Merge pull request #12 from virtualcell/image-geometry

simulate vcml model with image-based goemetry

Author: jcschaff

examples/geometry/bunny.tgz

@@ -0,0 +1,16 @@
+import matplotlib.pyplot as plt
+from tensorstore._tensorstore import TensorStore  # type: ignore[import-untyped]
+
+from pyvcell._internal.simdata.n5_data import vcell_n5_datastore
+from pyvcell.sim_results.var_types import NDArray2D
+
+url = "https://vcell-dev.cam.uchc.edu/n5Data/ACowan/4b5ac930c40d5ba.n5"
+dataset_name = "4248805214"
+data_store: TensorStore = vcell_n5_datastore(base_url=url, dataset_name=dataset_name)
+print(f"shape = {data_store.shape}")
+
+np_data: NDArray2D = data_store[:, :, 0, 0, 0].read().result()
+print("slice shape:", np_data.shape)
+
+plt.imshow(np_data)
+plt.show()

examples/models/Bunny.vcml

@@ -0,0 +1,19 @@
+import os
+from pathlib import Path
+
+from pyvcell.sbml.sbml_simulation import SbmlSpatialSimulation
+from pyvcell.sbml.sbml_spatial_model import SbmlSpatialModel
+
+model_fp = Path(os.getcwd()).parent / "models" / "Bunny_sbml.xml"
+
+# define editable spatial model and simulation instances
+spatial_model = SbmlSpatialModel(filepath=model_fp)
+spatial_model.copy_parameters()
+simulation = SbmlSpatialSimulation(sbml_model=spatial_model)
+result = simulation.run()
+
+print([(c.label, c.domain_name) for c in result.channel_data])
+result.plotter.plot_slice_2d(time_index=3, channel_name="s_in", z_index=result.zarr_dataset.shape[3] // 2)
+result.plotter.plot_slice_3d(time_index=3, channel_id="s_in")
+result.plotter.plot_concentrations()
+simulation.cleanup()

examples/models/Bunny_sbml.xml

@@ -0,0 +1,20 @@
+import os
+from pathlib import Path
+
+from pyvcell.vcml import VcmlReader
+from pyvcell.vcml.vcml_simulation import VcmlSpatialSimulation
+
+model_fp = Path(os.getcwd()).parent / "models" / "Bunny.vcml"
+
+bio_model = VcmlReader.biomodel_from_file(model_fp)
+sims = [sim for app in bio_model.applications for sim in app.simulations]
+
+# define editable spatial model and simulation instances
+simulation = VcmlSpatialSimulation(bio_model=bio_model)
+result = simulation.run(sims[0].name)
+
+print([c.label for c in result.channel_data])
+result.plotter.plot_slice_2d(time_index=3, channel_name="s_in", z_index=result.zarr_dataset.shape[3] // 2)
+result.plotter.plot_slice_3d(time_index=3, channel_id="s_in")
+result.plotter.plot_concentrations()
+simulation.cleanup()

examples/notebooks/_internal_n5_download_demo.ipynb

@@ -2,7 +2,7 @@
 name = "pyvcell"
 version = "0.1.4"
 description = "This is the python wrapper for vcell modeling and simulation"
-authors = ["Jim Schaff <fschaff@uchc.edu>"]
+authors = ["Jim Schaff <schaff@uchc.edu>"]
 repository = "https://github.com/virtualcell/pyvcell"
 documentation = "https://virtualcell.github.io/pyvcell/"
 readme = "README.md"
@@ -11,7 +11,7 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.10,<4.0"
+python = ">=3.11,<4.0"
 numexpr = "^2.10.0"
 zarr = "^2.17.2"
 h5py = "^3.11.0"
@@ -31,6 +31,9 @@ pyvista = "^0.44.2"
 python-libsbml = "^5.20.4"
 matplotlib = "^3.10.0"
 lxml = "^5.3.1"
+imageio = "^2.37.0"
+tensorstore = "^0.1.72"
+libvcell = "^0.0.6"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.2.0"

examples/scripts/_internal_n5_download_demo.py

@@ -0,0 +1,7 @@
+import tensorstore as ts  # type: ignore[import-untyped]
+from tensorstore._tensorstore import TensorStore  # type: ignore[import-untyped]
+
+
+def vcell_n5_datastore(base_url: str, dataset_name: str) -> TensorStore:
+    spec = {"driver": "n5", "kvstore": {"driver": "http", "base_url": base_url}, "path": dataset_name}
+    return ts.open(spec, read=True).result()