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1211 | class Scan:
"""Perform a parameter scan using the Fortran scan module."""
def __init__(self, models, solver):
"""Immediately run the run_scan() method.
:param models: physics and engineering model objects
:type models: process.main.Models
:param solver: which solver to use, as specified in solver.py
:type solver: str
"""
self.models = models
self.solver = solver
self.solver_handler = SolverHandler(models, solver)
self.run_scan()
def run_scan(self):
"""Call a solver over a range of values of one of the variables.
This method calls the optimisation routine VMCON a number of times, by
performing a sweep over a range of values of a particular variable. A
number of output variable values are written to the MFILE.DAT file at
each scan point, for plotting or other post-processing purposes.
"""
if scan_variables.isweep == 0:
# Solve single problem, rather than an array of problems (scan)
# doopt() can also run just an evaluation
start_time = time.time()
ifail = self.doopt()
write_output_files(
models=self.models, ifail=ifail, runtime=time.time() - start_time
)
show_errors(constants.NOUT)
return
if scan_variables.isweep > scan_variables.IPNSCNS:
raise ProcessValueError(
"Illegal value of isweep",
isweep=scan_variables.isweep,
IPNSCNS=scan_variables.IPNSCNS,
)
if scan_variables.scan_dim == 2:
self.scan_2d()
else:
self.scan_1d()
def doopt(self):
"""Run the optimiser or solver."""
ifail = self.solver_handler.run()
self.post_optimise(ifail)
return ifail
def post_optimise(self, ifail: int):
"""Called after calling the optimising equation solver from Python.
ifail : input integer : error flag
Parameters
----------
ifail: int :
"""
numerics.sqsumsq = sum(r**2 for r in numerics.rcm[: numerics.neqns]) ** 0.5
process_output.oheadr(constants.NOUT, "Numerics")
if self.solver == "fsolve":
process_output.ocmmnt(
constants.NOUT, "PROCESS has performed an fsolve (evaluation) run."
)
else:
process_output.ocmmnt(
constants.NOUT, "PROCESS has performed a VMCON (optimisation) run."
)
if ifail != 1:
process_output.ovarin(constants.NOUT, "Error flag", "(ifail)", ifail)
process_output.oheadr(
constants.IOTTY, "PROCESS COULD NOT FIND A FEASIBLE SOLUTION"
)
process_output.oblnkl(constants.IOTTY)
logger.critical(f"Solver returns with ifail /= 1. {ifail=}")
# Error code handler for VMCON
if self.solver == "vmcon":
self.verror(ifail)
process_output.oblnkl(constants.NOUT)
process_output.oblnkl(constants.IOTTY)
else:
# Solution found
if self.solver != "fsolve":
process_output.ocmmnt(
constants.NOUT, "and found a feasible set of parameters."
)
process_output.oheadr(
constants.IOTTY, "PROCESS found a feasible solution"
)
else:
process_output.ocmmnt(
constants.NOUT, "and found a consistent set of parameters."
)
process_output.oheadr(
constants.IOTTY, "PROCESS found a consistent solution"
)
process_output.oblnkl(constants.NOUT)
process_output.ovarin(constants.NOUT, "Error flag", "(ifail)", ifail)
if numerics.sqsumsq >= 1.0e-2:
process_output.oblnkl(constants.NOUT)
process_output.ocmmnt(
constants.NOUT,
"WARNING: Constraint residues are HIGH; consider re-running",
)
process_output.ocmmnt(
constants.NOUT,
" with lower values of EPSVMC to confirm convergence...",
)
process_output.ocmmnt(
constants.NOUT,
" (should be able to get down to about 1.0E-8 okay)",
)
process_output.oblnkl(constants.NOUT)
process_output.ocmmnt(
constants.IOTTY,
"WARNING: Constraint residues are HIGH; consider re-running",
)
process_output.ocmmnt(
constants.IOTTY,
" with lower values of EPSVMC to confirm convergence...",
)
process_output.ocmmnt(
constants.IOTTY,
" (should be able to get down to about 1.0E-8 okay)",
)
process_output.oblnkl(constants.IOTTY)
logger.warning(f"High final constraint residues. {numerics.sqsumsq=}")
process_output.ovarin(
constants.NOUT, "Number of iteration variables", "(nvar)", numerics.nvar
)
process_output.ovarin(
constants.NOUT,
"Number of constraints (total)",
"(neqns+nineqns)",
numerics.neqns + numerics.nineqns,
)
process_output.ovarin(
constants.NOUT, "Optimisation switch", "(ioptimz)", numerics.ioptimz
)
# Objective function output: none for fsolve
if self.solver != "fsolve":
process_output.ovarin(
constants.NOUT, "Figure of merit switch", "(minmax)", numerics.minmax
)
objf_name = f'"{numerics.lablmm[abs(numerics.minmax) - 1]}"'
numerics.objf_name = objf_name
process_output.ovarst(
constants.NOUT,
"Objective function name",
"(objf_name)",
numerics.objf_name,
)
process_output.ovarre(
constants.NOUT,
"Normalised objective function",
"(norm_objf)",
numerics.norm_objf,
"OP ",
)
process_output.ovarre(
constants.NOUT,
"Square root of the sum of squares of the constraint residuals",
"(sqsumsq)",
numerics.sqsumsq,
"OP ",
)
if self.solver != "fsolve":
process_output.ovarre(
constants.NOUT,
"VMCON convergence parameter",
"(convergence_parameter)",
global_variables.convergence_parameter,
"OP ",
)
process_output.ovarin(
constants.NOUT,
"Number of optimising solver iterations",
"(nviter)",
numerics.nviter,
"OP ",
)
process_output.oblnkl(constants.NOUT)
if self.solver == "fsolve":
if ifail == 1:
msg = "PROCESS has solved using fsolve."
else:
msg = "PROCESS failed to solve using fsolve."
process_output.write(
constants.NOUT,
f"{msg}\n",
)
else:
if ifail == 1:
string1 = "PROCESS has successfully optimised"
else:
string1 = "PROCESS has failed to optimise"
string2 = "minimise" if numerics.minmax > 0 else "maximise"
process_output.write(
constants.NOUT,
f"{string1} the optimisation parameters to {string2} the objective function: {objf_name}\n",
)
written_warning = False
# Output optimisation parameters
solution_vector_table = []
for i in range(numerics.nvar):
numerics.xcs[i] = numerics.xcm[i] * numerics.scafc[i]
name = numerics.lablxc[numerics.ixc[i] - 1]
solution_vector_table.append([name, numerics.xcs[i], numerics.xcm[i]])
xminn = 1.01 * numerics.itv_scaled_lower_bounds[i]
xmaxx = 0.99 * numerics.itv_scaled_upper_bounds[i]
# Write to output file if close to optimisation parameter bounds
if numerics.xcm[i] < xminn or numerics.xcm[i] > xmaxx:
if not written_warning:
written_warning = True
process_output.ocmmnt(
constants.NOUT,
(
"Certain operating limits have been reached,"
"\n as shown by the following optimisation parameters that are"
"\n at or near to the edge of their prescribed range :\n"
),
)
xcval = numerics.xcm[i] * numerics.scafc[i]
if numerics.xcm[i] < xminn:
location, bound = "below", "lower"
bounds = numerics.itv_scaled_lower_bounds
else:
location, bound = "above", "upper"
bounds = numerics.itv_scaled_upper_bounds
process_output.write(
constants.NOUT,
f" {name:<30}= {xcval} is at or {location} its {bound} bound:"
f" {bounds[i] * numerics.scafc[i]}",
)
# Write optimisation parameters to mfile
process_output.ovarre(
constants.MFILE,
numerics.lablxc[numerics.ixc[i] - 1],
f"(itvar{i + 1:03d})",
numerics.xcs[i],
)
if numerics.boundu[i] == numerics.boundl[i]:
xnorm = 1.0
else:
xnorm = min(
max(
(numerics.xcm[i] - numerics.itv_scaled_lower_bounds[i])
/ (
numerics.itv_scaled_upper_bounds[i]
- numerics.itv_scaled_lower_bounds[i]
),
0.0,
),
1.0,
)
process_output.ovarre(
constants.MFILE,
f"{name} (final value/initial value)",
f"(xcm{i + 1:03d})",
numerics.xcm[i],
)
process_output.ovarre(
constants.MFILE,
f"{name} (range normalised)",
f"(nitvar{i + 1:03d})",
xnorm,
)
process_output.ovarre(
constants.MFILE,
f"{name} (upper bound)",
f"(boundu{i + 1:03d})",
numerics.itv_scaled_upper_bounds[i] * numerics.scafc[i],
)
process_output.ovarre(
constants.MFILE,
f"{name} (lower bound)",
f"(boundl{i + 1:03d})",
numerics.itv_scaled_lower_bounds[i] * numerics.scafc[i],
)
# Write optimisation parameter headings to output file
process_output.osubhd(
constants.NOUT, "The solution vector is comprised as follows :"
)
process_output.write(
constants.NOUT,
tabulate(
solution_vector_table,
headers=["", "Final value", "Final / initial"],
numalign="left",
),
)
process_output.osubhd(
constants.NOUT,
"The following equality constraint residues should be close to zero:",
)
con1, con2, err, sym, lab = constraints.constraint_eqns(
numerics.neqns + numerics.nineqns, -1
)
# Write equality constraints to mfile
equality_constraint_table = []
for i in range(numerics.neqns):
name = numerics.lablcc[numerics.icc[i] - 1]
equality_constraint_table.append([
name,
sym[i],
f"{con2[i]} {lab[i]}",
f"{err[i]} {lab[i]}",
con1[i],
])
process_output.ovarre(
constants.MFILE,
f"{name:<33} normalised residue",
f"(eq_con{numerics.icc[i]:03d})",
con1[i],
)
process_output.ovarre(
constants.MFILE,
f"{name:<33} residual",
f"(res_eq_con{numerics.icc[i]:03d})",
err[i],
)
process_output.ovarre(
constants.MFILE,
f"{name} constraint value",
f"(val_eq_con{numerics.icc[i]:03d})",
con2[i],
)
process_output.ovarre(
constants.MFILE,
f"{name} units",
f"(eq_units_con{numerics.icc[i]:03d})",
f"'{lab[i]}'",
)
# Write equality constraints to output file
process_output.write(
constants.NOUT,
tabulate(
equality_constraint_table,
headers=[
"",
"",
"Physical constraint",
"Constraint residue",
"Normalised residue",
],
numalign="left",
),
)
# Write inequality constraints
if numerics.nineqns > 0:
inequality_constraint_table = []
# Inequalities not necessarily satisfied when evaluating
process_output.osubhd(
constants.NOUT,
"Negative inequality constraint (normalised) residuals indicate a constraint is satisfied.",
)
if self.solver == "fsolve":
process_output.osubhd(
constants.NOUT,
"This MFile was produced via an evaluation, not an optimisation, and so the constraints "
"might be violated.",
)
for i in range(numerics.neqns, numerics.neqns + numerics.nineqns):
name = numerics.lablcc[numerics.icc[i] - 1]
constraint = constraints.ConstraintManager.evaluate_constraint(
int(numerics.icc[i])
)
inequality_constraint_table.append([
name,
f"{constraint.constraint_value} {constraint.units}",
constraint.symbol,
f"{constraint.constraint_bound} {constraint.units}",
f"{constraint.residual} {constraint.units}",
f"{constraint.normalised_residual}",
])
process_output.ovarre(
constants.MFILE,
f"{name} normalised residue",
f"(ineq_con{numerics.icc[i]:03d})",
-constraint.normalised_residual,
)
process_output.ovarre(
constants.MFILE,
f"{name} physical value",
f"(ineq_value_con{numerics.icc[i]:03d})",
constraint.constraint_value,
)
process_output.ovarre(
constants.MFILE,
f"{name} symbol",
f"(ineq_symbol_con{numerics.icc[i]:03d})",
f"'{constraint.symbol}'",
)
process_output.ovarre(
constants.MFILE,
f"{name} units",
f"(ineq_units_con{numerics.icc[i]:03d})",
f"'{constraint.units}'",
)
process_output.ovarre(
constants.MFILE,
f"{name} physical bound",
f"(ineq_bound_con{numerics.icc[i]:03d})",
constraint.constraint_bound,
)
process_output.write(
constants.NOUT,
tabulate(
inequality_constraint_table,
headers=[
"",
"Physical constraint",
"",
"Physical constraint bound",
"Constraint residue",
"Normalised residue",
],
numalign="left",
),
)
def verror(self, ifail: int):
"""Routine to print out relevant messages in the case of an
unfeasible result from a VMCON (optimisation) run
ifail : input integer : error flag
This routine prints out relevant messages in the case of
an unfeasible result from a VMCON (optimisation) run.
Parameters
----------
ifail: int :
"""
if ifail == -1:
process_output.ocmmnt(constants.NOUT, "User-terminated execution of VMCON.")
process_output.ocmmnt(constants.IOTTY, "User-terminated execution of VMCON.")
elif ifail == 0:
process_output.ocmmnt(
constants.NOUT, "Improper input parameters to the VMCON routine."
)
process_output.ocmmnt(constants.NOUT, "PROCESS coding must be checked.")
process_output.ocmmnt(
constants.IOTTY, "Improper input parameters to the VMCON routine."
)
process_output.ocmmnt(constants.IOTTY, "PROCESS coding must be checked.")
elif ifail == 2:
process_output.ocmmnt(
constants.NOUT,
"The maximum number of calls has been reached without solution.",
)
process_output.ocmmnt(
constants.NOUT,
"The code may be stuck in a minimum in the residual space that is significantly above zero.",
)
process_output.oblnkl(constants.NOUT)
process_output.ocmmnt(
constants.NOUT, "There is either no solution possible, or the code"
)
process_output.ocmmnt(
constants.NOUT, "is failing to escape from a deep local minimum."
)
process_output.ocmmnt(
constants.NOUT,
"Try changing the variables in IXC, or modify their initial values.",
)
process_output.ocmmnt(
constants.IOTTY,
"The maximum number of calls has been reached without solution.",
)
process_output.ocmmnt(
constants.IOTTY,
"The code may be stuck in a minimum in the residual space that is significantly above zero.",
)
process_output.oblnkl(constants.NOUT)
process_output.oblnkl(constants.IOTTY)
process_output.ocmmnt(
constants.IOTTY, "There is either no solution possible, or the code"
)
process_output.ocmmnt(
constants.IOTTY, "is failing to escape from a deep local minimum."
)
process_output.ocmmnt(
constants.IOTTY,
"Try changing the variables in IXC, or modify their initial values.",
)
elif ifail == 3:
process_output.ocmmnt(
constants.NOUT, "The line search required the maximum of 10 calls."
)
process_output.ocmmnt(
constants.NOUT, "A feasible solution may be difficult to achieve."
)
process_output.ocmmnt(
constants.NOUT, "Try changing or adding variables to IXC."
)
process_output.ocmmnt(
constants.IOTTY, "The line search required the maximum of 10 calls."
)
process_output.ocmmnt(
constants.IOTTY, "A feasible solution may be difficult to achieve."
)
process_output.ocmmnt(
constants.IOTTY, "Try changing or adding variables to IXC."
)
elif ifail == 4:
process_output.ocmmnt(
constants.NOUT, "An uphill search direction was found."
)
process_output.ocmmnt(
constants.NOUT, "Try changing the equations in ICC, or"
)
process_output.ocmmnt(constants.NOUT, "adding new variables to IXC.")
process_output.ocmmnt(
constants.IOTTY, "An uphill search direction was found."
)
process_output.ocmmnt(
constants.IOTTY, "Try changing the equations in ICC, or"
)
process_output.ocmmnt(constants.IOTTY, "adding new variables to IXC.")
elif ifail == 5:
process_output.ocmmnt(
constants.NOUT, "The quadratic programming technique was unable to"
)
process_output.ocmmnt(constants.NOUT, "find a feasible point.")
process_output.oblnkl(constants.NOUT)
process_output.ocmmnt(
constants.NOUT, "Try changing or adding variables to IXC, or modify"
)
process_output.ocmmnt(
constants.NOUT,
"their initial values (especially if only 1 optimisation",
)
process_output.ocmmnt(constants.NOUT, "iteration was performed).")
process_output.ocmmnt(
constants.IOTTY, "The quadratic programming technique was unable to"
)
process_output.ocmmnt(constants.IOTTY, "find a feasible point.")
process_output.oblnkl(constants.IOTTY)
process_output.ocmmnt(
constants.IOTTY, "Try changing or adding variables to IXC, or modify"
)
process_output.ocmmnt(
constants.IOTTY,
"their initial values (especially if only 1 optimisation",
)
process_output.ocmmnt(constants.IOTTY, "iteration was performed).")
elif ifail == 6:
process_output.ocmmnt(
constants.NOUT, "The quadratic programming technique was restricted"
)
process_output.ocmmnt(
constants.NOUT, "by an artificial bound, or failed due to a singular"
)
process_output.ocmmnt(constants.NOUT, "matrix.")
process_output.ocmmnt(
constants.NOUT, "Try changing the equations in ICC, or"
)
process_output.ocmmnt(constants.NOUT, "adding new variables to IXC.")
process_output.ocmmnt(
constants.IOTTY, "The quadratic programming technique was restricted"
)
process_output.ocmmnt(
constants.IOTTY, "by an artificial bound, or failed due to a singular"
)
process_output.ocmmnt(constants.IOTTY, "matrix.")
process_output.ocmmnt(
constants.IOTTY, "Try changing the equations in ICC, or"
)
process_output.ocmmnt(constants.IOTTY, "adding new variables to IXC.")
def scan_1d(self):
"""Run a 1-D scan."""
# initialise dict which will contain ifail values for each scan point
scan_1d_ifail_dict = {}
for iscan in range(1, scan_variables.isweep + 1):
self.scan_1d_write_point_header(iscan)
start_time = time.time()
ifail = self.doopt()
scan_1d_ifail_dict[iscan] = ifail
write_output_files(
models=self.models, ifail=ifail, runtime=time.time() - start_time
)
show_errors(constants.NOUT)
logging_model_handler.clear_logs()
# outvar now contains results
self.scan_1d_write_plot()
print(
" ****************************************** Scan Convergence Summary ****************************************** \n"
)
sweep_values = scan_variables.sweep[: scan_variables.isweep]
nsweep_var_name, _ = self.scan_select(
scan_variables.nsweep, scan_variables.sweep, scan_variables.isweep
)
converged_count = 0
# offsets for aligning the converged/unconverged column
max_sweep_value_length = len(str(np.max(sweep_values)).replace(".", ""))
offsets = [
max_sweep_value_length - len(str(sweep_val).replace(".", ""))
for sweep_val in sweep_values
]
for iscan in range(1, scan_variables.isweep + 1):
if scan_1d_ifail_dict[iscan] == 1:
converged_count += 1
print(
f"Scan {iscan:02d}: {nsweep_var_name} = {sweep_values[iscan - 1]} "
+ " " * offsets[iscan - 1]
+ "\u001b[32mCONVERGED \u001b[0m"
)
else:
print(
f"Scan {iscan:02d}: {nsweep_var_name} = {sweep_values[iscan - 1]} "
+ " " * offsets[iscan - 1]
+ "\u001b[31mUNCONVERGED \u001b[0m"
)
converged_percentage = converged_count / scan_variables.isweep * 100
print(f"\nConvergence Percentage: {converged_percentage:.2f}%")
def scan_2d(self):
"""Run a 2-D scan."""
# Initialise intent(out) arrays
self.scan_2d_init()
iscan = 1
# initialise array which will contain ifail values for each scan point
scan_2d_ifail_list = np.zeros(
(scan_variables.NOUTVARS, scan_variables.IPNSCNS),
dtype=np.float64,
order="F",
)
for iscan_1 in range(1, scan_variables.isweep + 1):
for iscan_2 in range(1, scan_variables.isweep_2 + 1):
self.scan_2d_write_point_header(iscan, iscan_1, iscan_2)
start_time = time.time()
ifail = self.doopt()
write_output_files(
models=self.models, ifail=ifail, runtime=time.time() - start_time
)
show_errors(constants.NOUT)
logging_model_handler.clear_logs()
scan_2d_ifail_list[iscan_1][iscan_2] = ifail
iscan = iscan + 1
print(
" ****************************************** Scan Convergence Summary ****************************************** \n"
)
sweep_1_values = scan_variables.sweep[: scan_variables.isweep]
sweep_2_values = scan_variables.sweep_2[: scan_variables.isweep_2]
nsweep_var_name, _ = self.scan_select(
scan_variables.nsweep, scan_variables.sweep, scan_variables.isweep
)
nsweep_2_var_name, _ = self.scan_select(
scan_variables.nsweep_2, scan_variables.sweep_2, scan_variables.isweep_2
)
converged_count = 0
scan_point = 1
# offsets for aligning the converged/unconverged column
max_sweep1_value_length = len(str(np.max(sweep_1_values)).replace(".", ""))
max_sweep2_value_length = len(str(np.max(sweep_2_values)).replace(".", ""))
offsets = np.zeros(
(scan_variables.isweep, scan_variables.isweep_2), dtype=int, order="F"
)
for count1, sweep1 in enumerate(sweep_1_values):
for count2, sweep2 in enumerate(sweep_2_values):
offsets[count1][count2] = (
max_sweep1_value_length
- len(str(sweep1).replace(".", ""))
+ max_sweep2_value_length
- len(str(sweep2).replace(".", ""))
)
for iscan_1 in range(1, scan_variables.isweep + 1):
for iscan_2 in range(1, scan_variables.isweep_2 + 1):
if scan_2d_ifail_list[iscan_1][iscan_2] == 1:
converged_count += 1
print(
f"Scan {scan_point:02d}: ({nsweep_var_name} = {sweep_1_values[iscan_1 - 1]}, {nsweep_2_var_name} = {sweep_2_values[iscan_2 - 1]}) "
+ " " * offsets[iscan_1 - 1][iscan_2 - 1]
+ "\u001b[32mCONVERGED \u001b[0m"
)
scan_point += 1
else:
print(
f"Scan {scan_point:02d}: ({nsweep_var_name} = {sweep_1_values[iscan_1 - 1]}, {nsweep_2_var_name} = {sweep_2_values[iscan_2 - 1]}) "
+ " " * offsets[iscan_1 - 1][iscan_2 - 1]
+ "\u001b[31mUNCONVERGED \u001b[0m"
)
scan_point += 1
converged_percentage = (
converged_count / (scan_variables.isweep * scan_variables.isweep_2) * 100
)
print(f"\nConvergence Percentage: {converged_percentage:.2f}%")
def scan_2d_init(self):
process_output.ovarin(
constants.MFILE,
"Number of first variable scan points",
"(isweep)",
scan_variables.isweep,
)
process_output.ovarin(
constants.MFILE,
"Number of second variable scan points",
"(isweep_2)",
scan_variables.isweep_2,
)
process_output.ovarin(
constants.MFILE,
"Scanning first variable number",
"(nsweep)",
scan_variables.nsweep,
)
process_output.ovarin(
constants.MFILE,
"Scanning second variable number",
"(nsweep_2)",
scan_variables.nsweep_2,
)
process_output.ovarin(
constants.MFILE,
"Scanning second variable number",
"(nsweep_2)",
scan_variables.nsweep_2,
)
process_output.ovarin(
constants.MFILE,
"Scanning second variable number",
"(nsweep_2)",
scan_variables.nsweep_2,
)
def scan_1d_write_point_header(self, iscan: int):
global_variables.iscan_global = iscan
global_variables.vlabel, global_variables.xlabel = self.scan_select(
scan_variables.nsweep, scan_variables.sweep, iscan
)
process_output.oblnkl(constants.NOUT)
process_output.ostars(constants.NOUT, 110)
process_output.write(
constants.NOUT,
f"***** Scan point {iscan} of {scan_variables.isweep} : {global_variables.xlabel}"
f", {global_variables.vlabel} = {scan_variables.sweep[iscan - 1]} "
"*****",
)
process_output.ostars(constants.NOUT, 110)
process_output.oblnkl(constants.MFILE)
process_output.ovarin(constants.MFILE, "Scan point number", "(iscan)", iscan)
print(
f"Starting scan point {iscan} of {scan_variables.isweep} : "
f"{global_variables.xlabel} , {global_variables.vlabel}"
f" = {scan_variables.sweep[iscan - 1]}"
)
def scan_2d_write_point_header(self, iscan, iscan_1, iscan_2):
iscan_r = scan_variables.isweep_2 - iscan_2 + 1 if iscan_1 % 2 == 0 else iscan_2
# Makes iscan available globally (read-only)
global_variables.iscan_global = iscan
global_variables.vlabel, global_variables.xlabel = self.scan_select(
scan_variables.nsweep, scan_variables.sweep, iscan_1
)
global_variables.vlabel_2, global_variables.xlabel_2 = self.scan_select(
scan_variables.nsweep_2, scan_variables.sweep_2, iscan_r
)
process_output.oblnkl(constants.NOUT)
process_output.ostars(constants.NOUT, 110)
process_output.write(
constants.NOUT,
f"***** 2D Scan point {iscan} of {scan_variables.isweep * scan_variables.isweep_2} : "
f"{global_variables.vlabel} = {scan_variables.sweep[iscan_1 - 1]} and"
f" {global_variables.vlabel_2} = {scan_variables.sweep_2[iscan_r - 1]} "
"*****",
)
process_output.ostars(constants.NOUT, 110)
process_output.oblnkl(constants.MFILE)
process_output.ovarin(constants.MFILE, "Scan point number", "(iscan)", iscan)
print(
f"Starting scan point {iscan}: {global_variables.xlabel}, "
f"{global_variables.vlabel} = {scan_variables.sweep[iscan_1 - 1]}"
f" and {global_variables.xlabel_2}, "
f"{global_variables.vlabel_2} = {scan_variables.sweep_2[iscan_r - 1]} "
)
return iscan_r
def scan_1d_write_plot(self):
if scan_variables.first_call_1d:
process_output.ovarin(
constants.MFILE,
"Number of scan points",
"(isweep)",
scan_variables.isweep,
)
process_output.ovarin(
constants.MFILE,
"Scanning variable number",
"(nsweep)",
scan_variables.nsweep,
)
scan_variables.first_call_1d = False
def scan_select(self, nwp, swp, iscn):
match nwp:
case 1:
physics_variables.aspect = swp[iscn - 1]
case 2:
divertor_variables.pflux_div_heat_load_max_mw = swp[iscn - 1]
case 3:
constraint_variables.p_plant_electric_net_required_mw = swp[iscn - 1]
case 4:
physics_variables.hfact = swp[iscn - 1]
case 5:
tfcoil_variables.oacdcp = swp[iscn - 1]
case 6:
constraint_variables.pflux_fw_neutron_max_mw = swp[iscn - 1]
case 7:
physics_variables.beamfus0 = swp[iscn - 1]
case 9:
physics_variables.temp_plasma_electron_vol_avg_kev = swp[iscn - 1]
case 10:
numerics.boundu[14] = swp[iscn - 1]
case 11:
physics_variables.beta_norm_max = swp[iscn - 1]
case 12:
current_drive_variables.f_c_plasma_bootstrap_max = swp[iscn - 1]
case 13:
numerics.boundu[9] = swp[iscn - 1]
case 16:
physics_variables.rmajor = swp[iscn - 1]
case 17:
constraint_variables.b_tf_inboard_max = swp[iscn - 1]
case 18:
constraint_variables.eta_cd_norm_hcd_primary_max = swp[iscn - 1]
case 19:
numerics.boundl[15] = swp[iscn - 1]
case 20:
constraint_variables.t_burn_min = swp[iscn - 1]
case 22:
if cost_variables.i_plant_availability == 1:
raise ProcessValueError(
"Do not scan f_t_plant_available if i_plant_availability=1"
)
cost_variables.f_t_plant_available = swp[iscn - 1]
case 24:
constraint_variables.p_fusion_total_max_mw = swp[iscn - 1]
case 25:
physics_variables.kappa = swp[iscn - 1]
case 26:
physics_variables.triang = swp[iscn - 1]
case 27:
constraint_variables.tbrmin = swp[iscn - 1]
case 28:
physics_variables.b_plasma_toroidal_on_axis = swp[iscn - 1]
case 29:
impurity_radiation_module.coreradius = swp[iscn - 1]
case 31:
constraint_variables.f_alpha_energy_confinement_min = swp[iscn - 1]
case 32:
numerics.epsvmc = swp[iscn - 1]
case 38:
numerics.boundu[128] = swp[iscn - 1]
case 39:
numerics.boundu[130] = swp[iscn - 1]
case 40:
numerics.boundu[134] = swp[iscn - 1]
case 41:
build_variables.dr_blkt_outboard = swp[iscn - 1]
case 42:
impurity_radiation_module.f_nd_impurity_electrons[8] = swp[iscn - 1]
impurity_radiation_module.f_nd_impurity_electron_array[8] = (
impurity_radiation_module.f_nd_impurity_electrons[8]
)
case 44:
tfcoil_variables.sig_tf_case_max = swp[iscn - 1]
case 45:
tfcoil_variables.temp_tf_superconductor_margin_min = swp[iscn - 1]
case 46:
numerics.boundu[151] = swp[iscn - 1]
case 48:
tfcoil_variables.n_tf_wp_pancakes = int(swp[iscn - 1])
case 49:
tfcoil_variables.n_tf_wp_layers = int(swp[iscn - 1])
case 50:
impurity_radiation_module.f_nd_impurity_electrons[12] = swp[iscn - 1]
impurity_radiation_module.f_nd_impurity_electron_array[12] = (
impurity_radiation_module.f_nd_impurity_electrons[12]
)
case 51:
physics_variables.f_p_div_lower = swp[iscn - 1]
case 52:
physics_variables.rad_fraction_sol = swp[iscn - 1]
case 53:
numerics.boundu[156] = swp[iscn - 1]
case 54:
tfcoil_variables.b_crit_upper_nbti = swp[iscn - 1]
case 55:
build_variables.dr_shld_inboard = swp[iscn - 1]
case 56:
heat_transport_variables.p_cryo_plant_electric_max_mw = swp[iscn - 1]
case 57:
numerics.boundl[1] = swp[iscn - 1]
case 58:
build_variables.dr_fw_plasma_gap_inboard = swp[iscn - 1]
case 59:
build_variables.dr_fw_plasma_gap_outboard = swp[iscn - 1]
case 60:
tfcoil_variables.sig_tf_wp_max = swp[iscn - 1]
case 61:
rebco_variables.copperaoh_m2_max = swp[iscn - 1]
case 62:
pfcoil_variables.coheof = swp[iscn - 1]
case 63:
build_variables.dr_cs = swp[iscn - 1]
case 64:
pfcoil_variables.ohhghf = swp[iscn - 1]
case 65:
cs_fatigue_variables.n_cycle_min = swp[iscn - 1]
case 66:
pfcoil_variables.oh_steel_frac = swp[iscn - 1]
case 67:
cs_fatigue_variables.t_crack_vertical = swp[iscn - 1]
case 68:
fwbs_variables.inlet_temp_liq = swp[iscn - 1]
case 69:
fwbs_variables.outlet_temp_liq = swp[iscn - 1]
case 70:
fwbs_variables.blpressure_liq = swp[iscn - 1]
case 71:
fwbs_variables.n_liq_recirc = swp[iscn - 1]
case 72:
fwbs_variables.bz_channel_conduct_liq = swp[iscn - 1]
case 73:
fwbs_variables.pnuc_fw_ratio_dcll = swp[iscn - 1]
case 74:
fwbs_variables.f_nuc_pow_bz_struct = swp[iscn - 1]
case 75:
fwbs_variables.dx_fw_module = swp[iscn - 1]
case 76:
heat_transport_variables.eta_turbine = swp[iscn - 1]
case 77:
cost_variables.startupratio = swp[iscn - 1]
case 78:
cost_variables.fkind = swp[iscn - 1]
case 79:
current_drive_variables.eta_ecrh_injector_wall_plug = swp[iscn - 1]
case 80:
tfcoil_variables.fcoolcp = swp[iscn - 1]
case 81:
tfcoil_variables.n_tf_coil_turns = swp[iscn - 1]
case _:
raise ProcessValueError("Illegal scan variable number", nwp=nwp)
return ScanVariables(int(nwp)).value
|