Added benchmarks for plots

.github/workflows/codspeed.yaml

@@ -0,0 +1,55 @@
+name: Codspeed Test
+
+on:
+ push:
+ branches: [main]
+ tags:
+ - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ pull_request:
+ branches: "*"
+
+jobs:
+ codspeed-test:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -e {0} # -e to fail on error
+
+ strategy:
+ fail-fast: false
+ matrix:
+ python: ["3.12"]
+
+ env:
+ PYTHON: ${{ matrix.python }}
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python ${{ matrix.python }}
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ matrix.python }}
+
+ - name: Get pip cache dir
+ id: pip-cache-dir
+ run: |
+ echo "::set-output name=dir::$(pip cache dir)"
+ - name: Restore pip cache
+ uses: actions/cache@v3
+ with:
+ path: ${{ steps.pip-cache-dir.outputs.dir }}
+ key: pip-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('**/pyproject.toml') }}
+ restore-keys: |
+ pip-${{ runner.os }}-${{ env.pythonLocation }}-
+ - name: Install test dependencies
+ run: |
+ python -m pip install --upgrade pip wheel
+ pip install pytest-cov
+ - name: Install dependencies
+ run: |
+ pip install --pre -e ".[dev,test]"
+ - name: Test performance
+ uses: CodSpeedHQ/action@v3
+ with:
+ token: ${{ secrets.CODSPEED_TOKEN }}
+ run: pytest tests/ --codspeed

.github/workflows/test.yaml

@@ -56,7 +56,7 @@ jobs:
  PLATFORM: ${{ matrix.os }}
  DISPLAY: :42
  run: |
- pytest -v --cov --color=yes --cov-report=xml
+ pytest -v --cov --color=yes --cov-report=xml -m "not benchmark"
  # - name: Generate GH action "groundtruth" figures as artifacts, uncomment if needed
  # if: always()
  # uses: actions/upload-artifact@v3

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
  "bump2version",
+ "pytest-codspeed>=2.0.0",
 ]
 docs = [
  "sphinx>=4.5",
@@ -48,6 +49,7 @@ test = [
  "pytest",
  "pytest-cov",
  "pooch", # for scipy.datasets module
+	"pytest-codspeed",
 ]
 # this will be used by readthedocs and will make pip also look for pre-releases, generally installing the latest available version
 pre = [

tests/performance/test_render_large_elements.py

@@ -0,0 +1,148 @@
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+import spatialdata_plot # noqa: F401
+from geopandas import GeoDataFrame
+from numpy.random import default_rng
+from shapely.affinity import translate
+from shapely.geometry import MultiPolygon, Polygon
+from spatialdata._core.spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, ShapesModel
+
+RNG = np.random.default_rng(seed=42)
+sc.pl.set_rcParams_defaults()
+matplotlib.use("agg") # same as GitHub action runner
+_ = spatialdata_plot
+
+
+def create_large_spatialdata(
+ c: int,
+ y: int,
+ x: int,
+ scale_factors: list[int],
+ n_points: int,
+ n_circles: int,
+ n_polygons: int,
+ n_multipolygons: int,
+) -> SpatialData:
+ rng = default_rng(seed=0)
+
+ # create random image cxy
+ image_data = rng.random((c, y, x))
+ image = Image2DModel.parse(image_data, dims=["c", "y", "x"])
+
+ # create random labels yx
+ labels_data = rng.integers(0, 256, size=(y, x)).astype(np.uint8)
+ labels = Labels2DModel.parse(labels_data, dims=["y", "x"])
+
+ # create multiscale versions
+ multiscale_image = Image2DModel.parse(image_data, dims=["c", "y", "x"], scale_factors=scale_factors)
+ multiscale_labels = Labels2DModel.parse(labels_data, dims=["y", "x"], scale_factors=scale_factors)
+
+ # create random xy points
+ points_data = rng.random((n_points, 2)) * [x, y]
+ points_df = pd.DataFrame(points_data, columns=["x", "y"])
+ points = PointsModel.parse(points_df.to_numpy())
+
+ # create random circles
+ circles = ShapesModel.parse(points_df.to_numpy(), geometry=0, radius=10)
+
+ def generate_random_polygons(n: int, bbox: tuple[int, int]) -> list[Polygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ polygons: list[Polygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ polygons.append(poly)
+ return polygons
+
+ # create random polygons
+ polygons = GeoDataFrame(geometry=generate_random_polygons(n_polygons, (0, max(x, y))))
+ polygons = ShapesModel.parse(polygons)
+
+ def generate_random_multipolygons(n: int, bbox: tuple[int, int]) -> list[MultiPolygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ multipolygons: list[MultiPolygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly1 = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ poly2 = translate(poly1, xoff=maxx // 4, yoff=maxy // 4)
+ multipolygons.append(MultiPolygon([poly1, poly2]))
+ return multipolygons
+
+ # create random multipolygons (2 polygons each)
+ multipolygons = GeoDataFrame(geometry=generate_random_multipolygons(n_multipolygons, (0, max(x, y))))
+ multipolygons = ShapesModel.parse(multipolygons)
+
+ return SpatialData(
+ images={"image": image, "multiscale_image": multiscale_image},
+ labels={"labels": labels, "multiscale_labels": multiscale_labels},
+ points={"points": points},
+ shapes={"circles": circles, "polygons": polygons, "multipolygons": multipolygons},
+ )
+
+
+sdata = create_large_spatialdata(
+ c=2,
+ y=500,
+ x=500,
+ scale_factors=[2, 2, 2],
+ n_points=500,
+ n_circles=500,
+ n_polygons=500,
+ n_multipolygons=500,
+)
+
+
+@pytest.mark.parametrize("element", ["image", "multiscale_image"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_image(element: str):
+ sdata.pl.render_images(element=element).pl.show()
+
+
+@pytest.mark.parametrize("element", ["labels", "multiscale_labels"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_labels(element: str):
+ sdata.pl.render_labels(element=element).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_circles(method: str):
+ sdata.pl.render_shapes(element="circles", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_polygons(method: str):
+ sdata.pl.render_shapes(element="polygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_multipolygons(method: str):
+ sdata.pl.render_shapes(element="multipolygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_points(method: str):
+ sdata.pl.render_points(element="points", method=method).pl.show()

.github/workflows/codspeed.yaml

@@ -0,0 +1,55 @@
+name: Codspeed Test
+
+on:
+ push:
+ branches: [main]
+ tags:
+ - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ pull_request:
+ branches: "*"
+
+jobs:
+ codspeed-test:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -e {0} # -e to fail on error
+
+ strategy:
+ fail-fast: false
+ matrix:
+ python: ["3.12"]
+
+ env:
+ PYTHON: ${{ matrix.python }}
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python ${{ matrix.python }}
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ matrix.python }}
+
+ - name: Get pip cache dir
+ id: pip-cache-dir
+ run: |
+ echo "::set-output name=dir::$(pip cache dir)"
+ - name: Restore pip cache
+ uses: actions/cache@v3
+ with:
+ path: ${{ steps.pip-cache-dir.outputs.dir }}
+ key: pip-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('**/pyproject.toml') }}
+ restore-keys: |
+ pip-${{ runner.os }}-${{ env.pythonLocation }}-
+ - name: Install test dependencies
+ run: |
+ python -m pip install --upgrade pip wheel
+ pip install pytest-cov
+ - name: Install dependencies
+ run: |
+ pip install --pre -e ".[dev,test]"
+ - name: Test performance
+ uses: CodSpeedHQ/action@v3
+ with:
+ token: ${{ secrets.CODSPEED_TOKEN }}
+ run: pytest tests/ --codspeed

.github/workflows/test.yaml

@@ -56,7 +56,7 @@ jobs:
  PLATFORM: ${{ matrix.os }}
  DISPLAY: :42
  run: |
- pytest -v --cov --color=yes --cov-report=xml
+ pytest -v --cov --color=yes --cov-report=xml -m "not benchmark"
  # - name: Generate GH action "groundtruth" figures as artifacts, uncomment if needed
  # if: always()
  # uses: actions/upload-artifact@v3

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
  "bump2version",
+ "pytest-codspeed>=2.0.0",
 ]
 docs = [
  "sphinx>=4.5",
@@ -48,6 +49,7 @@ test = [
  "pytest",
  "pytest-cov",
  "pooch", # for scipy.datasets module
+	"pytest-codspeed",
 ]
 # this will be used by readthedocs and will make pip also look for pre-releases, generally installing the latest available version
 pre = [

tests/performance/test_render_large_elements.py

@@ -0,0 +1,148 @@
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+import spatialdata_plot # noqa: F401
+from geopandas import GeoDataFrame
+from numpy.random import default_rng
+from shapely.affinity import translate
+from shapely.geometry import MultiPolygon, Polygon
+from spatialdata._core.spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, ShapesModel
+
+RNG = np.random.default_rng(seed=42)
+sc.pl.set_rcParams_defaults()
+matplotlib.use("agg") # same as GitHub action runner
+_ = spatialdata_plot
+
+
+def create_large_spatialdata(
+ c: int,
+ y: int,
+ x: int,
+ scale_factors: list[int],
+ n_points: int,
+ n_circles: int,
+ n_polygons: int,
+ n_multipolygons: int,
+) -> SpatialData:
+ rng = default_rng(seed=0)
+
+ # create random image cxy
+ image_data = rng.random((c, y, x))
+ image = Image2DModel.parse(image_data, dims=["c", "y", "x"])
+
+ # create random labels yx
+ labels_data = rng.integers(0, 256, size=(y, x)).astype(np.uint8)
+ labels = Labels2DModel.parse(labels_data, dims=["y", "x"])
+
+ # create multiscale versions
+ multiscale_image = Image2DModel.parse(image_data, dims=["c", "y", "x"], scale_factors=scale_factors)
+ multiscale_labels = Labels2DModel.parse(labels_data, dims=["y", "x"], scale_factors=scale_factors)
+
+ # create random xy points
+ points_data = rng.random((n_points, 2)) * [x, y]
+ points_df = pd.DataFrame(points_data, columns=["x", "y"])
+ points = PointsModel.parse(points_df.to_numpy())
+
+ # create random circles
+ circles = ShapesModel.parse(points_df.to_numpy(), geometry=0, radius=10)
+
+ def generate_random_polygons(n: int, bbox: tuple[int, int]) -> list[Polygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ polygons: list[Polygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ polygons.append(poly)
+ return polygons
+
+ # create random polygons
+ polygons = GeoDataFrame(geometry=generate_random_polygons(n_polygons, (0, max(x, y))))
+ polygons = ShapesModel.parse(polygons)
+
+ def generate_random_multipolygons(n: int, bbox: tuple[int, int]) -> list[MultiPolygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ multipolygons: list[MultiPolygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly1 = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ poly2 = translate(poly1, xoff=maxx // 4, yoff=maxy // 4)
+ multipolygons.append(MultiPolygon([poly1, poly2]))
+ return multipolygons
+
+ # create random multipolygons (2 polygons each)
+ multipolygons = GeoDataFrame(geometry=generate_random_multipolygons(n_multipolygons, (0, max(x, y))))
+ multipolygons = ShapesModel.parse(multipolygons)
+
+ return SpatialData(
+ images={"image": image, "multiscale_image": multiscale_image},
+ labels={"labels": labels, "multiscale_labels": multiscale_labels},
+ points={"points": points},
+ shapes={"circles": circles, "polygons": polygons, "multipolygons": multipolygons},
+ )
+
+
+sdata = create_large_spatialdata(
+ c=2,
+ y=500,
+ x=500,
+ scale_factors=[2, 2, 2],
+ n_points=500,
+ n_circles=500,
+ n_polygons=500,
+ n_multipolygons=500,
+)
+
+
+@pytest.mark.parametrize("element", ["image", "multiscale_image"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_image(element: str):
+ sdata.pl.render_images(element=element).pl.show()
+
+
+@pytest.mark.parametrize("element", ["labels", "multiscale_labels"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_labels(element: str):
+ sdata.pl.render_labels(element=element).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_circles(method: str):
+ sdata.pl.render_shapes(element="circles", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_polygons(method: str):
+ sdata.pl.render_shapes(element="polygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_multipolygons(method: str):
+ sdata.pl.render_shapes(element="multipolygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_points(method: str):
+ sdata.pl.render_points(element="points", method=method).pl.show()

.github/workflows/codspeed.yaml

@@ -0,0 +1,55 @@
+name: Codspeed Test
+
+on:
+ push:
+ branches: [main]
+ tags:
+ - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ pull_request:
+ branches: "*"
+
+jobs:
+ codspeed-test:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -e {0} # -e to fail on error
+
+ strategy:
+ fail-fast: false
+ matrix:
+ python: ["3.12"]
+
+ env:
+ PYTHON: ${{ matrix.python }}
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python ${{ matrix.python }}
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ matrix.python }}
+
+ - name: Get pip cache dir
+ id: pip-cache-dir
+ run: |
+ echo "::set-output name=dir::$(pip cache dir)"
+ - name: Restore pip cache
+ uses: actions/cache@v3
+ with:
+ path: ${{ steps.pip-cache-dir.outputs.dir }}
+ key: pip-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('**/pyproject.toml') }}
+ restore-keys: |
+ pip-${{ runner.os }}-${{ env.pythonLocation }}-
+ - name: Install test dependencies
+ run: |
+ python -m pip install --upgrade pip wheel
+ pip install pytest-cov
+ - name: Install dependencies
+ run: |
+ pip install --pre -e ".[dev,test]"
+ - name: Test performance
+ uses: CodSpeedHQ/action@v3
+ with:
+ token: ${{ secrets.CODSPEED_TOKEN }}
+ run: pytest tests/ --codspeed

.github/workflows/test.yaml

@@ -56,7 +56,7 @@ jobs:
  PLATFORM: ${{ matrix.os }}
  DISPLAY: :42
  run: |
- pytest -v --cov --color=yes --cov-report=xml
+ pytest -v --cov --color=yes --cov-report=xml -m "not benchmark"
  # - name: Generate GH action "groundtruth" figures as artifacts, uncomment if needed
  # if: always()
  # uses: actions/upload-artifact@v3

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
  "bump2version",
+ "pytest-codspeed>=2.0.0",
 ]
 docs = [
  "sphinx>=4.5",
@@ -48,6 +49,7 @@ test = [
  "pytest",
  "pytest-cov",
  "pooch", # for scipy.datasets module
+	"pytest-codspeed",
 ]
 # this will be used by readthedocs and will make pip also look for pre-releases, generally installing the latest available version
 pre = [

tests/performance/test_render_large_elements.py

@@ -0,0 +1,148 @@
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+import spatialdata_plot # noqa: F401
+from geopandas import GeoDataFrame
+from numpy.random import default_rng
+from shapely.affinity import translate
+from shapely.geometry import MultiPolygon, Polygon
+from spatialdata._core.spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, ShapesModel
+
+RNG = np.random.default_rng(seed=42)
+sc.pl.set_rcParams_defaults()
+matplotlib.use("agg") # same as GitHub action runner
+_ = spatialdata_plot
+
+
+def create_large_spatialdata(
+ c: int,
+ y: int,
+ x: int,
+ scale_factors: list[int],
+ n_points: int,
+ n_circles: int,
+ n_polygons: int,
+ n_multipolygons: int,
+) -> SpatialData:
+ rng = default_rng(seed=0)
+
+ # create random image cxy
+ image_data = rng.random((c, y, x))
+ image = Image2DModel.parse(image_data, dims=["c", "y", "x"])
+
+ # create random labels yx
+ labels_data = rng.integers(0, 256, size=(y, x)).astype(np.uint8)
+ labels = Labels2DModel.parse(labels_data, dims=["y", "x"])
+
+ # create multiscale versions
+ multiscale_image = Image2DModel.parse(image_data, dims=["c", "y", "x"], scale_factors=scale_factors)
+ multiscale_labels = Labels2DModel.parse(labels_data, dims=["y", "x"], scale_factors=scale_factors)
+
+ # create random xy points
+ points_data = rng.random((n_points, 2)) * [x, y]
+ points_df = pd.DataFrame(points_data, columns=["x", "y"])
+ points = PointsModel.parse(points_df.to_numpy())
+
+ # create random circles
+ circles = ShapesModel.parse(points_df.to_numpy(), geometry=0, radius=10)
+
+ def generate_random_polygons(n: int, bbox: tuple[int, int]) -> list[Polygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ polygons: list[Polygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ polygons.append(poly)
+ return polygons
+
+ # create random polygons
+ polygons = GeoDataFrame(geometry=generate_random_polygons(n_polygons, (0, max(x, y))))
+ polygons = ShapesModel.parse(polygons)
+
+ def generate_random_multipolygons(n: int, bbox: tuple[int, int]) -> list[MultiPolygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ multipolygons: list[MultiPolygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly1 = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ poly2 = translate(poly1, xoff=maxx // 4, yoff=maxy // 4)
+ multipolygons.append(MultiPolygon([poly1, poly2]))
+ return multipolygons
+
+ # create random multipolygons (2 polygons each)
+ multipolygons = GeoDataFrame(geometry=generate_random_multipolygons(n_multipolygons, (0, max(x, y))))
+ multipolygons = ShapesModel.parse(multipolygons)
+
+ return SpatialData(
+ images={"image": image, "multiscale_image": multiscale_image},
+ labels={"labels": labels, "multiscale_labels": multiscale_labels},
+ points={"points": points},
+ shapes={"circles": circles, "polygons": polygons, "multipolygons": multipolygons},
+ )
+
+
+sdata = create_large_spatialdata(
+ c=2,
+ y=500,
+ x=500,
+ scale_factors=[2, 2, 2],
+ n_points=500,
+ n_circles=500,
+ n_polygons=500,
+ n_multipolygons=500,
+)
+
+
+@pytest.mark.parametrize("element", ["image", "multiscale_image"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_image(element: str):
+ sdata.pl.render_images(element=element).pl.show()
+
+
+@pytest.mark.parametrize("element", ["labels", "multiscale_labels"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_labels(element: str):
+ sdata.pl.render_labels(element=element).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_circles(method: str):
+ sdata.pl.render_shapes(element="circles", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_polygons(method: str):
+ sdata.pl.render_shapes(element="polygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_multipolygons(method: str):
+ sdata.pl.render_shapes(element="multipolygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_points(method: str):
+ sdata.pl.render_points(element="points", method=method).pl.show()

.github/workflows/codspeed.yaml

@@ -0,0 +1,55 @@
+name: Codspeed Test
+
+on:
+ push:
+ branches: [main]
+ tags:
+ - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ pull_request:
+ branches: "*"
+
+jobs:
+ codspeed-test:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -e {0} # -e to fail on error
+
+ strategy:
+ fail-fast: false
+ matrix:
+ python: ["3.12"]
+
+ env:
+ PYTHON: ${{ matrix.python }}
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python ${{ matrix.python }}
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ matrix.python }}
+
+ - name: Get pip cache dir
+ id: pip-cache-dir
+ run: |
+ echo "::set-output name=dir::$(pip cache dir)"
+ - name: Restore pip cache
+ uses: actions/cache@v3
+ with:
+ path: ${{ steps.pip-cache-dir.outputs.dir }}
+ key: pip-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('**/pyproject.toml') }}
+ restore-keys: |
+ pip-${{ runner.os }}-${{ env.pythonLocation }}-
+ - name: Install test dependencies
+ run: |
+ python -m pip install --upgrade pip wheel
+ pip install pytest-cov
+ - name: Install dependencies
+ run: |
+ pip install --pre -e ".[dev,test]"
+ - name: Test performance
+ uses: CodSpeedHQ/action@v3
+ with:
+ token: ${{ secrets.CODSPEED_TOKEN }}
+ run: pytest tests/ --codspeed

.github/workflows/test.yaml

@@ -56,7 +56,7 @@ jobs:
  PLATFORM: ${{ matrix.os }}
  DISPLAY: :42
  run: |
- pytest -v --cov --color=yes --cov-report=xml
+ pytest -v --cov --color=yes --cov-report=xml -m "not benchmark"
  # - name: Generate GH action "groundtruth" figures as artifacts, uncomment if needed
  # if: always()
  # uses: actions/upload-artifact@v3

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
  "bump2version",
+ "pytest-codspeed>=2.0.0",
 ]
 docs = [
  "sphinx>=4.5",
@@ -48,6 +49,7 @@ test = [
  "pytest",
  "pytest-cov",
  "pooch", # for scipy.datasets module
+	"pytest-codspeed",
 ]
 # this will be used by readthedocs and will make pip also look for pre-releases, generally installing the latest available version
 pre = [

tests/performance/test_render_large_elements.py

@@ -0,0 +1,148 @@
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+import spatialdata_plot # noqa: F401
+from geopandas import GeoDataFrame
+from numpy.random import default_rng
+from shapely.affinity import translate
+from shapely.geometry import MultiPolygon, Polygon
+from spatialdata._core.spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, ShapesModel
+
+RNG = np.random.default_rng(seed=42)
+sc.pl.set_rcParams_defaults()
+matplotlib.use("agg") # same as GitHub action runner
+_ = spatialdata_plot
+
+
+def create_large_spatialdata(
+ c: int,
+ y: int,
+ x: int,
+ scale_factors: list[int],
+ n_points: int,
+ n_circles: int,
+ n_polygons: int,
+ n_multipolygons: int,
+) -> SpatialData:
+ rng = default_rng(seed=0)
+
+ # create random image cxy
+ image_data = rng.random((c, y, x))
+ image = Image2DModel.parse(image_data, dims=["c", "y", "x"])
+
+ # create random labels yx
+ labels_data = rng.integers(0, 256, size=(y, x)).astype(np.uint8)
+ labels = Labels2DModel.parse(labels_data, dims=["y", "x"])
+
+ # create multiscale versions
+ multiscale_image = Image2DModel.parse(image_data, dims=["c", "y", "x"], scale_factors=scale_factors)
+ multiscale_labels = Labels2DModel.parse(labels_data, dims=["y", "x"], scale_factors=scale_factors)
+
+ # create random xy points
+ points_data = rng.random((n_points, 2)) * [x, y]
+ points_df = pd.DataFrame(points_data, columns=["x", "y"])
+ points = PointsModel.parse(points_df.to_numpy())
+
+ # create random circles
+ circles = ShapesModel.parse(points_df.to_numpy(), geometry=0, radius=10)
+
+ def generate_random_polygons(n: int, bbox: tuple[int, int]) -> list[Polygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ polygons: list[Polygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ polygons.append(poly)
+ return polygons
+
+ # create random polygons
+ polygons = GeoDataFrame(geometry=generate_random_polygons(n_polygons, (0, max(x, y))))
+ polygons = ShapesModel.parse(polygons)
+
+ def generate_random_multipolygons(n: int, bbox: tuple[int, int]) -> list[MultiPolygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ multipolygons: list[MultiPolygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly1 = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ poly2 = translate(poly1, xoff=maxx // 4, yoff=maxy // 4)
+ multipolygons.append(MultiPolygon([poly1, poly2]))
+ return multipolygons
+
+ # create random multipolygons (2 polygons each)
+ multipolygons = GeoDataFrame(geometry=generate_random_multipolygons(n_multipolygons, (0, max(x, y))))
+ multipolygons = ShapesModel.parse(multipolygons)
+
+ return SpatialData(
+ images={"image": image, "multiscale_image": multiscale_image},
+ labels={"labels": labels, "multiscale_labels": multiscale_labels},
+ points={"points": points},
+ shapes={"circles": circles, "polygons": polygons, "multipolygons": multipolygons},
+ )
+
+
+sdata = create_large_spatialdata(
+ c=2,
+ y=500,
+ x=500,
+ scale_factors=[2, 2, 2],
+ n_points=500,
+ n_circles=500,
+ n_polygons=500,
+ n_multipolygons=500,
+)
+
+
+@pytest.mark.parametrize("element", ["image", "multiscale_image"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_image(element: str):
+ sdata.pl.render_images(element=element).pl.show()
+
+
+@pytest.mark.parametrize("element", ["labels", "multiscale_labels"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_labels(element: str):
+ sdata.pl.render_labels(element=element).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_circles(method: str):
+ sdata.pl.render_shapes(element="circles", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_polygons(method: str):
+ sdata.pl.render_shapes(element="polygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_multipolygons(method: str):
+ sdata.pl.render_shapes(element="multipolygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_points(method: str):
+ sdata.pl.render_points(element="points", method=method).pl.show()

.github/workflows/codspeed.yaml

@@ -0,0 +1,55 @@
+name: Codspeed Test
+
+on:
+ push:
+ branches: [main]
+ tags:
+ - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ pull_request:
+ branches: "*"
+
+jobs:
+ codspeed-test:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -e {0} # -e to fail on error
+
+ strategy:
+ fail-fast: false
+ matrix:
+ python: ["3.12"]
+
+ env:
+ PYTHON: ${{ matrix.python }}
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python ${{ matrix.python }}
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ matrix.python }}
+
+ - name: Get pip cache dir
+ id: pip-cache-dir
+ run: |
+ echo "::set-output name=dir::$(pip cache dir)"
+ - name: Restore pip cache
+ uses: actions/cache@v3
+ with:
+ path: ${{ steps.pip-cache-dir.outputs.dir }}
+ key: pip-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('**/pyproject.toml') }}
+ restore-keys: |
+ pip-${{ runner.os }}-${{ env.pythonLocation }}-
+ - name: Install test dependencies
+ run: |
+ python -m pip install --upgrade pip wheel
+ pip install pytest-cov
+ - name: Install dependencies
+ run: |
+ pip install --pre -e ".[dev,test]"
+ - name: Test performance
+ uses: CodSpeedHQ/action@v3
+ with:
+ token: ${{ secrets.CODSPEED_TOKEN }}
+ run: pytest tests/ --codspeed

.github/workflows/test.yaml

@@ -56,7 +56,7 @@ jobs:
  PLATFORM: ${{ matrix.os }}
  DISPLAY: :42
  run: |
- pytest -v --cov --color=yes --cov-report=xml
+ pytest -v --cov --color=yes --cov-report=xml -m "not benchmark"
  # - name: Generate GH action "groundtruth" figures as artifacts, uncomment if needed
  # if: always()
  # uses: actions/upload-artifact@v3

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
  "bump2version",
+ "pytest-codspeed>=2.0.0",
 ]
 docs = [
  "sphinx>=4.5",
@@ -48,6 +49,7 @@ test = [
  "pytest",
  "pytest-cov",
  "pooch", # for scipy.datasets module
+	"pytest-codspeed",
 ]
 # this will be used by readthedocs and will make pip also look for pre-releases, generally installing the latest available version
 pre = [

tests/performance/test_render_large_elements.py

@@ -0,0 +1,148 @@
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+import spatialdata_plot # noqa: F401
+from geopandas import GeoDataFrame
+from numpy.random import default_rng
+from shapely.affinity import translate
+from shapely.geometry import MultiPolygon, Polygon
+from spatialdata._core.spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, ShapesModel
+
+RNG = np.random.default_rng(seed=42)
+sc.pl.set_rcParams_defaults()
+matplotlib.use("agg") # same as GitHub action runner
+_ = spatialdata_plot
+
+
+def create_large_spatialdata(
+ c: int,
+ y: int,
+ x: int,
+ scale_factors: list[int],
+ n_points: int,
+ n_circles: int,
+ n_polygons: int,
+ n_multipolygons: int,
+) -> SpatialData:
+ rng = default_rng(seed=0)
+
+ # create random image cxy
+ image_data = rng.random((c, y, x))
+ image = Image2DModel.parse(image_data, dims=["c", "y", "x"])
+
+ # create random labels yx
+ labels_data = rng.integers(0, 256, size=(y, x)).astype(np.uint8)
+ labels = Labels2DModel.parse(labels_data, dims=["y", "x"])
+
+ # create multiscale versions
+ multiscale_image = Image2DModel.parse(image_data, dims=["c", "y", "x"], scale_factors=scale_factors)
+ multiscale_labels = Labels2DModel.parse(labels_data, dims=["y", "x"], scale_factors=scale_factors)
+
+ # create random xy points
+ points_data = rng.random((n_points, 2)) * [x, y]
+ points_df = pd.DataFrame(points_data, columns=["x", "y"])
+ points = PointsModel.parse(points_df.to_numpy())
+
+ # create random circles
+ circles = ShapesModel.parse(points_df.to_numpy(), geometry=0, radius=10)
+
+ def generate_random_polygons(n: int, bbox: tuple[int, int]) -> list[Polygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ polygons: list[Polygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ polygons.append(poly)
+ return polygons
+
+ # create random polygons
+ polygons = GeoDataFrame(geometry=generate_random_polygons(n_polygons, (0, max(x, y))))
+ polygons = ShapesModel.parse(polygons)
+
+ def generate_random_multipolygons(n: int, bbox: tuple[int, int]) -> list[MultiPolygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ multipolygons: list[MultiPolygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly1 = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ poly2 = translate(poly1, xoff=maxx // 4, yoff=maxy // 4)
+ multipolygons.append(MultiPolygon([poly1, poly2]))
+ return multipolygons
+
+ # create random multipolygons (2 polygons each)
+ multipolygons = GeoDataFrame(geometry=generate_random_multipolygons(n_multipolygons, (0, max(x, y))))
+ multipolygons = ShapesModel.parse(multipolygons)
+
+ return SpatialData(
+ images={"image": image, "multiscale_image": multiscale_image},
+ labels={"labels": labels, "multiscale_labels": multiscale_labels},
+ points={"points": points},
+ shapes={"circles": circles, "polygons": polygons, "multipolygons": multipolygons},
+ )
+
+
+sdata = create_large_spatialdata(
+ c=2,
+ y=500,
+ x=500,
+ scale_factors=[2, 2, 2],
+ n_points=500,
+ n_circles=500,
+ n_polygons=500,
+ n_multipolygons=500,
+)
+
+
+@pytest.mark.parametrize("element", ["image", "multiscale_image"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_image(element: str):
+ sdata.pl.render_images(element=element).pl.show()
+
+
+@pytest.mark.parametrize("element", ["labels", "multiscale_labels"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_labels(element: str):
+ sdata.pl.render_labels(element=element).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_circles(method: str):
+ sdata.pl.render_shapes(element="circles", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_polygons(method: str):
+ sdata.pl.render_shapes(element="polygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_multipolygons(method: str):
+ sdata.pl.render_shapes(element="multipolygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_points(method: str):
+ sdata.pl.render_points(element="points", method=method).pl.show()

.github/workflows/codspeed.yaml

@@ -0,0 +1,55 @@
+name: Codspeed Test
+
+on:
+ push:
+ branches: [main]
+ tags:
+ - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ pull_request:
+ branches: "*"
+
+jobs:
+ codspeed-test:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -e {0} # -e to fail on error
+
+ strategy:
+ fail-fast: false
+ matrix:
+ python: ["3.12"]
+
+ env:
+ PYTHON: ${{ matrix.python }}
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python ${{ matrix.python }}
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ matrix.python }}
+
+ - name: Get pip cache dir
+ id: pip-cache-dir
+ run: |
+ echo "::set-output name=dir::$(pip cache dir)"
+ - name: Restore pip cache
+ uses: actions/cache@v3
+ with:
+ path: ${{ steps.pip-cache-dir.outputs.dir }}
+ key: pip-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('**/pyproject.toml') }}
+ restore-keys: |
+ pip-${{ runner.os }}-${{ env.pythonLocation }}-
+ - name: Install test dependencies
+ run: |
+ python -m pip install --upgrade pip wheel
+ pip install pytest-cov
+ - name: Install dependencies
+ run: |
+ pip install --pre -e ".[dev,test]"
+ - name: Test performance
+ uses: CodSpeedHQ/action@v3
+ with:
+ token: ${{ secrets.CODSPEED_TOKEN }}
+ run: pytest tests/ --codspeed

.github/workflows/test.yaml

@@ -56,7 +56,7 @@ jobs:
  PLATFORM: ${{ matrix.os }}
  DISPLAY: :42
  run: |
- pytest -v --cov --color=yes --cov-report=xml
+ pytest -v --cov --color=yes --cov-report=xml -m "not benchmark"
  # - name: Generate GH action "groundtruth" figures as artifacts, uncomment if needed
  # if: always()
  # uses: actions/upload-artifact@v3

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
  "bump2version",
+ "pytest-codspeed>=2.0.0",
 ]
 docs = [
  "sphinx>=4.5",
@@ -48,6 +49,7 @@ test = [
  "pytest",
  "pytest-cov",
  "pooch", # for scipy.datasets module
+	"pytest-codspeed",
 ]
 # this will be used by readthedocs and will make pip also look for pre-releases, generally installing the latest available version
 pre = [

tests/performance/test_render_large_elements.py

@@ -0,0 +1,148 @@
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+import spatialdata_plot # noqa: F401
+from geopandas import GeoDataFrame
+from numpy.random import default_rng
+from shapely.affinity import translate
+from shapely.geometry import MultiPolygon, Polygon
+from spatialdata._core.spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, ShapesModel
+
+RNG = np.random.default_rng(seed=42)
+sc.pl.set_rcParams_defaults()
+matplotlib.use("agg") # same as GitHub action runner
+_ = spatialdata_plot
+
+
+def create_large_spatialdata(
+ c: int,
+ y: int,
+ x: int,
+ scale_factors: list[int],
+ n_points: int,
+ n_circles: int,
+ n_polygons: int,
+ n_multipolygons: int,
+) -> SpatialData:
+ rng = default_rng(seed=0)
+
+ # create random image cxy
+ image_data = rng.random((c, y, x))
+ image = Image2DModel.parse(image_data, dims=["c", "y", "x"])
+
+ # create random labels yx
+ labels_data = rng.integers(0, 256, size=(y, x)).astype(np.uint8)
+ labels = Labels2DModel.parse(labels_data, dims=["y", "x"])
+
+ # create multiscale versions
+ multiscale_image = Image2DModel.parse(image_data, dims=["c", "y", "x"], scale_factors=scale_factors)
+ multiscale_labels = Labels2DModel.parse(labels_data, dims=["y", "x"], scale_factors=scale_factors)
+
+ # create random xy points
+ points_data = rng.random((n_points, 2)) * [x, y]
+ points_df = pd.DataFrame(points_data, columns=["x", "y"])
+ points = PointsModel.parse(points_df.to_numpy())
+
+ # create random circles
+ circles = ShapesModel.parse(points_df.to_numpy(), geometry=0, radius=10)
+
+ def generate_random_polygons(n: int, bbox: tuple[int, int]) -> list[Polygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ polygons: list[Polygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ polygons.append(poly)
+ return polygons
+
+ # create random polygons
+ polygons = GeoDataFrame(geometry=generate_random_polygons(n_polygons, (0, max(x, y))))
+ polygons = ShapesModel.parse(polygons)
+
+ def generate_random_multipolygons(n: int, bbox: tuple[int, int]) -> list[MultiPolygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ multipolygons: list[MultiPolygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly1 = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ poly2 = translate(poly1, xoff=maxx // 4, yoff=maxy // 4)
+ multipolygons.append(MultiPolygon([poly1, poly2]))
+ return multipolygons
+
+ # create random multipolygons (2 polygons each)
+ multipolygons = GeoDataFrame(geometry=generate_random_multipolygons(n_multipolygons, (0, max(x, y))))
+ multipolygons = ShapesModel.parse(multipolygons)
+
+ return SpatialData(
+ images={"image": image, "multiscale_image": multiscale_image},
+ labels={"labels": labels, "multiscale_labels": multiscale_labels},
+ points={"points": points},
+ shapes={"circles": circles, "polygons": polygons, "multipolygons": multipolygons},
+ )
+
+
+sdata = create_large_spatialdata(
+ c=2,
+ y=500,
+ x=500,
+ scale_factors=[2, 2, 2],
+ n_points=500,
+ n_circles=500,
+ n_polygons=500,
+ n_multipolygons=500,
+)
+
+
+@pytest.mark.parametrize("element", ["image", "multiscale_image"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_image(element: str):
+ sdata.pl.render_images(element=element).pl.show()
+
+
+@pytest.mark.parametrize("element", ["labels", "multiscale_labels"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_labels(element: str):
+ sdata.pl.render_labels(element=element).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_circles(method: str):
+ sdata.pl.render_shapes(element="circles", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_polygons(method: str):
+ sdata.pl.render_shapes(element="polygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_multipolygons(method: str):
+ sdata.pl.render_shapes(element="multipolygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_points(method: str):
+ sdata.pl.render_points(element="points", method=method).pl.show()

.github/workflows/codspeed.yaml

@@ -0,0 +1,55 @@
+name: Codspeed Test
+
+on:
+ push:
+ branches: [main]
+ tags:
+ - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ pull_request:
+ branches: "*"
+
+jobs:
+ codspeed-test:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -e {0} # -e to fail on error
+
+ strategy:
+ fail-fast: false
+ matrix:
+ python: ["3.12"]
+
+ env:
+ PYTHON: ${{ matrix.python }}
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python ${{ matrix.python }}
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ matrix.python }}
+
+ - name: Get pip cache dir
+ id: pip-cache-dir
+ run: |
+ echo "::set-output name=dir::$(pip cache dir)"
+ - name: Restore pip cache
+ uses: actions/cache@v3
+ with:
+ path: ${{ steps.pip-cache-dir.outputs.dir }}
+ key: pip-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('**/pyproject.toml') }}
+ restore-keys: |
+ pip-${{ runner.os }}-${{ env.pythonLocation }}-
+ - name: Install test dependencies
+ run: |
+ python -m pip install --upgrade pip wheel
+ pip install pytest-cov
+ - name: Install dependencies
+ run: |
+ pip install --pre -e ".[dev,test]"
+ - name: Test performance
+ uses: CodSpeedHQ/action@v3
+ with:
+ token: ${{ secrets.CODSPEED_TOKEN }}
+ run: pytest tests/ --codspeed

.github/workflows/test.yaml

@@ -56,7 +56,7 @@ jobs:
  PLATFORM: ${{ matrix.os }}
  DISPLAY: :42
  run: |
- pytest -v --cov --color=yes --cov-report=xml
+ pytest -v --cov --color=yes --cov-report=xml -m "not benchmark"
  # - name: Generate GH action "groundtruth" figures as artifacts, uncomment if needed
  # if: always()
  # uses: actions/upload-artifact@v3

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
  "bump2version",
+ "pytest-codspeed>=2.0.0",
 ]
 docs = [
  "sphinx>=4.5",
@@ -48,6 +49,7 @@ test = [
  "pytest",
  "pytest-cov",
  "pooch", # for scipy.datasets module
+	"pytest-codspeed",
 ]
 # this will be used by readthedocs and will make pip also look for pre-releases, generally installing the latest available version
 pre = [

tests/performance/test_render_large_elements.py

@@ -0,0 +1,148 @@
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+import spatialdata_plot # noqa: F401
+from geopandas import GeoDataFrame
+from numpy.random import default_rng
+from shapely.affinity import translate
+from shapely.geometry import MultiPolygon, Polygon
+from spatialdata._core.spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, ShapesModel
+
+RNG = np.random.default_rng(seed=42)
+sc.pl.set_rcParams_defaults()
+matplotlib.use("agg") # same as GitHub action runner
+_ = spatialdata_plot
+
+
+def create_large_spatialdata(
+ c: int,
+ y: int,
+ x: int,
+ scale_factors: list[int],
+ n_points: int,
+ n_circles: int,
+ n_polygons: int,
+ n_multipolygons: int,
+) -> SpatialData:
+ rng = default_rng(seed=0)
+
+ # create random image cxy
+ image_data = rng.random((c, y, x))
+ image = Image2DModel.parse(image_data, dims=["c", "y", "x"])
+
+ # create random labels yx
+ labels_data = rng.integers(0, 256, size=(y, x)).astype(np.uint8)
+ labels = Labels2DModel.parse(labels_data, dims=["y", "x"])
+
+ # create multiscale versions
+ multiscale_image = Image2DModel.parse(image_data, dims=["c", "y", "x"], scale_factors=scale_factors)
+ multiscale_labels = Labels2DModel.parse(labels_data, dims=["y", "x"], scale_factors=scale_factors)
+
+ # create random xy points
+ points_data = rng.random((n_points, 2)) * [x, y]
+ points_df = pd.DataFrame(points_data, columns=["x", "y"])
+ points = PointsModel.parse(points_df.to_numpy())
+
+ # create random circles
+ circles = ShapesModel.parse(points_df.to_numpy(), geometry=0, radius=10)
+
+ def generate_random_polygons(n: int, bbox: tuple[int, int]) -> list[Polygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ polygons: list[Polygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ polygons.append(poly)
+ return polygons
+
+ # create random polygons
+ polygons = GeoDataFrame(geometry=generate_random_polygons(n_polygons, (0, max(x, y))))
+ polygons = ShapesModel.parse(polygons)
+
+ def generate_random_multipolygons(n: int, bbox: tuple[int, int]) -> list[MultiPolygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ multipolygons: list[MultiPolygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly1 = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ poly2 = translate(poly1, xoff=maxx // 4, yoff=maxy // 4)
+ multipolygons.append(MultiPolygon([poly1, poly2]))
+ return multipolygons
+
+ # create random multipolygons (2 polygons each)
+ multipolygons = GeoDataFrame(geometry=generate_random_multipolygons(n_multipolygons, (0, max(x, y))))
+ multipolygons = ShapesModel.parse(multipolygons)
+
+ return SpatialData(
+ images={"image": image, "multiscale_image": multiscale_image},
+ labels={"labels": labels, "multiscale_labels": multiscale_labels},
+ points={"points": points},
+ shapes={"circles": circles, "polygons": polygons, "multipolygons": multipolygons},
+ )
+
+
+sdata = create_large_spatialdata(
+ c=2,
+ y=500,
+ x=500,
+ scale_factors=[2, 2, 2],
+ n_points=500,
+ n_circles=500,
+ n_polygons=500,
+ n_multipolygons=500,
+)
+
+
+@pytest.mark.parametrize("element", ["image", "multiscale_image"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_image(element: str):
+ sdata.pl.render_images(element=element).pl.show()
+
+
+@pytest.mark.parametrize("element", ["labels", "multiscale_labels"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_labels(element: str):
+ sdata.pl.render_labels(element=element).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_circles(method: str):
+ sdata.pl.render_shapes(element="circles", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_polygons(method: str):
+ sdata.pl.render_shapes(element="polygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_multipolygons(method: str):
+ sdata.pl.render_shapes(element="multipolygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_points(method: str):
+ sdata.pl.render_points(element="points", method=method).pl.show()

.github/workflows/codspeed.yaml

@@ -0,0 +1,55 @@
+name: Codspeed Test
+
+on:
+ push:
+ branches: [main]
+ tags:
+ - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
+ pull_request:
+ branches: "*"
+
+jobs:
+ codspeed-test:
+ runs-on: ubuntu-latest
+ defaults:
+ run:
+ shell: bash -e {0} # -e to fail on error
+
+ strategy:
+ fail-fast: false
+ matrix:
+ python: ["3.12"]
+
+ env:
+ PYTHON: ${{ matrix.python }}
+
+ steps:
+ - uses: actions/checkout@v3
+ - name: Set up Python ${{ matrix.python }}
+ uses: actions/setup-python@v3
+ with:
+ python-version: ${{ matrix.python }}
+
+ - name: Get pip cache dir
+ id: pip-cache-dir
+ run: |
+ echo "::set-output name=dir::$(pip cache dir)"
+ - name: Restore pip cache
+ uses: actions/cache@v3
+ with:
+ path: ${{ steps.pip-cache-dir.outputs.dir }}
+ key: pip-${{ runner.os }}-${{ env.pythonLocation }}-${{ hashFiles('**/pyproject.toml') }}
+ restore-keys: |
+ pip-${{ runner.os }}-${{ env.pythonLocation }}-
+ - name: Install test dependencies
+ run: |
+ python -m pip install --upgrade pip wheel
+ pip install pytest-cov
+ - name: Install dependencies
+ run: |
+ pip install --pre -e ".[dev,test]"
+ - name: Test performance
+ uses: CodSpeedHQ/action@v3
+ with:
+ token: ${{ secrets.CODSPEED_TOKEN }}
+ run: pytest tests/ --codspeed

.github/workflows/test.yaml

@@ -56,7 +56,7 @@ jobs:
  PLATFORM: ${{ matrix.os }}
  DISPLAY: :42
  run: |
- pytest -v --cov --color=yes --cov-report=xml
+ pytest -v --cov --color=yes --cov-report=xml -m "not benchmark"
  # - name: Generate GH action "groundtruth" figures as artifacts, uncomment if needed
  # if: always()
  # uses: actions/upload-artifact@v3

pyproject.toml

@@ -31,6 +31,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = [
  "bump2version",
+ "pytest-codspeed>=2.0.0",
 ]
 docs = [
  "sphinx>=4.5",
@@ -48,6 +49,7 @@ test = [
  "pytest",
  "pytest-cov",
  "pooch", # for scipy.datasets module
+	"pytest-codspeed",
 ]
 # this will be used by readthedocs and will make pip also look for pre-releases, generally installing the latest available version
 pre = [

tests/performance/test_render_large_elements.py

@@ -0,0 +1,148 @@
+import matplotlib
+import numpy as np
+import pandas as pd
+import pytest
+import scanpy as sc
+import spatialdata_plot # noqa: F401
+from geopandas import GeoDataFrame
+from numpy.random import default_rng
+from shapely.affinity import translate
+from shapely.geometry import MultiPolygon, Polygon
+from spatialdata._core.spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel, PointsModel, ShapesModel
+
+RNG = np.random.default_rng(seed=42)
+sc.pl.set_rcParams_defaults()
+matplotlib.use("agg") # same as GitHub action runner
+_ = spatialdata_plot
+
+
+def create_large_spatialdata(
+ c: int,
+ y: int,
+ x: int,
+ scale_factors: list[int],
+ n_points: int,
+ n_circles: int,
+ n_polygons: int,
+ n_multipolygons: int,
+) -> SpatialData:
+ rng = default_rng(seed=0)
+
+ # create random image cxy
+ image_data = rng.random((c, y, x))
+ image = Image2DModel.parse(image_data, dims=["c", "y", "x"])
+
+ # create random labels yx
+ labels_data = rng.integers(0, 256, size=(y, x)).astype(np.uint8)
+ labels = Labels2DModel.parse(labels_data, dims=["y", "x"])
+
+ # create multiscale versions
+ multiscale_image = Image2DModel.parse(image_data, dims=["c", "y", "x"], scale_factors=scale_factors)
+ multiscale_labels = Labels2DModel.parse(labels_data, dims=["y", "x"], scale_factors=scale_factors)
+
+ # create random xy points
+ points_data = rng.random((n_points, 2)) * [x, y]
+ points_df = pd.DataFrame(points_data, columns=["x", "y"])
+ points = PointsModel.parse(points_df.to_numpy())
+
+ # create random circles
+ circles = ShapesModel.parse(points_df.to_numpy(), geometry=0, radius=10)
+
+ def generate_random_polygons(n: int, bbox: tuple[int, int]) -> list[Polygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ polygons: list[Polygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ polygons.append(poly)
+ return polygons
+
+ # create random polygons
+ polygons = GeoDataFrame(geometry=generate_random_polygons(n_polygons, (0, max(x, y))))
+ polygons = ShapesModel.parse(polygons)
+
+ def generate_random_multipolygons(n: int, bbox: tuple[int, int]) -> list[MultiPolygon]:
+ minx = miny = bbox[0]
+ maxx = maxy = bbox[1]
+ multipolygons: list[MultiPolygon] = []
+ for _ in range(n):
+ x = rng.uniform(minx, maxx)
+ y = rng.uniform(miny, maxy)
+ poly1 = Polygon(
+ [
+ (x + rng.uniform(0, maxx // 4), y + rng.uniform(0, maxy // 4)),
+ (x + rng.uniform(0, maxx // 4), y),
+ (x, y + rng.uniform(0, maxy // 4)),
+ ]
+ )
+ poly2 = translate(poly1, xoff=maxx // 4, yoff=maxy // 4)
+ multipolygons.append(MultiPolygon([poly1, poly2]))
+ return multipolygons
+
+ # create random multipolygons (2 polygons each)
+ multipolygons = GeoDataFrame(geometry=generate_random_multipolygons(n_multipolygons, (0, max(x, y))))
+ multipolygons = ShapesModel.parse(multipolygons)
+
+ return SpatialData(
+ images={"image": image, "multiscale_image": multiscale_image},
+ labels={"labels": labels, "multiscale_labels": multiscale_labels},
+ points={"points": points},
+ shapes={"circles": circles, "polygons": polygons, "multipolygons": multipolygons},
+ )
+
+
+sdata = create_large_spatialdata(
+ c=2,
+ y=500,
+ x=500,
+ scale_factors=[2, 2, 2],
+ n_points=500,
+ n_circles=500,
+ n_polygons=500,
+ n_multipolygons=500,
+)
+
+
+@pytest.mark.parametrize("element", ["image", "multiscale_image"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_image(element: str):
+ sdata.pl.render_images(element=element).pl.show()
+
+
+@pytest.mark.parametrize("element", ["labels", "multiscale_labels"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_labels(element: str):
+ sdata.pl.render_labels(element=element).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_circles(method: str):
+ sdata.pl.render_shapes(element="circles", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_polygons(method: str):
+ sdata.pl.render_shapes(element="polygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_multipolygons(method: str):
+ sdata.pl.render_shapes(element="multipolygons", method=method).pl.show()
+
+
+@pytest.mark.parametrize("method", ["matplotlib", "datashader"])
+@pytest.mark.benchmark
+def test_plot_can_render_large_points(method: str):
+ sdata.pl.render_points(element="points", method=method).pl.show()