, \
     get_dimensionality, get_sort_order, get_unit_values, reshape_to_Ndims
 from .io_utils import transformToReal
+from ..viz.jupyter_utils import simple_ndim_visualizer
 
 
 class PycroDataset(h5py.Dataset):
@@ -339,3 +340,26 @@ class PycroDataset(h5py.Dataset):
             return transformToReal(data_slice), success
         else:
             return data_slice, success
+
+    def visualize(self, slice_dict=None, **kwargs):
+        """
+        Interactive visualization of this dataset. Only available on jupyter notebooks
+
+        Parameters
+        ----------
+        slice_dict : dictionary, optional
+            Slicing instructions
+        """
+        # TODO: Robust implementation that allows slicing
+        if len(self.pos_dim_labels + self.spec_dim_labels) > 4:
+            raise NotImplementedError('Unable to support visualization of more than 4 dimensions. Try slicing')
+        data_mat = self.get_n_dim_form()
+        pos_dim_names = self.pos_dim_labels[::-1]
+        spec_dim_names = self.spec_dim_labels
+        pos_dim_units_old = get_attr(self.h5_pos_inds, 'units')
+        spec_dim_units_old = get_attr(self.h5_spec_inds, 'units')
+        pos_ref_vals = get_unit_values(self.h5_pos_inds, self.h5_pos_vals, is_spec=False)
+        spec_ref_vals = get_unit_values(self.h5_spec_inds, self.h5_spec_vals, is_spec=True)
+
+        simple_ndim_visualizer(data_mat, pos_dim_names, pos_dim_units_old, spec_dim_names, spec_dim_units_old,
+                               pos_ref_vals=pos_ref_vals, spec_ref_vals=spec_ref_vals, **kwargs)

pycroscopy/viz/__init__.py

@@ -9,10 +9,11 @@ Submodules
 
     be_viz_utils
     plot_utils
+    jupyter_utils
 
 """
 from . import tests
 from . import plot_utils
 from . import be_viz_utils
 
-__all__ = ['plot_utils', 'be_viz_utils']
+__all__ = ['plot_utils', 'be_viz_utils', 'jupyter_utils']

pycroscopy/viz/jupyter_utils.py

@@ -73,7 +73,7 @@ def simple_ndim_visualizer(data_mat, pos_dim_names, pos_dim_units_old, spec_dim_
 
     def get_slicing_tuple(slice_dict):
         slice_list = []
-        for dim_name in pos_dim_names+spec_dim_names:
+        for dim_name in pos_dim_names + spec_dim_names:
             cur_slice = slice(None)
             if slice_dict[dim_name] is not None:
                 cur_slice = slice(slice_dict[dim_name], slice_dict[dim_name]+1)
@@ -141,7 +141,7 @@ def simple_ndim_visualizer(data_mat, pos_dim_names, pos_dim_units_old, spec_dim_
     for name, unit in zip(spec_dim_names, spec_dim_units_old):
         spec_dim_units[name] = unit
 
-    data_type, data_names, data_funcs  = check_data_type(data_mat)
+    data_type, data_names, data_funcs = check_data_type(data_mat)
 
     sub_data = data_mat
     component_name = 'Real'
@@ -170,8 +170,8 @@ def simple_ndim_visualizer(data_mat, pos_dim_names, pos_dim_units_old, spec_dim_
     axes[0].set_title('Spatial Map for\n' + component_title + '\n' + spec_titles)
     if pos_plot_2d:
         img_spat, cbar_spat = single_img_cbar_plot(axes[0], current_spatmap,
-                                                                 x_size=data_mat.shape[1], y_size=data_mat.shape[0],
-                                                                 clim=clims)
+                                                   x_size=data_mat.shape[1], y_size=data_mat.shape[0],
+                                                   clim=clims)
         axes[0].set_xlabel(pos_dim_names[1] + ' (' + pos_dim_units_old[1] + ')')
         axes[0].set_ylabel(pos_dim_names[0] + ' (' + pos_dim_units_old[0] + ')')
         main_vert_line = axes[0].axvline(x=spgram_slicing[pos_dim_names[1]], color='k')
@@ -193,9 +193,9 @@ def simple_ndim_visualizer(data_mat, pos_dim_names, pos_dim_units_old, spec_dim_
         axes[1].set_xlabel(spec_dim_names[1] + ' (' + spec_dim_units_old[1] + ')')
         axes[1].set_ylabel(spec_dim_names[0] + ' (' + spec_dim_units_old[0] + ')')
         img_spec, cbar_spec = single_img_cbar_plot(axes[1], current_spgram,
-                                                                 x_size=data_mat.shape[len(pos_dim_names) + 1],
-                                                                 y_size=data_mat.shape[len(pos_dim_names)],
-                                                                 cbar_label=component_name, clim=clims)
+                                                   x_size=data_mat.shape[len(pos_dim_names) + 1],
+                                                   y_size=data_mat.shape[len(pos_dim_names)],
+                                                   cbar_label=component_name, clim=clims)
     else:
         axes[1].set_xlabel(spec_xdim + ' (' + spec_dim_units[spec_xdim] + ')')
         if current_spgram.shape[0] != spec_ref_vals[spec_xdim].size:
@@ -218,7 +218,7 @@ def simple_ndim_visualizer(data_mat, pos_dim_names, pos_dim_units_old, spec_dim_
         slice_dict['component'] = data_names
 
     # stupid and hacky way of doing this:
-    global_vars = {'sub_data': sub_data, 'component_title':component_title}
+    global_vars = {'sub_data': sub_data, 'component_title': component_title}
 
     def update_plots(**kwargs):
         component_name = kwargs.get('component', None)