Scan rewrite

process/core/caller.py

@@ -9,13 +9,14 @@
 from tabulate import tabulate
 
 from process.core import constants
-from process.core.final import finalise
+from process.core import process_output as po
 from process.core.io.mfile import MFile
 from process.core.process_output import OutputFileManager, ovarre
 from process.core.solver import constraints
 from process.core.solver.iteration_variables import set_scaled_iteration_variable
 from process.core.solver.objectives import objective_function
 from process.data_structure.blanket_variables import BlktModelTypes
+from process.data_structure.numerics import PROCESSRunMode
 from process.models.tfcoil.base import TFConductorModel
 from process.models.tfcoil.superconducting import SuperconductingTFTurnType
 
@@ -396,6 +397,103 @@ def _call_models_once(self, xc: np.ndarray):
         # FISPACT and LOCA model (not used)- removed
 
 
+def finalise(models, data, ifail: int, non_idempotent_msg: str | None = None):
+    """Routine to print out the final point in the scan.
+
+    Writes to OUT.DAT and MFILE.DAT.
+
+    Parameters
+    ----------
+    models : process.main.Models
+        physics and engineering model objects
+    data: DataStructure
+        data structure object to provide data to evaluate the constraints
+    ifail : int
+        error flag
+    non_idempotent_msg : None | str, optional
+        warning about non-idempotent variables, defaults to None
+    """
+    if ifail == 1:
+        po.oheadr(constants.NOUT, "Final Feasible Point")
+    else:
+        po.oheadr(constants.NOUT, "Final UNFEASIBLE Point")
+
+    # Output relevant to no optimisation
+    if data.numerics.ioptimz == PROCESSRunMode.EVALUATION:
+        output_evaluation(data)
+
+    # Print non-idempotence warning to OUT.DAT only
+    if non_idempotent_msg:
+        po.oheadr(constants.NOUT, "NON-IDEMPOTENT VARIABLES")
+        po.ocmmnt(constants.NOUT, non_idempotent_msg)
+
+    # Write output to OUT.DAT and MFILE.DAT
+    models.write(data, constants.NOUT)
+
+
+def output_evaluation(data):
+    """Write output for an evaluation run of PROCESS
+
+    Parameters
+    ----------
+    data: DataStructure
+        data structure object to provide data to evaluate the constraints
+    """
+    po.oheadr(constants.NOUT, "Numerics")
+    po.ocmmnt(constants.NOUT, "PROCESS has performed an evaluation run.")
+    po.oblnkl(constants.NOUT)
+
+    # Evaluate objective function
+    norm_objf = objective_function(data.numerics.minmax, data)
+    po.ovarre(constants.MFILE, "Normalised objective function", "(norm_objf)", norm_objf)
+
+    # Print the residuals of the constraint equations
+
+    residual_error, value, residual, symbols, units = constraints.constraint_eqns(
+        data.numerics.neqns + data.numerics.nineqns, -1, data
+    )
+
+    labels = [
+        data.numerics.lablcc[j - 1]
+        for j in data.numerics.icc[: data.numerics.neqns + data.numerics.nineqns]
+    ]
+
+    def _fmt(a, units):
+        return [f"{c} {u}" for c, u in zip(a, units, strict=False)]
+
+    po.write(
+        constants.NOUT,
+        tabulate(
+            {
+                "Constraint Name": labels,
+                "Constraint Type": symbols,
+                "Physical constraint": _fmt(value, units),
+                "Constraint residual": _fmt(residual, units),
+                "Normalised residual": residual_error,
+            },
+            headers="keys",
+        ),
+    )
+
+    for i in range(data.numerics.neqns):
+        constraint_id = data.numerics.icc[i]
+        po.ovarre(
+            constants.MFILE,
+            f"{labels[i]} normalised residue",
+            f"(eq_con{constraint_id:03d})",
+            residual_error[i],
+        )
+
+    for i in range(data.numerics.nineqns):
+        constraint_id = data.numerics.icc[data.numerics.neqns + i]
+        po.ovarre(
+            constants.MFILE,
+            f"{labels[data.numerics.neqns + i]}",
+            f"(ineq_con{constraint_id:03d})",
+            residual_error[data.numerics.neqns + i],
+        )
+
+
 def write_output_files(
     models: Models, data: DataStructure, ifail: int, *, runtime: float | None = None
 ):

process/core/final.py

@@ -1,105 +0,0 @@
-"""Final output at the end of a scan."""
-
-from tabulate import tabulate
-
-from process.core import constants
-from process.core import output as op
-from process.core import process_output as po
-from process.core.solver import constraints
-from process.core.solver.objectives import objective_function
-from process.data_structure.numerics import PROCESSRunMode
-
-
-def finalise(models, data, ifail: int, non_idempotent_msg: str | None = None):
-    """Routine to print out the final point in the scan.
-
-    Writes to OUT.DAT and MFILE.DAT.
-
-    Parameters
-    ----------
-    models : process.main.Models
-        physics and engineering model objects
-    data: DataStructure
-        data structure object to provide data to evaluate the constraints
-    ifail : int
-        error flag
-    non_idempotent_msg : None | str, optional
-        warning about non-idempotent variables, defaults to None
-    """
-    if ifail == 1:
-        po.oheadr(constants.NOUT, "Final Feasible Point")
-    else:
-        po.oheadr(constants.NOUT, "Final UNFEASIBLE Point")
-
-    # Output relevant to no optimisation
-    if data.numerics.ioptimz == PROCESSRunMode.EVALUATION:
-        output_evaluation(data)
-
-    # Print non-idempotence warning to OUT.DAT only
-    if non_idempotent_msg:
-        po.oheadr(constants.NOUT, "NON-IDEMPOTENT VARIABLES")
-        po.ocmmnt(constants.NOUT, non_idempotent_msg)
-
-    # Write output to OUT.DAT and MFILE.DAT
-    op.write(models, data, constants.NOUT)
-
-
-def output_evaluation(data):
-    """Write output for an evaluation run of PROCESS
-
-    Parameters
-    ----------
-    data: DataStructure
-        data structure object to provide data to evaluate the constraints
-    """
-    po.oheadr(constants.NOUT, "Numerics")
-    po.ocmmnt(constants.NOUT, "PROCESS has performed an evaluation run.")
-    po.oblnkl(constants.NOUT)
-
-    # Evaluate objective function
-    norm_objf = objective_function(data.numerics.minmax, data)
-    po.ovarre(constants.MFILE, "Normalised objective function", "(norm_objf)", norm_objf)
-
-    # Print the residuals of the constraint equations
-
-    residual_error, value, residual, symbols, units = constraints.constraint_eqns(
-        data.numerics.neqns + data.numerics.nineqns, -1, data
-    )
-
-    labels = [
-        data.numerics.lablcc[j]
-        for j in [
-            i - 1
-            for i in data.numerics.icc[: data.numerics.neqns + data.numerics.nineqns]
-        ]
-    ]
-    physical_constraint = [f"{c} {u}" for c, u in zip(value, units, strict=False)]
-    physical_residual = [f"{c} {u}" for c, u in zip(residual, units, strict=False)]
-
-    table_data = {
-        "Constraint Name": labels,
-        "Constraint Type": symbols,
-        "Physical constraint": physical_constraint,
-        "Constraint residual": physical_residual,
-        "Normalised residual": residual_error,
-    }
-
-    po.write(constants.NOUT, tabulate(table_data, headers="keys"))
-
-    for i in range(data.numerics.neqns):
-        constraint_id = data.numerics.icc[i]
-        po.ovarre(
-            constants.MFILE,
-            f"{labels[i]} normalised residue",
-            f"(eq_con{constraint_id:03d})",
-            residual_error[i],
-        )
-
-    for i in range(data.numerics.nineqns):
-        constraint_id = data.numerics.icc[data.numerics.neqns + i]
-        po.ovarre(
-            constants.MFILE,
-            f"{labels[data.numerics.neqns + i]}",
-            f"(ineq_con{constraint_id:03d})",
-            residual_error[data.numerics.neqns + i],
-        )

process/core/input.py

@@ -1138,24 +1138,15 @@ def bounds(self) -> tuple[NumberType | None, NumberType | None]:
         "scan",
         int,
         choices=range(IPNSCNS + 1),
+        array=True,
     ),
     "nsweep": InputVariable(
         "scan",
         int,
         choices=range(1, IPNSCNV + 1),
-    ),
-    "isweep_2": InputVariable(
-        "scan",
-        int,
-        choices=range(IPNSCNS + 1),
-    ),
-    "nsweep_2": InputVariable(
-        "scan",
-        int,
-        choices=range(1, IPNSCNV + 1),
+        array=True,
     ),
     "sweep": InputVariable("scan", float, array=True),
-    "sweep_2": InputVariable("scan", float, array=True),
     "impvardiv": InputVariable(
         "reinke",
         int,

process/core/io/plot/scans.py

@@ -36,6 +36,7 @@
 from matplotlib.ticker import MultipleLocator, PercentFormatter
 
 from process.core.io.mfile import MFile
+from process.core.io.mfile.cli import mfile
 from process.core.io.variable_metadata import var_dicts as meta
 from process.core.scan import ScanVariables
 
@@ -532,9 +533,9 @@ def plot_1d_scan(
         # This uses exclusively the last output_name2 defined in an earlier loop ignoring
         # all other output_name2s...
         if output_name == "plasma_current_MA":
-            extra_str = f"plasma_current{f'_vs_{output_name2}' if len(output_names2) > 0 else ''}"  # ruff:ignore[line-too-long]
+            extra_str = f"plasma_current{f'_vs_{output_name2}' if len(output_names2) > 0 else ''}"  # noqa: E501
         elif stack_plots and output_names[-1] == output_name:
-            extra_str = f"{output_name}{f'_vs_{output_name2}' if len(output_names2) > 0 else '_vs_'.join(output_names)}"  # ruff:ignore[line-too-long]
+            extra_str = f"{output_name}{f'_vs_{output_name2}' if len(output_names2) > 0 else '_vs_'.join(output_names)}"  # noqa: E501
         else:
             extra_str = (
                 f"{output_name}{f'_vs_{output_name2}' if len(output_names2) > 0 else ''}"