Enable FutureWarnings/DeprecationWarnings as errors

python/cucim/pyproject.toml

@@ -129,6 +129,12 @@ testpaths = [
     "src",
     "tests",
 ]
+filterwarnings = [
+    "error::FutureWarning",
+    "error::DeprecationWarning",
+    # https://github.com/cupy/cupy/blob/main/cupy/testing/_helper.py#L56
+    "ignore:pkg_resources is deprecated as an API:DeprecationWarning",
+]
 
 [tool.ruff]
 # see: https://docs.astral.sh/ruff/rules/

python/cucim/src/cucim/clara/converter/tiff.py

@@ -73,11 +73,12 @@ def svs2tif(
     logger.info("      num_workers: %d", num_workers)
     logger.info("      compression: %s", compression)
     logger.info("  output filename: %s", output_filename)
-
+    compressionargs = None
     if compression is not None:
         # handles only jpeg or None (no compression)
         if compression.lower() == "jpeg":
-            compression = ("jpeg", 95)
+            compression = "jpeg"
+            compressionargs = {"level": 95}
         else:
             raise ValueError(
                 f"Unsupported compression: {compression}."
@@ -198,9 +199,10 @@ def svs2tif(
                         resolution=(
                             x_resolution // 2**level,
                             y_resolution // 2**level,
-                            resolution_unit,
                         ),
+                        resolutionunit=resolution_unit,
                         compression=compression,  # requires imagecodecs
+                        compressionargs=compressionargs,
                         subfiletype=subfiletype,
                     )
                 logger.info("Done.")

python/cucim/src/cucim/core/operations/morphology/_distance_transform.py

@@ -157,7 +157,7 @@ def distance_transform_edt(
         if len(unique_sampling) == 1:
             # In the isotropic case, can use the kernels without sample scaling
             # and just adjust the final distance accordingly.
-            scalar_sampling = float(unique_sampling)
+            scalar_sampling = float(unique_sampling.item())
             sampling = None
 
     if image.ndim == 3:

python/cucim/src/cucim/skimage/_vendored/_pearsonr.py

@@ -253,14 +253,14 @@ def pearsonr(x, y, *, disable_checks=False):
     # As explained in the docstring, the p-value can be computed as
     #     p = 2*dist.cdf(-abs(r))
     # where dist is the beta distribution on [-1, 1] with shape parameters
-    # a = b = n/2 - 1.  `special.btdtr` is the CDF for the beta distribution
+    # a = b = n/2 - 1.  `special.betainc` is the CDF for the beta distribution
     # on [0, 1].  To use it, we make the transformation  x = (r + 1)/2; the
     # shape parameters do not change.  Then -abs(r) used in `cdf(-abs(r))`
     # becomes x = (-abs(r) + 1)/2 = 0.5*(1 - abs(r)).  (r is cast to float64
-    # to avoid a TypeError raised by btdtr when r is higher precision.)
+    # to avoid a TypeError raised by betainc when r is higher precision.)
     ab = n / 2 - 1
-    # scalar valued, so use special.btdtr from SciPy, not CuPy
-    prob = 2 * special.btdtr(ab, ab, 0.5 * (1.0 - abs(r)))
+    # scalar valued, so use special.betainc from SciPy, not CuPy
+    prob = 2 * special.betainc(ab, ab, 0.5 * (1.0 - abs(r)))
     if disable_checks:
         # warn only based on output values to avoid overhead of host/device
         # synchronization needed for the disabled checks above.

python/cucim/src/cucim/skimage/exposure/exposure.py

@@ -370,7 +370,7 @@ def cumulative_distribution(image, nbins=256):
     >>> image = img_as_float(cp.array(data.camera()))
     >>> hi = exposure.histogram(image)
     >>> cdf = exposure.cumulative_distribution(image)
-    >>> cp.alltrue(cdf[0] == cp.cumsum(hi[0])/float(image.size))
+    >>> cp.all(cdf[0] == cp.cumsum(hi[0])/float(image.size))
     array(True)
     """
     hist, bin_centers = histogram(image, nbins)

python/cucim/src/cucim/skimage/transform/tests/test_warps.py

@@ -881,7 +881,7 @@ def test_linear_warp_polar(dtype):
     assert warped.dtype == _supported_float_type(dtype)
     profile = warped.mean(axis=0)
     peaks = cp.asnumpy(peak_local_max(profile))
-    assert np.alltrue([peak in radii for peak in peaks])
+    assert np.all([peak in radii for peak in peaks])
 
 
 @pytest.mark.parametrize("dtype", [cp.float16, cp.float32, cp.float64])
@@ -906,7 +906,7 @@ def test_log_warp_polar(dtype):
     peaks_coord = peak_local_max(profile)
     peaks_coord.sort(axis=0)
     gaps = cp.asnumpy(peaks_coord[1:] - peaks_coord[:-1])
-    assert np.alltrue([x >= 38 and x <= 40 for x in gaps])
+    assert np.all([x >= 38 and x <= 40 for x in gaps])
 
 
 def test_invalid_scaling_polar():

python/cucim/tests/util/gen_tiff.py

@@ -20,7 +20,7 @@
 import numpy as np
 from tifffile import TiffWriter
 
-COMPRESSION_MAP = {"jpeg": ("jpeg", 95), "deflate": "deflate", "raw": None}
+COMPRESSION_MAP = {"jpeg": "jpeg", "deflate": "deflate", "raw": None}
 
 
 class TiffGenerator:
@@ -57,8 +57,11 @@ def save_image(
             raise RuntimeError("'image_data' is neithor list or numpy.ndarray")
 
         compression = COMPRESSION_MAP.get(compression)
+        compressionargs = None
         if not compression:
-            compression = ("jpeg", 95)
+            compression = "jpeg"
+        if compression == "jpeg":
+            compressionargs = {"level": 95}
 
         # save as tif
         tiff_file_name = str(
@@ -84,6 +87,7 @@ def save_image(
                     photometric="RGB",
                     planarconfig="CONTIG",
                     compression=compression,  # requires imagecodecs
+                    compressionargs=compressionargs,
                     subfiletype=1 if level else 0,
                     resolution=level_resolution,
                     resolutionunit=resolutionunit,