Merge pull request #655 from bioimage-io/add_tests

add more tests

.github/workflows/build.yml

@@ -44,7 +44,7 @@ jobs:
       - name: install additinal deps for a full pyright check
         run: pip install json_schema_for_humans
         if: matrix.is-dev-version
-      - run: ruff check **/*.py # ignore notebooks for now
+      - run: ruff check
         if: matrix.is-dev-version
       - run: pyright --version
         if: matrix.is-dev-version

bioimageio/spec/model/v0_4.py

@@ -520,12 +520,14 @@ class ProcessingDescrBase(NodeWithExplicitlySetFields):
 
 
 class BinarizeKwargs(ProcessingKwargs):
+    """key word arguments for `BinarizeDescr`"""
+
     threshold: float
     """The fixed threshold"""
 
 
 class BinarizeDescr(ProcessingDescrBase):
-    """BinarizeDescr the tensor with a fixed threshold.
+    """BinarizeDescr the tensor with a fixed `BinarizeKwargs.threshold`.
     Values above the threshold will be set to one, values below the threshold to zero.
     """
 
@@ -534,21 +536,29 @@ class BinarizeDescr(ProcessingDescrBase):
 
 
 class ClipKwargs(ProcessingKwargs):
+    """key word arguments for `ClipDescr`"""
+
     min: float
     """minimum value for clipping"""
     max: float
     """maximum value for clipping"""
 
 
 class ClipDescr(ProcessingDescrBase):
-    """Set tensor values below min to min and above max to max."""
+    """Clip tensor values to a range.
+
+    Set tensor values below `ClipKwargs.min` to `ClipKwargs.min`
+    and above `ClipKwargs.max` to `ClipKwargs.max`.
+    """
 
     name: Literal["clip"] = "clip"
 
     kwargs: ClipKwargs
 
 
 class ScaleLinearKwargs(ProcessingKwargs):
+    """key word arguments for `ScaleLinearDescr`"""
+
     axes: Annotated[Optional[AxesInCZYX], Field(examples=["xy"])] = None
     """The subset of axes to scale jointly.
     For example xy to scale the two image axes for 2d data jointly."""
@@ -597,6 +607,8 @@ def kwargs(self) -> ProcessingKwargs:
 
 
 class ZeroMeanUnitVarianceKwargs(ProcessingKwargs):
+    """key word arguments for `ZeroMeanUnitVarianceDescr`"""
+
     mode: Literal["fixed", "per_dataset", "per_sample"] = "fixed"
     """Mode for computing mean and variance.
     |     mode    |             description              |
@@ -642,6 +654,15 @@ class ZeroMeanUnitVarianceDescr(ProcessingDescrBase):
 
 
 class ScaleRangeKwargs(ProcessingKwargs):
+    """key word arguments for `ScaleRangeDescr`
+
+    For `min_percentile`=0.0 (the default) and `max_percentile`=100 (the default)
+    this processing step normalizes data to the [0, 1] intervall.
+    For other percentiles the normalized values will partially be outside the [0, 1]
+    intervall. Use `ScaleRange` followed by `ClipDescr` if you want to limit the
+    normalized values to a range.
+    """
+
     mode: Literal["per_dataset", "per_sample"]
     """Mode for computing percentiles.
     |     mode    |             description              |
@@ -654,10 +675,10 @@ class ScaleRangeKwargs(ProcessingKwargs):
     For example xy to normalize the two image axes for 2d data jointly."""
 
     min_percentile: Annotated[Union[int, float], Interval(ge=0, lt=100)] = 0.0
-    """The lower percentile used for normalization."""
+    """The lower percentile used to determine the value to align with zero."""
 
     max_percentile: Annotated[Union[int, float], Interval(gt=1, le=100)] = 100.0
-    """The upper percentile used for normalization
+    """The upper percentile used to determine the value to align with one.
     Has to be bigger than `min_percentile`.
     The range is 1 to 100 instead of 0 to 100 to avoid mistakenly
     accepting percentiles specified in the range 0.0 to 1.0."""
@@ -691,6 +712,8 @@ class ScaleRangeDescr(ProcessingDescrBase):
 
 
 class ScaleMeanVarianceKwargs(ProcessingKwargs):
+    """key word arguments for `ScaleMeanVarianceDescr`"""
+
     mode: Literal["per_dataset", "per_sample"]
     """Mode for computing mean and variance.
     |     mode    |             description              |

bioimageio/spec/model/v0_5.py

@@ -789,11 +789,15 @@ class ProcessingDescrBase(NodeWithExplicitlySetFields, ABC):
 
 
 class BinarizeKwargs(ProcessingKwargs):
+    """key word arguments for `BinarizeDescr`"""
+
     threshold: float
     """The fixed threshold"""
 
 
 class BinarizeAlongAxisKwargs(ProcessingKwargs):
+    """key word arguments for `BinarizeDescr`"""
+
     threshold: NotEmpty[List[float]]
     """The fixed threshold values along `axis`"""
 
@@ -803,7 +807,9 @@ class BinarizeAlongAxisKwargs(ProcessingKwargs):
 
 class BinarizeDescr(ProcessingDescrBase):
     """Binarize the tensor with a fixed threshold.
-    Values above the threshold will be set to one, values below the threshold to zero.
+
+    Values above `BinarizeKwargs.threshold`/`BinarizeAlongAxisKwargs.threshold`
+    will be set to one, values below the threshold to zero.
     """
 
     id: Literal["binarize"] = "binarize"
@@ -818,6 +824,8 @@ class ClipDescr(ProcessingDescrBase):
 
 
 class EnsureDtypeKwargs(ProcessingKwargs):
+    """key word arguments for `EnsureDtypeDescr`"""
+
     dtype: Literal[
         "float32",
         "float64",
@@ -834,11 +842,15 @@ class EnsureDtypeKwargs(ProcessingKwargs):
 
 
 class EnsureDtypeDescr(ProcessingDescrBase):
+    """cast the tensor data type to `EnsureDtypeKwargs.dtype` (if not matching)"""
+
     id: Literal["ensure_dtype"] = "ensure_dtype"
     kwargs: EnsureDtypeKwargs
 
 
 class ScaleLinearKwargs(ProcessingKwargs):
+    """key word arguments for `ScaleLinearDescr`"""
+
     gain: float = 1.0
     """multiplicative factor"""
 
@@ -857,6 +869,8 @@ def _validate(self) -> Self:
 
 
 class ScaleLinearAlongAxisKwargs(ProcessingKwargs):
+    """key word arguments for `ScaleLinearDescr`"""
+
     axis: Annotated[NonBatchAxisId, Field(examples=["channel"])]
     """The axis of of gains/offsets values."""
 
@@ -912,8 +926,7 @@ def kwargs(self) -> ProcessingKwargs:
 
 
 class FixedZeroMeanUnitVarianceKwargs(ProcessingKwargs):
-    """Normalize with fixed, precomputed values for mean and variance.
-    See `zero_mean_unit_variance` for data dependent normalization."""
+    """key word arguments for `FixedZeroMeanUnitVarianceDescr`"""
 
     mean: float
     """The mean value to normalize with."""
@@ -923,8 +936,7 @@ class FixedZeroMeanUnitVarianceKwargs(ProcessingKwargs):
 
 
 class FixedZeroMeanUnitVarianceAlongAxisKwargs(ProcessingKwargs):
-    """Normalize with fixed, precomputed values for mean and variance.
-    See `zero_mean_unit_variance` for data dependent normalization."""
+    """key word arguments for `FixedZeroMeanUnitVarianceDescr`"""
 
     mean: NotEmpty[List[float]]
     """The mean value(s) to normalize with."""
@@ -949,7 +961,13 @@ def _mean_and_std_match(self) -> Self:
 
 
 class FixedZeroMeanUnitVarianceDescr(ProcessingDescrBase):
-    """Subtract a given mean and divide by a given variance."""
+    """Subtract a given mean and divide by the standard deviation.
+
+    Normalize with fixed, precomputed values for
+    `FixedZeroMeanUnitVarianceKwargs.mean` and `FixedZeroMeanUnitVarianceKwargs.std`
+    Use `FixedZeroMeanUnitVarianceAlongAxisKwargs` for independent scaling along given
+    axes.
+    """
 
     id: Literal["fixed_zero_mean_unit_variance"] = "fixed_zero_mean_unit_variance"
     kwargs: Union[
@@ -958,6 +976,8 @@ class FixedZeroMeanUnitVarianceDescr(ProcessingDescrBase):
 
 
 class ZeroMeanUnitVarianceKwargs(ProcessingKwargs):
+    """key word arguments for `ZeroMeanUnitVarianceDescr`"""
+
     axes: Annotated[
         Optional[Sequence[AxisId]], Field(examples=[("batch", "x", "y")])
     ] = None
@@ -979,6 +999,15 @@ class ZeroMeanUnitVarianceDescr(ProcessingDescrBase):
 
 
 class ScaleRangeKwargs(ProcessingKwargs):
+    """key word arguments for `ScaleRangeDescr`
+
+    For `min_percentile`=0.0 (the default) and `max_percentile`=100 (the default)
+    this processing step normalizes data to the [0, 1] intervall.
+    For other percentiles the normalized values will partially be outside the [0, 1]
+    intervall. Use `ScaleRange` followed by `ClipDescr` if you want to limit the
+    normalized values to a range.
+    """
+
     axes: Annotated[
         Optional[Sequence[AxisId]], Field(examples=[("batch", "x", "y")])
     ] = None
@@ -989,10 +1018,10 @@ class ScaleRangeKwargs(ProcessingKwargs):
     Default: Scale all axes jointly."""
 
     min_percentile: Annotated[float, Interval(ge=0, lt=100)] = 0.0
-    """The lower percentile used for normalization."""
+    """The lower percentile used to determine the value to align with zero."""
 
     max_percentile: Annotated[float, Interval(gt=1, le=100)] = 100.0
-    """The upper percentile used for normalization
+    """The upper percentile used to determine the value to align with one.
     Has to be bigger than `min_percentile`.
     The range is 1 to 100 instead of 0 to 100 to avoid mistakenly
     accepting percentiles specified in the range 0.0 to 1.0."""
@@ -1023,8 +1052,7 @@ class ScaleRangeDescr(ProcessingDescrBase):
 
 
 class ScaleMeanVarianceKwargs(ProcessingKwargs):
-    """Scale a tensor's data distribution to match another tensor's mean/std.
-    `out  = (tensor - mean) / (std + eps) * (ref_std + eps) + ref_mean.`"""
+    """key word arguments for `ScaleMeanVarianceKwargs`"""
 
     reference_tensor: TensorId
     """Name of tensor to match."""
@@ -1044,7 +1072,9 @@ class ScaleMeanVarianceKwargs(ProcessingKwargs):
 
 
 class ScaleMeanVarianceDescr(ProcessingDescrBase):
-    """Scale the tensor s.t. its mean and variance match a reference tensor."""
+    """Scale a tensor's data distribution to match another tensor's mean/std.
+    `out  = (tensor - mean) / (std + eps) * (ref_std + eps) + ref_mean.`
+    """
 
     id: Literal["scale_mean_variance"] = "scale_mean_variance"
     kwargs: ScaleMeanVarianceKwargs

pyproject.toml

@@ -49,8 +49,11 @@ testpaths = ["bioimageio/spec", "tests", "scripts"]
 [tool.ruff]
 line-length = 88
 target-version = "py312"
-include = ["*.py", "*.pyi", "**/pyproject.toml", "*.ipynb"]
-exclude = ["scripts/pdoc/original.py", "scripts/pdoc/patched.py"]
+exclude = [
+    "bioimageio_cache",
+    "scripts/pdoc/original.py",
+    "scripts/pdoc/patched.py",
+]
 
 [tool.coverage.report]
 exclude_also = ["if TYPE_CHECKING:", "assert_never\\("]

scripts/generate_version_submodule_imports.py

@@ -164,7 +164,7 @@ def process(info: Info, check: bool):
                 )
             )
     else:
-        with package_init.open("w", newline="\n") as f:
+        with package_init.open("w", newline="\n", encoding="utf-8") as f:
             _ = f.write(updated)
 
 

scripts/interactive_docs/__main__.py

@@ -1,13 +1,13 @@
-from typing_extensions import assert_never
 import sys
 from pathlib import Path
 
+from typing_extensions import assert_never
+
 from bioimageio.spec import SpecificResourceDescr
 from scripts.generate_json_schemas import MAJOR_MINOR_VERSION
 
 from . import generate_docs
 
-
 html_result: "str | Exception" = generate_docs(
     raw_type=SpecificResourceDescr, root_path=["Delivery"]
 )
@@ -21,7 +21,7 @@
     )
     docs_output_path.parent.mkdir(parents=True, exist_ok=True)
     print(f"[INFO] Writing interactive docs to {docs_output_path}")
-    with open(docs_output_path, "w") as f:
+    with open(docs_output_path, "w", encoding="utf-8") as f:
         _ = f.write(html_result)
         print(f"Wrote {_} bytes to {docs_output_path}")
 else:

scripts/interactive_docs/hint.py

@@ -716,7 +716,11 @@ def do_parse(
             return Unrecognized(raw_hint)
         type_args: Tuple[Type[Any], Type[Any]] = getattr(raw_hint, "__args__")
         key_type = type_args[0]
-        if key_type != str and key_type != int and key_type != typing.Union[int, str]:
+        if (
+            key_type is not str
+            and key_type is not int
+            and key_type != typing.Union[int, str]
+        ):
             return ParsingError(
                 f"Mappings with keys that are not ints or strings is not supported yet: {raw_hint}"
             )
@@ -992,15 +996,15 @@ def to_type_widget(
 
     def get_example(self) -> Example:
         hint_type = self.hint_type
-        if hint_type == int:
+        if hint_type is int:
             return Example(123456)
-        if hint_type == float:
+        if hint_type is float:
             return Example(3.14)
-        if hint_type == bool:
+        if hint_type is bool:
             return Example(True)
-        if hint_type == str:
+        if hint_type is str:
             return Example("some free-format string")
-        if hint_type == type(None):
+        if hint_type is type(None):
             return Example(None)
         return Example("--- NO EXAMPLES PROVIDED ---")
 

setup.py

@@ -52,6 +52,7 @@
             test_extras := [
                 "deepdiff",
                 "filelock",  # for session fixtures due to pytest-xdist
+                "h5py",
                 "lxml",
                 "psutil",  # parallel pytest with '-n auto'
                 "pytest-cov",

tests/test_bioimageio_collection.py

@@ -87,6 +87,7 @@ def test_rdf(
         "10.5281/zenodo.5764892/1.1",  # affable-shark/1.1
         "ambitious-sloth/1.2",
         "breezy-handbag/1",
+        "faithful-chicken/1",
         "ilastik/ilastik/1",
         "uplifting-ice-cream/1",
     ],