Merge pull request #161 from pycroscopy/use_dict3

Use dict3

.gitignore

@@ -250,3 +250,5 @@ _autosummary/pyTEMlib.sidpy_tools.rst
 _autosummary/pyTEMlib.simulation_tools.rst
 _autosummary/pyTEMlib.version.rst
 _autosummary/pyTEMlib.xrpa_x_sections.rst
+notebooks/Imaging/Adaptive_Fourier_Filter-Copy1.ipynb
+notebooks/Spectroscopy/EELS_286.hf5

pyTEMlib/file_tools.py

@@ -82,11 +82,12 @@ class FileWidget(ipywidgets.DOMWidget):
 
     """
 
-    def __init__(self, dir_name=None, extension=['*']):
+    def __init__(self, dir_name=None, extension=['*'], sum_frames=False):
         self.save_path = False
         self.dir_dictionary = {}
         self.dir_list = ['.', '..']
         self.display_list = ['.', '..']
+        self.sum_frames = sum_frames
 
         self.dir_name = '.'
         if dir_name is None:
@@ -148,7 +149,7 @@ def select_main(self, value=0):
         self.datasets = {}
         #self.loaded_datasets.value = self.dataset_list[0]
         self.dataset_list = []
-        self.datasets = open_file(self.file_name)
+        self.datasets = open_file(self.file_name, sum_frames=self.sum_frames)
         self.dataset_list = []
         for key in self.datasets.keys():
             self.dataset_list.append(f'{key}: {self.datasets[key].title}')
@@ -593,7 +594,7 @@ def h5_group_to_dict(group, group_dict={}):
     return group_dict
 
 
-def open_file(filename=None,  h5_group=None, write_hdf_file=False):  # save_file=False,
+def open_file(filename=None,  h5_group=None, write_hdf_file=False, sum_frames=False):  # save_file=False,
     """Opens a file if the extension is .hf5, .ndata, .dm3 or .dm4
 
     If no filename is provided the QT open_file windows opens (if QT_available==True)
@@ -680,7 +681,7 @@ def open_file(filename=None,  h5_group=None, write_hdf_file=False):  # save_file
                 print('This file type needs hyperspy to be installed to be able to be read')
                 return
         elif extension == '.emd':
-            reader = SciFiReaders.EMDReader(filename)
+            reader = SciFiReaders.EMDReader(filename, sum_frames=sum_frames)
 
         elif 'edax' in extension.lower():
             if 'h5' in extension:

pyTEMlib/image_tools.py

@@ -731,15 +731,27 @@ def onmove(self, event):
             self.line_verts[moved_point] = self.new_point
             self.set_linewidth()
 
-def get_profile(dataset, line):
+def get_profile(dataset, line, spline_order=-1):
+    """
+    This function extracts a line profile from a given dataset. The line profile is a representation of the data values 
+    along a specified line in the dataset. This function works for both image and spectral image data types.
+
+    Args:
+        dataset (sidpy.Dataset): The input dataset from which to extract the line profile.
+        line (list): A list specifying the line along which the profile should be extracted.
+        spline_order (int, optional): The order of the spline interpolation to use. Default is -1, which means no interpolation.
+
+    Returns:
+        profile_dataset (sidpy.Dataset): A new sidpy.Dataset containing the line profile.
+
+
+    """
     xv, yv = get_line_selection_points(line)
-
-
     if dataset.data_type.name == 'IMAGE':
         dataset.get_image_dims()
         xv /= (dataset.x[1] - dataset.x[0])
         yv /= (dataset.y[1] - dataset.y[0])
-        profile = scipy.ndimage.map_coordinates(np.array(dataset), [xv,yv])
+        profile = scipy.ndimage.map_coordinates(np.array(dataset), [xv, yv])
 
         profile_dataset = sidpy.Dataset.from_array(profile.sum(axis=0))
         profile_dataset.data_type='spectrum'
@@ -753,19 +765,21 @@ def get_profile(dataset, line):
 
     if dataset.data_type.name == 'SPECTRAL_IMAGE':
         spectral_axis = dataset.get_spectral_dims(return_axis=True)[0]
-        profile = np.zeros([xv.shape[1], 2, len(spectral_axis)])
-        data =np.array(dataset)
-
-        for index_x in range(xv.shape[1]):
-            for  index_y  in range(xv.shape[0]):
-                x = xv[index_y, index_x]
-                y = yv[index_y, index_x]
-                profile[index_x, 0] +=data[int(x),int(y)]
+        if spline_order > -1:
+            xv, yv, zv = get_line_selection_points_interpolated(line, z_length=dataset.shape[2])
+            profile = scipy.ndimage.map_coordinates(np.array(dataset), [xv, yv, zv], order=spline_order)
+            profile = profile.sum(axis=0)
+            profile = np.stack([profile, profile], axis=1)
+            start = xv[0, 0, 0]
+        else:
+            profile = get_line_profile(np.array(dataset), xv, yv, len(spectral_axis))
+            start = xv[0, 0]
+        print(profile.shape)
         profile_dataset = sidpy.Dataset.from_array(profile)
         profile_dataset.data_type='spectral_image'
         profile_dataset.units = dataset.units
         profile_dataset.quantity = dataset.quantity
-        profile_dataset.set_dimension(0, sidpy.Dimension(np.linspace(xv[0,0], xv[-1,-1], profile_dataset.shape[0]), 
+        profile_dataset.set_dimension(0, sidpy.Dimension(np.arange(profile_dataset.shape[0])+start, 
                                                   name='x', units=dataset.x.units, quantity=dataset.x.quantity,
                                                   dimension_type='spatial'))
         profile_dataset.set_dimension(1, sidpy.Dimension([0, 1], 
@@ -776,6 +790,42 @@ def get_profile(dataset, line):
     return profile_dataset
 
 
+
+def get_line_selection_points_interpolated(line, z_length=1):
+
+    start_point = line.line_verts[3]
+    right_point = line.line_verts[0]
+    low_point = line.line_verts[2]
+
+    if start_point[0] > right_point[0]:
+        start_point = line.line_verts[0]
+        right_point = line.line_verts[3]
+        low_point = line.line_verts[1]
+    m = (right_point[1] - start_point[1]) / (right_point[0] - start_point[0])
+    length_x = int(abs(start_point[0]-right_point[0]))
+    length_v = int(np.linalg.norm(start_point-right_point))
+
+    linewidth = int(abs(start_point[1]-low_point[1]))
+    x = np.linspace(0,length_x, length_v)
+    y = np.linspace(0,linewidth, line.line_width)
+    if z_length > 1:
+        z = np.linspace(0, z_length, z_length)
+        xv, yv, zv = np.meshgrid(x, y, np.arange(z_length))
+        x = np.atleast_2d(x).repeat(z_length, axis=0).T
+        y = np.atleast_2d(y).repeat(z_length, axis=0).T
+    else:
+        xv, yv = np.meshgrid(x, y)
+
+
+    yv = yv + x*m + start_point[1] 
+    xv = (xv.swapaxes(0,1) -y*m ).swapaxes(0,1) + start_point[0]
+
+    if z_length > 1:
+        return xv, yv, zv
+    else:   
+        return xv, yv
+
+
 def get_line_selection_points(line):
 
     start_point = line.line_verts[3]
@@ -801,6 +851,16 @@ def get_line_selection_points(line):
     return xx, yy
 
 
+def get_line_profile(data, xv, yv, z_length):
+    profile = np.zeros([len(xv[0]), 2, z_length])
+    for index_x in range(xv.shape[1]):
+        for  index_y  in range(xv.shape[0]):
+            x = int(xv[index_y, index_x])
+            y = int(yv[index_y, index_x])
+            if x< data.shape[0] and x>0 and y < data.shape[1] and y>0:
+                profile[index_x, 0] +=data[x, y]
+    return profile
+
 
 def histogram_plot(image_tags):
     """interactive histogram"""