Commit 4e9ef0e6 authored by Mark Hymers's avatar Mark Hymers

Add first tutorial

Signed-off-by: Mark Hymers's avatarMark Hymers <>
parent 97ec6f07
from anamnesis import AbstractAnam, register_class
class SimplePerson(AbstractAnam):
hdf5_outputs = ['name', 'age']
def __init__(self, name='Unknown', age=0):
AbstractAnam.__init__(self) = name
self.age = age
import h5py
from anamnesis import obj_from_hdf5file
import test_classes1
# Load the class from the HDF5 file
s = obj_from_hdf5file('test_script1.hdf5')
# Show that we have reconstructed the object
# Demonstrate how to specifically choose which group to load
s2 = obj_from_hdf5file('test_script1.hdf5', 'person')
# Show that we have reconstructed the object
import h5py
from anamnesis import obj_from_hdf5group, ClassRegister
# Register our class prefix so that we autoload our objects. This
# allows loading all classes whose fully resolved name
# starts with test_classes1; e.g. test_classes1.SimplePerson
# Open our HDF5 file
f = h5py.File('test_script1.hdf5', 'r')
# Load the class from the HDF5 file using our
# obj_from_hdf5group method
s = obj_from_hdf5group(f['person'])
# Show that we have reconstructed the object
# Close our HDF5 file
import h5py
from test_classes1 import SimplePerson
# Create a person
s = SimplePerson('Fred', 42)
# Serialise the person to disk
f = h5py.File('test_script1.hdf5', 'w')
Tutorial 1 - Using Serialised Objects
Tutorial 1 - Using a Simple Serialised Object
The simplest use of anamnesis allows the serialisation of classes to and from hdf5
with relatively little extra code.
In order to both reading to and writing from HDF5 files to work, there are
four basic steps
1. Inherit from the `anamnesis.AbstractAnam` class and call the class constructor
2. Ensure that your class constructor (`__init__`) can be called with no arguments (you may
pass arguments to it but they must have default values)
3. Call the `anamnesis.register_class` function with the class
4. Populate the `hdf5_outputs` class variable with a list of member variable names necessary for serialisation/de-serialisation
Note that anamnesis uses the fully qualified class name when autoloading during
the unserialising (loading) of object. If you want to use locally defined
classes, you will have to ensure that they have been manually imported. For
our examples, we will place our classes in the files `` (where
X is an ineger) and ensure that we can import this file into Python (i.e. it is
on the `PYTHONPATH` or in the current working directory).
Our first example class is going to be a simple model of a person's name
and age. We place the following code in ``
.. literalinclude::
:language: python
If we examine the `person` group in the HDF5 file, we can see that the
class member variables:
.. image:: test_classes1.png
We can now write a script which will serial our data into an HDF5 file.
We specify the group name when writing out.
.. literalinclude::
:language: python
And write another script which loads the class back in. Because this
class is not registered, we need to make sure that we have imported
the module first. First of all, we can load from the file; if
we know there is only one group in the file, we do not even need
to specify the group name:
.. literalinclude::
:language: python
If we want to load multiple objects from the same HDF5 file, we can
open the file once and then use a function which loads from
the group of the opened file. We can also tell anamnesis that
it should autoload modules which start with a certain name.
Both of these possibilities are demonstrated in the script
.. literalinclude::
:language: python
TODO: Basic example
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