def test_attach_plugins(tempdir):
class MyController(object):
pass
write_text(
tempdir / 'plugin1.py',
dedent('''
from phy import IPlugin
class MyPlugin1(IPlugin):
def attach_to_controller(self, controller):
controller.plugin1 = True
'''))
class MyPlugin2(IPlugin):
def attach_to_controller(self, controller):
controller.plugin2 = True
contents = dedent('''
c = get_config()
c.Plugins.dirs = ['%s']
c.MyController.plugins = ['MyPlugin1']
''' % tempdir)
write_text(tempdir / 'phy_config.py', contents)
controller = MyController()
attach_plugins(controller, plugins=['MyPlugin2'], config_dir=tempdir)
assert controller.plugin1 == controller.plugin2 is True
def _make_dataset(tempdir, param='dense', has_spike_attributes=True):
np.random.seed(0)
# Download the dataset.
paths = list(map(download_test_file, _FILES))
# Copy the dataset to a temporary directory.
for path in paths:
to_path = tempdir / path.name
# Skip sparse arrays if is_sparse is False.
if param == 'sparse' and ('_ind.' in str(to_path) or 'spike_ids.' in str(to_path)):
continue
logger.debug("Copying file to %s.", to_path)
shutil.copy(path, to_path)
# Some changes to files if 'misc' fixture parameter.
if param == 'misc':
# Remove spike_clusters and recreate it from spike_templates.
_remove(tempdir / 'spike_clusters.npy')
# Replace spike_times.npy, in samples, by spikes.times.npy, in seconds.
if (tempdir / 'spike_times.npy').exists():
st = np.load(tempdir / 'spike_times.npy').squeeze()
st_r = st + np.random.randint(low=-20000, high=+20000, size=st.size)
assert st_r.shape == st.shape
# Reordered spikes.
np.save(tempdir / 'spike_times_reordered.npy', st_r)
np.save(tempdir / 'spikes.times.npy', st / 25000.) # sample rate
_remove(tempdir / 'spike_times.npy')
# Buggy TSV file should not cause a crash.
write_text(tempdir / 'error.tsv', '')
# Remove some non-necessary files.
_remove(tempdir / 'template_features.npy')
_remove(tempdir / 'pc_features.npy')
_remove(tempdir / 'channel_probes.npy')
_remove(tempdir / 'channel_shanks.npy')
_remove(tempdir / 'amplitudes.npy')
_remove(tempdir / 'whitening_mat.npy')
_remove(tempdir / 'whitening_mat_inv.npy')
_remove(tempdir / 'sim_binary.dat')
# Spike attributes.
if has_spike_attributes:
write_array(tempdir / 'spike_fail.npy', np.full(10, np.nan)) # wrong number of spikes
write_array(tempdir / 'spike_works.npy', np.random.rand(314))
write_array(tempdir / 'spike_randn.npy', np.random.randn(314, 2))
# TSV file with cluster data.
write_tsv(
tempdir / 'cluster_Amplitude.tsv', [{'cluster_id': 1, 'Amplitude': 123.4}],
first_field='cluster_id')
write_tsv(
tempdir / 'cluster_metrics.tsv', [
{'cluster_id': 2, 'met1': 123.4, 'met2': 'hello world 1'},
{'cluster_id': 3, 'met1': 5.678},
{'cluster_id': 5, 'met2': 'hello world 2'},
])
template_path = tempdir / paths[0].name
return template_path
def test_discover_plugins(tempdir, no_native_plugins):
path = tempdir / 'my_plugin.py'
contents = '''from phy import IPlugin\nclass MyPlugin(IPlugin): pass'''
write_text(path, contents)
plugins = discover_plugins([tempdir])
assert plugins
assert plugins[0].__name__ == 'MyPlugin'
def py_config(tempdir):
# Create and load a config file.
config_contents = """
c = get_config()
c.MyConfigurable.my_var = 1.0
"""
path = tempdir / 'config.py'
write_text(path, config_contents)
return path
def json_config(tempdir):
# Create and load a config file.
config_contents = """
{
"MyConfigurable": {
"my_var": 1.0
}
}
"""
path = tempdir / 'config.json'
write_text(path, config_contents)
return path
def _make_dataset(tempdir, param='dense', has_spike_attributes=True):
# Download the dataset.
paths = list(map(download_test_file, _FILES))
# Copy the dataset to a temporary directory.
for path in paths:
to_path = tempdir / path.name
# Skip sparse arrays if is_sparse is False.
if param == 'sparse' and ('_ind.' in str(to_path) or 'spike_ids.' in str(to_path)):
continue
logger.debug("Copying file to %s.", to_path)
shutil.copy(path, to_path)
# Some changes to files if 'misc' fixture parameter.
if param == 'misc':
# Remove spike_clusters and recreate it from spike_templates.
_remove(tempdir / 'spike_clusters.npy')
# Replace spike_times.npy, in samples, by spikes.times.npy, in seconds.
if (tempdir / 'spike_times.npy').exists():
st = np.load(tempdir / 'spike_times.npy')
np.save(tempdir / 'spikes.times.npy', st / 25000.) # sample rate
_remove(tempdir / 'spike_times.npy')
# Buggy TSV file should not cause a crash.
write_text(tempdir / 'error.tsv', '')
# Remove some non-necessary files.
_remove(tempdir / 'template_features.npy')
_remove(tempdir / 'pc_features.npy')
_remove(tempdir / 'channel_shanks.npy')
_remove(tempdir / 'amplitudes.npy')
_remove(tempdir / 'whitening_mat.npy')
_remove(tempdir / 'whitening_mat_inv.npy')
_remove(tempdir / 'sim_binary.dat')
# Spike attributes.
if has_spike_attributes:
write_array(tempdir / 'spike_fail.npy', np.full(10, np.nan)) # wrong number of spikes
write_array(tempdir / 'spike_works.npy', np.random.rand(314))
write_array(tempdir / 'spike_randn.npy', np.random.randn(314, 2))
template_path = tempdir / paths[0].name
return template_path