Add microdata_example

examples/microdata_example.py

+"""
+===========================================
+Writing to hdf5 using the Microdata objects
+===========================================
+
+
+
+"""
+
+# Code source: Chris Smith -- cq6@ornl.gov
+# Liscense: MIT
+
+import numpy as np
+import pycroscopy as px
+
+
+# Create some MicroDatasets and MicroDataGroups that will be written to the file.
+# With h5py, groups and datasets must be created from the top down,
+# but the Microdata objects allow us to build them in any order and link them later.
+
+# First create some data
+data1 = np.random.rand(5, 7)
+
+# Now use the array to build the dataset.  This dataset will live
+# directly under the root of the file.  The MicroDataset class also implements the
+# compression and chunking parameters from h5py.Dataset.
+ds_main = px.MicroDataset('Main_Data', data=data1, parent='/')
+
+# We can also create an empty dataset and write the values in later
+# With this method, it is neccessary to specify the dtype and maxshape kwarg parameters.
+ds_empty = px.MicroDataset('Empty_Data', data=[], dtype=np.float32, maxshape=[7, 5, 3])
+
+# We can also create groups and add other MicroData objects as children.
+# If the group's parent is not given, it will be set to root.
+data_group = px.MicroDataGroup('Data_Group', parent='/')
+
+root_group = px.MicroDataGroup('/')
+
+# After creating the group, we then add an existing object as its child.
+data_group.addChildren([ds_empty])
+root_group.addChildren([ds_main, data_group])
+
+# The showTree method allows us to view the data structure before the hdf5 file is
+# created.
+root_group.showTree()
+
+# Now that we have created the objects, we can write them to an hdf5 file
+# First we specify the path to the file
+h5_path = 'microdata_test.h5'
+
+# Then we use the ioHDF5 class to build the file from our objects.
+hdf = px.ioHDF5(h5_path)
+
+# The writeData method builds the hdf5 file using the structure defined by the
+# MicroData objects.  It returns a list of references to all h5py objects in the
+# 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]
+
+print(np.allclose(h5_main[()], data1))
+
+# As mentioned above, we can now write to the Empty_Data object
+data2 = np.random.rand(*h5_empty.shape)
+h5_empty[:] = data2[:]
+
+# Now that we are using h5py objects, we must use flush to write the data to file
+# after it has been altered.
+# We need the file object to do this.  It can be accessed as an attribute of the
+# hdf object.
+h5_file = hdf.file
+h5_file.flush()
+
+# Now that we are done, we should close the file so that it can be accessed elsewhere.
+h5_file.close()