Using sidpy instead of pyUSID wherever appropriate

jupyter_notebooks/BE_Processing.ipynb

@@ -26,11 +26,11 @@
 
 ``` python
 # Make sure needed packages are installed and up-to-date
 import sys
 !conda install --yes --prefix {sys.prefix} numpy scipy matplotlib scikit-learn Ipython ipywidgets h5py
-!{sys.executable} -m pip install -U --no-deps pycroscopy  # this will automatically install pyUSID as well
+!{sys.executable} -m pip install -U --no-deps pycroscopy  # this will automatically install sidpy and pyUSID as well
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
@@ -104,11 +104,11 @@
 You can select desired file type by choosing the second option in the pull down menu on the bottom right of the file window
 
 %% Cell type:code id: tags:
 
 ``` python
-input_file_path = usid.io_utils.file_dialog(caption='Select translated .h5 file or raw experiment data',
+input_file_path = sidpy.io.interface_utils.file_dialog(caption='Select translated .h5 file or raw experiment data',
                                           file_filter='Parameters for raw BE data (*.txt *.mat *xls *.xlsx);; \
                                           Translated file (*.h5)')
 
 (data_dir, filename) = os.path.split(input_file_path)
 
@@ -152,17 +152,17 @@
 
 %% Cell type:code id: tags:
 
 ``` python
 print('Datasets and datagroups within the file:\n------------------------------------')
-usid.hdf_utils.print_tree(h5_file)
+sidpy.hdf.hdf_utils.print_tree(h5_file)
  
 print('\nThe main dataset:\n------------------------------------')
 print(h5_main)
 
 print('\nMetadata or attributes in the measurement datagroup\n------------------------------------')
-for key, val in usid.hdf_utils.get_attributes(h5_main.parent.parent).items():
+for key, val in sidpy.hdf.hdf_utils.get_attributes(h5_main.parent.parent).items():
     print('{} : {}'.format(key, val))
 ```
 
 %% Cell type:markdown id: tags:
 
@@ -177,29 +177,29 @@
 pos_labels = h5_main.pos_dim_labels
 print(pos_labels, pos_dims)
 
 h5_meas_grp = h5_main.parent.parent
 
-parm_dict = usid.hdf_utils.get_attributes(h5_meas_grp)
+parm_dict = sidpy.hdf.hdf_utils.get_attributes(h5_meas_grp)
 
-expt_type = usid.hdf_utils.get_attr(h5_file, 'data_type')
+expt_type = sidpy.hdf.hdf_utils.get_attr(h5_file, 'data_type')
 
 is_ckpfm = expt_type == 'cKPFMData'
 if is_ckpfm:
     num_write_steps = parm_dict['VS_num_DC_write_steps']
     num_read_steps = parm_dict['VS_num_read_steps']
     num_fields = 2
     
 if expt_type != 'BELineData':
-    vs_mode = usid.hdf_utils.get_attr(h5_meas_grp, 'VS_mode')
+    vs_mode = sidpy.hdf.hdf_utils.get_attr(h5_meas_grp, 'VS_mode')
     try:
-        field_mode = usid.hdf_utils.get_attr(h5_meas_grp, 'VS_measure_in_field_loops')
+        field_mode = sidpy.hdf.hdf_utils.get_attr(h5_meas_grp, 'VS_measure_in_field_loops')
     except KeyError:
         print('field mode could not be found. Setting to default value')
         field_mode = 'out-of-field'
     try:
-        vs_cycle_frac = usid.hdf_utils.get_attr(h5_meas_grp, 'VS_cycle_fraction')
+        vs_cycle_frac = sidpy.hdf.hdf_utils.get_attr(h5_meas_grp, 'VS_cycle_fraction')
     except KeyError:
         print('VS cycle fraction could not be found. Setting to default value')
         vs_cycle_frac = 'full'
 ```