add cast, floor and ceil algorithm

CHANGES.md

@@ -12,6 +12,7 @@
 * move `rescale` and `color_formula` QueryParameters dependencies in `ImageRenderingParams` class  **breaking change**
 * handle image rescaling and color_formula within `titiler.core.utils.render_image` function  **breaking change**
 * add `render_func: Callable[..., Tuple[bytes, str]] = render_image` attribute in `TilerFactory` class
+* add `castToInt`, `Floor`, `Ceil` algorithms
 
 ### titiler.application
 
@@ -32,6 +33,8 @@
 
 * Updated WMTS Capabilities template to avoid inserting extra new lines (author @AndrewAnnex, https://github.com/developmentseed/titiler/pull/1052).
 * Updated WMTS endpoint in titiler.mosaic and titiler.core to return layer bounds in coordinate ordering matching CRS order if WGS84 is not used (author @AndrewAnnex, https://github.com/developmentseed/titiler/pull/1052).
+* Remove `python3.8` support (author @pratapvardhan, https://github.com/developmentseed/titiler/pull/1058)
+* Add `python3.13` support (author @pratapvardhan, https://github.com/developmentseed/titiler/pull/1058)
 
 ## 0.19.2 (2024-11-28)
 

src/titiler/core/tests/test_algorithms.py

@@ -235,3 +235,39 @@ def test_terrainrgb():
     assert out.array.shape == (3, 256, 256)
     assert out.array.dtype == "uint8"
     assert out.array[0, 0, 0] is numpy.ma.masked
+
+
+def test_ops():
+    """test ops: cast, ceil and floor."""
+    arr = numpy.ma.MaskedArray(
+        numpy.random.randint(0, 5000, (1, 256, 256)).astype("float32"),
+        mask=numpy.zeros((1, 256, 256), dtype="bool"),
+    )
+    arr.data[0, 0, 0] = 1.6
+    arr.mask[0, 1:100, 1:100] = True
+
+    img = ImageData(arr)
+    assert img.array.dtype == numpy.float32
+
+    algo = default_algorithms.get("cast")()
+    out = algo(img)
+    assert out.array.shape == (1, 256, 256)
+    assert out.array.dtype == "uint8"
+    assert out.array[0, 0, 0] == 1
+    assert out.array[0, 1, 1] is numpy.ma.masked
+
+    assert img.array.dtype == numpy.float32
+    algo = default_algorithms.get("floor")()
+    out = algo(img)
+    assert out.array.shape == (1, 256, 256)
+    assert out.array.dtype == "uint8"
+    assert out.array[0, 0, 0] == 1
+    assert out.array[0, 1, 1] is numpy.ma.masked
+
+    assert img.array.dtype == numpy.float32
+    algo = default_algorithms.get("ceil")()
+    out = algo(img)
+    assert out.array.shape == (1, 256, 256)
+    assert out.array.dtype == "uint8"
+    assert out.array[0, 0, 0] == 2
+    assert out.array[0, 1, 1] is numpy.ma.masked

src/titiler/core/titiler/core/algorithm/__init__.py

@@ -13,13 +13,17 @@
 from titiler.core.algorithm.base import BaseAlgorithm
 from titiler.core.algorithm.dem import Contours, HillShade, TerrainRGB, Terrarium
 from titiler.core.algorithm.index import NormalizedIndex
+from titiler.core.algorithm.ops import CastToInt, Ceil, Floor
 
 default_algorithms: Dict[str, Type[BaseAlgorithm]] = {
     "hillshade": HillShade,
     "contours": Contours,
     "normalizedIndex": NormalizedIndex,
     "terrarium": Terrarium,
     "terrainrgb": TerrainRGB,
+    "cast": CastToInt,
+    "ceil": Ceil,
+    "floor": Floor,
 }
 
 

src/titiler/core/titiler/core/algorithm/dem.py

@@ -9,6 +9,8 @@
 
 from titiler.core.algorithm.base import BaseAlgorithm
 
+__all__ = ["HillShade", "Contours", "Terrarium", "TerrainRGB"]
+
 
 class HillShade(BaseAlgorithm):
     """Hillshade."""

src/titiler/core/titiler/core/algorithm/index.py

@@ -7,6 +7,8 @@
 
 from titiler.core.algorithm.base import BaseAlgorithm
 
+__all__ = ["NormalizedIndex"]
+
 
 class NormalizedIndex(BaseAlgorithm):
     """Normalized Difference Index."""

src/titiler/core/titiler/core/algorithm/ops.py

@@ -0,0 +1,82 @@
+"""titiler.core.algorithm Ops."""
+
+from typing import Sequence
+
+import numpy
+from rio_tiler.models import ImageData
+
+from titiler.core.algorithm.base import BaseAlgorithm
+
+__all__ = ["CastToInt", "Ceil", "Floor"]
+
+
+class CastToInt(BaseAlgorithm):
+    """Cast data to Integer."""
+
+    title: str = "Cast data to Integer"
+    description: str = "Cast data to Integer."
+
+    # metadata
+    output_dtype: str = "uint8"
+    output_min: Sequence[int] = [0]
+    output_max: Sequence[int] = [255]
+
+    def __call__(self, img: ImageData) -> ImageData:
+        """Cast Data."""
+        return ImageData(
+            img.array.astype("uint8"),
+            assets=img.assets,
+            crs=img.crs,
+            bounds=img.bounds,
+            band_names=img.band_names,
+            metadata=img.metadata,
+            cutline_mask=img.cutline_mask,
+        )
+
+
+class Ceil(BaseAlgorithm):
+    """Round data to the smallest integer."""
+
+    title: str = "Round data to the smallest integer"
+    description: str = "Round data to the smallest integer."
+
+    # metadata
+    output_dtype: str = "uint8"
+    output_min: Sequence[int] = [0]
+    output_max: Sequence[int] = [255]
+
+    def __call__(self, img: ImageData) -> ImageData:
+        """Cast Data."""
+        return ImageData(
+            numpy.ceil(img.array).astype("uint8"),
+            assets=img.assets,
+            crs=img.crs,
+            bounds=img.bounds,
+            band_names=img.band_names,
+            metadata=img.metadata,
+            cutline_mask=img.cutline_mask,
+        )
+
+
+class Floor(BaseAlgorithm):
+    """Round data to the largest integer."""
+
+    title: str = "Round data to the largest integer"
+    description: str = "Round data to the largest integer."
+
+    # metadata
+    output_dtype: str = "uint8"
+    output_min: Sequence[int] = [0]
+    output_max: Sequence[int] = [255]
+
+    def __call__(self, img: ImageData) -> ImageData:
+        """Cast Data."""
+        return ImageData(
+            numpy.floor(img.array).astype("uint8"),
+            assets=img.assets,
+            crs=img.crs,
+            bounds=img.bounds,
+            band_names=img.band_names,
+            metadata=img.metadata,
+            cutline_mask=img.cutline_mask,
+        )