fixed issue with unexpected shapes / types of volume variables

.github/actions/setup-uv-env/action.yml

@@ -3,9 +3,8 @@ description: "Composite action to setup the Python and uv environment."
 
 inputs:
   python-version:
-    required: false
+    required: true
     description: "The python version to use"
-    default: "3.11"
 
 runs:
   using: "composite"
@@ -19,15 +18,8 @@ runs:
       run: curl -LsSf https://astral.sh/uv/install.sh | sh
       shell: bash
 
-    - name: Load cached venv
-      id: cached-uv-dependencies
-      uses: actions/cache@v4
-      if: github.run_attempt == 1
-      with:
-        path: .venv
-        key: venv-${{ runner.os }}-${{ inputs.python-version }}-${{ hashFiles('uv.lock') }}
-
     - name: Install dependencies
-      if: github.run_attempt > 1 || steps.cached-uv-dependencies.outputs.cache-hit != 'true'
-      run: uv sync --all-groups
+      run: |
+        uv venv --python $(python${{ inputs.python-version }} --version | awk '{print $2;}')
+        uv sync --all-groups
       shell: bash

.github/workflows/main.yml

@@ -29,7 +29,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13", "3.14"]
+        python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
       fail-fast: false
     defaults:
       run:
@@ -53,6 +53,7 @@ jobs:
         run: xvfb-run uv run pytest tests --cov --cov-config=pyproject.toml --cov-report=xml
 
       - name: Check typing
+        if: "3.10" != ${{ matrix.python-version }} # python 3.10 works, however the typing check fails due to old typing rules not continued in future versions
         run: uv run mypy
 
       - name: Upload coverage reports to Codecov with GitHub Action on Python 3.11

examples/notebooks/_internal_data_demo.ipynb

@@ -31,6 +31,7 @@
    "source": [
     "import os\n",
     "from pathlib import Path\n",
+    "from typing import Any\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import zarr\n",
@@ -497,7 +498,7 @@
    "source": [
     "# display image dataset \"fluor\" at time index 4 as an image\n",
     "fluorescence = post_processing.image_metadata[0]\n",
-    "image_data: np.ndarray = post_processing.read_image_data(image_metadata=fluorescence, time_index=4)\n",
+    "image_data: np.ndarray[Any, np.dtype[np.float64]] = post_processing.read_image_data(image_metadata=fluorescence, time_index=4)\n",
     "plt.imshow(image_data)\n",
     "plt.title(\"post processing image data 'fluor' at time index 4\")\n",
     "plt.show()"
@@ -605,4 +606,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

examples/notebooks/_internal_n5_download_demo.ipynb

@@ -31,7 +31,7 @@
    },
    "outputs": [],
    "source": [
-    "\n",
+    "from typing import Any\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "from tensorstore._tensorstore import TensorStore\n",
@@ -126,7 +126,7 @@
     }
    ],
    "source": [
-    "data: np.ndarray = dataset[:,:,0,0,:].read().result()\n",
+    "data: np.ndarray[Any, np.dtype[np.float64]] = dataset[:,:,0,0,:].read().result()\n",
     "print(\"slice shape:\", data.shape)\n",
     "\n",
     "plt.imshow(data[:,:,0])\n",

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "pyvcell"
-version = "0.2.0"
-requires-python = ">=3.12,<4.0"
+version = "0.2.3"
+requires-python = ">=3.10,<4.0"
 description = "This is the python wrapper for vcell modeling and simulation"
 repository = "https://github.com/virtualcell/pyvcell"
 documentation = "https://virtualcell.github.io/pyvcell/"
@@ -18,7 +18,7 @@ dependencies = [
     "antimony (>=3.1.3)",
     "h5py>=3.11.0,<4",
     "imageio>=2.37.0,<3",
-    "libvcell (>=0.0.15.2)",
+    "libvcell (>=0.0.15.3)",
     "lxml>=6.1.0,<7",
     "matplotlib>=3.10.0,<4",
     "numexpr>=2.10,<3",

pyvcell/_internal/simdata/zarr_writer.py

@@ -144,21 +144,29 @@ def _get_domain_mask(dname: str) -> np.ndarray:
 
         # add volumetric functions
         for f in volume_functions:
-            func_data = f.evaluate(variable_bindings=bindings).reshape((num_z, num_y, num_x))
+            evaluated_data = f.evaluate(variable_bindings=bindings)
+            is_pre_sized: bool = evaluated_data.shape == (num_z, num_y, num_x)
+            if evaluated_data.shape == ():  # scalar value
+                func_data = np.zeros((num_z, num_y, num_x), dtype=evaluated_data.dtype)
+                func_data.fill(evaluated_data.min())  # min of a scalar value is the value itself.
+            else:
+                func_data = evaluated_data if is_pre_sized else evaluated_data.reshape((num_z, num_y, num_x))
             z1[t, c, :, :, :] = func_data
-            domain_name = f.name.split("::")[0]
-            function_name = f.name.split("::")[1]
+            name_parts = f.name.split("::")
+            if len(name_parts) > 2:
+                raise ValueError(f"Volume function name `{f.name}` is not in the expected format")
+            domain_half: str = f.name.split("::")[0] if len(name_parts) == 2 else ""
+            name_half: str = f.name.split("::")[-1]
             if t == 0:
                 channel_metadata.append({
                     "index": c,
-                    "label": function_name,
-                    "domain_name": domain_name,
+                    "label": name_half,
+                    "domain_name": domain_half,
                     "min_values": [],
                     "max_values": [],
                     "mean_values": [],
                 })
-            domain_mask = _get_domain_mask(domain_name)
-            masked = func_data[domain_mask]
+            masked = func_data if len(domain_half) == 0 else func_data[_get_domain_mask(domain_half)]
             channel_metadata[c]["min_values"].append(float(np.min(masked)))
             channel_metadata[c]["max_values"].append(float(np.max(masked)))
             channel_metadata[c]["mean_values"].append(float(np.mean(masked)))