def test_hasattribute_exceptions_for_optional_deps():
'''test whether HasAttributes raises exceptions for optional features'''
required_dep = base.register("Data", DummyDataProvider())
optional_dep = base.register('OptionalData', DummyDataWithZeroDivision())
comp = Dummy()
data = comp.get_optional_data()
assert True
def test_hasattribute_exceptions_for_optional_deps():
'''test whether HasAttributes raises exceptions for optional features'''
required_dep = base.register("Data", DummyDataProvider())
optional_dep = base.register('OptionalData', DummyDataWithZeroDivision())
comp = Dummy()
data = comp.get_optional_data()
assert True
def test_cluster_component_smart_update():
feature_comp = base.register("FeatureSource", DummyFeatureExtractor())
cluster_comp = components.ClusterAnalyzer("k_means", 2)
labs = cluster_comp.labels # this is a workaround to call _cluster()
# cluster_comp should NOT recluster when updated, if the number of
# spikes didn't change
cluster_comp.delete_cells(1) # modify lablels
labels_orig = cluster_comp.labels.copy()
feature_comp.add_feature('new_feature')
feature_comp.update()
labels_new_feat = cluster_comp.labels.copy()
# cluster_comp should recluster when updated, if the number of
# spikes DID change
feature_comp.add_spikes(10)
feature_comp.update()
labels_new_spikes = cluster_comp.labels.copy()
test1 = (labels_orig == labels_new_feat).all()
test2 = len(labels_new_spikes) != len(labels_new_feat)
ok_(test1 and test2)
# spike detection/extraction properties
contact = 3
detection_type = "max"
thresh = "auto"
filter_freq = (800.0, 100.0)
sp_win = [-0.6, 0.8]
path = filter(None, os.environ['DATAPATH'].split(os.sep)) + [hdf5filename]
hdf5file = os.path.join(os.sep, *path)
io = components.PyTablesSource(hdf5file, dataset)
io_filter = components.FilterStack()
base.register("RawSource", io)
base.register("EventsOutput", io)
base.register("SignalSource", io_filter)
base.register("SpikeMarkerSource",
components.SpikeDetector(contact=contact,
thresh=thresh,
type=detection_type,
sp_win=sp_win,
resample=1,
align=True))
base.register("SpikeSource", components.SpikeExtractor(sp_win=sp_win))
base.register("FeatureSource", components.FeatureExtractor())
base.register("LabelSource", components.ClusterAnalyzer("gmm", 4))
browser = components.SpikeBrowser()
feature_plot = components.PlotFeaturesTimeline()
from spike_sort.io import neo_filters
####################################
# Adjust these fields for your needs
sp_win = [-0.6, 0.8]
url = 'https://portal.g-node.org/neo/axon/File_axon_1.abf'
path = 'file_axon.abf'
import urllib
urllib.urlretrieve(url, path)
io = neo_filters.NeoSource(path)
base.register("SignalSource", io)
base.register(
"SpikeMarkerSource",
components.SpikeDetector(contact=0,
thresh='auto',
type='max',
sp_win=sp_win,
resample=1,
align=True))
base.register("SpikeSource", components.SpikeExtractor(sp_win=sp_win))
browser = components.SpikeBrowser()
browser.show()
def test_missing_optional_dependency():
required_dep = base.register("Data", DummyDataProvider())
comp = Dummy()
ok_(comp.opt_con is None)
def test_register_new_feature_by_register():
dep_comp = DummyDataProvider()
added_comp = base.register("Data", dep_comp)
comp = Dummy()
ok_(comp.get_data() == 'some data')
assert dep_comp is added_comp
def test_missing_optional_dependency():
required_dep = base.register("Data", DummyDataProvider())
comp = Dummy()
ok_(comp.opt_con is None)
def test_register_new_feature_by_register():
dep_comp = DummyDataProvider()
added_comp = base.register("Data", dep_comp)
comp = Dummy()
ok_(comp.get_data() == 'some data')
assert dep_comp is added_comp
# spike detection/extraction properties
contact = 3
detection_type = "max"
thresh = "auto"
filter_freq = (800.0, 100.0)
sp_win = [-0.6, 0.8]
path = filter(None, os.environ['DATAPATH'].split(os.sep)) + [hdf5filename]
hdf5file = os.path.join(os.sep, *path)
io = components.PyTablesSource(hdf5file, dataset)
io_filter = components.FilterStack()
base.register("RawSource", io)
base.register("EventsOutput", io)
base.register("SignalSource", io_filter)
base.register(
"SpikeMarkerSource",
components.SpikeDetector(contact=contact,
thresh=thresh,
type=detection_type,
sp_win=sp_win,
resample=1,
align=True))
base.register("SpikeSource", components.SpikeExtractor(sp_win=sp_win))
base.register("FeatureSource", components.FeatureExtractor())
base.register("LabelSource", components.ClusterAnalyzer("gmm", 4))
browser = components.SpikeBrowser()
from spike_beans import components, base
from spike_sort.io import neo_filters
####################################
# Adjust these fields for your needs
sp_win = [-0.6, 0.8]
url = 'https://portal.g-node.org/neo/axon/File_axon_1.abf'
path = 'file_axon.abf'
import urllib
urllib.urlretrieve(url, path)
io = neo_filters.NeoSource(path)
base.register("SignalSource", io)
base.register("SpikeMarkerSource",
components.SpikeDetector(contact=0,
thresh='auto',
type='max',
sp_win=sp_win,
resample=1,
align=True))
base.register("SpikeSource", components.SpikeExtractor(sp_win=sp_win))
browser = components.SpikeBrowser()
browser.show()
