refactor(apps): convert apps to use MynaApp subclasses

examples/cluster_solidification/input_rve.yaml

@@ -39,7 +39,7 @@ steps:
         batch: True
         np: 24
         mpiexec: mpirun
-        mpiargs: --bind-to none
+        mpiflags: --bind-to none
 data:
   build:
     datatype: Peregrine

examples/microstructure_region/input.yaml

@@ -15,11 +15,11 @@ steps:
       exaca-mesh: 10.0e-6
       np: 2
       mpiexec: mpirun
-      mpiargs: --bind-to none
+      mpiflags: --bind-to none
     execute:
       np: 2
       mpiexec: mpirun
-      mpiargs: --bind-to none
+      mpiflags: --bind-to none
 - exaca:
     class: microstructure_region
     application: exaca

examples/microstructure_region_slice/input.yaml

@@ -16,7 +16,7 @@ steps:
       batch: True
       np: 2
       mpiexec: mpirun
-      mpiargs: --bind-to none
+      mpiflags: --bind-to none
 - exaca:
     class: microstructure_region_slice
     application: exaca

examples/rve_part_center/input.yaml

@@ -21,7 +21,7 @@ steps:
       overwrite: True
       np: 16
       mpiexec: mpirun
-      mpiargs: --bind-to none
+      mpiflags: --bind-to none
 data:
   build:
     datatype: Peregrine

examples/solidification_region_reduced/input.yaml

@@ -16,7 +16,7 @@ steps:
     execute:
       np: 2
       mpiexec: mpirun
-      mpiargs: --bind-to none
+      mpiflags: --bind-to none
 data:
   build:
     datatype: Peregrine

examples/solidification_region_reduced_stl/input.yaml

@@ -16,11 +16,11 @@ steps:
       exaca-mesh: 10.0e-6
       np: 2
       mpiexec: mpirun
-      mpiargs: --bind-to none
+      mpiflags: --bind-to none
     execute:
       np: 2
       mpiexec: mpirun
-      mpiargs: --bind-to none
+      mpiflags: --bind-to none
 data:
   build:
     datatype: Peregrine

examples/vtk_to_exodus_region/input.yaml

@@ -16,7 +16,7 @@ steps:
       batch: True
       np: 2
       mpiexec: mpirun
-      mpiargs: --bind-to none
+      mpiflags: --bind-to none
 - exaca:
     class: microstructure_region
     application: exaca

src/myna/application/bnpy/bnpy.py

@@ -7,7 +7,6 @@
 # License: 3-clause BSD, see https://opensource.org/licenses/BSD-3-Clause.
 #
 from myna.core.app.base import MynaApp
-from myna.core.workflow.load_input import load_input
 import glob
 import os
 
@@ -18,17 +17,12 @@ def __init__(self):
         self.app_type = "bnpy"
         self.sF = 0.5
         self.gamma = 8
-        self.settings = load_input(os.environ["MYNA_INPUT"])
-        self.input_dir = os.path.dirname(os.environ["MYNA_INPUT"])
+        self.input_dir = os.path.dirname(self.input_file)
         self.resource_dir = os.path.join(self.input_dir, "myna_resources")
-        self.resource_template_dir = os.path.join(
-            self.resource_dir, *self.name.split("/")
-        )
-        self.training_dir = os.path.join(
-            self.resource_template_dir, "training_supervoxels"
-        )
-        self.make_directory_structure()
+        self.resource_template_dir = None
+        self.training_dir = None
 
+    def parse_shared_arguments(self):
         self.parser.add_argument(
             "--model",
             default=None,
@@ -43,6 +37,11 @@ def __init__(self):
             help="flag to use pre-trained model",
         )
 
+    def parse_execute_arguments(self):
+        self.update_resource_paths()
+        self.parse_shared_arguments()
+        self.parse_known_args()
+
     def get_latest_model_path(self):
         latest_model = sorted(
             glob.glob(os.path.join(self.get_model_dir_path(), "*")), reverse=True
@@ -62,3 +61,14 @@ def get_model_dir_path(self):
     def make_directory_structure(self):
         os.makedirs(self.resource_template_dir, exist_ok=True)
         os.makedirs(self.training_dir, exist_ok=True)
+
+    def update_resource_paths(self):
+        if self.class_name is None:
+            return
+        self.resource_template_dir = os.path.join(
+            self.resource_dir, *self.name.split("/")
+        )
+        self.training_dir = os.path.join(
+            self.resource_template_dir, "training_supervoxels"
+        )
+        self.make_directory_structure()

src/myna/application/bnpy/cluster_solidification/__init__.py

@@ -8,3 +8,7 @@
 #
 """Myna application for applying a clustering algorithm to solidification data
 (G and V magnitudes and orientations)"""
+
+from .app import BnpyClusterSolidification
+
+__all__ = ["BnpyClusterSolidification"]

src/myna/application/bnpy/cluster_solidification/app.py

@@ -6,10 +6,61 @@
 #
 # License: 3-clause BSD, see https://opensource.org/licenses/BSD-3-Clause.
 #
+import os
+import pandas as pd
 from myna.application.bnpy import Bnpy
 
 
 class BnpyClusterSolidification(Bnpy):
     def __init__(self):
         super().__init__()
         self.class_name = "cluster_solidification"
+
+    def parse_execute_arguments(self):
+        self.parser.add_argument(
+            "--thermal",
+            default=None,
+            type=str,
+            help='thermal step name, for example: "--thermal 3dthesis"',
+        )
+        super().parse_execute_arguments()
+
+    def execute(self):
+        from .execute import run_clustering, train_voxel_model
+
+        self.parse_execute_arguments()
+        train_model = self.args.train_model
+        overwrite = self.args.overwrite
+
+        myna_files = self.get_step_output_paths()
+        thermal_step_name = self.args.thermal
+        if thermal_step_name is None:
+            thermal_step_name = self.last_step_name
+        thermal_files = self.get_step_output_paths(thermal_step_name)
+
+        if train_model:
+            train_voxel_model(
+                myna_files, thermal_files, self.sF, self.gamma, os.path.dirname(self.input_file)
+            )
+
+        output_files = []
+        for case_dir, thermal_file in zip(
+            self.get_case_dirs(output_paths=myna_files), thermal_files
+        ):
+            print("Running clustering for:")
+            print(f"- {case_dir=}")
+            print(f"- {thermal_file=}")
+            output_files.append(
+                run_clustering(
+                    case_dir,
+                    thermal_file,
+                    self.sF,
+                    self.gamma,
+                    overwrite,
+                    os.path.dirname(self.input_file),
+                )
+            )
+
+        for filepath, mynafile in zip(output_files, myna_files):
+            df = pd.read_csv(filepath)
+            df.to_csv(mynafile, index=False)

src/myna/application/bnpy/cluster_solidification/execute.py

@@ -9,12 +9,11 @@
 import os
 import pandas as pd
 import numpy as np
-from myna.core.workflow.load_input import load_input
 import myna.application.bnpy as myna_bnpy
 import glob
 import matplotlib.pyplot as plt
 from myna.application.bnpy import get_representative_distribution
-from .app import BnpyClusterSolidification
+from myna.application.bnpy.cluster_solidification import BnpyClusterSolidification
 
 
 def reduce_thermal_file_to_df(thermal_file):
@@ -330,61 +329,7 @@ def run_clustering(
 
 def main():
     app = BnpyClusterSolidification()
-
-    # Set up argparse
-    parser = app.parser
-    parser.add_argument(
-        "--thermal",
-        default=None,
-        type=str,
-        help='thermal step name, for example: "--thermal 3dthesis"',
-    )
-
-    # Parse command line arguments
-    args = parser.parse_args()
-    settings = load_input(os.environ["MYNA_INPUT"])
-    train_model = args.train_model
-    overwrite = args.overwrite
-
-    # Get expected Myna output files
-    step_name = os.environ["MYNA_STEP_NAME"]
-    myna_files = settings["data"]["output_paths"][step_name]
-    thermal_step_name = args.thermal
-    if thermal_step_name is None:
-        thermal_step_name = os.environ["MYNA_LAST_STEP_NAME"]
-    thermal_files = settings["data"]["output_paths"][thermal_step_name]
-
-    # Assemble training data and train model
-    gamma = 8
-    sF = 0.5
-    if train_model:
-        train_voxel_model(
-            myna_files, thermal_files, sF, gamma, os.path.dirname(app.input_file)
-        )
-
-    # Run clustering using trained model
-    output_files = []
-    for case_dir, thermal_file in zip(
-        [os.path.dirname(x) for x in myna_files], thermal_files
-    ):
-        print("Running clustering for:")
-        print(f"- {case_dir=}")
-        print(f"- {thermal_file=}")
-        output_files.append(
-            run_clustering(
-                case_dir,
-                thermal_file,
-                sF,
-                gamma,
-                overwrite,
-                os.path.dirname(app.input_file),
-            )
-        )
-
-    # Post-process results to convert to Myna format
-    for filepath, mynafile in zip(output_files, myna_files):
-        df = pd.read_csv(filepath)
-        df.to_csv(mynafile, index=False)
+    app.execute()
 
 
 if __name__ == "__main__":

src/myna/application/bnpy/cluster_supervoxel/__init__.py

@@ -9,3 +9,7 @@
 """Myna application for applying a clustering algorithm to supervoxelized data
 containing composition data, intended to be run after the bnpy/cluster_solidification
 app"""
+
+from .app import BnpyClusterSupervoxel
+
+__all__ = ["BnpyClusterSupervoxel"]

src/myna/application/bnpy/cluster_supervoxel/app.py

@@ -6,10 +6,89 @@
 #
 # License: 3-clause BSD, see https://opensource.org/licenses/BSD-3-Clause.
 #
+import glob
+import os
 from myna.application.bnpy import Bnpy
 
 
 class BnpyClusterSupervoxel(Bnpy):
     def __init__(self):
         super().__init__()
         self.class_name = "cluster_supervoxel"
+
+    def parse_execute_arguments(self):
+        self.parser.add_argument(
+            "--cluster",
+            default="",
+            type=str,
+            help="input cluster step name, for example: --cluster cluster",
+        )
+        self.parser.add_argument(
+            "--voxel-model",
+            dest="voxel_model",
+            default="myna_resources/cluster_solidification/voxel_model-sF=0.5-gamma=8",
+            type=str,
+            help="path to model for voxel clustering",
+        )
+        self.parser.add_argument(
+            "--res",
+            default=250.0e-6,
+            type=float,
+            help="resolution to use for super-voxel size, in meters, for example: --res 250.0e-6",
+        )
+        super().parse_execute_arguments()
+
+    def execute(self):
+        from .execute import run, train_supervoxel_model
+
+        self.parse_execute_arguments()
+
+        try:
+            import bnpy
+        except ImportError:
+            raise ImportError(
+                'Myna bnpy app requires "pip install .[bnpy]" optional dependencies!'
+            )
+
+        voxel_model_path = self.args.voxel_model.replace("/", os.sep)
+        voxel_model_path = sorted(
+            glob.glob(os.path.join(voxel_model_path, "*")), reverse=True
+        )[0]
+        voxel_model_path = sorted(
+            glob.glob(os.path.join(voxel_model_path, "*")), reverse=True
+        )[0]
+        voxel_model, _ = bnpy.load_model_at_lap(voxel_model_path, None)
+        self.n_voxel_clusters = max(voxel_model.allocModel.K, 2)
+
+        myna_files = self.get_step_output_paths()
+        cluster_step_name = self.args.cluster
+        if cluster_step_name == "":
+            cluster_step_name = self.last_step_name
+        voxel_cluster_files = self.get_step_output_paths(cluster_step_name)
+
+        supervoxel_composition_filename = "supervoxel_composition.csv"
+        self.sF = 0.5
+        self.gamma = 8.0
+        if self.args.train_model:
+            trained_model_path, composition_files = train_supervoxel_model(
+                myna_files,
+                voxel_cluster_files,
+                self,
+                comp_file_name=supervoxel_composition_filename,
+            )
+        else:
+            trained_model_path = self.get_latest_model_path()
+            composition_files = [
+                os.path.join(os.path.dirname(myna_file), supervoxel_composition_filename)
+                for myna_file in myna_files
+            ]
+
+        print("- Clustering supervoxel data:")
+        for myna_file, composition_file in zip(myna_files, composition_files):
+            print(f"  - {composition_file=}")
+            run(
+                myna_file,
+                composition_file,
+                trained_model_path,
+                self,
+            )