ci: update pytest

pytest changed its warning/exception order (see pytest-dev/pytest#9036). This updates our tests to fit the new spec.

For kymographs, this meant explicitly passing a motion blur parameter such that we don't issue that error instead of what we are looking for.
For the dwelltime analysis, we have to make sure we don't get any of the deprecation warnings so that we can see the warning we are testing for.
For touchdown, we have to explicitly silence the covariance matrix is undefined error (which actually already tells us that the problem will be non-identifiable).

lumicks/pylake/force_calibration/tests/test_touchdown.py

@@ -145,6 +145,7 @@ def test_exp_sine_fits(decay, amplitude, frequency, phase_shift):
     np.testing.assert_allclose(par, frequency, rtol=1e-6)
 
 
+@pytest.mark.filterwarnings("ignore:Covariance of the parameters could not be estimated")
 def test_insufficient_data(mack_parameters):
     stage_positions, simulation = simulate_touchdown(102.5, 103.5, 0.01, mack_parameters)
 

lumicks/pylake/kymotracker/tests/conftest.py

@@ -66,7 +66,7 @@ def two_gaussians_1d():
 
 @pytest.fixture
 def blank_kymo():
-    return generate_kymo(
+    kymo = generate_kymo(
         "",
         np.ones((1, 10)),
         pixel_size_nm=1000,
@@ -75,6 +75,8 @@ def blank_kymo():
         samples_per_pixel=1,
         line_padding=0,
     )
+    kymo._motion_blur_constant = 0
+    return kymo
 
 
 @pytest.fixture

lumicks/pylake/kymotracker/tests/test_kymotrack.py

@@ -867,7 +867,9 @@ def test_fit_binding_times_nonzero(blank_kymo, blank_kymo_track_args):
         r"analysis. If you wish to not see this warning, filter the tracks with "
         r"`lk.filter_tracks` with a minimum length of 2 samples.",
     ):
-        dwelltime_model = tracks.fit_binding_times(1)
+        dwelltime_model = tracks.fit_binding_times(
+            n_components=1, observed_minimum=True, discrete_model=False
+        )
         np.testing.assert_equal(dwelltime_model.dwelltimes, [4, 4, 4, 4])
         np.testing.assert_equal(dwelltime_model._observation_limits[0], 4)
         np.testing.assert_allclose(dwelltime_model.lifetimes[0], [0.4])
@@ -1145,6 +1147,7 @@ def test_kymotrack_group_diffusion_filter():
     image = np.random.randint(0, 20, size=(10, 10, 3))
     kwargs = dict(line_time_seconds=10e-3, start=np.int64(20e9), pixel_size_um=0.05, name="test")
     kymo = _kymo_from_array(image, "rgb", **kwargs)
+    kymo._motion_blur_constant = 0
 
     base_coordinates = (
         np.arange(1, 10),
@@ -1408,7 +1411,12 @@ def test_ensemble_diffusion_different_attributes():
     line_times = (1, 0.5)
     pixel_sizes = (0.1, 0.05)
     kwargs = [{"line_time_seconds": t, "pixel_size_um": s} for t in line_times for s in pixel_sizes]
-    kymos = [_kymo_from_array(np.random.poisson(5, (25, 25, 3)), "rgb", **k) for k in kwargs]
+    kymos = []
+    for k in kwargs:
+        kymo = _kymo_from_array(np.random.poisson(5, (25, 25, 3)), "rgb", **k)
+        kymo._motion_blur_constant = 0
+        kymos.append(kymo)
+
     tracks = [
         KymoTrackGroup(
             [KymoTrack(np.arange(5), np.random.uniform(3, 5, 5), k, "green", 0) for _ in range(5)]