moved core components of package to new subpackage core. updated inputs

docs/_autosummary/_autosummary/pycroscopy.io.hdf_utils.rst

@@ -9,24 +9,24 @@ pycroscopy\.io\.hdf\_utils
 
    .. autosummary::
    
-      buildReducedSpec
+      build_reduced_spec_dsets
       calc_chunks
-      checkAndLinkAncillary
-      checkIfMain
+      check_and_link_ancillary
+      check_if_main
       check_for_old
-      copyAttributes
-      copyRegionRefs
+      copy_attributes
+      copy_region_refs
       copy_main_attributes
-      createRefFromIndices
+      create_region_reference
       create_empty_dataset
       create_spec_inds_from_vals
-      findDataset
-      findH5group
-      getAuxData
-      getDataSet
-      getH5DsetRefs
-      getH5GroupRefs
-      getH5RegRefIndices
+      find_dataset
+      find_results_groups
+      get_auxillary_datasets
+      get_datasets
+      get_h5_obj_refs
+      get_group_refs
+      get_indices_for_region_ref
       get_all_main
       get_attr
       get_attributes
@@ -36,14 +36,14 @@ pycroscopy\.io\.hdf\_utils
       get_sort_order
       get_source_dataset
       get_unit_values
-      linkRefAsAlias
-      linkRefs
+      link_h5_obj_as_alias
+      link_h5_objects_as_attrs
       link_as_main
       print_tree
-      reducingRefCopy
-      reshape_from_Ndims
-      reshape_to_Ndims
-      simpleRefCopy
+      copy_reg_ref_reduced_dim
+      reshape_from_n_dims
+      reshape_to_n_dims
+      simple_region_ref_copy
    
    
 

docs/_autosummary/backreferences/pycroscopy.hdf_utils.buildReducedSpec.examples

 
 
-Examples using ``pycroscopy.hdf_utils.buildReducedSpec``
+Examples using ``pycroscopy.hdf_utils.build_reduced_spec_dsets``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.checkAndLinkAncillary.examples

 
 
-Examples using ``pycroscopy.hdf_utils.checkAndLinkAncillary``
+Examples using ``pycroscopy.hdf_utils.check_and_link_ancillary``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.checkIfMain.examples

 
 
-Examples using ``pycroscopy.hdf_utils.checkIfMain``
+Examples using ``pycroscopy.hdf_utils.check_if_main``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.findDataset.examples

 
 
-Examples using ``pycroscopy.hdf_utils.findDataset``
+Examples using ``pycroscopy.hdf_utils.find_dataset``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.findH5group.examples

 
 
-Examples using ``pycroscopy.hdf_utils.findH5group``
+Examples using ``pycroscopy.hdf_utils.find_results_groups``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.getAuxData.examples

 
 
-Examples using ``pycroscopy.hdf_utils.getAuxData``
+Examples using ``pycroscopy.hdf_utils.get_auxillary_datasets``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.getDataSet.examples

 
 
-Examples using ``pycroscopy.hdf_utils.getDataSet``
+Examples using ``pycroscopy.hdf_utils.get_datasets``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.getH5DsetRefs.examples

 
 
-Examples using ``pycroscopy.hdf_utils.getH5DsetRefs``
+Examples using ``pycroscopy.hdf_utils.get_h5_obj_refs``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.linkRefs.examples

 
 
-Examples using ``pycroscopy.hdf_utils.linkRefs``
+Examples using ``pycroscopy.hdf_utils.link_h5_objects_as_attrs``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.reshape_from_Ndims.examples

 
 
-Examples using ``pycroscopy.hdf_utils.reshape_from_Ndims``
+Examples using ``pycroscopy.hdf_utils.reshape_from_n_dims``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.hdf_utils.reshape_to_Ndims.examples

 
 
-Examples using ``pycroscopy.hdf_utils.reshape_to_Ndims``
+Examples using ``pycroscopy.hdf_utils.reshape_to_n_dims``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/_autosummary/backreferences/pycroscopy.io.hdf_utils.getH5DsetRefs.examples

 
 
-Examples using ``pycroscopy.io.hdf_utils.getH5DsetRefs``
+Examples using ``pycroscopy.io.hdf_utils.get_h5_obj_refs``
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 .. raw:: html

docs/auto_examples/data_analysis/plot_spectral_unmixing.rst

@@ -111,7 +111,7 @@ We will begin by downloading the BE-PFM dataset from Github
     h5_main = h5_meas_grp['Channel_000/Raw_Data']
 
     # Extracting the X axis - vector of frequencies
-    h5_spec_vals = px.hdf_utils.getAuxData(h5_main,'Spectroscopic_Values')[-1]
+    h5_spec_vals = px.hdf_utils.get_auxillary_datasets(h5_main,'Spectroscopic_Values')[-1]
     freq_vec = np.squeeze(h5_spec_vals.value) * 1E-3
 
     print('Data currently of shape:', h5_main.shape)

docs/auto_examples/dev_tutorials/plot_tutorial_01_5_microdata.rst

@@ -193,8 +193,8 @@ new file.
     h5_refs = hdf.writeData(root_group, print_log=True)
 
     # We can use these references to get the h5py dataset and group objects
-    h5_main = px.io.hdf_utils.getH5DsetRefs(['Main_Data'], h5_refs)[0]
-    h5_empty = px.io.hdf_utils.getH5DsetRefs(['Empty_Data'], h5_refs)[0]
+    h5_main = px.io.hdf_utils.get_h5_obj_refs(['Main_Data'], h5_refs)[0]
+    h5_empty = px.io.hdf_utils.get_h5_obj_refs(['Empty_Data'], h5_refs)[0]
 
 
 

docs/auto_examples/dev_tutorials/plot_tutorial_02_writing_to_h5.py

@@ -85,6 +85,8 @@ import os
 from warnings import warn
 
 # Package for downloading online files:
+import pycroscopy.core.io.translator
+
 try:
     # This package is not part of anaconda and may need to be installed.
     import wget
@@ -277,9 +279,9 @@ px.plot_utils.plot_cluster_results_together(np.reshape(labels, (num_rows, num_co
 #
 # In this case, `centroids` has `k` positions all in one dimension. Thus the matrix needs to be reshaped to `k x 1`
 
-ds_labels_spec_inds, ds_labels_spec_vals = px.io.translators.utils.build_ind_val_dsets([1], labels=['Label'])
-ds_cluster_inds, ds_cluster_vals = px.io.translators.utils.build_ind_val_dsets([centroids.shape[0]], is_spectral=False,
-                                                                               labels=['Cluster'])
+ds_labels_spec_inds, ds_labels_spec_vals = pycroscopy.core.io.translator.build_ind_val_dsets([1], labels=['Label'])
+ds_cluster_inds, ds_cluster_vals = pycroscopy.core.io.translator.build_ind_val_dsets([centroids.shape[0]], is_spectral=False,
+                                                                                     labels=['Cluster'])
 labels_mat = np.uint32(labels.reshape([-1, 1]))
 
 # Rename the datasets

docs/auto_examples/dev_tutorials/plot_tutorial_02_writing_to_h5.rst

@@ -192,7 +192,7 @@ instead.
     y_label = px.hdf_utils.get_attr(h5_main, 'quantity') + ' [' + px.hdf_utils.get_attr(h5_main, 'units') + ']'
 
     # Get the voltage vector that this data was acquired as a function of:
-    h5_spec_vals = px.hdf_utils.getAuxData(h5_main, 'Spectroscopic_Values')[0]
+    h5_spec_vals = px.hdf_utils.get_auxillary_datasets(h5_main, 'Spectroscopic_Values')[0]
     volt_vec = np.squeeze(h5_spec_vals[()])
 
     # Get the descriptor for this
@@ -530,12 +530,12 @@ Once the tree is prepared (previous cell), ioHDF5 will handle all the file writi
 
     h5_clust_refs = hdf.writeData(cluster_grp, print_log=True)
 
-    h5_labels = px.hdf_utils.getH5DsetRefs(['Labels'], h5_clust_refs)[0]
-    h5_centroids = px.hdf_utils.getH5DsetRefs(['Mean_Response'], h5_clust_refs)[0]
-    h5_clust_inds = px.hdf_utils.getH5DsetRefs(['Cluster_Indices'], h5_clust_refs)[0]
-    h5_clust_vals = px.hdf_utils.getH5DsetRefs(['Cluster_Values'], h5_clust_refs)[0]
-    h5_label_inds = px.hdf_utils.getH5DsetRefs(['Label_Spectroscopic_Indices'], h5_clust_refs)[0]
-    h5_label_vals = px.hdf_utils.getH5DsetRefs(['Label_Spectroscopic_Values'], h5_clust_refs)[0]
+    h5_labels = px.hdf_utils.get_h5_obj_refs(['Labels'], h5_clust_refs)[0]
+    h5_centroids = px.hdf_utils.get_h5_obj_refs(['Mean_Response'], h5_clust_refs)[0]
+    h5_clust_inds = px.hdf_utils.get_h5_obj_refs(['Cluster_Indices'], h5_clust_refs)[0]
+    h5_clust_vals = px.hdf_utils.get_h5_obj_refs(['Cluster_Values'], h5_clust_refs)[0]
+    h5_label_inds = px.hdf_utils.get_h5_obj_refs(['Label_Spectroscopic_Indices'], h5_clust_refs)[0]
+    h5_label_vals = px.hdf_utils.get_h5_obj_refs(['Label_Spectroscopic_Values'], h5_clust_refs)[0]
 
 
 
@@ -653,20 +653,20 @@ rather easy by a few pycroscopy functions.
 
 
     # we already got the reference to the spectroscopic values in the first few cells
-    h5_spec_inds = px.hdf_utils.getAuxData(h5_main, 'Spectroscopic_Indices')[0]
+    h5_spec_inds = px.hdf_utils.get_auxillary_datasets(h5_main, 'Spectroscopic_Indices')[0]
 
-    px.hdf_utils.checkAndLinkAncillary(h5_labels,
+    px.hdf_utils.check_and_link_ancillary(h5_labels,
                           ['Position_Indices', 'Position_Values'],
                           h5_main=h5_main)
-    px.hdf_utils.checkAndLinkAncillary(h5_labels,
+    px.hdf_utils.check_and_link_ancillary(h5_labels,
                           ['Spectroscopic_Indices', 'Spectroscopic_Values'],
                           anc_refs=[h5_label_inds, h5_label_vals])
 
-    px.hdf_utils.checkAndLinkAncillary(h5_centroids,
+    px.hdf_utils.check_and_link_ancillary(h5_centroids,
                           ['Spectroscopic_Indices', 'Spectroscopic_Values'],
                           anc_refs=[h5_spec_inds, h5_spec_vals])
 
-    px.hdf_utils.checkAndLinkAncillary(h5_centroids,
+    px.hdf_utils.check_and_link_ancillary(h5_centroids,
                           ['Position_Indices', 'Position_Values'],
                           anc_refs=[h5_clust_inds, h5_clust_vals])
 

docs/auto_examples/dev_tutorials/plot_tutorial_03_multidimensional_data.rst

@@ -191,10 +191,10 @@ slice the data. For that we need the ancillary datasets that support this main d
 
 
     # pycroscopy has a convenient function to access datasets linked to a given dataset:
-    h5_spec_ind = px.hdf_utils.getAuxData(h5_main, 'Spectroscopic_Indices')[0]
-    h5_spec_val = px.hdf_utils.getAuxData(h5_main, 'Spectroscopic_Values')[0]
-    h5_pos_ind = px.hdf_utils.getAuxData(h5_main, 'Position_Indices')[0]
-    h5_pos_val = px.hdf_utils.getAuxData(h5_main, 'Position_Values')[0]
+    h5_spec_ind = px.hdf_utils.get_auxillary_datasets(h5_main, 'Spectroscopic_Indices')[0]
+    h5_spec_val = px.hdf_utils.get_auxillary_datasets(h5_main, 'Spectroscopic_Values')[0]
+    h5_pos_ind = px.hdf_utils.get_auxillary_datasets(h5_main, 'Position_Indices')[0]
+    h5_pos_val = px.hdf_utils.get_auxillary_datasets(h5_main, 'Position_Values')[0]
 
 
 
@@ -536,7 +536,7 @@ make up this function.
 .. code-block:: python
 
 
-    ds_nd, success, labels = px.hdf_utils.reshape_to_Ndims(h5_main, get_labels=True)
+    ds_nd, success, labels = px.hdf_utils.reshape_to_n_dims(h5_main, get_labels=True)
     print('Shape of the N-dimensional dataset:', ds_nd.shape)
     print(labels)
 

docs/auto_examples/dev_tutorials/plot_tutorial_05_data_processing.rst

@@ -131,7 +131,7 @@ Note that:
             super(ShoGuess, self).__init__(h5_main, cores=cores)
 
             # find the frequency vector
-            h5_spec_vals = px.hdf_utils.getAuxData(h5_main, 'Spectroscopic_Values')[-1]
+            h5_spec_vals = px.hdf_utils.get_auxillary_datasets(h5_main, 'Spectroscopic_Values')[-1]
             self.freq_vec = np.squeeze(h5_spec_vals.value) * 1E-3
 
         def _create_results_datasets(self):
@@ -140,8 +140,8 @@ Note that:
             Just as the raw data is stored in the pycroscopy format, the results also need to conform to the same
             standards. Hence, the create_datasets function can appear to be a little longer than one might expect.
             """
-            h5_spec_inds = px.hdf_utils.getAuxData(self.h5_main, auxDataName=['Spectroscopic_Indices'])[0]
-            h5_spec_vals = px.hdf_utils.getAuxData(self.h5_main, auxDataName=['Spectroscopic_Values'])[0]
+            h5_spec_inds = px.hdf_utils.get_auxillary_datasets(self.h5_main, auxDataName=['Spectroscopic_Indices'])[0]
+            h5_spec_vals = px.hdf_utils.get_auxillary_datasets(self.h5_main, auxDataName=['Spectroscopic_Values'])[0]
 
             self.step_start_inds = np.where(h5_spec_inds[0] == 0)[0]
             self.num_udvs_steps = len(self.step_start_inds)
@@ -152,7 +152,7 @@ Note that:
 
             not_freq = px.hdf_utils.get_attr(h5_spec_inds, 'labels') != 'Frequency'
 
-            ds_sho_inds, ds_sho_vals = px.hdf_utils.buildReducedSpec(h5_spec_inds, h5_spec_vals, not_freq,
+            ds_sho_inds, ds_sho_vals = px.hdf_utils.build_reduced_spec_dsets(h5_spec_inds, h5_spec_vals, not_freq,
                                                                      self.step_start_inds)
 
             dset_name = self.h5_main.name.split('/')[-1]
@@ -162,18 +162,18 @@ Note that:
 
             h5_sho_grp_refs = self.hdf.writeData(sho_grp)
 
-            self.h5_guess = px.hdf_utils.getH5DsetRefs(['Guess'], h5_sho_grp_refs)[0]
+            self.h5_guess = px.hdf_utils.get_h5_obj_refs(['Guess'], h5_sho_grp_refs)[0]
             self.h5_results_grp = self.h5_guess.parent
-            h5_sho_inds = px.hdf_utils.getH5DsetRefs(['Spectroscopic_Indices'],
+            h5_sho_inds = px.hdf_utils.get_h5_obj_refs(['Spectroscopic_Indices'],
                                                      h5_sho_grp_refs)[0]
-            h5_sho_vals = px.hdf_utils.getH5DsetRefs(['Spectroscopic_Values'],
+            h5_sho_vals = px.hdf_utils.get_h5_obj_refs(['Spectroscopic_Values'],
                                                      h5_sho_grp_refs)[0]
 
             # Reference linking before actual fitting
-            px.hdf_utils.linkRefs(self.h5_guess, [h5_sho_inds, h5_sho_vals])
+            px.hdf_utils.link_h5_objects_as_attrs(self.h5_guess, [h5_sho_inds, h5_sho_vals])
             # Linking ancillary position datasets:
-            aux_dsets = px.hdf_utils.getAuxData(self.h5_main, auxDataName=['Position_Indices', 'Position_Values'])
-            px.hdf_utils.linkRefs(self.h5_guess, aux_dsets)
+            aux_dsets = px.hdf_utils.get_auxillary_datasets(self.h5_main, auxDataName=['Position_Indices', 'Position_Values'])
+            px.hdf_utils.link_h5_objects_as_attrs(self.h5_guess, aux_dsets)
             print('Finshed creating datasets')
 
         def compute(self, *args, **kwargs):
@@ -258,7 +258,7 @@ dimensional matrix in accordance with the pycroscopy data format.
     h5_main = h5_meas_grp['Channel_000/Raw_Data']
 
     # Extracting the X axis - vector of frequencies
-    h5_spec_vals = px.hdf_utils.getAuxData(h5_main, 'Spectroscopic_Values')[-1]
+    h5_spec_vals = px.hdf_utils.get_auxillary_datasets(h5_main, 'Spectroscopic_Values')[-1]
     freq_vec = np.squeeze(h5_spec_vals.value) * 1E-3