diff --git a/.gitignore b/.gitignore
index 68bc17f9..72cf5d55 100644
--- a/.gitignore
+++ b/.gitignore
@@ -158,3 +158,9 @@ cython_debug/
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
+
+# Icarus-specific files
+examples/example_outputs
+src/icarus.egg-info/PKG-INFO
+.vscode
+moose-config.json
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
index f288702d..4362b491 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,674 +1,502 @@
- GNU GENERAL PUBLIC LICENSE
- Version 3, 29 June 2007
+ GNU LESSER GENERAL PUBLIC LICENSE
+ Version 2.1, February 1999
- Copyright (C) 2007 Free Software Foundation, Inc.
+ Copyright (C) 1991, 1999 Free Software Foundation, Inc.
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+
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diff --git a/README.md b/README.md
index 10d5b18d..35021787 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,62 @@
-# icarus
-Machine learning for fusion simulation validation.
+# Icarus
+
+A fully customisable set of simulation-driven machine learning tools that interacts with MOOSE and can be used to assess the agreement between an experiment and simulation; that is, to validate the simulation with experimental data and provide the engineer with a probable reason for any mismatches to allow further investigation and diagnosis.
+
+## Installation
+### Virtual Environment
+
+Icarus is intended for use in a virtual environment. You can create a new virtual environment with
+
+```
+python -m venv venv
+```
+
+and activate it with
+```
+source venv/bin/activate
+```
+
+### Standard & Developer Installation
+
+Clone `icarus` to your local system along with submodules using
+
+```
+git clone --recurse-submodules git@github.com:aurora-multiphysics/icarus.git
+```
+
+`cd` to the root directory of `icarus` and run
+```
+pip install .
+```
+
+Alternatively, to create an editable/developer installation of `icarus`
+```
+pip install -e .
+```
+
+### MOOSE App
+
+`icarus` is intended for use with any MOOSE app
+It has been developed and tested using the `proteus` MOOSE app which can be found here: https://github.com/aurora-multiphysics/proteus. Follow the build instructions found on this page to install `proteus`.
+
+## Getting Started
+
+The examples folder shows how to use `icarus` for different input files and with different levels of user control. Make sure to include a moose_config.json file wherever you run it, and change the values contained to match your system.
+
+Icarus requires a moose_config.json file to be present wherever it is run; if there is not one already present, it will create one with the following values:
+```
+{
+ "main_path": "path/to/moose",
+ "app_path": "path/to/YourMooseApp",
+ "app_name": "YourMooseApp-opt"
+}
+```
+
+Make sure to change these values to match your system before running Icarus.
+
+## Contributors
+
+- Isaac Magee, UK Atomic Energy Authority, (iomags2211)
+- Lloyd Fletcher, UK Atomic Energy Authority, (TheScepticalRabbit)
+- Luke Humphrey, UK Atomic Energy Authority, (lukethehuman)
+
diff --git a/examples/ex1_2d_full_control.py b/examples/ex1_2d_full_control.py
new file mode 100644
index 00000000..06aa3b17
--- /dev/null
+++ b/examples/ex1_2d_full_control.py
@@ -0,0 +1,144 @@
+"""
+Example file showing how to use Icarus and detailing customisable options.
+
+This example runs an input file to produce a 2D temperature field with
+different parameters perturbed. Any results from the default input file
+are considered valid, while any other results are invalid. The datasets
+generated are split into training and validation datasets and labelled,
+and then a machine learning model is trained using the training dataset
+to distinguish between valid and invalid datasets (and classify the
+invalid sets depending on which parameter has been altered, if desired).
+The model is then validated using the validation datasets to see if it
+correctly predicted the labels.
+
+(c) Copyright UKAEA 2025.
+"""
+
+import sklearn
+from icarus import (DatasetGenerator,
+ ModelBuilder)
+
+
+def main():
+ """Run all of the other functions.
+ """
+ # Required parameters - change to desired values
+
+ # Input and output paths
+ input_file_path = "scripts/moose/plate_2d_thermal.i"
+ output_file_path = "examples/example_outputs/ex1_outputs/"
+
+ # Perturbation parameters, leave blank (ie parameters = {}) to use
+ # auto-generated tkinter interface
+ # Format as {param_name, [param_class, [param_values]]}
+ # where param_name is the name of the parameter from the input file
+ # param_class is the classification of the parameter
+ # geom - geometry, bc - boundary condtion, mat_prop - material property
+ # and param_values is a list of values the parameter should take on
+ parameters = {
+ "max_temp": ["bc", [750, 1000, 1250, 1500, 1750]],
+ "thermal_conductivity": ["mat_prop", [55, 65, 76, 85, 95]]
+ }
+
+ # Number of validation values to use for each parameter, respectively.
+ # This defines how many different perturbed values each parameter will
+ # take to create validation datasets to assess the quality of the model.
+ num_validation_values = [3, 3]
+
+ # Parallelisation options
+ n_tasks, n_threads = 1, 2 # Parallelisation of each simulation
+ num_para_runs = 2 # Number of concurrent simulations
+
+ # Ratio of invalid:valid datasets
+ # This defines how many datasets will be generated from the default
+ # input file (no perturbations -> labelled 0 for valid dataset),
+ # as the right ratio is important to ensure the model can recognise
+ # a valid dataset without assuming all datasets are valid.
+ datasets_per_ground_truth = 3
+
+ # Modelling parameters - change to desired values:
+
+ # Classifier framework
+ # For a comprehensive list of available classifiers, please refer
+ # to the relevant documentation:
+ # https://scikit-learn.org/stable/supervised_learning.html
+ classifier_framework = sklearn.ensemble.RandomForestClassifier
+
+ # Classifier parameters
+ # Note that not all parameters are required for every classifier.
+ # Any irrelevant or unsupported parameters for the selected model
+ # will be automatically ignored during model creation.
+ # For a comprehensive list of available parameters for each classifier,
+ # please refer to the relevant documentation:
+ # https://scikit-learn.org/stable/modules/classes.html#classifier
+ classifier_params = {
+ "n_estimators": 100,
+ "random_state": 42,
+ "kernel": "linear",
+ "C": 0.025,
+ "max_depth": 5
+ }
+
+ # The field being analysed as used by your input script
+ field_key = "temperature"
+
+ # Analysis sensor type
+ # (thermocouples for temperature, disp_sensors for displacement,
+ # or strain_gauges for strain)
+ sensor_type = "thermocouples"
+
+ # Sensor arrangement (x_sensors, y_sensors, z_sensors)
+ # Defines how many sensors are present in each dimension,
+ # with sensors being uniformly distributed.
+ # e.g. (3,2,1) would result in a 2D arrangement as follows:
+ # _______________
+ # | |
+ # | x x x |
+ # | x x x |
+ # |_______________|
+ sensors = (3, 2, 1)
+
+ # Number of spatial dimensions being used
+ dims = 2
+
+ # Whether the sensors should include errors or give exact values.
+ # Including errors means the model is more likely to miscategorise,
+ # but a successful model will be much better in reality, where errors
+ # are unavoidable.
+ errors = False
+
+ # Whether the model should be a multi-classifier rather than binary,
+ # so it can distinguish between perturbations to different classes of
+ # invalid parameters (geometry, BCs, material properties) rather than
+ # just valid and invalid results
+ multi = True
+
+ # Whether the unlabelled data should be deleted
+ delete_datasets = False
+
+ # Whether the model should be saved as a .pkl file
+ # and what it should be called
+ save = False
+ model_name = "ex1_2d_model"
+
+ # Initialise dataset generator to generate unlabelled datasets
+ dataset_generator = DatasetGenerator(input_file_path,
+ n_tasks, n_threads,
+ parameters, num_validation_values,
+ datasets_per_ground_truth,
+ output_file_path,
+ num_para_runs)
+
+ # Generate the unlabelled perturbed, validation and ground truths datasets
+ dataset_generator.generate_datasets()
+
+ # Initialise model builder to set up, run, and (optionally)
+ # save the chosen model
+ model = ModelBuilder(output_file_path, field_key, sensor_type, sensors,
+ dims, errors, multi, delete_datasets, save)
+
+ # Set up, run, and (optionally) save the chosen model
+ model.build_model(classifier_framework, classifier_params, model_name)
+
+if __name__ == "__main__":
+ main()
diff --git a/pyproject.toml b/pyproject.toml
index 02bf61c8..3a940775 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,22 +5,28 @@ build-backend = "setuptools.build_meta"
[project]
name = "icarus"
version = "0.1.0"
-description = "A machine learning tool for fusion simulation validation."
+description = "Machine learning for fusion simulation validation"
authors = [
- { name = "arjavp-ukaea", email = "arjav.poudel@ukaea.uk" },
+ { name = "iomags2211", email = "isaac.magee@ukaea.uk" },
+ { name = "arjavp-ukaea", email = "arjav.poudel@ukaea.uk"},
{ name = "barisc-ukaea", email = "baris.cavusoglu@ukaea.uk"},
- { name = "lukethehuman", email = "luke.humphrey@ukaea.uk"},
+ { name = "lukethehuman", email = "luke.humphrey@ukaea.uk"}
]
license = { file = "LICENSE" }
readme = "README.md"
classifiers = [
- "Programming Language :: Python :: 3",
- "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+ "Programming Language :: Python :: 3",
+ "License :: GNU LGPL",
"Operating System :: OS Independent",
]
requires-python = ">=3.11"
dependencies = [
"numpy>=1.26.0",
+ "sklearn>=1.6.0",
+ "mooseherder>=0.1.0",
+ "pyvale>=2025.5.3",
+ "joblib>=1.4.0",
+ "pytest>=8.2.0"
]
[project.urls]
diff --git a/scripts/moose/monoblock_3d_thermal.i b/scripts/moose/monoblock_3d_thermal.i
new file mode 100644
index 00000000..33a4e756
--- /dev/null
+++ b/scripts/moose/monoblock_3d_thermal.i
@@ -0,0 +1,753 @@
+#-------------------------------------------------------------------------
+# This is a MOOSE input file to simulate the temperature field of a 3D monoblock.
+# Author: Luke Humphrey, Lloyd Fletcher
+# (c) Copyright UKAEA 2024.
+#
+#-------------------------------------------------------------------------
+# DESCRIPTION
+#
+# Input file for a thermal simulation of a divertor monoblock.
+#
+# The monoblock is comprised of a copper-chromium-zirconium (CuCrZr) pipe
+# surrounded by tungsten armour with an OFHC copper pipe interlayer in between.
+#
+# Temperature-variant material properties are implemented via linear
+# interpolation from available data. Some of these material properties are not
+# used for this thermal simulation, but are in place for a thermomechanical
+# model including thermal expansion.
+#
+# Parameters describing the geometry are present at the top of the file above
+# the MOOSE tree structure. These parameters can be modified to produce a
+# monoblock design with the specified geometry.
+#
+# The mesh uses first order elements with a nominal mesh refinement of one
+# division per millimetre.
+#
+# The incoming heat is modelled as a constant heat flux on the top surface of
+# the block (i.e. the plasma-facing side). The outgoing heat is modelled as a
+# convective heat flux on the internal surface of the copper pipe. Besides this
+# heat flux, coolant flow is not modelled; the fluid region is treated as void.
+#
+# The boundary conditions are the incoming heat flux on the top surface, and
+# the coolant temperature. The solve is steady state and outputs temperature.
+
+#-------------------------------------------------------------------------
+# PARAMETER DEFINITIONS
+
+end_time = 30
+time_step = 0.5
+
+#_*
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Geometry
+PI = 3.141592653589793
+
+pipeThick = 0.0015 # m
+pipeIntDiam = 0.012 # m
+pipeExtDiam = ${fparse pipeIntDiam + 2*pipeThick}
+
+intLayerThick = 0.001 # m
+intLayerIntDiam = ${pipeExtDiam}
+intLayerExtDiam = ${fparse intLayerIntDiam + 2*intLayerThick}
+
+monoBThick = 0.003 # m
+monoBWidth = ${fparse intLayerExtDiam + 2*monoBThick}
+monoBArmHeight = 0.008 # m
+monoBDepth = 0.012 # m
+
+pipeIntCirc = ${fparse PI * pipeIntDiam}
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Mesh Sizing
+meshRefFact = 1
+meshDens = 1000 # divisions per metre (nominal)
+
+# Mesh Order
+secondOrder = false
+orderString = FIRST
+
+# Note: some of the following values must be even integers. This is in some
+# cases a requirement for the meshing functions used, else is is to ensure a
+# division is present at the centreline, thus allowing zero-displacement
+# boundary conditions to be applied to the centre nodes. These values are
+# halved, rounded to int, then doubled to ensure the result is an even int.
+
+# Number of divisions along the top section of the monoblock armour.
+monoBArmDivs = ${fparse int(monoBArmHeight * meshDens * meshRefFact)}
+
+# Number of divisions around each quadrant of the circumference of the pipe,
+# interlayer, and radial section of the monoblock armour.
+pipeCircSectDivs = ${fparse 2 * int(monoBWidth/2 * meshDens * meshRefFact / 2)}
+
+# Number of radial divisions for the pipe, interlayer, and radial section of
+# the monoblock armour respectively.
+pipeRadDivs = ${fparse max(int(pipeThick * meshDens * meshRefFact), 3)}
+intLayerRadDivs = ${fparse max(int(intLayerThick * meshDens * meshRefFact), 5)}
+monoBRadDivs = ${
+ fparse max(int((monoBWidth-intLayerExtDiam)/2 * meshDens * meshRefFact), 5)
+}
+
+# Number of divisions along monoblock depth (i.e. z-dimension).
+extrudeDivs = ${fparse max(2 * int(monoBDepth * meshDens * meshRefFact / 2), 4)}
+
+monoBElemSize = ${fparse monoBDepth / extrudeDivs}
+tol = ${fparse monoBElemSize / 10}
+ctol = ${fparse pipeIntCirc / (8 * 4 * pipeCircSectDivs)}
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Material Properties
+# Mono-Block/Armour = Tungsten
+# Interlayer = Oxygen-Free High-Conductivity (OFHC) Copper
+# Cooling pipe = Copper Chromium Zirconium (CuCrZr)
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Loads and BCs
+stressFreeTemp = 20 # degC
+coolantTemp = 500 # degC
+surfHeatFlux = 100000000.0 # W/m^2
+
+#**
+#-------------------------------------------------------------------------
+
+[Mesh]
+ second_order = ${secondOrder}
+
+ [mesh_monoblock]
+ type = PolygonConcentricCircleMeshGenerator
+ num_sides = 4
+ polygon_size = ${fparse monoBWidth / 2}
+ polygon_size_style = apothem # i.e. distance from centre to edge
+ ring_radii = '
+ ${fparse pipeIntDiam / 2}
+ ${fparse pipeExtDiam / 2}
+ ${fparse intLayerExtDiam / 2}
+ '
+ num_sectors_per_side = '
+ ${pipeCircSectDivs}
+ ${pipeCircSectDivs}
+ ${pipeCircSectDivs}
+ ${pipeCircSectDivs}
+ '
+ ring_intervals = '1 ${pipeRadDivs} ${intLayerRadDivs}'
+ background_intervals = ${monoBRadDivs}
+ preserve_volumes = on
+ flat_side_up = true
+ ring_block_names = 'void pipe interlayer'
+ background_block_names = monoblock
+ interface_boundary_id_shift = 1000
+ external_boundary_name = monoblock_boundary
+ generate_side_specific_boundaries = true
+ []
+
+ [mesh_armour]
+ type = GeneratedMeshGenerator
+ dim = 2
+ xmin = ${fparse monoBWidth /-2}
+ xmax = ${fparse monoBWidth / 2}
+ ymin = ${fparse monoBWidth / 2}
+ ymax = ${fparse monoBWidth / 2 + monoBArmHeight}
+ nx = ${pipeCircSectDivs}
+ ny = ${monoBArmDivs}
+ boundary_name_prefix = armour
+ []
+
+ [combine_meshes]
+ type = StitchedMeshGenerator
+ inputs = 'mesh_monoblock mesh_armour'
+ stitch_boundaries_pairs = 'monoblock_boundary armour_bottom'
+ clear_stitched_boundary_ids = true
+ []
+
+ [delete_void]
+ type = BlockDeletionGenerator
+ input = combine_meshes
+ block = void
+ new_boundary = internal_boundary
+ []
+
+ [merge_block_names]
+ type = RenameBlockGenerator
+ input = delete_void
+ old_block = '4 0'
+ new_block = 'armour armour'
+ []
+
+ [merge_boundary_names]
+ type = RenameBoundaryGenerator
+ input = merge_block_names
+ old_boundary = 'armour_top
+ armour_left 10002 15002
+ armour_right 10004 15004
+ 10003 15003'
+ new_boundary = 'top
+ left left left
+ right right right
+ bottom bottom'
+ []
+
+ [extrude]
+ type = AdvancedExtruderGenerator
+ input = merge_boundary_names
+ direction = '0 0 1'
+ heights = ${monoBDepth}
+ num_layers = ${extrudeDivs}
+ []
+
+ [name_node_centre_x_bottom_y_back_z]
+ type = BoundingBoxNodeSetGenerator
+ input = extrude
+ bottom_left = '${fparse -ctol}
+ ${fparse (monoBWidth/-2)-ctol}
+ ${fparse -tol}'
+ top_right = '${fparse ctol}
+ ${fparse (monoBWidth/-2)+ctol}
+ ${fparse tol}'
+ new_boundary = centre_x_bottom_y_back_z
+ []
+ [name_node_centre_x_bottom_y_front_z]
+ type = BoundingBoxNodeSetGenerator
+ input = name_node_centre_x_bottom_y_back_z
+ bottom_left = '${fparse -ctol}
+ ${fparse (monoBWidth/-2)-ctol}
+ ${fparse monoBDepth-tol}'
+ top_right = '${fparse ctol}
+ ${fparse (monoBWidth/-2)+ctol}
+ ${fparse monoBDepth+tol}'
+ new_boundary = centre_x_bottom_y_front_z
+ []
+ [name_node_left_x_bottom_y_centre_z]
+ type = BoundingBoxNodeSetGenerator
+ input = name_node_centre_x_bottom_y_front_z
+ bottom_left = '${fparse (monoBWidth/-2)-ctol}
+ ${fparse (monoBWidth/-2)-ctol}
+ ${fparse (monoBDepth/2)-tol}'
+ top_right = '${fparse (monoBWidth/-2)+ctol}
+ ${fparse (monoBWidth/-2)+ctol}
+ ${fparse (monoBDepth/2)+tol}'
+ new_boundary = left_x_bottom_y_centre_z
+ []
+ [name_node_right_x_bottom_y_centre_z]
+ type = BoundingBoxNodeSetGenerator
+ input = name_node_left_x_bottom_y_centre_z
+ bottom_left = '${fparse (monoBWidth/2)-ctol}
+ ${fparse (monoBWidth/-2)-ctol}
+ ${fparse (monoBDepth/2)-tol}'
+ top_right = '${fparse (monoBWidth/2)+ctol}
+ ${fparse (monoBWidth/-2)+ctol}
+ ${fparse (monoBDepth/2)+tol}'
+ new_boundary = right_x_bottom_y_centre_z
+ []
+[]
+
+[Variables]
+ [temperature]
+ family = LAGRANGE
+ order = ${orderString}
+ initial_condition = ${coolantTemp}
+ []
+[]
+
+[Kernels]
+ [heat_conduction]
+ type = HeatConduction
+ variable = temperature
+ []
+ [time_derivative]
+ type = HeatConductionTimeDerivative
+ variable = temperature
+ []
+[]
+
+[Functions]
+ [cucrzr_thermal_expansion]
+ type = PiecewiseLinear
+ xy_data = '
+ 20 1.67e-05
+ 50 1.7e-05
+ 100 1.73e-05
+ 150 1.75e-05
+ 200 1.77e-05
+ 250 1.78e-05
+ 300 1.8e-05
+ 350 1.8e-05
+ 400 1.81e-05
+ 450 1.82e-05
+ 500 1.84e-05
+ 550 1.85e-05
+ 600 1.86e-05
+ '
+ []
+ [copper_thermal_expansion]
+ type = PiecewiseLinear
+ xy_data = '
+ 20 1.67e-05
+ 50 1.7e-05
+ 100 1.72e-05
+ 150 1.75e-05
+ 200 1.77e-05
+ 250 1.78e-05
+ 300 1.8e-05
+ 350 1.81e-05
+ 400 1.82e-05
+ 450 1.84e-05
+ 500 1.85e-05
+ 550 1.87e-05
+ 600 1.88e-05
+ 650 1.9e-05
+ 700 1.91e-05
+ 750 1.93e-05
+ 800 1.96e-05
+ 850 1.98e-05
+ 900 2.01e-05
+ '
+ []
+ [tungsten_thermal_expansion]
+ type = PiecewiseLinear
+ xy_data = '
+ 20 4.5e-06
+ 100 4.5e-06
+ 200 4.53e-06
+ 300 4.58e-06
+ 400 4.63e-06
+ 500 4.68e-06
+ 600 4.72e-06
+ 700 4.76e-06
+ 800 4.81e-06
+ 900 4.85e-06
+ 1000 4.89e-06
+ 1200 4.98e-06
+ 1400 5.08e-06
+ 1600 5.18e-06
+ 1800 5.3e-06
+ 2000 5.43e-06
+ 2200 5.57e-06
+ 2400 5.74e-06
+ 2600 5.93e-06
+ 2800 6.15e-06
+ 3000 6.4e-06
+ 3200 6.67e-06
+ '
+ []
+[]
+
+[Materials]
+ [cucrzr_thermal_conductivity]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 318
+ 50 324
+ 100 333
+ 150 339
+ 200 343
+ 250 345
+ 300 346
+ 350 347
+ 400 347
+ 450 346
+ 500 346
+ '
+ variable = temperature
+ property = thermal_conductivity
+ block = 'pipe'
+ []
+ [copper_thermal_conductivity]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 401
+ 50 398
+ 100 395
+ 150 391
+ 200 388
+ 250 384
+ 300 381
+ 350 378
+ 400 374
+ 450 371
+ 500 367
+ 550 364
+ 600 360
+ 650 357
+ 700 354
+ 750 350
+ 800 347
+ 850 344
+ 900 340
+ 950 337
+ 1000 334
+ '
+ variable = temperature
+ property = thermal_conductivity
+ block = 'interlayer'
+ []
+ [tungsten_thermal_conductivity]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 173
+ 50 170
+ 100 165
+ 150 160
+ 200 156
+ 250 151
+ 300 147
+ 350 143
+ 400 140
+ 450 136
+ 500 133
+ 550 130
+ 600 127
+ 650 125
+ 700 122
+ 750 120
+ 800 118
+ 850 116
+ 900 114
+ 950 112
+ 1000 110
+ 1100 108
+ 1200 105
+ '
+ variable = temperature
+ property = thermal_conductivity
+ block = 'armour'
+ []
+
+ [cucrzr_density]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 8900
+ 50 8886
+ 100 8863
+ 150 8840
+ 200 8816
+ 250 8791
+ 300 8797
+ 350 8742
+ 400 8716
+ 450 8691
+ 500 8665
+ '
+ variable = temperature
+ property = density
+ block = 'pipe'
+ []
+ [copper_density]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 8940
+ 50 8926
+ 100 8903
+ 150 8879
+ 200 8854
+ 250 8829
+ 300 8802
+ 350 8774
+ 400 8744
+ 450 8713
+ 500 8681
+ 550 8647
+ 600 8612
+ 650 8575
+ 700 8536
+ 750 8495
+ 800 8453
+ 850 8409
+ 900 8363
+ '
+ variable = temperature
+ property = density
+ block = 'interlayer'
+ []
+ [tungsten_density]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 19300
+ 50 19290
+ 100 19280
+ 150 19270
+ 200 19250
+ 250 19240
+ 300 19230
+ 350 19220
+ 400 19200
+ 450 19190
+ 500 19180
+ 550 19170
+ 600 19150
+ 650 19140
+ 700 19130
+ 750 19110
+ 800 19100
+ 850 19080
+ 900 19070
+ 950 19060
+ 1000 19040
+ 1100 19010
+ 1200 18990
+ '
+ variable = temperature
+ property = density
+ block = 'armour'
+ []
+
+ [cucrzr_elastic_modulus]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 128000000000.0
+ 50 127000000000.0
+ 100 127000000000.0
+ 150 125000000000.0
+ 200 123000000000.0
+ 250 121000000000.0
+ 300 118000000000.0
+ 350 116000000000.0
+ 400 113000000000.0
+ 450 110000000000.0
+ 500 106000000000.0
+ 550 100000000000.0
+ 600 95000000000.0
+ 650 90000000000.0
+ 700 86000000000.0
+ '
+ variable = temperature
+ property = elastic_modulus
+ block = 'pipe'
+ []
+ [copper_elastic_modulus]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 117000000000.0
+ 50 116000000000.0
+ 100 114000000000.0
+ 150 112000000000.0
+ 200 110000000000.0
+ 250 108000000000.0
+ 300 105000000000.0
+ 350 102000000000.0
+ 400 98000000000.0
+ '
+ variable = temperature
+ property = elastic_modulus
+ block = 'interlayer'
+ []
+ [tungsten_elastic_modulus]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 398000000000.0
+ 50 398000000000.0
+ 100 397000000000.0
+ 150 397000000000.0
+ 200 396000000000.0
+ 250 396000000000.0
+ 300 395000000000.0
+ 350 394000000000.0
+ 400 393000000000.0
+ 450 391000000000.0
+ 500 390000000000.0
+ 550 388000000000.0
+ 600 387000000000.0
+ 650 385000000000.0
+ 700 383000000000.0
+ 750 381000000000.0
+ 800 379000000000.0
+ 850 376000000000.0
+ 900 374000000000.0
+ 950 371000000000.0
+ 1000 368000000000.0
+ 1100 362000000000.0
+ 1200 356000000000.0
+ '
+ variable = temperature
+ property = elastic_modulus
+ block = 'armour'
+ []
+
+ [cucrzr_specific_heat]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 390
+ 50 393
+ 100 398
+ 150 402
+ 200 407
+ 250 412
+ 300 417
+ 350 422
+ 400 427
+ 450 432
+ 500 437
+ 550 442
+ 600 447
+ 650 452
+ 700 458
+ '
+ variable = temperature
+ property = specific_heat
+ block = 'pipe'
+ []
+ [copper_specific_heat]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 388
+ 50 390
+ 100 394
+ 150 398
+ 200 401
+ 250 406
+ 300 410
+ 350 415
+ 400 419
+ 450 424
+ 500 430
+ 550 435
+ 600 441
+ 650 447
+ 700 453
+ 750 459
+ 800 466
+ 850 472
+ 900 479
+ 950 487
+ 1000 494
+ '
+ variable = temperature
+ property = specific_heat
+ block = 'interlayer'
+ []
+ [tungsten_specific_heat]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 20 129
+ 50 130
+ 100 132
+ 150 133
+ 200 135
+ 250 136
+ 300 138
+ 350 139
+ 400 141
+ 450 142
+ 500 144
+ 550 145
+ 600 147
+ 650 148
+ 700 150
+ 750 151
+ 800 152
+ 850 154
+ 900 155
+ 950 156
+ 1000 158
+ 1100 160
+ 1200 163
+ '
+ variable = temperature
+ property = specific_heat
+ block = 'armour'
+ []
+
+ [cucrzr_elasticity]
+ type = ComputeVariableIsotropicElasticityTensor
+ args = temperature
+ youngs_modulus = elastic_modulus
+ poissons_ratio = 0.33
+ block = 'pipe'
+ []
+ [copper_elasticity]
+ type = ComputeVariableIsotropicElasticityTensor
+ args = temperature
+ youngs_modulus = elastic_modulus
+ poissons_ratio = 0.33
+ block = 'interlayer'
+ []
+ [tungsten_elasticity]
+ type = ComputeVariableIsotropicElasticityTensor
+ args = temperature
+ youngs_modulus = elastic_modulus
+ poissons_ratio = 0.29
+ block = 'armour'
+ []
+
+ [cucrzr_expansion]
+ type = ComputeInstantaneousThermalExpansionFunctionEigenstrain
+ temperature = temperature
+ stress_free_temperature = ${stressFreeTemp}
+ thermal_expansion_function = cucrzr_thermal_expansion
+ eigenstrain_name = thermal_expansion_eigenstrain
+ block = 'pipe'
+ []
+ [copper_expansion]
+ type = ComputeInstantaneousThermalExpansionFunctionEigenstrain
+ temperature = temperature
+ stress_free_temperature = ${stressFreeTemp}
+ thermal_expansion_function = copper_thermal_expansion
+ eigenstrain_name = thermal_expansion_eigenstrain
+ block = 'interlayer'
+ []
+ [tungsten_expansion]
+ type = ComputeInstantaneousThermalExpansionFunctionEigenstrain
+ temperature = temperature
+ stress_free_temperature = ${stressFreeTemp}
+ thermal_expansion_function = tungsten_thermal_expansion
+ eigenstrain_name = thermal_expansion_eigenstrain
+ block = 'armour'
+ []
+
+ [coolant_heat_transfer_coefficient]
+ type = PiecewiseLinearInterpolationMaterial
+ xy_data = '
+ 1 4
+ 100 109.1e3
+ 150 115.9e3
+ 200 121.01e3
+ 250 128.8e3
+ 295 208.2e3
+ '
+ variable = temperature
+ property = heat_transfer_coefficient
+ boundary = 'internal_boundary'
+ []
+[]
+
+[BCs]
+ [heat_flux_in]
+ type = FunctionNeumannBC
+ variable = temperature
+ boundary = 'top'
+ function = '${fparse surfHeatFlux}*(1-exp(-t))'
+ []
+ [heat_flux_out]
+ type = ConvectiveHeatFluxBC
+ variable = temperature
+ boundary = 'internal_boundary'
+ T_infinity = ${coolantTemp}
+ heat_transfer_coefficient = heat_transfer_coefficient
+ []
+[]
+
+[Preconditioning]
+ [smp]
+ type = SMP
+ full = true
+ []
+[]
+
+[Executioner]
+ type = Transient
+ solve_type = 'PJFNK'
+ petsc_options_iname = '-pc_type -pc_hypre_type'
+ petsc_options_value = 'hypre boomeramg'
+ end_time = ${end_time}
+ dt = ${time_step}
+[]
+
+[Postprocessors]
+ [max_temp]
+ type = ElementExtremeValue
+ variable = temperature
+ []
+[]
+
+[Outputs]
+ exodus = true
+[]
diff --git a/scripts/moose/plate_2d_thermal.i b/scripts/moose/plate_2d_thermal.i
new file mode 100644
index 00000000..27f69efa
--- /dev/null
+++ b/scripts/moose/plate_2d_thermal.i
@@ -0,0 +1,79 @@
+#-------------------------------------------------------------------------
+# This is a MOOSE input file to simulate the temperature field of a 2D plate.
+# (c) Copyright UKAEA 2024.
+#-------------------------------------------------------------------------
+
+#_* Variables Block
+max_temp = 500
+init_temp = 20
+xmax = 10
+ymax = 2
+thermal_conductivity = 45
+specific_heat = 0.5
+prop_values = 8000
+bc1_value = ${init_temp}
+bc1_boundary = left
+bc2_value = ${max_temp}
+bc2_boundary = right
+#**
+
+[Mesh]
+ [generated]
+ type = GeneratedMeshGenerator
+ dim = 2
+ nx = 20
+ ny = 10
+ xmax = ${xmax}
+ ymax = ${ymax}
+ []
+[]
+
+[Variables]
+ [temperature]
+ initial_condition = ${init_temp}
+ []
+[]
+
+[Kernels]
+ [heat_conduction]
+ type = HeatConduction
+ variable = temperature
+ []
+[]
+
+[Materials]
+ [thermal]
+ type = HeatConductionMaterial
+ thermal_conductivity = ${thermal_conductivity}
+ specific_heat = ${specific_heat}
+ []
+ [density]
+ type = GenericConstantMaterial
+ prop_names = 'density'
+ prop_values = ${prop_values}
+ []
+[]
+
+[BCs]
+ [t_left]
+ type = DirichletBC
+ variable = temperature
+ value = ${bc1_value}
+ boundary = ${bc1_boundary}
+ []
+ [t_right]
+ type = FunctionDirichletBC
+ variable = temperature
+ function = ${bc2_value}
+ boundary = ${bc2_boundary}
+ []
+[]
+
+[Executioner]
+ type = Steady
+[]
+
+[Outputs]
+ console = false
+ exodus = true
+[]
diff --git a/src/icarus/__init__.py b/src/icarus/__init__.py
new file mode 100644
index 00000000..6f03855c
--- /dev/null
+++ b/src/icarus/__init__.py
@@ -0,0 +1,14 @@
+# -*- coding: utf-8 -*-
+"""
+Icarus
+"""
+
+from icarus.moosesetup import MooseSetup
+from icarus.userinterface import UserInterface
+from icarus.datasetgenerator import DatasetGenerator
+from icarus.modelbuilder import ModelBuilder
+
+__all__ = ["MooseSetup",
+ "UserInterface",
+ "DatasetGenerator",
+ "ModelBuilder"]
diff --git a/src/icarus/datasetgenerator.py b/src/icarus/datasetgenerator.py
new file mode 100644
index 00000000..6331dd32
--- /dev/null
+++ b/src/icarus/datasetgenerator.py
@@ -0,0 +1,273 @@
+"""
+DatasetGenerator module to generate unlabelled datasets for Icarus.
+
+(c) Copyright UKAEA 2025.
+"""
+
+import random
+import math
+from pathlib import Path
+from mooseherder import (MooseHerd,
+ InputModifier,
+ DirectoryManager,
+ SweepReader)
+from icarus import MooseSetup, UserInterface
+
+
+class DatasetGenerator:
+ """Generate the required unlabelled datasets
+ """
+ def __init__(self, input_file_path: str, n_tasks: int, n_threads: int,
+ parameters: dict[str, list], num_validation_values: list[int],
+ datasets_per_ground_truth: int, output_file_path: str,
+ num_para_runs: int = 2) -> None:
+ """__init__
+
+ Parameters
+ ----------
+ input_file_path : str
+ The path to the input file used.
+ n_tasks : int
+ The number of tasks for parallelisation.
+ n_threads : int
+ The number of threads for parallelisation.
+ parameters : dict[str, list]
+ Dictionary containing the name of the parameter and the list of
+ values for that parameter to take for each simulation to be run.
+ num_validation_values : list[int]
+ Contains a list of the number of validation datasets to generate
+ for each parameter.
+ datasets_per_ground_truth : int
+ Contains the ratio of perturbed:ground truth datasets.
+ output_file_path : str
+ Contains the path to the folder where the datasets and model(s)
+ will be stored.
+ num_para_runs : int, optional
+ Number of parallel runs for running the simulations, by default 2.
+
+ Raises
+ ----------
+ ValueError
+ If there are no parameters entered.
+ FileNotFoundError
+ If any of the required output file paths don't exist.
+ """
+ moose_setup = MooseSetup(input_file_path, output_file_path,
+ n_tasks, n_threads)
+ self.moose_runner, self.moose_modifier = \
+ moose_setup.setup_moose_runner()
+
+ self.num_para_runs = num_para_runs
+
+ found_vars = self.moose_modifier.get_vars()
+ if parameters == {}:
+ num_validation_values, parameters = \
+ UserInterface().accept_parameters(found_vars)
+
+ else:
+ if len(num_validation_values) != len(parameters) \
+ or 0 in num_validation_values:
+ raise ValueError(
+ "Invalid number of validation values specified")
+ for param_name, param_data in parameters.items():
+ if param_name not in found_vars:
+ raise ValueError(
+ f"Parameter {param_name} not in input file. Exiting.")
+ if param_data[0].lower() not in ["geom", "bc", "mat_prop"]:
+ raise ValueError(
+ f"Invalid parameter class {param_data[0]}. Exiting.")
+ if len(param_data[1]) < 1:
+ raise ValueError("Insufficient parameter values. Exiting.")
+
+ if not parameters:
+ raise ValueError(f"Unacceptable parameters {parameters}. \
+ Must be defined. Exiting.")
+
+ self.parameters = parameters
+ self.param_names = [key for key in parameters.keys()]
+ self.param_classes = [value[0] for value in parameters.values()]
+ self.param_values = [value[1] for value in parameters.values()]
+
+ self.num_validation_values = num_validation_values
+
+ self.datasets_per_ground_truth = datasets_per_ground_truth
+
+ if not Path(output_file_path).exists():
+ raise FileNotFoundError(f"Output file path {output_file_path} \
+ not found. Exiting.")
+
+ self.output_file_path = output_file_path
+
+
+ def generate_ground_truths(self, base_dir: Path, num_ground_truths: int) \
+ -> None:
+ """Generate the ground truth by running the input file
+ with no modifications, and save the results to the required base_dir
+ under the sub_dir_name "ground_truth". Creates a specified number of
+ ground_truth datasets
+
+ Parameters
+ ----------
+ base_dir : Path
+ Contains the base directory to save ground_truth sub-directory for
+ running the simulations.
+ num_ground_truths : list[int]
+ Number of ground_truth datasets to generate.
+ """
+ if num_ground_truths <= 0:
+ raise ValueError("Some ground truths must be generated. Exiting.")
+ dir_manager = MooseSetup.setup_directory_manager(base_dir,
+ 'ground_truth',
+ num_ground_truths)
+ moose_vars = [[{}]] * num_ground_truths
+ self.run_herd(dir_manager, moose_vars, num_ground_truths)
+
+
+ def generate_dataset(self, base_dir: Path, param_name: str,
+ param_class: str, param_values: list[float]) -> None:
+ """Generate the perturbed and validation datasets by running the
+ input file with modifications to specified parameter, and save the
+ results to the required base_dir under a sub_dir_named for the
+ perturbed parameter.
+
+ Parameters
+ ----------
+ base_dir : Path
+ Contains the base directory to save perturbed_param sub-directory
+ for running the simulations.
+ param_name : str
+ The name of the perturbed parameter.
+ param_class : str
+ The class of the perturbed parameter
+ (geometry, BC or material property).
+ param_values : list[float]
+ List of values for the currently selected parameter.
+ """
+ moose_vars = []
+ n_dirs = len(param_values)
+ dir_manager = MooseSetup.setup_directory_manager(
+ base_dir, str(f"{param_class}_{param_name}"), n_dirs)
+ for param in param_values:
+ moose_vars.append([{str(param_name): param}])
+ self.run_herd(dir_manager, moose_vars, n_dirs)
+
+
+ def generate_validation_values(self, param_values: list[float],
+ num_validation_values: int) -> list[float]:
+ """Generate a specified number of validation values
+ for the selected parameter, so that the model can be tested to see if
+ it can correctly determine when the parameter has been perturbed to a
+ value that was not present in the training dataset.
+
+ Parameters
+ ----------
+ param_values : list[float]
+ List of training values for the currently selected parameter(s).
+ num_validation_values : int
+ Number of validation values to generate for each parameter.
+
+ Returns
+ -------
+ validation_values : list[float]
+ List containing a list(s) of validation values for each parameter,
+ to be used to determine the value of the InputModifier for that
+ parameter for the run to be saved under
+ validation_datasets/param_name.
+ """
+ validation_values = []
+
+ min_val = min(param_values)
+ max_val = max(param_values)
+
+ while len(validation_values) < num_validation_values:
+ validation_value = random.uniform(min_val, max_val)
+ if validation_value not in param_values and \
+ validation_value not in validation_values:
+ validation_values.append(validation_value)
+
+ return validation_values
+
+
+ def generate_datasets(self) -> None:
+ """Convenience function to run all aspects of the DatasetGenerator
+ class.
+
+ Parameters
+ ----------
+ num_validation_values : list[int]
+ Contains a list of the number of validation datasets to generate
+ for each parameter
+ datasets_per_ground_truth : int
+ Contains the ratio of perturbed:ground truth datasets.
+ """
+ perturbed_path, perturbed_vals = Path(
+ str(self.output_file_path+"perturbed_datasets/")), None
+ validation_path, validation_vals = Path(
+ str(self.output_file_path+"validation_datasets/")), None
+
+ paths = {perturbed_path: perturbed_vals,
+ validation_path: validation_vals}
+
+ for name, param_class, values, n_valid in \
+ zip(self.param_names, self.param_classes, self.param_values,
+ self.num_validation_values):
+ validation_values = self.generate_validation_values(values,
+ n_valid)
+
+ paths[perturbed_path] = values
+ paths[validation_path] = validation_values
+
+ for path, vals in paths.items():
+ self.generate_dataset(path, name, param_class, vals)
+
+ for path in paths:
+ num_datasets = sum(1 for d in path.iterdir() if d.is_dir())
+ num_ground_truths = math.ceil(
+ num_datasets/self.datasets_per_ground_truth)
+ self.generate_ground_truths(path, num_ground_truths)
+
+
+ def run_herd(self, dir_manager: DirectoryManager,
+ moose_vars: InputModifier, n_para: int = 1,
+ keep_flag: bool = False) -> None:
+ """Run parametric sweeps of simulation chains in
+ parallel with configurable parallelisation options.
+
+ Takes a list of SimRunner objects and a corresponding list of
+ InputModifiers to insert the variables into the input scripts for the
+ SimRunners. Will first call all InputModifiers in the specified order
+ and then call run on all the SimRunners in order. Uses the
+ DirectoryManager class to log the directories in which each parallel
+ worker is creating input files and running simulations. Uses the
+ SweepReader class to read the output from one or more calls to
+ mooseherd.run_para().
+
+ Parameters
+ ----------
+ dir_manager : DirectoryManager
+ Used to control how many and which directories are used to run the
+ simulations.
+ moose_vars : InputModifier
+ Used to extract and modify the variables in the input file.
+ Specifies the comment character. Variable definition blocks should
+ begin #comment character#* and end #comment character#**
+ n_para : int, optional
+ Sets the number of simulation chains to run in parallel,
+ by default 1.
+ keep_flag : bool, optional
+ Flag used for allowing multiple calls to run to keep everything or
+ to overwrite each time, by default False - overwrite inputs and
+ outputs with multiple calls
+ """
+
+ herd = MooseHerd([self.moose_runner],
+ [self.moose_modifier],
+ dir_manager)
+ herd.set_num_para_sims(n_para=n_para)
+ herd.set_keep_flag(keep_flag)
+ for _ in range(self.num_para_runs):
+ herd.run_para(moose_vars)
+
+ sweep_reader = SweepReader(dir_manager, num_para_read=4)
+ sweep_reader.read_all_output_keys()
+ sweep_reader.read_results_para()
diff --git a/src/icarus/modelbuilder.py b/src/icarus/modelbuilder.py
new file mode 100644
index 00000000..906da739
--- /dev/null
+++ b/src/icarus/modelbuilder.py
@@ -0,0 +1,406 @@
+"""
+ModelBuilder module to create and analyse the Icarus machine learning model.
+
+(c) Copyright UKAEA 2025.
+"""
+
+from pathlib import Path
+import shutil
+import inspect
+import joblib
+import numpy as np
+from sklearn.metrics import accuracy_score
+from mooseherder import ExodusReader, SimData
+
+
+class ModelBuilder:
+ """Used to create and analyse the machine learning model
+ """
+ def __init__(self, output_file_path: str, field_key: str, sensor_type: str,
+ sensors: list, dims: int = 2, errors: bool = False,
+ multi: bool = False, delete_datasets: bool = False,
+ save: bool = True) -> None:
+ """__init__
+
+ Parameters
+ ----------
+ output_file_path : str
+ Path to the location that all outputs should be saved.
+ field_key : str
+ The field key used for the analysis field in the input file.
+ sensor_type : str, optional
+ The sensor type used to analyse the field in the
+ experiment/simulation.
+ sensors : list, optional
+ The number of sensors to in each dimension (x,y,z).
+ dims : int, optional
+ The dimensionality of the problem, by default 2
+ errors : bool, optional
+ Allows the user to determine whether or not the sensor array
+ should incorporate errors,
+ by default False.
+ multi : bool, optional
+ Allows the user to specify whether to use a multi-classifier,
+ by default False (meaning use a binary classifier).
+ delete_datasets : bool
+ Allows the user to determine whether or not to delete the
+ unlabelled datasets.
+ save : bool
+ Allows the user to determine whether or not to save the model
+ and the labelled datasets.
+
+ Raises
+ ----------
+ FileNotFoundError
+ If any of the required output file paths don't exist.
+ ValueError
+ If any of sensor_type, sensors, dims, errors, multi,
+ delete_datasets, or save are unacceptable.
+ """
+ import pyvale
+ self.pyvale = pyvale
+
+ if not Path(output_file_path).exists():
+ raise FileNotFoundError(
+ f"Output file path {output_file_path} not found. Exiting.")
+ self.output_file_path = output_file_path
+
+ self.field_key = field_key
+
+ if sensor_type not in ["thermocouples",
+ "disp_sensors",
+ "strain_gauges"]:
+ raise ValueError(f"Unacceptable sensor type {sensor_type}. \
+ Must be either thermocouples, disp_sensors, \
+ or strain_gauges. Exiting.")
+ self.sensor_type = sensor_type
+
+ for sensor in sensors:
+ if sensor == 0:
+ raise ValueError("Invalid sensor array {sensors}. \
+ Can't have a dimension with zero sensors. \
+ Exiting.")
+ if len(sensors) != 3 or sensors is None:
+ raise ValueError("Invalid sensor array {sensors}. \
+ Must have a number of sensors for exactly \
+ 3 dimensions. Exiting.")
+ self.sensors = sensors
+
+ if dims not in [1, 2, 3]:
+ raise ValueError(f"Dimensions must be 1, 2 or 3, not {dims}. \
+ Exiting.")
+ self.dims = dims
+
+ self.errors = errors
+ self.multi = multi
+ self.delete_datasets = delete_datasets
+ self.save = save
+
+ self.labelled_dataset_cols = (sensors[0]*sensors[1]*sensors[2])+1
+
+
+ def sensor_array(self, sim_data: SimData) -> "pyvale.SensorArrayPoint":
+ """Generate the array of sensors used to generate the labelled
+ datasets required for training the model.
+
+ Parameters
+ ----------
+ sim_data : SimData
+ The unlabelled datasets.
+
+ Returns
+ -------
+ sens_array : pyvale.SensorArrayPoint
+ The desired sensor array used to extract the values of the chosen
+ field at given points.
+ """
+ sensx, sensy, sensz = self.sensors[0], self.sensors[1], self.sensors[2]
+
+ sim_data.coords = sim_data.coords*1000.0
+
+ xmax = np.max(sim_data.coords[:, 0])
+ xmin = np.min(sim_data.coords[:, 0])
+
+ ymax, ymin, zmax, zmin = 0.0, 0.0, 0.0, 0.0
+ if self.dims > 1:
+ ymax = np.max(sim_data.coords[:, 1])
+ ymin = np.min(sim_data.coords[:, 1])
+ if self.dims > 2:
+ zmax = np.max(sim_data.coords[:, 2])
+ zmin = np.min(sim_data.coords[:, 2])
+
+ n_sens = (sensx, sensy, sensz)
+ x_lims = (xmin, xmax)
+ y_lims = (ymin, ymax)
+ z_lims = (zmin, zmax)
+ sens_pos = self.pyvale.create_sensor_pos_array(n_sens, x_lims,
+ y_lims, z_lims)
+ sens_data = self.pyvale.SensorData(positions=sens_pos)
+
+ errors_map = {
+ True: "basic_errs",
+ False: "no_errs"
+ }
+
+ func_name = f"{self.sensor_type}_{errors_map[self.errors]}"
+ factory = self.pyvale.SensorArrayFactory
+ func = getattr(factory, func_name)
+ sens_array = func(sim_data, sens_data, elem_dims=self.dims,
+ field_name=self.field_key)
+
+ return sens_array
+
+
+ def generate_training_dataset(self) \
+ -> list[list[list[float], int]]:
+ """Create a dataset by extracting values from the outputs of each run,
+ using ExodusReader class to read the data. Assigns a label to each
+ dataset depending on which (if any) parameter has been perturbed.
+
+ Raises
+ ----------
+ ValueError
+ If the training and/or validation datasets failed to generate.
+
+ Returns
+ ----------
+ labelled_dataset: np.array[np.array[list[float], int]]
+ 2D array containing the list of extracted measurements for each
+ dataset and its corresponding label. To be used to train/validate
+ the model.
+ """
+ labelled_dataset = np.empty((0, self.labelled_dataset_cols))
+ folder_path = Path(self.output_file_path+"perturbed_datasets/")
+
+ for file_path in folder_path.rglob('*.e'):
+ if "ground_truth" in str(file_path):
+ label = 0
+ else:
+ if self.multi:
+ labels = {"ground_truth": 0, "geom": 1,
+ "bc": 2, "mat_prop": 3}
+ for param_class, class_label in labels.items():
+ if param_class in str(file_path):
+ label = class_label
+ else:
+ label = 1
+
+ sim_data = ExodusReader(file_path).read_all_sim_data()
+ sens_array = self.sensor_array(sim_data)
+ measurements = sens_array.get_measurements()[:, 0, 1]
+
+ measurements = np.append(measurements, label)
+ labelled_dataset = np.vstack([labelled_dataset, measurements])
+
+ if len(labelled_dataset) <= 1:
+ raise ValueError("Training and/or validation datasets \
+ failed to generate. Exiting.")
+
+ return labelled_dataset
+
+
+ def generate_validation_dataset(self) \
+ -> list[list[list[float], int]]:
+ """Create a dataset by extracting values from the outputs of each run,
+ using ExodusReader class to read the data. Assigns a label to each
+ dataset depending on which (if any) parameter has been perturbed.
+
+ Raises
+ ----------
+ ValueError
+ If the training and/or validation datasets failed to generate.
+
+ Returns
+ ----------
+ labelled_dataset: np.array[np.array[list[float], int]]
+ 2D array containing the list of extracted measurements for each
+ dataset and its corresponding label. To be used to train/validate
+ the model.
+ """
+ labelled_dataset = np.empty((0, self.labelled_dataset_cols))
+ folder_path = Path(self.output_file_path+"validation_datasets/")
+
+ for file_path in folder_path.rglob('*.e'):
+ if "ground_truth" in str(file_path):
+ label = 0
+ else:
+ if self.multi:
+ labels = {"ground_truth": 0, "geom": 1,
+ "bc": 2, "mat_prop": 3}
+ for param_class, class_label in labels.items():
+ if param_class in str(file_path):
+ label = class_label
+ else:
+ label = 1
+
+ sim_data = ExodusReader(file_path).read_all_sim_data()
+ sens_array = self.sensor_array(sim_data)
+ measurements = sens_array.get_measurements()[:, 0, 1]
+
+ measurements = np.append(measurements, label)
+ labelled_dataset = np.vstack([labelled_dataset, measurements])
+
+ if len(labelled_dataset) <= 1:
+ raise ValueError("Training and/or validation datasets \
+ failed to generate. Exiting.")
+
+ return labelled_dataset
+
+
+ def generate_labelled_datasets(self):
+ """Generate both the training and validation labelled datasets.
+
+ Returns
+ ----------
+ training_dataset : np.array[np.array[list[float], int]]
+ The labelled training dataset.
+ validation_dataset : np.array[np.array[list[float], int]]
+ The labelled validation dataset.
+ """
+ training_dataset, validation_dataset = \
+ self.generate_training_dataset(), \
+ self.generate_validation_dataset()
+
+ return training_dataset, validation_dataset
+
+
+ def filter_parameters(self, classifier_framework, classifier_params):
+ """Filter the parameters to only use those which are valid for the
+ chosen classifier framework.
+
+ Returns
+ ----------
+ filtered_params : dict
+ The selected parameters that apply to the chosen classifier.
+ """
+ # Filter parameters according to chosen classifier
+ sig = inspect.signature(classifier_framework.__init__)
+ valid_params = set(sig.parameters.keys()) - {"self"}
+ filtered_params = {k: v for k, v in classifier_params.items()
+ if k in valid_params}
+
+ # Make sure there are some filtered parameters to be used
+ if len(filtered_params) == 0:
+ raise ValueError("Invalid parameters for {classifier_framework}: \
+ {classifier_params}")
+
+ # Print ignored parameters
+ ignored = set(classifier_params) - set(filtered_params)
+ print(f"Ignored parameters for {classifier_framework}: {ignored}")
+
+ return filtered_params
+
+
+ def delete_data(self, perturbed_path: Path, validation_path: Path) -> None:
+ """Delete the unlabelled datasets and just saves the labelled dataset
+
+ Parameters
+ ----------
+ perturbed_path : Path
+ Path to the save location for the perturbed datasets.
+ validation_path : Path
+ Path to the save location for the validation datasets.
+ """
+ for path in [perturbed_path, validation_path]:
+ for folder in path.iterdir():
+ if folder.is_dir():
+ shutil.rmtree(folder)
+
+
+ def output_model_metrics(self, y_val: list[float], y_pred: list[float]) \
+ -> None:
+ """Output the actual + predicted labels, as well as the final
+ accuracy score for the model.
+
+ Parameters
+ ----------
+ y_val : list[float]
+ The actual labels for the validation dataset.
+ y_pred : list[float]
+ The predicted labels for the validation dataset.
+ """
+ for i, (actual, predicted) in enumerate(zip(y_val, y_pred), start=1):
+ print(f"Row {i}: Actual Label = {actual}, Predicted Label = \
+ {predicted}")
+
+ val_accuracy = accuracy_score(y_val, y_pred)
+ print(f"Validation Accuracy: {val_accuracy * 100:.2f}%")
+
+
+ def save_datasets_and_model(self, classifier, model_name: str,
+ training_dataset: list[list[list[float], int]],
+ validation_dataset: list[list[list[float],
+ int]]):
+ """Save the labelled datasets and the model if specified.
+
+ Parameters
+ ----------
+ classifier
+ The machine learning model classifier.
+ model_name : str
+ The name of the model the saved.
+ training_dataset : np.array[np.array[list[float]
+ The labelled training dataset.
+ validation_dataset : np.array[np.array[list[float]
+ The labelled validation dataset.
+ """
+ if self.save:
+ header = ",".join([f"Sensor {i} reading" for i in range(
+ 1, self.labelled_dataset_cols)] + ["Label"])
+
+ joblib.dump(classifier,
+ str(self.output_file_path)+model_name+'_model.pkl')
+
+ perturbed_path = Path(self.output_file_path+"/perturbed_datasets")
+ validation_path = Path(self.output_file_path+"/labelled_datasets")
+
+ np.savetxt(
+ perturbed_path/"labelled_dataset.txt",
+ training_dataset, fmt="%d", delimiter=",", header=header,
+ comments='')
+
+ np.savetxt(
+ validation_path/"labelled_dataset.txt",
+ validation_dataset, fmt="%d", delimiter=",", header=header,
+ comments='')
+
+
+ def build_model(self, classifier_framework,
+ classifier_params: dict[str, float],
+ model_name: str):
+ """Convenience function to build the model using the other functions
+ present in the ModelBuilder class.
+
+ Parameters
+ ----------
+ classifier_framework
+ The chosen framework for the machine learning classifier.
+ classifier_params : dict[str, float]
+ The list of parameters for the classifier specified by the user.
+ model_name : str
+ The name of the model to be saved.
+ """
+ # Generate labelled training and validation datasets and saves them
+ training_dataset, validation_dataset = \
+ self.generate_labelled_datasets()
+
+ # Split data according to modelling requirements
+ x_train, y_train, x_val, y_val = training_dataset[:, :-1], \
+ training_dataset[:, -1], validation_dataset[:, :-1], \
+ validation_dataset[:, -1]
+
+ # Build the specified model and predict the labels of the validation
+ # datasets
+ filtered_params = self.filter_parameters(classifier_framework,
+ classifier_params)
+ classifier = classifier_framework(**filtered_params)
+ classifier.fit(x_train, y_train)
+ y_pred = classifier.predict(x_val)
+
+ # Output model metrics
+ self.output_model_metrics(y_val, y_pred)
+
+ # Save model and labelled datasets if specified
+ self.save_datasets_and_model(classifier, model_name,
+ training_dataset,
+ validation_dataset)
diff --git a/src/icarus/moosesetup.py b/src/icarus/moosesetup.py
new file mode 100644
index 00000000..ad8ad924
--- /dev/null
+++ b/src/icarus/moosesetup.py
@@ -0,0 +1,133 @@
+"""
+MooseSetup module to carry out the setup required for Icarus.
+
+(c) Copyright UKAEA 2025.
+"""
+
+from pathlib import Path
+import os
+from mooseherder import (MooseRunner,
+ MooseConfig,
+ InputModifier,
+ DirectoryManager)
+
+
+class MooseSetup:
+ """Setup the file system, MOOSE Runners and Directory Managers required to
+ run the simulation and generate the datasets.
+ """
+ def __init__(self, input_file_path: str, output_file_path: str,
+ n_tasks: int, n_threads: int) -> None:
+ """__init__
+
+ Parameters
+ ----------
+ input_file_path : str
+ The path to the input file used.
+ output_file_path : str
+ The path to where the outputs will be stored.
+ n_tasks : int
+ The number of tasks for parallelisation.
+ n_threads : int
+ The number of threads for parallelisation.
+ """
+ self.input_file_path = input_file_path
+ self.output_file_path = output_file_path
+ self.n_tasks, self.n_threads = n_tasks, n_threads
+
+ config_file_path = Path.cwd()/'moose-config.json'
+ if not os.path.exists(config_file_path):
+ with open(config_file_path, "w", encoding="utf-8") as file:
+ file.write("""
+ {
+ "main_path": "path/to/projects/moose",
+ "app_path": "path/to/projects/babbler",
+ "app_name": "babbler-opt"
+ }
+ """)
+
+ for file_path in [Path(self.output_file_path+"perturbed_datasets"),
+ Path(self.output_file_path+"validation_datasets")]:
+ os.makedirs(file_path, exist_ok=True)
+
+ def setup_moose_runner(self) -> tuple[MooseRunner, InputModifier]:
+ """Constructor for MOOSE runner taking a MooseConfig object
+ that contains the paths to the main MOOSE install, the MOOSE app and
+ the MOOSE app name.
+
+ Raises
+ ----------
+ FileNotFoundError
+ If the input file path is unacceptable (not found or not an input
+ file), or if the moose-config.json file is unacceptable.
+ ValueError
+ If there are no parameters found in the input file.
+
+ Returns
+ ----------
+ moose_runner : MooseRunner
+ Constructed MOOSE runner used to run the input file with modified
+ variables.
+ moose_modifier : InputModifier
+ Used to extract and modify the variables in the input file.
+ Specifies the comment character. Variable definition blocks should
+ begin #comment character#* and end #comment character#**
+ """
+ if self.input_file_path[-2:] != ".i" or \
+ not Path(self.input_file_path).exists():
+ raise FileNotFoundError(f"Unacceptable input file path {self.input_file_path}. \
+ Must both exist and end in .i")
+
+ moose_input = Path(self.input_file_path)
+
+ moose_modifier = InputModifier(moose_input, '#', '')
+ if len(moose_modifier.get_vars()) == 0:
+ raise ValueError("No parameters found in input file. \
+ Check input file and try again.")
+
+ try:
+ moose_config = MooseConfig().read_config(
+ Path.cwd()/'moose-config.json')
+ except FileNotFoundError as e:
+ raise FileNotFoundError("JSON file moose-config.json not found, \
+ or points to non-existent MOOSE app.") \
+ from e
+
+ moose_runner = MooseRunner(moose_config)
+ moose_runner.set_run_opts(n_tasks=self.n_tasks,
+ n_threads=self.n_threads, redirect_out=False)
+
+ return moose_runner, moose_modifier
+
+ @staticmethod
+ def setup_directory_manager(base_dir: str, sub_dir_name: str,
+ n_dirs: int = 1) -> DirectoryManager:
+ """Set up directory manager to manage directories for running
+ simulations in parallel with the mooseherd. Clears existing
+ directories and creates specified new ones with given names
+
+ Parameters
+ ----------
+ base_dir : Path
+ Sets the base directory to create sub-directories for running the
+ simulations. The base directory must exist.
+ sub_dir_name : str
+ String to be used at the start of the created sub-directores.
+ Default on creation is 'sim-workdir'. Populates the list of run
+ directories using the new sub directory name.
+ n_dirs : int, optional
+ Number of directories to be created., by default 1
+
+ Returns
+ -------
+ dir_manager : DirectoryManager
+ Used to control how many and which directories are used to run the
+ simulations.
+ """
+ dir_manager = DirectoryManager(n_dirs=n_dirs)
+ dir_manager.set_base_dir(base_dir)
+ dir_manager.set_sub_dir_name(sub_dir_name)
+ dir_manager.clear_dirs()
+ dir_manager.create_dirs()
+
+ return dir_manager
diff --git a/src/icarus/userinterface.py b/src/icarus/userinterface.py
new file mode 100644
index 00000000..77cc7c33
--- /dev/null
+++ b/src/icarus/userinterface.py
@@ -0,0 +1,204 @@
+"""
+UserInterface module to provide tkinter user interface to control Icarus.
+
+(c) Copyright UKAEA 2025.
+"""
+
+from tkinter import Tk, Label, Entry, Button, Frame, \
+ Checkbutton, BooleanVar, StringVar
+
+
+class UserInterface:
+ """User interface enabling users to control aspects of the
+ modelling process.
+ """
+ def __init__(self):
+ pass
+
+ def accept_parameters(self, parameters: dict[str, float]) \
+ -> tuple[list[int], dict[str, list[float]]]:
+ """Allow user to select which parameters to perturb, and the range,
+ interval of perturbation values, and number of validation values to
+ be used for each parameter.
+
+ Parameters
+ ----------
+ parameters : dict[str, float]
+ Dictionary of parameter names and their corresponding values in
+ the default input file.
+ Used to allow users to select which parameters to modify, and show
+ them the default values so they don't include them again.
+
+ Returns
+ ----------
+ num_val_values : list[int]
+ Number of validation values to use for each parameter
+ parameters : dict[str, list[float]]
+ Dictionary of parameter names and their corresponding list of
+ values for perturbation.
+ """
+
+ def submit_parameters():
+ """Save user-specified values to their corresponding variables to
+ allow creation of param_values list, and close the tkinter window.
+
+ Raises
+ ----------
+ ValueError
+ If the range or interval specified are invalid
+ Value Error
+ If there are fewer than 2 values for perturbation
+ Value Error
+ If there are fewer than 2 values for validation
+ """
+ nonlocal parameters, num_val_values
+ parameters = {}
+ num_val_values = []
+
+ for row in rows:
+ if row['checkbox_var'].get(): # Check if checkbox is ticked
+ param_name = row['param_name']
+ try:
+ param_class = str(row['param_class'].get())
+ default_val = float(row['default_val'])
+ min_val = float(row['min_val'].get())
+ max_val = float(row['max_val'].get())
+ interval = float(row['interval'].get())
+ num_validation_values = \
+ int(row['num_validation_values'].get())
+
+ if param_class.lower() not in ["geom", "bc",
+ "mat_prop"]:
+ raise ValueError("Invalid \
+ parameter classification")
+
+ if min_val >= max_val or interval <= 0:
+ raise ValueError("Invalid range or interval")
+
+ param_data = []
+ param_data.append(param_class)
+
+ param_values = []
+ current_val = min_val
+ while current_val <= max_val:
+ if current_val != default_val:
+ param_values.append(current_val)
+ current_val += interval
+
+ if len(param_values) < 2:
+ raise ValueError("Insufficient \
+ perturbation values")
+
+ if num_validation_values < 2:
+ raise ValueError("Insufficient validation values")
+
+ param_data.append(param_values)
+ num_val_values.append(num_validation_values)
+ parameters[param_name] = param_data
+
+ except ValueError as e:
+ error_label.config(text=f"Error: {str(e)}", fg="red")
+ return # Stop execution if an error occurs
+
+ if len(parameters) == 0:
+ error_label.config(text="No parameters submitted", fg="red")
+ return
+
+ param_root.destroy()
+
+ param_root = Tk()
+
+ table_frame = Frame(param_root)
+ table_frame.grid(row=2, column=0, columnspan=2, padx=10, pady=10)
+
+ headers = ["Select", "Param Name", "Param Class", "Default Value",
+ "Min Value", "Max Value", "Interval",
+ "No. Validation Values"]
+ for col, header in enumerate(headers):
+ Label(table_frame, text=header).grid(row=0, column=col,
+ padx=5, pady=5)
+
+ params = []
+ for parameter_name, parameter_value in parameters.items():
+ try:
+ if float(parameter_value) <= 10:
+ x = parameter_value
+ elif float(parameter_value) > 10 and parameter_value < 100:
+ x = 10
+ else:
+ x = parameter_value/2
+ params.append({"param_name": parameter_name,
+ "param_class": "geom/bc/mat_prop",
+ "default_val": parameter_value,
+ "min_val": parameter_value+x,
+ "max_val": parameter_value+5*x,
+ "interval": x, "num_validation_values": 3})
+ except ValueError:
+ params.append({"param_name": parameter_name,
+ "param_class": "geom/bc/mat_prop",
+ "default_val": parameter_value,
+ "min_val": "", "max_val": "",
+ "interval": "", "num_validation_values": ""})
+
+ rows = []
+ for i, param in enumerate(params):
+ row = {}
+
+ row['checkbox_var'] = BooleanVar(value=False)
+
+ checkbox = Checkbutton(table_frame, variable=row['checkbox_var'])
+ checkbox.grid(row=i+1, column=0, padx=5, pady=5)
+
+ Label(table_frame, text=param['param_name']).grid(row=i+1,
+ column=1, padx=5,
+ pady=5)
+ row['param_name'] = param['param_name']
+
+ row['param_class'] = StringVar(value=param['param_class'])
+ param_class_entry = Entry(table_frame,
+ textvariable=row['param_class'],
+ width=15)
+ param_class_entry.grid(row=i+1, column=2, padx=5, pady=5)
+
+ Label(table_frame, text=param['default_val']).grid(row=i+1,
+ column=3,
+ padx=5, pady=5)
+ row['default_val'] = param['default_val']
+
+ row['min_val'] = StringVar(value=param['min_val'])
+ min_val_entry = Entry(table_frame, textvariable=row['min_val'],
+ width=10)
+ min_val_entry.grid(row=i+1, column=4, padx=5, pady=5)
+
+ row['max_val'] = StringVar(value=param['max_val'])
+ max_val_entry = Entry(table_frame, textvariable=row['max_val'],
+ width=10)
+ max_val_entry.grid(row=i+1, column=5, padx=5, pady=5)
+
+ row['interval'] = StringVar(value=param['interval'])
+ interval_entry = Entry(table_frame, textvariable=row['interval'],
+ width=10)
+ interval_entry.grid(row=i+1, column=6, padx=5, pady=5)
+
+ row['num_validation_values'] = StringVar(
+ value=param['num_validation_values'])
+ num_validation_values_entry = Entry(
+ table_frame, textvariable=row['num_validation_values'],
+ width=10)
+ num_validation_values_entry.grid(row=i+1, column=7, padx=5, pady=5)
+
+ rows.append(row)
+
+ parameters = None
+ num_val_values = None
+
+ submit_button = Button(param_root, text="Submit",
+ command=submit_parameters)
+ submit_button.grid(row=3, column=0, columnspan=2, pady=10)
+
+ error_label = Label(param_root, text="", fg="red")
+ error_label.grid(row=4, column=0, columnspan=2)
+
+ param_root.mainloop()
+
+ return num_val_values, parameters
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 00000000..2542f927
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1,3 @@
+"""
+Icarus Test
+"""
diff --git a/tests/datasetgenerator_test.py b/tests/datasetgenerator_test.py
new file mode 100644
index 00000000..c9f26f1e
--- /dev/null
+++ b/tests/datasetgenerator_test.py
@@ -0,0 +1,43 @@
+"""
+Test suite for the DatasetGenerator module of Icarus.
+
+(c) Copyright UKAEA 2024.
+"""
+
+#import pytest
+from unittest.mock import patch
+from icarus import DatasetGenerator
+
+
+values = [10, 20, 30, 40, 50]
+with patch.object(DatasetGenerator, '__init__', lambda self: None):
+ dataset_generator = DatasetGenerator()
+
+def test_correct_number_of_validation_values_generated():
+ """Test to make sure the correct number of validation values are generated
+ """
+ validation_values = dataset_generator.generate_validation_values(values, 3)
+ assert len(validation_values) == 3, f"Incorrect number of validation values generated: was \
+ {len(validation_values)}, should've been 3"
+
+
+def test_validation_values_within_range():
+ """Test to make correct validation values are generated
+ """
+ validation_values = dataset_generator.generate_validation_values(values, 3)
+ min_val = min(values)
+ max_val = max(values)
+ for val in validation_values:
+ assert val > min_val, "Validation value generated outside of acceptable range"
+ assert val < max_val, "Validation value generated outside of acceptable range"
+
+
+def test_no_duplicate_validation_values():
+ """Test to make sure that no duplicate validation values are generated
+ """
+ validation_values = dataset_generator.generate_validation_values(values, 3)
+ for val in validation_values:
+ assert val not in values, "Validation value duplicate of parameter value"
+
+ assert len(validation_values) == len(set(validation_values)), \
+ "Same validation value generated multiple times"
diff --git a/tests/moosesetup_test.py b/tests/moosesetup_test.py
new file mode 100644
index 00000000..48c018d7
--- /dev/null
+++ b/tests/moosesetup_test.py
@@ -0,0 +1,55 @@
+"""
+Test suite for the MooseSetup module of Icarus.
+
+(c) Copyright UKAEA 2024.
+"""
+
+from unittest.mock import patch, MagicMock
+import pytest
+from icarus import MooseSetup
+
+
+def test_invalid_file_extension_raises_file_not_found():
+ """Test to make sure the correct error is raised when input file
+ is invalid (doesn't end .i).
+ """
+ setup = MooseSetup(input_file_path="input.txt", output_file_path="",
+ n_tasks=4, n_threads=4)
+ with pytest.raises(FileNotFoundError,
+ match="Unacceptable input file path input.txt. \
+ Must both exist and end in .i"):
+ setup.setup_moose_runner()
+
+
+def test_nonexistent_input_file_raises_file_not_found(tmp_path):
+ """Test to make sure the correct error is raised when input file
+ doesn't exist.
+ """
+ fake_file = tmp_path / "input.i"
+ setup = MooseSetup(input_file_path=str(fake_file), output_file_path="",
+ n_tasks=4, n_threads=4)
+
+ with pytest.raises(FileNotFoundError,
+ match=f"Unacceptable input file path {str(fake_file)}. \
+ Must both exist and end in .i"):
+ setup.setup_moose_runner()
+
+
+@patch("icarus.moosesetup.InputModifier")
+def test_no_parameters_found_raises_value_error(mock_input_modifier, tmp_path):
+ """Test to make sure the correct error is raised when no parameters
+ are found
+ """
+ input_file = tmp_path / "input.i"
+ input_file.write_text("Dummy content")
+
+ mock_instance = MagicMock()
+ mock_instance.get_vars.return_value = {}
+ mock_input_modifier.return_value = mock_instance
+
+ setup = MooseSetup(input_file_path=str(input_file), output_file_path="",
+ n_tasks=4, n_threads=4)
+
+ with pytest.raises(ValueError, match="No parameters found in input file. \
+ Check input file and try again."):
+ setup.setup_moose_runner()