Fix issues after rebase

Author: oerc0122

MDANSE/Src/MDANSE/Framework/Converters/MDAnalysis.py

@@ -71,10 +71,10 @@ class MDAnalysis(Converter):
         choices=mda._READERS.keys() | {None}, aliases={"AUTO": None}, default=None
     )
     continuous = Boolean(label="Continuous frame stitching", default=False)
-    coordinate_files = ManyPath(
+    coordinate_files = ManyPath[MDAnalysisTopology](
         mode="r",
         label="Coordinate file",
-        depends={
+        on_set_depends={
             "topology_format": "topology_format",
             "coordinate_format": "coordinate_format",
             "topology": "topology_file",

MDANSE/Src/MDANSE/Framework/Jobs/DynamicIncoherentStructureFactor.py

@@ -92,7 +92,7 @@ def initialize(self):
 
         self.generator = self.q_vectors.generator
         self.numberOfSteps = len(self.trajectory.atom_indices)
-        self._nQShells = self.generator.n_shells
+        self._nQShells = len(self.generator.q_vectors)
         self._nFrames = self.frame_window.n_frames
         self._atoms = self.trajectory.atom_names
         self.resolution = self.instrument_resolution.run_resolution
@@ -213,7 +213,7 @@ def run_step(self, index):
 
         disf_per_q_shell = {
             q: np.zeros((self._nFrames,), dtype=np.float64)
-            for q in self.generator.shells
+            for q in self.generator.q_vectors
         }
 
         n_configs = self.frame_window.n_configs

MDANSE/Src/MDANSE/Framework/Jobs/IJob.py

@@ -17,8 +17,6 @@
 
 import abc
 import json
-import multiprocessing
-import os
 import pprint
 import queue
 import random
@@ -41,7 +39,7 @@
 from MDANSE.Framework.Jobs.JobStatus import JobStates, JobStatus
 from MDANSE.Framework.OutputVariables.IOutputVariable import OutputData
 from MDANSE.Framework.Parameters.Parameters import Configurable
-from MDANSE.MLogging import FMT, LOG
+from MDANSE.MLogging import FMT, LOG, LogLevels
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
@@ -650,7 +648,7 @@ def finalize(self):
             return None
         return templateFile
 
-    def add_log_file_handler(self, filename: Path, level: str) -> None:
+    def add_log_file_handler(self, filename: Path, level: LogLevels) -> None:
         """Adds a file handle which is used to write the jobs logs.
 
         Parameters
@@ -668,7 +666,7 @@ def add_log_file_handler(self, filename: Path, level: str) -> None:
         # with multiprocessing jobs
         fh.set_name(str(filename))
         fh.setFormatter(FMT)
-        fh.setLevel(level)
+        fh.setLevel(level.value)
         LOG.addHandler(fh)
         LOG.debug(f"Log handler added for {filename}.")
 

MDANSE/Src/MDANSE/Framework/Parameters/BaseTypes.py

@@ -427,7 +427,7 @@ def validate(self, value: Path | str, deps: Depends, /) -> Path | None:
         return value
 
 
-class ManyPath(PathParam):
+class ManyPath(PathParam[CB]):
     """Parameter for specifying multiple files simultaneously.
 
     Files can be defined as either a Sequence of paths or

MDANSE/Src/MDANSE/Framework/Parameters/Choices.py

@@ -197,8 +197,5 @@ def validate(self, value: str | type[SC], deps: Depends) -> SC:
         value = cast("type[SC]", super().validate(value, deps))
         # Instantiate the object.
         return value(
-            **{
-                key: deps[arg] if isinstance(arg, DescID) else arg
-                for key, arg in self.instance_args.items()
-            }
+            **{key: deps.get(arg, arg) for key, arg in self.instance_args.items()}
         )

MDANSE/Src/MDANSE/Framework/Parameters/Parameters.py

@@ -86,7 +86,7 @@ def description_summary(self) -> str:
     @property
     def texttable_lengths(self) -> tuple[int, int, int]:
         """Return lengths of columns in texttable."""
-        return len(self.name), len(self.default), 50
+        return len(self.name), len(str(self.default)), 50
 
 
 @runtime_checkable
@@ -375,7 +375,7 @@ def build_doc_texttable(cls, doclist: Iterable[ParamInfo]) -> str:
         columns = ("Parameter", "Default value", "Description")
 
         size_iter = prepend(map(len, columns), (v.texttable_lengths for v in doclist))
-        len_name, len_default, len_desc = unzip(size_iter)
+        len_name, len_default, len_desc = unzip([(x, y, z) for x, y, z in size_iter])
         sizes = (max(len_name), max(len_default), max(len_desc))
 
         data_line = "| " + "| ".join(f"{{}}:<{size}" for size in sizes) + "|\n"
@@ -904,7 +904,7 @@ def validate(self, value: P, deps: Depends, /) -> T:
 
         elif self.choices and is_enum(self.choices):  # Enum
             try:
-                out = self.choices(out).value
+                out = self.choices(out)
             except ValueError:
                 raise ConfigError(
                     f"Value ({out!r}) not in choices ({cjoin(choice.name for choice in self.choices)})."

MDANSE/Src/MDANSE/Framework/Parameters/QVectors.py

@@ -15,14 +15,20 @@
 #
 from __future__ import annotations
 
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from MDANSE.Framework.Parameters.Choices import SubclassChoice
-from MDANSE.Framework.Parameters.Parameters import ConfigError, Configurable
+from MDANSE.Framework.Parameters.Parameters import (
+    ConfigError,
+    Configurable,
+    ConfigureDescriptor,
+    CustomConfig,
+)
+from MDANSE.Framework.Parameters.UtilTypes import Depends, DescID, PredictionResult
 from MDANSE.Framework.QVectors.IQVectors import IQVectors
 
-from .Parameters import CustomConfig
-from .UtilTypes import DescID
+if TYPE_CHECKING:
+    from collections.abc import Iterator
 
 
 class QVectors(CustomConfig):
@@ -48,7 +54,8 @@ def __set__(self, owner: Configurable, value):
 
         match value:
             case (generator, params):
-                self.generator, self.generator.configuration = generator, params
+                self.generator = generator
+                self.generator.configuration = params
             case dict():
                 self.generator.configuration = value
             case IQVectors():
@@ -58,9 +65,18 @@ def __set__(self, owner: Configurable, value):
             case _:
                 super().__set__(owner, value)
 
-    def vaidate(self, value: Configurable):
+    def validate(self, conf: ConfigureDescriptor, value: Configurable):
         try:
-            self.generator.generate()
-        except Exception as err:
-            raise ConfigError("Invalid or incomplete QVector configuration.") from err
-        return value
+            return self.generator.validate(conf, value)
+        except Exception:
+            return value
+
+    def preview_output_axis(self) -> Iterator[PredictionResult]:
+        """Output the values of |Q| from current parameters.
+
+        Yields
+        ------
+        PredictionResult
+            Prediction.
+        """
+        yield PredictionResult("Q Shells", self.generator.shells, "1/nm")

MDANSE/Src/MDANSE/Framework/Parsers/MDAnalysis.py

@@ -43,6 +43,7 @@ class MDAnalysisTopology(Parser):
 
     def __init__(self, _desc, filename: Sequence[Path | str], deps: Depends):
         self.filename = tuple(Path(pth).expanduser() for pth in filename)
+        print(_desc, filename, deps)
         self.trajectory = self.load(deps)
 
     def load(self, deps):

MDANSE/Src/MDANSE/Framework/QVectors/GridQVectors.py

@@ -21,6 +21,7 @@
 from more_itertools import map_reduce
 
 from MDANSE.Framework.Parameters import Float, Range
+from MDANSE.Framework.QVectors.IQVectors import IQVectors
 from MDANSE.Framework.QVectors.LatticeQVectors import LatticeQVectors
 
 
@@ -54,17 +55,19 @@ class GridQVectors(LatticeQVectors):
         default=0.01,
     )
 
-    def _generate(self):
+    def _get_grid(self):
         nh = len(self.hrange)
         nk = len(self.krange)
         nl = len(self.lrange)
 
-        hkls = np.mgrid[
+        return np.mgrid[
             self.hrange[0] : self.hrange[-1] + 1,
             self.krange[0] : self.krange[-1] + 1,
             self.lrange[0] : self.lrange[-1] + 1,
-        ]
-        hkls = hkls.reshape(3, nh * nk * nl)
+        ].reshape(3, nh * nk * nl)
+
+    def _get_qgroups(self):
+        hkls = self._get_grid()
 
         # The k matrix (3,n_hkls)
         vects = self.hkl_to_qvectors(hkls, self._unit_cell)
@@ -78,7 +81,12 @@ def _generate(self):
         inds = np.digitize(dists, bins) - 1
 
         dists = enumerate(bins[inds])
+
         q_groups = map_reduce(dists, itemgetter(1), itemgetter(0))
+        return hkls, vects, {key: q_groups[key] for key in sorted(q_groups)}
+
+    def _generate(self):
+        hkls, vects, q_groups = self._get_qgroups()
         # Functions rely on ordered dicts for some reason?
         self.q_groups = {key: q_groups[key] for key in sorted(q_groups)}
 
@@ -102,4 +110,4 @@ def _generate(self):
 
     @property
     def shells(self):
-        return list(self.q_groups.keys())
+        return sorted(self.q_vectors)

MDANSE/Src/MDANSE/Framework/QVectors/IQVectors.py

@@ -15,20 +15,20 @@
 #
 from __future__ import annotations
 
-import abc
 from typing import TYPE_CHECKING
 
 import numpy as np
 
 from MDANSE.Core.SubclassFactory import SubclassFactory
-from MDANSE.Framework.Parameters.Parameters import Configurable
-from MDANSE.Framework.Parameters.UtilTypes import PredictionResult
+from MDANSE.Framework.Parameters.Parameters import (
+    ConfigError,
+    Configurable,
+    ConfigureDescriptor,
+)
 from MDANSE.MLogging import LOG
 from MDANSE.MolecularDynamics.Trajectory import Trajectory
 
 if TYPE_CHECKING:
-    from collections.abc import Iterator
-
     from MDANSE.Framework.OutputVariables.IOutputVariable import OutputData
     from MDANSE.MolecularDynamics.UnitCell import UnitCell
 
@@ -68,6 +68,18 @@ def generate(self) -> bool:
         )
         return False
 
+    def validate(self, conf: ConfigureDescriptor, value: Configurable):
+        try:
+            # Force the setting (if we've reached this far)
+            setattr(self, conf.private_name, value)
+            self.generate()
+        except Exception as err:
+            import traceback
+
+            traceback.print_exception(err)
+            raise ConfigError("Invalid or incomplete QVector configuration.") from err
+        return value
+
     @staticmethod
     def qvectors_to_hkl(
         vector_array: np.array,
@@ -161,13 +173,3 @@ def write_vectors_to_file(self, output_data: OutputData):
                     units="au",
                     axis="vector_generator/coordinates|index",
                 )
-
-    def preview_output_axis(self) -> Iterator[PredictionResult]:
-        """Output the values of |Q| from current parameters.
-
-        Yields
-        ------
-        PredictionResult
-            Prediction.
-        """
-        yield PredictionResult("Q Shells", self.shells, "1/nm")