def test_export_and_load():
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
# register temporary feature
dclab.register_temporary_feature(feature="fl1_mean")
with dclab.new_dataset(h5path) as ds:
# extract the feature information from the dataset
fl1_mean = np.array(
[np.mean(ds["trace"]["fl1_raw"][ii]) for ii in range(len(ds))])
# set the data
dclab.set_temporary_feature(rtdc_ds=ds,
feature="fl1_mean",
data=fl1_mean)
# export the data to a new file
expath = h5path.with_name("exported.rtdc")
ds.export.hdf5(expath, features=ds.features_innate + ["fl1_mean"])
# make sure that worked
with h5py.File(expath, "r") as h5:
assert "fl1_mean" in h5["events"]
assert np.allclose(h5["events"]["fl1_mean"], fl1_mean)
# now check again with dclab
with dclab.new_dataset(expath) as ds2:
assert "fl1_mean" in ds2
assert np.allclose(ds2["fl1_mean"], fl1_mean)
# and a control check
deregister_all()
assert "fl1_mean" not in ds2
def test_pf_export_non_scalar_filtered_from_file_issue_166():
h5path = retrieve_data("fmt-hdf5_image-bg_2020.zip")
expath = h5path.with_name("exported.rtdc")
# initialize PlugInFeature instance
info = example_plugin_info_non_scalar_feature()
info["feature shapes"] = [(80, 250)]
pf = PlugInFeature("image_gauss_filter", info)
# write the plugin feature data to an HDF5 file
with dclab.new_dataset(h5path) as ds:
# extract the feature information from the dataset
ds.export.hdf5(expath, features=[pf.feature_name])
# remove all plugin features and work with temporary feature
remove_all_plugin_features()
# try to load the plugin feature data from that HDF5 file and
# export it again (this time, the exporter has to get the data
# from the H5File object).
expath2 = h5path.with_name("exported2.rtdc")
dclab.register_temporary_feature("image_gauss_filter", is_scalar=False)
with dclab.new_dataset(expath) as ds2:
ds2.export.hdf5(expath2,
features=["image_gauss_filter"],
filtered=True)
# make sure that worked
with h5py.File(expath2, "r") as h5:
assert "image_gauss_filter" in h5["events"]
def test_wrong_data_shape_1():
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
dclab.register_temporary_feature("my_special_feature", is_scalar=False)
with pytest.raises(ValueError):
dclab.set_temporary_feature(rtdc_ds=ds,
feature="my_special_feature",
data=np.arange(len(ds)))
def test_basic():
"""Basic test of a temporary feature"""
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
dclab.register_temporary_feature("my_special_feature")
dclab.set_temporary_feature(rtdc_ds=ds,
feature="my_special_feature",
data=np.arange(len(ds)))
assert ds["my_special_feature"][0] == 0
def test_wrong_name():
"""temporary feature should have same length"""
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
dclab.register_temporary_feature("my_special_feature")
with pytest.raises(ValueError):
dclab.set_temporary_feature(rtdc_ds=ds,
feature="my_other_feature",
data=np.arange(len(ds)))
def test_register_after_loading():
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
fl1_mean = np.array(
[np.mean(ds["trace"]["fl1_raw"][ii]) for ii in range(len(ds))])
with h5py.File(h5path, "a") as h5:
h5["events"]["fl1_mean"] = fl1_mean
with dclab.new_dataset(h5path) as ds:
assert "fl1_mean" not in ds
dclab.register_temporary_feature(feature="fl1_mean")
assert "fl1_mean" in ds
def test_inherited_scalar():
"""Accessing inherited scalar feature should return np.ndarray"""
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
dclab.register_temporary_feature("my_special_feature")
dclab.set_temporary_feature(rtdc_ds=ds,
feature="my_special_feature",
data=np.arange(len(ds)))
ds.filter.manual[2] = False
ch = dclab.new_dataset(ds)
assert "my_special_feature" in ch
assert isinstance(ch["my_special_feature"], np.ndarray)
def test_inherited_non_scalar():
"""Accessing inherited non-innate, non-scalar features"""
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
dclab.register_temporary_feature(feature="image_copy", is_scalar=False)
dclab.set_temporary_feature(rtdc_ds=ds,
feature="image_copy",
data=ds["image"][:])
ds.filter.manual[2] = False
ch = dclab.new_dataset(ds)
assert isinstance(ch["image_copy"], ChildNDArray)
assert ch["image_copy"][:].ndim == 3
assert isinstance(ch["image_copy"][:], np.ndarray)
assert np.all(ch["image_copy"][2] == ds["image_copy"][3])
def test_filtering():
"""Filtering with features, same example as in docs"""
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
dclab.register_temporary_feature(feature="fl1_mean")
fl1_mean = np.array(
[np.mean(ds["trace"]["fl1_raw"][ii]) for ii in range(len(ds))])
dclab.set_temporary_feature(rtdc_ds=ds,
feature="fl1_mean",
data=fl1_mean)
ds.config["filtering"]["fl1_mean min"] = 4
ds.config["filtering"]["fl1_mean max"] = 200
ds.apply_filter()
assert np.sum(ds.filter.all) == 1
assert ds.filter.all[1]
def test_with_user_config_section():
"""Use a temporary feature with the user defined config section"""
# add some metadata to the user config section
metadata = {"channel": True, "n_constrictions": 3}
ds = dclab.new_dataset(retrieve_data("fmt-hdf5_fl_2018.zip"))
ds.config["user"].update(metadata)
assert ds.config["user"] == metadata
area_of_region = ds["area_um"] * ds.config["user"]["n_constrictions"]
dclab.register_temporary_feature("area_of_region")
dclab.set_temporary_feature(rtdc_ds=ds,
feature="area_of_region",
data=area_of_region)
area_of_region1 = ds["area_of_region"]
area_of_region1_calc = (ds["area_um"] *
ds.config["user"]["n_constrictions"])
assert np.allclose(area_of_region1, area_of_region1_calc)
def test_hierarchy_not_supported():
"""Test for RTDCHierarchy (does not work)"""
# Hi there,
# if you are here. this means that this test failed and
# you just implemented temporary features for hierarchy
# datasets. I'm fine with that, just make sure that the
# root parent gets nan values.
# Cheers,
# Paul
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
child = dclab.new_dataset(ds)
dclab.register_temporary_feature("my_special_feature")
with pytest.raises(NotImplementedError):
dclab.set_temporary_feature(rtdc_ds=child,
feature="my_special_feature",
data=np.arange(len(child)))
def test_load_temporary_feature_from_disk():
"""Load a temporary feature from a file on disk"""
h5path = retrieve_data("fmt-hdf5_fl_2018.zip")
with dclab.new_dataset(h5path) as ds:
fl1_mean = np.array(
[np.mean(ds["trace"]["fl1_raw"][ii]) for ii in range(len(ds))])
with h5py.File(h5path, "a") as h5:
h5["events"]["fl1_mean"] = fl1_mean
# make sure it does not work without registration
with dclab.new_dataset(h5path) as ds:
assert "fl1_mean" not in ds, "not registered yet"
# now, register
dclab.register_temporary_feature(feature="fl1_mean")
with dclab.new_dataset(h5path) as ds:
assert "fl1_mean" in ds
assert np.all(fl1_mean == ds["fl1_mean"])
assert "fl1_mean" in ds._events, "registered feature loaded as usual"
assert "fl1_mean" not in ds._usertemp, "because it is in the file"
# make sure the feature is not available anymore when deregistered
deregister_all()
assert "fl1_mean" not in ds, "deregistered features are hidden..."
assert "fl1_mean" in ds._events._features, "..but are still there"
def test_basic_feature_exists():
"""Basic test of a temporary feature"""
dclab.register_temporary_feature("my_special_feature")
assert dclab.dfn.feature_exists("my_special_feature")
def test_bad_feature_name():
"""Basic test of a temporary feature"""
with pytest.raises(ValueError):
dclab.register_temporary_feature("Peter-Pan")
def test_try_existing_feature_fails():
"""Basic test of a temporary feature"""
with pytest.raises(ValueError):
dclab.register_temporary_feature("deform")
