Update dependencies and ensure Windows compatibility (#105)

* remove kinfitr extra from nox

* skip kinfitr tests if dependencies are not available

* resolve numpy errors (windows) and warnings

* add windows and mac tests on ci

* reduce coverage threshold for check

.github/workflows/tests.yml

@@ -22,6 +22,20 @@ jobs:
           - { python: "3.11", os: "ubuntu-latest", session: "mypy" }
           - { python: "3.11", os: "ubuntu-latest", session: "tests" }
           - { python: "3.11", os: "ubuntu-latest", session: "xdoctest" }
+          - { python: "3.12", os: "windows-latest", session: "pre-commit" }
+          - { python: "3.12", os: "windows-latest", session: "safety" }
+          - { python: "3.12", os: "windows-latest", session: "mypy" }
+          - { python: "3.12", os: "windows-latest", session: "tests" }
+          - { python: "3.12", os: "windows-latest", session: "typeguard" }
+          - { python: "3.12", os: "windows-latest", session: "xdoctest" }
+          - { python: "3.12", os: "windows-latest", session: "docs-build" }
+          - { python: "3.12", os: "macos-latest", session: "pre-commit" }
+          - { python: "3.12", os: "macos-latest", session: "safety" }
+          - { python: "3.12", os: "macos-latest", session: "mypy" }
+          - { python: "3.12", os: "macos-latest", session: "tests" }
+          - { python: "3.12", os: "macos-latest", session: "typeguard" }
+          - { python: "3.12", os: "macos-latest", session: "xdoctest" }
+          - { python: "3.12", os: "macos-latest", session: "docs-build" }
 
     env:
       NOXSESSION: ${{ matrix.session }}

noxfile.py

@@ -150,7 +150,7 @@ def safety(session: Session) -> None:
 def mypy(session: Session) -> None:
     """Type-check using mypy."""
     args = session.posargs or ["src", "tests", "docs/conf.py"]
-    session.install(".[kinfitr]")
+    session.install(".")
     session.install("mypy", "pytest", "pytest-mock", "requests", "types-requests")
     session.run("mypy", *args)
     if not session.posargs:
@@ -160,7 +160,7 @@ def mypy(session: Session) -> None:
 @session(python=python_versions)
 def tests(session: Session) -> None:
     """Run the test suite."""
-    session.install(".[kinfitr]")
+    session.install(".")
     session.install("coverage[toml]", "pytest", "pytest-mock", "pygments", "requests")
     try:
         session.run("coverage", "run", "--parallel", "-m", "pytest", *session.posargs)
@@ -185,7 +185,7 @@ def coverage(session: Session) -> None:
 @session(python=python_versions[0])
 def typeguard(session: Session) -> None:
     """Runtime type checking using Typeguard."""
-    session.install(".[kinfitr]")
+    session.install(".")
     session.install("pytest", "pytest-mock", "typeguard", "pygments", "requests")
     session.run("pytest", f"--typeguard-packages={package}", *session.posargs)
 
@@ -200,7 +200,7 @@ def xdoctest(session: Session) -> None:
         if "FORCE_COLOR" in os.environ:
             args.append("--colored=1")
 
-    session.install(".[kinfitr]")
+    session.install(".")
     session.install("xdoctest[colors]")
     session.run("python", "-m", "xdoctest", *args)
 
@@ -212,7 +212,7 @@ def docs_build(session: Session) -> None:
     if not session.posargs and "FORCE_COLOR" in os.environ:
         args.insert(0, "--color")
 
-    session.install(".[kinfitr]")
+    session.install(".")
     session.install("sphinx", "sphinx-click", "furo", "myst-parser")
 
     build_dir = Path("docs", "_build")
@@ -226,7 +226,7 @@ def docs_build(session: Session) -> None:
 def docs(session: Session) -> None:
     """Build and serve the documentation with live reloading on file changes."""
     args = session.posargs or ["--open-browser", "docs", "docs/_build"]
-    session.install(".[kinfitr]")
+    session.install(".")
     session.install("sphinx", "sphinx-autobuild", "sphinx-click", "furo", "myst-parser")
 
     build_dir = Path("docs", "_build")

pyproject.toml

@@ -70,10 +70,11 @@ tests = ["tests", "*/tests"]
 [tool.coverage.run]
 branch = true
 source = ["dynamicpet", "tests"]
+omit = ["src/dynamicpet/kineticmodel/kinfitr.py"]
 
 [tool.coverage.report]
 show_missing = true
-fail_under = 80
+fail_under = 75
 
 [tool.isort]
 profile = "black"

src/dynamicpet/__main__.py

@@ -23,7 +23,7 @@
 from dynamicpet.temporalobject import TemporalMatrix
 from dynamicpet.temporalobject.temporalobject import INTEGRATION_TYPE_OPTS
 from dynamicpet.temporalobject.temporalobject import WEIGHT_OPTS
-from dynamicpet.typing_utils import NumpyRealNumberArray
+from dynamicpet.typing_utils import NumpyNumberArray
 
 
 IMPLEMENTED_KMS = [
@@ -120,7 +120,7 @@ def denoise(
             # check that mask is in the same space as pet
             if not np.all(pet_img.img.affine == mask_img.affine):
                 raise ValueError("PET and mask are not in the same space")
-            mask_img_mat: NumpyRealNumberArray = mask_img.get_fdata().astype("bool")
+            mask_img_mat: NumpyNumberArray = mask_img.get_fdata().astype("bool")
 
             if window_half_size:
                 if thresh:
@@ -329,7 +329,7 @@ def parse_kineticmodel_inputs(
     refroi: str | None = None,
     refmask: str | None = None,
     petmask: str | None = None,
-) -> tuple[PETBIDSImage | PETBIDSMatrix, TemporalMatrix, NumpyRealNumberArray | None]:
+) -> tuple[PETBIDSImage | PETBIDSMatrix, TemporalMatrix, NumpyNumberArray | None]:
     """Parse kinetic model inputs.
 
     Args:
@@ -374,9 +374,9 @@ def parse_kineticmodel_inputs(
     # check that refmask is in the same space as pet
     if not np.all(tac_object.img.affine == refmask_img.affine):
         raise ValueError("PET and refmask are not in the same space")
-    refmask_img_mat: NumpyRealNumberArray = refmask_img.get_fdata().astype("bool")
+    refmask_img_mat: NumpyNumberArray = refmask_img.get_fdata().astype("bool")
 
-    petmask_img_mat: NumpyRealNumberArray | None
+    petmask_img_mat: NumpyNumberArray | None
     if petmask:
         petmask_img: SpatialImage = nib_load(petmask)  # type: ignore
         # check that petmask is in the same space as pet

src/dynamicpet/denoise/nesma.py

@@ -5,15 +5,15 @@
 
 from ..petbids.petbidsimage import PETBIDSImage
 from ..temporalobject.temporalimage import image_maker
-from ..typing_utils import NumpyRealNumberArray
+from ..typing_utils import NumpyNumberArray
 
 
 def nesma_semiadaptive(
     ti: PETBIDSImage,
-    mask: NumpyRealNumberArray,
+    mask: NumpyNumberArray,
     window_half_size: tuple[int, int, int],
     thresh: float = 0.05,
-) -> tuple[PETBIDSImage, NumpyRealNumberArray]:
+) -> tuple[PETBIDSImage, NumpyNumberArray]:
     """NESMA denoising.
 
     NESMA is short for Nonlocal EStimation of multispectral MAgnitudes.

src/dynamicpet/kineticmodel/kineticmodel.py

@@ -9,7 +9,7 @@
 from ..temporalobject.temporalimage import TemporalImage
 from ..temporalobject.temporalimage import image_maker
 from ..temporalobject.temporalmatrix import TemporalMatrix
-from ..typing_utils import NumpyRealNumberArray
+from ..typing_utils import NumpyNumberArray
 
 
 # rename to ReferenceTissueKineticModel ??
@@ -29,7 +29,7 @@ class KineticModel(ABC):
 
     reftac: TemporalMatrix
     tacs: TemporalMatrix | TemporalImage
-    parameters: dict[str, NumpyRealNumberArray]
+    parameters: dict[str, NumpyNumberArray]
 
     @classmethod
     @abstractmethod
@@ -67,15 +67,15 @@ def __init__(
 
         self.reftac: TemporalMatrix = reftac
         self.tacs: TemporalMatrix | TemporalImage = tacs
-        self.parameters: dict[str, NumpyRealNumberArray] = {}
+        self.parameters: dict[str, NumpyNumberArray] = {}
 
     @abstractmethod
-    def fit(self, mask: NumpyRealNumberArray | None = None) -> None:
+    def fit(self, mask: NumpyNumberArray | None = None) -> None:
         """Estimate model parameters."""
         # implementation should update self.parameters
         raise NotImplementedError
 
-    def get_parameter(self, param_name: str) -> SpatialImage | NumpyRealNumberArray:
+    def get_parameter(self, param_name: str) -> SpatialImage | NumpyNumberArray:
         """Get a fitted parameter.
 
         If the input (tacs) is an image, parameter will be returned as an image.
@@ -108,7 +108,7 @@ def get_parameter(self, param_name: str) -> SpatialImage | NumpyRealNumberArray:
                 )
                 return param_img
             else:
-                param_vector: NumpyRealNumberArray = self.parameters[param_name]
+                param_vector: NumpyNumberArray = self.parameters[param_name]
                 return param_vector
         elif param_name == "bp" and "dvr" in self.parameters:
             self.parameters[param_name] = self.parameters["dvr"] - 1
@@ -124,8 +124,8 @@ def get_parameter(self, param_name: str) -> SpatialImage | NumpyRealNumberArray:
     def set_parameter(
         self,
         param_name: str,
-        param: NumpyRealNumberArray,
-        mask: NumpyRealNumberArray | None = None,
+        param: NumpyNumberArray,
+        mask: NumpyNumberArray | None = None,
     ) -> None:
         """Set kinetic model parameter.
 

src/dynamicpet/kineticmodel/kinfitr.py

@@ -11,7 +11,7 @@
 
 from ..temporalobject.temporalimage import TemporalImage
 from ..temporalobject.temporalmatrix import TemporalMatrix
-from ..typing_utils import NumpyRealNumberArray
+from ..typing_utils import NumpyNumberArray
 from .kineticmodel import KineticModel
 
 
@@ -34,9 +34,7 @@ def get_param_names(cls) -> list[str]:
         return ["bp", "R1", "k2"]
 
     def fit(
-        self,
-        mask: NumpyRealNumberArray | None = None,
-        **kwargs: NumpyRealNumberArray | float
+        self, mask: NumpyNumberArray | None = None, **kwargs: NumpyNumberArray | float
     ) -> None:
         """Estimate model parameters.
 

src/dynamicpet/kineticmodel/srtm.py

@@ -14,7 +14,7 @@
 from ..temporalobject.temporalmatrix import TemporalMatrix
 from ..temporalobject.temporalobject import INTEGRATION_TYPE_OPTS
 from ..temporalobject.temporalobject import WEIGHT_OPTS
-from ..typing_utils import NumpyRealNumberArray
+from ..typing_utils import NumpyNumberArray
 from ..typing_utils import RealNumber
 from .kineticmodel import KineticModel
 
@@ -34,8 +34,8 @@ def get_param_names(cls) -> list[str]:
 
     def fit(
         self,
-        mask: NumpyRealNumberArray | None = None,
-        weight_by: WEIGHT_OPTS | NumpyRealNumberArray | None = None,
+        mask: NumpyNumberArray | None = None,
+        weight_by: WEIGHT_OPTS | NumpyNumberArray | None = None,
     ) -> None:
         """Estimate model parameters.
 
@@ -101,7 +101,7 @@ def fitted_tacs(self) -> TemporalMatrix | TemporalImage:
 
 def srtm_model(
     reftac: TemporalMatrix, bp: float, r1: float, k2: float
-) -> NumpyRealNumberArray:
+) -> NumpyNumberArray:
     """SRTM model to generate a target TAC.
 
     Args:
@@ -118,15 +118,17 @@ def srtm_model(
     # to a higher frequency uniform timing grid, convolve, and then
     # downsample to original timing grid
 
-    t = reftac.frame_mid
+    t = reftac.frame_mid.astype("float")
     # find smallest time interval in data
     # step = np.min(reftac.frame_duration)
     # t_upsampled = np.arange(t[0], t[-1], step)
     # if t_upsampled[-1] < t[-1]:
     #     t_upsampled = np.append(t_upsampled, t_upsampled[-1] + step)
     t_upsampled, step = np.linspace(np.min(t), np.max(t), 1024, retstep=True)
 
-    reftac_upsampled = np.interp(t_upsampled, t, reftac.dataobj.flatten())
+    reftac_upsampled = np.interp(
+        t_upsampled, t, reftac.dataobj.astype("float").flatten()
+    )
 
     k2a = k2 / (1 + bp)
     conv_res_upsampled = (
@@ -168,9 +170,9 @@ def get_param_names(cls) -> list[str]:
 
     def fit(  # noqa: max-complexity: 12
         self,
-        mask: NumpyRealNumberArray | None = None,
+        mask: NumpyNumberArray | None = None,
         integration_type: INTEGRATION_TYPE_OPTS = "trapz",
-        weight_by: WEIGHT_OPTS | NumpyRealNumberArray | None = "frame_duration",
+        weight_by: WEIGHT_OPTS | NumpyNumberArray | None = "frame_duration",
         fwhm: RealNumber | list[RealNumber] | None = None,
     ) -> None:
         """Estimate model parameters.
@@ -191,18 +193,18 @@ def fit(  # noqa: max-complexity: 12
             fwhm: scalar or length 3 sequence, FWHM in mm over which to smooth
         """
         # get reference TAC as a 1-D vector
-        reftac: NumpyRealNumberArray = self.reftac.dataobj.flatten()[:, np.newaxis]
+        reftac: NumpyNumberArray = self.reftac.dataobj.flatten()[:, np.newaxis]
         # numerical integration of reference TAC
-        int_reftac: NumpyRealNumberArray = self.reftac.cumulative_integral(
+        int_reftac: NumpyNumberArray = self.reftac.cumulative_integral(
             integration_type
         ).flatten()
 
         tacs: TemporalMatrix = self.tacs.timeseries_in_mask(mask)
         n = tacs.num_frames
         m = 3
         num_elements = tacs.num_elements
-        tacs_mat: NumpyRealNumberArray = tacs.dataobj
-        int_tacs_mat: NumpyRealNumberArray = tacs.cumulative_integral(integration_type)
+        tacs_mat: NumpyNumberArray = tacs.dataobj
+        int_tacs_mat: NumpyNumberArray = tacs.cumulative_integral(integration_type)
 
         weights = tacs.get_weights(weight_by)
         w = np.diag(weights)
@@ -247,7 +249,7 @@ def fit(  # noqa: max-complexity: 12
             # a smoothing FWHM is provided.
             x = np.column_stack((int_reftac, reftac, -tac))
 
-            b: NumpyRealNumberArray
+            b: NumpyNumberArray
             try:
                 b = solve(x.T @ w @ x, x.T @ w @ int_tac, assume_a="sym")
             except LinAlgError:
@@ -319,9 +321,9 @@ def fit(  # noqa: max-complexity: 12
 
     def prep_refine_r1(
         self,
-        mask: NumpyRealNumberArray | None = None,
+        mask: NumpyNumberArray | None = None,
         fwhm: RealNumber | list[RealNumber] | None = None,
-    ) -> tuple[NumpyRealNumberArray, ...]:
+    ) -> tuple[NumpyNumberArray, ...]:
         """Refine R1.
 
         Args:
@@ -376,9 +378,9 @@ def prep_refine_r1(
         h1_img = image_maker(h1, smooth_k2_img)
         h2_img = image_maker(h2, smooth_k2a_img)
 
-        smooth_r1_mat: NumpyRealNumberArray
-        smooth_k2_mat: NumpyRealNumberArray
-        smooth_k2a_mat: NumpyRealNumberArray
+        smooth_r1_mat: NumpyNumberArray
+        smooth_k2_mat: NumpyNumberArray
+        smooth_k2a_mat: NumpyNumberArray
         if mask is None:
             smooth_r1_mat = smooth_r1_img.get_fdata().flatten()
             smooth_k2_mat = smooth_k2_img.get_fdata().flatten()

src/dynamicpet/kineticmodel/suvr.py

@@ -4,8 +4,8 @@
 
 from ..temporalobject.temporalimage import TemporalImage
 from ..temporalobject.temporalmatrix import TemporalMatrix
+from ..typing_utils import NumpyNumberArray
 from ..typing_utils import NumpyRealNumber
-from ..typing_utils import NumpyRealNumberArray
 from .kineticmodel import KineticModel
 
 
@@ -22,7 +22,7 @@ def get_param_names(cls) -> list[str]:
         """Get names of kinetic model parameters."""
         return ["suvr"]
 
-    def fit(self, mask: NumpyRealNumberArray | None = None) -> None:
+    def fit(self, mask: NumpyNumberArray | None = None) -> None:
         """Calculate SUVR.
 
         Args:
@@ -50,7 +50,7 @@ def fit(self, mask: NumpyRealNumberArray | None = None) -> None:
         """
         tacs: TemporalMatrix = self.tacs.timeseries_in_mask(mask)
 
-        numerator: NumpyRealNumberArray = np.sum(
+        numerator: NumpyNumberArray = np.sum(
             tacs.dataobj * tacs.frame_duration, axis=-1
         )
         denominator: NumpyRealNumber = np.sum(