/usr/lib/python2.7/dist-packages/neo/test/iotest/test_hdf5io.py is in python-neo 0.3.3-2.
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"""
Tests of neo.io.hdf5io
Usually I run these tests like that. I add neo root folder to the pythonpath
(usually by adding the neo.pth with the path to the cloned repository to, say,
/usr/lib/python2.6/dist-packages/) and run
python <path to the neo repo>/test/io/test_hdf5io.py
For the moment only basic tests are active.
#TODO add performance testing!!
"""
# needed for python 3 compatibility
from __future__ import absolute_import
from datetime import datetime
from hashlib import md5
import logging
import os
import sys
try:
import unittest2 as unittest
except ImportError:
import unittest
import numpy as np
import quantities as pq
from neo.core import SpikeTrain, Segment, Block
from neo.test.tools import (assert_neo_object_is_compliant,
assert_objects_equivalent,
assert_same_sub_schema)
from neo.test.iotest.common_io_test import BaseTestIO
from neo.description import (class_by_name, classes_necessary_attributes,
classes_recommended_attributes,
implicit_relationship, many_to_many_relationship,
name_by_class, one_to_many_relationship)
from neo.io.hdf5io import NeoHdf5IO, HAVE_TABLES
#==============================================================================
TEST_ANNOTATIONS = [1, 0, 1.5, "this is a test", datetime.now(), None]
def get_fake_value(attr): # attr = (name, type, [dim, [dtype]])
""" returns default value for a given attribute based on description.py """
if attr[1] == pq.Quantity or attr[1] == np.ndarray:
size = []
for i in range(int(attr[2])):
size.append(np.random.randint(100) + 1)
to_set = np.random.random(size) * pq.millisecond # let it be ms
if attr[0] == 't_start':
to_set = 0.0 * pq.millisecond
if attr[0] == 't_stop':
to_set = 1.0 * pq.millisecond
if attr[0] == 'sampling_rate':
to_set = 10000.0 * pq.Hz
if attr[1] == np.ndarray:
to_set = np.array(to_set, dtype=attr[3])
if attr[1] == str:
to_set = str(np.random.randint(100000))
if attr[1] == int:
to_set = np.random.randint(100)
if attr[1] == datetime:
to_set = datetime.now()
return to_set
def fake_NEO(obj_type="Block", cascade=True, _follow_links=True):
""" Create a fake NEO object of a given type. Follows one-to-many
and many-to-many relationships if cascade. RC, when requested cascade, will
not create RGCs to avoid dead-locks.
_follow_links - an internal variable, indicates whether to create objects
with 'implicit' relationships, to avoid duplications. Do not use it. """
kwargs = {} # assign attributes
attrs = classes_necessary_attributes[obj_type] + \
classes_recommended_attributes[obj_type]
for attr in attrs:
kwargs[attr[0]] = get_fake_value(attr)
obj = class_by_name[obj_type](**kwargs)
if cascade:
if obj_type == "Block":
_follow_links = False
if obj_type in one_to_many_relationship:
rels = one_to_many_relationship[obj_type]
if obj_type == "RecordingChannelGroup":
rels += many_to_many_relationship[obj_type]
if not _follow_links and obj_type in implicit_relationship:
for i in implicit_relationship[obj_type]:
if not i in rels:
logging.debug("LOOK HERE!!!" + str(obj_type))
rels.remove(i)
for child in rels:
setattr(obj, child.lower() + "s", [fake_NEO(child, cascade,
_follow_links)])
if obj_type == "Block": # need to manually create 'implicit' connections
# connect a unit to the spike and spike train
u = obj.recordingchannelgroups[0].units[0]
st = obj.segments[0].spiketrains[0]
sp = obj.segments[0].spikes[0]
u.spiketrains.append(st)
u.spikes.append(sp)
# connect RCG with ASA
asa = obj.segments[0].analogsignalarrays[0]
obj.recordingchannelgroups[0].analogsignalarrays.append(asa)
# connect RC to AS, IrSAS and back to RGC
rc = obj.recordingchannelgroups[0].recordingchannels[0]
rc.recordingchannelgroups.append(obj.recordingchannelgroups[0])
rc.analogsignals.append(obj.segments[0].analogsignals[0])
seg = obj.segments[0]
rc.irregularlysampledsignals.append(seg.irregularlysampledsignals[0])
# add some annotations, 80%
at = dict([(str(x), TEST_ANNOTATIONS[x]) for x in
range(len(TEST_ANNOTATIONS))])
obj.annotate(**at)
return obj
class HDF5Commontests(BaseTestIO, unittest.TestCase):
ioclass = NeoHdf5IO
files_to_test = ['test.h5']
files_to_download = files_to_test
@unittest.skipIf(sys.version_info[0] > 2, "not Python 3 compatible")
@unittest.skipUnless(HAVE_TABLES, "requires PyTables")
def setUp(self):
BaseTestIO.setUp(self)
class hdf5ioTest: # inherit this class from unittest.TestCase when ready
"""
Tests for the hdf5 library.
"""
#@unittest.skipIf(sys.version_info[0] > 2, "not Python 3 compatible")
#@unittest.skipUnless(HAVE_TABLES, "requires PyTables")
def setUp(self):
self.test_file = "test.h5"
def tearDown(self):
if os.path.exists(self.test_file):
os.remove(self.test_file)
def test_create(self):
"""
Create test file with signals, segments, blocks etc.
"""
iom = NeoHdf5IO(filename=self.test_file)
b1 = fake_NEO() # creating a structure
iom.save(b1) # saving
# must be assigned after save
self.assertTrue(hasattr(b1, "hdf5_path"))
iom.close()
iom.connect(filename=self.test_file)
b2 = iom.get(b1.hdf5_path) # new object
assert_neo_object_is_compliant(b2)
assert_same_sub_schema(b1, b2)
def test_property_change(self):
""" Make sure all attributes are saved properly after the change,
including quantities, units, types etc."""
iom = NeoHdf5IO(filename=self.test_file)
for obj_type in class_by_name.keys():
obj = fake_NEO(obj_type, cascade=False)
iom.save(obj)
self.assertTrue(hasattr(obj, "hdf5_path"))
replica = iom.get(obj.hdf5_path, cascade=False)
assert_objects_equivalent(obj, replica)
def test_relations(self):
"""
make sure the change in relationships is saved properly in the file,
including correct M2M, no redundancy etc. RC -> RCG not tested.
"""
def assert_children(self, obj, replica):
obj_type = name_by_class[obj]
self.assertEqual(md5(str(obj)).hexdigest(),
md5(str(replica)).hexdigest())
if obj_type in one_to_many_relationship:
rels = one_to_many_relationship[obj_type]
if obj_type == "RecordingChannelGroup":
rels += many_to_many_relationship[obj_type]
for child_type in rels:
ch1 = getattr(obj, child_type.lower() + "s")
ch2 = getattr(replica, child_type.lower() + "s")
self.assertEqual(len(ch1), len(ch2))
for i, v in enumerate(ch1):
self.assert_children(ch1[i], ch2[i])
iom = NeoHdf5IO(filename=self.test_file)
for obj_type in class_by_name.keys():
obj = fake_NEO(obj_type, cascade=True)
iom.save(obj)
self.assertTrue(hasattr(obj, "hdf5_path"))
replica = iom.get(obj.hdf5_path, cascade=True)
self.assert_children(obj, replica)
def test_errors(self):
""" some tests for specific errors """
f = open("thisisafakehdf.h5", "w") # wrong file type
f.write("this is not an HDF5 file. sorry.")
f.close()
self.assertRaises(TypeError, NeoHdf5IO(filename="thisisafakehdf.h5"))
iom = NeoHdf5IO(filename=self.test_file) # wrong object path test
self.assertRaises(LookupError, iom.get("/wrong_path"))
some_object = np.array([1, 2, 3]) # non NEO object test
self.assertRaises(AssertionError, iom.save(some_object))
def test_attr_changes(self):
""" gets an object, changes its attributes, saves it, then compares how
good the changes were saved. """
iom = NeoHdf5IO(filename=self.test_file)
for obj_type in class_by_name.keys():
obj = fake_NEO(obj_type=obj_type, cascade=False)
iom.save(obj)
orig_obj = iom.get(obj.hdf5_path)
attrs = (classes_necessary_attributes[obj_type] +
classes_recommended_attributes[obj_type])
for attr in attrs:
if hasattr(orig_obj, attr[0]):
setattr(obj, attr[0], get_fake_value(attr))
iom.save(orig_obj)
test_obj = iom.get(orig_obj.hdf5_path)
assert_objects_equivalent(orig_obj, test_obj)
# changes!!! in attr AS WELL AS in relations!!
# test annotations
# test naming - paths
# unicode!!
# add a child, then remove, then check it's removed
# update/removal of relations b/w RC and AS which are/not are in the
# same segment
class HDF5MoreTests(unittest.TestCase):
@unittest.skipIf(sys.version_info[0] > 2, "not Python 3 compatible")
@unittest.skipUnless(HAVE_TABLES, "requires PyTables")
def test_store_empty_spike_train(self):
spiketrain0 = SpikeTrain([], t_start=0.0, t_stop=100.0, units="ms")
spiketrain1 = SpikeTrain([23.4, 45.6, 67.8],
t_start=0.0, t_stop=100.0, units="ms")
segment = Segment(name="a_segment")
segment.spiketrains.append(spiketrain0)
segment.spiketrains.append(spiketrain1)
block = Block(name="a_block")
block.segments.append(segment)
iom = NeoHdf5IO(filename="test987.h5")
iom.save(block)
iom.close()
iom = NeoHdf5IO(filename="test987.h5")
block1 = iom.get("/Block_0")
self.assertEqual(block1.segments[0].spiketrains[0].t_stop, 100.0)
self.assertEqual(len(block1.segments[0].spiketrains[0]), 0)
self.assertEqual(len(block1.segments[0].spiketrains[1]), 3)
iom.close()
os.remove("test987.h5")
if __name__ == '__main__':
unittest.main()
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