ci(pre-commit.ci): autoupdate

.pre-commit-config.yaml

@@ -5,24 +5,24 @@ ci:
 
 repos:
   - repo: https://github.com/abravalheri/validate-pyproject
-    rev: v0.25
+    rev: '0.26'
     hooks:
       - id: validate-pyproject
 
   - repo: https://github.com/adhtruong/mirrors-typos
-    rev: v1.47.2
+    rev: v1.50.3
     hooks:
       - id: typos
         args: [--force-exclude]  # omitting --write-changes
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.15.19
+    rev: v0.16.10
     hooks:
       - id: ruff-check
       - id: ruff-format
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v2.1.0
+    rev: v2.4.0
     hooks:
       - id: mypy
         files: "^src/"

README.md

@@ -77,10 +77,8 @@ from microsim.util import ortho_plot
 # define the parameters of the simulation
 sim = ms.Simulation(
     truth_space=ms.ShapeScaleSpace(shape=(128, 512, 512), scale=(0.02, 0.01, 0.01)),
-    output_space={'downscale': 8},
-    sample=ms.Sample(
-        labels=[ms.MatsLines(density=0.5, length=30, azimuth=5, max_r=1)]
-    ),
+    output_space={"downscale": 8},
+    sample=ms.Sample(labels=[ms.MatsLines(density=0.5, length=30, azimuth=5, max_r=1)]),
     modality=ms.Confocal(pinhole_au=0.2),
     output_path="au02.tiff",
 )

docs/api.md

@@ -29,7 +29,7 @@ Simulation(
     truth_space=ms.ShapeScaleSpace(shape=(256, 1024, 1024), scale=(0.02, 0.01, 0.01)),
     # downscale output to 160nm x 80nm x 80nm pixels with shape (32, 128, 128)
     output_space=ms.DownscaledSpace(downscale=8),
-    sample=ms.Sample(labels=[])  # ...
+    sample=ms.Sample(labels=[]),  # ...
 )
 ```
 

docs/index.md

@@ -46,10 +46,8 @@ Construct and run a [`microsim.Simulation`][] object.
 
     sim = ms.Simulation(
         truth_space=ms.ShapeScaleSpace(shape=(128, 512, 512), scale=(0.02, 0.01, 0.01)),
-        output_space={'downscale': 8},
-        sample=ms.Sample(
-            labels=[ms.MatsLines(density=0.5, length=30, azimuth=5, max_r=1)]
-        ),
+        output_space={"downscale": 8},
+        sample=ms.Sample(labels=[ms.MatsLines(density=0.5, length=30, azimuth=5, max_r=1)]),
         modality=ms.Confocal(pinhole_au=0.2),
     )
     result = sim.run()
@@ -67,20 +65,20 @@ Construct and run a [`microsim.Simulation`][] object.
     from microsim.util import ortho_plot
 
     sim = Simulation(
-        truth_space={'shape': (128, 512, 512), 'scale': (0.02, 0.01, 0.01)},
-        output_space={'downscale': 8},
+        truth_space={"shape": (128, 512, 512), "scale": (0.02, 0.01, 0.01)},
+        output_space={"downscale": 8},
         sample=dict(
             labels=[
-                {   
-                    'type': 'matslines',
-                    'density': 0.5,
-                    'length': 30,
-                    'azimuth': 5,
-                    'max_r': 1,
+                {
+                    "type": "matslines",
+                    "density": 0.5,
+                    "length": 30,
+                    "azimuth": 5,
+                    "max_r": 1,
                 }
             ]
         ),
-        modality={'type': 'confocal', 'pinhole_au': 0.2}
+        modality={"type": "confocal", "pinhole_au": 0.2},
     )
     result = sim.run()
     ortho_plot(result)
@@ -126,7 +124,7 @@ the result of the simulation to a file.
 from microsim import Simulation
 from pathlib import Path
 
-spec = Path('confocal.json').read_text()
+spec = Path("confocal.json").read_text()
 sim = Simulation.model_validate_json(spec)
 sim.run()
 ```

docs/stages.md

@@ -37,7 +37,7 @@ stage, this volume will be downsampled to the final image shape and scale
     from microsim import Simulation
 
     sim = Simulation(
-        truth_space={'shape': (512, 1024, 1024), 'scale': (0.04, 0.02, 0.02)},
+        truth_space={"shape": (512, 1024, 1024), "scale": (0.04, 0.02, 0.02)},
         output_space={"downscale": 4},
     )
     ```
@@ -75,10 +75,15 @@ fluorophores present at each voxel in the volume.
     sim = Simulation(
         # ...,
         sample={
-            'labels': [
+            "labels": [
                 {
-                    'distribution': {'type': 'matslines', 'density': 0.5, 'length': 30, 'azimuth': 5},
-                    'fluorophore': 'EGFP'
+                    "distribution": {
+                        "type": "matslines",
+                        "density": 0.5,
+                        "length": 30,
+                        "azimuth": 5,
+                    },
+                    "fluorophore": "EGFP",
                 }
             ],
         },
@@ -240,9 +245,19 @@ channel 1. This is why both F and C remain at this stage.
             {
                 "name": "Green",
                 "filters": [
-                    {"type": "bandpass", "bandcenter": 470, "bandwidth": 40, "placement": "EX"},
+                    {
+                        "type": "bandpass",
+                        "bandcenter": 470,
+                        "bandwidth": 40,
+                        "placement": "EX",
+                    },
                     {"type": "longpass", "cuton": 495, "placement": "BS"},
-                    {"type": "bandpass", "bandcenter": 525, "bandwidth": 50, "placement": "EM"}
+                    {
+                        "type": "bandpass",
+                        "bandcenter": 525,
+                        "bandwidth": 50,
+                        "placement": "EM",
+                    },
                 ],
             }
         ],
@@ -350,8 +365,12 @@ bin in the detector.
     sim = Simulation(
         # ...,
         channels=["4yL4ggAo::491"],
-        objective_lens={"numerical_aperture": 1.4, "immersion_medium_ri": 1.515, 'specimen_ri': 1.33},
-        modality={"type": "confocal", 'pinhole_au': 1.2},
+        objective_lens={
+            "numerical_aperture": 1.4,
+            "immersion_medium_ri": 1.515,
+            "specimen_ri": 1.33,
+        },
+        modality={"type": "confocal", "pinhole_au": 1.2},
     )
     ```