Feature: support fans v0.4 (#33)

* bump fans version

* remove pointless entry-points

* def input generator tool

* use input generator tool

* change macroscale_loading type to List

* code cleanup

* filename -> filepath

* move results input to metadata.options and add results_prefix input under metadata.options

* satisfy ruff

Author: ethan-shanahan

pyproject.toml

@@ -23,15 +23,15 @@ dependencies = [
 ]
 
 # Entry Points
-[project.entry-points."aiida.data"]
-"fans" = "aiida_fans.data:FANSParameters"
+# [project.entry-points."aiida.data"]
+# "fans" = "aiida_fans.data:FANSParameters"
 [project.entry-points."aiida.calculations"]
 "fans.stashed" = "aiida_fans.calculations:FansStashedCalculation"
 "fans.fragmented" = "aiida_fans.calculations:FansFragmentedCalculation"
 [project.entry-points."aiida.parsers"]
 "fans" = "aiida_fans.parsers:FansParser"
-[project.entry-points."aiida.cmdline.data"]
-"fans" = "aiida_fans.cli:data_cli"
+# [project.entry-points."aiida.cmdline.data"]
+# "fans" = "aiida_fans.cli:data_cli"
 
 # Build System
 [build-system]
@@ -64,7 +64,7 @@ dependencies = {aiida-fans = "==0.1.5"}
 # [tool.pixi.feature.aiida]
 # dependencies = {aiida-core = "2.6.*"}
 [tool.pixi.feature.fans]
-dependencies = {fans = "0.3.*"}
+dependencies = {fans = "0.4.*"}
 [tool.pixi.feature.ruff]
 dependencies = {ruff = "*"}
 tasks = {fmt = "ruff check", dummy = "echo dummy", my-dummy="echo my-dummy"}

src/aiida_fans/calculations.py

@@ -8,9 +8,11 @@
 from aiida.common.folders import Folder
 from aiida.engine import CalcJob
 from aiida.engine.processes.process_spec import CalcJobProcessSpec
-from aiida.orm import ArrayData, Dict, Float, Int, List, SinglefileData, Str
+from aiida.orm import Dict, Float, Int, List, SinglefileData, Str
 from h5py import File as h5File
 
+from aiida_fans.helpers import make_input_dict
+
 
 class FansCalcBase(CalcJob):
     """Base class of all calculations using FANS."""
@@ -20,9 +22,8 @@ def define(cls, spec: CalcJobProcessSpec) -> None:
         """Define inputs, outputs, and exit codes of the calculation."""
         super().define(spec)
 
-        # Metadata
+        # Default Metadata
         spec.inputs["metadata"]["label"].default = "FANS"
-        # spec.inputs["metadata"]["dry_run"].default = True
         ## Processing Power
         spec.inputs["metadata"]["options"]["withmpi"].default = True
         spec.inputs["metadata"]["options"]["resources"].default = {
@@ -35,6 +36,10 @@ def define(cls, spec: CalcJobProcessSpec) -> None:
         ## Parser
         spec.inputs["metadata"]["options"]["parser_name"].default = "fans"
 
+        # Custom Metadata
+        spec.input("metadata.options.results_prefix", valid_type=str, default="")
+        spec.input("metadata.options.results", valid_type=list, default=[])
+
         # Input Ports
         ## Microstructure Definition
         spec.input_namespace("microstructure")
@@ -53,9 +58,7 @@ def define(cls, spec: CalcJobProcessSpec) -> None:
         spec.input("error_parameters.type", valid_type=Str)
         spec.input("error_parameters.tolerance", valid_type=Float)
         ## Macroscale Loading Conditions
-        spec.input("macroscale_loading", valid_type=ArrayData)
-        ## Results Specification
-        spec.input("results", valid_type=List)
+        spec.input("macroscale_loading", valid_type=List)
 
         # Output Ports
         spec.output("output", valid_type=SinglefileData)
@@ -107,28 +110,8 @@ def prepare_for_submission(self, folder: Folder) -> CalcInfo:
                     copyfileobj(source, target)
 
         # input.json as dict
-        input_dict = {
-            ## Microstructure Definition
-            "ms_filename": str(ms_filepath), # path to stashed microstructure
-            "ms_datasetname": self.inputs.microstructure.datasetname.value,
-            "ms_L": self.inputs.microstructure.L.get_list(),
-            ## Problem Type and Material Model
-            "problem_type": self.inputs.problem_type.value,
-            "matmodel": self.inputs.matmodel.value,
-            "material_properties": self.inputs.material_properties.get_dict(),
-            ## Solver Settings
-            "method": self.inputs.method.value,
-            "n_it": self.inputs.n_it.value,
-            "error_parameters": {
-                "measure": self.inputs.error_parameters.measure.value,
-                "type": self.inputs.error_parameters.type.value,
-                "tolerance": self.inputs.error_parameters.tolerance.value
-            },
-            ## Macroscale Loading Conditions
-            "macroscale_loading": [a[1].tolist() for a in self.inputs.macroscale_loading.get_iterarrays()],
-            ## Results Specification
-            "results": self.inputs.results.get_list()
-        }
+        input_dict = make_input_dict(self)
+        input_dict["microstructure"]["filepath"] = str(ms_filepath)
         # write input.json to working directory
         with folder.open(self.options.input_filename, "w", "utf8") as json:
             dump(input_dict, json, indent=4)
@@ -154,28 +137,8 @@ def prepare_for_submission(self, folder: Folder) -> CalcInfo:
                         h5_src.copy(datasetname, h5_dest, name=datasetname)
 
         # input.json as dict
-        input_dict = {
-            ## Microstructure Definition
-            "ms_filename": "microstructure.h5", # path to fragmented microstructure
-            "ms_datasetname": self.inputs.microstructure.datasetname.value,
-            "ms_L": self.inputs.microstructure.L.get_list(),
-            ## Problem Type and Material Model
-            "problem_type": self.inputs.problem_type.value,
-            "matmodel": self.inputs.matmodel.value,
-            "material_properties": self.inputs.material_properties.get_dict(),
-            ## Solver Settings
-            "method": self.inputs.method.value,
-            "n_it": self.inputs.n_it.value,
-            "error_parameters": {
-                "measure": self.inputs.error_parameters.measure.value,
-                "type": self.inputs.error_parameters.type.value,
-                "tolerance": self.inputs.error_parameters.tolerance.value
-            },
-            ## Macroscale Loading Conditions
-            "macroscale_loading": [a[1].tolist() for a in self.inputs.macroscale_loading.get_iterarrays()],
-            ## Results Specification
-            "results": self.inputs.results.get_list()
-        }
+        input_dict = make_input_dict(self)
+        input_dict["microstructure"]["filepath"] = "microstructure.h5"
         # write input.json to working directory
         with folder.open(self.options.input_filename, "w", "utf8") as json:
             dump(input_dict, json, indent=4)

src/aiida_fans/helpers.py

@@ -1,30 +1,38 @@
-"""Tools required by aiida_fans."""
+"""Tools required by aiida-fans."""
 
-import json
+from typing import Any
 
-from aiida.orm import ArrayData, Dict, Float, Int, List, SinglefileData, Str
+from aiida.engine import CalcJob
 from numpy import allclose, ndarray
 
 
-class InputEncoder(json.JSONEncoder):
-    """Prepares a dictionary of calcjob inputs for json representation."""
-
-    def default(self, obj):
-        """Converts aiida datatypes to their python counterparts."""
-        match obj:
-            case Str() | Int() | Float():
-                return obj.value
-            case List():
-                return obj.get_list()
-            case Dict():
-                return obj.get_dict()
-            case ArrayData():
-                return [a[1].tolist() for a in obj.get_iterarrays()]  #! Caution: may be disordered
-            case SinglefileData():
-                return obj.filename
-            case _:
-                # Let the base class default method raise the TypeError
-                return super().default(obj)
+def make_input_dict(job: CalcJob) -> dict[str, Any]:
+    """Prepares a dictionary that maps to an input.json from calcjob inputs."""
+    return {
+        ## Microstructure Definition
+        "microstructure": {
+            "filepath": None, # path to stashed microstructure, must be overwritten by impl
+            "datasetname": job.inputs.microstructure.datasetname.value,
+            "L": job.inputs.microstructure.L.get_list()
+        },
+        "results_prefix": job.inputs.metadata.options.results_prefix,
+        ## Problem Type and Material Model
+        "problem_type": job.inputs.problem_type.value,
+        "matmodel": job.inputs.matmodel.value,
+        "material_properties": job.inputs.material_properties.get_dict(),
+        ## Solver Settings
+        "method": job.inputs.method.value,
+        "n_it": job.inputs.n_it.value,
+        "error_parameters": {
+            "measure": job.inputs.error_parameters.measure.value,
+            "type": job.inputs.error_parameters.type.value,
+            "tolerance": job.inputs.error_parameters.tolerance.value
+        },
+        ## Macroscale Loading Conditions
+        "macroscale_loading": job.inputs.macroscale_loading.get_list(),
+        ## Results Specification
+        "results": job.inputs.metadata.options.results
+    }
 
 def arraydata_equal(first: dict[str, ndarray], second: dict[str, ndarray]) -> bool:
     """Return whether two dicts of arrays are roughly equal."""

src/aiida_fans/parsers.py

@@ -26,7 +26,11 @@ def parse(self, **kwargs) -> ExitCode | None:
             return self.exit_codes.ERROR_MISSING_OUTPUT
 
         with h5File(output_path) as h5:
-            results = h5[self.node.inputs.microstructure.datasetname.value]
+            results = h5[
+                self.node.inputs.microstructure.datasetname.value + \
+                    "_results/" + \
+                        self.node.get_option('results_prefix')
+            ]
             results.visititems(self.parse_h5)
 
         if self.results_dict:

src/aiida_fans/utils.py

@@ -10,7 +10,7 @@
 from aiida_fans.helpers import arraydata_equal
 
 
-def aiida_type(value : Any) -> type[Data]:
+def aiida_type(value: Any) -> type[Data]:
     """Find the corresponding AiiDA datatype for a variable with pythonic type.
 
     Args:
@@ -39,7 +39,7 @@ def aiida_type(value : Any) -> type[Data]:
         case _:
             raise NotImplementedError(f"Received an input of value:  {value}    with type:  {type(value)}")
 
-def fetch(label : str, value : Any) -> list[Node]:
+def fetch(label: str, value: Any) -> list[Node]:
     """Return a list of nodes matching the label and value provided.
 
     Args:
@@ -61,12 +61,16 @@ def fetch(label : str, value : Any) -> list[Node]:
     else:
         array_nodes = []
         for array_node in nodes:
-            array_value = {k:v for k, v in [(name, array_node.get_array(name)) for name in array_node.get_arraynames()]}
+            array_value = {
+                k: v for k, v in [
+                    (name, array_node.get_array(name)) for name in array_node.get_arraynames() # type: ignore
+                ]
+            }
             if arraydata_equal(value, array_value):
                 array_nodes.append(array_node)
         return array_nodes
 
-def generate(label : str, value : Any) -> Node:
+def generate(label: str, value: Any) -> Node:
     """Return a single node with the label and value provided.
 
     Uses an existing node when possible, but otherwise creates one instead.
@@ -89,7 +93,7 @@ def generate(label : str, value : Any) -> Node:
     else:
         raise RuntimeError
 
-def convert(ins : dict[str, Any], path : list[str] = []):
+def convert(ins: dict[str, Any], path: list[str] = []):
     """Takes a dictionary of inputs and converts the values to their respective Nodes.
 
     Args:
@@ -104,7 +108,7 @@ def convert(ins : dict[str, Any], path : list[str] = []):
         else:
             ins[k] = generate(".".join([*path, k]), v)
 
-def compile_query(ins : dict[str,Any], qb : QueryBuilder) -> None:
+def compile_query(ins: dict[str,Any], qb: QueryBuilder) -> None:
     """Interate over the converted input dictionary and append to the QueryBuilder for each node.
 
     Args:
@@ -175,6 +179,10 @@ def execute_fans(
             print("ERROR: Calculation strategy must be either 'Fragmented' or 'Stashed'.")
             raise ValueError
 
+    # move results_prefix and results items to metadata.options
+    inputs.setdefault("metadata", {}).setdefault("options", {})["results_prefix"] = inputs.pop("results_prefix", "")
+    inputs.setdefault("metadata", {}).setdefault("options", {})["results"] = inputs.pop("results", [])
+
     # fetch the inputs if possible or otherwise create them
     convert(inputs)