def test_get_feat_range_opt():
keys = ["area_um", "deform", "fl1_max"]
dicts = [example_data_dict(s, keys) for s in [10, 100, 12, 382]]
# add a fl2_max column with negative numbers
for dd in dicts:
lend = len(dd["deform"])
dd["fl2_max"] = np.linspace(-1, 5000, lend)
dd["userdef1"] = np.linspace(-2, -3, lend)
dd["userdef2"] = np.linspace(-1, 1, lend)
#dicts.append(otherdict)
anal = analysis.Analysis([dclab.new_dataset(d) for d in dicts])
assert anal.get_feat_range_opt(feature="deform") == (0, .2)
assert np.allclose(anal.get_feat_range_opt(feature="area_um"),
(0.000507009623987198, 0.9989715591819257))
assert np.allclose(anal.get_feat_range_opt(feature="fl1_max"),
(0.0036766675498647317, 0.9970937546290722))
assert np.allclose(anal.get_feat_range_opt(feature="fl1_max", scale="log"),
(0.0036766675498647317, 0.9970937546290722))
assert np.allclose(anal.get_feat_range_opt(feature="fl2_max"), (-1, 5000))
assert np.allclose(anal.get_feat_range_opt(feature="fl2_max", scale="log"),
(1, 5000))
assert np.allclose(anal.get_feat_range_opt(feature="userdef1"), (-3, -2))
assert np.allclose(
anal.get_feat_range_opt(feature="userdef1", scale="log"), (.1, 1))
assert np.allclose(anal.get_feat_range_opt(feature="userdef2"), (-1, 1))
assert np.allclose(
anal.get_feat_range_opt(feature="userdef2", scale="log"),
(0.051197602631569354, 1.0))
def test_axes_usable():
keys = ["area_um", "circ"]
dicts = [example_data_dict(s, keys=keys) for s in [10, 100, 12, 382]]
anal = analysis.Analysis([dclab.new_dataset(d) for d in dicts])
axes = anal.GetUsableAxes()
for ax in keys:
assert ax in axes
def test_data_size():
dicts = [example_data_dict(s) for s in [10, 100, 12, 382]]
anal = analysis.Analysis([dclab.new_dataset(d) for d in dicts])
minsize = anal.ForceSameDataSize()
assert minsize == 10
for mm in anal.measurements:
assert np.sum(mm._filter) == minsize
def test_get_config_value():
ddict = example_data_dict(size=8472)
ds1 = dclab.new_dataset(ddict)
ds2 = dclab.new_dataset(ddict)
ds1.config["filtering"]["deform max"] = .5
ds2.config["filtering"]["deform max"] = .2
ds1.config["filtering"]["deform min"] = .0
ds2.config["filtering"]["deform min"] = .0
anal = analysis.Analysis([ds1, ds2])
assert anal.get_config_value(section="filtering", key="deform min") == 0
try:
anal.get_config_value(section="filtering", key="deform max")
except ValueError:
pass
else:
raise ValueError("different values should raise error")
def test_basic():
ddict = example_data_dict(size=8472)
ds = dclab.new_dataset(ddict)
anal = analysis.Analysis([ds])
assert len(anal.measurements) == 1