def test_get_min_max_plot():
"""See #22"""
path = pathlib.Path(__file__).parent / "data" / "calibration_beads_47.rtdc"
# initiate the pipeline
pl = pipeline.Pipeline()
slot_id = pl.add_slot(path=path)
# add a filter
filt_id = pl.add_filter()
filt = pl.get_filter(filt_id)
# get the current min/max values
amin, amax = pl.get_min_max("area_um")
# modify the filter
filt.boxdict["area_um"] = {"start": amin,
"end": (amin + amax)/2,
"active": True}
# make the filter active in the filter ray
pl.set_element_active(slot_id, filt_id)
# add a plot
plot_id = pl.add_plot()
pl.set_element_active(slot_id, plot_id)
# get the new min/max values
amin2, amax2 = pl.get_min_max("area_um", plot_id=plot_id)
assert amin == amin2
assert amax2 <= (amin + amax) / 2
def test_apply_filter_ray():
path = pathlib.Path(__file__).parent / "data" / "calibration_beads_47.rtdc"
# initiate the pipeline
pl = pipeline.Pipeline()
slot_id = pl.add_slot(path=path)
# add a filter
filt_id1 = pl.add_filter()
filt1 = pl.get_filter(filt_id1)
amin, amax = pl.get_min_max("area_um")
filt1.boxdict["area_um"] = {"start": amin,
"end": (amin + amax)/2,
"active": True}
pl.set_element_active(slot_id, filt_id1)
# and another one
filt_id2 = pl.add_filter()
filt2 = pl.get_filter(filt_id2)
dmin, dmax = pl.get_min_max("deform")
filt2.boxdict["deform"] = {"start": dmin,
"end": (dmin + dmax)/2,
"active": True}
pl.set_element_active(slot_id, filt_id2)
ds_ref = pl.get_dataset(0) # this will run through the filter ray
ds0 = dclab.new_dataset(path)
ds_ext = pl.apply_filter_ray(ds0, slot_id) # this should do the same thing
for feat in ds_ref.features_loaded:
if dclab.dfn.scalar_feature_exists(feat):
assert np.allclose(ds_ref[feat], ds_ext[feat]), feat
assert np.all(ds_ref.filter.all == ds_ext.filter.all)
def test_get_min_max_inf():
# generate fake dataset
path = pathlib.Path(__file__).parent / "data" / "calibration_beads_47.rtdc"
with dclab.new_dataset(path) as ds:
config = copy.deepcopy(ds.config)
tmp = tempfile.mktemp(".rtdc", prefix="example_filter_inf_")
ds2 = dclab.new_dataset({"deform": np.linspace(0, .01, 100),
"area_um": np.linspace(20, 200, 100),
"area_ratio": np.linspace(1, 1.1, 100)
})
ds2.config.update(config)
ds2["area_ratio"][0] = np.inf
ds2["area_ratio"][1] = np.nan
ds2.export.hdf5(tmp, features=["area_um", "deform", "area_ratio"])
# initiate the pipeline
pl = pipeline.Pipeline()
pl.add_slot(path=tmp)
pl.add_filter()
# get the current min/max values
amin, amax = pl.get_min_max("area_ratio")
assert amin == ds2["area_ratio"][2]
assert amax == 1.1
try:
pathlib.Path(tmp).unlink()
except BaseException:
pass
def make_pipeline(nslots=2, nfilters=3, nplots=1, paths=None):
"""Create a sample pipeline
- Each filter has a different box filter
- Every other filter/plot will be set active
"""
if paths is None:
here = pathlib.Path(__file__).parent
paths = [here / "data" / "calibration_beads_47.rtdc"]
# circular iterator over paths
pathcycle = itertools.cycle(paths)
# initiate the pipeline
pl = pipeline.Pipeline()
for _ in range(nslots):
pl.add_slot(path=next(pathcycle))
feats = pl.get_features(scalar=True)
for jj in range(nfilters):
filt_id = pl.add_filter()
filt = pl.get_filter(filt_id)
feat = feats[jj]
fmin, fmax = pl.get_min_max(feat)
# modify the filter
filt.boxdict[filt_id] = {
"start": fmin,
"end": (fmin + fmax) / 2,
"active": True
}
# set every other filter active
active = True
for slot_id in pl.slot_ids:
for filt_id in pl.filter_ids:
pl.set_element_active(slot_id, filt_id, active)
active = not active
# add a plot
for _ in range(nplots):
pl.add_plot()
# set every other plot active
active = True
for slot_id in pl.slot_ids:
for plot_id in pl.plot_ids:
pl.set_element_active(slot_id, plot_id, active)
active = not active
return pl