Add basic file readers

src/miv_data/__init__.py

@@ -0,0 +1,85 @@
+from typing import Iterator
+
+from neuroh5.io import (
+    read_population_names,
+    read_population_ranges,
+    read_cell_attribute_info,
+    scatter_read_trees,
+    scatter_read_cell_attributes,
+    scatter_read_graph,
+)
+from mpi4py import MPI
+from miv_data import types
+
+
+def read_cells_meta_data(
+    filepath: str, comm: MPI.Comm = MPI.COMM_WORLD
+) -> types.CellsMetaData:
+    rank = comm.Get_rank()
+    comm0 = comm.Split(int(rank == 0), 0)
+    cell_attribute_info = None
+    population_ranges = None
+    population_names = None
+    if rank == 0:
+        population_names = read_population_names(filepath, comm0)
+        (population_ranges, _) = read_population_ranges(filepath, comm0)
+        cell_attribute_info = read_cell_attribute_info(
+            filepath, population_names, comm=comm0
+        )
+    population_ranges = comm.bcast(population_ranges, root=0)
+    population_names = comm.bcast(population_names, root=0)
+    cell_attribute_info = comm.bcast(cell_attribute_info, root=0)
+
+    comm0.Free()
+
+    return types.CellsMetaData(
+        population_names=population_names,
+        population_ranges=population_ranges,
+        cell_attribute_info=cell_attribute_info,
+    )
+
+
+def read_coordinates(
+    filepath: str, population: str, comm: MPI.Comm = MPI.COMM_WORLD
+) -> Iterator[tuple[int, tuple[float, float, float]]]:
+    cell_attr_dict = scatter_read_cell_attributes(
+        filepath,
+        population,
+        namespaces=["Generated Coordinates"],
+        return_type="tuple",
+        comm=comm,
+    )
+    coords_iter, coords_attr_info = cell_attr_dict["Generated Coordinates"]
+    x_index = coords_attr_info.get("X Coordinate", None)
+    y_index = coords_attr_info.get("Y Coordinate", None)
+    z_index = coords_attr_info.get("Z Coordinate", None)
+    for gid, cell_coords in coords_iter:
+        yield gid, (
+            cell_coords[x_index][0],
+            cell_coords[y_index][0],
+            cell_coords[z_index][0],
+        )
+
+
+def read_trees(
+    filepath: str, population: str, comm: MPI.Comm = MPI.COMM_WORLD
+) -> Iterator[tuple[int, types.Tree]]:
+    (trees, forestSize) = scatter_read_trees(filepath, population, comm=comm)
+    yield from trees
+
+
+def read_projections(
+    filepath: str,
+    pre: types.PreSynapticPopulationName,
+    post: types.PostSynapticPopulationName,
+    comm: MPI.Comm = MPI.COMM_WORLD,
+) -> Iterator[tuple[int, tuple[list[int], types.Projection]]]:
+    (graph, a) = scatter_read_graph(
+        filepath,
+        comm=comm,
+        io_size=1,
+        projections=[(pre, post)],
+        namespaces=["Synapses", "Connections"],
+    )
+
+    yield from graph[post][pre]

src/miv_data/types.py

@@ -0,0 +1,22 @@
+from pydantic import BaseModel
+
+PopulationName = str
+PostSynapticPopulationName = PopulationName
+PreSynapticPopulationName = PopulationName
+
+
+class CellsMetaData(BaseModel):
+    """Cells metadata"""
+    population_names: list[PopulationName]
+    population_ranges: dict[PopulationName, tuple[int, int]]
+    cell_attribute_info: dict[PopulationName, dict[str, list[str]]]
+
+    def has(self, population: PopulationName, attribute: str) -> bool:
+        return attribute in self.cell_attribute_info.get(population, {})
+
+
+class Tree(BaseModel):
+    """TBD"""
+
+class Projection(BaseModel):
+    """TBD"""