DM-45226: Change np.float_ to np.float64

mypy.ini

@@ -94,6 +94,3 @@ warn_unreachable = False
 warn_unused_ignores = False
 allow_untyped_globals = True
 allow_redefinition = True
-
-[mypy-lsst.summit.utils.version]
-ignore_missing_imports = True

pyproject.toml

@@ -6,3 +6,6 @@ known_first_party = ["lsst", "tests"]
 [tool.black]
 line-length = 110
 target-version = ["py311"]
+
+[tool.lsst_versions]
+write_to = "python/lsst/summit/utils/version.py"

python/lsst/summit/utils/imageExaminer.py

@@ -47,8 +47,8 @@
 
 
 def gauss(
-    x: float | npt.NDArray[np.float_], a: float, x0: float, sigma: float
-) -> float | npt.NDArray[np.float_]:
+    x: float | npt.NDArray[np.float64], a: float, x0: float, sigma: float
+) -> float | npt.NDArray[np.float64]:
     return a * np.exp(-((x - x0) ** 2) / (2 * sigma**2))
 
 
@@ -312,7 +312,7 @@ def _calcBbox(self, centroid: tuple[float, float]) -> geom.Box2I:
 
         return bbox
 
-    def getStarBoxData(self) -> npt.NDArray[np.float_]:
+    def getStarBoxData(self) -> npt.NDArray[np.float64]:
         """Get the image data for the star.
 
         Calculates the maximum valid box, and uses that to return the image
@@ -330,8 +330,8 @@ def getStarBoxData(self) -> npt.NDArray[np.float_]:
         return self.exp.image[bbox].array
 
     def getMeshGrid(
-        self, data: npt.NDArray[np.float_]
-    ) -> tuple[npt.NDArray[np.float_], npt.NDArray[np.float_]]:
+        self, data: npt.NDArray[np.float64]
+    ) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:
         """Get the meshgrid for a data array.
 
         Parameters

python/lsst/summit/utils/peekExposure.py

@@ -53,7 +53,7 @@
 IDX_SENTINEL = -99999
 
 
-def _estimateMode(data: npt.NDArray[np.float_], frac: float = 0.5) -> float:
+def _estimateMode(data: npt.NDArray[np.float64], frac: float = 0.5) -> float:
     """Estimate the mode of a 1d distribution.
 
     Finds the smallest interval containing the fraction ``frac`` of the data,

python/lsst/summit/utils/spectrumExaminer.py

@@ -131,8 +131,8 @@ def calcGoodSpectrumSection(self, threshold: int = 5, windowSize: int = 5) -> tu
 
     def fit(self) -> None:
         def gauss(
-            x: float | npt.NDArray[np.float_], a: float, x0: float, sigma: float
-        ) -> float | npt.NDArray[np.float_]:
+            x: float | npt.NDArray[np.float64], a: float, x0: float, sigma: float
+        ) -> float | npt.NDArray[np.float64]:
             return a * np.exp(-((x - x0) ** 2) / (2 * sigma**2))
 
         data = self.spectrumData[self.goodSlice]

python/lsst/summit/utils/utils.py

@@ -127,7 +127,7 @@ def countPixels(maskedImage: afwImage.MaskedImage, maskPlane: str) -> int:
     return len(np.where(np.bitwise_and(maskedImage.mask.array, bit))[0])
 
 
-def quickSmooth(data: npt.NDArray[np.float_], sigma: float = 2) -> npt.NDArray[np.float_]:
+def quickSmooth(data: npt.NDArray[np.float64], sigma: float = 2) -> npt.NDArray[np.float64]:
     """Perform a quick smoothing of the image.
 
     Not to be used for scientific purposes, but improves the stretch and
@@ -150,7 +150,7 @@ def quickSmooth(data: npt.NDArray[np.float_], sigma: float = 2) -> npt.NDArray[n
     return smoothData
 
 
-def argMax2d(array: npt.NDArray[np.float_]) -> tuple[tuple[float, float], bool, list[tuple[float, float]]]:
+def argMax2d(array: npt.NDArray[np.float64]) -> tuple[tuple[float, float], bool, list[tuple[float, float]]]:
     """Get the index of the max value of an array and whether it's unique.
 
     If its not unique, returns a list of the other locations containing the
@@ -339,7 +339,7 @@ def fluxesFromFootprints(
     footprints: afwDetect.FootprintSet | afwDetect.Footprint | Iterable[afwDetect.Footprint],
     parentImage: afwImage.Image,
     subtractImageMedian: bool = False,
-) -> npt.NDArray[np.float_]:
+) -> npt.NDArray[np.float64]:
     """Calculate the flux from a set of footprints, given the parent image,
     optionally subtracting the whole-image median from each pixel as a very
     rough background subtraction.
@@ -1052,7 +1052,7 @@ def getCdf(data: np.ndarray, scale: int, nBinsMax: int = 300_000) -> tuple[np.nd
     return cdf, minVal, maxVal
 
 
-def getQuantiles(data: npt.NDArray[np.float_], nColors: int) -> npt.NDArray[np.float_]:
+def getQuantiles(data: npt.NDArray[np.float64], nColors: int) -> npt.NDArray[np.float64]:
     """Get a set of boundaries that equally distribute data into
     nColors intervals. The output can be used to make a colormap of nColors
     colors.
@@ -1096,7 +1096,7 @@ def getQuantiles(data: npt.NDArray[np.float_], nColors: int) -> npt.NDArray[np.f
     return boundaries
 
 
-def digitizeData(data: npt.NDArray[np.float_], nColors: int = 256) -> npt.NDArray[np.integer]:
+def digitizeData(data: npt.NDArray[np.float64], nColors: int = 256) -> npt.NDArray[np.integer]:
     """
     Scale data into nColors using its cumulative distribution function.
 

types.txt → requirements.txt

@@ -3,3 +3,6 @@ numpy==1.24.4
 matplotlib
 astro-metadata-translator
 humanize
+lsst-utils
+lsst-daf-butler
+lsst-pipe-base