Merge pull request #147 from pycroscopy/dev_mani

Dev mani

extras_require.txt

@@ -0,0 +1,2 @@
+mpi4py
+pyqt5

setup.py

@@ -25,6 +25,8 @@
                 'ipywidgets>=5.2.2',
                 'ipython>=5.1.0,<6;python_version<"3.3"',  # IPython 6.0+ does not support Python < 3.5
                 'ipython>=6.0;python_version>="3.3"',  # Beginning with IPython 6.0, Python 3.3 and above is required.
+                'sklearn',
+                'scipy'
                 ]
 
 setup(
@@ -66,8 +68,8 @@
     include_package_data=True,
     # https://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-dependencies
     extras_require={
-        'MPI':  ["mpi4py"],
-        'File_Widgets': ['pyqt5'],
+        'MPI': ["mpi4py"],
+        'File_Widgets': ['pyqt5']
     },
     # If there are data files included in your packages that need to be
     # installed, specify them here.  If using Python 2.6 or less, then these

sidpy/proc/__init__.py

@@ -3,5 +3,6 @@
 """
 
 from . import comp_utils
+from . import fitter
 
-__all__ = ['comp_utils']
+__all__ = ['comp_utils', 'fitter']

sidpy/sid/dataset.py

@@ -178,7 +178,7 @@ def hdf_close(self):
             print(self.h5_dataset)
 
     @classmethod
-    def from_array(cls, x, title='generic', chunks='auto',  lock=False,
+    def from_array(cls, x, title='generic', chunks='auto', lock=False,
                    datatype='UNKNOWN', units='generic', quantity='generic',
                    modality='generic', source='generic', **kwargs):
         """
@@ -262,7 +262,7 @@ def like_data(self, data, title=None, chunks='auto', lock=False, **kwargs):
         checkdims = kwargs.get('checkdims', True)
 
         new_data = self.from_array(data, chunks=chunks, lock=lock)
-        
+
         new_data.data_type = self.data_type
 
         # units
@@ -278,9 +278,9 @@ def like_data(self, data, title=None, chunks='auto', lock=False, **kwargs):
                 new_data.title = self.title
             else:
                 new_data.title = self.title + '_new'
-        
-        new_data.title = title_prefix+new_data.title+title_suffix
-        
+
+        new_data.title = title_prefix + new_data.title + title_suffix
+
         # quantity
         if reset_quantity:
             new_data.quantity = 'generic'
@@ -300,7 +300,7 @@ def like_data(self, data, title=None, chunks='auto', lock=False, **kwargs):
                     try:
                         scale = get_slope(self._axes[dim])
                         # axis = self._axes[dim].copy()
-                        axis = Dimension(np.arange(new_data.shape[dim])*scale, self._axes[dim].name)
+                        axis = Dimension(np.arange(new_data.shape[dim]) * scale, self._axes[dim].name)
                         axis.quantity = self._axes[dim].quantity
                         axis.units = self._axes[dim].units
                         axis.dimension_type = self._axes[dim].dimension_type
@@ -393,7 +393,7 @@ def __validate_dim(self, ind, name):
             raise TypeError('Dimension must be an integer')
         if (0 > ind) or (ind >= self.ndim):
             raise IndexError('Dimension must be an integer between 0 and {}'
-                             ''.format(self.ndim-1))
+                             ''.format(self.ndim - 1))
         for key, dim in self._axes.items():
             if key != ind:
                 if name == dim.name:
@@ -571,7 +571,7 @@ def set_thumbnail(self, figure=None, thumbnail_size=128):
 
         import imageio
         # Thumbnail configurations for matplotlib
-        kwargs = {'figsize': (1,1), 'colorbar': False, 'set_title': False}
+        kwargs = {'figsize': (1, 1), 'colorbar': False, 'set_title': False}
         view = self.plot(figure=figure, **kwargs)
         for axis in view.axes:
             axis.set_axis_off()
@@ -609,8 +609,8 @@ def get_extent(self, dimensions):
         extent = []
         for ind, dim in enumerate(dimensions):
             temp = self._axes[dim].values
-            start = temp[0] - (temp[1] - temp[0])/2
-            end = temp[-1] + (temp[-1] - temp[-2])/2
+            start = temp[0] - (temp[1] - temp[0]) / 2
+            end = temp[-1] + (temp[-1] - temp[-2]) / 2
             if ind == 1:
                 extent.append(end)  # y-axis starts on top
                 extent.append(start)
@@ -950,7 +950,6 @@ def min(self, axis=None, keepdims=False, split_every=None, out=None):
                                 checkdims=False)
         return result, locals().copy()
 
-
     @reduce_dims
     def max(self, axis=None, keepdims=False, split_every=None, out=None):
 
@@ -974,9 +973,9 @@ def mean(self, axis=None, dtype=None, keepdims=False, split_every=None, out=None
                                              split_every=split_every, out=out), title_prefix='mean_aggregate_',
                                 checkdims=False)
         return result, locals().copy()
-    
+
     @reduce_dims
-    def std(self, axis=None, dtype=None, keepdims=False, ddof = 0, split_every=None, out=None):
+    def std(self, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, out=None):
 
         result = self.like_data(super().std(axis=axis, dtype=dtype, keepdims=keepdims,
                                             ddof=0, split_every=split_every, out=out),
@@ -991,7 +990,7 @@ def var(self, axis=None, dtype=None, keepdims=False, ddof=0, split_every=None, o
                                             ddof=ddof, split_every=split_every, out=out),
                                 title_prefix='var_aggregate_', checkdims=False)
         return result, locals().copy()
-    
+
     @reduce_dims
     def argmin(self, axis=None, split_every=None, out=None):
 
@@ -1106,7 +1105,6 @@ def reshape(self, *shape, merge_chunks=True):
             new_shape = shape[0]
         else:
             new_shape = shape
-        print(new_shape)
         return super().reshape(*new_shape, merge_chunks)
 
     @reduce_dims
@@ -1183,13 +1181,17 @@ def fold(self, dim_order=None, method=None):
         ----------
         
         dim_order: List of lists or tuple of tuples 
-            -Each element correponds to the order of axes in the corresponding 
+            -Each element corresponds to the order of axes in the corresponding 
             new axis after the collapse
             -Default: None
         method: str
-            -'spaspec' collapses the original dataset to a 2D dataset, where 
+            -'spaspec': collapses the original dataset to a 2D dataset, where 
             spatail dimensions form the zeroth axis and spectral dimensions 
             form the first axis
+            -'spa': combines all the spatial dimensions into a single dimension and 
+            the combined dimension will be first
+            -'spec': combines all the spectral dimensions into a single dimension and 
+            the combined dimension will be last
             -Uses the user defined dim_order when set to None
             -Default: None
 
@@ -1207,7 +1209,7 @@ def fold(self, dim_order=None, method=None):
 
             dim_order_list = [list(x) for x in dim_order]
 
-        # Book-keeping for uncollapsing
+        # Book-keeping for unfolding
         fold_attr = {'shape': self.shape, '_axes': self._axes.copy()}
 
         if method == 'spaspec':
@@ -1223,6 +1225,34 @@ def fold(self, dim_order=None, method=None):
                                               part of the last collapsed dimension')
                     dim_order_list[1].extend(dim)
 
+        if method == 'spa':
+            dim_order_list = [[]]
+            for dim, axis in self._axes.items():
+                if axis.dimension_type == DimensionType.SPATIAL:
+                    dim_order_list[0].extend(dim)
+                else:
+                    dim_order_list.append([dim])
+
+            if len(dim_order_list[0]) == 0:
+                raise NotImplementedError("No spatial dimensions found and the method is set to 'spa' ")
+            if len(dim_order_list[0]) == 1:
+                warnings.warn('Only one spatial dimension found\
+                                Folding returns the original dataset')
+
+        if method == 'spec':
+            dim_order_list = [[]]
+            for dim, axis in self._axes.items():
+                if axis.dimension_type == DimensionType.Spectral:
+                    dim_order_list[-1].extend(dim)
+                else:
+                    dim_order_list.insert(-1, [dim])
+
+            if len(dim_order_list[-1]) == 0:
+                raise NotImplementedError("No spectral dimensions found and the method is set to 'spec'")
+            if len(dim_order_list[-1]) == 1:
+                warnings.warn('Only one spatial dimension found\
+                                Folding returns the original dataset')
+
         # We need the flattened list to transpose the original array
         dim_order_flattened = [item for sublist in dim_order_list for item in sublist]
 
@@ -1280,8 +1310,10 @@ def unfold(self):
                                        title=self.title.replace('folded_', ''), checkdims=False)
 
         unfolded_dset._axes = {}
-        for i, dim in enumerate(old_axes):
-            unfolded_dset._axes[dim] = old_axes[dim].copy()
+        for i, dim in old_axes.items():
+            unfolded_dset.set_dimension(i, dim.copy())
+
+        del unfolded_dset.metadata['fold_attr']
         return unfolded_dset
 
     # Following methods are to be edited

sidpy/viz/dataset_viz.py

@@ -316,53 +316,67 @@ def __init__(self, dset, figure=None, **kwargs):
 
         # self.axis = self.fig.add_axes([0.0, 0.2, .9, .7])
         self.ind = 0
+        self.plot_fit_labels = False
 
         self.number_of_slices = self.dset.shape[self.stack_dim]
+
+        if self.set_title:
+            if 'fit_dataset'  in dir(dset):
+                if dset.fit_dataset:
+                    if dset.metadata['fit_parms_dict']['fit_parameters_labels'] is not None: 
+                        self.plot_fit_labels = True
+                        img_titles = dset.metadata['fit_parms_dict']['fit_parameters_labels']
+                        self.image_titles = ['Fitting Parm: ' + img_titles[k] for k in range(len(img_titles))]
+                    else:
+                        self.image_titles = 'Fitting Maps: ' + dset.title + '\n use scroll wheel to navigate images'    
+                else:
+                    self.image_titles = 'Fitting Maps: ' + dset.title + '\n use scroll wheel to navigate images'
+            else:
+                self.image_titles = 'Image stack: ' + dset.title + '\n use scroll wheel to navigate images'
+
         self.axis = None
         self.plot_image(**kwargs)
         self.axis = plt.gca()
-        if self.set_title:
-            self.axis.set_title('image stack: ' + dset.title + '\n use scroll wheel to navigate images')
-            self.img.axes.figure.canvas.mpl_connect('scroll_event', self._onscroll)
-
-
-            import ipywidgets as iwgt
-            self.play = iwgt.Play(
-                value=0,
-                min=0,
-                max=self.number_of_slices,
-                step=1,
-                interval=500,
-                description="Press play",
-                disabled=False
-            )
-            self.slider = iwgt.IntSlider(
-                value=0,
-                min=0,
-                max=self.number_of_slices,
-                continuous_update=False,
-                description="Frame:")
-            # set the slider function
-            iwgt.interactive(self._update, frame=self.slider)
-            # link slider and play function
-            iwgt.jslink((self.play, 'value'), (self.slider, 'value'))
-
-            # We add a button to average the images
-            self.button = iwgt.widgets.ToggleButton(
-                value=False,
-                description='Average',
-                disabled=False,
-                button_style='',  # 'success', 'info', 'warning', 'danger' or ''
-                tooltip='Average Images of Stack')
-
-            self.button.observe(self._average_slices, 'value')
-
-            # set play and slider widgets next to each other
-            widg = iwgt.HBox([self.play, self.slider, self.button])
-            display(widg)
-
-            # self.anim = animation.FuncAnimation(self.fig, self._updatefig, interval=200, blit=False, repeat=True)
-            self._update()
+        #self.axis.set_title(image_titles)
+        self.img.axes.figure.canvas.mpl_connect('scroll_event', self._onscroll)
+
+        import ipywidgets as iwgt
+        self.play = iwgt.Play(
+            value=0,
+            min=0,
+            max=self.number_of_slices,
+            step=1,
+            interval=500,
+            description="Press play",
+            disabled=False
+        )
+        self.slider = iwgt.IntSlider(
+            value=0,
+            min=0,
+            max=self.number_of_slices,
+            continuous_update=False,
+            description="Frame:")
+        # set the slider function
+        iwgt.interactive(self._update, frame=self.slider)
+        # link slider and play function
+        iwgt.jslink((self.play, 'value'), (self.slider, 'value'))
+
+        # We add a button to average the images
+        self.button = iwgt.widgets.ToggleButton(
+            value=False,
+            description='Average',
+            disabled=False,
+            button_style='',  # 'success', 'info', 'warning', 'danger' or ''
+            tooltip='Average Images of Stack')
+
+        self.button.observe(self._average_slices, 'value')
+
+        # set play and slider widgets next to each other
+        widg = iwgt.HBox([self.play, self.slider, self.button])
+        display(widg)
+
+        # self.anim = animation.FuncAnimation(self.fig, self._updatefig, interval=200, blit=False, repeat=True)
+        self._update()
 
     def plot_image(self, **kwargs):
 
@@ -436,7 +450,10 @@ def _update(self, frame=0):
         self.selection[self.stack_dim] = slice(frame, frame + 1)
         self.img.set_data((self.dset[tuple(self.selection)].squeeze()).T)
         self.img.axes.figure.canvas.draw_idle()
-
+        if self.plot_fit_labels:
+            self.axis.set_title(self.image_titles[frame])
+        else:
+            self.axis.set_title(self.image_titles)
 
 class SpectralImageVisualizer(object):
     """

tests/hdf/test_hdf_utils.py

@@ -7,6 +7,7 @@
 from __future__ import division, print_function, unicode_literals, \
     absolute_import
 import unittest
+import tempfile
 import os
 import sys
 import tempfile
@@ -19,6 +20,7 @@
 from sidpy.base.dict_utils import flatten_dict
 from sidpy.hdf import hdf_utils
 
+
 from . import data_utils
 
 if sys.version_info.major == 3:
@@ -638,8 +640,9 @@ def test_wrong_type(self):
 class TestValidateH5ObjsInSameFile(unittest.TestCase):
 
     def test_diff_file(self):
-        file_path_1 = 'source.h5'
-        file_path_2 = 'sink.h5'
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            file_path_1 = tmp_dir + 'source.h5'
+            file_path_2 = tmp_dir + 'sink.h5'
         data_utils.delete_existing_file(file_path_1)
         h5_f1 = h5py.File(file_path_1, mode='w')
         h5_main = h5_f1.create_dataset('main', data=np.arange(5))
@@ -649,8 +652,8 @@ def test_diff_file(self):
         with self.assertRaises(ValueError):
             hdf_utils.validate_h5_objs_in_same_h5_file(h5_main, h5_other)
 
-        os.remove(file_path_1)
-        os.remove(file_path_2)
+        #os.remove(file_path_1)
+        #os.remove(file_path_2)
 
     def test_same_file(self):
         file_path = 'test_same_file.h5'