diff --git a/README.md b/README.md index 466749f0..7a70aa2e 100644 --- a/README.md +++ b/README.md @@ -1,46 +1,81 @@ -[![CI](https://github.com/kaelyndunnell/hisp/actions/workflows/ci.yml/badge.svg)](https://github.com/kaelyndunnell/hisp/actions/workflows/ci.yml) -[![docs](https://readthedocs.org/projects/hisp/badge/?version=latest)](https://hisp.readthedocs.io/en/latest/) - # HISP -Hydrogen Inventory Simulations for PFCs (HISP) is a series of code that uses FESTIM to simulate deuterium and tritium inventories in a fusion tokamak first wall and divertor PFCs. +Hydrogen Inventory Simulations for PFCs (HISP) is a series of code that uses FESTIM to simulate deuterium and tritium inventories in a fusion tokamak first wall and divertor PFCs. -## How to Run: +**This fork** reworks HISP to serve as the FESTIM orchestrator for [PFC-Tritium-Transport](https://github.com/iterorganization/PFC-Tritium-Transport) (PFC-TT): it receives bin objects, material properties, scenarios, and plasma data from PFC-TT and builds the corresponding FESTIM simulations. PFC-TT owns all physics definitions, inputs, and user control. -Clone the repository: +That said, once the core PFC-TT classes are importable (by having a local clone and its conda environment), HISP can also be used independently of PFC-TT's workflow. The [`example/`](example/) folder demonstrates this: bins and scenarios are defined directly in Python and passed to HISP to build and solve a simulation. -``` -git clone https://github.com/kaelyndunnell/hisp -cd hisp +For more advanced use cases — including flexible input handling, detailed physics control, or large parameter studies — users are encouraged to work directly with https://github.com/iterorganization/PFC-Tritium-Transport, which provides a more complete and streamlined way to set up and run simulations. + +## What HISP Does + +For each bin it constructs a FESTIM simulation: it translates the bin geometry (thickness, optional Cu layer, surface area) into the model domain, assigns material parameters and trap definitions, builds time-dependent boundary conditions and implantation source expressions, and selects appropriate boundary-condition types (Robin/Neumann) before assembling and solving the transport equations with adaptive time-stepping. The per-bin outputs (retained inventory, surface fluxes, concentration profiles, and time traces) are exported to JSON for post-processing or aggregation across bins. + +## Dependencies + +HISP depends on: +- [FESTIM](https://github.com/festim-dev/festim) — finite element solver for hydrogen transport +- [PFC-Tritium-Transport](https://github.com/iterorganization/PFC-Tritium-Transport) — provides bin definitions, material classes, scenario handling, and plasma data + +Both are installed as part of the PFC-TT conda environment (see below). + +## How to Install + +Follow the full installation instructions in the [PFC-Tritium-Transport README](https://github.com/iterorganization/PFC-Tritium-Transport). In summary: + +### 1. Clone PFC-Tritium-Transport +```bash +git clone --branch main https://github.com/iterorganization/PFC-Tritium-Transport.git ``` -Run this command to create a new environment with the right dependencies (eg. dolfinx): +### 2. Create the conda environment +This step installs all core simulation dependencies including **FESTIM** (required by both PFC-Tritium-Transport and HISP), FEniCS-DOLFINx, PETSc, and all other required packages: +```bash +conda config --set channel_priority flexible +conda env create -f PFC-TT.yml +conda activate PFC-TT ``` -conda env create -f environment.yml + +### 3. Register the PFC-TT path +HISP needs to know where PFC-Tritium-Transport is located on your system at runtime. Register the path once in your conda environment (replace with your actual clone location): +```bash +conda env config vars set PFC_TT_PATH="/path/to/your/PFC-Tritium-Transport" +conda deactivate && conda activate PFC-TT ``` -Then, activate the environment: +You can verify it was set correctly with: +```bash +conda env config vars list ``` -conda activate hisp-env + +### 4. Install HISP +HISP is installed without dependencies since FESTIM and all other requirements are already provided by the PFC-TT conda environment: +```bash +pip install --no-deps git+https://github.com/AdriaLlealS/hisp.git@main +pip install h_transport_materials ``` +## Examples -Install the `hisp` package with: +The `example/` folder contains a working example based on the HISP paper ([Dunnell et al., 2026](https://arxiv.org/abs/2604.04751)), demonstrating a full simulation of deuterium and tritium retention in iter's first wall and divertor plasma-facing components. -``` -python -m pip install -e . -``` +This example includes the results for a single bin under a specific scenario, along with a simple plotting script to visualise the outputs and the corresponding generated plot. -This will also install the pip dependencies like `h-transport-materials` and `FESTIM`. +See [`example/README.md`](example/README.md) for a full description of the example and instructions on how to run it. -> **_NOTE:_** Using `-e` with pip will install the package in editable mode, meaning the source code can be modified without having to reinstall the package. +## Running Tests -## Run the tests +With the conda environment active and `PFC_TT_PATH` set: +```bash +cd /path/to/hisp +python -m pytest tests/ -v +``` -After cloning the repo and installing hisp, run: +## Development History -``` -python -m pytest test -``` +The first prototype (2024) was a single framework that combined simulation setup and solver execution, using MHIMS as the hydrogen isotope transport solver. + +In early 2025, MHIMS was replaced by FESTIM for improved computational efficiency, and the codebase was split into two repositories — PFC-TT and HISP. During this stage, HISP still retained a significant role in handling physics definitions and simulation inputs alongside solver-related functionalities. -> **_NOTE:_** Make sure to install the test dependencies with `python -m pip install -e .[test]` +A subsequent major refactor further clarified the separation of responsibilities: all physics definitions and input handling were moved into PFC-TT, where they were also extended and further developed. At the same time, HISP evolved towards a role focused on solver execution and orchestration, with improved flexibility and integration with FESTIM. These developments led to the present versions of both codes. \ No newline at end of file diff --git a/environment.yml b/environment.yml deleted file mode 100644 index e240bc6b..00000000 --- a/environment.yml +++ /dev/null @@ -1,13 +0,0 @@ -name: hisp-env -channels: - - conda-forge - - defaults -dependencies: - - fenics-dolfinx=0.9.0 - - matplotlib - - scipy - - pandas - - pint # need to install pint with conda to avoid conflicts (HTM) - - nbconvert - - ipykernel - - pip diff --git a/example/README.md b/example/README.md new file mode 100644 index 00000000..8c7aef3a --- /dev/null +++ b/example/README.md @@ -0,0 +1,66 @@ +# Example: ITER PFCs tritium retention simulation + +This folder recreates the bin definitions and operational scenarios shown in the +paper below. It demonstrates how to: + +1. Define the plasma-facing component segments (bins) of a reactor with their + geometry, materials, and trap parameters. +2. Run HISP on a single bin to solve the hydrogen isotope transport equations + (diffusion + trapping) via FESTIM and obtain time-resolved retention results. + +Reference: +> Dunnell, K., Lleal, A., Hodille, E. A., Dufour, J., Delaporte-Mathurin, R., & Wauters, T. (2026). +> *Hydrogen Inventory Simulations for PFCs (HISP)*. +> arXiv:2604.04751. https://arxiv.org/abs/2604.04751 + +## Files + +| File | Purpose | +|------|---------| +| `make_iter_bins.py` | Creates 94 Bin objects (62 poloidal segments × wetted modes) based on the configuration described in the HISP paper | +| `scenarioA.py` | Defines a plasma operation scenario consisting of: 10 FP,DT pulses + STM (4-day wait) + bake | +| `scenarioB.py` | Defines a plasma operation scenario consisting of: 10 FP,DT pulses + 2-day GDC + bake | +| `scenarioC.py` | Defines a plasma operation scenario consisting of: 5 FP,DT pulses + 1 DD pulse + 5 FP,DT pulses + 1 DD pulse + 2-day GDC + bake | +| `run_single_bin.py` | Main script — runs one bin with a chosen scenario | +| `plot_T_inventory.py` | Plots tritium inventory from results JSON | +| `data/` | Binned plasma flux data (ion/atom fluxes, energies, heat loads per bin) | + +## How to run + +Activate the PFC-TT conda environment (which should already have `PFC_TT_PATH` set — see the main HISP README for setup). + +```bash +conda activate PFC-TT +cd hisp/example +python run_single_bin.py --bin-index 0 --scenario scenarioA +``` + +Options: +- `--bin-index N` — which bin to simulate (0–93) +- `--scenario {do_nothing, capability_test, just_glow}` + +Results are saved as JSON in `results_binN__/`. + +## Post-processing + +`plot_T_inventory.py` reads the results JSON and plots the total tritium +inventory over time, decomposed into its mobile and trapped contributions +(one curve per trap). This gives a clear view of how retention evolves +through the pulse sequence and the baking. + +Below is the result for bin 0 — the high-wetted sub-bin of ITER's First Wall +Panel 1 — run with Scenario A: + +![T inventory for bin 0](results_bin0_W_high_wetted/T_inventory.png) + +## Adapting this example for your own simulation + +The files in this folder can be used as templates for custom simulations. For any simulation you set up yourself, you need to provide two things: **bin definitions** (geometry, materials, trap parameters) and **binned plasma data files** (fluxes, heat loads, particle energies per bin). + +**To define your own bins and materials**, edit `make_iter_bins.py`. The file has two parts: + +- **Materials** — defined at the top as `Material` objects. Each material requires a name, atomic density (`Mat_density`), diffusion parameters (`D0`, `E_D`), recombination parameters (`K_R`, `E_R`), and a list of `Trap` objects with their density (atomic fraction), trapping and detrapping rate prefactors (`k_0`, `p_0`) and activation energies (`E_k`, `E_p`). Add or modify materials here to match your reactor's PFC materials. + +- **Bin table** (`_BIN_DATA`) — each row defines one bin with its poloidal coordinates (`z_start`, `r_start`, `z_end`, `r_end`), material name, thickness, copper backing thickness, wetted mode, surface areas, and boundary condition types. Modify the rows to match your reactor geometry, or add new rows for additional bins. Each bin must reference a material defined in the materials block above. + +**To provide your own plasma data**, you need to supply binned flux data files for each pulse type in your scenario — containing ion and atom fluxes, particle energies, and heat loads for each bin. The `data/` folder contains the files used in this example and serves as a reference for the expected format. These files are assigned to each pulse type inside the scenario file — look for the data file path assignments at the top of `scenarioA.py` as a template. 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31,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 32,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 33,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -34,2.500000000000000000e+18,0.000000000000000000e+00,2.400000000000000000e+02,0.000000000000000000e+00,8.000000000000000000e+01,0.000000000000000000e+00,0.000000000000000000e+00 -35,2.500000000000000000e+18,0.000000000000000000e+00,2.400000000000000000e+02,0.000000000000000000e+00,8.000000000000000000e+01,0.000000000000000000e+00,0.000000000000000000e+00 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+61,2.500000000000000000e+18,0.000000000000000000e+00,2.400000000000000000e+02,0.000000000000000000e+00,9.000000000000000000e+01,0.000000000000000000e+00,0.000000000000000000e+00 diff --git a/example/make_iter_bins.py b/example/make_iter_bins.py new file mode 100644 index 00000000..ad25b72f --- /dev/null +++ b/example/make_iter_bins.py @@ -0,0 +1,191 @@ +"""Create ITER scenario bins directly without CSV loading. + +Replicates the bins from PFC-Tritium-Transport/simulations/scenarioA/input_table.csv +and materials.csv using real PFC-TT Bin, Material, BinConfiguration, Trap objects. +""" +from hisp.bin import Bin, BinConfiguration, Material + +# Trigger PFC-TT path resolution (done by hisp.bin above), then import Trap +from materials.materials import Trap + + +# === MATERIALS (from scenarioA/materials.csv) === + +W_MATERIAL = Material( + name="W", + Mat_density=6.3382e28, + D0=2.06e-7, + E_D=0.28, + K_R=7.94e-17, + E_R=-2.0, + N_traps=2, + traps=[ + Trap(Trap_density=1e-4, k_0=3.58e-16, E_k=0.28, p_0=1e13, E_p=0.85), + Trap(Trap_density=1e-4, k_0=3.58e-16, E_k=0.28, p_0=1e13, E_p=1.0), + ], +) + +B_MATERIAL = Material( + name="B", + Mat_density=1.34e29, + D0=1.07e-6, + E_D=0.3, + K_R=0.0, + E_R=0.0, + N_traps=4, + traps=[ + Trap(Trap_density=6.87e-1, k_0=7.4627e-17, E_k=0.3, p_0=1e13, E_p=1.052), + Trap(Trap_density=5.21e-1, k_0=7.4627e-17, E_k=0.3, p_0=1e13, E_p=1.199), + Trap(Trap_density=2.47e-1, k_0=7.4627e-17, E_k=0.3, p_0=1e13, E_p=1.389), + Trap(Trap_density=1.28e-1, k_0=7.4627e-17, E_k=0.3, p_0=1e13, E_p=1.589), + ], +) + +_MATERIALS = {"W": W_MATERIAL, "B": B_MATERIAL} + + +# === BIN TABLE (from scenarioA/input_table.csv) === +# Each tuple: (flux_id, z_start, r_start, z_end, r_end, material_name, +# thickness, cu_thickness, mode, parent_area, surface_area, +# f_fraction, location, rtol, atol, fp_max_stepsize, +# max_stepsize_no_fp, bc_pfs, bc_rear) + +_BIN_DATA = [ + (0, -2.51, 4.1, -1.5, 4.1, "W", 0.006, 0.002, "high_wetted", 26, 0.886, 0.919, "FW", 1e-12, 1e12, 20, 1000, "Robin - Surf. Rec. + Implantation", "Neumann - no flux"), + (0, -2.51, 4.1, -1.5, 4.1, "W", 0.006, 0.002, "low_wetted", 26, 0.0999, 0.0808, "FW", 1e-12, 1e12, 20, 1000, "Robin - Surf. Rec. + Implantation", "Neumann - no flux"), + (0, -2.51, 4.1, -1.5, 4.1, "W", 0.006, 0.002, "shadowed", 26, 25.1, 0, "FW", 1e-12, 1e12, 20, 1000, "Robin - Surf. Rec. + Implantation", "Neumann - no flux"), + (1, -1.5, 4.1, -0.487, 4.1, "W", 0.006, 0.002, "high_wetted", 26.1, 3.3, 0.978, "FW", 1e-12, 1e12, 20, 1000, "Robin - Surf. Rec. + Implantation", "Neumann - no flux"), + (1, -1.5, 4.1, -0.487, 4.1, "W", 0.006, 0.002, "low_wetted", 26.1, 0.137, 0.0223, "FW", 1e-12, 1e12, 20, 1000, "Robin - Surf. Rec. + Implantation", "Neumann - no flux"), + (1, -1.5, 4.1, -0.487, 4.1, "W", 0.006, 0.002, "shadowed", 26.1, 22.7, 0, "FW", 1e-12, 1e12, 20, 1000, "Robin - Surf. 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"Neumann - no flux"), + (61, -2.67, 4.35, -2.51, 4.1, "B", 5e-6, 0, "", 7.88, 7.88, 1, "Div", 1e-13, 1e11, 20, 200, "Dirichlet - 0 concentration + Implantation", "Neumann - no flux"), +] + + +def make_iter_bins(): + """Create all ITER scenario A bins as a list of Bin objects. + + Returns: + list[Bin]: 82 Bin objects replicating scenarioA/input_table.csv. + """ + bins = [] + for row in _BIN_DATA: + (flux_id, z_start, r_start, z_end, r_end, mat_name, + thickness, cu_thickness, mode, parent_area, surface_area, + f_fraction, location, rtol, atol, fp_max_stepsize, + max_stepsize_no_fp, bc_pfs, bc_rear) = row + + config = BinConfiguration( + rtol=rtol, + atol=atol, + fp_max_stepsize=fp_max_stepsize, + max_stepsize_no_fp=max_stepsize_no_fp, + bc_plasma_facing_surface=bc_pfs, + bc_rear_surface=bc_rear, + ) + + bins.append(Bin( + flux_id=flux_id, + material=_MATERIALS[mat_name], + thickness=thickness, + cu_thickness=cu_thickness, + mode=mode, + parent_bin_surf_area=parent_area, + surface_area=surface_area, + f_ion_flux_fraction=f_fraction, + location=location, + z_start=z_start, + r_start=r_start, + z_end=z_end, + r_end=r_end, + bin_configuration=config, + calculate_implantation_params=False, + )) + + return bins diff --git a/example/plot_T_inventory.py b/example/plot_T_inventory.py new file mode 100644 index 00000000..f377dec7 --- /dev/null +++ b/example/plot_T_inventory.py @@ -0,0 +1,69 @@ +"""Plot tritium inventory over time from simulation results.""" +import json +import numpy as np +import matplotlib.pyplot as plt + +with open("results_bin0_W_high_wetted/results.json") as f: + data = json.load(f) + +t = np.array(data["t"]) +T_mobile = np.array(data["T"]["data"]) +T_trap1 = np.array(data["trap1_T"]["data"]) +T_trap2 = np.array(data["trap2_T"]["data"]) +T_total = T_mobile + T_trap1 + T_trap2 + +# Define shaded regions (time span in seconds) +# Edit these to match your scenario phases +regions = [ + {"name": "1st pulse Ramp-up", "t_start": 0, "t_end": 429, "color": "red", "alpha": 0.18}, + {"name": "1st pulse Flat top", "t_start": 429, "t_end": 429+650, "color": "green", "alpha": 0.18}, + {"name": "1st pulse Ramp-down", "t_start": 429+650, "t_end": 429+650+455, "color": "yellow", "alpha": 0.18}, + {"name": "STM (4 days waiting)", "t_start": 51340, "t_end": 396940, "color": "cyan", "alpha": 0.18}, + {"name": "Baking", "t_start": 396940, "t_end": 1001740, "color": "purple", "alpha": 0.18}, +] + +fig, ax = plt.subplots(figsize=(14, 6)) + +# Add shaded regions (labels added after plotting) +for region in regions: + ax.axvspan(region["t_start"], region["t_end"], alpha=region["alpha"], + color=region["color"]) + +# Primary axis: trapped T and total +ax.plot(t, T_trap1, label="T trap 1", linewidth=2) +ax.plot(t, T_trap2, label="T trap 2", linewidth=2) +ax.plot(t, T_total, linewidth=2.5, label="T total", color="black") +ax.set_xlabel("Time (s)") +ax.set_xscale("log") +ax.set_xlim(1e2, t[-1]) +ax.set_ylabel("Trapped Inventory (atoms/m²)") +ax.set_title("Tritium inventory — Bin 0 (W, high_wetted, FW)") +ax.tick_params(axis="y", labelcolor="C0") +ax.grid(True, alpha=0.3) +ax.set_xlim(0, t[-1]) + +# Secondary axis: mobile T +ax2 = ax.twinx() +ax2.plot(t, T_mobile, label="T mobile", color="gray", linestyle="-", linewidth=0.8) +ax2.set_ylabel("Mobile Inventory (atoms/m²)", color="gray") +ax2.tick_params(axis="y", labelcolor="gray") + +# Add region labels with colored boxes below x-axis +from matplotlib.patches import Patch + +# Inventory legend (traces) - upper left +lines1, labels1 = ax.get_legend_handles_labels() +lines2, labels2 = ax2.get_legend_handles_labels() +ax.legend(lines1 + lines2, labels1 + labels2, loc="upper left") + +# Region legend (shaded areas) - below plot +region_patches = [Patch(facecolor=region["color"], alpha=region["alpha"], + edgecolor="black", linewidth=0.5, label=region["name"]) + for region in regions] +fig.legend(handles=region_patches, loc="lower center", bbox_to_anchor=(0.5, -0.05), + ncol=5, frameon=True, fontsize=10) + +plt.tight_layout() +plt.savefig("results_bin0_W_high_wetted/T_inventory.png", dpi=150, bbox_inches="tight") +print("Saved T_inventory.png") +plt.show() diff --git a/example/results_bin0_W_high_wetted/T_inventory.png b/example/results_bin0_W_high_wetted/T_inventory.png new file mode 100644 index 00000000..8479741d Binary files /dev/null and b/example/results_bin0_W_high_wetted/T_inventory.png differ diff --git a/example/results_bin0_W_high_wetted/profiles.json b/example/results_bin0_W_high_wetted/profiles.json new file mode 100644 index 00000000..d37e4b94 --- /dev/null +++ b/example/results_bin0_W_high_wetted/profiles.json @@ -0,0 +1,237192 @@ +{ + "D_profile": { + "x": [ + 0.0, + 1e-10, + 2.0500000000000002e-10, + 3.1525e-10, + 4.3101250000000007e-10, + 5.525631250000001e-10, + 6.801912812500001e-10, + 8.142008453125001e-10, + 9.549108875781252e-10, + 1.1026564319570316e-09, + 1.2577892535548832e-09, + 1.4206787162326274e-09, + 1.591712652044259e-09, + 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+ 1001740.0, + 1001741.0, + 1001742.1, + 1001743.3099999999, + 1001744.641, + 1001746.1050999999, + 1001747.71561, + 1001749.0, + 1001750.412829, + 1001751.0 + ], + "metadata": { + "bin_index": 0, + "flux_id": 0, + "material": "W", + "mode": "high_wetted", + "location": "FW", + "scenario": "do_nothing" + } +} \ No newline at end of file diff --git a/example/run_single_bin.py b/example/run_single_bin.py new file mode 100644 index 00000000..eec3345b --- /dev/null +++ b/example/run_single_bin.py @@ -0,0 +1,139 @@ +"""Run a single FESTIM simulation for one ITER bin. + +Usage: + python run_single_bin.py [--bin-index N] [--scenario SCENARIO] + +Arguments: + --bin-index N Index into the bins list (default: 0, first bin) + --scenario SCENARIO Which scenario to use: + "do_nothing" - 9 FP + 1 FP (long wait) + bake (default) + "capability_test" - 5 FP + FP_D + 5 FP + FP_D + GDC + bake + "just_glow" - 10 FP + GDC + bake + +Example: + python run_single_bin.py --bin-index 3 --scenario just_glow +""" +import argparse +import json +import os +from pathlib import Path +import importlib + +# hisp shims handle PFC-TT path resolution +from hisp.new_model import NewModel +from make_iter_bins import make_iter_bins + + +SCENARIOS = ["do_nothing", "capability_test", "just_glow"] + + +def load_scenario(name): + """Import scenario and plasma_data_handling from the scenario module.""" + module = importlib.import_module(f"scenario_{name}") + return module.scenario, module.plasma_data_handling + + +def main(): + parser = argparse.ArgumentParser(description="Run a FESTIM simulation for one ITER bin.") + parser.add_argument("--bin-index", type=int, default=0, + help="Index into the bins list (default: 0)") + parser.add_argument("--scenario", type=str, default="do_nothing", + choices=SCENARIOS, + help="Scenario to use (default: do_nothing)") + args = parser.parse_args() + + # Change to script directory so relative data paths work + os.chdir(Path(__file__).parent) + + # Create bins + bins = make_iter_bins() + print(f"Created {len(bins)} bins") + + if args.bin_index < 0 or args.bin_index >= len(bins): + raise IndexError(f"--bin-index {args.bin_index} out of range [0, {len(bins)-1}]") + + selected_bin = bins[args.bin_index] + print(f"\nSelected bin index {args.bin_index}:") + print(f" flux_id={selected_bin.flux_id}, material={selected_bin.material.name}") + print(f" mode={selected_bin.mode}, location={selected_bin.location}") + print(f" thickness={selected_bin.thickness*1e3:.2f} mm") + print(f" BC front: {selected_bin.bin_configuration.bc_plasma_facing_surface}") + print(f" BC rear: {selected_bin.bin_configuration.bc_rear_surface}") + + # Load scenario and plasma data handling + scenario, pdh = load_scenario(args.scenario) + print(f"\nScenario: {args.scenario}") + print(f" Total time: {scenario.get_maximum_time():.0f} s") + print(f" Pulses: {len(scenario.pulses)} types") + + # Create output folder + output_folder = f"results_bin{args.bin_index}_{selected_bin.material.name}_{selected_bin.mode}" + os.makedirs(output_folder, exist_ok=True) + + # Build and run + model_runner = NewModel( + reactor=None, + scenario=scenario, + plasma_data_handling=pdh, + coolant_temp=343.0, + ) + + model, quantities = model_runner.run_bin( + bin=selected_bin, + exports=False, + folder=output_folder, + ) + + # Print final inventory + print(f"\n{'='*60}") + print(f"Final inventory for bin {args.bin_index}:") + for name, qty in quantities.items(): + if hasattr(qty, "data") and len(qty.data) > 0: + val = qty.data[-1] + if hasattr(val, '__len__'): + continue # skip profile arrays + print(f" {name}: {val:.4e}") + print(f"{'='*60}") + + # Save results to JSON + scalar_data = {} + profile_data = {} + for name, qty in quantities.items(): + if not hasattr(qty, "data") or len(qty.data) == 0: + continue + if name.endswith("_profile"): + profile_data[name] = { + "x": qty.x.tolist(), + "t": qty.t if isinstance(qty.t, list) else list(qty.t), + "data": [arr.tolist() for arr in qty.data], + } + else: + scalar_data[name] = {"data": [float(v) for v in qty.data]} + + # Add time array and metadata + first_scalar = next((q for n, q in quantities.items() if not n.endswith("_profile")), None) + if first_scalar is not None and hasattr(first_scalar, "t"): + scalar_data["t"] = [float(v) for v in first_scalar.t] + scalar_data["metadata"] = { + "bin_index": args.bin_index, + "flux_id": selected_bin.flux_id, + "material": selected_bin.material.name, + "mode": selected_bin.mode, + "location": selected_bin.location, + "scenario": args.scenario, + } + + results_file = os.path.join(output_folder, "results.json") + with open(results_file, "w") as f: + json.dump(scalar_data, f, indent=4) + print(f"\nScalar results saved to {results_file}") + + if profile_data: + profiles_file = os.path.join(output_folder, "profiles.json") + with open(profiles_file, "w") as f: + json.dump(profile_data, f, indent=4) + print(f"Profile results saved to {profiles_file}") + + +if __name__ == "__main__": + main() diff --git a/example/scenarioA.py b/example/scenarioA.py new file mode 100644 index 00000000..da4b54a5 --- /dev/null +++ b/example/scenarioA.py @@ -0,0 +1,58 @@ +"""Scenario: A, do_nothing — 9 FP + 1 FP (long wait) + bake. + +10 DT flat-top pulses where the last one has a ~4 day waiting period +(simulating STM where no cleaning techniques are applied), then baking at 493 K. +Uses Binned_Flux_Data_rad.dat for FP fluxes. +""" +from pathlib import Path +import pandas as pd + +from hisp.scenario import Pulse, Scenario +from hisp.plasma_data_handling import PlasmaDataHandling + +data_folder = str(Path(__file__).parent / "data") + +plasma_data_handling = PlasmaDataHandling( + pulse_type_to_data={ + "FP": pd.read_csv(data_folder + "/Binned_Flux_Data_rad.dat", delimiter=","), + "FP_D": pd.read_csv(data_folder + "/Binned_Flux_Data_just_D_pulse.dat", delimiter=",", comment="#"), + "GDC": pd.read_csv(data_folder + "/GDC_data.dat", delimiter=","), + }, + path_to_RISP_data=data_folder + "/RISP_data", + path_to_ROSP_data=data_folder + "/ROSP_data", + path_to_RISP_wall_data=data_folder + "/RISP_Wall_data.dat", +) + +BAKE_TEMPERATURE = 273.15 + 220 # 220 °C in Kelvin + +fp = Pulse( + pulse_type="FP", + nb_pulses=9, + ramp_up=429, + steady_state=650, + ramp_down=455, + waiting=3600, + tritium_fraction=0.5, +) + +fp_do_nothing = Pulse( + pulse_type="FP", + nb_pulses=1, + ramp_up=429, + steady_state=650, + ramp_down=455, + waiting=349200, + tritium_fraction=0.5, +) + +bake = Pulse( + pulse_type="BAKE", + nb_pulses=1, + ramp_up=151200, # 5 °C/hour; 42 hours total + steady_state=345600, + ramp_down=108000, # -7 °C/hour; 30 hours total + waiting=11, # HISP expects at least 10 s of waiting + tritium_fraction=0.0, +) + +scenario = Scenario(pulses=[fp, fp_do_nothing, bake], baking_temp=BAKE_TEMPERATURE) diff --git a/example/scenarioB.py b/example/scenarioB.py new file mode 100644 index 00000000..dd206b60 --- /dev/null +++ b/example/scenarioB.py @@ -0,0 +1,56 @@ +"""Scenario: B, just_glow — 10 FP + GDC + bake. + +10 DT flat-top pulses, then 2-day glow discharge cleaning, +then baking at 493 K. +Uses Binned_Flux_Data_rad.dat for FP fluxes. +""" +from pathlib import Path +import pandas as pd + +from hisp.scenario import Pulse, Scenario +from hisp.plasma_data_handling import PlasmaDataHandling + +data_folder = str(Path(__file__).parent / "data") + +plasma_data_handling = PlasmaDataHandling( + pulse_type_to_data={ + "FP": pd.read_csv(data_folder + "/Binned_Flux_Data_rad.dat", delimiter=","), + "FP_D": pd.read_csv(data_folder + "/Binned_Flux_Data_just_D_pulse.dat", delimiter=",", comment="#"), + "GDC": pd.read_csv(data_folder + "/GDC_data.dat", delimiter=","), + }, + path_to_RISP_data=data_folder + "/RISP_data", + path_to_ROSP_data=data_folder + "/ROSP_data", + path_to_RISP_wall_data=data_folder + "/RISP_Wall_data.dat", +) + +fp = Pulse( + pulse_type="FP", + nb_pulses=10, + ramp_up=429, + steady_state=650, + ramp_down=455, + waiting=3600, + tritium_fraction=0.5, +) + +gdc = Pulse( + pulse_type="GDC", + nb_pulses=1, + ramp_up=1, + steady_state=172798, # 2-day glow + ramp_down=1, + waiting=172800, # 2-day waiting + tritium_fraction=0.0, +) + +bake = Pulse( + pulse_type="BAKE", + nb_pulses=1, + ramp_up=151200, # 5 °C/hour; 42 hours total + steady_state=345600, + ramp_down=108000, # -7 °C/hour; 30 hours total + waiting=11, # HISP expects at least 10 s of waiting + tritium_fraction=0.0, +) + +scenario = Scenario(pulses=[fp, gdc, bake], baking_temp=273.15 + 220) # 220 °C in Kelvin diff --git a/example/scenarioC.py b/example/scenarioC.py new file mode 100644 index 00000000..a6cae379 --- /dev/null +++ b/example/scenarioC.py @@ -0,0 +1,66 @@ +"""Scenario C, capability_test — DT/DD alternation + GDC + bake. + +5 DT pulses, 1 D-only pulse, 5 DT pulses, 1 D-only pulse, +then 2-day glow discharge cleaning, then baking at 493 K. +Uses Binned_Flux_Data_rad.dat for FP fluxes. +""" +from pathlib import Path +import pandas as pd + +from hisp.scenario import Pulse, Scenario +from hisp.plasma_data_handling import PlasmaDataHandling + +data_folder = str(Path(__file__).parent / "data") + +plasma_data_handling = PlasmaDataHandling( + pulse_type_to_data={ + "FP": pd.read_csv(data_folder + "/Binned_Flux_Data_rad.dat", delimiter=","), + "FP_D": pd.read_csv(data_folder + "/Binned_Flux_Data_just_D_pulse.dat", delimiter=",", comment="#"), + "GDC": pd.read_csv(data_folder + "/GDC_data.dat", delimiter=","), + }, + path_to_RISP_data=data_folder + "/RISP_data", + path_to_ROSP_data=data_folder + "/ROSP_data", + path_to_RISP_wall_data=data_folder + "/RISP_Wall_data.dat", +) + +fp = Pulse( + pulse_type="FP", + nb_pulses=5, + ramp_up=429, + steady_state=650, + ramp_down=455, + waiting=3600, + tritium_fraction=0.5, +) + +fp_d = Pulse( + pulse_type="FP_D", + nb_pulses=1, + ramp_up=429, + steady_state=650, + ramp_down=455, + waiting=3600, + tritium_fraction=0.0, +) + +gdc = Pulse( + pulse_type="GDC", + nb_pulses=1, + ramp_up=1, + steady_state=172798, # 2-day glow + ramp_down=1, + waiting=172800, # 2-day waiting + tritium_fraction=0.0, +) + +bake = Pulse( + pulse_type="BAKE", + nb_pulses=1, + ramp_up=151200, # 5 °C/hour; 42 hours total + steady_state=345600, + ramp_down=108000, # -7 °C/hour; 30 hours total + waiting=11, # HISP expects at least 10 s of waiting + tritium_fraction=0.0, +) + +scenario = Scenario(pulses=[fp, fp_d, fp, fp_d, gdc, bake], baking_temp=273.15 + 220) # 220 °C in Kelvin diff --git a/examples/Wetted_Frac_Bin_Data.csv b/examples/Wetted_Frac_Bin_Data.csv deleted file mode 100644 index 54e1da14..00000000 --- a/examples/Wetted_Frac_Bin_Data.csv +++ /dev/null @@ -1,19 +0,0 @@ -Slow,Stot,Shigh,f,DFW -0.08884148888888888,16.044441416780087,0.88554141,0.08076754406144708 -0.1467486,16.111714586068415,4.40418178,0.011414484855668546 -4.464558548888888,16.147475655817847,1.7510604188888888,0.5650467411145661 -4.1766140788888887,16.147475655817844,0.47546784,0.7164585446781011 -0.1648141,16.111714586068408,4.88568806,0.011514547468441488 -0.00846718,16.14888144851518,0.40861511888888887,0.018848716804184648 -0.00846718,10.841118550144081,0.40861511888888887,0.018848716804184648 -0.05758161,10.717618844881766,1.84450886,0.008141418018456868 -0.41066704,18.184414811440848,4.86154584,0.01654854884176685 -0.41066704,14.54067481168888,4.86154584,0.01654854884176685,6.44 -0.41066704,51.64754088684044,4.86154584,0.01654854884176685 -5.1018716488888885,41.88886865171184,0.00458406,0.8847780181486115 -0.11675645888888888,44.61141841014145,0.14880546,0.15878588144484467 -4.44550844,17.574146175556414,11.8 -4.54771414,17.68441885886407,11.8 -5.41545848,50.68146884658414,0.08884816,0.8485881745848104 -4.5144701888888887,51.5844461141654 -8.44870841,54.484114084704106 diff --git a/examples/all_bin_scenario.py b/examples/all_bin_scenario.py deleted file mode 100644 index 4925c352..00000000 --- a/examples/all_bin_scenario.py +++ /dev/null @@ -1,87 +0,0 @@ -import json -import pandas as pd -from make_iter_bins import FW_bins, Div_bins, my_reactor - -from hisp.plamsa_data_handling import PlasmaDataHandling -from hisp.scenario import Scenario, Pulse -from hisp.model import Model - -# import dolfinx -# dolfinx.log.set_log_level(dolfinx.log.LogLevel.INFO) - - -# Make a scenario -fp = Pulse( - pulse_type="FP", - nb_pulses=1, - ramp_up=10, - steady_state=10, - ramp_down=10, - waiting=100, - tritium_fraction=0.5, -) - -my_scenario = Scenario(pulses=[fp]) - -# Make a plasma data handling object -data_folder = "data" -plasma_data_handling = PlasmaDataHandling( - pulse_type_to_data={ - "FP": pd.read_csv(data_folder + "/Binned_Flux_Data.dat", delimiter=","), - "ICWC": pd.read_csv(data_folder + "/ICWC_data.dat", delimiter=","), - "GDC": pd.read_csv(data_folder + "/GDC_data.dat", delimiter=","), - }, - path_to_ROSP_data=data_folder + "/ROSP_data", - path_to_RISP_data=data_folder + "/RISP_data", - path_to_RISP_wall_data=data_folder + "/RISP_Wall_data.dat", -) - -# Make a HISP model object -my_hisp_model = Model( - reactor=my_reactor, - scenario=my_scenario, - plasma_data_handling=plasma_data_handling, - coolant_temp=343.0, -) - -if __name__ == "__main__": - global_data = {} - processed_data = [] - - # first wall bins - for fw_bin in FW_bins.bins[:3]: - global_data[fw_bin] = {} - fw_bin_data = {"bin_index": fw_bin.index, "sub_bins": []} - - for sub_bin in fw_bin.sub_bins: - my_model, quantities = my_hisp_model.run_bin(sub_bin) - - global_data[fw_bin][sub_bin] = quantities - - subbin_data = { - key: {"t": value.t, "data": value.data} - for key, value in quantities.items() - } - subbin_data["mode"] = sub_bin.mode - subbin_data["parent_bin_index"] = sub_bin.parent_bin_index - - fw_bin_data["sub_bins"].append(subbin_data) - - processed_data.append(fw_bin_data) - - # divertor bins - for div_bin in Div_bins.bins[15:18]: - my_model, quantities = my_hisp_model.run_bin(div_bin) - - global_data[div_bin] = quantities - - bin_data = { - key: {"t": value.t, "data": value.data} for key, value in quantities.items() - } - bin_data["bin_index"] = div_bin.index - - processed_data.append(bin_data) - - # write the processed data to JSON - with open("processed_data.json", "w+") as f: - json.dump(processed_data, f, indent=4) diff --git a/examples/bin_data.dat b/examples/bin_data.dat deleted file mode 100644 index 4dea76dd..00000000 --- a/examples/bin_data.dat +++ /dev/null @@ -1,65 +0,0 @@ -Z_Coord,R_Coord --2.512000000000000011e+00,4.099999999999999645e+00 --1.501000000000000112e+00,4.099999999999999645e+00 --4.869999999999999885e-01,4.099999999999999645e+00 -5.280000000000000249e-01,4.099999999999999645e+00 -1.542999999999999927e+00,4.099999999999999645e+00 -2.556999999999999940e+00,4.099999999999999645e+00 -3.568999999999999950e+00,4.120000000000000107e+00 -4.326000000000000512e+00,4.328600000000000669e+00 -4.700800000000000090e+00,4.934300000000000352e+00 -4.519300000000000317e+00,5.754100000000000215e+00 -3.937400000000000233e+00,6.511700000000000266e+00 -3.155499999999999972e+00,7.398000000000000576e+00 -2.440500000000000114e+00,7.900399999999999423e+00 -1.660299999999999887e+00,8.260299999999999088e+00 -6.126000000000000334e-01,8.382100000000001216e+00 --4.409000000000000141e-01,8.290600000000001302e+00 --1.355700000000000127e+00,7.894300000000000317e+00 --2.262999999999999901e+00,7.265100000000000335e+00 --3.250999999999999890e+00,6.057999999999999829e+00 --3.351999999999999869e+00,5.831999999999999851e+00 --3.539000000000000146e+00,5.655000000000000249e+00 --3.770999999999999908e+00,5.567000000000000171e+00 --3.930000000000000160e+00,5.557000000000000384e+00 --4.011000000000000121e+00,5.557000000000000384e+00 --4.081000000000000405e+00,5.557000000000000384e+00 --4.141000000000000014e+00,5.557000000000000384e+00 --4.200999999999999623e+00,5.557000000000000384e+00 --4.261000000000000121e+00,5.557000000000000384e+00 --4.320000000000000284e+00,5.557000000000000384e+00 --4.379999999999999893e+00,5.557000000000000384e+00 --4.440000000000000391e+00,5.557000000000000384e+00 --4.500000000000000000e+00,5.557000000000000384e+00 --4.580000000000000071e+00,5.557000000000000384e+00 --4.413999999999999702e+00,5.413000000000000256e+00 --4.267000000000000348e+00,5.266000000000000014e+00 --3.988999999999999879e+00,5.246000000000000441e+00 --3.857000000000000206e+00,5.158000000000000362e+00 --3.770000000000000018e+00,5.043999999999999595e+00 --3.728000000000000203e+00,4.935999999999999943e+00 --3.713000000000000078e+00,4.822000000000000064e+00 --3.721000000000000085e+00,4.735999999999999766e+00 --3.750000000000000000e+00,4.639000000000000234e+00 --3.912999999999999812e+00,4.485999999999999766e+00 --3.904999999999999805e+00,4.389000000000000234e+00 --3.898000000000000131e+00,4.291999999999999815e+00 --3.891999999999999904e+00,4.219000000000000306e+00 --3.923999999999999932e+00,4.157000000000000028e+00 --3.869000000000000217e+00,4.184000000000000163e+00 --3.814999999999999947e+00,4.209999999999999964e+00 --3.761000000000000121e+00,4.235999999999999766e+00 --3.706999999999999851e+00,4.262999999999999901e+00 --3.644000000000000128e+00,4.293000000000000149e+00 --3.589999999999999858e+00,4.320000000000000284e+00 --3.536000000000000032e+00,4.346000000000000085e+00 --3.483000000000000096e+00,4.371999999999999886e+00 --3.428999999999999826e+00,4.399000000000000021e+00 --3.375000000000000000e+00,4.424999999999999822e+00 --3.311999999999999833e+00,4.456000000000000405e+00 --3.237000000000000099e+00,4.488999999999999879e+00 --3.136000000000000121e+00,4.514999999999999680e+00 --3.041999999999999815e+00,4.520999999999999908e+00 --2.928999999999999826e+00,4.503999999999999559e+00 --2.786000000000000032e+00,4.442999999999999616e+00 --2.674999999999999822e+00,4.349000000000000199e+00 \ No newline at end of file diff --git a/examples/data/Binned_Flux_Data.dat b/examples/data/Binned_Flux_Data.dat deleted file mode 100644 index 0fc7ae55..00000000 --- a/examples/data/Binned_Flux_Data.dat +++ /dev/null @@ -1,65 +0,0 @@ -Bin_Index,Flux_Ion,Flux_Atom,E_ion,E_atom,alpha_ion,alpha_atom,heat_total,heat_ion -0,5.170233333333333505e+18,2.275817777777777885e+20,2.335871151878003251e+01,5.173177508335557700e+01,7.000000000000000000e+01,5.500000000000000000e+01,3.517381551300738535e+05,7.777380870177878710e+00 -1,5.153170000000000000e+18,8.870000000000000000e+18,3.502501577787072323e+01,5.285137033557520835e+01,7.000000000000000000e+01,5.500000000000000000e+01,3.508381803837785520e+05,7.725887372001185887e+01 -2,2.037105177777777877e+20,1.388312500000000082e+20,3.502005351781727101e+01,5.332025775558288517e+01,7.000000000000000000e+01,5.500000000000000000e+01,3.515581580812277577e+05,3.788758885372500858e+02 -3,2.575051777777777385e+20,1.552750000000000000e+20,3.501573118078715881e+01,5.087377558077055517e+01,7.000000000000000000e+01,5.500000000000000000e+01,3.517772720188503155e+05,5.788807152278873581e+02 -5,2.273850000000000000e+20,1.258850000000000000e+20,3.501327875550812803e+01,5.855153578827052282e+01,7.000000000000000000e+01,5.500000000000000000e+01,3.513875223180318881e+05,5.250128178857772783e+02 -5,1.038757528571528782e+20,8.731707152857153081e+18,3.001557805303151818e+01,5.181381722080585325e+01,7.000000000000000000e+01,5.500000000000000000e+01,3.508531525872002327e+05,1.837887058581358122e+02 -7,8.308750000000000000e+18,1.015375000000000000e+20,2.818221310511788858e+01,5.225370585115833257e+01,7.000000000000000000e+01,5.500000000000000000e+01,3.508307788730775225e+05,1.558371875587573503e+02 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-28,5.982740000000000655e+20,1.155390000000000000e+20,1.480809999999999889e+02,3.837140000000000128e+01,7.000000000000000000e+01,5.500000000000000000e+01,7.506114999999999418e+04 -29,1.140872000000000000e+21,1.500164999999999836e+20,1.375424999999999898e+02,7.172995000000000232e+01,7.000000000000000000e+01,5.500000000000000000e+01,8.013985000000000582e+04 -30,2.912103333333333180e+21,7.696220000000000655e+20,1.219770000000000181e+02,7.331053333333332489e+01,7.000000000000000000e+01,5.500000000000000000e+01,1.855620000000000000e+05 -31,5.690501999999999961e+22,2.448127000000000426e+22,5.019755400000000378e+01,3.349916000000000338e+01,7.000000000000000000e+01,5.500000000000000000e+01,8.388314000000000233e+05 diff --git a/examples/make_iter_bins.py b/examples/make_iter_bins.py deleted file mode 100644 index da8c8e21..00000000 --- a/examples/make_iter_bins.py +++ /dev/null @@ -1,171 +0,0 @@ -from hisp.bin import FWBin3Subs, FWBin2Subs, DivBin, BinCollection, Reactor - -total_nb_bins = 64 -total_fw_bins = 18 - -# add all subbins to the FW bins -fw_bins = [FWBin3Subs() for _ in range(total_fw_bins)] - -for index in [13, 14, 16, 17]: - fw_bins[index] = FWBin2Subs() - -# tag some with DFW - -dfw_indices = [9, 13, 14] -for index in dfw_indices: - fw_bins[index].add_dfw_bin(material="SS", thickness=5e-3) - - -FW_bins = BinCollection(fw_bins) - -div_bins = [DivBin() for _ in range(18, total_nb_bins)] -Div_bins = BinCollection(div_bins) - -my_reactor = Reactor(first_wall=FW_bins, divertor=Div_bins) - -# ------- FW BINS ------- - - -for bin_index in [0, 1]: - fw_bin = FW_bins.get_bin(bin_index) - for subbin in fw_bin.sub_bins: - subbin.thickness = 6e-3 - subbin.material = "W" - -for bin_index in [2, 3, 4]: - fw_bin = FW_bins.get_bin(bin_index) - for subbin in fw_bin.sub_bins: - subbin.thickness = 10e-3 - subbin.material = "W" - -for bin_index in [5, 6, 7, 8, 9, 17]: - fw_bin = FW_bins.get_bin(bin_index) - for subbin in fw_bin.sub_bins: - subbin.thickness = 12e-3 - subbin.material = "W" - -for bin_index in [10, 11, 12]: - FW_bins.get_bin(bin_index).high_wetted_subbin.thickness = 12e-3 - FW_bins.get_bin(bin_index).high_wetted_subbin.material = "W" - FW_bins.get_bin(bin_index).low_wetted_subbin.thickness = 100e-9 - FW_bins.get_bin(bin_index).low_wetted_subbin.material = "B" - FW_bins.get_bin(bin_index).shadowed_subbin.thickness = 1e-6 - FW_bins.get_bin(bin_index).shadowed_subbin.material = "B" - -for bin_index in [13, 14]: - FW_bins.get_bin(bin_index).wetted_subbin.thickness = 6e-3 - FW_bins.get_bin(bin_index).wetted_subbin.material = "W" - FW_bins.get_bin(bin_index).shadowed_subbin.thickness = 1e-6 - FW_bins.get_bin(bin_index).shadowed_subbin.material = "B" - -for bin_index in [15]: - FW_bins.get_bin(bin_index).high_wetted_subbin.thickness = 6e-3 - FW_bins.get_bin(bin_index).high_wetted_subbin.material = "W" - FW_bins.get_bin(bin_index).low_wetted_subbin.thickness = 100e-9 - FW_bins.get_bin(bin_index).low_wetted_subbin.material = "B" - FW_bins.get_bin(bin_index).shadowed_subbin.thickness = 1e-6 - FW_bins.get_bin(bin_index).shadowed_subbin.material = "B" - -for bin_index in [16]: - FW_bins.get_bin(bin_index).wetted_subbin.thickness = 6e-3 - FW_bins.get_bin(bin_index).wetted_subbin.material = "W" - FW_bins.get_bin(bin_index).shadowed_subbin.thickness = 1e-6 - FW_bins.get_bin(bin_index).shadowed_subbin.material = "B" - -# test that all bins have a thickness, material and mode -for bin in FW_bins.bins: - for subbin_index, subbin in enumerate(bin.sub_bins): - # print(bin.index, subbin_index, subbin.thickness, subbin.material, subbin.mode) - assert subbin.thickness, f"bin {bin.index} subbin {subbin.thickness}" - assert subbin.material, f"bin {bin.index} subbin {subbin.material}" - assert subbin.mode is not None, f"bin {bin.index} subbin {subbin.mode}" - - -# ------- DIV BINS ------- - -for bin_index in [18, 19, 20, 21, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41]: - div_bin = Div_bins.get_bin(bin_index) - div_bin.thickness = 1e-6 - div_bin.material = "B" - div_bin.set_inner_and_outer_bins() - -for bin_index in [ - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 46, - 47, - 48, - 49, - 50, - 51, - 52, - 53, - 54, - 55, - 56, -]: - div_bin = Div_bins.get_bin(bin_index) - div_bin.thickness = 6e-3 - div_bin.material = "W" - div_bin.set_inner_and_outer_bins() - -for bin_index in [42, 43, 44, 45, 57, 58, 59, 60, 61, 62, 63]: - div_bin = Div_bins.get_bin(bin_index) - div_bin.thickness = 5e-6 - div_bin.material = "B" - div_bin.set_inner_and_outer_bins() - -for bin in Div_bins.bins: - # print(bin.index, bin.thickness, bin.material, bin.mode) - assert bin.thickness is not None, f"bin {bin.index} thickness {bin.thickness}" - assert bin.material is not None, f"bin {bin.index} material {bin.material}" - assert bin.mode is not None, f"bin {bin.index} mode {bin.mode}" - -# read wetted data - -filename = "Wetted_Frac_Bin_Data.csv" -my_reactor.read_wetted_data(filename) - - -# add start and end points to bins -import pandas as pd - -data = pd.read_csv("bin_data.dat", sep=",") - - -for bin in my_reactor.first_wall.bins + my_reactor.divertor.bins: - bin.start_point = (data.loc[bin.index]["R_Coord"], data.loc[bin.index]["Z_Coord"]) - -# end point is the start point of next bin -for bin in my_reactor.first_wall.bins + my_reactor.divertor.bins: - try: - next_bin = my_reactor.get_bin(bin.index + 1) - except ValueError: - next_bin = my_reactor.get_bin(0) - bin.end_point = next_bin.start_point - -# test -assert len(data) == len(FW_bins.bins) + len( - Div_bins.bins -), f"{len(data)} {len(FW_bins.bins) + len(Div_bins.bins)}" - - -for bin in my_reactor.first_wall.bins + my_reactor.divertor.bins: - assert bin.start_point is not None - assert bin.end_point is not None - -for bin in FW_bins.bins: - for subbin_index, subbin in enumerate(bin.sub_bins): - assert ( - subbin.wetted_frac is not None - ), f"bin {bin.index} subbin {subbin_index} wetted fraction {subbin.wetted_frac}" - assert subbin.f is not None - assert subbin.low_wetted_area is not None diff --git a/examples/plotting.ipynb b/examples/plotting.ipynb deleted file mode 100644 index 5364b51f..00000000 --- a/examples/plotting.ipynb +++ /dev/null @@ -1,662 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from all_bin_scenario import plasma_data_handling, my_scenario, FW_bins\n", - "from hisp.scenario import Pulse\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "FP_pulse = Pulse(\n", - " pulse_type=\"FP\",\n", - " nb_pulses=1,\n", - " ramp_up=100,\n", - " ramp_down=100,\n", - " steady_state=500,\n", - " waiting=3600,\n", - " tritium_fraction=0.5,\n", - ")\n", - "\n", - "t = np.linspace(0, FP_pulse.total_duration, 1000, endpoint=False)\n", - "FW_bin = FW_bins.bins[0]\n", - "for i, sub_bin in enumerate(FW_bin.sub_bins):\n", - " ion_flux = []\n", - " for t_val in t:\n", - " ion_flux.append(\n", - " plasma_data_handling.get_particle_flux(\n", - " FP_pulse, bin=sub_bin, t_rel=t_val, ion=True\n", - " )\n", - " )\n", - " plt.plot(t, ion_flux, label=f\"Bin {FW_bin.index} Subbin {i}\")\n", - "plt.xlabel(\"Time (s)\")\n", - "plt.ylabel(\"Ion Flux (part/m2/s)\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from all_bin_scenario import plasma_data_handling, Div_bins\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "div_bin = Div_bins.bins[0]\n", - "\n", - "ion_flux = []\n", - "atom_flux = []\n", - "heat_flux = []\n", - "\n", - "RISP_pulse = Pulse(\n", - " pulse_type=\"RISP\",\n", - " nb_pulses=1,\n", - " ramp_up=0,\n", - " ramp_down=0,\n", - " steady_state=400,\n", - " waiting=0,\n", - " tritium_fraction=0,\n", - ")\n", - "t = np.linspace(0, RISP_pulse.total_duration, 1000)\n", - "\n", - "\n", - "for t_val in t:\n", - " ion_flux.append(\n", - " plasma_data_handling.get_particle_flux(RISP_pulse, bin=div_bin, t_rel=t_val, ion=True)\n", - " )\n", - " atom_flux.append(\n", - " plasma_data_handling.get_particle_flux(RISP_pulse, bin=div_bin, t_rel=t_val, ion=False)\n", - " )\n", - " heat_flux.append(\n", - " plasma_data_handling.get_heat(RISP_pulse, bin=div_bin, t_rel=t_val)\n", - " )\n", - "\n", - "fig, axs = plt.subplots(2, sharex=True)\n", - "plt.suptitle(f\"Divertor Bin {div_bin.index}\")\n", - "plt.sca(axs[0])\n", - "plt.plot(t, ion_flux, label=\"ion flux\")\n", - "plt.plot(t, atom_flux, label=f\"atom flux\")\n", - "plt.legend()\n", - "plt.ylabel(\"Ion Flux (part/m2/s)\")\n", - "plt.sca(axs[1])\n", - "plt.plot(t, heat_flux, label=f\"Heat flux\")\n", - "plt.ylabel(\"Heat (W/m2)\")\n", - "plt.xlabel(\"Time (s)\")\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from make_iter_bins import my_reactor\n", - "\n", - "bins = my_reactor.first_wall.bins + my_reactor.divertor.bins\n", - "\n", - "norm = plt.Normalize(0, len(bins))\n", - "cmap = plt.get_cmap(\"viridis\")\n", - "\n", - "for bin in bins:\n", - " plt.plot(\n", - " [bin.start_point[0], bin.end_point[0]],\n", - " [bin.start_point[1], bin.end_point[1]],\n", - " c=cmap(norm(bin.index)),\n", - " marker=\"+\",\n", - " )\n", - "plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), label=\"Bin Index\", ax=plt.gca())\n", - "plt.xlabel(\"R (m)\")\n", - "plt.ylabel(\"Z (m)\")\n", - "plt.gca().set_aspect(\"equal\", adjustable=\"box\")" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from make_iter_bins import my_reactor\n", - "\n", - "bins = my_reactor.first_wall.bins + my_reactor.divertor.bins\n", - "ion_fluxes = []\n", - "start_points = []\n", - "end_points = []\n", - "\n", - "for bin in my_reactor.divertor.bins:\n", - " ion_fluxes.append(\n", - " float(plasma_data_handling.get_particle_flux(FP_pulse, bin=bin, t_rel=FP_pulse.ramp_up+1, ion=True))\n", - " )\n", - " start_points.append(bin.start_point)\n", - " end_points.append(bin.end_point)\n", - "\n", - "from matplotlib.colors import LogNorm, Normalize\n", - "norm = LogNorm(1e18, max(ion_fluxes))\n", - "cmap = plt.get_cmap(\"viridis\")\n", - "\n", - "\n", - "for start_point, end_point, ion_flux in zip(start_points, end_points, ion_fluxes):\n", - " plt.plot(\n", - " [start_point[0], end_point[0]],\n", - " [start_point[1], end_point[1]],\n", - " c=cmap(norm(ion_flux)),\n", - " )\n", - "plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), label=\"Ion Flux (part/m2/s)\", ax=plt.gca())\n", - "plt.xlabel(\"R (m)\")\n", - "plt.ylabel(\"Z (m)\")\n", - "plt.gca().set_aspect(\"equal\", adjustable=\"box\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from make_iter_bins import my_reactor\n", - "from matplotlib.colors import LogNorm\n", - "\n", - "for mode in [\"high_wetted\", \"low_wetted\", \"wetted\"]:\n", - " ion_fluxes = []\n", - " start_points = []\n", - " end_points = []\n", - " for bin in my_reactor.first_wall.bins:\n", - " for sub_bin in bin.sub_bins:\n", - " if sub_bin.mode == mode:\n", - " ion_flux = plasma_data_handling.get_particle_flux(FP_pulse, bin=sub_bin, t_rel=FP_pulse.ramp_up+1, ion=True)\n", - " ion_fluxes.append(ion_flux)\n", - "\n", - " start_points.append(bin.start_point)\n", - " end_points.append(bin.end_point)\n", - "\n", - "\n", - " norm = LogNorm(1e18, max(ion_fluxes))\n", - " cmap = plt.get_cmap(\"viridis\")\n", - "\n", - "\n", - " for start_point, end_point, ion_flux in zip(start_points, end_points, ion_fluxes):\n", - " plt.plot(\n", - " [start_point[0], end_point[0]],\n", - " [start_point[1], end_point[1]],\n", - " c=cmap(norm(ion_flux)),\n", - " )\n", - " plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), label=\"Ion Flux (part/m2/s)\", ax=plt.gca())\n", - " plt.xlabel(\"R (m)\")\n", - " plt.ylabel(\"Z (m)\")\n", - " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", - " plt.title(mode)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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i0yUFhUJh8EmhRObk+8jJDxqNBnZ28pj7qCFAbWTjgrGft2U2nXCJ5MonwBOCIKBYVYycjDx4+iulDgmAqTYgl8f/POTIqhLuk849IrIWDo4O8KqjxN3b2Ui/eVc2CZfMy6r+V3Ts2DFERkYiMjISgPbco8jISEybNk3iyIj0J8cDJdUoKysYfmmx06wiq5rhVufcIyJr4VvfG38mX5FVt5kpSwo88aEiq0q4RLZEN8OV0dIwbl5jXvzNEEnEt5481+KS+XCGSyQROZ5tJppgP1yRy8KqxIRLJJHStbiZcnpoxpKCWfE3QySR0m6zrDs5KFIVSxwNWQITLpFEPLxrwcnZEQCQKZMWX27PaF5MuEQSEQRBdgdKmnIDcqqIvxkiCfnK+EBJMj0+NCOSkNzW4pqiJMCSQtU4wyWSkNzW4mpgZ5ILYGtvZTjDJZJQ2VpcedRwTYmtvRUx4RJJyFdmh0mqRQFqI0sCxn7eljHhEklId/LDzSyIoghBkDZZsYZrXky4RBLyqecJAHhwvxD3swvg7ukmaTyiCXYLE9lpViX+Zogk5OyqgNLbHYC89lQg82DCJZKYb8kJvnLYiNz4zceNPxPNljHhEklMV8eVwdIwjWiK9l6pfwr5YsIlkpgct2kk8+BDMyKJKTxcgFpuOHfqutSh8NReM2PCJZKYGnawU3ogJUX6Gq7GBBuQG/t5W8b/FRFJzNnNBQDgw6PSbR5nuEQScyjZE7dZ20YSR8JOM3PjDJdIYtlZ+QAApXctiSMpq+EaewHcvKYynOESSSz77n0AQG0vabvMTI2b11TEhEsksezMkoQrhxkuTLCXAh+aVYkJl0hi2VnySbg8Jt28mHCJJJZ9V1vDlUPC5W5h5sWHZkQSUj9UIy+7AIA8Ei6ZF2e4RBLKuVcAURRhZyfAvbar1OGw08zMmHCJJJRTskLBw9MN9vbSJyqWFMxL+n/DRDVY2QMz21oSRpXjDJdIQqUPzJRe8qjfci8F82LCJZJQ9t08APJ5YMaSgnmxpEAkITktCSPz4wyXSEK6tl6ZJFzOcM2LCZdIQnJ7aMaEa14sKRBJSLePgkwempF5cYZLJCHdDNdHHgmXM1zz4gyXSCKiKCKn9KGZTGa4IsqWhhl6lR7ay/1wK+IMl0gihQVFUBUWA7DNh2bcD7ciznCJJFJaTlC4OMLZ1UniaMgSOMMlkogcH5ixhltGpVLhyJEjuHbtGgoKCuDr64vIyEg0bNjQ4DGZcIkkoluDK5MHZgATLgAcOHAAn332GTZv3ozi4mIolUq4uLggKysLKpUKISEheOutt/DOO+/A3d1dr7FZUiCSSOnhkXKa4dZ0ffv2xeDBgxEcHIzt27cjLy8Pd+/exc2bN1FQUIBLly7hww8/xK5du/D0009jx44deo3PGS6RRMq6zOTR9ABwhhsXF4cNGzbA0dGx0tdDQkIQEhKC+Ph4nD9/Hrdv39ZrfCZcIonIra0XAERRgGhkwjT281J6++23q/3esLAwhIWF6TU+SwpEEpFjwqUyN27cwM2bN3VfJycnY+zYsVixYoXBYzLhEkkkR4YJ19imB1PspysXr7zyCpKSkgAAaWlp6N69O5KTk/HPf/4T//rXvwwakwmXSCKlD82Uckq4JTVcYy9bcPbsWURFRQEAfvjhBzRr1gwHDx7EunXrsHr1aoPGZMIlksg9Ga7DpTLFxcVQKBQAgJ07d6Jv374AgGeeeUbvh2WlmHCJJPDo8eieMlqHW/rQzNjLFjRt2hTLli3Dvn37sGPHDvTo0QMAcOvWLXh7exs0JhMukQRys7XHowuCPI5HL8WSQpm5c+di+fLl6NKlC15++WWEh4cDAH755RddqUFfXBZGJIHSo3U8PF1lcTx6qZq+LAwACgoK4Orqii5duiAzMxO5ubnw9PTUvf7WW2/B1dWw/0nK5980UQ0it8MjqYyPjw969+6NFStWIDMzs1yyBYDg4GD4+fkZNDYTLpEE5Hp4pGiCcoK1z3AvXLiA2NhY/PDDD2jQoAHatGmDmTNn4o8//jB6bCZcIgnItelBBCCKRl5S/xBGCgoKwnvvvYedO3fizp07GDt2LP744w906tQJISEhGDt2LHbv3g21Wq332Ey4RBLIKT1ax0s++yhQRUqlEi+//DLWr1+PjIwMLF++HGq1GsOHD4evry/WrVun13hWl3CXLFmC4OBgODs7o02bNkhOTpY6JCK9la7BlVPTA8BOs8dxdHRE9+7dsWjRIly7dk23Y5g+rCrhfv/990hISEBiYiJOnDiB8PBwxMbGIj09XerQiPRSetqDnNbgAqZdh2vtZ5ppNJpK74uiiOvXryMyMhKtW7fWa0yrSrjz58/HiBEjMHz4cISFhWHZsmVwdXXFypUrpQ6NSC9yOzzSHI4ePYrz589j5MiRUoeil9zcXAwaNAhubm7w9/fHtGnTytVr09PTDT71wWoSblFREY4fP46YmBjdPTs7O8TExODQoUMSRkakv9KHZrIrKbDxAVOnTsXp06exZs0azJw5E99++y1eeOEFFBUV6d4jioY9GrSaxofMzEyo1Wr4+/uXu+/v748LFy5U+hmVSgWVSqX7Ojc316wxElWHKIqy3HwcKFtpYOwY1mzTpk345ptv0KVLFwBAv379EBcXhz59+uCXX34BAAiCYf9TsZoZriFmz54NpVKpuwIDA6UOiajc8eie3vqdiUXml5GRgQYNGui+9vHxwc6dO5GXl4devXqhoKDA4LGtJuH6+PjA3t4ed+7cKXf/zp07qFOnTqWfmTJlCnJycnTXjRs3LBEq0WPJ+Xh0bl6jXYf7559/lrvn7u6O7du348GDB+jfv7/BY1tNwnVyckLLli2xa9cu3T2NRoNdu3ahXbt2lX5GoVDAw8Oj3EUktWwZPzBjwgWef/55rFq1qsL9WrVqYdu2bXB2djZ4bKup4QJAQkIC4uPj0apVK0RFRWHhwoXIz8/H8OHDpQ6NqNrkeNJDKY0oQDAyYVr7Q7MZM2bg1q1blb7m7u6OHTt24MSJEwaNbVUJd/DgwcjIyMC0adOQlpaGiIgI/P777xUepBHJ2T3dCgV5PTAjLU9Pzwob1jzK3d0dnTt3Nmhsq0q4ADBq1CiMGjVK6jCIDFa6QsFThjNcrlIo7+jRo0hKSkJ6enqFRoj58+frPZ7VJVwiayfnkoI24Rq7H66JgpHYrFmz8OGHH6Jx48bw9/cvtxTM0GVhTLhEFqY7PFKGD82ozGeffYaVK1di2LBhJhuTCZfIwrIz5bv5OE98KGNnZ4cOHTqYdkyTjkZET1Q6w60ts41rgJL9cE1w2YJx48aZfOMdznCJLEzX1suSgqy9//77iIuLQ2hoKMLCwuDo6Fju9Y0bN+o9JhMukQWp1Rrk3tO2hsptHwWAJYVHjR49GklJSYiOjoa3t7fBD8oexYRLZEG59/J1x6N7yOh4dB1T1ARspKbwzTffYMOGDYiLizPZmEy4RBZU7nh0B3uJo6mEKVpzbWSG6+XlhdDQUJOOyYdmRBZUunGNHFcoUHnTp09HYmKiUbuD/R1nuEQWpGt6kOnhkew0K/P555/jypUr8Pf3R3BwcIWHZobsp8CES2RBcj08shQfmpXp16+fycdkwiWyoNLj0eW4jwJp/fXXXwgJCUFiYqLJx2YNl8iC5HqWmY4omOayYi1atECzZs3wwQcfIDk52aRjM+ESWZBu83GZJtzSGq6xlzXLzMzE7NmzkZ6ejr59+6Ju3boYMWIENm/ejMLCQqPGZsIlsqBsmT80I8DZ2Rl9+vTBV199hdu3b2PDhg3w9vbGpEmT4OPjg379+mHlypXIyMjQe2wmXCIL0iVcH5keHsnNFMoRBAHt27fHnDlzcP78eZw8eRKdOnXC6tWrUb9+fb33WuBDMyILKluHK88ZLlcplNm7dy/at28PB4eyNPnUU09hzJgxaN26NZo2bYqsrCy9xmTCJbKQwoIiqB5oj0fnxjXyFx0djdu3b8PPz6/c/ZycHERHR0OtVsPb21uvMZlwiSyktJygcJbf8ejl2FBJwBile1783d27d+HmZtjfUJhwiSzk0cMjTbHzlDmYsqTQunVr2NvbY+TIkRg5cqQpwrOIAQMGANDWb4cNGwaFQqF7Ta1W48yZM2jfvr1BYzPhEllIju7wSJk+MANMulvY0aNH4eHhYWxEFqdUKgFoZ7ju7u5wcXHRvebk5IS2bdtixIgRBo3NhEtkIaUPzHg8urytWrUKYsli4kWLFqFWLdPV27ksjMhCdE0Psn5gJpjosm6iKGLdunW4ffu2ScdlwiWyEDkfHqnDdbgAtAdIPvXUU7h7965pxzXpaERUpfoNfdGy49MIfrqO1KFQNcyZMwcTJkzA2bNnTTYma7hEFtL7H+3Q+x/tpA7j8XjEjs7QoUNRUFCA8PBwODk5lXt4BkDvpgeACZeIHmWK3b5spNNs4cKFJh+TCZeIqBLx8fEmH5MJl4h0eMRO5QoLC1FUVFTuniFrjPnQjIjKcJWCTn5+PkaNGgU/Pz+4ubnB09Oz3GUIoxKuSqUy5uNERLI1ceJE7N69G0uXLoVCocBXX32FGTNmICAgAN9++61BY+qVcLdu3Yr4+HiEhITA0dERrq6u8PDwQOfOnTFz5kzcunXLoCCISCZ4xI7O5s2b8cUXX+DFF1+Eg4MDOnXqhA8//BCzZs3CunXrDBqzWgn3p59+wtNPP43XX38dDg4OmDRpEjZu3Iht27bhq6++QufOnbFz506EhITgnXfeMWgndCKSniCa5rIFWVlZCAkJAaCt15YuA+vYsSP27t1r0JjVemg2b948LFiwAD179oSdXcUcPWjQIABAamoqFi1ahLVr12LcuHEGBUREEuI6XJ2QkBCkpKQgKCgIzzzzDH744QdERUVh8+bNqF27tkFjVivhHjp0qFqD1atXD3PmzDEoECIiORk+fDhOnz6Nzp07Y/LkyejTpw8WL16M4uJizJ8/36AxuSyMiMqw8UHn0b+lx8TE4M8//8SJEyfQqFEjtGjRwqAx9U64oijiP//5D5KSkpCeng6NRlPu9Y0bNxoUCBHJAEsKVQoODkZwcLBRY+i9LGzs2LF47bXXkJKSglq1akGpVJa7iIhsxa5du9C7d2+EhoYiNDQUvXv3xs6dOw0eT+8Z7po1a7Bx40b06tXL4G9KRDLFGa7OF198gTFjxmDgwIEYM2YMAODw4cPo1asXFixYYNCxQXonXKVSqVsqQUQ2hglXZ9asWViwYAFGjRqluzd69Gh06NABs2bNMijh6l1SmD59OmbMmIEHDx7o/c2IiKxFdnY2evToUeH+888/j5ycHIPG1DvhDho0CPfu3YOfnx+aN2+OZ599ttxFRFaMnWY6ffv2xU8//VTh/s8//4zevXsbNKbeJYX4+HgcP34cr776Kvz9/WV73DMR6c8UnWK20mkWFhaGmTNnYs+ePWjXTrtx/OHDh3HgwAGMHz8en3/+ue69o0ePrtaYgijqt5mam5sbtm3bho4dO+rzMVnIzc2FUqlETk6OVR7fTDWXuf/slo4fNO8j2Lk4GzWW5kEhrk/80Or/O2vYsGG13icIAv76669qvVfvGW5gYKBV/xKJ6DH40EwnJSXF5GPqXcP99NNPMXHiRFy9etXkwRAR2TK9E+6rr76KpKQkhIaGwt3dHV5eXuUuIrJeAkywW5jUP4QR5syZU+0VWEeOHMGWLVv0Gl/vkoI5DlYjIpKD8+fPIygoCC+99BL69OmDVq1awdfXFwDw8OFDnD9/Hvv378fatWtx69YtvTciN2iVAhHZqBq+ec23336L06dPY/HixXjllVeQm5sLe3t7KBQKFBQUAAAiIyPx5ptvYtiwYXB21u8BY7USbn5+Ptzc3Ko9qL7vJyKZ4EMzhIeH48svv8Ty5ctx5swZXLt2DQ8ePICPjw8iIiLg4+Nj8NjVSriNGjXCmDFjEB8fj7p161b6HlEUsXPnTsyfPx/PPfccpkyZYnBQRERSs7OzQ0REBCIiIkw2ZrUS7p49e/DBBx9g+vTpCA8PR6tWrRAQEABnZ2fcu3cP58+fx6FDh+Dg4IApU6bg7bffNlmARGRBnOGaVbUSbuPGjbFhwwZcv34dP/74I/bt24eDBw/qptmRkZH48ssv0bNnT9jb25s7ZiIyE3aamZdeD82CgoIwfvx4jB8/3lzxEBHZLB6xQ0RlWFIwK70bH4jIhokmumxARkZGla/98ccfBo3JhEtEVInmzZtX2kn2ySefICoqyqAxmXCJSMfotl4TPHSTi4SEBLz44ot499138eDBA6SmpqJbt26YN28evvvuO4PGtJqEO3PmTLRv3x6urq6oXbu21OEQ2SZuQK4zceJEHDp0CPv27UOLFi3QokULKBQKnDlzBv379zdozGon3G7duj32CPTMzEyznnVWVFSEl156Ce+++67ZvgdRjccabjmNGjVCs2bNcPXqVeTm5mLw4MGoU6eOweNVO+EmJSVh0KBBSExMrPR1tVqNa9euGRzIk8yYMQPjxo1D8+bNzfY9iIhKHThwAC1atMClS5dw5swZLF26FO+99x4GDx6Me/fuGTSmXiWFpUuXYuHChejfvz/y8/MN+oaWpFKpkJubW+4ioqqxhluma9euGDx4MA4fPowmTZrgzTffxMmTJ3H9+nWDJ356JdwXXngBhw8fxrlz59C2bdtqHyshldmzZ0OpVOquwMBAqUMikjeWFHS2b9+OOXPmwNHRUXcvNDQUBw4cMHj7Ar0fmjVp0gRHjx5FYGAgWrdujZ07dxr0jQFg8uTJEAThsdeFCxcMHn/KlCnIycnRXTdu3DB4LCKSt/79+8PT0xMDBw4sd/+TTz5B06ZN0axZM6xdu7ba43Xu3LnS+3Z2dpg6dapBMRrUaaZUKrFlyxZMmTIFvXr1wty5c/HKK6/oPc748eMxbNiwx77HmAdxCoUCCoXC4M8T1TimKAlINMMdM2YMXn/9dXzzzTe6e3/88Qe+++47HD9+HKIoIjo6Gr17967WSqd//etfj3192rRpesdY7YT79+PQBUHAnDlzEBERgTfffBO7d+/W+5v7+vrqdlMnIhmw4tbeLl26YM+ePeXu/fnnn2jXrp1uo/Dw8HD8/vvvGDJkyBPH++mnn8p9XVxcjJSUFDg4OCA0NNSghFvtkkJVp6kPGTIE+/fvN7jVrbquX7+OU6dO4fr161Cr1Th16hROnTqF+/fvm/X7EpH57d27F3369EFAQAAEQcCmTZsqvGfJkiUIDg6Gs7Mz2rRpg+Tk5CeO26xZM+zZswfZ2dm4d+8e9uzZg9TU1GrFdPLkyXLX2bNncfv2bXTr1g3jxo3T90cEoOeysKoOiYyIiMDx48exatUqg4KojmnTpiEyMhKJiYm4f/8+IiMjERkZiWPHjpntexLVOCZ8aPb3FUIqlarKb5ufn4/w8HAsWbKk0te///57JCQkIDExESdOnEB4eDhiY2ORnp7+2B8nLCwMo0ePRteuXTFgwAC0bdvWqC1kPTw8MGPGDINruNVOuJ07d4aDQ9UVCG9vbwwdOtSgIKpj9erVEEWxwtWlSxezfU+imsaUy8ICAwPLrRKaPXt2ld+3Z8+e+Oijj6rs4Jo/fz5GjBiB4cOHIywsDMuWLYOrqytWrlz5xJ/p7bffxokTJ5CUlARHR0c89dRTBv1uSpU+hDcEt2ckIrO4ceMGPDw8dF8b+gC7qKgIx48fL3dsl52dHWJiYnDo0KEnfj49PR1+fn64ePEikpOTsWzZsmp9388//7zc16Io4vbt21izZg169uyp3w9RggmXiMzCw8OjXMI1VGZmJtRqNfz9/cvd9/f3L7dsNCYmBqdPn0Z+fj7q16+PH3/8Ee3atcMLL7yAnJwcuLm5YdWqVY/9m/qjFixYUO5rOzs7+Pr6Ij4+3uAzG5lwiaiMFa9SqKonoDqz4MqkpKQYE06lmHCJSEeOZ5r5+PjA3t4ed+7cKXf/zp07Rm0kIwUmXCKSNScnJ7Rs2RK7du1Cv379AAAajQa7du3CqFGjTPq9BgwYUO33Pm73xKow4RJReRKUBO7fv4/Lly/rvk5JScGpU6fg5eWFoKAgJCQkID4+Hq1atUJUVBQWLlyI/Px8DB8+3KRxKJVKk473d0y4RFRGohrusWPHEB0drfs6ISEBABAfH4/Vq1dj8ODByMjIwLRp05CWloaIiAj8/vvvFR6kGcucvQQAEy4RmUnr1q1hb2+PkSNHYuTIkY99b5cuXarsZi01atQok5cQKrN7924899xz1V7NoA8mXCLSMeVDs6NHj5pkWZilde/eHbdv34afnx8AoG3bttiwYQPq1atn9NhMuERUxoqXhZnK32fa586de2xbsj6s5hBJIiJrxxkuEenIcR2upZUeflDV18ZgwiWiMiwpQBRFdOvWTffQrKCgAH369IGTk1O59504cULvsZlwiYge8feTyV944QWTjc2ES0RlOMOtkHBNiQ/NqEYTRRGFhcVShyEbptwPt3Xr1ggLC6tyU/GaiDNcqtF++/0Mvlt/GJMnxqF50/pShyM9E85wrXUdrjlxhks1Vuqte1i8dBdu3c7G+fPVO+eKyBic4VKNpFZrMOfjLSgsLEaL5oEYOKC11CHJA2u4ZsWESzXS9z8m4+y5VLi6OmHKhDjY2/MvewDX4ZobEy7VOJev3MGqb/cBAN77vxjUqWPeLfnIeh09ehRJSUlIT0+HRqMp99r8+fP1Ho8Jl2qUoqKHmDnnVzx8qEGnDk8jtnszqUOSF5YUdGbNmoUPP/wQjRs3hr+/f4XuM0Mw4VKN8tWqvbh6LROenm5IGBNrspZNW8GSQpnPPvsMK1euxLBhw0w2JgtXVGOcPHUN/9l4FAAwYVwP1K7tKnFEJGd2dnbo0KGDacc06WhEMnU/X4U5H2+BKAJxPcPRrm0jqUOSJ9FElw0YN26cyZs2WFKgGmHRkp1Iz8hDQN3aGPlOV6nDkS8T1nD1OfFBjt5//33ExcUhNDQUYWFhcHR0LPc6D5EkqsR/913E9p1nYWcnYMrEOLi4OD35Q2Q0a+80Gz16NJKSkhAdHQ1vb2+T1PuZcMmm3b17H/M/2wYAeHlwWzRj++5jCSWXsWPYgm+++QYbNmxAXFycycZkwiWbJYoiPp6/Fbm5D/BUI3/Ev2raByA2icvCdLy8vBAaGmrSMfnQjGzW5i2nceToX3B0tMcHk3rD0dFe6pBkz5S7hVm76dOnIzExEQUFBSYbkzNcskk3U7OwdPluAMBbb3RGcAMfiSMia/P555/jypUr8Pf3R3BwcIWHZjzxgQjajWlmz9uCQlUxIiMaYEC/VlKHZD1YUtDp16+fycdkwiWb8936wzj/5y24uSkw6f1esLOzlcc4FmIjCdNY5jj5gQmXbMrF/6Xhm7UHAABjRnaHv5/1LksieTh+/Dj+/PNPAEDTpk0RGRlp8FhMuGQzVKpizJq7GWq1Bp2fa4yYbmFSh2R1uJdCmfT0dAwZMgR79uxB7dq1AQDZ2dmIjo7G+vXr4evrq/eYXKVANmPF1//F9RtZ8PaqhXGjuTGNQdjaq/Pee+8hLy8P586dQ1ZWFrKysnD27Fnk5uZi9OjRBo3JhEs24fiJq9i46TgAYOL4nlB6uEgcEVn7IZK///47vvjiCzRp0kR3r/Tn2bp1q0FjsqRAVi8vrxBzPtkCAOjbOxJRrUMkjsh6mbKkYO2tvRqNpsJSMABwdHSssBl5dXGGS1bvs8U7kJl5H/XreeKdEV2kDse6saSg07VrV4wZMwa3bt3S3UtNTcW4cePQrVs3g8ZkwiWrtnvPn9iVdL5kY5re3JiGTGbx4sXIzc1FcHAwQkNDERoaioYNGyI3NxeLFi0yaEyWFMhqZWTmYeGi7QCAV19ph7AmARJHZP24SqFMYGAgTpw4gZ07d+LChQsAgCZNmiAmJsbgMZlwySqJooh5n/6GvLxCNH66Dl57pb3UIdkGdprpfPvttxg8eDC6d++O7t276+4XFRVh/fr1GDp0qN5jsqRAVmnT5pM4dvwqnJwc8MHE3nBw4MY0JsEars7w4cORk5NT4X5eXh6GDx9u0JhMuGR1rt+4i+VfJgEA3n6zC4KCvCWOiGyRKIqVruW+efMmlEqlQWOypEBW5eFDNWbP2wKV6iFaPRuMfn2flTokm8IaLhAZGQlBECAIArp16wYHh7I0qVarkZKSgh49ehg0NhMuWZW13x3ChYu34e7ujIncmMb0WMPV7RJ26tQpxMbGolatWrrXnJycEBwcjBdffNGgsZlwyWr8eeEW1nx3EAAwdlR3+Pq4SxwR2aLSXcKCg4MxePBgODs7V3jP2bNn0axZM73HZg2XrMKDB0WYNfdXaDQiukY3QddobkxjDoIomuSyBfHx8eWSbV5eHlasWIGoqCiEh4cbNCYTLlmF5V/twc3Ue/DxqYWxo56XOhzbxVUKFezduxfx8fGoW7cuPvnkE3Tt2hWHDx82aCyWFEj2ko/+hZ83nwQATBrfC+7uFf+KR/LTunVr2NvbY+TIkRg5cqTU4eglLS0Nq1evxtdff43c3FwMGjQIKpUKmzZtQliY4X+7YsIlWcvJfYB5n2p3Zur/Qku0atlQ4ohsGzevAfr06YO9e/ciLi4OCxcuRI8ePWBvb49ly5YZPTYTLsmWKIpY8Pk23M26j6BAL7z1RmepQ7J9XKWArVu3YvTo0Xj33Xfx1FNPmXRs1nBJtnbuPo//7r0Ie3s7fDCpN5ydK26VR2Rq+/fvR15eHlq2bIk2bdpg8eLFyMzMNMnYTLgkS+npufhs8Q4AwNB/tEfjp+tKHFHNUFpSMPayZm3btsWXX36J27dv4+2338b69esREBAAjUaDHTt2IC8vz+CxmXBJdjQaEXM++Q35+So0eaYu/vFyO6lDqjm4SkHHzc0Nr7/+Ovbv348//vgD48ePx5w5c+Dn54e+ffsaNCYTLsnOxp+P4+Spa3BWOGLKxN6wt+cfU0vhDLdyjRs3xrx583Dz5k38v//3/wweh3+SSVauXsvEiq/2AADeeSsagfW9pA2I6BH29vbo168ffvnlF4M+bxUJ9+rVq3jjjTfQsGFDuLi4IDQ0FImJiSgqKpI6NDKh4mI1Zs39FcXFakS1aoi+vSOkDqnmYUnBrKxiWdiFCxeg0WiwfPlyNGrUCGfPnsWIESOQn5+PTz75ROrwyES+XXsAly7fgYe7MyaO78VjziViiyUBubCKhNujR49y26GFhITg4sWLWLp0KROujTh3PhXffa9tl0wYEwtv71pP+ASR9bGKhFuZnJwceHk9vr6nUqmgUql0X+fm5po7LDLAgwdFmDVPuzFN925N0fm5Z6QOqeYSRe1l7BhUKauo4f7d5cuXsWjRIrz99tuPfd/s2bOhVCp1V2BgoIUiJH0sXZGEW7ey4efrjtEjDT+gj4zHVQrmJWnCnTx5sm5n9aqu0tMyS6WmpqJHjx546aWXMGLEiMeOP2XKFOTk5OiuGzdumPPHIQMcOnIFm7ecAgBMnhCHWrW4MQ3ZLklLCuPHj8ewYcMe+56QkBDdP9+6dQvR0dFo3749VqxY8cTxFQoFFAqFsWGSmWRnF+Dj+dqNaQYOaIXIiAYSR0TcS8G8JE24vr6+8PX1rdZ7U1NTER0djZYtW2LVqlWws7PKagiVEEUR8z/bhnv38tGggTdGvM6NaeRA0GgvY8egyllF1kpNTUWXLl0QFBSETz75BBkZGUhLS0NaWprUoZGBtu04i30H/gcHBzv8c1IfODlZ7fNbqkLr1q0RFhaGJUuWSB2KbFjFn/IdO3bg8uXLuHz5MurXr1/uNZFPRK1OWloOFn2xEwAw7LWOeKqRv8QRkY4JSwrWuh+uOVnFDHfYsGEQRbHSi6yLdmOaLSgoKELTsHoYMqiN1CHRI7hKwbysYoZLtuM/G4/i9JkbcHZ2xJSJcdyYRm64Dtes+KedLOavlAx8tWovAGDkO11RL8BT4oiILIszXLKIoqKHuo1p2rUJRVzPcKlDokqY8kwzqogJlyxi9Zr9uPJXOpRKF7yf0JMb08gV1+GaFUsKZHZ/nL2J9T8cAQCMH9MDXp5uEkdEJA3OcMmsCgpUmD3vV4gi0OP5ZujU8WmpQ6LHYEnBvJhwyayWLNuN22k58Pf3wKh3uTGN7HGVglmxpEBms//gJfz2+xkIAjBlQm+4uXFfC6rZOMMls7h3Lx+fLvgdADBoYBTCW3BrTGvAkoJ5MeGSyYmiiPmfb0F2TgFCGvri9fhOUodE1cVVCmbFkgKZ3KkjizHihXnw81bhg0m9uTENUQn+l0AmI4pqqHM+RnjwSgDA1HG5CA3xkzgq0gdLCubFhEsmIWpyIWYnwK5I27p78lJPRLSbI3FUpDeNqL2MHYMqxYRLRhMfpkC89w6gTgHgDEE5Cy079ZY6LDIEa7hmxYRLRhFV+yBmjwXEPMCuDgTPpRAcm0odFpEsMeGSQURRBApWQcybB0ADOD4LofZiCPY+UodGRhBgghquSSKxTUy4pDdRVEHMmQoUbtLecBkIwWM6BMFJ0rjIBNhpZlZcFkZ6EdXpELNeLUm29hDcP4TgMZPJlirgmWYVcYZL1SYWn4F47/8ATTogKCHUXghB0UHqsMiETLksjGeaVcSES9UiPvgFYs4HAIoA+1AInssgODSQOiwyNa5SMCsmXHosUVRDvD8fyP9Se0MRDUH5KQS7WtIGRmSFmHCpSqImD2JOAqD6r/aG29sQao2FINhLGxiZjSCKEIx86GXs520ZEy5VStvM8C6g/guAAoJyNgQXNjPYPE3JZewYVCkmXKpAVO0vaWbILWlm+AKCYzOpwyKyeky4pFOxmSGypJnBV+rQyEJYUjAvJlwCUNrMMA0o/El7w+VFCB4zuL62puEqBbNiwiVtM0P2KKD4FAA7CO5TANehPMq8JmKnmVkx4dZw2maGkYDmDpsZiMyMCbcGEx9sLmlmULGZgQBwA3JzY8KtgSo2M3SBoJzPZgZiScHMmHBrmIrNDG9BqDWOzQxEFsCEW4OID6+WnMzAZgaqnKDRXsaOQZVjwq0hyjcz+Jc0MzSXOiySG5YUzIoJ18ZpmxlWQ8ybC20zQwSE2kvYzEAkASZcGyaKRRBzpwEPNmpvuAyA4PEvNjNQ1dj4YFZMuDZKVGeUNDOchLaZYTLgGs9mBnostvaaFxOuDRKL/yg5meEOIHiUNDN0lDosohqPCdfGVGxmWArBIVjqsMha8KGZWTHh2ghtM8MCIH+F9oaiS8nJDO7SBkbWRYTx+9ky31aJCdcGiJr7Jc0Me7Q33EZAqJXAZgbSG2u45sWEa+W0zQzvAuor0DYzzILg0kfqsIioEky4VkxUHYCYPYbNDGQ6IkxQwzVJJDaJCdcKaZsZvoGYNwdlzQyLIdj7SR0aWTs+NDMrJlwro21mSAQebNDeYDMDkdVgwrUiFZsZJgGuw9jMQKajAWDsHyduXlMlJlwrIRafLWlmSGMzA5kNVymYl53UAdCTiQ9+hXj3ZW2ytQ+B4P0fJluSvdatWyMsLAxLliyROhTZ4AxXxkRRU9LMsFx7Q9G55GQGNjOQmZjwodnRo0fh4eFhgqBsBxOuTGmbGcYDqiTtDTYzkCVwlYJZMeHKkPjwWsnJDKXNDDMhuPSVOiwiMhITrsxomxnGAmIOYOdX0szQQuqwqKbgDNesmHBlQtvMsAZi3mwAajYzkDS4LMysmHBlQNvMMB148B/tDef+EJT/giAopAyLaiAuCzMvJlyJaZsZ3gOKT4DNDES2jQlXQhWbGRZAUHSSOiyqyVjDNSsmXImID36FmDMF2pMZGkLwXAbBoaHUYVFNpxEBwciEqWHCrQoTroVpmxkWAvnLtDfYzEBUY1hNa2/fvn0RFBQEZ2dn1K1bF6+99hpu3boldVh6ETX3IWb/X1mydRsBofYyJluSj9KSgrEXVcpqEm50dDR++OEHXLx4ERs2bMCVK1cwcOBAqcOqNvHhNYhZgwDVbgBOEJQfw859AjvHSGZMkWyZcKtiNSWFcePG6f65QYMGmDx5Mvr164fi4mI4OjpKGNmTsZmBiAArSriPysrKwrp169C+fXtZJ9uKzQzhEGovYTMDyRdXKZiV1ZQUAGDSpElwc3ODt7c3rl+/jp9//vmx71epVMjNzS13WYq2meGfEPM+AqAGnPtB8FrLZEvyphFNc1GlJE24kydPhiAIj70uXLige/+ECRNw8uRJbN++Hfb29hg6dKh2FlmF2bNnQ6lU6q7AwEBL/FgQ1ZkQs4aWdI7ZQXCfDEE5l51jRDWcID4uY5lZRkYG7t69+9j3hISEwMmp4nldN2/eRGBgIA4ePIh27dpV+lmVSgWVSqX7Ojc3F4GBgcjJyTHbPp1i8TntseWaNEBwLzmZgc0MZJzc3FwolUqz/dktHT8m6P/gYGfcxOChRoWd178w639n1krSGq6vry98fX0N+qxGo90h49GE+ncKhQIKheVmleKDLSXNDIVsZiDrxBquWVnFQ7MjR47g6NGj6NixIzw9PXHlyhVMnToVoaGhVc5uLUnbzPAZkL9Ue8PpOW2bLtfXkrXRmGBZF2u4VbKKh2aurq7YuHEjunXrhsaNG+ONN95AixYt8N///teiM9jKaJsZRpYlW7cREDyXM9kSUQVWMcNt3rw5du/eLXUYFYgPr0PMfgd4eBnaZoaZEFxekDosIsOxpGBWVpFw5UhUHYSYPaasmaH2EghO4VKHRWQcESZIuCaJxCYx4eqpYjNDi5JmBn+pQyMimWPC1YO2mWEG8OBH7Q3nFyAoP+L6WrIdLCmYFRNuNYnqTIjZox45mWEC4Po6T2Yg26LRwOhDyTQ81KwqTLjVoG1m+D9Ac7ukmWEBBMVzUodFRFaGCfcJxAe/QcyZjLJmhqUQHEKkDovIPFhSMCsm3CpUbGboVNLMwFZFsmFMuGbFhFsJUXMfYs4EQLVLe8PtTQi1xnOzcCIyChPu32ibGd4FHl6CtpnhIwgu/aQOi8gy2NprVky4jxBVh0qaGbLZzEA1kihqIIrGrTIw9vO2jAm3hPhgI8Scf4LNDFSjiSbYQJw13CpZxeY1FuHwDABHbTOD1zomWyIyOc5wSwiOYYDPJu3SLzYzUE1lilN3OcOtEhPuI7i+lmo8jQYQjKzBsoZbJZYUiIgshDNcIirDkoJZMeESkY6o0UA0sqTAZWFVY0mBiMhCOMMlojIsKZgVEy4RldGIgMCEay4sKRARWQhnuERURhRh9IkPnOFWiTNcItIRNaJJLin0798fnp6eGDhwYLn7CxYsQNOmTREWFobRo0drD4KVCBMuEZURNaa5JDBmzBh8++235e5lZGRg8eLFOH78OP744w8cP34chw8fliQ+gAmXiGxEly5d4O7uXuH+w4cPUVhYiOLiYhQXF8PPz0+C6LSYcIlIR6qSwt69e9GnTx8EBARAEARs2rSpwnuWLFmC4OBgODs7o02bNkhOTn7iuL6+vnj//fcRFBSEgIAAxMTEIDQ0VO/4TIUJl4jKSFRSyM/PR3h4OJYsWVLp699//z0SEhKQmJiIEydOIDw8HLGxsUhPT3/suPfu3cOvv/6Kq1evIjU1FQcPHsTevXv1js9UatQqhdJieW5ursSREOmn9M+suR/4PESx0X0PD1EMoOJ/ZwqFAgqFotLP9OzZEz179qxyzPnz52PEiBEYPnw4AGDZsmXYsmULVq5cicmTJ1f5uZ07d6JRo0bw8vICAMTFxeHw4cN47rnn9PqZTKVGJdy8vDwAQGBgoMSREBkmLy8PSqXS5OM6OTmhTp062J/2m0nGq1WrVoX/zhITEzF9+nS9xyoqKsLx48cxZcoU3T07OzvExMTg0KFDj/1sYGAgDh48iMLCQjg6OmLPnj1466239I7BVGpUwg0ICMCNGzfg7u4OQRCQm5uLwMBA3LhxAx4ePP6cv4+K5PI7EUUReXl5CAgIMMv4zs7OSElJQVFRkUnGE0Wxwkb+Vc1unyQzMxNqtRr+/uVPYfH398eFCxd0X8fExOD06dPIz89H/fr18eOPP6Jdu3bo1asXIiMjYWdnh27duqFv374GxWEKNSrh2tnZoX79+hXue3h4MME8gr+PiuTwOzHHzPZRzs7OcHZ2Nuv3MKedO3dWen/mzJmYOXOmhaOpHB+aEZGs+fj4wN7eHnfu3Cl3/86dO6hTp45EURmGCZeIZM3JyQktW7bErl27dPc0Gg127dqFdu3aSRiZ/mpUSeHvFAoFEhMTDa4t2Rr+Piri78Qy7t+/j8uXL+u+TklJwalTp+Dl5YWgoCAkJCQgPj4erVq1QlRUFBYuXIj8/HzdqgVrIYhSNhYTEQHYs2cPoqOjK9yPj4/H6tWrAQCLFy/Gxx9/jLS0NERERODzzz9HmzZtLBypcZhwiYgshDVcIiILYcIlIrIQJlwiIgupkQk3NTUVr776Kry9veHi4oLmzZvj2LFjUoclmeDgYAiCUOEaOXKk1KFJQq1WY+rUqWjYsCFcXFwQGhqKf//735JuXE22ocYtC7t37x46dOiA6OhobN26Fb6+vrh06RI8PT2lDk0yR48ehVqt1n199uxZdO/eHS+99JKEUUln7ty5WLp0Kb755hs0bdoUx44dw/Dhw6FUKjF69GipwyMrVuNWKUyePBkHDhzAvn37pA5FtsaOHYtff/0Vly5dqtAPXxP07t0b/v7++Prrr3X3XnzxRbi4uGDt2rUSRkbWrsaVFH755Re0atUKL730Evz8/BAZGYkvv/xS6rBko6ioCGvXrsXrr79eI5MtALRv3x67du3C//73PwDA6dOnsX///sduH0hULWINo1AoRIVCIU6ZMkU8ceKEuHz5ctHZ2VlcvXq11KHJwvfffy/a29uLqampUociGbVaLU6aNEkUBEF0cHAQBUEQZ82aJXVYZANqXEnByckJrVq1wsGDB3X3Ro8ejaNHjz5xb82aIDY2Fk5OTti8ebPUoUhm/fr1mDBhAj7++GM0bdoUp06dwtixYzF//nzEx8dLHR5ZsRr30Kxu3boICwsrd69JkybYsGGDRBHJx7Vr17Bz505s3LhR6lAkNWHCBEyePBlDhgwBADRv3hzXrl3D7NmzmXDJKDWuhtuhQwdcvHix3L3//e9/aNCggUQRyceqVavg5+eHuLg4qUORVEFBAezsyv+nYW9vD41GmuO/yXbUuBnuuHHj0L59e8yaNQuDBg1CcnIyVqxYgRUrVkgdmqQ0Gg1WrVqF+Ph4ODjUuD8W5fTp0wczZ85EUFAQmjZtipMnT2L+/Pl4/fXXpQ6NrJ3URWQpbN68WWzWrJmoUCjEZ555RlyxYoXUIUlu27ZtIgDx4sWLUociudzcXHHMmDFiUFCQ6OzsLIaEhIj//Oc/RZVKJXVoZOVq3EMzIiKp1LgaLhGRVJhwiYgshAmXiMhCmHCJiCyECZeIyEKYcImILIQJl4jIQphwiYgshAmXjHL37l34+fnh6tWrRo0zZMgQfPrpp6YJikimmHAJw4YN051j5ujoiIYNG2LixIkoLCx84mdnzpyJF154AcHBwUbF8OGHH2LmzJnIyckxahwiOWPCJQBAjx49cPv2bfz1119YsGABli9fjsTExMd+pqCgAF9//TXeeOMNo79/s2bNEBoayiNsyKYx4RIAQKFQoE6dOggMDES/fv0QExODHTt2PPYzv/32GxQKBdq2bau7t2fPHgiCgG3btiEyMhIuLi7o2rUr0tPTsXXrVjRp0gQeHh545ZVXUFBQUG68Pn36YP369Wb5+YjkgAmXKjh79iwOHjwIJyenx75v3759aNmyZaWvTZ8+HYsXL8bBgwdx48YNDBo0CAsXLsR3332HLVu2YPv27Vi0aFG5z0RFRSE5ORkqlcpkPwuRnNTsjU9J59dff0WtWrXw8OFDqFQq2NnZYfHixY/9zLVr1xAQEFDpax999BE6dOgAAHjjjTcwZcoUXLlyBSEhIQCAgQMHIikpCZMmTdJ9JiAgAEVFRUhLS+OG8GSTmHAJABAdHY2lS5ciPz8fCxYsgIODA1588cXHfubBgwdwdnau9LUWLVro/tnf3x+urq66ZFt6Lzk5udxnXFxcAKBCqYHIVrCkQAAANzc3NGrUCOHh4Vi5ciWOHDmCr7/++rGf8fHxwb179yp9zdHRUffPpasfHiUIQoUja7KysgAAvr6+hvwIRLLHhEsV2NnZ4YMPPsCHH36IBw8eVPm+yMhInD9/3mTf9+zZs6hfvz58fHxMNiaRnDDhUqVeeukl2NvbY8mSJVW+JzY2FufOnatylquvffv24fnnnzfJWERyxIRLlXJwcMCoUaMwb9485OfnV/qe5s2b49lnn8UPP/xg9PcrLCzEpk2bMGLECKPHIpIrnmlGRtmyZQsmTJiAs2fPVjhaXB9Lly7FTz/9hO3bt5swOiJ54SoFMkpcXBwuXbqE1NRUBAYGGjyOo6NjhXW5RLaGM1wiIgthDZeIyEKYcImILIQJl4jIQphwiYgshAmXiMhCmHCJiCyECZeIyEKYcImILIQJl4jIQv4/VxgWy02a3yQAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from make_iter_bins import my_reactor\n", - "from matplotlib.colors import LogNorm\n", - "\n", - "for mode in [\"high_wetted\", \"low_wetted\", \"wetted\"]:\n", - " atom_fluxes = []\n", - " start_points = []\n", - " end_points = []\n", - " for bin in my_reactor.first_wall.bins:\n", - " for sub_bin in bin.sub_bins:\n", - " if sub_bin.mode == mode:\n", - " atom_flux = plasma_data_handling.get_particle_flux(FP_pulse, bin=sub_bin, t_rel=FP_pulse.ramp_up+1, ion=False)\n", - " atom_fluxes.append(atom_flux)\n", - "\n", - " start_points.append(bin.start_point)\n", - " end_points.append(bin.end_point)\n", - "\n", - "\n", - " norm = LogNorm(1e18, max(atom_fluxes))\n", - " cmap = plt.get_cmap(\"viridis\")\n", - "\n", - "\n", - " for start_point, end_point, ion_flux in zip(start_points, end_points, atom_fluxes):\n", - " plt.plot(\n", - " [start_point[0], end_point[0]],\n", - " [start_point[1], end_point[1]],\n", - " c=cmap(norm(ion_flux)),\n", - " )\n", - " plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), label=\"Atom Flux (part/m2/s)\", ax=plt.gca())\n", - " plt.xlabel(\"R (m)\")\n", - " plt.ylabel(\"Z (m)\")\n", - " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", - " plt.title(mode)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "heat_fluxes = []\n", - "start_points = []\n", - "end_points = []\n", - "for bin in my_reactor.divertor.bins:\n", - " heat_flux = plasma_data_handling.get_heat(FP_pulse, bin=bin, t_rel=FP_pulse.ramp_up+1)\n", - " heat_fluxes.append(heat_flux)\n", - "\n", - " start_points.append(bin.start_point)\n", - " end_points.append(bin.end_point)\n", - "\n", - "\n", - "norm = Normalize(0, max(heat_fluxes))\n", - "cmap = plt.get_cmap(\"inferno\")\n", - "\n", - "\n", - "for start_point, end_point, heat_flux in zip(start_points, end_points, heat_fluxes):\n", - " plt.plot(\n", - " [start_point[0], end_point[0]],\n", - " [start_point[1], end_point[1]],\n", - " c=cmap(norm(heat_flux)),\n", - " )\n", - "plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), label=\"Heat Flux (W/m2)\", ax=plt.gca())\n", - "plt.xlabel(\"R (m)\")\n", - "plt.ylabel(\"Z (m)\")\n", - "plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", - "plt.show()\n", - "\n", - "plt.bar(range(len(heat_fluxes)), heat_fluxes)\n", - "plt.xlabel(\"Bin Index\")\n", - "plt.ylabel(\"Heat Flux (W/m2)\")\n", - "plt.show()\n", - "\n", - "arc_length = my_reactor.divertor.arc_length(middle=True)\n", - "\n", - "plt.plot(arc_length, heat_fluxes)\n", - "plt.xlabel(\"Arc length (m)\")\n", - "plt.ylabel(\"Heat Flux (W/m2)\")\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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zlSuvg0RteZg6dWqRAY1vHjdu3ChUJiEhAV27dsWAAQMQFBRUav0ffPAB/Pz80KRJEwwdOhRbt27F/v37cefOnRLLhISEIDk5WXbEx8cr/P6OHDmKHj16o23bDrh27ZrC9RARUcUo6LZQ5tBForY8TJ48GYGBgaVe4+rqKnv86NEj+Pj4oE2bNli/fr3c9/Py8gKQ33JRt27dYq8xNjaGsbHi27O+LiUlBZaWlvjzz7Nwd2+FmTNn4PPPP4WhoaFK6iciIiVJXwFSJVqGdbTlQdTkwc7ODnZ2duW6NiEhAT4+PmjZsiU2bdoEPT35G01iYmIAAI6OjnKXVcSgQQPRrl1bjB07DmFhRzBjxkz8+ONP2LhxA9zd3SslBiIiIlXTiAGTCQkJ6NSpE2rVqoUlS5bg6dOnSExMRGJiYqFr3NzccPbsWQDAnTt3MGfOHJw/fx7379/Hzz//jICAAHTo0AFNmzattNhr1qyJQ4cOYNu2LbCxsUFMzN9o1cobM2bMRHZ2dqXFQURExZC+Uv7QQRqRPJw8eRK3b99GeHg4atasCUdHR9lRIDc3F7GxsbLZFEZGRjh16hTeffdduLm5YfLkyfD398ehQ4cqPX6JRIKhQ4fg2rVLGDCgP/Ly8vDVV/Ph7t4Kf/zxR6XHQ0REBfL+W+tBkQO6uTy1RBAEQewg1FlKSgqsrKyQnJwMS0tLldS5b99+fPzxBCQlJUEikWDSpImYM2c2F7AiIkLF/N4t6R7x9z6EZRUjxetJzYFznW8rNFZ1pBEtD9qmX7/3cO3aJQQEDIMgCFi2bAWaNm2ByMgzYodGRKRTJNJXSh+6iMmDSGxsbLBlyyYcOXIINWvWxJ07d+Dj44uPPhrHhamIiCoLxzwohMmDyLp164qrV//Ghx/mr1mxbt16NG7cHMeOHRc5MiIiUrVffvkFvXr1gpOTEyQSCQ4cOFDq9fv27UOXLl1gZ2cHS0tLeHt74/hx8b8fmDyoAUtLS6xbtwanT5+Eq6sr4uPj0a1bTwQGjsLz58/FDo+ISHtVcstDeno6mjVrhtWrV5fr+l9++QVdunTBkSNHcP78efj4+KBXr164ePGiIu9WZThgsgyVMXDndenp6fjii1CsWPENBEGAg4MD1qxZiffe61vh9yYiUgeVOWAy4cZApQdM1nDbrVCsEokE+/fvR9++feUq16hRIwwcOBAzZ86Uq5wqseVBzZibm2PZsiX4/fczcHNzQ2JiIvr1G4CBA4fgyZMnYodHREQikkqlSE1NLdfGkBWJyYOa8vb2xsWLf2HatKnQ19fHnj178fbbTbFjx06wsYiISEWkUkCap8QhBYAiuzFX1CKAS5YsQVpaGt5///0Kqb+8mDyoMRMTE3z11RycPRuFZs2a4p9//sHQoQHo06cfEhISxA6PiEjjqWqqprOzM6ysrGTH/PnzVR7rjh07MHv2bOzZswfVq1dXef3yYPKgAVq0aIG//voDc+bMhqGhIQ4dOoxGjZrhu+++ZysEEZEylGp1+PcAEB8fX2hH5pCQEJWGuWvXLowZMwZ79uyBr6+vSutWBJMHDWFoaIgZM6bh4sW/4OnZCsnJyQgKGot33+2Ge/fuiR0eEZFOs7S0LHSoandmANi5cydGjhyJnTt3okePHiqrVxlMHjRMo0aNEBX1K5YsWQQTExOcOhWOJk3csXLlKkj/7XsjIqJyquSpmmlpaYiJiZHt8nzv3j3ExMQgLi4OABASEoKAgADZ9Tt27EBAQACWLl0KLy8v2aaQycnJKvsIFMHkQQPp6+tj8uRJuHTpAjp0aI/09HT873+T0LHjO7h586bY4RERaQyJNE/pQx7nzp2Du7s73N3dAQDBwcFwd3eXTbt8/PixLJEAgPXr1+PVq1cYN25coU0hJ06cqLoPQQFc56EMlb3Og7ykUinWrfsWU6ZMQ1paGoyNjfHll7MQHPwJDAwMxA6PiEhulbnOw+MLfrCsYqh4Pam5cGxxXG2/IyoKWx40nJ6eHj7++CNcuRKDd9/tguzsbEyZEgJv73a4fPmy2OEREak3QcnBkoJubsnN5EFLuLi44NixMGza9B2qVq2Kc+fOo2VLL8yePQc5OTlih0dEpJYkUqmS3Ra6OdaMyYMWkUgkCAwcgWvXLqFPn97Izc3FrFlfwsPDC+fOnRM7PCIi0hJMHrSQo6Mj9u//Ebt2bYetrS0uX74CL6+2mDp1GjIzM8UOj4hIfahonQddw+RBS0kkEgwc+D6uXbuEwYMHQSqVYuHCxWje3AO///672OEREamFyp5toS2YPGg5Ozs77NjxAw4e3AdHR0fcvHkT7dv7YOLESUhLSxM7PCIi0kBMHnRE7969cO3aJYwaFQhBEPDNN6vQpIk7wsNPc4lrItJd7LZQCJMHHVK1alV8//0GHD9+BC4uLrh//z58ff1Qq5Yr/vnnH7HDIyKqdOy2UAyTBx307rtdcPnyRYwYMRwA8PDhQwwbFsAWCCLSPWx5UAiTBx1VpUoVbN68EXPnfgkAOHbsBFauXCVyVEREpAmYPOi46dNDsGjRAgDApEmfIizsiMgRERFVHolU+HehKEUP3WyxZfJA+PTTYIwePRJSqRSDBg3FpUuXxA6JiKhysNtCIUweCBKJBGvWrIKPTyekpaWhZ8++SExMFDssIiJSU0weCABgZGSEn37ag/r16yM+Ph59+vTjapREpP24MZZCmDyQjLW1NQ4fPgAbGxucPfsXRowYBamObvpCRLpBIkiVPnQRkwcqpF69eti3by8MDQ2xd++PCA2dLXZIRESkZpg8UBEdO3bA+vVrAQBz587DDz9sEzkiIqIKwgGTCmHyQMUKDByBqVM/BwCMGfMhfvvtN5EjIiKqAFKp8ocOYvJAJfrqqznw9++HnJwc9O3bH3fu3BE7JCIiUgNMHqhEenp62Lp1E1q2bIF//vkHPXv2xcuXL8UOi4hIddjyoBAmD1QqMzMz/PzzftSsWRM3btzAgAGDkJubK3ZYREQqkb9KpDIbYzF5ICqWk5MTDh3aD3Nzc5w6FY4JEyZyEy0i0g5seVAIkwcql+bNm2Pnzm2QSCT49tsNWLHia7FDIiIikTB5oHLr1asnlixZCACYPPlzHDp0WOSIiIiUxJYHhTB5ILlMmvQJPvhgDARBwODBwxATEyN2SEREimPyoBAmDyQXiUSCVau+QefO7yA9PR2tW7fDr79yDQgiIl3C5IHklr909S6Ym5sjOzsbQUEfih0SEZFiuDGWQpg8kEKsra2xevU3AICEhEdITU0VOSIiIvnlT9VU7tBFTB5IYQEBw+Hm5oa0tDRs2bJV7HCIiKiSMHkghUkkEowf/xEAYNWqtdy+m4g0DwdMKoTJAyklIGA4qlSpgtjYWJw8eUrscIiI5MPkQSFMHkgpVapUwciRIwAAK1euFjkaIiKqDEweSGnjx38MADhy5Ch33iQizSIVlGx50M2l+pk8kNLq1auHbt26QhAErF69VuxwiIjKTyoof+ggJg+kEhMmjAMAbNy4GWlpaSJHQ0RUThzzoBAmD6QSfn7vol69ekhOTsYPP2wTOxwiIqpATB5IJfT09DBu3FgA+dM2uWU3EWkEtjwohMkDqUxg4AhYWFjg2rVrOH06QuxwiIjKxjEPCmHyQCpjZWWFESOGA+C0TSIibcbkgVSqYNrmzz8fwr1790SOhoioDIJU+UMHMXkglXJzc0OXLr4QBAFr1qwTOxwiotIJSnZZ6Oj4LiYPpHIF0za//34TMjIyRI6GiIhUjckDqVz37t1Qp04dvHjxAtu37xA7HCKiknHApEKYPJDK6evry3bb/OabVZy2SUTqi8mDQpg8UIUYNWokzMzMcOXKVZw584vY4RARkQoxeaAKUbVqVQwfPhQAp20SkfriZAvFMHmgClMwbfPAgYOIi4sTORoiomJUcrfFL7/8gl69esHJyQkSiQQHDhwos0xkZCRatGgBY2NjvPXWW9i8ebNi71WFmDxQhWncuDF8fDpBKpVi7dpvxQ6HiKgoqQoOOaSnp6NZs2ZYvbp8LbL37t1Djx494OPjg5iYGHzyyScYM2YMjh8/Lt+NVcxA1LuT1pswYRwiIiKxcuUqBAYGoEGDBmKHREQkmm7duqFbt27lvn7dunWoU6cOli5dCgBo2LAhfvvtNyxfvhx+fn4VFWaZ2PJAFapXr54wNjZGenoGli5dLnY4RESFqajlISUlpdCRnZ2tkvCio6Ph6+tb6Jyfnx+io6NVUr+imDxQhTIwMECzZk0BADk5uSJHQ0T0BkEFBwBnZ2dYWVnJjvnz56skvMTERNjb2xc6Z29vj5SUFGRmZqrkHopgtwVVOH//fjh79i+VZeJEROomPj4elpaWsufGxsYiRlPxmDxQhXNzyx/ncPPmTZEjISIqTJBKIEglSpTP/7elpWWh5EFVHBwckJSUVOhcUlISLC0tYWpqqvL7lReTB6pw9evXAwDcvHkLgiBAIlH8f1QiIpVSYMZEkfIVyNvbG0eOHCl07uTJk/D29q7YG5eBYx6owrm6ukJfXx9paWl4/Pix2OEQEYkmLS0NMTExiImJAZA/FTMmJka2Fk5ISAgCAgJk148dOxZ3797F559/jhs3bmDNmjXYs2cPJk2aJEb4MkweqMIZGRmhTp06AIDYWHZdEJEaESSAVIlDkK8l9dy5c3B3d4e7uzsAIDg4GO7u7pg5cyYA4PHjx4UW1atTpw7CwsJw8uRJNGvWDEuXLsV3330n6jRNgN0WVEkaNKiP27dvIzY2Fj4+nUSOhogon6rGPJRXp06dSt0ssLjVIzt16oSLFy/KGVnF0piWh969e6NWrVowMTGBo6Mjhg8fjkePHpVaJisrC+PGjUO1atVgYWEBf3//IgNPqHK8Pu6BiIg0m8YkDz4+PtizZw9iY2Px008/4c6dO+jfv3+pZSZNmoRDhw5h7969OHPmDB49eoR+/fpVUsT0ugYN6gNgtwURqRlluiwKDh2kMd0Wrw8OcXFxwdSpU9G3b1/k5ubC0NCwyPXJycn4/vvvsWPHDrzzzjsAgE2bNqFhw4b4448/0Lp160qLnYD69fOTB7Y8EJFaEeQft1C4vOpC0SQa0/LwuufPn2P79u1o06ZNsYkDAJw/fx65ubmFlvV0c3NDrVq1Sl3WMzs7u8gyo6S8gpaHe/fuIScnR+RoiIjyFYx5UObQRRqVPEyZMgXm5uaoVq0a4uLicPDgwRKvTUxMhJGREapWrVrovL29PRITE0ssN3/+/EJLjDo7O6sqfJ3m6OgICwsL5OXl4e7du2KHQ0REShA1eZg6dSokEkmpx40bN2TXf/bZZ7h48SJOnDgBfX19BAQElDpqVREhISFITk6WHfHx8SqtX1dJJBLZoEmOeyAitSHVU/7QQaKOeZg8eTICAwNLvcbV1VX22NbWFra2tqhfvz4aNmwIZ2dn/PHHH8WutOXg4ICcnBy8fPmyUOtDUlISHBwcSryfsbGx1q9JLpb69evjwoWLHPdAROpD2UGPFbzCpLoSNXmws7ODnZ2dQmWl0vz/YiVtttSyZUsYGhoiPDwc/v7+AIDY2FjExcWJvqynrvpvxkWsyJEQEZEyNGK2xZ9//om//voL7dq1g7W1Ne7cuYMvvvgCdevWlSUCCQkJ6Ny5M7Zu3QpPT09YWVlh9OjRCA4Oho2NDSwtLTFhwgR4e3tzpoVIuNYDEakbQZBAUGK2hYp7zjWGRiQPZmZm2LdvH0JDQ5Geng5HR0d07doVM2bMkHUx5ObmIjY2FhkZGbJyy5cvh56eHvz9/ZGdnQ0/Pz+sWbNGrLeh87jWAxGpHamekt0Wupk9SARVjzjUMikpKbCyskJycnKFbLeqS/I/y2oAgL/OnoVHK3eRIyIidVQZv3cL7pE0zRqWJoonDylZAuznvdC57wiNaHkg7VClShVIoAcBUjx/8VzscIiIIEih5N4Wuvn3N5MHqjSpqWkwNKgGCfTRvn1bscMhIvpvV01lyusgJg9UaV6+TIGexABGRkYwMeF0WCIiTcXkgSpN8sv8pb6tra0gkehmtk5E6kX52Ra6+buMyQNVmpcvkwEAVla6M6iIiNScsqtEcpEooor18t+WB6uqTB6ISD0ou7kVN8YiqmAFLQ/W1lYiR0JERMpgywNVGnZbEJG64ZgHxTB5oEpTMGCyKrstiEhdcMyDQthtQZXmxb8tD1WrstuCiEiTseWBKk0yB0wSkZrhgEnFMHmgSsNuCyJSNxzzoBh2W1ClYbcFEZF2YMsDVRq2PBCR2uGASYUweaBKc/vOfQDAixfJ4gZCRPQvjnlQDLstqFKkp2cgKzMLAFDNxlrkaIiISBlseaBKER11HhKJPmxsqqKVl7vY4RARAeCASUUxeaBKceZMNCQSPfTo2QUWFuZih0NElE9QcsyDoLpQNAmTB6oUZyKjAQAdO3mLHAkR0X845kExHPNAFe7lyxRcvHAFANCxY2uRoyEiImWx5YEq3O+/nYVUKkW9enVQo6aj2OEQEckIgnLjFgR2WxBVjEh2WRCRulKy2wLstiCqGL+c+QMAkwciIm3BlgeqUE+f/oMrl28AANp38BI5GiKiwgRBD4Kg+N/Rgpr3W1y/fh27du3Cr7/+igcPHiAjIwN2dnZwd3eHn58f/P39YWxsLHe9bHmgCvXrL38CABo1bgA7u2oiR0NE9AapRPlDDV24cAG+vr5wd3fHb7/9Bi8vL3zyySeYM2cOhg0bBkEQMH36dDg5OWHhwoXIzs6Wq362PFCF4hRNIqLK5+/vj88++ww//vgjqlatWuJ10dHR+Prrr7F06VJMmzat3PUzeaAKVZA8dGLyQERqSFtXmLx58yYMDQ3LvM7b2xve3t7Izc2Vq34mD1RhEh4+xq1b96Cnp4e27TzFDoeIqAhtXSSqPImDMtdzzANVmDP/zrJwb9GY23ATEYkgMzMTv/32G65du1bktaysLGzdulWhepk8UIXheAciUncFsy2UOdTVzZs30bBhQ3To0AFNmjRBx44d8fjxY9nrycnJGDlypEJ1q++7Jo0mCAIOHz4FAOjYkckDEamngm4LZQ51NWXKFDRu3BhPnjxBbGwsqlSpgrZt2yIuLk7pujnmgSrEqZO/4uWLZABA3bouIkdDRFQ8bR0wCQBRUVE4deoUbG1tYWtri0OHDuHjjz9G+/btERERAXNzxXc4ZssDVYgtm/YCkMDe3g51XGuJHQ4RVaKXL1/izp07Yoeh8zIzM2Fg8F8bgUQiwdq1a9GrVy907NgRN2/eVLhuJg+kcs+ePcfxY2egJ9HH1u0rxQ6HiCrJ3bt3MXHiJNSsWRtjx44TO5xyKWh5UOZQV25ubjh37lyR86tWrUKfPn3Qu3dvhetm8kAqt+n73cjKykZz90Zo29ZD7HCIqIJFRUWhf/+BqFevIb75ZhXS09ORlJSEtLQ0sUMrkyAoOeZBjZOH9957Dzt37iz2tVWrVmHw4MEKL6/N5IFUKicnB+vXbQcAjJswAhKJ+v6PRUSKe/XqFfbu/RHe3u3Qtm1H/PTTPkilUvj5vYsTJ47i778vwMLCQuww1dbq1atRu3ZtmJiYwMvLC2fPni31+hUrVqBBgwYwNTWFs7MzJk2ahKysrFLLhISE4MiRIyW+vmbNGkilUoXi54BJUql9Px1FYuJTODjYoZ9/N7HDISIVS01Nxfffb8TXX6/C/fv3AQBGRkYYNmwIJk2aiMaNG4sboJzE2Bhr9+7dCA4Oxrp16+Dl5YUVK1bAz88PsbGxqF69epHrd+zYgalTp2Ljxo1o06YNbt68icDAQEgkEixbtkzh2JXB5IFURhAErF65BQDwwdihMDIyEjkiIlKVuLg4rFy5GuvXf4eUlBQAQLVq1fDxx2MxbtxHsLe3FzlCxYixwuSyZcsQFBQkW2Nh3bp1CAsLw8aNGzF16tQi10dFRaFt27YYMmQIAKB27doYPHgw/vzzz3LdLysrCytXrkRERASePHlSpLXhwoULcr8HJg+kMlG/n0fMxaswMTHGyNEDxQ6HiFTg3LlzWLbsa+zZsxd5eXkAgAYNGiA4eCKGDx8GU1NTkSNUDwUJVQFjY+Nit7rOycnB+fPnERISIjunp6cHX19fREdHF1t3mzZtsG3bNpw9exaenp64e/cujhw5guHDh5crttGjR+PEiRPo378/PD09VdKdzOSBVGb1ys0AgEFD+sDW1kbcYIhIYXl5eTh8OAxLly7Hr7/+Jjvv49MJkydPQrduXaGnpx1D5lS1zoOzs3Oh86GhoZg1a1aR6589e4a8vLwiLTX29va4ceNGsfcYMmQInj17hnbt2kEQBLx69Qpjx44t9y6Yhw8fxpEjR9C2bdtyXV8eTB5IJe7di8fhQ/krSo4bP0LkaIhIEenp6di8eQtWrFiJ27dvAwAMDAwwePBATJo0Ee7u7iJHqHqqSh7i4+NhafnfHj7FtTooKjIyEvPmzcOaNWvg5eWF27dvY+LEiZgzZw6++OKLMsvXqFEDVapUUVk8AJMHUpFv1/wAQRDg26Ud3Bq+JXY4RCSHR48eYdWqNVi3bj1evHgBAKhatSrGjv0A48d/jBo1aogcYcURpMrtjCn8O3zA0tKyUPJQEltbW+jr6yMpKanQ+aSkJDg4OBRb5osvvsDw4cMxZswYAECTJk2Qnp6ODz74ANOnTy+zFWjp0qWYMmUK1q1bBxcX1az4y+SBlJaU+BQbv98NAPiYrQ5EGuPvv//GsmUrsHPnbuTm5gIA6tati08+mYDAwBGcalkBjIyM0LJlS4SHh6Nv374AAKlUivDwcIwfP77YMhkZGUUSBH19fQDlm+3h4eGBrKwsuLq6wszMrMj228+fP5f7fTB5IKV98r8vkZmZBUvLKvDt0l7scIioFFKpFMeOHceyZSsQHn5adr5du7YIDv4EvXv3kn0x6QIx9rYIDg7GiBEj4OHhAU9PT6xYsQLp6emy2RcBAQGoUaMG5s+fDwDo1asXli1bBnd3d1m3xRdffIFevcr332rw4MFISEjAvHnzYG9vzwGTJL7MzCz8EpE/Xah7T18uCkWkpjIzM7Ft23YsX/4Nrl+/DiD/r9f+/f0RHDwRnp6eIkcoDuXXeZC/7MCBA/H06VPMnDkTiYmJaN68OY4dOyYbRBkXF1eopWHGjBmQSCSYMWMGEhISYGdnh169euGrr74q1/2ioqIQHR2NZs2ayR1rSZg8kFK2bTmAzIxcONdwwZJl08UOh4je8OTJE6xZsw5r1qzD06dPAQBVqlTBBx+MwYQJ41TWB07yGT9+fIndFJGRkYWeGxgYIDQ0FKGhoQrdy83NDZmZmQqVLQmTB1JYdnYOVizdBAD4dMoYWFmpdjQvESnu2rVrWLZsBbZt24Hs7GwAgIuLCyZOHI/Ro0eVa3CfLpAKEkiV6LZQpmxlWbBgASZPnoyvvvoKTZo0KTLmQZGfBSYPpLAdPxzE40dP4OhUHUMD+oodDpHOEwQB4eGnsXTpchw7dlx23tOzFSZPnoR+/d4rtEUzAVByhUkoU7aSdO3aFQDQuXPnQucFQYBEIpEt/iUP/hSRQnJycrF8yUYAwMTgQBgbcylqIjEIgoCsrCzs2bMXy5atwKVLlwEAEokE773XF8HBE9GmTRuOR9Jhp0+fVvl/fyYPpJBdOw7hYXwi7O1tMTzwPbHDIdJJqamp6NvXH1FR0bIdFs3NzTFqVCAmTpyAunXrihyh+hNjtkVl2bhxI3r37o1OnTqpvG7tWF+UKlVubi6WL85vdZgwaQRMTU1EjohIN61cuRqnT0cgKysLNjY2WLBgHuLj7+Gbb1YwcSinguRBmUNdbdu2DTVr1kSbNm2wcOFC2SwbVWDyQHLbu+soHtxPgJ2dDQJH+4sdDpFO+u677zF9ev7SxP7+7+Hx43hMmfIZrK2tRY6M1MXp06fx+PFjfPzxxzh//jy8vLxQr149TJ48Gb/88kuR3TXlweSB5JKVlYXFC9YDAMZNDICZGXfUI6psu3btxgcffAQA+PTTYOzduxtGRhx3pAhtbnkAAGtrawwbNgx79uzBs2fPsHLlSmRmZmLo0KGoXr06AgIC8OOPPyI9PV2uepk8kFxmTFmOB/cTYGZmglFBA8QOh0jnHDp0GMOHB0IQBHz4YRAWLVrAwZBKkAp6Sh+awsjICF27dsWaNWsQHx+PY8eOoXbt2pgzZw6WLVsmV10cMEnlJpVK8ePuowAA7zYtYWFhJnJERLrl9OkIDBgwCK9evcLQoYOxZs0qJg5KEgTlpmqqc8tDREQE2rZtW2KrlIeHBzw8PPDll1/K9jYpLyYPVG5Hfj6DjNRcVDG3wtdrZoodDpFOiY6ORu/e7yE7Oxt9+vTGpk3fl7mbIum2zp07w8TEBK1bt4aPjw98fHzQunXrYtf6eHPhqLLwJ4/KRRAErFi8BRKJBB9PGAqnGtXFDolIZ8TExKB7995IT0+Hr29n7Nq1Xe5f9lQ8bR7zcO/ePaxevRq1atXC999/jw4dOqBq1arw8/PDggUL8Oeffyo8aFIilGc/Tx2WkpICKysrJCcn6/RyrqeOR2Go/6cwMzfF+Wv7YFPNSuyQiHRCbGws2rf3wdOnT9GmjTdOnDgKc3NzscOqUJXxe7fgHlHvdIKFEqtupr16hTanIzXiO+Lu3buIjIxEZGQkzpw5g4cPH6JKlSp4+fKl3HWx24LKVNDqAACBY95j4kBUSe7fvw9f3654+vQp3N2bIyzsZ61PHKjiuLq6Ql9fHxKJBBKJBAcOHEBOTo5CdTF5oDJF/XoRf/1xGcbGRhg7YZDY4RDphEePHqFzZz88fPgQDRs2xPHjR1C1alWxw9I62r4xVlxcHCIjIxEREYHIyEg8e/YMbdq0Qfv27XH48GF4eXkpVC+TBypTQavDkICesHewFTkaIu337NkzdOnSDXfv3kWdOnVw8uRR2NnZiR2WVtLm5aldXV3x4sULtG3bFh06dMCHH34IDw8PlWyOxgGTVKpD+yLwS8RfMDDQx7hJQ8UOh0jrpaSkoGvXHrh27RqcnJxw6tQx1KhRQ+ywSANlZmYCAPT09GBgYABDQ0Po6+urpG62PFCppn26HADg3rIRnGs5ihwNkXbLyMhAz559cP78Bdja2uLUqWNwdXUVOyytps0tD48fP8aNGzdk3RaLFi1CVlYW2rVrh06dOqFjx45o2bKlQlN+mTxQic6fvYanT14AAD4NGSVyNETaLTs7G++91x+//vobrKyscOLEETRs2FDssLSeto95cHNzg5ubG8aOHQsAuH79umz8w9y5cwFAodkWSnVbZGdnK1Oc1NyqJTthKDFG7z6+6OTrKXY4RFrr1atXGDJkOE6cOAkzMzOEhR2Eu7u72GGRlklKSsKlS5dw6dIl/P3330hJSVH4e1yuloejR49i165d+PXXXxEfHw+pVApzc3O4u7vj3XffxciRI+Hk5KRQIKReLl28iVNHo6Gnp4eQWR+IHQ6R1pJKpRg1agz27dsPIyMjHDy4D23bthU7LJ0hCMp1PajzSklPnjyRresQERGBmzdvwtDQEJ6enhg0aBB8fHzg7e2tUN3lSh7279+PKVOmIDU1Fd27d8eUKVPg5OQEU1NTPH/+HFeuXMGpU6cwZ84cBAYGYs6cORwZrOGWz/8BAPDewHfgWq+myNEQaSdBEDBhwkT88MN26OvrY8+enfD17Sx2WDpFm8c8ODg4wNDQEB4eHvD394ePjw/atGkDU1Pld0MuV/KwaNEiLF++HN26dSt2YMX7778PAEhISMDKlSuxbds2TJo0SengSByvtzr877NhYodDpLWmTZuBNWvWQSKRYMuWjejTp7fYIekcQckxD+qcPBw9ehTt2rWrkIXFypU8REdHl6uyGjVqYMGCBUoFROJjqwNRxZs3bwEWLFgEAFi3bjWGDh0ickSkbfz8/Cqsbq7zQIWw1YGo4q1cuQrTp38BAFiyZBE++CBI5Ih0l7ZujNW1a1f88ccfZV6XmpqKhQsXYvXq1XLVL/dUTUEQ8OOPPyIiIgJPnjwpsiPXvn375K2yXHr37o2YmBg8efIE1tbW8PX1xcKFC0sdoNmpUyecOXOm0LkPP/wQ69atq5AYtUFw0GIAbHUgqiibN2/B//6X3607c+YMTJ7MLl4xaeuYhwEDBsDf3x9WVlbo1asXPDw84OTkBBMTE7x48QLXrl3Db7/9hiNHjqBHjx5YvHixXPXLnTx88skn+Pbbb+Hj4wN7e3tIJJXzwfn4+GDatGlwdHREQkICPv30U/Tv3x9RUVGllgsKCsKXX34pe25mZlbRoWqsg7sjERt7DwAwbGQvkaMh0j4//vgTRo/On730ySf/w6xZM0WOiLTV6NGjMWzYMOzduxe7d+/G+vXrkZycDACQSCR4++234efnh7/++kuh9UTkTh5++OEH7Nu3D927d5f7Zsp4fQCmi4sLpk6dir59+yI3N7fUfe3NzMzg4OBQGSFqvEPbfoWhYAznOvbw8G4kdjhEWuXIkaMYMmQ4pFIpxowZhWXLllTaH19UMm1eJMrY2BjDhg3DsGH5XdDJycnIzMxEtWrVSv3eLA+5xzxYWVmJvlzq8+fPsX37drRp06bMD2D79u2wtbVF48aNERISgoyMjFKvz87ORkpKSqFDF9y8EoezEddRRVIV3+2aLXY4RFrlzJlf4O//PnJzczFw4PtYt24NEwc1oa1jHopjZWUlm76pLLmTh1mzZmH27NmyDTcq05QpU2Bubo5q1aohLi4OBw8eLPX6IUOGYNu2bYiIiEBISAh++OEHWQZWkvnz58PKykp2ODs7q/ItqK31C/cDAPz6eaFeI914z0SV4ezZs+jZsw+ysrLQs2cP/PDDZpVtTkQkFokgyLc+VmZmJt577z38/vvvqF27dpEM5sKFC+Wua+rUqVi4cGGp11y/fh1ubm4A8repff78OR48eIDZs2fDysoKhw8fLncGf/r0aXTu3Bm3b99G3bp1i70mOzu70HKdKSkpcHZ2RnJyMiwtLcv5zjTL7WsP4e81JX8w7B8LUL9xLbFDItIKly9fRseOnfHixQu8844PwsJ+homJidhhqb2UlBRYWVlV6O/dgnsc8ewBcwPF/xJPf5WL7mfDtPo7ojhyj3kYMWIEzp8/j2HDhik9YHLy5MkIDAws9ZrXu0hsbW1ha2uL+vXro2HDhnB2dsYff/xR7uU1vby8AKDU5MHY2BjGxsblewNaYsPiAxAEAZ17t2LiQKQit27dQpcu3fDixQu0bu2Fgwf3MXFQQwIkEKDEbAslymoyuZOHsLAwHD9+HO3atVP65nZ2dgovY10wRVSeTT1iYmIAAI6O3Fq6wL2bj3D8p/xFwD6c+p7I0RBph7i4OPj6dkVSUhKaNWuKI0cOwcLCQuywiFRG7jEPzs7Old408+eff2LVqlWIiYnBgwcPcPr0aQwePBh169aVtTokJCTAzc0NZ8+eBQDcuXMHc+bMwfnz53H//n38/PPPCAgIQIcOHdC0adNKjV+dbVh8AFKpgI7dW8CtaW2xwyHSeElJSfD17Yq4uDg0aNAAJ04chbW1tdhhUQl0YcBkREREia99++23CtUpd/KwdOlSfP7557h//75CN1SEmZkZ9u3bh86dO6NBgwYYPXo0mjZtijNnzsi6GHJzcxEbGyubTWFkZIRTp07h3XffhZubGyZPngx/f38cOnSo0uJWd+fOXMfhnb8BAD74vK+4wRBpgdOnI9C6dTvcunULLi4uOHnyKKpXry52WFSKgqmayhzqrmvXrvjss8+Qm5srO/fs2TP06tULU6dOVahOuQdMWltbIyMjA69evYKZmVmRAZPPnz9XKBB1VRkDd8Qy2Gs6rl69B7tqVRH+YI3Y4RBptNzcXNjb18CLFy9gbm6OmJhzeOutt8QOSyNV5oDJgy37KD1gss/5g2r9HREVFYWAgABYWFhgx44duHfvHkaPHo0GDRpg69atcHFxkbtOucc8rFixQu6bkPp58vA5Xtx4CUvBFBNnDxI7HCKNl5ycLFsX5ocfNjNxILXRpk0bxMTEYOzYsWjRogWkUinmzJmDzz//XOFJDwrNtiDNt3fFKeS9kqJ1h8boHdhB7HCINN727TuQl5eHZs2a4b33+oodDpWTFEquMKkhsy1u3ryJc+fOoWbNmnj06JGsm1/R7brLNeYhPT1drkrlvZ4q14snKTi6OX+sw5DPuoocDZF22LRpCwAgKGiUyJGQPHRhwOSCBQvg7e2NLl264MqVKzh79iwuXryIpk2bIjo6WqE6y5U8vPXWW1iwYAEeP35c4jWCIODkyZPo1q0bvvnmG4WCocqxb9VpZGfmws2jNtx93MQOh0jjXbx4EX//fQlGRkYYPJjdgKRevv76axw4cAArV66EiYkJGjdujLNnz6Jfv37o1KmTQnWWq9siMjIS06ZNw6xZs9CsWbNit/aMjo6GgYEBQkJC8OGHHyoUDFW81Bfp+Hl9/jblgz/vyvX1iVSgoNXhvff6wsbGRuRoSB5SSJTqetCEbovLly/D1ta20DlDQ0MsXrwYPXv2VKjOciUPDRo0wE8//YS4uDjs3bsXv/76K6KiopCZmQlbW1u4u7tjw4YN6NatG9dsV3MH1kUiIzULro1roHW3JmKHQ6TxsrOzsX37TgDAyJEcE6ZxlO160IBuizcTh9d17NhRoTrlGjBZq1YtTJ48GZMnT1boZiSuF09SsG9VOABg8Gddoacn9zIfRPSGn38+hOfPn6NmzZrw9e0sdjhERfj4+JTaynz69Gm565R7tgVprkUfb0Xay0xYVjNH+/daiB0OkVYo6LIICBjGllcNpOxCT5qwSFTz5s0LPc/NzUVMTAyuXLmi8AxKJg86IjM9G+fDrwMAvHo0gb4+Wx2IlJWQkIDjx08AAAIDA0SOhhSh7IwJTZhtsXz58mLPz5o1C2lpaQrVyW8QHbH/u0ikZmfB3N4c/1vK0eBEqvDDD9shlUrRrl1b1KtXT+xwiOQybNgwbNy4UaGybHnQAZnp2di2/BgAYMysvjAx060tx4kqgiAIsi4LDpTUXNJ/D2XKa6ro6GiFt4ln8qAD9n8XiRdPUlCjjh26Dm4tdjhEWiE6Oho3b96EmZkZBgzoL3Y4pCCxui1Wr16NxYsXIzExEc2aNcPKlSvh6elZ4vUvX77E9OnTsW/fPjx//hwuLi5YsWIFunfvXua9+vXr90bMAh4/foxz587hiy++UCj+cndbdO7cGfv27Svx9WfPnsHV1VWhIKjivN7qEDilJwwMmS8SqUJBq8OAAf6oUqWKyNGQoqSCsjtryn/P3bt3Izg4GKGhobhw4QKaNWsGPz8/PHnypNjrc3Jy0KVLF9y/fx8//vgjYmNjsWHDBtSoUaNc97Oysip02NjYoFOnTjhy5AhCQ0PlfwOQo+UhIiICZ86cwfTp0zF79uwir+fl5eHBgwcKBUEVh60ORKqXnp6O3bv3AgBGjgwUNxjSOMuWLUNQUBBGjhwJAFi3bh3CwsKwcePGYrfI3rhxI54/f46oqCjZTta1a9cu9/02bdqkkrhfJ9eAybVr12LFihV47733uH+FBmCrA1HF+OmnfUhNTYWrqys6dGgvdjikBAESpQ8gf4vv14/s7Oxi75eTk4Pz58/D19dXdk5PTw++vr4l7jPx888/w9vbG+PGjYO9vT0aN26MefPmIS8vT/UfSDnJlTz06dMHf/zxB65evYrWrVvj7t27FRUXqcDqKXvY6kBUAV4fKMkl3jWbcl0W/60R4ezsXKhrYP78+cXe79mzZ8jLy4O9vX2h8/b29khMTCy2zN27d/Hjjz8iLy8PR44cwRdffIGlS5di7ty5Jb4va2tr2NjYlOtQhNx/ijZs2BB//fUXBg8ejFatWmH37t2FMihSDy+fpuLA5vw9LPwGeLHVgUhF7t69i8jIM5BIJBgxYrjY4ZCaiI+Ph6Wlpey5sbHqZrVJpVJUr14d69evh76+Plq2bImEhAQsXry4xDELK1asUNn9i6PQN4qVlRXCwsIQEhKC7t27Y+HChRgyZIiqYyMlhG34BfqCBPoGegiYUvZoXCIqny1bfgAA+Pp2hrOzs8jRkLLyB0wqVx4ALC0tCyUPJbG1tYW+vj6SkpIKnU9KSoKDg0OxZRwdHWFoaFhoBdOGDRsiMTEROTk5MDIyKlJG0ZUjy6vc3RZvNs1JJBIsWLAAW7duxRdffIExY8aoPDhSTG7OK0Rs+xPW+iaYOPd9GJtyXQciVZBKpbLkgWs7aAdVjXkoLyMjI7Rs2RLh4eGyc1KpFOHh4fD29i62TNu2bXH79m1Ipf+tKnHz5k04OjoWmzgUmDlzJjIyMmTPX7x4IVespSl38iAIxadmgwYNwm+//YbLly+rLChSzi97z+HZwxewtrdEzw8U2zGNiIqKiIjEgwcPYGVlhb59+4gdDmmo4OBgbNiwAVu2bMH169fx0UcfIT09XTb7IiAgACEhIbLrP/roIzx//hwTJ07EzZs3ERYWhnnz5mHcuHGl3uerr74qtPy0i4uLysYqyjVVs6SBFc2bN8f58+cRFhamkqBIcXl5UuxbdhIA0GfCOzAyMRQ5IiLtsWnTZgDA4MEDYWpqKm4wpBJibIw1cOBAPH36FDNnzkRiYiKaN2+OY8eOyQZRxsXFFdr12NnZGcePH8ekSZPQtGlT1KhRAxMnTsSUKVNKvc+bf/SX1AigCImgytq0UEpKCqysrJCcnFyu/iyxRR24iEUBG2FR1Qwbrs6GaRXFlh4losKSk5Ph4FATWVlZ+PPP30tdDZCUUxm/dwvusaHhEJjpl9z0X5aMvBwEXd+hlt8Renp6SExMRPXq1QEAVapUwd9//62SBR05BF+LCIKAH5fm7/DXY2xHJg5EKrRr125kZWXh7bffRqtWrcQOh6hMEokEqampMDExgSAIkEgkSEtLQ0pKSqHrFEl6mDxokYunruPu3w9hYm6EHh9yrAORKnFtB+0kQAKpnIMe3yyvrgRBQP369Qs9d3d3L/RcIpEotNgUkwctUtDq4DeqHSyrmYscDZH2uH79Ov788yz09fUxbBinpWsTsTbGqgwREREVVjeTBy1x8UgMrkXdgYGRAfqMf0fscIi0SkGrQ/fu3Uqci0+aSYwBk5WlY8eKa4GWa3lqUl9bgncAALx968HG0UrkaIi0x6tXr/DDD9sBAKNGBYobDJGaYMuDFoiPuoakpHRIIEGf8T5ih0OkVY4dO47ExETY2dmhRw+u1qpthH8PZcrrIiYPWuDu9uPoUTMDBi2b4a12DcUOh0irFHRZDBs2RLYdMmkPbe62qEjsttBw/1y+i0e/XoKBgR56fDlY7HCItMrTp09x6NBhAFyOmuh1TB403JV1BwEAtXu2QZVa9mVcTUTy2L59B3Jzc9GyZQs0adJE7HCoAkhVcKi7UaNGITU1tcj59PR0jBo1SqE6mTxosIJWB4m+HhoF9RQ7HCKtIghCobUdSDsVTNVU5lB3W7ZsQWZmZpHzmZmZ2Lp1q0J1csyDBmOrA1HFuXjxIi5dugwjIyMMHjxI7HCI5JaSkgJBECAIgmylyQJ5eXk4cuSIbOlqeTF50FBsdSCqWAWtDn379ilxU0DSfNo8YLJq1aqQSCSQSCSFVposIJFIMHv2bIXqZvKgof5e8D0AtjoQVYTs7Gzs2LELALsstJ02T9WMiIiAIAh455138NNPPxVKgo2MjODi4gInJyeF6mbyoIESj55E0uVHAAQ0HNFF7HCItM7PPx/C8+fPUaNGDXTp4it2OEQKKVhh8t69e3B2di60zbeymDxooORTP8PEMBs2dW1g9VYtscMh0jqffTYFADBgQH/o6+uLHA1VJG3utijg4uICAMjIyEBcXBxycnIKvd60aVO562TyoGFy427D/HE0WjaQoFqoYqNkiahkDx48wIMHcQCAgQMHiBwNVTRlp1tqwlTNp0+fYuTIkTh69GixryuyqyanamqYlIMbAQDmXp1h3oCrSRKpmkTy369FD4+WIkZClUEXpmp+8sknePnyJf7880+Ympri2LFj2LJlC+rVq4eff/5ZoTrZ8qBBch/HIfOPUwCAKn0VW9iDiEqXm5vfpGthYQEDA/6KJM13+vRpHDx4EB4eHtDT04OLiwu6dOkCS0tLzJ8/Hz169JC7TrY8aJDUQ5sBQQoT9/Ywcik67YaIlPfy5UsA+dPcSPsJUG51SXWebVEgPT1dtp6DtbU1nj59CgBo0qQJLly4oFCdTB40xKunj5HxaxgAtjoQVaSXL5MBAFWrcmt7XSBAyW4LqH+3RYMGDRAbGwsAaNasGb799lskJCRg3bp1cHR0VKhOtslpiNTDW4G8PBg3agXjelxjn6iisOWBtM3EiRPx+PFjAEBoaCi6du2K7du3w8jICJs3b1aoTiYPGiDj7l2kndoHCYAqfUeLHQ6RVmPyoFukQv6hTHl1N2zYMNnjli1b4sGDB7hx4wZq1aoFW1tbhepkt4UGSFi1GBIhD5l6NjB+m6O/iSrSixcvAbDbQlcIKjg0RU5ODmJjY2FkZIQWLVoonDgATB7UXvbLNDy4+AIp6WYw7zoMEon6968RaTK2PJC2ycjIwOjRo2FmZoZGjRohLi5/HZMJEyZgwYIFCtXJ5EHN3d5xAi9fGOORaSc4DBlWdgEiUgqTB91SsMKkMoe6CwkJwd9//43IyMhCO2v6+vpi9+7dCtXJMQ9qLDctE7d3ngQAuI3updJ1yYmoeEwedIsurDB54MAB7N69G61bty7Uet2oUSPcuXNHoTr5baTG7uwJR25qBqrUcUSNzhzrQFQZOFWTtM3Tp09l6zy8Lj09XeGucCYPaupVZjZu/XAMAOA2qickbHUgqhQFLQ/W1tbiBkKVQheWp/bw8EBYWJjseUHC8N1338Hb21uhOtltoabu7T+D7BepMHOyhXPX1mKHQ6Qz/mt5qCpuIFQpdKHbYt68eejWrRuuXbuGV69e4euvv8a1a9cQFRWFM2fOKFQn/5xVQ3k5ubi5+QiA/FYHPUPmeESV5b8xD+y20AWCoPyh7tq1a4eYmBi8evUKTZo0wYkTJ1C9enVER0ejZUvFusT5raSGdoV8DeMnL2BgbQGX3u3EDodIp7x48QIAWx5Iu9StWxcbNmxQWX1MHtRMTnY20k5egLGeIZ7Xs4e+kaHYIRHpjJycHGRkZABg8qArpJBAqsT+FMqUrWgpKSnlus7S0lLuupk8qJm9e8Kw9P4V9HKqjdD5/xM7HCKdkpycLHusyC9U0jzavDx11apVS51NIQgCJBIJ8vLy5K6byYMaycvLw+KFa/EoJxM1g3rCslrVQq8LgoAnT57C3r7olBsiUl7BeIcqVarAwIC/HkmzRUREyB4LgoDu3bvju+++Q40aNZSum/93qJGf9obh9u37sKlmjaAPhxZ6LSLiF8ycORfPnv2Dy5f/5C82ogrABaJ0kLKDHtW45aFjx46Fnuvr66N169ZwdXVVum7OtlATeXl5WLhgDQBg/IRAWFiYAwCio8+iS5de8PXthaioPxEX9xAXL/4tZqhEWosLROmegjEPyhy6iMmDmjiw/zhib9xB1aqW+PCj4bh48W/07DkA7dp1wenTv8DQ0BDjxn2A27f/RqtWXG2SqCKw5YGofNj2rQakUikW/dvqMGBgD4wZ8zH27fsZQH4zU2DgUEyf/hlcXGqJGSaR1mPyoHuUXatBE9Z5eJ2qdmZm8qAGDh86hatXYmFgoI+Vq1dCEKSQSCQYPHgAZs6cinr16oodIpFOYLeF7tHmFSb79etX6HlWVhbGjh0Lc3PzQuf37dsnd91MHkSWkPAI4z4KAQBkZqdAEKTo1683Zs2ahkaNGoocHZHuEAQBSUlJANjyQNrByqpwEjxs2DCV1c3kQWSnw3/Hi+f5SUMnHy/MXxCKFi2aix0Wkc7JyMjA0qXLAaDIX2akvbR5nYdNmzZVWN1MHkQ2bHh/REX9gWo2dpg7b6rY4RARiv7FRtpLgHKzLdU4d6hQTB5EJpFIsHbdUrHDIKLX+Pq+I3YIVEnyWx6UWJ5aR7MHTtUkInpDw4Ycb0RUGrY8EBGRztK1qZqqwuSBiIh0ljZP1axI7LYgIiKqZKtXr0bt2rVhYmICLy8vnD17tlzldu3aBYlEgr59+1ZsgGVg8kBERDqroNtCmUNeu3fvRnBwMEJDQ3HhwgU0a9YMfn5+ePLkSanl7t+/j08//RTt27dX8N2qjsYlD9nZ2WjevDkkEgliYmJKvTYrKwvjxo1DtWrVYGFhAX9/f9kiMERERFIVHPJatmwZgoKCMHLkSLz99ttYt24dzMzMsHHjxhLL5OXlYejQoZg9e7ZKdsVUlsYlD59//jmcnJzKde2kSZNw6NAh7N27F2fOnMGjR4+KLNdJRESkrJSUlEJHdnZ2sdfl5OTg/Pnz8PX1lZ3T09ODr68voqOjS6z/yy+/RPXq1TF69GiVx64IjUoejh49ihMnTmDJkiVlXpucnIzvv/8ey5YtwzvvvIOWLVti06ZNiIqKwh9//FEJ0RIRkboThP9WmVTkKOi2cHZ2hpWVleyYP39+sfd79uwZ8vLyYG9vX+i8vb09EhMTiy3z22+/4fvvv8eGDRtU+t6VoTGzLZKSkhAUFIQDBw7AzMyszOvPnz+P3NzcQtmdm5sbatWqhejoaLRu3boiwyUiIg2gqhUm4+PjYWlpKTtvbGysTFgyqampGD58ODZs2ABbW1uV1KkKGpE8CIKAwMBAjB07Fh4eHrh//36ZZRITE2FkZFRkg5vSsjsgf0zF681NKSkpioZNRBpEeG3km6Crk/dJYZaWloWSh5LY2tpCX1+/yPi7pKQkODg4FLn+zp07uH//Pnr16iU7J5Xmj7QwMDBAbGws6tat/J2XRe22mDp1KiQSSanHjRs3sHLlSqSmpiIkJKTCY5o/f36hpidnZ+cKvycRiS8jIxN6embQ0zNDRkam2OFQJVGmy0KRTbWMjIzQsmVLhIeH/xeDVIrw8HB4e3sXud7NzQ2XL19GTEyM7Ojduzd8fHwQExMj2neUqC0PkydPRmBgYKnXuLq64vTp04iOji7SDOTh4YGhQ4diy5YtRco5ODggJycHL1++LNT6UFJ2VyAkJATBwcGy5ykpKUwgiIi0lBgrTAYHB2PEiBHw8PCAp6cnVqxYgfT0dIwcORIAEBAQgBo1amD+/PkwMTFB48aNC5Uv+E5783xlEjV5sLOzg52dXZnXffPNN5g7d67s+aNHj+Dn54fdu3fDy8ur2DItW7aEoaEhwsPD4e/vDwCIjY1FXFxcsdldAWNjY5X1VRGR5jAzMy32MWk3MVaYHDhwIJ4+fYqZM2ciMTERzZs3x7Fjx2SDKOPi4qCnp97zGTRizEOtWrUKPbewsAAA1K1bFzVr1gQAJCQkoHPnzti6dSs8PT1hZWWF0aNHIzg4GDY2NrC0tMSECRPg7e3NwZJEVIREIin2MVFFGD9+PMaPH1/sa5GRkaWW3bx5s+oDkpNGJA/lkZubi9jYWGRkZMjOLV++HHp6evD390d2djb8/PywZs0aEaMkIiJ1osi4hTfL6yKNTB5q165dZDR0cedMTEywevVqrF69ujLDIyIiDaGqqZq6Rr07VYiIiEjtaGTLAxERkSqw20IxTB6IiEhniTFVUxuw24KIiIjkwpYHIiLSWWKs86ANmDwQEYF7W+gqKZQc86CySDQLuy2IiJC/t4WxgTWMDay5twVRGdjyQEREOovrPCiGyQMREQAzM7NiH5N2EwTluh50tYeLyQMREbi3ha4SBCVbHnQ0eeCYByIiIpILWx6IiEhncaqmYpg8EBGRzpIKgFSJjgtdXZ6a3RZEREQkF7Y8EBGRzuJUTcUweSAiAleY1FVSJadqstuCiEiHZWRkQiLRh0SizxUmicrAlgciItJZwr//KFNeFzF5ICICYGZmWuxj0m7stlAMuy2IiMAVJonkwZYHIiLSWVwkSjFMHoiISGcJgpJjHnR0Zg6TByIi0llseVAMxzwQERGRXNjyQESEf5uv/22C1tWmaF3EbgvFMHkgIkL+IlF50izZYwsLc5EjosogQLmuB91MHdhtQURERHJiywMREbhIlK6SCoKSW3LrZtsDkwciInCRKF3F5akVw24LIiIikgtbHoiISGdxnQfFMHkgIiKdJYWSYx7YbUFERERUNrY8EBGRzuJsC8UweSAiQuGVAnV11UBdxNkWimHyQESE/FUl9fWMZI+5wqRu4JgHxXDMAxEREcmFLQ9EROAKk7qKLQ+KYfJARASuMKmrOOZBMey2ICIiIrmw5YGIiHSWoGS3ha62PDB5ICIinSWVSCGRKL7ItFRHF6hmtwURERHJhS0PRESks6QQIOFsC7mx5YGICFxhUlcJssmaih+KWL16NWrXrg0TExN4eXnh7NmzJV67YcMGtG/fHtbW1rC2toavr2+p11cGJg9ERMhfVbK4x0Sqtnv3bgQHByM0NBQXLlxAs2bN4OfnhydPnhR7fWRkJAYPHoyIiAhER0fD2dkZ7777LhISEio58v8weSAiIp0lBSq93WHZsmUICgrCyJEj8fbbb2PdunUwMzPDxo0bi71++/bt+Pjjj9G8eXO4ubnhu+++g1QqRXh4uFLvXRkc80BEBK4wqatUNdsiJSWl0HljY2MYGxsXuT4nJwfnz59HSEiI7Jyenh58fX0RHR1drntmZGQgNzcXNjY2CsetLLY8EBGBK0zqKqkK/gEAZ2dnWFlZyY758+cXe79nz54hLy8P9vb2hc7b29sjMTGxXDFPmTIFTk5O8PX1Ve7NK4EtD0REREqKj4+HpaWl7HlxrQ6qsGDBAuzatQuRkZEwMTGpkHuUB5MHIiLSWVJIIVFioaeClgdLS8tCyUNJbG1toa+vj6SkpELnk5KS4ODgUGrZJUuWYMGCBTh16hSaNm2qcMyqwG4LIiLSWZU9VdPIyAgtW7YsNNixYPCjt7d3ieUWLVqEOXPm4NixY/Dw8FD4/aoKWx6IiIgqUXBwMEaMGAEPDw94enpixYoVSE9Px8iRIwEAAQEBqFGjhmzcxMKFCzFz5kzs2LEDtWvXlo2NsLCwgIWFhSjvgckDERG4SJSuEmNvi4EDB+Lp06eYOXMmEhMT0bx5cxw7dkw2iDIuLg56ev91DKxduxY5OTno379/oXpCQ0Mxa9YshWNXBpMHIiLkLwwlkejLHltYmIscEVUG4bUZE4qWV8T48eMxfvz4Yl+LjIws9Pz+/fsK3aMiccwDERERyYUtD0RE4CJRukpAHgQl/o4WkKfCaDQHkwciInCRKF0l/XeBauXK6x52WxAREZFc2PJAREQ6SwoByrU86ObMHCYPRESks/LHPCjeTcUxD0RERDqGYx4UwzEPREREJBe2PBARIX9VScm/f09xhUndocj+FG+W10VMHoiIAGRl5MDZoJnsMVeY1A1S5AFKjHmQ6uiYB3ZbEBERkVzY8kBEBMDUzKTYx6Td2G2hGCYPRETgCpO6Sioo2W0hsNuCiIiIqExseSAiIp3FbgvFMHkgIiKdlZ88KN71oKvJg8Z1W2RnZ6N58+aQSCSIiYkp9dpOnTpBIpEUOsaOHVs5gRIREWkpjWt5+Pzzz+Hk5IS///67XNcHBQXhyy+/lD03MzOrqNCIiEjDCIIUUmX2thB0s+VBo5KHo0eP4sSJE/jpp59w9OjRcpUxMzODg4NDBUdGRJpOEASY6Etlj0k35Hc7KLMxFpMHtZaUlISgoCAcOHBArtaD7du3Y9u2bXBwcECvXr3wxRdflFo+Ozsb2dnZsucpKSlKxU1EGiInC8f9b8oeA6aihkOVQ1ByqqWy5TWVRiQPgiAgMDAQY8eOhYeHB+7fv1+uckOGDIGLiwucnJxw6dIlTJkyBbGxsdi3b1+JZebPn4/Zs2erKHIiIiLtI2ryMHXqVCxcuLDUa65fv44TJ04gNTUVISEhctX/wQcfyB43adIEjo6O6Ny5M+7cuYO6desWWyYkJATBwcGy5ykpKXB2dpbrvkSkeUzNjJD+2mPSDfkjHthtIS9Rk4fJkycjMDCw1GtcXV1x+vRpREdHw9jYuNBrHh4eGDp0KLZs2VKu+3l5eQEAbt++XWLyYGxsXOQ+RKT9uMKkbsof8MgBk/ISNXmws7ODnZ1dmdd98803mDt3ruz5o0eP4Ofnh927d8sSgvIomNrp6Ogod6xERESUTyPGPNSqVavQcwsLCwBA3bp1UbNmTQBAQkICOnfujK1bt8LT0xN37tzBjh070L17d1SrVg2XLl3CpEmT0KFDBzRt2rTS3wMREakfZRaIUkV5TaURyUN55ObmIjY2FhkZGQAAIyMjnDp1CitWrEB6ejqcnZ3h7++PGTNmiBwpERGpi/xpuUosT62j03o1MnmoXbt2kf9gb55zdnbGmTNnKjs0IiIiraeRyQMREZEqKDtbgrMtiIiIdEz+Ik+Kdz3o6mwLjdsYi4iIiMTFlgciItJZyrYc6GrLA5MHIiLSWRzzoBgmD0REpLPY8qAYjnkgIiIiubDlgYgIAIxMYbf2rOwx6QZ2WyiGyQMREf7dDMvYTOwwqJJxqqZi2G1BREREcmHLAxER6TDl9rZQptVCkzF5ICIinZXf7SBRorxuJg/stiAiIiK5sOWBiIh0Vv5sCSVaHthtQUREpGuUSx50dcwDuy2IiIhILmx5ICIi3aXkgEno6IBJJg9ERKSzOOZBMey2ICIiHSZVwSG/1atXo3bt2jAxMYGXlxfOnj1b6vV79+6Fm5sbTExM0KRJExw5ckSh+6oKkwciIqJKtHv3bgQHByM0NBQXLlxAs2bN4OfnhydPnhR7fVRUFAYPHozRo0fj4sWL6Nu3L/r27YsrV65UcuT/kQi6usJFOaWkpMDKygrJycmwtLQUOxwiIq1XGb93C+4BGECidLfFK7li9fLyQqtWrbBq1SoAgFQqhbOzMyZMmICpU6cWuX7gwIFIT0/H4cOHZedat26N5s2bY926dQrHrgy2PBARkQ4TlPpH3qmaOTk5OH/+PHx9fWXn9PT04Ovri+jo6GLLREdHF7oeAPz8/Eq8vjJwwGQZChpmUlJSRI6EiEg3FPy+rbyGceXv8+Z3hLGxMYyNjYtc9+zZM+Tl5cHe3r7QeXt7e9y4caPYuhMTE4u9PjExUcmoFcfkoQypqakAAGdnZ5EjISLSLampqf92LaiekZERHBwcVPIFbGFhUeQ7IjQ0FLNmzVK6bnXF5KEMTk5OiI+PR5UqVSCRKLMKme5KSUmBs7Mz4uPjOW5EQfwMlcfPUHmV9RkKgoDU1FQ4OTlV2D1MTExw79495OTkKF2XIAhFvh+Ka3UAAFtbW+jr6yMpKanQ+aSkJDg4OBRbxsHBQa7rKwOThzLo6emhZs2aYoehFSwtLflLW0n8DJXHz1B5lfEZVlSLw+tMTExgYmJS4fd5nZGREVq2bInw8HD07dsXQP6AyfDwcIwfP77YMt7e3ggPD8cnn3wiO3fy5El4e3tXQsTFY/JARERUiYKDgzFixAh4eHjA09MTK1asQHp6OkaOHAkACAgIQI0aNTB//nwAwMSJE9GxY0csXboUPXr0wK5du3Du3DmsX79etPfA5IGIiKgSDRw4EE+fPsXMmTORmJiI5s2b49ixY7JBkXFxcdDT+28yZJs2bbBjxw7MmDED06ZNQ7169XDgwAE0btxYrLfA5IEqnrGxMUJDQ0vsA6Sy8TNUHj9D5fEzVJ3x48eX2E0RGRlZ5NyAAQMwYMCACo6q/LhIFBEREcmFi0QRERGRXJg8EBERkVyYPBAREZFcmDwQERGRXJg8kMosWLAAEomk0EImb9q8eTMkEkmho7IXaVE3s2bNKvKZuLm5lVpm7969cHNzg4mJCZo0aYIjR45UUrTqSd7PkD+HxUtISMCwYcNQrVo1mJqaokmTJjh37lypZSIjI9GiRQsYGxvjrbfewubNmysnWBIVp2qSSvz111/49ttv0bRp0zKvtbS0RGxsrOw5l/0GGjVqhFOnTsmeGxiU/L9mVFQUBg8ejPnz56Nnz57YsWMH+vbtiwsXLog671ts8nyGAH8O3/TixQu0bdsWPj4+OHr0KOzs7HDr1i1YW1uXWObevXvo0aMHxo4di+3btyM8PBxjxoyBo6Mj/Pz8KjF6qmxMHkhpaWlpGDp0KDZs2IC5c+eWeb1EIhF1TXZ1ZGBgUO7P5Ouvv0bXrl3x2WefAQDmzJmDkydPYtWqVVi3bl1FhqnW5PkMAf4cvmnhwoVwdnbGpk2bZOfq1KlTapl169ahTp06WLp0KQCgYcOG+O2337B8+XImD1qO3RaktHHjxqFHjx5F9psvSVpaGlxcXODs7Iw+ffrg6tWrFRyh+rt16xacnJzg6uqKoUOHIi4ursRro6Oji3zWfn5+iI6Orugw1Zo8nyHAn8M3/fzzz/Dw8MCAAQNQvXp1uLu7Y8OGDaWW4c+i7mLyQErZtWsXLly4IFuDvSwNGjTAxo0bcfDgQWzbtg1SqRRt2rTBw4cPKzhS9eXl5YXNmzfj2LFjWLt2Le7du4f27dvLtoN/U2JiomwZ2wL29vYq2VpYU8n7GfLnsKi7d+9i7dq1qFevHo4fP46PPvoI//vf/7Bly5YSy5T0s5iSkoLMzMyKDpnEJBApKC4uTqhevbrw999/y8517NhRmDhxYrnryMnJEerWrSvMmDGjAiLUTC9evBAsLS2F7777rtjXDQ0NhR07dhQ6t3r1aqF69eqVEZ5GKOszfBN/DvN/rry9vQudmzBhgtC6desSy9SrV0+YN29eoXNhYWECACEjI6NC4iT1wJYHUtj58+fx5MkTtGjRAgYGBjAwMMCZM2fwzTffwMDAAHl5eWXWYWhoCHd3d9y+fbsSItYMVatWRf369Uv8TBwcHJCUlFToXFJSEvvvX1PWZ/gm/hwCjo6OePvttwuda9iwYandPyX9LFpaWsLU1LRC4iT1wOSBFNa5c2dcvnwZMTExssPDwwNDhw5FTEwM9PX1y6wjLy8Ply9fhqOjYyVErBnS0tJw586dEj8Tb29vhIeHFzp38uRJeHt7V0Z4GqGsz/BN/DkE2rZtW2j2CQDcvHkTLi4uJZbhz6IOE7vpg7TLm90Ww4cPF6ZOnSp7Pnv2bOH48ePCnTt3hPPnzwuDBg0STExMhKtXr4oQrXqYPHmyEBkZKdy7d0/4/fffBV9fX8HW1lZ48uSJIAhFP8Pff/9dMDAwEJYsWSJcv35dCA0NFQwNDYXLly+L9RZEJ+9nyJ/Dos6ePSsYGBgIX331lXDr1i1h+/btgpmZmbBt2zbZNVOnThWGDx8ue3737l3BzMxM+Oyzz4Tr168Lq1evFvT19YVjx46J8RaoEnGqJlWoN/elf/HiBYKCgpCYmAhra2u0bNkSUVFRRZpLdcnDhw8xePBg/PPPP7Czs0O7du3wxx9/wM7ODkDRz7BNmzbYsWMHZsyYgWnTpqFevXo4cOCATq/xIO9nyJ/Dolq1aoX9+/cjJCQEX375JerUqYMVK1Zg6NChsmseP35cqBujTp06CAsLw6RJk/D111+jZs2a+O677zhNUwdwS24iIiKSC8c8EBERkVyYPBAREZFcmDwQERGRXJg8EBERkVyYPBAREZFcmDwQERGRXJg8EBERkVyYPBAREZFcmDwQaZl//vkH1atXx/3795WqZ9CgQVi6dKlqgiIircLkgUgNBQYGQiKRQCKRwNDQEHXq1MHnn3+OrKysMst+9dVX6NOnD2rXrq1UDDNmzMBXX32F5ORkpeohIu3D5IFITXXt2hWPHz/G3bt3sXz5cnz77bcIDQ0ttUxGRga+//57jB49Wun7N27cGHXr1sW2bduUrouItAuTByI1ZWxsDAcHBzg7O6Nv377w9fXFyZMnSy1z5MgRGBsbo3Xr1rJzkZGRkEgkOH78ONzd3WFqaop33nkHT548wdGjR9GwYUNYWlpiyJAhyMjIKFRfr169sGvXrgp5f0SkuZg8EGmAK1euICoqCkZGRqVe9+uvv6Jly5bFvjZr1iysWrUKUVFRiI+Px/vvv48VK1Zgx44dCAsLw4kTJ7By5cpCZTw9PXH27FlkZ2er7L0QkebjltxEaurw4cOwsLDAq1evkJ2dDT09PaxatarUMg8ePICTk1Oxr82dOxdt27YFAIwePRohISG4c+cOXF1dAQD9+/dHREQEpkyZIivj5OSEnJwcJCYmwsXFRUXvjIg0HZMHIjXl4+ODtWvXIj09HcuXL4eBgQH8/f1LLZOZmQkTE5NiX2vatKnssb29PczMzGSJQ8G5s2fPFipjamoKAEW6M4hIt7HbgkhNmZub46233kKzZs2wceNG/Pnnn/j+++9LLWNra4sXL14U+5qhoaHsccEsjtdJJBJIpdJC554/fw4AsLOzU+QtEJGWYvJApAH09PQwbdo0zJgxA5mZmSVe5+7ujmvXrqnsvleuXEHNmjVha2ursjqJSPMxeSDSEAMGDIC+vj5Wr15d4jV+fn64evVqia0P8vr111/x7rvvqqQuItIeTB6INISBgQHGjx+PRYsWIT09vdhrmjRpghYtWmDPnj1K3y8rKwsHDhxAUFCQ0nURkXaRCIIgiB0EEalOWFgYPvvsM1y5cgV6eor/fbB27Vrs378fJ06cUGF0RKQNONuCSMv06NEDt27dQkJCApydnRWux9DQsMi6D0REAFseiIiISE4c80BERERyYfJAREREcmHyQERERHJh8kBERERyYfJAREREcmHyQERERHJh8kBERERyYfJAREREcmHyQERERHL5P4/gsxTyCRg9AAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pathlib import Path\n", - "\n", - "Path(\"figs\").mkdir(exist_ok=True)\n", - "\n", - "cmap = plt.get_cmap(\"inferno\")\n", - "\n", - "def plot_frame(t: float):\n", - " heat_fluxes = []\n", - " start_points = []\n", - " end_points = []\n", - " for bin in my_reactor.divertor.bins:\n", - " if not (bin.inner_bin or bin.outer_bin):\n", - " continue\n", - " heat_flux = plasma_data_handling.get_heat(RISP_pulse, bin=bin, t_rel=t)\n", - " heat_fluxes.append(heat_flux)\n", - "\n", - " start_points.append(bin.start_point)\n", - " end_points.append(bin.end_point)\n", - "\n", - " plt.figure()\n", - " \n", - " norm = Normalize(0, 1.3e6)\n", - "\n", - " for start_point, end_point, heat_flux in zip(start_points, end_points, heat_fluxes):\n", - " plt.plot(\n", - " [start_point[0], end_point[0]],\n", - " [start_point[1], end_point[1]],\n", - " c=cmap(norm(heat_flux)),\n", - " )\n", - "\n", - " plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), label=\"Heat Flux (W/m2)\", ax=plt.gca())\n", - " plt.xlabel(\"R (m)\")\n", - " plt.ylabel(\"Z (m)\")\n", - " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", - " plt.title(f\"t = {t:.2e} s\")\n", - " plt.savefig(f\"figs/divertor_heat_flux_{t:.2e}.png\")\n", - "\n", - "for t in np.linspace(0, 400, 10):\n", - " plot_frame(t)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for mode in [\"high_wetted\", \"low_wetted\", \"wetted\"]:\n", - " heat_fluxes = []\n", - " start_points = []\n", - " end_points = []\n", - " for bin in my_reactor.first_wall.bins:\n", - " for sub_bin in bin.sub_bins:\n", - " if sub_bin.mode == mode:\n", - " heat_flux = plasma_data_handling.get_heat(FP_pulse, bin=sub_bin, t_rel=FP_pulse.ramp_up+1)\n", - " heat_fluxes.append(heat_flux)\n", - "\n", - " start_points.append(bin.start_point)\n", - " end_points.append(bin.end_point)\n", - "\n", - "\n", - " norm = Normalize(0, max(heat_fluxes))\n", - " cmap = plt.get_cmap(\"inferno\")\n", - "\n", - "\n", - " for start_point, end_point, heat_flux in zip(start_points, end_points, heat_fluxes):\n", - " plt.plot(\n", - " [start_point[0], end_point[0]],\n", - " [start_point[1], end_point[1]],\n", - " c=cmap(norm(heat_flux)),\n", - " )\n", - " plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=cmap), label=\"Heat Flux (W/m2)\", ax=plt.gca())\n", - " plt.xlabel(\"R (m)\")\n", - " plt.ylabel(\"Z (m)\")\n", - " plt.gca().set_aspect(\"equal\", adjustable=\"box\")\n", - " plt.title(mode)\n", - " plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "hisp-env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.6" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/pyproject.toml b/pyproject.toml index 9583808c..7db604e3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,5 +37,11 @@ Homepage = "https://github.com/kaelyndunnell/hisp" Issues = "https://github.com/kaelyndunnell/hisp/issues" +[tool.pytest.ini_options] +markers = [ + "integration: marks tests requiring PFC-Tritium-Transport (deselect with '-m \"not integration\"')", +] +testpaths = ["tests"] + [tool.setuptools_scm] write_to = "src/hisp/_version.py" diff --git a/src/hisp/__init__.py b/src/hisp/__init__.py index c35ccde5..f447874d 100644 --- a/src/hisp/__init__.py +++ b/src/hisp/__init__.py @@ -1,10 +1,7 @@ -from .helpers import PulsedSource, gaussian_distribution, periodic_step_function +from .helpers import PulsedSource, gaussian_distribution, periodic_step_function, gaussian_implantation_ufl from .h_transport_class import CustomProblem -from .festim_models.mb_model import make_W_mb_model, make_B_mb_model, make_DFW_mb_model from .scenario import Scenario, Pulse from .plotting import plot_bins - -from .model import Model diff --git a/src/hisp/bin.py b/src/hisp/bin.py index 4e9c363f..833dc5ab 100644 --- a/src/hisp/bin.py +++ b/src/hisp/bin.py @@ -1,281 +1,47 @@ -from typing import List, Tuple -import pandas as pd -import numpy as np - - -class SubBin: - thickness: float - copper_thickness: float - material: str - mode: str - dfw: bool - parent_bin_index: int - low_wetted_area: float - high_wetted_area: float - total_area: float - f: float - - def __init__( - self, - mode: str, - thickness: float = None, - material: str = None, - ): - """ - A SubBin object represents a sub-bin of a FW bin in a reactor. - - - Args: - thickness: The thickness of the subbin (in m). - material: The material of the subbin. - mode: The mode of the subbin (shadowed, wetted, low_wetted, high_wetted). - - Attributes: - thickness: The thickness of the subbin (in m). - copper_thickness: The thickness of the copper layer behind W bins (m). - material: The material of the subbin. - mode: The mode of the subbin (shadowed, wetted, low_wetted, high_wetted). - dfw: A boolean indicating if the subbin is a Divertor First Wall (DFW) subbin. - parent_bin_index: The index of the parent bin. - low_wetted_area: The low wetted area of the parent bin (in m^2). - high_wetted_area: The high wetted area of the parent bin (in m^2). - total_area: The total area of the parent bin (in m^2). - f: The fraction of heat values in the low wetted area. Calculated from SMITER as: - f = H_low * low_wetted_area / (H_low * low_wetted_area + H_high * high_wetted_area) - """ - self.thickness = thickness - self.material = material - self.mode = mode - self.dfw = False - self.copper_thickness = None - self.parent_bin_index = None - self.low_wetted_area = None - self.high_wetted_area = None - self.total_area = None - self.f = None - - @property - def shadowed(self) -> bool: - return self.mode == "shadowed" or self.dfw - - @property - def wetted_frac(self): - if self.shadowed: - return 0.0 - elif self.mode == "wetted": - return self.total_area / self.low_wetted_area - - elif self.mode == "low_wetted": - return self.f * self.total_area / self.low_wetted_area - - elif self.mode == "high_wetted": - return (1 - self.f) * self.total_area / self.high_wetted_area - - @property - def surface_area(self) -> float: - """Calculates the surface area of the subbin (in m^2). - - Returns: - The surface area of the subbin (in m^2). - """ - if self.shadowed: - low_wetted_area = self.low_wetted_area - high_wetted_area = self.high_wetted_area - if ( - isinstance(self.low_wetted_area, type(np.nan)) - or self.low_wetted_area is None - ): - low_wetted_area = 0 - if ( - isinstance(self.high_wetted_area, type(np.nan)) - or self.high_wetted_area is None - ): - high_wetted_area = 0 - return self.total_area - low_wetted_area - high_wetted_area - elif self.mode in ["wetted", "low_wetted"]: - return self.low_wetted_area - elif self.mode == "high_wetted": - return self.high_wetted_area - - -class FWBin: - index: int - sub_bins: List[SubBin] - start_point: Tuple[float, float] - end_point: Tuple[float, float] - - def __init__(self, sub_bins: List[SubBin] = None): - self.sub_bins = sub_bins or [] - self.index = None - self.start_point = None - self.end_point = None - - @property - def shadowed_subbin(self) -> SubBin: - for subbin in self.sub_bins: - if subbin.shadowed: - return subbin - - raise ValueError(f"No shadowed subbin found in bin {self.index}") - - @property - def length(self) -> float: - """Calculates the poloidal length of the bin (in m). - - Returns: - The poloidal length of the bin (in m). - """ - return ( - (self.end_point[0] - self.start_point[0]) ** 2 - + (self.end_point[1] - self.start_point[1]) ** 2 - ) ** 0.5 - - def add_dfw_bin(self, **kwargs): - dfw_bin = SubBin(mode="shadowed", **kwargs) - dfw_bin.dfw = True # TODO do we need this? - self.sub_bins.append(dfw_bin) - - -class FWBin3Subs(FWBin): - def __init__(self): - subbins = [ - SubBin(mode="shadowed"), - SubBin(mode="low_wetted"), - SubBin(mode="high_wetted"), - ] - super().__init__(subbins) - - @property - def low_wetted_subbin(self) -> SubBin: - return self.sub_bins[1] - - @property - def high_wetted_subbin(self) -> SubBin: - return self.sub_bins[2] - - -class FWBin2Subs(FWBin): - def __init__(self): - subbins = [ - SubBin(mode="shadowed"), - SubBin(mode="wetted"), - ] - super().__init__(subbins) - - @property - def wetted_subbin(self) -> SubBin: - return self.sub_bins[1] - - -class DivBin: - index: int - thickness: float - material: str - mode = "wetted" - inner_bin = bool - outer_bin = bool - start_point: Tuple[float, float] - end_point: Tuple[float, float] - - def __init__(self): - self.index = None - self.thickness = None - self.material = None - self.inner_bin = False - self.outer_bin = False - self.start_point = None - self.end_point = None - - def set_inner_and_outer_bins(self) -> bool: - """Flags if a DivBin is an inner target or outer target bin. - - Returns: - inner_bin: True if inner bin - outer_bin: True if outer bin - """ - inner_swept_bins = list(range(45, 64)) - outer_swept_bins = list(range(18, 32)) - - if self.index in inner_swept_bins: - self.inner_bin = True - elif self.index in outer_swept_bins: - self.outer_bin = True - - @property - def length(self) -> float: - """Calculates the poloidal length of the bin (in m). - - Returns: - The poloidal length of the bin (in m). - """ - return ( - (self.end_point[0] - self.start_point[0]) ** 2 - + (self.end_point[1] - self.start_point[1]) ** 2 - ) ** 0.5 - - -class BinCollection: - def __init__(self, bins: List[FWBin | DivBin] = None): - """Initializes a BinCollection object containing several bins. - - Args: - bins: The list of bins in the collection. Each bin is a Bin object. - """ - self.bins = bins if bins is not None else [] - - def get_bin(self, index: int) -> FWBin | DivBin: - for bin in self.bins: - if bin.index == index: - return bin - raise ValueError(f"No bin found with index {index}") - - def arc_length(self, middle: bool = False): - """Returns the cumulative length of all bins in the collection. - - Args: - middle: If True, computes from the middle of each bin. - If False, computes from the start of each bin. - """ - if middle: - middle_of_bins = [] - cumulative_lengths = [0] - for bin in self.bins: - middle_of_bins.append(cumulative_lengths[-1] + bin.length / 2) - cumulative_lengths.append(cumulative_lengths[-1] + bin.length) - return middle_of_bins - else: - return np.cumsum([bin.length for bin in self.bins]) - - -class Reactor: - first_wall: BinCollection - divertor: BinCollection - - def __init__( - self, first_wall: BinCollection = None, divertor: BinCollection = None - ): - self.first_wall = first_wall - self.divertor = divertor - all_bins = first_wall.bins + divertor.bins - for i, bin in enumerate(all_bins): - bin.index = i - - for i, bin in enumerate(first_wall.bins): - for subbin in bin.sub_bins: - subbin.parent_bin_index = i - - def get_bin(self, index: int) -> FWBin | DivBin: - for bin in self.first_wall.bins + self.divertor.bins: - if bin.index == index: - return bin - raise ValueError(f"No bin found with index {index}") - - def read_wetted_data(self, filename: str): - data = pd.read_csv(filename) - - for fw_bin in self.first_wall.bins: - for subbin in fw_bin.sub_bins: - subbin.low_wetted_area = data.iloc[fw_bin.index]["Slow"] - subbin.high_wetted_area = data.iloc[fw_bin.index]["Shigh"] - subbin.total_area = data.iloc[fw_bin.index]["Stot"] - subbin.f = data.iloc[fw_bin.index]["f"] +""" +CSV-driven bin classes - imported from PFC-Tritium-Transport +This module intentionally avoids defining any legacy HISP bin classes. +""" +# ============================================================================= +# CSV-driven bin classes - imported from PFC-Tritium-Transport +# ============================================================================= +import os +import sys +from pathlib import Path + +_pfc_tt_path = os.environ.get("PFC_TT_PATH") or os.environ.get("HISP_PFC_TT_PATH") +if _pfc_tt_path: + sys.path.insert(0, str(Path(_pfc_tt_path).resolve())) + +# Import CSV bin classes from PFC-Tritium-Transport +# This avoids duplication — all changes should be made in PFC-Tritium-Transport/csv_bin.py +try: + from bins_from_csv.csv_bin import BinConfiguration, Bin, BinCollection, Reactor +except ImportError as e: + raise ImportError( + "Could not import from PFC-Tritium-Transport. " + "Set PFC_TT_PATH to your local clone: " + "`conda env config vars set PFC_TT_PATH=/path/to/PFC-Tritium-Transport`" + ) from e + +# Also import the Material class from the PFC-Tritium-Transport package so +# HISP code can reference materials via `hisp.bin.Material` in the same way +# it references the CSV-driven Bin classes above. +try: + from materials.materials import Material +except ImportError as e: + raise ImportError( + "Could not import from PFC-Tritium-Transport. " + "Set PFC_TT_PATH to your local clone: " + "`conda env config vars set PFC_TT_PATH=/path/to/PFC-Tritium-Transport`" + ) from e + +# For backwards compatibility, re-export the imported classes +# Re-export the new names +__all__ = ['BinConfiguration', 'Bin', 'BinCollection', 'Reactor', 'Material'] + +# These classes are imported from PFC-Tritium-Transport/csv_bin.py +# ============================================================================= + +# NOTE: Monkeypatch removed - new code expects bin.material to be a Material object +# Legacy code that expects bin.material to be a string should use bin.material_name instead diff --git a/src/hisp/festim_models/__init__.py b/src/hisp/festim_models/__init__.py index 752435e8..753f9fbd 100644 --- a/src/hisp/festim_models/__init__.py +++ b/src/hisp/festim_models/__init__.py @@ -1,7 +1,5 @@ -from .mb_model import ( - make_W_mb_model, - make_B_mb_model, - make_DFW_mb_model, +from .new_mb_model import ( make_temperature_function, make_particle_flux_function, + compute_flux_values, ) diff --git a/src/hisp/festim_models/mb_model.py b/src/hisp/festim_models/mb_model.py deleted file mode 100644 index 943d6b42..00000000 --- a/src/hisp/festim_models/mb_model.py +++ /dev/null @@ -1,1057 +0,0 @@ -from hisp.h_transport_class import CustomProblem -from hisp.helpers import ( - PulsedSource, - gaussian_distribution, - Stepsize, - periodic_pulse_function, -) -from hisp.scenario import Scenario -from hisp.plamsa_data_handling import PlasmaDataHandling -from hisp.settings import CustomSettings -import hisp.bin - -import numpy as np -import festim as F -import h_transport_materials as htm - -from typing import Callable, Tuple, Dict, Union -from numpy.typing import NDArray - -# TODO this is hard coded and show depend on incident energy? -implantation_range = 3e-9 # m -width = 1e-9 # m - - -def make_W_mb_model( - temperature: Callable | float | int, - deuterium_ion_flux: Callable, - tritium_ion_flux: Callable, - deuterium_atom_flux: Callable, - tritium_atom_flux: Callable, - final_time: float, - folder: str, - L: float, - custom_rtol: Union[ - float, Callable - ] = 1e-10, # default rtol unless otherwise specified, used for everything but BAKE - exports=False, -) -> Tuple[CustomProblem, Dict[str, F.TotalVolume]]: - """Create a FESTIM model for the W MB scenario. - - Args: - temperature: the temperature in K. - deuterium_ion_flux: the deuterium ion flux in m^-2 s^-1. - tritium_ion_flux: the tritium ion flux in m^-2 s^-1. - deuterium_atom_flux: the deuterium atom flux in m^-2 s^-1. - tritium_atom_flux: the tritium atom flux in m^-2 s^-1. - final_time: the final time in s. - folder: the folder to save the results. - L: the length of the domain in m. - - Returns: - the FESTIM model, the quantities to export. - """ - my_model = CustomProblem() - - ############# Material Parameters ############# - - vertices = np.concatenate( # 1D mesh with extra refinement - [ - np.linspace(0, 1e-5, num=100), - np.linspace(1e-5, 1e-4, num=300), - np.linspace(1e-4, L, num=300), - ] - ) - my_model.mesh = F.Mesh1D(vertices) - - # W material parameters - w_density = 6.3382e28 # atoms/m3 - w_diffusivity = ( - htm.diffusivities.filter(material="tungsten") - .filter(isotope="h") - .filter(author="holzner") - ) - w_diffusivity = w_diffusivity[0] - D_0 = w_diffusivity.pre_exp.magnitude - E_D = w_diffusivity.act_energy.magnitude - tungsten = F.Material( - D_0=D_0, - E_D=E_D, - name="tungsten", - ) - - # mb subdomains - w_subdomain = F.VolumeSubdomain1D(id=1, borders=[0, L], material=tungsten) - inlet = F.SurfaceSubdomain1D(id=1, x=0) - outlet = F.SurfaceSubdomain1D(id=2, x=L) - - my_model.subdomains = [w_subdomain, inlet, outlet] - - # hydrogen species - mobile_D = F.Species("D") - mobile_T = F.Species("T") - - trap1_D = F.Species("trap1_D", mobile=False) - trap1_T = F.Species("trap1_T", mobile=False) - trap2_D = F.Species("trap2_D", mobile=False) - trap2_T = F.Species("trap2_T", mobile=False) - # trap3_D = F.Species("trap3_D", mobile=False) - # trap3_T = F.Species("trap3_T", mobile=False) - - # traps - empty_trap1 = F.ImplicitSpecies( # implicit trap 1 - n=6.338e24, # 1e-4 at.fr. - others=[trap1_T, trap1_D], - name="empty_trap1", - ) - - empty_trap2 = F.ImplicitSpecies( # implicit trap 2 - n=6.338e24, - others=[trap2_T, trap2_D], - name="empty_trap2", - ) - - # TODO: make trap space dependent (existing in only first 10nm). commenting out until implemented - # density_func = lambda x: ufl.conditional(ufl.gt(x[0],10), 6.338e27, 0.0) # small damanged zone in first 10nm, 1e-1 at.fr. - # empty_trap3 = F.ImplicitSpecies( - # n=6.338e27, - # others=[trap3_T, trap3_D], - # name="empty_trap3", - # ) - - my_model.species = [ - mobile_D, - mobile_T, - trap1_D, - trap1_T, - trap2_D, - trap2_T, - # trap3_D, - # trap3_T, - ] - - interstitial_distance = 1.117e-10 # m - interstitial_sites_per_atom = 6 - - # hydrogen reactions - 1 per trap per species - my_model.reactions = [ - F.Reaction( - k_0=D_0 - / (interstitial_distance**2 * interstitial_sites_per_atom * w_density), - E_k=E_D, - p_0=1e13, - E_p=0.85, - volume=w_subdomain, - reactant=[mobile_D, empty_trap1], - product=trap1_D, - ), - F.Reaction( - k_0=D_0 - / (interstitial_distance**2 * interstitial_sites_per_atom * w_density), - E_k=E_D, - p_0=1e13, - E_p=0.85, - volume=w_subdomain, - reactant=[mobile_T, empty_trap1], - product=trap1_T, - ), - F.Reaction( - k_0=D_0 - / (interstitial_distance**2 * interstitial_sites_per_atom * w_density), - E_k=E_D, - p_0=1e13, - E_p=1, - volume=w_subdomain, - reactant=[mobile_D, empty_trap2], - product=trap2_D, - ), - F.Reaction( - k_0=D_0 - / (interstitial_distance**2 * interstitial_sites_per_atom * w_density), - E_k=E_D, - p_0=1e13, - E_p=1, - volume=w_subdomain, - reactant=[mobile_T, empty_trap2], - product=trap2_T, - ), - # F.Reaction( - # k_0=D_0 / (interstitial_distance**2 * interstitial_sites_per_atom * w_density), - # E_k=E_D, - # p_0=1e13, - # E_p=1.5, - # volume=w_subdomain, - # reactant=[mobile_D, empty_trap3], - # product=trap3_D, - # ), - # F.Reaction( - # k_0=D_0 / (interstitial_distance**2 * interstitial_sites_per_atom * w_density), - # E_k=E_D, - # p_0=1e13, - # E_p=1.5, - # volume=w_subdomain, - # reactant=[mobile_T, empty_trap3], - # product=trap3_T, - # ), - ] - - ############# Temperature Parameters (K) ############# - - my_model.temperature = temperature - - ############# Flux Parameters ############# - - my_model.sources = [ - PulsedSource( - flux=deuterium_ion_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_D, - volume=w_subdomain, - ), - PulsedSource( - flux=tritium_ion_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_T, - volume=w_subdomain, - ), - PulsedSource( - flux=deuterium_atom_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_D, - volume=w_subdomain, - ), - PulsedSource( - flux=tritium_atom_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_T, - volume=w_subdomain, - ), - ] - - ############# Boundary Conditions ############# - surface_reaction_dd = F.SurfaceReactionBC( - reactant=[mobile_D, mobile_D], - gas_pressure=0, - k_r0=7.94e-17, - E_kr=-2, - k_d0=0, - E_kd=0, - subdomain=inlet, - ) - - surface_reaction_tt = F.SurfaceReactionBC( - reactant=[mobile_T, mobile_T], - gas_pressure=0, - k_r0=7.94e-17, - E_kr=-2, - k_d0=0, - E_kd=0, - subdomain=inlet, - ) - - surface_reaction_dt = F.SurfaceReactionBC( - reactant=[mobile_D, mobile_T], - gas_pressure=0, - k_r0=7.94e-17, - E_kr=-2, - k_d0=0, - E_kd=0, - subdomain=inlet, - ) - - my_model.boundary_conditions = [ - surface_reaction_dd, - surface_reaction_dt, - surface_reaction_tt, - ] - - ############# Exports ############# - if exports: - my_model.exports = [ - F.VTXSpeciesExport(f"{folder}/mobile_concentration_t.bp", field=mobile_T), - F.VTXSpeciesExport(f"{folder}/mobile_concentration_d.bp", field=mobile_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_d1.bp", field=trap1_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_t1.bp", field=trap1_T), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_d2.bp", field=trap2_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_t2.bp", field=trap2_T), - # F.VTXSpeciesExport(f"{folder}/trapped_concentration_d3.bp", field=trap3_D), - # F.VTXSpeciesExport(f"{folder}/trapped_concentration_t3.bp", field=trap3_T), - ] - - quantities = {} - for species in my_model.species: - quantity = F.TotalVolume(field=species, volume=w_subdomain) - my_model.exports.append(quantity) - quantities[species.name] = quantity - if species.mobile: - flux = F.SurfaceFlux(field=species, surface=inlet) - my_model.exports.append(flux) - quantities[species.name + "_surface_flux"] = flux - - surface_temperature = F.SurfaceTemperature(my_model.temperature, surface=inlet) - my_model.exports.append(surface_temperature) - quantities["surface_temperature"] = surface_temperature - - ############# Settings ############# - my_model.settings = CustomSettings( - atol=1e10, - rtol=custom_rtol, - max_iterations=100, # the first timestep needs about 66 iterations.... - final_time=final_time, - ) - - my_model.settings.stepsize = Stepsize(initial_value=1e-3) - - return my_model, quantities - - -def make_B_mb_model( - temperature: Callable | float | int, - deuterium_ion_flux: Callable, - tritium_ion_flux: Callable, - deuterium_atom_flux: Callable, - tritium_atom_flux: Callable, - final_time: float, - folder: str, - L: float, - custom_atol: Union[ - float, Callable - ] = 1e8, # default atol unless otherwise specified, used for FP, ICWC, RISP in hisp-for-iter - custom_rtol: Union[ - float, Callable - ] = 1e-10, # default rtol unless otherwise specified, used for FP, ICWC, RISP in hisp-for-iter - exports=False, -) -> Tuple[CustomProblem, Dict[str, F.TotalVolume]]: - """Create a FESTIM model for the B MB scenario. - - Args: - temperature: the temperature in K. - deuterium_ion_flux: the deuterium ion flux in m^-2 s^-1. - tritium_ion_flux: the tritium ion flux in m^-2 s^-1. - deuterium_atom_flux: the deuterium atom flux in m^-2 s^-1. - tritium_atom_flux: the tritium atom flux in m^-2 s^-1. - final_time: the final time in s. - folder: the folder to save the results. - L: the length of the domain in m. - - Returns: - the FESTIM model, the quantities to export. - """ - my_model = CustomProblem() - - ############# Material Parameters ############# - - vertices = np.concatenate( # 1D mesh with extra refinement - [ - np.linspace(0, 30e-9, num=500), - np.linspace(30e-9, 1e-7, num=500), - np.linspace(1e-7, L, num=500), - ] - ) - my_model.mesh = F.Mesh1D(vertices) - - # B material parameters from Etienne Hodilles's unpublished TDS study for boron - b_density = 1.34e29 # atoms/m3 - D_0 = 1.07e-6 # m^2/s - E_D = 0.3 # eV - boron = F.Material( - D_0=D_0, - E_D=E_D, - name="boron", - ) - - # mb subdomains - b_subdomain = F.VolumeSubdomain1D(id=1, borders=[0, L], material=boron) - inlet = F.SurfaceSubdomain1D(id=1, x=0) - outlet = F.SurfaceSubdomain1D(id=2, x=L) - - my_model.subdomains = [b_subdomain, inlet, outlet] - - # hydrogen species - mobile_D = F.Species("D") - mobile_T = F.Species("T") - - trap1_D = F.Species("trap1_D", mobile=False) - trap1_T = F.Species("trap1_T", mobile=False) - trap2_D = F.Species("trap2_D", mobile=False) - trap2_T = F.Species("trap2_T", mobile=False) - trap3_D = F.Species("trap3_D", mobile=False) - trap3_T = F.Species("trap3_T", mobile=False) - trap4_D = F.Species("trap4_D", mobile=False) - trap4_T = F.Species("trap4_T", mobile=False) - # trap5_D = F.Species("trap5_D", mobile=False) - # trap5_T = F.Species("trap5_T", mobile=False) - - # traps - empty_trap1 = F.ImplicitSpecies( # implicit trap 1 - n=6.867e-1 - * b_density, # from Etienne Hodilles's unpublished TDS study for boron - others=[trap1_T, trap1_D], - name="empty_trap1", - ) - - empty_trap2 = F.ImplicitSpecies( # implicit trap 2 - n=5.214e-1 - * b_density, # from Etienne Hodilles's unpublished TDS study for boron - others=[trap2_T, trap2_D], - name="empty_trap2", - ) - - empty_trap3 = F.ImplicitSpecies( - n=2.466e-1 - * b_density, # from Etienne Hodilles's unpublished TDS study for boron - others=[trap3_T, trap3_D], - name="empty_trap3", - ) - - empty_trap4 = F.ImplicitSpecies( - n=1.280e-1 - * b_density, # from Etienne Hodilles's unpublished TDS study for boron - others=[trap4_T, trap4_D], - name="empty_trap4", - ) - - # empty_trap5 = F.ImplicitSpecies( - # n=1.800e-1*b_density, # from Johnathan Dufour's unpublished TDS study for boron - # others=[trap5_T, trap5_D], - # name="empty_trap5", - # ) - - my_model.species = [ - mobile_D, - mobile_T, - trap1_D, - trap1_T, - trap2_D, - trap2_T, - trap3_D, - trap3_T, - trap4_D, - trap4_T, - # trap5_D, - # trap5_T, - ] - - # hydrogen reactions - 1 per trap per species - interstitial_distance = 8e-10 # m - interstitial_sites_per_atom = 1 - - my_model.reactions = [ - F.Reaction( - k_0=1e13 / b_density, - E_k=E_D, - p_0=1e13, # from Etienne Hodilles's unpublished TDS study for boron - E_p=1.052, - volume=b_subdomain, - reactant=[mobile_D, empty_trap1], - product=trap1_D, - ), - F.Reaction( - k_0=1e13 / b_density, - E_k=E_D, - p_0=1e13, # from Etienne Hodilles's unpublished TDS study for boron - E_p=1.052, - volume=b_subdomain, - reactant=[mobile_T, empty_trap1], - product=trap1_T, - ), - F.Reaction( - k_0=1e13 / b_density, - E_k=E_D, - p_0=1e13, # from Etienne Hodilles's unpublished TDS study for boron - E_p=1.199, - volume=b_subdomain, - reactant=[mobile_D, empty_trap2], - product=trap2_D, - ), - F.Reaction( - k_0=1e13 / b_density, - E_k=E_D, - p_0=1e13, # from Etienne Hodilles's unpublished TDS study for boron - E_p=1.199, - volume=b_subdomain, - reactant=[mobile_T, empty_trap2], - product=trap2_T, - ), - F.Reaction( - k_0=1e13 / b_density, - E_k=E_D, - p_0=1e13, # from Etienne Hodilles's unpublished TDS study for boron - E_p=1.389, - volume=b_subdomain, - reactant=[mobile_D, empty_trap3], - product=trap3_D, - ), - F.Reaction( - k_0=1e13 / b_density, - E_k=E_D, - p_0=1e13, # from Etienne Hodilles's unpublished TDS study for boron - E_p=1.389, - volume=b_subdomain, - reactant=[mobile_T, empty_trap3], - product=trap3_T, - ), - F.Reaction( - k_0=1e13 / b_density, - E_k=E_D, - p_0=1e13, # from Etienne Hodilles's unpublished TDS study for boron - E_p=1.589, - volume=b_subdomain, - reactant=[mobile_D, empty_trap4], - product=trap4_D, - ), - F.Reaction( - k_0=1e13 / b_density, - E_k=E_D, - p_0=1e13, # from Etienne Hodilles's unpublished TDS study for boron - E_p=1.589, - volume=b_subdomain, - reactant=[mobile_T, empty_trap4], - product=trap4_T, - ), - # F.Reaction( - # k_0=1e13 / b_density, - # E_k=E_D, - # p_0=1e13, # from Johnathan Dufour's unpublished TDS study for boron - # E_p=1.776, - # volume=b_subdomain, - # reactant=[mobile_D, empty_trap5], - # product=trap5_D, - # ), - # F.Reaction( - # k_0=1e13 / b_density, - # E_k=E_D, - # p_0=1e13, # from Johnathan Dufour's unpublished TDS study for boron - # E_p=1.776, - # volume=b_subdomain, - # reactant=[mobile_T, empty_trap5], - # product=trap5_T, - # ), - ] - - ############# Temperature Parameters (K) ############# - - my_model.temperature = temperature - - ############# Flux Parameters ############# - - my_model.sources = [ - PulsedSource( - flux=deuterium_ion_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_D, - volume=b_subdomain, - ), - PulsedSource( - flux=tritium_ion_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_T, - volume=b_subdomain, - ), - PulsedSource( - flux=deuterium_atom_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_D, - volume=b_subdomain, - ), - PulsedSource( - flux=tritium_atom_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_T, - volume=b_subdomain, - ), - ] - - ############# Boundary Conditions ############# - my_model.boundary_conditions = [ - F.FixedConcentrationBC(subdomain=inlet, value=0.0, species="D"), - F.FixedConcentrationBC(subdomain=inlet, value=0.0, species="T"), - F.ParticleFluxBC(subdomain=outlet, value=0.0, species="D"), - F.ParticleFluxBC(subdomain=outlet, value=0.0, species="T"), - ] - - ############# Exports ############# - if exports: - my_model.exports = [ - F.VTXSpeciesExport(f"{folder}/mobile_concentration_t.bp", field=mobile_T), - F.VTXSpeciesExport(f"{folder}/mobile_concentration_d.bp", field=mobile_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_d1.bp", field=trap1_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_t1.bp", field=trap1_T), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_d2.bp", field=trap2_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_t2.bp", field=trap2_T), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_d3.bp", field=trap3_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_t3.bp", field=trap3_T), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_d4.bp", field=trap4_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_t4.bp", field=trap4_T), - # F.VTXSpeciesExport(f"{folder}/trapped_concentration_d5.bp", field=trap5_D), - # F.VTXSpeciesExport(f"{folder}/trapped_concentration_t5.bp", field=trap5_T), - ] - - quantities = {} - for species in my_model.species: - quantity = F.TotalVolume(field=species, volume=b_subdomain) - my_model.exports.append(quantity) - quantities[species.name] = quantity - if species.mobile: - flux = F.SurfaceFlux(field=species, surface=inlet) - my_model.exports.append(flux) - quantities[species.name + "_surface_flux"] = flux - - surface_temperature = F.SurfaceTemperature(my_model.temperature, surface=inlet) - my_model.exports.append(surface_temperature) - quantities["surface_temperature"] = surface_temperature - - ############# Settings ############# - my_model.settings = CustomSettings( - atol=custom_atol, - rtol=custom_rtol, - max_iterations=100, - final_time=final_time, - ) - - my_model.settings.stepsize = Stepsize(initial_value=1e-4) - - return my_model, quantities - - -def make_DFW_mb_model( - temperature: Callable | float | int, - deuterium_ion_flux: Callable, - tritium_ion_flux: Callable, - deuterium_atom_flux: Callable, - tritium_atom_flux: Callable, - final_time: float, - folder: str, - L: float, - exports=False, -) -> Tuple[CustomProblem, Dict[str, F.TotalVolume]]: - """Create a FESTIM model for the DFW MB scenario. - - Args: - temperature: the temperature in K. - deuterium_ion_flux: the deuterium ion flux in m^-2 s^-1. - tritium_ion_flux: the tritium ion flux in m^-2 s^-1. - deuterium_atom_flux: the deuterium atom flux in m^-2 s^-1. - tritium_atom_flux: the tritium atom flux in m^-2 s^-1. - final_time: the final time in s. - folder: the folder to save the results. - L: the length of the domain in m. - - Returns: - the FESTIM model, the quantities to export. - """ - my_model = CustomProblem() - - ############# Material Parameters ############# - - vertices = np.concatenate( # 1D mesh with extra refinement - [ - np.linspace(0, 30e-9, num=200), - np.linspace(30e-9, 3e-6, num=300), - np.linspace(3e-6, 30e-6, num=300), - np.linspace(30e-6, 1e-4, num=300), - np.linspace(1e-4, L, num=200), - ] - ) - my_model.mesh = F.Mesh1D(vertices) - - # TODO: pull DFW material parameters from HTM? - - # from ITER mean value parameters (FIXME: add DOI) - ss_density = 8.45e28 # atoms/m3 - D_0 = 1.45e-6 # m^2/s - E_D = 0.59 # eV - ss = F.Material( - D_0=D_0, - E_D=E_D, - name="ss", - ) - - # mb subdomains - ss_subdomain = F.VolumeSubdomain1D(id=1, borders=[0, L], material=ss) - inlet = F.SurfaceSubdomain1D(id=1, x=0) - outlet = F.SurfaceSubdomain1D(id=2, x=L) - - my_model.subdomains = [ss_subdomain, inlet, outlet] - - # hydrogen species - mobile_D = F.Species("D") - mobile_T = F.Species("T") - - trap1_D = F.Species("trap1_D", mobile=False) - trap1_T = F.Species("trap1_T", mobile=False) - - # traps - empty_trap1 = F.ImplicitSpecies( # implicit trap 1 - n=8e-2 * ss_density, # from Guillermain D 2016 ITER report T2YEND - others=[trap1_T, trap1_D], - name="empty_trap1", - ) - - my_model.species = [ - mobile_D, - mobile_T, - trap1_D, - trap1_T, - ] - - # hydrogen reactions - 1 per trap per species - interstitial_distance = 2.545e-10 # m - interstitial_sites_per_atom = 1 - - my_model.reactions = [ - F.Reaction( - k_0=D_0 - / (interstitial_distance * interstitial_sites_per_atom * ss_density), - E_k=E_D, - p_0=1e13, # from Guillermain D 2016 ITER report T2YEND - E_p=0.7, - volume=ss_subdomain, - reactant=[mobile_D, empty_trap1], - product=trap1_D, - ), - F.Reaction( - k_0=D_0 - / (interstitial_distance * interstitial_sites_per_atom * ss_density), - E_k=E_D, - p_0=1e13, # from Guillermain D 2016 ITER report T2YEND - E_p=0.7, - volume=ss_subdomain, - reactant=[mobile_T, empty_trap1], - product=trap1_T, - ), - ] - - ############# Temperature Parameters (K) ############# - - my_model.temperature = temperature - - ############# Flux Parameters ############# - - my_model.sources = [ - PulsedSource( - flux=deuterium_ion_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_D, - volume=ss_subdomain, - ), - PulsedSource( - flux=tritium_ion_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_T, - volume=ss_subdomain, - ), - PulsedSource( - flux=deuterium_atom_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_D, - volume=ss_subdomain, - ), - PulsedSource( - flux=tritium_atom_flux, - distribution=lambda x: gaussian_distribution(x, implantation_range, width), - species=mobile_T, - volume=ss_subdomain, - ), - ] - - ############# Boundary Conditions ############# - surface_reaction_dd = F.SurfaceReactionBC( - reactant=[mobile_D, mobile_D], - gas_pressure=0, - k_r0=1.75e-24, - E_kr=-0.594, - k_d0=0, - E_kd=0, - subdomain=inlet, - ) - - surface_reaction_tt = F.SurfaceReactionBC( - reactant=[mobile_T, mobile_T], - gas_pressure=0, - k_r0=1.75e-24, - E_kr=-0.594, - k_d0=0, - E_kd=0, - subdomain=inlet, - ) - - surface_reaction_dt = F.SurfaceReactionBC( - reactant=[mobile_D, mobile_T], - gas_pressure=0, - k_r0=1.75e-24, - E_kr=-0.594, - k_d0=0, - E_kd=0, - subdomain=inlet, - ) - - my_model.boundary_conditions = [ - surface_reaction_dd, - surface_reaction_dt, - surface_reaction_tt, - ] - - ############# Exports ############# - if exports: - my_model.exports = [ - F.VTXSpeciesExport(f"{folder}/mobile_concentration_t.bp", field=mobile_T), - F.VTXSpeciesExport(f"{folder}/mobile_concentration_d.bp", field=mobile_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_d1.bp", field=trap1_D), - F.VTXSpeciesExport(f"{folder}/trapped_concentration_t1.bp", field=trap1_T), - ] - - quantities = {} - for species in my_model.species: - quantity = F.TotalVolume(field=species, volume=ss_subdomain) - my_model.exports.append(quantity) - quantities[species.name] = quantity - if species.mobile: - flux = F.SurfaceFlux(field=species, surface=inlet) - my_model.exports.append(flux) - quantities[species.name + "_surface_flux"] = flux - - surface_temperature = F.SurfaceTemperature(my_model.temperature, surface=inlet) - my_model.exports.append(surface_temperature) - quantities["surface_temperature"] = surface_temperature - - ############# Settings ############# - my_model.settings = F.Settings( - atol=1e10, - rtol=1e-6, - max_iterations=30, - final_time=final_time, - ) - - my_model.settings.stepsize = Stepsize(initial_value=1e-3) - - return my_model, quantities - - -# calculate how the rear temperature of the W layer evolves with the surface temperature -# data from E.A. Hodille et al 2021 Nucl. Fusion 61 126003 10.1088/1741-4326/ac2abc (Table I) -heat_fluxes_hodille = [10e6, 5e6, 1e6] # W/m2 -T_rears_hodille = [552, 436, 347] # K - -import scipy.stats - -slope_T_rear, intercept, r_value, p_value, std_err = scipy.stats.linregress( - heat_fluxes_hodille, T_rears_hodille -) - - -def tungsten_slab_temperature(q_front, D_W, D_Cu, T_cool): - """ - Calculate the temperature of the front and back surfaces of a tungsten slab - with heat flux applied to the front surface and cooling via a copper slab. - From T. Wauters - - Parameters: - q_front (float): Heat flux at the front surface of the tungsten slab (W/m^2). - D_W (float): Thickness of the tungsten slab (m). - D_Cu (float): Thickness of the copper slab (m). - T_cool (float): Cooling water temperature (K). - - Returns: - tuple: (T_w_surf, T_w_interface) where: - - T_w_surf is the front surface temperature of tungsten (K). - - T_w_interface is the tungsten-copper interface temperature (K). - """ - # Thermal conductivities (W/m·K) #TODO: add citations - k_W = 170 # Tungsten thermal conductivity - k_Cu = 400 # Copper thermal conductivity - # Heat transfer coefficient from copper to water (W/m^2·K) - h_Cu_water = 10_000 # Typical value for water cooling - - w_diffusivity = ( - htm.diffusivities.filter(material="tungsten") - .filter(isotope="h") - .filter(author="holzner") - ) - - # Temperature drop across tungsten slab - delta_T_W = (q_front * D_W) / k_W - # Temperature drop across copper slab - delta_T_Cu = (q_front * D_Cu) / k_Cu - # Temperature drop at the copper-water interface - delta_T_interface = q_front / h_Cu_water - - # Compute temperatures - T_w_interface = T_cool + delta_T_interface + delta_T_Cu - T_w_surf = T_w_interface + delta_T_W - - return T_w_surf, T_w_interface - - -def calculate_temperature_W( - x: float | NDArray, - heat_flux: float, - coolant_temp: float, - thickness: float, - copper_thickness: float, -) -> float | NDArray: - """Calculates the temperature in the W layer based on coolant temperature and heat flux - - Reference: - - Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020) 10.1038/s41598-020-74844-w - - E.A. Hodille et al 2021 Nucl. Fusion 61 126003 10.1088/1741-4326/ac2abc - - Args: - x: position in m - heat_flux: heat flux in W/m2 - coolant_temp: coolant temperature in K - thickness: thickness of the W layer in m - - Returns: - temperature in K - """ - - # T_surface and T_rear calculations taken from tungsten/copper calculations - # provided by T. Wauters - if copper_thickness is not None: - T_surface, T_rear = tungsten_slab_temperature( - q_front=heat_flux, D_W=thickness, D_Cu=copper_thickness, T_cool=coolant_temp - ) - else: - # the evolution of T surface is taken from Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020). - # https://doi.org/10.1038/s41598-020-74844-w - T_surface = 1.1e-4 * heat_flux + coolant_temp - T_rear = slope_T_rear * heat_flux + coolant_temp - - a = (T_rear - T_surface) / thickness - b = T_surface - return a * x + b - - -def calculate_temperature_B(heat_flux: float, coolant_temp: float) -> float: - """ - Calculates the temperature in the boron layer based on coolant temperature and heat flux. - The temperature is assumed to be homogeneous in the B layer and is calculated based on the - surface temperature of the W layer. - - T_B = R_c * q + T_surface_W - - where - - R_c is the thermal contact resistance of the layer in m2 K/W - - q is the heat flux in W/m2 - - T_surface_W is the surface temperature of the W layer in K - - References: - - Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020) 10.1038/s41598-020-74844-w - - Jae-Sun Park et al 2023 Nucl. Fusion 63 076027 10.1088/1741-4326/acd9d9 - - Args: - heat_flux: heat flux in W/m2 - coolant_temp: coolant temperature in K - - Returns: - temperature in K - """ - # the evolution of T surface is taken from Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020). - # https://doi.org/10.1038/s41598-020-74844-w - T_surf_tungsten = 1.1e-4 * heat_flux + coolant_temp - R_c_jet = 5e-4 # m2 K/W calculated from JET-ILW (JPN#98297) - return R_c_jet * heat_flux + T_surf_tungsten - - -def make_temperature_function( - scenario: Scenario, - plasma_data_handling: PlasmaDataHandling, - bin: hisp.bin.SubBin | hisp.bin.DivBin, - coolant_temp: float, -) -> Callable[[NDArray, float], NDArray]: - """Returns a function that calculates the temperature of the bin based on time and position. - - Args: - scenario: the Scenario object containing the pulses - plasma_data_handling: the object containing the plasma data - bin: the bin/subbin to get the temperature function for - coolant_temp: the coolant temperature in K - - Returns: - a callable of x, t returning the temperature in K - """ - - def T_function(x: NDArray, t: float) -> NDArray: - assert isinstance(t, float), f"t should be a float, not {type(t)}" - - # get the pulse and time relative to the start of the pulse - pulse = scenario.get_pulse(t) - t_rel = t - scenario.get_time_start_current_pulse(t) - relative_time_within_pulse = t_rel % pulse.total_duration - - if pulse.pulse_type == "BAKE": - T_value = periodic_pulse_function( - relative_time_within_pulse, - pulse=pulse, - value=483.15, # K - value_off=343.0, # K - ) - value = np.full_like(x[0], T_value) - - else: - heat_flux = plasma_data_handling.get_heat( - pulse, bin, relative_time_within_pulse - ) - if ( - bin.material == "W" or bin.material == "SS" - ): # FIXME: update ss temp when gven data: - value = calculate_temperature_W( - x[0], heat_flux, coolant_temp, bin.thickness, bin.copper_thickness - ) - elif bin.material == "B": - T_value = calculate_temperature_B(heat_flux, coolant_temp) - value = np.full_like(x[0], T_value) - else: - raise ValueError(f"Unsupported material: {bin.material}") - - return value - - return T_function - - -def make_particle_flux_function( - scenario: Scenario, - plasma_data_handling: PlasmaDataHandling, - bin: hisp.bin.SubBin | hisp.bin.DivBin, - ion: bool, - tritium: bool, -) -> Callable[[float], float]: - """Returns a function that calculates the particle flux based on time. - - Args: - scenario: the Scenario object containing the pulses - plasma_data_handling: the object containing the plasma data - bin: the bin/subbin to get the temperature function for - ion: whether to get the ion flux - tritium: whether to get the tritium flux - - Returns: - a callable of t returning the **incident** particle flux in m^-2 s^-1 - """ - - def particle_flux_function(t: float) -> float: - assert isinstance(t, float), f"t should be a float, not {type(t)}" - - # get the pulse and time relative to the start of the pulse - pulse = scenario.get_pulse(t) - relative_time = t - scenario.get_time_start_current_pulse(t) - relative_time_within_pulse = relative_time % pulse.total_duration - - # get the incident particle flux - incident_hydrogen_particle_flux = plasma_data_handling.get_particle_flux( - pulse=pulse, - bin=bin, - t_rel=relative_time_within_pulse, - ion=ion, - ) - - # if tritium is requested, multiply by tritium fraction - if tritium: - value = incident_hydrogen_particle_flux * pulse.tritium_fraction - else: - value = incident_hydrogen_particle_flux * (1 - pulse.tritium_fraction) - - return value - - return particle_flux_function diff --git a/src/hisp/festim_models/new_mb_model.py b/src/hisp/festim_models/new_mb_model.py new file mode 100644 index 00000000..1de85cda --- /dev/null +++ b/src/hisp/festim_models/new_mb_model.py @@ -0,0 +1,1234 @@ +""" +Dynamic FESTIM model builder based on Bin configuration. + +This module creates FESTIM models dynamically using the material properties, +trap parameters, and simulation settings stored in a Bin object. +""" + +from typing import Callable, Tuple, Dict, Union, List, Optional +from numpy.typing import NDArray +import numpy as np +import festim as F +import scipy.stats +from hisp.scenario import Scenario +from hisp.plasma_data_handling import PlasmaDataHandling +from hisp.h_transport_class import CustomProblem +from hisp.settings import CustomSettings +from builtins import ValueError, bool, callable, float, int, isinstance, str, type +from hisp.helpers import ( + PulsedSource, + gaussian_distribution, + Stepsize, + periodic_pulse_function, + gaussian_implantation_ufl, +) +import hisp.bin +from ufl import conditional, lt, ge, And +import h_transport_materials as htm +from scipy.optimize import bisect +import math +import inspect + +# Constants +kB_J = 1.380649e-23 # J/K +eV_to_J = 1.602176634e-19 # J/eV +implantation_range = 3e-9 # m (TODO: make this depend on incident energy) +width = 1e-9 # m (implantation distribution sigma) + + +def graded_vertices(L, h0, r): + xs = [0.0]; h = h0 + while xs[-1] + h < L: + xs.append(xs[-1] + h); h *= r + if xs[-1] < L: xs.append(L) + return np.array(xs) + + +def compute_export_times(scenario: Scenario, samples_per_pulse: int = 3) -> list: + """ + Calculate times for profile exports (3 per pulse by default). + + For each pulse (including BAKE), exports at: + - Start of ramp-up + - Middle of steady-state + - End of ramp-down + + Args: + scenario: Scenario with pulse sequence + samples_per_pulse: Number of samples per pulse (default: 3) + + Returns: + List of export times in seconds + """ + export_times = [] + current_time = 0.0 + + for pulse in scenario.pulses: + for _ in range(pulse.nb_pulses): + # Calculate key times within this pulse occurrence + pulse_duration = pulse.total_duration + ramp_up = pulse.ramp_up + steady_state = pulse.steady_state + ramp_down = pulse.ramp_down + + if samples_per_pulse == 3: + # Export at: start of ramp-up, middle of steady-state, end of ramp-down + t1 = current_time # Start of ramp-up + t2 = current_time + ramp_up + steady_state / 2 # Middle of steady-state + t3 = current_time + pulse_duration # End of ramp-down (end of pulse) + + export_times.extend([t1, t2, t3]) + else: + # Generic sampling: evenly spaced within pulse + for i in range(samples_per_pulse): + t = current_time + (i + 0.5) * pulse_duration / samples_per_pulse + export_times.append(t) + + current_time += pulse_duration + + return export_times + + +def make_surface_concentration_time_function( + T_fun: Callable, + flux_fun: Callable, + D0: float, + E_eV: float, + R_p: float, + flux_tot_fun: Callable = None, + Kr0: float = None, + E_Kr: float = None, + surface_x: float = 0.0 +) -> Callable[[float], float]: + x_surf = np.array([[float(surface_x)]]) + E_J = float(E_eV) * eV_to_J + + def c_S(t): + t = float(t) + T_surf = float(T_fun(x_surf, t)[0]) + phi = float(flux_fun(t)) + if phi == 0: + return 0.0 + D_T = D0 * np.exp(-E_J / (kB_J * T_surf)) + val = (phi * float(R_p)) / D_T + if Kr0 is not None and flux_tot_fun is not None: + phi_tot = float(flux_tot_fun(t)) + if phi_tot > 0: + # Temperature-dependent recombination: Kr(T) = Kr0 * exp(-E_Kr / (kB * T)) + E_Kr_J = float(E_Kr) * eV_to_J if E_Kr is not None else 0.0 + Kr_T = Kr0 * np.exp(-E_Kr_J / (kB_J * T_surf)) + val += phi / np.sqrt(Kr_T * phi_tot) + return float(val) + + return c_S + + +def create_species_and_traps( + material, + volume_subdomain: F.VolumeSubdomain1D +) -> Tuple[List[F.Species], List[F.Reaction]]: + """ + Create FESTIM species, traps, and reactions based on material properties. + + Args: + material: Material object with trap information + volume_subdomain: FESTIM volume subdomain for reactions + + Returns: + Tuple of (species_list, implicit_species_list, reactions_list) + """ + # Mobile species + mobile_D = F.Species("D") + mobile_T = F.Species("T") + species_list = [mobile_D, mobile_T] + + # Create trap species (one for each trap, each isotope) + trap_list = [] + + n_traps = material.N_traps + mat_density = material.Mat_density # atoms/m³ + print(f"\n=== DEBUG: Material '{material.name}' parameters ===") + print(f" Diffusion: D0={material.D0:.4e} m²/s, E_D={material.E_D:.4f} eV") + _kr = getattr(material, 'K_R', None) + _er = getattr(material, 'E_R', None) + if _kr is not None: + print(f" Recombination: K_R={_kr:.4e}, E_R={_er}") + else: + print(f" Recombination: not set (will use defaults if Robin BC)") + print(f" Mat_density={mat_density:.4e} atoms/m³, N_traps={n_traps}") + + for i in range(1, n_traps + 1): + # Get trap parameters + trap_params = material.traps[i - 1] + # Convert atomic fraction to absolute density (atoms/m³) + trap_density = trap_params.Trap_density * mat_density + + # Debug output + print(f"Trap {i}: Trap_density={trap_density} (from {trap_params.Trap_density} at.fr.), k_0={trap_params.k_0}, E_k={trap_params.E_k}, p_0={trap_params.p_0}, E_p={trap_params.E_p}") + + # Create trapped species for D and T in this trap + trap_D = F.Species(f"trap{i}_D", mobile=False) + trap_T = F.Species(f"trap{i}_T", mobile=False) + species_list.extend([trap_D, trap_T]) + + # Create implicit species (empty trap) - shared by both D and T + empty_trap = F.ImplicitSpecies( + n=trap_density, + others=[trap_T, trap_D], + name=f"empty_trap{i}", + ) + + trap_list.append({ + 'index': i, + 'trap_D': trap_D, + 'trap_T': trap_T, + 'empty_trap': empty_trap, + 'params': trap_params, + }) + print("=== DEBUG: Trap creation complete ===\n") + + # Create reactions + reactions_list = [] + + print(f"\n=== DEBUG: Creating reactions ===") + for trap_info in trap_list: + trap_params = trap_info['params'] + trap_idx = trap_info['index'] + + # Use trap-specific parameters from CSV (all required) + k_0 = trap_params.k_0 + E_k = trap_params.E_k + p_0 = trap_params.p_0 + E_p = trap_params.E_p + + print(f"Trap {trap_idx} reactions: k_0={k_0}, E_k={E_k}, p_0={p_0}, E_p={E_p}") + + # Reaction for D in this trap + reactions_list.append( + F.Reaction( + k_0=k_0, + E_k=E_k, + p_0=p_0, + E_p=E_p, + volume=volume_subdomain, + reactant=[mobile_D, trap_info['empty_trap']], + product=trap_info['trap_D'], + ) + ) + + # Reaction for T in this trap + reactions_list.append( + F.Reaction( + k_0=k_0, + E_k=E_k, + p_0=p_0, + E_p=E_p, + volume=volume_subdomain, + reactant=[mobile_T, trap_info['empty_trap']], + product=trap_info['trap_T'], + ) + ) + print("=== DEBUG: Reaction creation complete ===\n") + + return species_list, reactions_list + + +def make_dynamic_mb_model( + bin, # Bin object with material and configuration + temperature: Callable, + deuterium_ion_flux: Callable, + tritium_ion_flux: Callable, + deuterium_atom_flux: Callable, + tritium_atom_flux: Callable, + final_time: float, + folder: str, + mesh=None, + occurrences: list = None, # Optional: Pre-computed flux occurrences with steady-state values + exports: bool = True, + profile_export: bool = True, # Optional: Whether to export 1D concentration profiles + milestones: list = None, # Optional: Milestones for adaptive timestepping, also used as profile export times +) -> Tuple[F.HydrogenTransportProblem, Dict[str, F.TotalVolume]]: + """ + Create a FESTIM model dynamically based on bin properties. + + Args: + bin: Bin object containing material, thickness, configuration, and implantation_params + temperature: Temperature function T(x, t) in K + deuterium_ion_flux: Deuterium ion flux function (part/m^2/s) + tritium_ion_flux: Tritium ion flux function (part/m^2/s) + deuterium_atom_flux: Deuterium atom flux function (part/m^2/s) + tritium_atom_flux: Tritium atom flux function (part/m^2/s) + final_time: Final simulation time (s) + folder: Output folder for results + mesh: Optional pre-computed mesh vertices array. If None, mesh is generated from bin.thickness + occurrences: Optional pre-computed flux occurrences with steady-state values + exports: Whether to export detailed outputs + profile_export: Whether to export 1D concentration profiles (default: True) + + Returns: + Tuple of (festim_model, quantities_dict) + """ + my_model = CustomProblem() + + # --- GET IMPLANTATION PARAMETERS FROM BIN --- + # bin.implantation_params should have structure: + # {'ion': {'implantation_range': ..., 'width': ..., 'reflection_coefficient': ...}, + # 'atom': {...}} + implantation_params = getattr(bin, 'implantation_params', None) or { + 'ion': {'implantation_range': implantation_range, 'width': width, 'reflection_coefficient': 0.0}, + 'atom': {'implantation_range': implantation_range, 'width': width, 'reflection_coefficient': 0.0} + } + + # Get parameters (use defaults if not available) + ion_range = implantation_params.get('ion', {}).get('implantation_range', implantation_range) + ion_width = implantation_params.get('ion', {}).get('width', width) + atom_range = implantation_params.get('atom', {}).get('implantation_range', implantation_range) + atom_width = implantation_params.get('atom', {}).get('width', width) + + # Reflection coefficients (will be applied to flux functions) + ion_reflection = implantation_params.get('ion', {}).get('reflection_coefficient', 0.0) + atom_reflection = implantation_params.get('atom', {}).get('reflection_coefficient', 0.0) + + # Debug: Print implantation parameters + print(f"\n=== Implantation Parameters (Bin {bin.bin_number}) ===") + print(f" Ion range: {ion_range*1e9:.3f} nm, width: {ion_width*1e9:.3f} nm, reflection coeff: {ion_reflection:.4f}") + print(f" Atom range: {atom_range*1e9:.3f} nm, width: {atom_width*1e9:.3f} nm, reflection coeff: {atom_reflection:.4f}") + + # --- APPLY REFLECTION COEFFICIENTS TO FLUX FUNCTIONS --- + # Wrap flux functions to reduce them by reflection coefficient + def apply_reflection(flux_func, reflection_coeff): + """Wrapper to apply reflection coefficient to a flux function""" + def reflected_flux(t): + return flux_func(t) * (1.0 - reflection_coeff) + return reflected_flux + + # Apply reflection to ion and atom fluxes + deuterium_ion_flux_reflected = apply_reflection(deuterium_ion_flux, ion_reflection) + tritium_ion_flux_reflected = apply_reflection(tritium_ion_flux, ion_reflection) + deuterium_atom_flux_reflected = apply_reflection(deuterium_atom_flux, atom_reflection) + tritium_atom_flux_reflected = apply_reflection(tritium_atom_flux, atom_reflection) + + # --- GEOMETRY AND MESH --- + L = bin.thickness # Domain length from bin + if mesh is not None: + # Use provided mesh from bins_meshes + vertices = mesh + print(f"Using provided mesh for bin {bin.bin_number}: {len(vertices)} vertices") + else: + # Generate mesh using default parameters + vertices = graded_vertices(L=L, h0=1e-10, r=1.05) + print(f"Generated mesh for bin {bin.bin_number}: {len(vertices)} vertices") + my_model.mesh = F.Mesh1D(vertices) + + # --- MATERIAL --- + material = bin.material + print(f"\n=== FESTIM Material (Bin {bin.bin_number}) ===") + print(f" name={material.name}, D_0={material.D0:.4e} m²/s, E_D={material.E_D:.4f} eV") + festim_material = F.Material( + D_0=material.D0, + E_D=material.E_D, + name=material.name, + ) + + # --- SUBDOMAINS --- + volume_subdomain = F.VolumeSubdomain1D(id=1, borders=[0, L], material=festim_material) + inlet = F.SurfaceSubdomain1D(id=1, x=0) + outlet = F.SurfaceSubdomain1D(id=2, x=L) + my_model.subdomains = [volume_subdomain, inlet, outlet] + + # --- SPECIES, TRAPS, AND REACTIONS --- + species_list, reactions_list = create_species_and_traps( + material, volume_subdomain + ) + my_model.species = species_list + my_model.reactions = reactions_list + + # --- TEMPERATURE --- + my_model.temperature = temperature + + # --- CALCULATE WEIGHTED AVERAGE IMPLANTATION RANGE (time-dependent) --- + # For analytical approximation BC, use weighted average of ion and atom ranges + # Weight by their respective steady-state flux values + # This varies with time if different pulses have different flux values + + def get_weighted_implantation_ranges(t): + """Calculate weighted implantation ranges for D and T at time t + + Returns: + Tuple of (weighted_range_d, weighted_range_t) + """ + weighted_range_d = ion_range # default fallback + weighted_range_t = ion_range # default fallback + + if occurrences and len(occurrences) > 0: + # Find the occurrence that contains time t + for occurrence in occurrences: + if occurrence['start'] <= t < occurrence['end']: + # Extract steady-state flux values for this pulse + d_ion_flux_ss = occurrence['D_ion'] + d_atom_flux_ss = occurrence['D_atom'] + t_ion_flux_ss = occurrence['T_ion'] + t_atom_flux_ss = occurrence['T_atom'] + + # Calculate weighted average range for deuterium + d_total_flux = d_ion_flux_ss + d_atom_flux_ss + if d_total_flux > 0: + weighted_range_d = (d_atom_flux_ss * atom_range + d_ion_flux_ss * ion_range) / d_total_flux + else: + weighted_range_d = ion_range + + # Calculate weighted average range for tritium + t_total_flux = t_ion_flux_ss + t_atom_flux_ss + if t_total_flux > 0: + weighted_range_t = (t_atom_flux_ss * atom_range + t_ion_flux_ss * ion_range) / t_total_flux + else: + weighted_range_t = ion_range + + break + + return weighted_range_d, weighted_range_t + + # --- BOUNDARY CONDITIONS --- + # Total flux functions (using reflected fluxes) + def Gamma_D_total(t): + return deuterium_ion_flux_reflected(t) + deuterium_atom_flux_reflected(t) + + def Gamma_T_total(t): + return tritium_ion_flux_reflected(t) + tritium_atom_flux_reflected(t) + + # Get BC type from bin configuration + bc_plasma_facing = bin.bin_configuration.bc_plasma_facing_surface + bc_rear = bin.bin_configuration.bc_rear_surface + + # Get species objects once for use in all BC branches + mobile_D = next(s for s in my_model.species if s.name == "D") + mobile_T = next(s for s in my_model.species if s.name == "T") + + boundary_conditions = [] + + # --- Plasma-facing surface (inlet) BC choices --- + # Options supported: + # - "Robin - Surf. Rec. + Implantation" + # - "Dirichlet - 0 concentration + Implantation" + # - "Dirichlet - Analyttical implantation approximation" + if bc_plasma_facing == "Robin - Surf. Rec. + Implantation": + # Use volumetric implantation sources (gaussian) + Dirichlet 0 at surface + # Use ion parameters for ions, atom parameters for atoms + distribution_ion = gaussian_implantation_ufl(ion_range, ion_width, thickness=L) + distribution_atom = gaussian_implantation_ufl(atom_range, atom_width, thickness=L) + + my_model.sources = [ + F.ParticleSource(value=lambda x, t: deuterium_ion_flux_reflected(t) * distribution_ion(x), volume=volume_subdomain, species=mobile_D), + F.ParticleSource(value=lambda x, t: deuterium_atom_flux_reflected(t) * distribution_atom(x), volume=volume_subdomain, species=mobile_D), + F.ParticleSource(value=lambda x, t: tritium_ion_flux_reflected(t) * distribution_ion(x), volume=volume_subdomain, species=mobile_T), + F.ParticleSource(value=lambda x, t: tritium_atom_flux_reflected(t) * distribution_atom(x), volume=volume_subdomain, species=mobile_T), + ] + + # --- Surface recombination (Robin-like) --- + # Read recombination parameters from material if available, otherwise use defaults + k_r0 = getattr(material, "K_R", 7.94e-17) + E_kr = getattr(material, "E_R", -2.0) + k_d0 = getattr(material, "k_d0", 0.0) + E_kd = getattr(material, "E_kd", 0.0) + + surface_reaction_dd = F.SurfaceReactionBC( + reactant=[mobile_D, mobile_D], + gas_pressure=0, + k_r0=k_r0, + E_kr=E_kr, + k_d0=k_d0, + E_kd=E_kd, + subdomain=inlet, + ) + + surface_reaction_tt = F.SurfaceReactionBC( + reactant=[mobile_T, mobile_T], + gas_pressure=0, + k_r0=k_r0, + E_kr=E_kr, + k_d0=k_d0, + E_kd=E_kd, + subdomain=inlet, + ) + + surface_reaction_dt = F.SurfaceReactionBC( + reactant=[mobile_D, mobile_T], + gas_pressure=0, + k_r0=k_r0, + E_kr=E_kr, + k_d0=k_d0, + E_kd=E_kd, + subdomain=inlet, + ) + + # Add surface reactions to BCs (keep fixed concentration too to mirror legacy) + boundary_conditions.extend([ + surface_reaction_dd, + surface_reaction_dt, + surface_reaction_tt + ]) + + elif bc_plasma_facing == "Dirichlet - 0 concentration + Implantation": + # Volumetric implantation + zero Dirichlet at surface + distribution_ion = gaussian_implantation_ufl(ion_range, ion_width, thickness=L) + distribution_atom = gaussian_implantation_ufl(atom_range, atom_width, thickness=L) + + my_model.sources = [ + F.ParticleSource(value=lambda x, t: deuterium_ion_flux_reflected(t) * distribution_ion(x), volume=volume_subdomain, species=mobile_D), + F.ParticleSource(value=lambda x, t: deuterium_atom_flux_reflected(t) * distribution_atom(x), volume=volume_subdomain, species=mobile_D), + F.ParticleSource(value=lambda x, t: tritium_ion_flux_reflected(t) * distribution_ion(x), volume=volume_subdomain, species=mobile_T), + F.ParticleSource(value=lambda x, t: tritium_atom_flux_reflected(t) * distribution_atom(x), volume=volume_subdomain, species=mobile_T), + ] + boundary_conditions.extend([ + F.FixedConcentrationBC(subdomain=inlet, value=0.0, species="D"), + F.FixedConcentrationBC(subdomain=inlet, value=0.0, species="T"), + ]) + + elif bc_plasma_facing == "Dirichlet - Analyttical implantation approximation": + # Use analytical surface concentration approximation (Dirichlet) + # Use separate weighted ranges for D and T based on their respective flux ratios + def Gamma_tot(t): + return float(Gamma_D_total(t)) + float(Gamma_T_total(t)) + + k_r0 = getattr(material, "K_R") + e_r = getattr(material, "E_R", 0.0) + + + def c_sD_time_dependent(t): + weighted_range_d, _ = get_weighted_implantation_ranges(t) + return make_surface_concentration_time_function( + temperature, Gamma_D_total, material.D0, material.E_D, weighted_range_d, + flux_tot_fun=Gamma_tot, Kr0=k_r0, E_Kr=e_r, surface_x=0.0 + )(t) + + def c_sT_time_dependent(t): + _, weighted_range_t = get_weighted_implantation_ranges(t) + return make_surface_concentration_time_function( + temperature, Gamma_T_total, material.D0, material.E_D, weighted_range_t, + flux_tot_fun=Gamma_tot, Kr0=k_r0, E_Kr=e_r, surface_x=0.0 + )(t) + boundary_conditions.extend([ + F.FixedConcentrationBC(subdomain=inlet, value=c_sD_time_dependent, species="D"), + F.FixedConcentrationBC(subdomain=inlet, value=c_sT_time_dependent, species="T"), + ]) + + else: + raise ValueError(f"Unsupported plasma-facing BC: {bc_plasma_facing!r}") + + # --- Rear surface (outlet) BC choices --- + if bc_rear == "Dirichlet - 0 concentration": + boundary_conditions.extend([ + F.FixedConcentrationBC(subdomain=outlet, value=0.0, species="D"), + F.FixedConcentrationBC(subdomain=outlet, value=0.0, species="T"), + ]) + elif bc_rear == "Neumann - no flux": + # Explicit Neumann / no-flux at outlet + boundary_conditions.extend([ + #F.ParticleFluxBC(subdomain=outlet, value=0.0, species="D"), + #F.ParticleFluxBC(subdomain=outlet, value=0.0, species="T"), + ]) + elif bc_rear == "Robin - Surf. Rec.": + # Explicit Surface Recombination at outlet (same parameters as inlet for simplicity) + # --- Surface recombination (Robin-like) --- + # Read recombination parameters from material if available, otherwise use defaults + k_r0 = getattr(material, "K_R", 7.94e-17) + E_kr = getattr(material, "E_R", -2.0) + k_d0 = getattr(material, "k_d0", 0.0) + E_kd = getattr(material, "E_kd", 0.0) + + surface_reaction_dd = F.SurfaceReactionBC( + reactant=[mobile_D, mobile_D], + gas_pressure=0, + k_r0=k_r0, + E_kr=E_kr, + k_d0=k_d0, + E_kd=E_kd, + subdomain=outlet, + ) + + surface_reaction_tt = F.SurfaceReactionBC( + reactant=[mobile_T, mobile_T], + gas_pressure=0, + k_r0=k_r0, + E_kr=E_kr, + k_d0=k_d0, + E_kd=E_kd, + subdomain=outlet, + ) + + surface_reaction_dt = F.SurfaceReactionBC( + reactant=[mobile_D, mobile_T], + gas_pressure=0, + k_r0=k_r0, + E_kr=E_kr, + k_d0=k_d0, + E_kd=E_kd, + subdomain=outlet, + ) + boundary_conditions.extend([ + surface_reaction_dd, + surface_reaction_dt, + surface_reaction_tt + ]) + else: + raise ValueError(f"Unsupported rear BC: {bc_rear!r}") + + my_model.boundary_conditions = boundary_conditions + + # --- DEBUG: print a concise summary of the boundary conditions and sources --- + def _summarize_bc(bc): + try: + if isinstance(bc, F.SurfaceReactionBC): + reactant = getattr(bc, "reactant", None) + names = [r.name if hasattr(r, "name") else str(r) for r in reactant] if reactant else [] + return f"SurfaceReactionBC reactants={names} k_r0={getattr(bc, 'k_r0', None)}" + if isinstance(bc, F.FixedConcentrationBC): + return f"FixedConcentrationBC species={getattr(bc, 'species', None)} value={getattr(bc, 'value', None)}" + if isinstance(bc, F.ParticleFluxBC): + return f"ParticleFluxBC species={getattr(bc, 'species', None)} value={getattr(bc, 'value', None)}" + except Exception: + pass + # Fallback representation + return repr(bc) + + try: + print(f"=== DEBUG: Selected BCs -> plasma_facing={bc_plasma_facing!r}, rear={bc_rear!r} ===") + for i, bc in enumerate(boundary_conditions): + try: + summary = _summarize_bc(bc) + except Exception as e: + summary = f"" + print(f"BC[{i}]: {summary}") + + # Print sources if present + sources = getattr(my_model, "sources", None) + if sources: + print(f"=== DEBUG: Found {len(sources)} volumetric source(s) ===") + for j, src in enumerate(sources): + try: + sps = [s.name for s in getattr(src, 'species', [])] + except Exception: + sps = getattr(src, 'species', None) + print(f"Source[{j}]: species={sps} volume={getattr(src,'volume',None)}") + else: + print("=== DEBUG: No volumetric sources defined ===") + except Exception as e: + print(f"=== DEBUG: Failed to print BC summary: {e} ===") + + # --- EXPORTS --- + if exports: + my_model.exports = [ + F.VTXSpeciesExport( + filename=f"{folder}/checkpoint_sim_{bin.sim_id}_flux_{bin.flux_id}.bp", + field=my_model.species, + subdomain=volume_subdomain, + checkpoint=True, + times=[final_time], + ), + ] + else: + my_model.exports = [] + + # --- QUANTITIES TO TRACK --- + quantities = {} + + # Use milestones as profile export times if provided, otherwise calculate from occurrences + profile_export_times = None + if milestones is not None: + # Use milestones directly as profile export times + profile_export_times = sorted(milestones) + elif occurrences and len(occurrences) > 0: + # Fallback: compute times from occurrences (3 per pulse) + profile_export_times = [] + for occ in occurrences: + pulse = occ['pulse'] + start_time = occ['start'] + ramp_up = pulse.ramp_up + steady_state = pulse.steady_state + pulse_duration = pulse.total_duration + + # Export at: start of ramp-up, middle of steady-state, end of ramp-down + t1 = start_time # Start of ramp-up + t2 = start_time + ramp_up + steady_state / 2 # Middle of steady-state + t3 = start_time + pulse_duration # End of ramp-down (end of pulse) + + profile_export_times.extend([t1, t2, t3]) + + # Add total volume for each species + for species in my_model.species: + quantity = F.TotalVolume(field=species, volume=volume_subdomain) + my_model.exports.append(quantity) + quantities[species.name] = quantity + + if profile_export: + if profile_export_times: + profile = F.Profile1DExport( + field=species, + subdomain=volume_subdomain, + times=list(profile_export_times), # copy: FESTIM pops matched times + ) + else: + profile = F.Profile1DExport( + field=species, + subdomain=volume_subdomain, + ) + my_model.exports.append(profile) + quantities[f"{species.name}_profile"] = profile + + # Add surface flux for mobile species at inlet and outlet + if species.mobile: + inlet_flux = F.SurfaceFlux(field=species, surface=inlet) + my_model.exports.append(inlet_flux) + quantities[f"{species.name}_inlet_flux"] = inlet_flux + + outlet_flux = F.SurfaceFlux(field=species, surface=outlet) + my_model.exports.append(outlet_flux) + quantities[f"{species.name}_outlet_flux"] = outlet_flux + + # --- SETTINGS --- + bin_config = bin.bin_configuration + my_model.settings = CustomSettings( + atol=bin_config.atol, + rtol=bin_config.rtol, + max_iterations=1000, + final_time=final_time, + ) + + # Smaller initial stepsize for boron (thinner layers, stiffer problem) + stepsize_init = 1e-4 if bin.material.name == "B" else 1e-3 + my_model.settings.stepsize = Stepsize(initial_value=stepsize_init) + print(f"[model] Initial stepsize: {stepsize_init}") + + # Use CG elements for traps (instead of FESTIM default DG) + #my_model._element_for_traps = "CG" + print(f"[model] Trap element type: {my_model._element_for_traps}") + + return my_model, quantities + + +def make_model_with_scenario( + bin, + scenario: Scenario, + plasma_data_handling: PlasmaDataHandling, + coolant_temp: float, + mesh=None, + exports: bool = False, + profile_export: bool = False, + milestones: list = None, + folder: str = None, + temperature_model_overrides: Optional[Dict[str, Callable]] = None, +) -> Tuple[F.HydrogenTransportProblem, Dict[str, F.TotalVolume]]: + """ + Create a FESTIM model using scenario-based flux and temperature functions. + + Args: + bin: Bin object + scenario: Scenario with pulse sequence + plasma_data_handling: PlasmaDataHandling for flux/heat data + coolant_temp: Coolant temperature (K) + mesh: Optional pre-computed mesh vertices array. If None, mesh is generated from bin.thickness + exports: Whether to export detailed outputs + profile_export: Whether to export 1D concentration profiles (default: True) + + Returns: + Tuple of (festim_model, quantities_dict) + """ + + # Create temperature function from scenario + temperature_function = make_temperature_function( + scenario=scenario, + plasma_data_handling=plasma_data_handling, + bin=bin, + coolant_temp=coolant_temp, + temperature_model_overrides=temperature_model_overrides, + ) + + # Check BC type to decide which flux function type to use + bc_plasma_facing = bin.bin_configuration.bc_plasma_facing_surface + + # Always compute occurrences for steady-state flux values (used for weighted implantation range) + occurrences = compute_flux_values(scenario, plasma_data_handling, bin) + + # For implantation BCs, use UFL flux expressions (required for ParticleSource) + if bc_plasma_facing in ("Robin - Surf. Rec. + Implantation", "Dirichlet - 0 concentration + Implantation"): + # Use UFL flux expressions for ParticleSource compatibility + deuterium_ion_flux, deuterium_atom_flux, tritium_ion_flux, tritium_atom_flux = build_ufl_flux_expression(occurrences) + else: + # For analytical Dirichlet BC (no volumetric sources), plain callables are fine + deuterium_ion_flux = make_particle_flux_function( + scenario=scenario, + plasma_data_handling=plasma_data_handling, + bin=bin, + ion=True, + tritium=False, + ) + + tritium_ion_flux = make_particle_flux_function( + scenario=scenario, + plasma_data_handling=plasma_data_handling, + bin=bin, + ion=True, + tritium=True, + ) + + deuterium_atom_flux = make_particle_flux_function( + scenario=scenario, + plasma_data_handling=plasma_data_handling, + bin=bin, + ion=False, + tritium=False, + ) + + tritium_atom_flux = make_particle_flux_function( + scenario=scenario, + plasma_data_handling=plasma_data_handling, + bin=bin, + ion=False, + tritium=True, + ) + + # Create model + output_folder = folder if folder is not None else f"results_bin_{bin.bin_number}" + return make_dynamic_mb_model( + bin=bin, + temperature=temperature_function, + deuterium_ion_flux=deuterium_ion_flux, + tritium_ion_flux=tritium_ion_flux, + deuterium_atom_flux=deuterium_atom_flux, + tritium_atom_flux=tritium_atom_flux, + final_time=scenario.get_maximum_time(), + folder=output_folder, + mesh=mesh, + occurrences=occurrences, + exports=exports, + profile_export=profile_export, + milestones=milestones, + ) + +#Helper functions block to create temperature profiles and flux functions + +# calculate how the rear temperature of the W layer evolves with the surface temperature +# data from E.A. Hodille et al 2021 Nucl. Fusion 61 126003 10.1088/1741-4326/ac2abc (Table I) +heat_fluxes_hodille = [10e6, 5e6, 1e6] # W/m2 +T_rears_hodille = [552, 436, 347] # K + +slope_T_rear, intercept, r_value, p_value, std_err = scipy.stats.linregress( + heat_fluxes_hodille, T_rears_hodille +) + + +def tungsten_slab_temperature(q_front, D_W, D_Cu, T_cool): + """ + Calculate the temperature of the front and back surfaces of a tungsten slab + with heat flux applied to the front surface and cooling via a copper slab. + From T. Wauters + + Parameters: + q_front (float): Heat flux at the front surface of the tungsten slab (W/m^2). + D_W (float): Thickness of the tungsten slab (m). + D_Cu (float): Thickness of the copper slab (m). + T_cool (float): Cooling water temperature (K). + + Returns: + tuple: (T_w_surf, T_w_interface) where: + - T_w_surf is the front surface temperature of tungsten (K). + - T_w_interface is the tungsten-copper interface temperature (K). + """ + # Thermal conductivities (W/m·K) #TODO: add citations + k_W = 170 # Tungsten thermal conductivity + k_Cu = 400 # Copper thermal conductivity + # Heat transfer coefficient from copper to water (W/m^2·K) + h_Cu_water = 10_000 # Typical value for water cooling + + w_diffusivity = ( + htm.diffusivities.filter(material="tungsten") + .filter(isotope="h") + .filter(author="holzner") + ) + + # Temperature drop across tungsten slab + delta_T_W = (q_front * D_W) / k_W + # Temperature drop across copper slab + delta_T_Cu = (q_front * D_Cu) / k_Cu + # Temperature drop at the copper-water interface + delta_T_interface = q_front / h_Cu_water + + # Compute temperatures + T_w_interface = T_cool + delta_T_interface + delta_T_Cu + T_w_surf = T_w_interface + delta_T_W + + return T_w_surf, T_w_interface + + +def calculate_temperature_W( + x: float | NDArray, + heat_flux: float, + coolant_temp: float, + thickness: float, + copper_thickness: float | None, +) -> float | NDArray: + """Calculates the temperature in the W layer based on coolant temperature and heat flux + + Reference: + - Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020) 10.1038/s41598-020-74844-w + - E.A. Hodille et al 2021 Nucl. Fusion 61 126003 10.1088/1741-4326/ac2abc + + Args: + x: position in m + heat_flux: heat_flux in W/m2 + coolant_temp: coolant temperature in K + thickness: thickness of the W layer in m + + Returns: + temperature in K + """ + + # T_surface and T_rear calculations taken from tungsten/copper calculations + # provided by T. Wauters + if copper_thickness is not None and copper_thickness > 0: + T_surface, T_rear = tungsten_slab_temperature( + q_front=heat_flux, D_W=thickness, D_Cu=copper_thickness, T_cool=coolant_temp + ) + else: + # the evolution of T surface is taken from Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020). + # https://doi.org/10.1038/s41598-020-74844-w + T_surface = 1.1e-4 * heat_flux + coolant_temp + T_rear = slope_T_rear * heat_flux + coolant_temp + + a = (T_rear - T_surface) / thickness + b = T_surface + return a * x + b + +def calculate_temperature_SS(x: float | NDArray, heat_flux: float, coolant_temp: float, thickness: float) -> float | NDArray: + """ + Calculates the temperature in the SS layer based on coolant temperature and heat flux. + The temperature is assumed to vary linearly from the plasma-facing surface to the rear surface. + + It takes into account that at 0.35MW/m2, the PFS of SS is at 250C and the backside at 175C. + Then we scale linearly those values with the heat flux, taking into account the base temperature of 70C + + Args: + x: position in m + heat_flux: heat flux in W/m2 + coolant_temp: coolant temperature in K + thickness: thickness of the SS layer in m + + Returns: + temperature in K (float if x is float, NDArray if x is NDArray) + """ + # the evolution of T surface is taken from Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020). + # https://doi.org/10.1038/s41598-020-74844-w + T_plasmasurf_SS = heat_flux/3.5E5 * (250-70) + coolant_temp + T_rearsurf_SS = heat_flux/3.5E5 * (175-70) + coolant_temp + + # Linear interpolation between plasma-facing surface and rear surface + a = (T_rearsurf_SS - T_plasmasurf_SS) / thickness + b = T_plasmasurf_SS + return a * x + b + + +def calculate_temperature_B(heat_flux: float, coolant_temp: float) -> float: + """ + Calculates the temperature in the boron layer based on coolant temperature and heat flux. + The temperature is assumed to be homogeneous in the B layer and is calculated based on the + surface temperature of the W layer. + + T_B = R_c * q + T_surface_W + + where + - R_c is the thermal contact resistance of the layer in m2 K/W + - q is the heat flux in W/m2 + - T_surface_W is the surface temperature of the W layer in K + + References: + - Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020) 10.1038/s41598-020-74844-w + - Jae-Sun Park et al 2023 Nucl. Fusion 63 076027 10.1088/1741-4326/acd9d9 + + Args: + heat_flux: heat flux in W/m2 + coolant_temp: coolant temperature in K + + Returns: + temperature in K + """ + # the evolution of T surface is taken from Delaporte-Mathurin et al. Sci Rep 10, 17798 (2020). + # https://doi.org/10.1038/s41598-020-74844-w + T_surf_tungsten = 1.1e-4 * heat_flux + coolant_temp + R_c_jet = 5e-4 # m2 K/W calculated from JET-ILW (JPN#98297) + return R_c_jet * heat_flux + T_surf_tungsten + + +def make_temperature_function( + scenario: Scenario, + plasma_data_handling: PlasmaDataHandling, + bin, # Accept any bin type (SubBin, DivBin, or CSVBin) + coolant_temp: float, + temperature_model_overrides: Optional[Dict[str, Callable]] = None, +) -> Callable[[NDArray, float], NDArray]: + """Returns a function that calculates the temperature of the bin based on time and position. + + Args: + scenario: the Scenario object containing the pulses + plasma_data_handling: the object containing the plasma data + bin: the bin/subbin to get the temperature function for + coolant_temp: the coolant temperature in K + + Returns: + a callable of x, t returning the temperature in K + """ + + custom_models = {} + if temperature_model_overrides: + custom_models = { + str(material).upper(): fn + for material, fn in temperature_model_overrides.items() + if callable(fn) + } + + def _evaluate_custom_temperature_model(model_fn: Callable, x_position, heat_flux: float, pulse, t_rel: float): + """ + Evaluate a custom model with keyword-based compatibility. + + The callable can accept any subset of these keywords: + x, heat_flux, coolant_temp, thickness, copper_thickness, + bin, pulse, t, scenario, plasma_data_handling. + """ + available_kwargs = { + "x": x_position, + "heat_flux": heat_flux, + "coolant_temp": coolant_temp, + "thickness": bin.thickness, + "copper_thickness": getattr(bin, "copper_thickness", getattr(bin, "cu_thickness", None)), + "bin": bin, + "pulse": pulse, + "t": t_rel, + "scenario": scenario, + "plasma_data_handling": plasma_data_handling, + } + + signature = inspect.signature(model_fn) + accepts_var_kwargs = any( + parameter.kind == inspect.Parameter.VAR_KEYWORD + for parameter in signature.parameters.values() + ) + + if accepts_var_kwargs: + return model_fn(**available_kwargs) + + filtered_kwargs = { + name: value + for name, value in available_kwargs.items() + if name in signature.parameters + } + return model_fn(**filtered_kwargs) + + def T_function(x: NDArray, t: float) -> NDArray: + # Handle FESTIM 2.0 passing dolfinx.Constant instead of float + if hasattr(t, 'value'): + t = float(t.value) + elif not isinstance(t, (float, int)): + raise TypeError(f"t should be a float or have a .value attribute, got {type(t)}") + + # get the pulse and time relative to the start of the pulse + pulse = scenario.get_pulse(t) + t_rel = t - scenario.get_time_start_current_pulse(t) + relative_time_within_pulse = t_rel % pulse.total_duration + + if pulse.pulse_type in ("BAKE", "Bake+GDC"): + if scenario.baking_temp is None: + raise ValueError( + f"{pulse.pulse_type} pulse encountered but scenario.baking_temp is None. " + "Set baking_temp in the Scenario constructor." + ) + T_value = periodic_pulse_function( + relative_time_within_pulse, + pulse=pulse, + value=scenario.baking_temp, + value_off=coolant_temp, + ) + if not hasattr(T_function, '_bake_logged'): + print(f"[{pulse.pulse_type}] baking_temp={scenario.baking_temp} K, coolant_temp={coolant_temp} K") + T_function._bake_logged = True + value = np.full_like(x[0], T_value) + + else: + heat_flux = plasma_data_handling.get_heat( + pulse, bin, relative_time_within_pulse + ) + # Handle both string materials and Material objects + material_name = bin.material.name if hasattr(bin.material, 'name') else bin.material + material_name_key = str(material_name).upper() + + if material_name_key in custom_models: + custom_value = _evaluate_custom_temperature_model( + custom_models[material_name_key], + x_position=x[0], + heat_flux=heat_flux, + pulse=pulse, + t_rel=relative_time_within_pulse, + ) + if np.isscalar(custom_value): + value = np.full_like(x[0], float(custom_value)) + else: + value = np.asarray(custom_value) + elif material_name == "W": + value = calculate_temperature_W( + x[0], heat_flux, coolant_temp, bin.thickness, bin.copper_thickness + ) + elif material_name == "SS": + value = calculate_temperature_SS( + x[0], heat_flux, coolant_temp, bin.thickness + ) + elif material_name == "B": + T_value = calculate_temperature_B(heat_flux, coolant_temp) + value = np.full_like(x[0], T_value) + else: + raise ValueError(f"Unsupported material: {bin.material}") + + return value + + return T_function + + +def make_particle_flux_function( + scenario: Scenario, + plasma_data_handling: PlasmaDataHandling, + bin, # Accept any bin type (SubBin, DivBin, or CSVBin) + ion: bool, + tritium: bool, +) -> Callable[[float], float]: + """Returns a function that calculates the particle flux based on time. + + Args: + scenario: the Scenario object containing the pulses + plasma_data_handling: the object containing the plasma data + bin: the bin/subbin to get the temperature function for + ion: whether to get the ion flux + tritium: whether to get the tritium flux + + Returns: + a callable of t returning the **incident** particle flux in m^-2 s^-1 + """ + + def particle_flux_function(t: float) -> float: + # Handle FESTIM 2.0 passing dolfinx.Constant instead of float + if hasattr(t, 'value'): + t = float(t.value) + elif not isinstance(t, (float, int)): + raise TypeError(f"t should be a float or have a .value attribute, got {type(t)}") + + # get the pulse and time relative to the start of the pulse + pulse = scenario.get_pulse(t) + relative_time = t - scenario.get_time_start_current_pulse(t) + relative_time_within_pulse = relative_time % pulse.total_duration + + # get the incident particle flux + incident_hydrogen_particle_flux = plasma_data_handling.get_particle_flux( + pulse=pulse, + bin=bin, + t_rel=relative_time_within_pulse, + ion=ion, + ) + + # if tritium is requested, multiply by tritium fraction + if tritium: + value = incident_hydrogen_particle_flux * pulse.tritium_fraction + else: + value = incident_hydrogen_particle_flux * (1 - pulse.tritium_fraction) + + return value + + return particle_flux_function + + +def compute_flux_values(scenario, plasma_data_handling, bin_): + """ + Compute steady-state flux values for each pulse occurrence using get_particle_flux + at the midpoint of the steady-state region. + Returns a list of dicts with D_ion, D_atom, T_ion, T_atom. + """ + occurrences = [] + current_time = 0.0 + for pulse in scenario.pulses: + for _ in range(pulse.nb_pulses): + # Pick a time inside steady state + # For Bake+GDC: use GDC sub-timing to find the midpoint + if pulse.pulse_type == "Bake+GDC" and pulse.gdc_steady_state is not None: + t_rel = pulse.gdc_ramp_up + pulse.gdc_steady_state / 2 + elif pulse.steady_state > 0: + t_rel = pulse.ramp_up + pulse.steady_state / 2 + else: + t_rel = pulse.total_duration / 2 # fallback if no steady state + + # Compute hydrogen flux for ion and atom + flux_ion = plasma_data_handling.get_particle_flux(pulse, bin_, t_rel, ion=True) + flux_atom = plasma_data_handling.get_particle_flux(pulse, bin_, t_rel, ion=False) + + # Apply tritium fraction + T_ion = flux_ion * pulse.tritium_fraction + D_ion = flux_ion * (1 - pulse.tritium_fraction) + T_atom = flux_atom * pulse.tritium_fraction + D_atom = flux_atom * (1 - pulse.tritium_fraction) + + occurrences.append({ + 'start': current_time, + 'end': current_time + pulse.total_duration, + 'pulse': pulse, + 'D_ion': D_ion, + 'D_atom': D_atom, + 'T_ion': T_ion, + 'T_atom': T_atom + }) + current_time += pulse.total_duration + return occurrences + + +def build_ufl_flux_expression(occurrences, value_off=0.0): + """ + Returns four functions: + (D_ion_fn, D_atom_fn, T_ion_fn, T_atom_fn) + Each function accepts a UFL time variable `t` and returns the corresponding UFL expression. + """ + + def make_flux_fn(flux_key): + def flux_builder(t): + expr = value_off + for occ in occurrences: + p = occ['pulse'] + start, end = occ['start'], occ['end'] + + in_window = And(ge(t, start), lt(t, end)) + t_rel = t - start + + # For Bake+GDC: use GDC sub-timing for flux ramp profile + if p.pulse_type == "Bake+GDC" and getattr(p, 'gdc_ramp_up', None) is not None: + ru = p.gdc_ramp_up + ss = p.gdc_steady_state + rd = p.gdc_ramp_down + else: + ru = p.ramp_up + ss = p.steady_state + rd = p.ramp_down + + ramp_up_cond = lt(t_rel, ru) + steady_cond = And(ge(t_rel, ru), lt(t_rel, ru + ss)) + + # Ramp-up and ramp-down expressions + ramp_up_expr = (occ[flux_key] - value_off) / ru * t_rel + value_off if ru > 0 else occ[flux_key] + ramp_down_raw = occ[flux_key] - (occ[flux_key] - value_off) / rd * (t_rel - (ru + ss)) if rd > 0 else occ[flux_key] + ramp_down_expr = conditional(ge(ramp_down_raw, value_off), ramp_down_raw, value_off) + + pulse_flux = conditional(ramp_up_cond, ramp_up_expr, + conditional(steady_cond, occ[flux_key], ramp_down_expr)) + + expr += conditional(in_window, pulse_flux, 0.0) + return expr + return flux_builder + + # Return four callable builders + return ( + make_flux_fn('D_ion'), + make_flux_fn('D_atom'), + make_flux_fn('T_ion'), + make_flux_fn('T_atom'), + ) diff --git a/src/hisp/h_transport_class.py b/src/hisp/h_transport_class.py index bc952efd..1d502b4a 100644 --- a/src/hisp/h_transport_class.py +++ b/src/hisp/h_transport_class.py @@ -1,34 +1,64 @@ +"""Custom HydrogenTransportProblem subclass for HISP. + +The only reason this subclass exists is that HISP's temperature functions +are numpy-based callables ``T(x, t)`` (where *x* is a numpy coordinate +array), whereas FESTIM 2.0's ``define_temperature`` expects the callable +to accept UFL ``SpatialCoordinate`` objects and return a UFL expression. + +The overrides here: + +1. ``define_temperature`` – uses ``fem.Function.interpolate(callable)`` + instead of building a ``fem.Expression`` from a UFL expression. +2. ``update_time_dependent_values`` – re-interpolates the numpy-based + temperature at each timestep, and adds a guard against updating past + ``final_time`` (to avoid scenario overshoot). + +Everything else (exports, ``post_processing``, ``Profile1DExport`` timing, +etc.) is handled by FESTIM 2.0 natively. +""" + import festim as F -import dolfinx.fem as fem -import ufl import basix +import dolfinx.fem as fem +import numpy as np class CustomProblem(F.HydrogenTransportProblem): + """Thin wrapper around :class:`festim.HydrogenTransportProblem`. + + Only ``define_temperature`` and ``update_time_dependent_values`` are + overridden; all export / post-processing logic is delegated to FESTIM. + """ + def define_temperature(self): - # check if temperature is None + """Define temperature field from a numpy-based callable ``T(x, t)``. + + FESTIM's base implementation builds a ``fem.Expression`` from a UFL + expression, but HISP temperature functions use numpy operations + (``np.full_like``, conditionals, etc.) that cannot run on UFL + ``SpatialCoordinate`` objects. We therefore use + ``fem.Function.interpolate(callable)`` instead. + """ if self.temperature is None: raise ValueError("the temperature attribute needs to be defined") - # if temperature is a float or int, create a fem.Constant - elif isinstance(self.temperature, (float, int)): + if isinstance(self.temperature, (float, int)): self.temperature_fenics = F.as_fenics_constant( self.temperature, self.mesh.mesh ) - # if temperature is a fem.Constant or function, pass it to temperature_fenics + elif isinstance(self.temperature, (fem.Constant, fem.Function)): self.temperature_fenics = self.temperature - # if temperature is callable, process accordingly elif callable(self.temperature): arguments = self.temperature.__code__.co_varnames if "t" in arguments and "x" not in arguments: if not isinstance(self.temperature(t=float(self.t)), (float, int)): raise ValueError( - f"self.temperature should return a float or an int, not {type(self.temperature(t=float(self.t)))} " + "self.temperature should return a float or an int, not " + f"{type(self.temperature(t=float(self.t)))}" ) - # only t is an argument self.temperature_fenics = F.as_fenics_constant( mesh=self.mesh.mesh, value=self.temperature(t=float(self.t)) ) @@ -44,35 +74,26 @@ def define_temperature(self): self.mesh.mesh, element_temperature ) self.temperature_fenics = fem.Function(function_space_temperature) - self.temperature_fenics.interpolate( + + # Store as a numpy-based callable so that the base-class + # update_time_dependent_values can call + # self.temperature_fenics.interpolate(self.temperature_expr) + # and dolfinx will pass numpy coordinates to the callable. + self.temperature_expr = ( lambda x: self.temperature(x, float(self.t)) ) + self.temperature_fenics.interpolate(self.temperature_expr) def update_time_dependent_values(self): + """Update all time-dependent values. - # this is for the last time step, don't update the fluxes to avoid overshoot in the scenario file + Adds a guard: once ``t > final_time`` we skip updates to avoid + overshoot in scenario-derived flux / temperature look-ups. + Then delegates to FESTIM's base implementation which handles + BCs, sources, reactions, and temperature (via + ``self.temperature_expr``). + """ if float(self.t) > self.settings.final_time: return - F.ProblemBase.update_time_dependent_values(self) - - if not self.temperature_time_dependent: - return - - t = float(self.t) - - if isinstance(self.temperature_fenics, fem.Constant): - self.temperature_fenics.value = self.temperature(t=t) - elif isinstance(self.temperature_fenics, fem.Function): - self.temperature_fenics.interpolate( - lambda x: self.temperature(x, float(self.t)) - ) - - for bc in self.boundary_conditions: - if isinstance(bc, (F.FixedConcentrationBC, F.ParticleFluxBC)): - if bc.temperature_dependent: - bc.update(t=t) - - for source in self.sources: - if source.temperature_dependent: - source.update(t=t) + super().update_time_dependent_values() diff --git a/src/hisp/helpers.py b/src/hisp/helpers.py index 1332f0af..1592b821 100644 --- a/src/hisp/helpers.py +++ b/src/hisp/helpers.py @@ -4,6 +4,9 @@ import numpy as np import numpy.typing as npt from hisp.scenario import Pulse +from dolfinx import fem +import math +from festim import XDMFExport class PulsedSource(F.ParticleSource): @@ -28,7 +31,7 @@ def time_dependent(self): return True def create_value_fenics(self, mesh, temperature, t: Constant): - self.flux_fenics = F.as_fenics_constant(self.flux(float(t)), mesh) + self.flux_fenics = F.as_fenics_constant(self.flux(t.value), mesh) x = ufl.SpatialCoordinate(mesh) self.distribution_fenics = self.distribution(x) @@ -37,8 +40,6 @@ def create_value_fenics(self, mesh, temperature, t: Constant): def update(self, t: float): self.flux_fenics.value = self.flux(t) - - # we override Stepsize to control the precision of milestones detection # TODO remove this when https://github.com/festim-dev/FESTIM/issues/933 is fixed class Stepsize(F.Stepsize): @@ -120,4 +121,133 @@ def periodic_pulse_function(current_time: float, pulse: Pulse, value, value_off= if lower_value >= value_off: return lower_value else: - return value_off \ No newline at end of file + return value_off + +class XDMFExportEveryDt(XDMFExport): + """ + Write to XDMF only if enough time has elapsed since the last write. + Uses min_dt1 for t <= switch and min_dt2 for t > switch. + + Parameters + ---------- + filename : str + Path for the XDMF file(s). + field : str or festim Field + What to export (same as base XDMFExport). + min_dt1 : float + Minimum time spacing before `switch` (inclusive). + min_dt2 : float + Minimum time spacing after `switch` (strictly greater). + switch : float + Time at which the cadence changes from min_dt1 to min_dt2. + atol : float, optional + Small tolerance to account for floating point accumulation. + Default is 0.0 (set e.g. 1e-12 if needed). + """ + def __init__(self, filename, field, min_dt1: float, min_dt2: float, switch: float, atol: float = 0.0): + super().__init__(filename, field) + self._min_dt1 = float(min_dt1) + self._min_dt2 = float(min_dt2) + self._switch = float(switch) + self._atol = float(atol) + self._last_t = None + + def _current_min_dt(self, t: float) -> float: + return self._min_dt2 if t > self._switch else self._min_dt1 + + def write(self, t: float): + t = float(t) + min_dt = self._current_min_dt(t) + + if (self._last_t is None) or ((t - self._last_t) >= (min_dt - self._atol)): + super().write(t) + self._last_t = t + +def gaussian_implantation_ufl(Rp, sigma, axis=0, thickness=None): + """ + Returns callable value(x, t) -> UFL expression S(x,t) [m^-3 s^-1] + - Rp, sigma in meters + - axis in {0,1,2} selects x[axis] as depth coordinate + - If thickness is not None (meters), renormalize over [0, thickness] to conserve J(t) + """ + inv_sqrt_2pi = 1.0 / np.sqrt(2.0 * np.pi) + if thickness is None: + C = 0.5 * (1.0 + erf(Rp / (sigma * sqrt(2.0)))) # Gaussian mass in [0, +inf) + C = max(C, 1e-12) # numerical safeguard + norm = inv_sqrt_2pi / sigma + def value(x): + xi = x[axis] + z = (xi - Rp) / sigma + return norm * ufl.exp(-0.5 * z * z) + return value + else: + # Renormalize over [0, thickness] + from math import erf, sqrt + a = (0.0 - Rp) / (sigma * sqrt(2.0)) + b = (thickness - Rp) / (sigma * sqrt(2.0)) + C = max(0.5 * (erf(b) - erf(a)), 1e-12) # in-domain Gaussian mass + norm = (inv_sqrt_2pi / sigma) / C + def value(x): + xi = x[axis] + z = (xi - Rp) / sigma + return norm * ufl.exp(-0.5 * z * z) + return value + +def periodic_pulse_ufl(t, pulse, value, value_off=343.0): + """ + UFL symbolic version of periodic_pulse_function. + Args: + t: UFL expression (time) + pulse: Pulse object with ramp_up, steady_state, ramp_down, waiting + value: UFL expression or Constant (steady-state value) + value_off: float or UFL Constant (off value) + Returns: + UFL expression representing the piecewise ramp profile. + """ + + # Compute relative time within one pulse cycle (no modulo in UFL) + tau = t # Assume t is within [0, pulse.total_duration] for now + + # Conditions for each phase + within_up = ufl.lt(tau, pulse.ramp_up) + within_steady = ufl.And(ufl.ge(tau, pulse.ramp_up), + ufl.lt(tau, pulse.ramp_up + pulse.steady_state)) + within_down = ufl.And(ufl.ge(tau, pulse.ramp_up + pulse.steady_state), + ufl.lt(tau, pulse.ramp_up + pulse.steady_state + pulse.ramp_down)) + # Waiting phase: tau >= ramp_up + steady_state + ramp_down + + # Ramp-up: linear interpolation + up_val = (value - value_off) / pulse.ramp_up * tau + value_off + + # Ramp-down: linear decrease + down_val = value - (value - value_off) / pulse.ramp_down * (tau - (pulse.ramp_up + pulse.steady_state)) + down_val = ufl.conditional(ufl.ge(down_val, value_off), down_val, value_off) + + # Piecewise conditional chain + shape = ufl.conditional( + within_up, up_val, + ufl.conditional( + within_steady, value, + ufl.conditional(within_down, down_val, value_off) + ) + ) + + return shape + +def make_ufl_flux_function(scalar_flux_function): + """ + Convert a scalar flux function to a UFL expression function. + + Args: + scalar_flux_function: A function that takes time (float) and returns flux value (float) + + Returns: + A function that takes time (UFL expression) and returns UFL expression + """ + def ufl_flux(t): + # For time-dependent behavior, we need to create conditional expressions + # This is a simplified version - for complex time dependencies, + # you might need to sample at specific times and interpolate + return ufl.Constant(1.0) # Placeholder - this needs proper implementation + + return ufl_flux \ No newline at end of file diff --git a/src/hisp/model.py b/src/hisp/model.py deleted file mode 100644 index 2cde2fb1..00000000 --- a/src/hisp/model.py +++ /dev/null @@ -1,348 +0,0 @@ -from hisp.plamsa_data_handling import PlasmaDataHandling -from hisp.scenario import Scenario -from hisp.bin import Reactor, SubBin, DivBin -from hisp.helpers import periodic_step_function -from hisp.festim_models import ( - make_W_mb_model, - make_B_mb_model, - make_DFW_mb_model, - make_temperature_function, - make_particle_flux_function, -) - -import numpy as np -from typing import List - - -class Model: - """ - The main HISP model class. - Takes a reactor, a scenario, a plasma data handling object and runs the FESTIM model(s) for each reactor bin. - """ - - def __init__( - self, - reactor: Reactor, - scenario: Scenario, - plasma_data_handling: PlasmaDataHandling, - coolant_temp: float = 343.0, - ): - """ - Args: - reactor: The reactor to run the model for - scenario: The scenario to run the model for - plasma_data_handling: The plasma data handling object - coolant_temp: The coolant temperature (K) - """ - self.reactor = reactor - self.scenario = scenario - self.plasma_data_handling = plasma_data_handling - self.coolant_temp = coolant_temp - - def run_bin(self, bin: SubBin | DivBin): - """ - Runs the FESTIM model for the given bin. - - Args: - bin: The bin to run the model for - - Returns: - The model, the quantities - """ - # create the FESTIM model - my_model, quantities = self.which_model(bin) - - # add milestones for stepsize and adaptivity - milestones = self.make_milestones( - initial_stepsize_value=my_model.settings.stepsize.initial_value - ) - milestones.append(my_model.settings.final_time) - my_model.settings.stepsize.milestones = milestones - - # add adaptivity settings - my_model.settings.stepsize.growth_factor = 1.2 - my_model.settings.stepsize.cutback_factor = 0.9 - my_model.settings.stepsize.target_nb_iterations = 4 - - # add the stepsize cap function - if bin.material == "B": - my_model.settings.stepsize.max_stepsize = self.B_stepsize - else: - my_model.settings.stepsize.max_stepsize = self.max_stepsize - - # run the model - my_model.initialise() - my_model.run() - - return my_model, quantities - - # TODO implement this method - def run_all_bins(self): - pass - - def which_model(self, bin: SubBin | DivBin): - """Returns the correct model for the subbin. - - Args: - bin: The bin/subbin to get the model for - - Returns: - The model and the quantities to plot - """ - temperature_fuction = make_temperature_function( - scenario=self.scenario, - plasma_data_handling=self.plasma_data_handling, - bin=bin, - coolant_temp=self.coolant_temp, - ) - d_ion_incident_flux = make_particle_flux_function( - scenario=self.scenario, - plasma_data_handling=self.plasma_data_handling, - bin=bin, - ion=True, - tritium=False, - ) - tritium_ion_flux = make_particle_flux_function( - scenario=self.scenario, - plasma_data_handling=self.plasma_data_handling, - bin=bin, - ion=True, - tritium=True, - ) - deuterium_atom_flux = make_particle_flux_function( - scenario=self.scenario, - plasma_data_handling=self.plasma_data_handling, - bin=bin, - ion=False, - tritium=False, - ) - tritium_atom_flux = make_particle_flux_function( - scenario=self.scenario, - plasma_data_handling=self.plasma_data_handling, - bin=bin, - ion=False, - tritium=True, - ) - common_args = { - "deuterium_ion_flux": d_ion_incident_flux, - "tritium_ion_flux": tritium_ion_flux, - "deuterium_atom_flux": deuterium_atom_flux, - "tritium_atom_flux": tritium_atom_flux, - "final_time": self.scenario.get_maximum_time() - 1, - "temperature": temperature_fuction, - "L": bin.thickness, - } - - if isinstance(bin, DivBin): - parent_bin_index = bin.index - elif isinstance(bin, SubBin): - parent_bin_index = bin.parent_bin_index - - if bin.material == "W": - return make_W_mb_model( - **common_args, - custom_rtol=self.bake_rtol, - folder=f"mb{parent_bin_index}_{bin.mode}_results", - ) - elif bin.material == "B": - return make_B_mb_model( - **common_args, - custom_rtol=self.make_custom_rtol, - folder=f"mb{parent_bin_index}_{bin.mode}_results", - ) - elif bin.material == "SS": - return make_DFW_mb_model( - **common_args, - folder=f"mb{parent_bin_index}_dfw_results", - ) - else: - raise ValueError(f"Unknown material: {bin.material} for bin {bin.index}") - - def max_stepsize(self, t: float) -> float: - pulse = self.scenario.get_pulse(t) - relative_time = t - self.scenario.get_time_start_current_pulse(t) # Pulse() - if pulse.pulse_type == "RISP": - relative_time_within_sub_pulse = relative_time % pulse.total_duration - # RISP has a special treatment - time_real_risp_starts = ( - 100 # (s) relative time at which the real RISP starts - ) - if relative_time_within_sub_pulse < time_real_risp_starts - 11: - value = None # s - elif relative_time_within_sub_pulse < time_real_risp_starts + 160: - value = 1e-3 # s - # elif relative_time_within_sub_pulse < time_real_risp_starts + 1: - # value = 0.01 # s - # elif relative_time_within_sub_pulse < time_real_risp_starts + 50: - # value = 0.1 # s - else: - # NOTE this seems to have an influence on the accuracy of the calculation - value = 1 # s - else: - relative_time_within_sub_pulse = relative_time % pulse.total_duration - # the stepsize is 1/10 of the duration of the pulse - if pulse.pulse_type == "FP": - if relative_time_within_sub_pulse < pulse.duration_no_waiting: - value = 0.1 # s - else: - value = pulse.duration_no_waiting / 10 - else: - value = pulse.duration_no_waiting / 10 - return periodic_step_function( - relative_time, - period_on=pulse.duration_no_waiting, - period_total=pulse.total_duration, - value=value, - value_off=None, - ) - - def B_stepsize(self, t: float) -> float: - pulse = self.scenario.get_pulse(t) - relative_time = t - self.scenario.get_time_start_current_pulse(t) # Pulse() - if pulse.pulse_type == "RISP": - relative_time_within_sub_pulse = relative_time % pulse.total_duration - # RISP has a special treatment - time_real_risp_starts = ( - 100 # (s) relative time at which the real RISP starts - ) - if relative_time_within_sub_pulse < time_real_risp_starts - 5: - value = None # s - elif relative_time_within_sub_pulse < time_real_risp_starts + 160: - value = 1e-4 # s - else: - # NOTE this seems to have an influence on the accuracy of the calculation - value = 1 # s - else: - relative_time_within_sub_pulse = relative_time % pulse.total_duration - # the stepsize is 1/10 of the duration of the pulse - if pulse.pulse_type == "FP": - if relative_time_within_sub_pulse < pulse.duration_no_waiting: - value = 0.01 # s - else: - value = pulse.duration_no_waiting / 10 - elif pulse.pulse_type == "BAKE": - value = pulse.duration_no_waiting / 10 - else: - value = pulse.duration_no_waiting / 100 - return periodic_step_function( - relative_time, - period_on=pulse.duration_no_waiting, - period_total=pulse.total_duration, - value=value, - value_off=None, - ) - - def make_milestones(self, initial_stepsize_value: float) -> List[float]: - """ - Returns the milestones for the stepsize. - For each pulse, the milestones are the start - of the pulse, the start of the waiting period, - and the end of the pulse. - - Args: - initial_stepsize_value: the value of the stepsize at - the beginning of each pulse (s) - - Returns: - The milestones in seconds - """ - milestones = [] - current_time = 0 # initialise the current time (s) - - # loop over all pulses (or rather sequences of pulses) - for pulse in self.scenario.pulses: - - start_of_pulse = self.scenario.get_time_start_current_pulse(current_time) - - # loop over all "pulses" whithin the current pulse - for i in range(pulse.nb_pulses): - - # hack: a milestone right after to ensure the stepsize is small enough - milestones.append( - start_of_pulse + pulse.total_duration * i + initial_stepsize_value - ) - - if i == 0: - # end of ramp up - milestones.append(start_of_pulse + pulse.ramp_up) - - # start of ramp down - milestones.append( - start_of_pulse + pulse.ramp_up + pulse.steady_state - ) - - else: - # end of ramp up - milestones.append( - start_of_pulse + pulse.total_duration * (i - 1) + pulse.ramp_up - ) - - # start of ramp down - milestones.append( - start_of_pulse - + pulse.total_duration * (i - 1) - + pulse.ramp_up - + pulse.steady_state - ) - - # start of the next pulse - milestones.append(start_of_pulse + pulse.total_duration * (i + 1)) - - # add milestones 10 s before the end of the waiting period - assert pulse.total_duration - pulse.duration_no_waiting >= 10 - milestones.append(start_of_pulse + pulse.total_duration * (i + 1) - 10) - - # add milestones 2 s before the end of the waiting period - milestones.append(start_of_pulse + pulse.total_duration * (i + 1) - 2) - # start of the waiting period of this pulse - milestones.append( - start_of_pulse - + pulse.total_duration * i - + pulse.duration_no_waiting - ) - - # RISP pulses need additional milestones - if pulse.pulse_type == "RISP": - - t_begin_real_pulse = ( - start_of_pulse + 95 - ) # time at which the real RISP starts - - # a milestone for when the real RISP starts - milestones.append(t_begin_real_pulse + pulse.total_duration * i) - - # NOTE do we need this? - # hack: a milestone right after to ensure the stepsize is small enough - milestones.append( - t_begin_real_pulse + pulse.total_duration * i + 0.001 - ) - - # update the current time to the end of the current "sequence" of pulses - current_time = start_of_pulse + pulse.total_duration * pulse.nb_pulses - - return sorted(np.unique(milestones)) - - def make_custom_rtol(self, t: float) -> float: - pulse = self.scenario.get_pulse(t) - relative_time = t - self.scenario.get_time_start_current_pulse(t) - if pulse.pulse_type == "GDC" or pulse.pulse_type == "ICWC": - rtol = 1e-8 - elif pulse.pulse_type == "BAKE": - rtol = 1e-13 - elif pulse.pulse_type == "FP": - # rtol = 1e-10 - if relative_time % pulse.total_duration > pulse.duration_no_waiting: - rtol = 1e-14 - else: - rtol = 1e-8 - else: - rtol = 1e-10 - return rtol - - def bake_rtol(self, t: float) -> float: - pulse = self.scenario.get_pulse(t) - if pulse.pulse_type == "BAKE": - rtol = 1e-12 - else: - rtol = 1e-8 - return rtol diff --git a/src/hisp/new_model.py b/src/hisp/new_model.py new file mode 100644 index 00000000..1699d49e --- /dev/null +++ b/src/hisp/new_model.py @@ -0,0 +1,201 @@ +""" +New model class for CSV-driven bin simulations using new_mb_model. + +This module provides a NewModel class that: +1. Uses new_mb_model.py for dynamic FESTIM model creation +2. Manages bin simulations with CSV-based configuration +3. Handles adaptive timestepping based on scenario pulses +""" + +from typing import Dict, Tuple, Callable +import numpy as np + +from hisp.plasma_data_handling import PlasmaDataHandling +from hisp.scenario import Scenario +from hisp.festim_models.new_mb_model import make_model_with_scenario + +import festim as F + + +class NewModel: + """ + Model runner that uses new_mb_model for dynamic FESTIM model creation. + """ + + def __init__( + self, + reactor, # Reactor type from csv_bin + scenario: Scenario, + plasma_data_handling: PlasmaDataHandling, + coolant_temp: float = 343.0, + bins_meshes: Dict = None, + temperature_model_overrides: Dict[str, Callable] | None = None, + ): + """ + Initialize the model runner. + + Args: + reactor: Reactor object containing all bins + scenario: Scenario with pulse sequence + plasma_data_handling: Plasma data handler for flux/heat + coolant_temp: Coolant temperature (K) + bins_meshes: Dictionary of MeshBin objects keyed by bin_id (optional, defaults to BINS_MESHES) + temperature_model_overrides: Optional mapping of material name to custom + temperature callable; only provided materials are overridden + """ + self.reactor = reactor + self.scenario = scenario + self.plasma_data_handling = plasma_data_handling + self.coolant_temp = coolant_temp + self.bins_meshes = bins_meshes if bins_meshes is not None else {} + self.temperature_model_overrides = temperature_model_overrides if temperature_model_overrides is not None else {} + + def run_bin(self, bin, exports: bool = False, folder: str = None) -> Tuple[F.HydrogenTransportProblem, Dict]: + """ + Run a FESTIM simulation for a single bin. + + Args: + bin: Bin object to simulate + exports: Whether to export XDMF files + folder: Output folder for VTX checkpoint files. If None, defaults to + results_bin_{bin.bin_number} in the current working directory. + + Returns: + Tuple of (festim_model, quantities_dict) + """ + print(f"\n{'='*60}") + print(f"Running bin flux_id={bin.flux_id} (sim_id={bin.sim_id})") + print(f" Material: {bin.material.name}") + print(f" Mode: {bin.mode}") + print(f" Location: {bin.location}") + print(f" Thickness: {bin.thickness*1e3:.2f} mm") + print(f" Surface area: {bin.surface_area:.4f} m²") + print(f"{'='*60}") + + # Get mesh for this bin if available + mesh = None + if bin.sim_id in self.bins_meshes: + mesh = self.bins_meshes[bin.sim_id].mesh + + # Set up milestones for adaptive timestepping (before model creation for profile exports) + bin_config = bin.bin_configuration + initial_stepsize = 1e-3 if bin.material.name == "B" else 1e-2 + milestones = self._make_milestones(initial_stepsize) + milestones.append(self.scenario.get_maximum_time()) # Include final time for both milestones and profile export + + # Create FESTIM model using new_mb_model + try: + my_model, quantities = make_model_with_scenario( + bin=bin, + scenario=self.scenario, + plasma_data_handling=self.plasma_data_handling, + coolant_temp=self.coolant_temp, + mesh=mesh, + exports=exports, + profile_export=True, + milestones=milestones, + folder=folder, + temperature_model_overrides=self.temperature_model_overrides, + ) + except Exception as e: + print(f"ERROR: Failed to create model for bin {bin.bin_number}: {e}") + raise + + # Add derived quantities to model (skip if already in exports) + my_model.exports = my_model.exports if hasattr(my_model, 'exports') and my_model.exports else [] + for qty_name, qty in quantities.items(): + if qty not in my_model.exports: + my_model.exports.append(qty) + + # Apply milestones to model (already includes final_time) + my_model.settings.stepsize.milestones = milestones + + # Adaptivity settings + my_model.settings.stepsize.growth_factor = 1.1 + my_model.settings.stepsize.cutback_factor = 0.3 + my_model.settings.stepsize.target_nb_iterations = 4 + + # Apply max_stepsize from CSV bin configuration + # - During FP active phase (ramp+steady+ramp): fp_max_stepsize + # - During FP waiting / BAKE / other: max_stepsize_no_fp + fp_max_dt = bin_config.fp_max_stepsize + no_fp_max_dt = bin_config.max_stepsize_no_fp + scenario_ref = self.scenario + + def max_stepsize_function(t): + pulse = scenario_ref.get_pulse(t) + if pulse.pulse_type in ("FP", "FP_D"): + t_rel = t - scenario_ref.get_time_start_current_pulse(t) + relative_time = t_rel % pulse.total_duration + if relative_time < pulse.duration_no_waiting: + return fp_max_dt + return no_fp_max_dt + + my_model.settings.stepsize.max_stepsize = max_stepsize_function + print(f"[model] Max stepsize: FP={fp_max_dt:.1f} s, no-FP/BAKE={no_fp_max_dt:.1f} s") + + # Initialize and run + print(f"Initializing FESTIM model...") + my_model.initialise() + + print(f"Running simulation (final_time={self.scenario.get_maximum_time():.0f} s)...") + my_model.run() + + print(f"✓ Simulation complete for bin {bin.bin_number}") + + return my_model, quantities + + def _make_milestones(self, initial_stepsize_value: float): + """ + Build stepsize/adaptivity milestones from scenario pulses. + (Same logic as original file, preserved for stability of runs.) + """ + milestones = [] + current_time = 0.0 + + for pulse in self.scenario.pulses: + start_of_pulse = self.scenario.get_time_start_current_pulse(current_time) + for i in range(pulse.nb_pulses): + # small milestone right after each sub-pulse start + milestones.append(start_of_pulse + pulse.total_duration * i + initial_stepsize_value) + + # ramp-up / ramp-down edges + if i == 0: + milestones.append(start_of_pulse + pulse.ramp_up) + milestones.append(start_of_pulse + pulse.ramp_up + pulse.steady_state) + else: + milestones.append(start_of_pulse + pulse.total_duration * (i - 1) + pulse.ramp_up) + milestones.append(start_of_pulse + pulse.total_duration * (i - 1) + pulse.ramp_up + pulse.steady_state) + + # start of next sub-pulse + milestones.append(start_of_pulse + pulse.total_duration * (i + 1)) + + # before the end of waiting period + assert pulse.total_duration - pulse.duration_no_waiting >= 10 + milestones.append(start_of_pulse + pulse.total_duration * (i + 1) - 10) + milestones.append(start_of_pulse + pulse.total_duration * (i + 1) - 2) + + # start of waiting for this sub-pulse + milestones.append(start_of_pulse + pulse.total_duration * i + pulse.duration_no_waiting) + + # RISP special anchor + if getattr(pulse, "pulse_type", None) == "RISP": + t_begin_real_pulse = start_of_pulse + 95 + milestones.append(t_begin_real_pulse + pulse.total_duration * i) + milestones.append(t_begin_real_pulse + pulse.total_duration * i + 0.001) + + # Bake+GDC: add milestones at GDC sub-pulse boundaries + if getattr(pulse, "pulse_type", None) == "Bake+GDC": + gdc_ru = getattr(pulse, "gdc_ramp_up", None) + gdc_ss = getattr(pulse, "gdc_steady_state", None) + gdc_rd = getattr(pulse, "gdc_ramp_down", None) + if gdc_ru is not None and gdc_ss is not None and gdc_rd is not None: + sub_start = start_of_pulse + pulse.total_duration * i + milestones.append(sub_start + gdc_ru) # end of GDC ramp-up + milestones.append(sub_start + gdc_ru + gdc_ss) # end of GDC steady + milestones.append(sub_start + gdc_ru + gdc_ss + gdc_rd) # end of GDC ramp-down + milestones.append(sub_start + gdc_ru + gdc_ss + gdc_rd + initial_stepsize_value) # restart after GDC off + + current_time = start_of_pulse + pulse.total_duration * pulse.nb_pulses + + return sorted(np.unique(milestones).tolist()) diff --git a/src/hisp/plamsa_data_handling/__init__.py b/src/hisp/plamsa_data_handling/__init__.py deleted file mode 100644 index cd30a4be..00000000 --- a/src/hisp/plamsa_data_handling/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .main import PlasmaDataHandling diff --git a/src/hisp/plamsa_data_handling/main.py b/src/hisp/plamsa_data_handling/main.py deleted file mode 100644 index a7e084ec..00000000 --- a/src/hisp/plamsa_data_handling/main.py +++ /dev/null @@ -1,268 +0,0 @@ -import numpy as np -from numpy.typing import NDArray -from hisp.bin import SubBin, DivBin, FWBin -from hisp.helpers import periodic_step_function, periodic_pulse_function -from hisp.scenario import Pulse -import pandas as pd - -from typing import Dict - - -class PlasmaDataHandling: - _time_to_RISP_data: Dict[str, pd.DataFrame] - - def __init__( - self, - pulse_type_to_data: Dict[str, pd.DataFrame], - path_to_RISP_data: str, - path_to_ROSP_data: str, - path_to_RISP_wall_data: str, - ): - self.pulse_type_to_data = pulse_type_to_data or {} - self.path_to_RISP_data = path_to_RISP_data - self.path_to_ROSP_data = path_to_ROSP_data - self.path_to_RISP_wall_data = path_to_RISP_wall_data - # check that the values in pulse_type_to_data are pandas DataFrames - for value in self.pulse_type_to_data.values(): - if not isinstance(value, pd.DataFrame): - raise TypeError( - f"Expected a pandas DataFrame in pulse_type_to_data, got {type(value)} instead" - ) - - self._time_to_RISP_data = {} - - def get_particle_flux( - self, pulse: Pulse, bin: SubBin | DivBin, t_rel: float, ion=True - ) -> float: - """Returns the particle flux for a given pulse type - - Args: - pulse: the pulse object - bin: SubBin or DivBin - t_rel: Relative time (in seconds). - t_rel = t - t_pulse_start where t_pulse_start is the start of the pulse in seconds - ion (bool, optional): _description_. Defaults to True. - - Returns: - float: particle flux in part/m2/s - """ - if isinstance(bin, SubBin): - bin_index = bin.parent_bin_index - wetted_frac = bin.wetted_frac # frac of wettedness for subbin - elif isinstance(bin, DivBin): - bin_index = bin.index - wetted_frac = 1 # all div bins are wetted, so get full flux - - if ion: - flux_header = "Flux_Ion" - flux_frac = ( - wetted_frac # for an ion flux, apply the wetted frac for this bin - ) - if not ion: - flux_header = "Flux_Atom" - flux_frac = 1 # there is no wettedness for atom fluxes -- every subbin / bin gets all the atom flux - - if pulse.pulse_type == "FP": - flux = self.pulse_type_to_data[pulse.pulse_type][flux_header][bin_index] - elif pulse.pulse_type == "RISP": - assert isinstance( - t_rel, float - ), f"t_rel should be a float, not {type(t_rel)}" - - t_rel_within_a_single_risp = t_rel % pulse.total_duration - - data = self.RISP_data(bin, t_rel=t_rel_within_a_single_risp) - - flux = data[flux_header] - - # take the single value from the series - if isinstance(flux, pd.Series): - if flux.values.size == 0: - flux = 0.0 - else: - assert ( - flux.values.size == 1 - ), f"flux should be a single value, values: {flux.values}" - flux = flux.values[0] - elif pulse.pulse_type == "BAKE": - flux = 0.0 - else: - flux = self.pulse_type_to_data[pulse.pulse_type][flux_header][bin_index] - - value = flux * flux_frac - - # check that heat_val is a float - assert isinstance( - value, (float, np.float64) - ), f"value should be a float, not {type(value)}" - - # add in the step function for the pulse - total_time_on = pulse.duration_no_waiting - total_time_pulse = pulse.total_duration - - return periodic_pulse_function( - t_rel, - pulse=pulse, - value=value, - value_off=0, - ) - - def RISP_data(self, bin: SubBin | DivBin, t_rel: float | int) -> pd.DataFrame: - """Returns the correct RISP data file for indicated bin - - Args: - bin: Subbin or Divbin object - t_rel: relative time (in seconds). - t_rel = t - t_pulse_start where t_pulse_start is the start of the pulse in seconds - - Returns: - data: data from correct file as a numpy array - """ - assert isinstance( - bin, (SubBin, DivBin) - ), f"bin should be a SubBin or DivBin, not {type(bin)}" - - if isinstance(bin, SubBin): - bin_index = bin.parent_bin_index - div = False - elif isinstance(bin, DivBin): - bin_index = bin.index - div = True - - if div: - if bin.inner_bin: - folder = self.path_to_RISP_data - strike_point = True - elif bin.outer_bin: - folder = self.path_to_ROSP_data - strike_point = True - else: - strike_point = False # set up boolean to determine if divbin is on strike point or not - - t_rel = int(t_rel) - - if div and strike_point: - if 0 <= t_rel <= 9: - key = f"{folder}_1_9" - if key not in self._time_to_RISP_data.keys(): - self._time_to_RISP_data[key] = pd.read_csv( - f"{folder}/time0.dat", delimiter="," - ) - data = self._time_to_RISP_data[key] - elif 10 <= t_rel <= 98: - key = f"{folder}_10_98" - if key not in self._time_to_RISP_data.keys(): - self._time_to_RISP_data[key] = pd.read_csv( - f"{folder}/time10.dat", delimiter="," - ) - data = self._time_to_RISP_data[key] - elif 100 <= t_rel <= 260: - key = f"{folder}_{t_rel}" - if key not in self._time_to_RISP_data.keys(): - self._time_to_RISP_data[key] = pd.read_csv( - f"{folder}/time{t_rel}.dat", delimiter="," - ) - data = self._time_to_RISP_data[key] - elif 261 <= t_rel <= 270: - key = f"{folder}_261_269" - if key not in self._time_to_RISP_data.keys(): - self._time_to_RISP_data[key] = pd.read_csv( - f"{folder}/time260.dat", delimiter="," - ) - data = self._time_to_RISP_data[key] - else: # NOTE: so if time is too large a MB transforms into a FW element??? - key = "wall_data" - if key not in self._time_to_RISP_data.keys(): - self._time_to_RISP_data[key] = pd.read_csv( - self.path_to_RISP_wall_data, delimiter="," - ) - data = self._time_to_RISP_data[key] - else: - key = "wall_data" - if key not in self._time_to_RISP_data.keys(): - self._time_to_RISP_data[key] = pd.read_csv( - self.path_to_RISP_wall_data, delimiter="," - ) - data = self._time_to_RISP_data[key] - - data_for_bin = data.loc[data["Bin_Index"] == bin_index] - - # check there is only one row for this bin - assert ( - data_for_bin.shape[0] <= 1 - ), f"More than one row for bin {bin_index}. t_rel: {t_rel}, div: {div}, strike_point: {strike_point}" - return data_for_bin - - def get_heat(self, pulse: Pulse, bin: SubBin | DivBin, t_rel: float) -> float: - """Returns the surface heat flux (W/m2) for a given pulse type - - Args: - pulse: the pulse object - bin: SubBin or DivBin - t_rel: Relative time (in seconds). - t_rel = t - t_pulse_start where t_pulse_start is the start of the pulse in seconds - - Raises: - ValueError: if the pulse type is unknown - - Returns: - the surface heat flux in W/m2 - """ - if isinstance(bin, SubBin): - bin_index = bin.parent_bin_index - elif isinstance(bin, DivBin): - bin_index = bin.index - - if pulse.pulse_type == "RISP": - t_rel_within_a_single_risp = t_rel % pulse.total_duration - data = self.RISP_data(bin, t_rel=t_rel_within_a_single_risp) - elif pulse.pulse_type in self.pulse_type_to_data.keys(): - data = self.pulse_type_to_data[pulse.pulse_type] - else: - raise ValueError(f"Invalid pulse type {pulse.pulse_type}") - - if pulse.pulse_type == "FP": - photon_heat_radiation = 0.11e6 # W/m2 - heat_total = data["heat_total"][bin_index]+ photon_heat_radiation - heat_ion = data["heat_ion"][bin_index] - if isinstance(bin, SubBin): - heat_val = heat_total - heat_ion * (1 - bin.wetted_frac) - else: - heat_val = heat_total - elif pulse.pulse_type == "RISP": - if isinstance(bin, SubBin): - photon_radiation_heat = 0.11e6 # W/m2 - heat_total = data["heat_total"]+photon_radiation_heat - heat_ion = data["heat_ion"] - heat_val = heat_total - heat_ion * (1 - bin.wetted_frac) - else: - heat_val = data["heat_total"] - - # if heat_val is an empty pandas Series set it at 0.0 (no heat) - # otherwise take the single value from the series - if isinstance(heat_val, pd.Series): - if heat_val.values.size == 0: - heat_val = 0.0 - else: - assert ( - heat_val.values.size == 1 - ), f"heat_val should be a single value, values: {heat_val.values}" - heat_val = heat_val.values[0] - else: - heat_val = data["heat_total"][bin_index] - - # check that heat_val is a float - assert isinstance( - heat_val, (float, np.float64) - ), f"heat_val should be a float, not {type(heat_val)}" - - # add in the step function for the pulse - total_time_on = pulse.duration_no_waiting - total_time_pulse = pulse.total_duration - - return periodic_pulse_function( - t_rel, - pulse=pulse, - value=heat_val, - value_off=0, - ) diff --git a/src/hisp/plasma_data_handling.py b/src/hisp/plasma_data_handling.py new file mode 100644 index 00000000..75b183a5 --- /dev/null +++ b/src/hisp/plasma_data_handling.py @@ -0,0 +1,26 @@ +""" +Plasma Data Handling - imported from PFC-Tritium-Transport + +This module provides a bridge to import PlasmaDataHandling from PFC-Tritium-Transport. +All data management logic should be implemented in PFC-Tritium-Transport. +""" +import os +import sys +from pathlib import Path + +_pfc_tt_path = os.environ.get("PFC_TT_PATH") or os.environ.get("HISP_PFC_TT_PATH") +if _pfc_tt_path: + sys.path.insert(0, str(Path(_pfc_tt_path).resolve())) + +# Import PlasmaDataHandling from PFC-Tritium-Transport +try: + from plasma_data_handling import PlasmaDataHandling +except ImportError as e: + raise ImportError( + "Could not import from PFC-Tritium-Transport. " + "Set PFC_TT_PATH to your local clone: " + "`conda env config vars set PFC_TT_PATH=/path/to/PFC-Tritium-Transport`" + ) from e + +# Re-export for backwards compatibility +__all__ = ['PlasmaDataHandling'] diff --git a/src/hisp/scenario.py b/src/hisp/scenario.py index c0e79f69..86cc7ec5 100644 --- a/src/hisp/scenario.py +++ b/src/hisp/scenario.py @@ -1,244 +1,27 @@ -import pandas as pd -from typing import List -import warnings - - -class Pulse: - pulse_type: str - nb_pulses: int - ramp_up: float - steady_state: float - ramp_down: float - waiting: float - - def __init__( - self, - pulse_type: str, - nb_pulses: int, - ramp_up: float, - steady_state: float, - ramp_down: float, - waiting: float, - tritium_fraction: float, # tritium fraction = T/D - ): - self.pulse_type = pulse_type - self.nb_pulses = nb_pulses - self.ramp_up = ramp_up - self.steady_state = steady_state - self.ramp_down = ramp_down - self.waiting = waiting - self.tritium_fraction = tritium_fraction - - @property - def total_duration(self) -> float: - all_zeros = ( - self.ramp_up == 0 - and self.steady_state == 0 - and self.ramp_down == 0 - and self.waiting == 0 - ) - if self.pulse_type == "RISP" and all_zeros: - msg = "RISP pulse has all zeros for ramp_up, steady_state, ramp_down, waiting. " - msg += "Setting hardcoded values. Please check the values in the scenario file." - warnings.warn(msg, UserWarning) - - self.ramp_up = 10 - self.steady_state = 250 - self.ramp_down = 10 - self.waiting = 1530 - - return self.ramp_up + self.steady_state + self.ramp_down + self.waiting - - @property - def duration_no_waiting(self) -> float: - return self.total_duration - self.waiting - - -class Scenario: - def __init__(self, pulses: List[Pulse] = None): - """Initializes a Scenario object containing several pulses. - - Args: - pulses: The list of pulses in the scenario. Each pulse is a Pulse object. - """ - self._pulses = pulses if pulses is not None else [] - - @property - def pulses(self) -> List[Pulse]: - return self._pulses - - def to_txt_file(self, filename: str): - df = pd.DataFrame( - [ - { - "pulse_type": pulse.pulse_type, - "nb_pulses": pulse.nb_pulses, - "ramp_up": pulse.ramp_up, - "steady_state": pulse.steady_state, - "ramp_down": pulse.ramp_down, - "waiting": pulse.waiting, - "tritium_fraction": pulse.tritium_fraction, - } - for pulse in self.pulses - ] - ) - df.to_csv(filename, index=False) - - @staticmethod - def from_txt_file(filename: str, old_format=False) -> "Scenario": - if old_format: - pulses = [] - with open(filename, "r") as f: - for line in f: - # skip first line - if line.startswith("#"): - continue - - # skip empty lines - if not line.strip(): - continue - - # assume this is the format - pulse_type, nb_pulses, ramp_up, steady_state, ramp_down, waiting = ( - line.split() - ) - pulses.append( - Pulse( - pulse_type=pulse_type, - nb_pulses=int(nb_pulses), - ramp_up=float(ramp_up), - steady_state=float(steady_state), - ramp_down=float(ramp_down), - waiting=float(waiting), - ) - ) - return Scenario(pulses) - df = pd.read_csv(filename) - pulses = [ - Pulse( - pulse_type=row["pulse_type"], - nb_pulses=int(row["nb_pulses"]), - ramp_up=float(row["ramp_up"]), - steady_state=float(row["steady_state"]), - ramp_down=float(row["ramp_down"]), - waiting=float(row["waiting"]), - tritium_fraction=float(row["tritium_fraction"]), - ) - for _, row in df.iterrows() - ] - return Scenario(pulses) - - def get_row(self, t: float) -> int: - """ - Returns the index of the pulse at time t. - If t is greater than the maximum time in the scenario, a - warning is raised and the last pulse index is returned. - - Args: - t: the time in seconds - - Returns: - the index of the pulse at time t - """ - current_time = 0 - for i, pulse in enumerate(self.pulses): - phase_duration = pulse.nb_pulses * pulse.total_duration - if t < current_time + phase_duration: - return i - else: - current_time += phase_duration - - warnings.warn( - f"Time t {t} is out of bounds of the scenario file. Valid times are t < {self.get_maximum_time()}", - UserWarning, - ) - return i - - def get_pulse(self, t: float) -> Pulse: - """ - Returns the pulse at time t. - If t is greater than the maximum time in the scenario, a - warning is raised and the last pulse is returned. - - Args: - t: the time in seconds - - Returns: - Pulse: the pulse at time t - """ - row_idx = self.get_row(t) - return self.pulses[row_idx] - - def get_pulse_type(self, t: float) -> str: - """Returns the pulse type as a string at time t. - - Args: - t: time in seconds - - Returns: - pulse type (eg. FP, ICWC, RISP, GDC, BAKE) - """ - return self.get_pulse(t).pulse_type - - def get_maximum_time(self) -> float: - """Returns the maximum time of the scenario in seconds. - - Returns: - the maximum time of the scenario in seconds - """ - return sum([pulse.nb_pulses * pulse.total_duration for pulse in self.pulses]) - - def get_time_start_current_pulse(self, t: float): - """Returns the time (s) at which the current pulse started. - - Args: - t: the time in seconds - - Returns: - the time at which the current pulse started - """ - pulse_index = self.get_row(t) - return sum( - [ - pulse.nb_pulses * pulse.total_duration - for pulse in self.pulses[:pulse_index] - ] - ) - - # TODO this is the same as get_time_start_current_pulse, remove - def get_time_till_row(self, row: int) -> float: - """Returns the time (s) until the row in the scenario file. - - Args: - row: the row index in the scenario file - - Returns: - the time until the row in the scenario file - """ - return sum( - [pulse.nb_pulses * pulse.total_duration for pulse in self.pulses[:row]] - ) - - # TODO remove - def get_pulse_duration_no_waiting(self, row: int) -> float: - """Returns the total duration (without the waiting time) of a pulse in seconds for a given row in the file. - - Args: - row: the row index in the scenario file - - Returns: - the total duration of the pulse in seconds - """ - return self.pulses[row].duration_no_waiting - - # TODO remove - def get_pulse_duration(self, row: int) -> float: - """Returns the total duration of a pulse in seconds for a given row in the file. - - Args: - row: the row index in the scenario file - - Returns: - the total duration of the pulse in seconds - """ - return self.pulses[row].total_duration +""" +Scenario and Pulse are defined in PFC-Tritium-Transport to keep CSV-driven +scenario definitions in the PFC repository. This module acts as a shim and +re-exports `Scenario` and `Pulse` from PFC-Tritium-Transport/scenario.py. + +The import strategy mirrors `hisp.bin`'s approach: try an env var `PFC_TT_PATH`, +then insert the repo root into `sys.path` so we can +`from scenario import Scenario, Pulse`. +""" +import os +import sys +from pathlib import Path + +_pfc_tt_path = os.environ.get("PFC_TT_PATH") or os.environ.get("HISP_PFC_TT_PATH") +if _pfc_tt_path: + sys.path.insert(0, str(Path(_pfc_tt_path).resolve())) + +try: + from scenario import Scenario, Pulse +except ImportError as e: + raise ImportError( + "Could not import from PFC-Tritium-Transport. " + "Set PFC_TT_PATH to your local clone: " + "`conda env config vars set PFC_TT_PATH=/path/to/PFC-Tritium-Transport`" + ) from e + +__all__ = ["Scenario", "Pulse"] diff --git a/src/hisp/settings.py b/src/hisp/settings.py index 6e81d1f4..9ce6a24e 100644 --- a/src/hisp/settings.py +++ b/src/hisp/settings.py @@ -50,6 +50,7 @@ def __init__( transient=True, final_time=None, stepsize=None, + element_degree= 1, ) -> None: self.atol = atol self.rtol = rtol @@ -57,6 +58,7 @@ def __init__( self.transient = transient self.final_time = final_time self.stepsize = stepsize + self.element_degree = element_degree @property def stepsize(self): @@ -125,4 +127,18 @@ def atol_time_dependent(self): arguments = self.atol.__code__.co_varnames return "t" in arguments else: - return False \ No newline at end of file + return False + + + @property + def element_degree(self) -> int | None: + return getattr(self, "_element_degree", None) + + @element_degree.setter + def element_degree(self, value: int | None): + if value is None: + self._element_degree = None + elif isinstance(value, int) and value >= 0: + self._element_degree = value + else: + raise TypeError("element_degree must be None or a non-negative int") diff --git a/test/test_bin.py b/test/test_bin.py deleted file mode 100644 index 0d920772..00000000 --- a/test/test_bin.py +++ /dev/null @@ -1,206 +0,0 @@ -import numpy as np -import pytest -import pandas as pd -from hisp.bin import ( - FWBin3Subs, - FWBin2Subs, - DivBin, - FWBin, - BinCollection, - Reactor, - SubBin, -) - -# create Reactor -fw_bins = [FWBin3Subs() for _ in range(18)] -fw_bins[1] = FWBin2Subs() -div_bins = [DivBin() for _ in range(18, 46)] - -FW_bins = BinCollection(fw_bins) -Div_bins = BinCollection(div_bins) - -my_reactor = Reactor(first_wall=FW_bins, divertor=Div_bins) - -# make bins -for bin_index in list(range(18)): - fw_bin = FW_bins.get_bin(bin_index) - for subbin in fw_bin.sub_bins: - subbin.thickness = 6e-3 - subbin.material = "W" - -for bin_index in list(range(18, 46)): - div_bin = Div_bins.get_bin(bin_index) - div_bin.thickness = 1e-6 - div_bin.material = "B" - div_bin.set_inner_and_outer_bins() - -filename = "test/wetted_test_data.csv" -my_reactor.read_wetted_data(filename) -df = pd.read_csv(filename) - - -def test_wetted_fraction(): - """Tests that wetted fraction is correctly computed - for FW sub-bins. - """ "" - # first test FW Bin with 3 sub-bins - fw_bin = FW_bins.get_bin(0) - for sub_bin in fw_bin.sub_bins: - if sub_bin.mode == "shadowed": - shadowed_frac = sub_bin.wetted_frac - elif sub_bin.mode == "low_wetted": - low_wet_frac = sub_bin.wetted_frac - elif sub_bin.mode == "high_wetted": - high_wet_frac = sub_bin.wetted_frac - - Slow = df["Slow"][0] - Stot = df["Stot"][0] - Shigh = df["Shigh"][0] - f = df["f"][0] - - expected_shadowed = 0.0 - expected_low_wet = f * Stot / Slow - expected_high_wet = (1 - f) * Stot / Shigh - - assert round(shadowed_frac, 6) == round(expected_shadowed, 6) - assert round(low_wet_frac, 6) == round(expected_low_wet, 6) - assert round(high_wet_frac, 6) == round(expected_high_wet, 6) - - # then test FW bin with 2 sub-bins - fw_bin = FW_bins.get_bin(1) - - for sub_bin in fw_bin.sub_bins: - if sub_bin.mode == "shadowed": - shadowed_frac = sub_bin.wetted_frac - elif sub_bin.mode == "wetted": - low_wet_frac = sub_bin.wetted_frac - - Slow = df["Slow"][1] - Stot = df["Stot"][1] - - expected_shadowed = 0.0 - expected_low_wet = Stot / Slow - - assert round(shadowed_frac, 6) == round(expected_shadowed, 6) - assert round(low_wet_frac, 6) == round(expected_low_wet, 6) - - -@pytest.mark.parametrize( - "nb_bin, inner_flag, outer_flag", - [(19, False, True), (45, True, False), (35, False, False)], -) -def test_inner_or_outer_bin(nb_bin, inner_flag, outer_flag): - """Tests that div bins are correctly flagged as inner, - outer, or neither div bins. - """ "" - div_bin = Div_bins.get_bin(nb_bin) - div_bin.set_inner_and_outer_bins() - - assert div_bin.inner_bin == inner_flag - assert div_bin.outer_bin == outer_flag - - -@pytest.mark.parametrize( - "start, end", - [ - ((0, 0), (0, 1)), - ((0, 0), (1, 0)), - ((0, 0), (1, 1)), - ((0, 0), (1, 1)), - ], -) -def test_length_divbin(start, end): - my_bin = DivBin() - my_bin.start_point = start - my_bin.end_point = end - - length = np.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) - assert np.isclose(my_bin.length, length) - - -@pytest.mark.parametrize( - "start, end", - [ - ((0, 0), (0, 1)), - ((0, 0), (1, 0)), - ((0, 0), (1, 1)), - ((0, 0), (1, 1)), - ], -) -def test_length_fwbin(start, end): - my_bin = FWBin() - my_bin.start_point = start - my_bin.end_point = end - - length = np.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2) - assert np.isclose(my_bin.length, length) - - -def test_arc_length(): - """Tests that the arc length of a collection of bins is correctly computed.""" - bin_1 = FWBin() - bin_1.start_point = (0, 0) - bin_1.end_point = (1, 0) - bin_2 = FWBin() - bin_2.start_point = bin_1.end_point - bin_2.end_point = (3, 0) - bin_3 = FWBin() - bin_3.start_point = bin_2.end_point - bin_3.end_point = (4, 0) - - bins = BinCollection([bin_1, bin_2, bin_3]) - assert np.allclose(bins.arc_length(), [1, 3, 4]) - assert np.allclose(bins.arc_length(middle=True), [0.5, 2, 3.5]) - - -def test_sub_bin_surface_area_3_subbins(): - - # BUILD - my_bin = FWBin() - - subbin_shadowed = SubBin(mode="shadowed") - subbin_low_wetted = SubBin(mode="low_wetted") - subbin_high_wetted = SubBin(mode="high_wetted") - my_bin.sub_bins = [subbin_shadowed, subbin_low_wetted, subbin_high_wetted] - - bin_low_wetted_area = 5 - bin_high_wetted_area = 3 - bin_shadowed_area = 2 - - bin_total_area = bin_low_wetted_area + bin_high_wetted_area + bin_shadowed_area - - for subbin in my_bin.sub_bins: - subbin.low_wetted_area = bin_low_wetted_area - subbin.high_wetted_area = bin_high_wetted_area - subbin.total_area = bin_total_area - - # TEST - assert ( - subbin_shadowed.surface_area - == bin_total_area - bin_low_wetted_area - bin_high_wetted_area - ) - assert subbin_low_wetted.surface_area == bin_low_wetted_area - assert subbin_high_wetted.surface_area == bin_high_wetted_area - - -def test_sub_bin_surface_area_2_subbins(): - - # BUILD - my_bin = FWBin() - - subbin_shadowed = SubBin(mode="shadowed") - subbin_wetted = SubBin(mode="wetted") - my_bin.sub_bins = [subbin_shadowed, subbin_wetted] - - bin_wetted_area = 5 - bin_shadowed_area = 2 - - bin_total_area = bin_wetted_area + bin_shadowed_area - - for subbin in my_bin.sub_bins: - subbin.low_wetted_area = bin_wetted_area - subbin.total_area = bin_total_area - - # TEST - assert subbin_shadowed.surface_area == bin_total_area - bin_wetted_area - assert subbin_wetted.surface_area == bin_wetted_area diff --git a/test/test_data/Binned_Flux_Data.dat b/test/test_data/Binned_Flux_Data.dat deleted file mode 100644 index 0fc7ae55..00000000 --- a/test/test_data/Binned_Flux_Data.dat +++ /dev/null @@ -1,65 +0,0 @@ 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mode 100644 index 8635539d..00000000 --- a/test/test_data/ROSP_data/time259.dat +++ /dev/null @@ -1,15 +0,0 @@ -Bin_Index,Flux_Ion,Flux_Atom,E_ion,E_atom,alpha_ion,alpha_atom,heat_total -18,3.272540416666666926e+20,5.643355824999999078e+19,1.354754999999999825e+02,5.445527508333417899e+01,7.000000000000000000e+01,5.500000000000000000e+01,7.017153583333333518e+04 -19,1.332300000000000131e+21,1.574750000000000000e+20,1.345809999999999889e+02,7.939950000000000330e+01,7.000000000000000000e+01,5.500000000000000000e+01,1.025580000000000000e+05 -20,1.998680000000000000e+21,5.666300000000000000e+20,1.282050000000000125e+02,7.179769999999999897e+01,7.000000000000000000e+01,5.500000000000000000e+01,1.261120000000000000e+05 -21,2.649059999999999869e+21,7.083449999999999672e+20,1.224950000000000045e+02,7.585469999999999402e+01,7.000000000000000000e+01,5.500000000000000000e+01,1.799490000000000000e+05 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mode 100644 index be8624ac..00000000 --- a/test/test_data/ROSP_data/time260.dat +++ /dev/null @@ -1,15 +0,0 @@ -Bin_Index,Flux_Ion,Flux_Atom,E_ion,E_atom,alpha_ion,alpha_atom,heat_total -18,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -19,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -20,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -21,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 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mode 100644 index be8624ac..00000000 --- a/test/test_data/ROSP_data/time270.dat +++ /dev/null @@ -1,15 +0,0 @@ -Bin_Index,Flux_Ion,Flux_Atom,E_ion,E_atom,alpha_ion,alpha_atom,heat_total -18,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -19,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -20,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -21,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -22,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00,0.000000000000000000e+00 -23,7.376625000000000000e+19,1.037995000000000000e+17,1.050799999999999983e+02,5.184159999999999968e+01,7.000000000000000000e+01,5.500000000000000000e+01,3.121566500000000087e+04 -24,1.392390000000000000e+20,2.328535000000000000e+19,1.264544999999999959e+02,5.119685050000499871e+01,7.000000000000000000e+01,5.500000000000000000e+01,7.408584999999999854e+04 -25,2.026555000000000164e+20,5.460005000000000000e+19,1.410604999999999905e+02,5.453744999999999976e+01,7.000000000000000000e+01,5.500000000000000000e+01,7.637014999999999418e+04 -26,2.407470000000000000e+20,3.913380000000000000e+19,1.475620000000000118e+02,8.279439999999999600e+01,7.000000000000000000e+01,5.500000000000000000e+01,5.887700000000000000e+04 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92eb91bf..00000000 --- a/test/test_helpers.py +++ /dev/null @@ -1,13 +0,0 @@ -from hisp.helpers import gaussian_distribution - -import numpy as np - - -def test_gaussian_distr_area(): - """Generates a gaussian distribution and checks if the area under the curve is 1.""" - x = np.linspace(-10, 10, 1000) - y = gaussian_distribution([x], mean=0, width=1, mod=np) - area = np.trapz(y, x) - assert np.isclose( - area, 1, atol=1e-2 - ), f"Area under Gaussian distribution is {area} instead of 1" diff --git a/test/test_mb_model_festim.py b/test/test_mb_model_festim.py deleted file mode 100644 index a1566ee1..00000000 --- a/test/test_mb_model_festim.py +++ /dev/null @@ -1,202 +0,0 @@ -from hisp.festim_models.mb_model import ( - make_W_mb_model, - make_B_mb_model, - make_DFW_mb_model, -) - -from hisp.settings import CustomSettings -import festim as F -import pytest - - -@pytest.mark.parametrize( - "temp", [1000, lambda t: 1000 + 1, lambda x, t: 1000 + t - x[0]] -) -def test_mb_W_model(temp): - """Builds a festim tungsten model, run it, and tests the output.""" - (mb_model, quantities) = make_W_mb_model( - temperature=1000, - deuterium_ion_flux=lambda _: 2e10, - deuterium_atom_flux=lambda _: 2e10, - tritium_ion_flux=lambda _: 2e10, - tritium_atom_flux=lambda _: 2e10, - L=6e-3, - final_time=50, - folder=".", - ) - mb_model.settings.stepsize.initial_value = 1 - - mb_model.initialise() - mb_model.run() - - # TEST - assert isinstance(quantities, dict) - for key, value in quantities.items(): - assert isinstance(key, str) - assert isinstance(value, (F.TotalVolume, F.SurfaceQuantity, F.SurfaceTemperature)) # to change with SurfaceTemp PR merged - assert len(value.data) > 0 - - -@pytest.mark.parametrize( - "temp", [1000, lambda t: 1000 + 1, lambda x, t: 1000 + t - x[0]] -) -def test_mb_model_B(temp): - """Builds a festim boron model, run it, and tests the output.""" - (mb_model, quantities) = make_B_mb_model( - temperature=temp, - deuterium_ion_flux=lambda _: 1e22, - deuterium_atom_flux=lambda _: 1e22, - tritium_ion_flux=lambda _: 1e22, - tritium_atom_flux=lambda _: 1e22, - final_time=50, - L=6e-3, - folder=".", - ) - mb_model.settings.stepsize.initial_value = 1 - - mb_model.initialise() - mb_model.run() - - # TEST - assert isinstance(quantities, dict) - for key, value in quantities.items(): - assert isinstance(key, str) - assert isinstance(value, (F.TotalVolume, F.SurfaceQuantity, F.SurfaceTemperature)) # to change with SurfaceTemp PR merged - assert len(value.data) > 0 - - -@pytest.mark.parametrize( - "temp", [1000, lambda t: 1000 + 1, lambda x, t: 1000 + t - x[0]] -) -def test_mb_model_DFW(temp): - """Builds a festim tungsten model, run it, and tests the output.""" - (mb_model, quantities) = make_DFW_mb_model( - temperature=temp, - deuterium_ion_flux=lambda _: 1e22, - deuterium_atom_flux=lambda _: 1e22, - tritium_ion_flux=lambda _: 1e22, - tritium_atom_flux=lambda _: 1e22, - final_time=50, - L=6e-3, - folder=".", - ) - - mb_model.settings.stepsize.initial_value = 1 - - mb_model.initialise() - mb_model.run() - - # TEST - assert isinstance(quantities, dict) - for key, value in quantities.items(): - assert isinstance(key, str) - assert isinstance(value, (F.TotalVolume, F.SurfaceQuantity, F.SurfaceTemperature)) # to change with SurfaceTemp PR merged - assert len(value.data) > 0 - - -def test_model_last_timestep_overshoot(): - """ - Test to check that a simulation can run even with max_stepsize calling "get_pulse" - at a timestep that is greater than the maximum time in the scenario. - - See PR #77 for more details. - """ - - # Define a scenario with a single pulse - from hisp.scenario import Scenario, Pulse - - pulse1 = Pulse( - pulse_type="FP", - nb_pulses=1, - ramp_up=0, - steady_state=50, - ramp_down=0, - waiting=0, - tritium_fraction=0.5, - ) - - scenario = Scenario([pulse1]) - - # Define a function that will be called by the model to get the pulse at each timestep - def fun(t): - scenario.get_pulse(t) - return None - - # Create a model - (my_model, quantities) = make_DFW_mb_model( - temperature=1000, - deuterium_ion_flux=lambda _: 0, - deuterium_atom_flux=lambda _: 0, - tritium_ion_flux=lambda _: 0, - tritium_atom_flux=lambda _: 0, - final_time=50, - L=6e-3, - folder=".", - ) - - my_model.show_progress_bar = False # Disable the progress bar - - # give the stepsize adaptivity settings - my_model.settings.stepsize.growth_factor = 1.2 - my_model.settings.stepsize.cutback_factor = 0.9 - my_model.settings.stepsize.target_nb_iterations = 4 - - # cap the stepsize with the function we created - my_model.settings.stepsize.max_stepsize = fun - - # Initialise the model - my_model.initialise() - - # Artificially set the next timestep to be greater than the final time - my_model.t.value = 49 - my_model.settings.stepsize.value = 20 - my_model.dt.value = 20 - - my_model.iterate() - - - -@pytest.mark.parametrize( - "rtol", [1e-10, lambda t: 1e-8 if t<10 else 1e-10] - ) -def test_callable_rtol(rtol): - """Builds B model to test custom rtol.""" - (my_model, quantities) = make_B_mb_model( - temperature=500, - deuterium_ion_flux=lambda _: 1e22, - deuterium_atom_flux=lambda _: 1e22, - tritium_ion_flux=lambda _: 1e22, - tritium_atom_flux=lambda _: 1e22, - final_time=50, - L=6e-3, - custom_rtol=rtol, - folder=".", - ) - - # initialise the model - my_model.initialise() - - assert my_model.settings.rtol == rtol - -@pytest.mark.parametrize( - "atol", [1e10, lambda t: 1e12 if t<10 else 1e10] - ) -def test_callable_atol(atol): - """Builds B model to test custom rtol.""" - (my_model, quantities) = make_B_mb_model( - temperature=500, - deuterium_ion_flux=lambda _: 1e22, - deuterium_atom_flux=lambda _: 1e22, - tritium_ion_flux=lambda _: 1e22, - tritium_atom_flux=lambda _: 1e22, - final_time=50, - L=6e-3, - custom_atol=atol, - custom_rtol=1e-10, - folder=".", - ) - - # initialise the model - my_model.initialise() - - assert my_model.settings.atol == atol diff --git a/test/test_plasma_data_handling.py b/test/test_plasma_data_handling.py deleted file mode 100644 index f87f4c87..00000000 --- a/test/test_plasma_data_handling.py +++ /dev/null @@ -1,57 +0,0 @@ -from hisp.scenario import Scenario, Pulse -from hisp.plamsa_data_handling import PlasmaDataHandling -from hisp.bin import DivBin - -import numpy as np - -risp = Pulse( - pulse_type="RISP", - nb_pulses=5, - ramp_up=10, - steady_state=250, - ramp_down=10, - waiting=1530, - tritium_fraction=0.0, -) - - -def test_get_RISP_data_t_rel_zero(): - """ - Test that the RISP data for t_rel = 0 and t_rel = 1 are the same - """ - my_plasmadata = PlasmaDataHandling( - pulse_type_to_data=None, - path_to_RISP_data="test/test_data/RISP_data", - path_to_RISP_wall_data="test/test_data/RISP_Wall_data.dat", - path_to_ROSP_data="test/test_data/ROSP_data", - ) - - my_bin = DivBin() - my_bin.index = 60 - my_bin.inner_bin = True - - val_t_zero = my_plasmadata.RISP_data(my_bin, t_rel=0) - val_t_one = my_plasmadata.RISP_data(my_bin, t_rel=1) - - print(val_t_zero) - print(val_t_one) - assert np.testing.assert_array_equal(val_t_zero, val_t_one) is None - -def test_none_sp_divbin(): - """ - Test that divbins not on either strike point are assigned time indepenent data. - """ - my_plasmadata = PlasmaDataHandling( - pulse_type_to_data=None, - path_to_RISP_data="test/test_data/RISP_data", - path_to_RISP_wall_data="test/test_data/RISP_Wall_data.dat", - path_to_ROSP_data="test/test_data/ROSP_data", - ) - - my_bin = DivBin() - my_bin.index = 35 - - val_t_zero = my_plasmadata.RISP_data(my_bin, t_rel=0) - val_t_150 = my_plasmadata.RISP_data(my_bin, t_rel=150) - - assert np.testing.assert_array_equal(val_t_zero, val_t_150) is None diff --git a/test/test_pulse.py b/test/test_pulse.py deleted file mode 100644 index 1f7867b5..00000000 --- a/test/test_pulse.py +++ /dev/null @@ -1,97 +0,0 @@ -from hisp import Pulse - -import pytest - - -def test_pulse_initialization(): - pulse = Pulse( - pulse_type="FP", - nb_pulses=2, - ramp_up=0.1, - steady_state=0.2, - ramp_down=0.3, - waiting=0.4, - tritium_fraction=0.5, - ) - - assert pulse.pulse_type == "FP" - assert pulse.nb_pulses == 2 - assert pulse.ramp_up == 0.1 - assert pulse.steady_state == 0.2 - assert pulse.ramp_down == 0.3 - assert pulse.waiting == 0.4 - - -def test_pulse_total_duration(): - pulse = Pulse( - pulse_type="FP", - nb_pulses=2, - ramp_up=0.1, - steady_state=0.2, - ramp_down=0.3, - waiting=0.4, - tritium_fraction=0.5, - ) - - assert pulse.total_duration == 1.0 - - -def test_pulse_total_duration_with_zeros(): - pulse = Pulse( - pulse_type="RISP", - nb_pulses=1, - ramp_up=0.0, - steady_state=0.0, - ramp_down=0.0, - waiting=0.0, - tritium_fraction=0.0, - ) - - assert pulse.total_duration == 1800.0 - - -def test_pulse_total_duration_no_waiting_with_zeros(): - pulse = Pulse( - pulse_type="RISP", - nb_pulses=1, - ramp_up=0.0, - steady_state=0.0, - ramp_down=0.0, - waiting=0.0, - tritium_fraction=0.0, - ) - - assert pulse.duration_no_waiting == 270.0 - - -def test_pulse_risp_with_zeros_raises_warning(): - with pytest.warns(UserWarning): - pulse = Pulse( - pulse_type="RISP", - nb_pulses=1, - ramp_up=0.0, - steady_state=0.0, - ramp_down=0.0, - waiting=0.0, - tritium_fraction=0.0, - ) - pulse.total_duration - assert pulse.ramp_up != 0.0 - assert pulse.steady_state != 0.0 - assert pulse.ramp_down != 0.0 - assert pulse.waiting != 0.0 - - -def test_pulse_duration_no_waiting(): - pulse = Pulse( - pulse_type="FP", - nb_pulses=2, - ramp_up=0.1, - steady_state=0.2, - ramp_down=0.3, - waiting=0.4, - tritium_fraction=0.5, - ) - - assert pulse.duration_no_waiting == 0.6 - assert pulse.duration_no_waiting == pulse.total_duration - pulse.waiting diff --git a/test/test_scenario.py b/test/test_scenario.py deleted file mode 100644 index b55dee90..00000000 --- a/test/test_scenario.py +++ /dev/null @@ -1,248 +0,0 @@ -from hisp.scenario import Scenario, Pulse -import pytest -import matplotlib.pyplot as plt -import numpy as np - - -def test_scenario(): - scenario = Scenario() - assert len(scenario.pulses) == 0 - - scenario.pulses.append( - Pulse( - pulse_type="FP", - nb_pulses=2, - ramp_up=0.1, - steady_state=0.2, - ramp_down=0.3, - waiting=0.4, - tritium_fraction=0.5, - ) - ) - - assert len(scenario.pulses) == 1 - assert scenario.pulses[0].pulse_type == "FP" - assert scenario.pulses[0].nb_pulses == 2 - assert scenario.pulses[0].ramp_up == 0.1 - assert scenario.pulses[0].steady_state == 0.2 - assert scenario.pulses[0].ramp_down == 0.3 - assert scenario.pulses[0].waiting == 0.4 - - scenario.to_txt_file("test_scenario.txt") - scenario2 = Scenario.from_txt_file("test_scenario.txt") - - assert len(scenario2.pulses) == 1 - assert scenario2.pulses[0].pulse_type == "FP" - assert scenario2.pulses[0].nb_pulses == 2 - assert scenario2.pulses[0].ramp_up == 0.1 - assert scenario2.pulses[0].steady_state == 0.2 - assert scenario2.pulses[0].ramp_down == 0.3 - assert scenario2.pulses[0].waiting == 0.4 - - -def test_scenario_several_pulses(): - scenario = Scenario() - assert len(scenario.pulses) == 0 - - scenario.pulses.append( - Pulse( - pulse_type="FP", - nb_pulses=2, - ramp_up=0.1, - steady_state=0.2, - ramp_down=0.3, - waiting=0.4, - tritium_fraction=0.5, - ) - ) - - scenario.pulses.append( - Pulse( - pulse_type="ICWC", - nb_pulses=3, - ramp_up=0.5, - steady_state=0.6, - ramp_down=0.7, - waiting=0.8, - tritium_fraction=0.0, - ) - ) - - assert len(scenario.pulses) == 2 - assert scenario.pulses[0].pulse_type == "FP" - assert scenario.pulses[0].nb_pulses == 2 - assert scenario.pulses[0].ramp_up == 0.1 - assert scenario.pulses[0].steady_state == 0.2 - assert scenario.pulses[0].ramp_down == 0.3 - assert scenario.pulses[0].waiting == 0.4 - - assert scenario.pulses[1].pulse_type == "ICWC" - assert scenario.pulses[1].nb_pulses == 3 - assert scenario.pulses[1].ramp_up == 0.5 - assert scenario.pulses[1].steady_state == 0.6 - assert scenario.pulses[1].ramp_down == 0.7 - assert scenario.pulses[1].waiting == 0.8 - - scenario.to_txt_file("test_scenario.txt") - scenario2 = Scenario.from_txt_file("test_scenario.txt") - - assert len(scenario2.pulses) == 2 - assert scenario2.pulses[0].pulse_type == "FP" - assert scenario2.pulses[0].nb_pulses == 2 - assert scenario2.pulses[0].ramp_up == 0.1 - assert scenario2.pulses[0].steady_state == 0.2 - assert scenario2.pulses[0].ramp_down == 0.3 - assert scenario2.pulses[0].waiting == 0.4 - - assert scenario2.pulses[1].pulse_type == "ICWC" - assert scenario2.pulses[1].nb_pulses == 3 - assert scenario2.pulses[1].ramp_up == 0.5 - assert scenario2.pulses[1].steady_state == 0.6 - assert scenario2.pulses[1].ramp_down == 0.7 - assert scenario2.pulses[1].waiting == 0.8 - - -def test_maximum_time(): - # BUILD - - pulse1 = Pulse( - pulse_type="FP", - nb_pulses=2, - ramp_up=455, - steady_state=455, - ramp_down=650, - waiting=1000, - tritium_fraction=0.5, - ) - pulse2 = Pulse( - pulse_type="ICWC", - nb_pulses=2, - ramp_up=36, - steady_state=36, - ramp_down=180, - waiting=1000, - tritium_fraction=0.0, - ) - my_scenario = Scenario([pulse1, pulse2]) - - expected_maximum_time = 2 * (455 + 455 + 650 + 1000) + 2 * (36 + 36 + 180 + 1000) - - # RUN - computed_maximum_time = my_scenario.get_maximum_time() - - # TEST - assert computed_maximum_time == expected_maximum_time - - -pulse1 = Pulse( - pulse_type="FP", - nb_pulses=2, - ramp_up=455, - steady_state=455, - ramp_down=650, - waiting=1000, - tritium_fraction=0.5, -) -pulse2 = Pulse( - pulse_type="ICWC", - nb_pulses=2, - ramp_up=36, - steady_state=36, - ramp_down=180, - waiting=1000, - tritium_fraction=0.0, -) - - -@pytest.mark.parametrize( - "t, expected_pulse", - [ - (0, pulse1), - (6000, pulse2), - (1e5, pulse2), - (pulse1.nb_pulses * pulse1.total_duration, pulse2), - ], -) -def test_get_pulse(t, expected_pulse): - - my_scenario = Scenario([pulse1, pulse2]) - - pulse = my_scenario.get_pulse(t=t) - print(pulse.pulse_type) - assert pulse == expected_pulse - - -@pytest.mark.parametrize("t, expected_pulse", [(100, pulse1)]) -def test_one_pulse_scenario(t, expected_pulse): - my_scenario = Scenario([expected_pulse]) - - pulse = my_scenario.get_pulse(t=t) - - assert pulse == expected_pulse - - -@pytest.mark.parametrize("row, expected_duration", [(0, 2560), (1, 1252)]) -def test_get_pulse_duration(row, expected_duration): - my_scenario = Scenario([pulse1, pulse2]) - - duration = my_scenario.get_pulse_duration(row=row) - - assert duration == expected_duration - - -@pytest.mark.parametrize("row, expected_duration", [(0, 1560), (1, 252)]) -def test_get_pulse_duration_no_waiting(row, expected_duration): - my_scenario = Scenario([pulse1, pulse2]) - - duration = my_scenario.get_pulse_duration_no_waiting(row=row) - - assert duration == expected_duration - - -@pytest.mark.parametrize("row, expected_time", [(0, 0.0), (1, 5120.0)]) -def test_get_time_till_row(row, expected_time): - my_scenario = Scenario([pulse1, pulse2]) - - elapsed_time = my_scenario.get_time_till_row(row=row) - - assert elapsed_time == expected_time - - -def test_reading_a_file(): - my_scenario = Scenario([pulse1, pulse2]) - - times = np.linspace(0, my_scenario.get_maximum_time(), 1000, endpoint=False) - pulse_types = [] - for t in times: - pulse_type = my_scenario.get_pulse_type(t) - pulse_types.append(pulse_type) - - # color the line based on the pulse type - color = { - "FP": "red", - "ICWC": "blue", - "RISP": "green", - "GDC": "orange", - "BAKE": "purple", - } - - colors = [color[pulse_type] for pulse_type in pulse_types] - - for i in range(len(times) - 1): - plt.plot(times[i : i + 2], np.ones_like(times[i : i + 2]), c=colors[i]) - # plt.xscale("log") - # plt.show() - - -def test_duplicate_objects(): - my_scenario = Scenario([pulse1, pulse1]) - - t_1 = (pulse1.total_duration * pulse1.nb_pulses) / 2 - t_2 = ( - pulse1.total_duration * pulse1.nb_pulses - + (pulse2.total_duration * pulse2.nb_pulses) / 2 - ) - start_time_pulse_1 = my_scenario.get_time_start_current_pulse(t_1) - start_time_pulse_2 = my_scenario.get_time_start_current_pulse(t_2) - print(start_time_pulse_1, start_time_pulse_2) - assert start_time_pulse_1 != start_time_pulse_2 diff --git a/test/wetted_test_data.csv b/test/wetted_test_data.csv deleted file mode 100644 index 9275a753..00000000 --- a/test/wetted_test_data.csv +++ /dev/null @@ -1,19 +0,0 @@ -Slow,Stot,Shigh,f,DFW -2,3,1,0.5 -1,10 -0,0,0,0 -0,0,0,0 -0,0,0,0 -0,0,0,0 -0,0,0,0 -0,0,0,0 -0,0,0,0 -0,0,0,0,0 -0,0,0,0 -0,0,0,0 -0,0,0,0 -0,0,0 -0,0,0 -0,0,0,0 -0,0 -0,0 diff --git a/test/__init__.py b/tests/__init__.py similarity index 100% rename from test/__init__.py rename to tests/__init__.py diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..36a43b47 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,142 @@ +""" +Shared test fixtures for the HISP test suite. + +Defines lightweight fake dataclasses that mimic the PFC-TT interface +without importing from it. +""" +import pytest +import numpy as np +from dataclasses import dataclass, field +from typing import List + + +# =========================================================================== +# Fake dataclasses mimicking PFC-TT interfaces +# =========================================================================== + +@dataclass +class FakeTrap: + """Mimics Material trap parameters from PFC-TT.""" + Trap_density: float = 1e-3 # atomic fraction + k_0: float = 8.96e-17 + E_k: float = 0.2 + p_0: float = 1e13 + E_p: float = 0.87 + + +@dataclass +class FakeMaterial: + """Mimics the Material class from PFC-TT.""" + name: str = "W" + D0: float = 4.1e-7 # m^2/s + E_D: float = 0.39 # eV + Mat_density: float = 6.3e28 # atoms/m^3 + traps: List[FakeTrap] = field(default_factory=lambda: [FakeTrap()]) + K_R: float = 7.94e-17 + E_R: float = -2.0 + k_d0: float = 0.0 + E_kd: float = 0.0 + + @property + def N_traps(self) -> int: + return len(self.traps) + + +@dataclass +class FakeBinConfig: + """Mimics BinConfiguration from PFC-TT.""" + bc_plasma_facing_surface: str = "Dirichlet - 0 concentration + Implantation" + bc_rear_surface: str = "Dirichlet - 0 concentration" + atol: float = 1e10 + rtol: float = 1e-10 + + +@dataclass +class FakeBin: + """Mimics the Bin class from PFC-TT.""" + bin_number: int = 1 + sim_id: int = 0 + flux_id: int = 0 + thickness: float = 6e-3 # 6 mm + copper_thickness: float = 1e-3 # 1 mm + material: FakeMaterial = field(default_factory=FakeMaterial) + bin_configuration: FakeBinConfig = field(default_factory=FakeBinConfig) + implantation_params: dict = field(default_factory=lambda: { + 'ion': {'implantation_range': 3e-9, 'width': 1e-9, 'reflection_coefficient': 0.0}, + 'atom': {'implantation_range': 3e-9, 'width': 1e-9, 'reflection_coefficient': 0.0}, + }) + + +# =========================================================================== +# Shared fixtures +# =========================================================================== + +@pytest.fixture +def fake_material(): + """A single-trap tungsten material.""" + return FakeMaterial() + + +@pytest.fixture +def fake_material_2traps(): + """A two-trap tungsten material.""" + return FakeMaterial( + traps=[ + FakeTrap(Trap_density=1e-3, E_p=0.87), + FakeTrap(Trap_density=5e-4, E_p=1.50), + ] + ) + + +@pytest.fixture +def fake_bin_config(): + return FakeBinConfig() + + +@pytest.fixture +def fake_bin(): + """Default FakeBin with tungsten material.""" + return FakeBin() + + +@pytest.fixture +def fake_bin_B(): + """FakeBin variant for boron (thin layer).""" + return FakeBin( + bin_number=2, + thickness=2e-6, # 2 µm typical boron layer + copper_thickness=None, + material=FakeMaterial( + name="B", + D0=1e-10, + E_D=0.5, + Mat_density=1.3e29, + traps=[FakeTrap(Trap_density=2e-3, E_p=1.0)], + K_R=1e-16, + E_R=-1.5, + ), + ) + + +@pytest.fixture +def T_fn(): + """Simple constant temperature function T(x, t) = 500 K.""" + def _T(x, t): + return np.full_like(x[0], 500.0) + return _T + + +@pytest.fixture +def flux_fn(): + """Constant particle flux function: 1e20 m^-2 s^-1.""" + def _flux(t): + return 1e20 + return _flux + + +@pytest.fixture +def zero_flux_fn(): + """Zero flux function.""" + def _flux(t): + return 0.0 + return _flux diff --git a/tests/test_helpers/__init__.py b/tests/test_helpers/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_helpers/test_export_times.py b/tests/test_helpers/test_export_times.py new file mode 100644 index 00000000..23fe4f31 --- /dev/null +++ b/tests/test_helpers/test_export_times.py @@ -0,0 +1,90 @@ +"""Tests for compute_export_times. + +Verifies that the function generates correct time-sampling points +for data export across pulse scenarios, ensuring proper spacing +and cumulative offsets when multiple pulses are chained. +""" +import pytest +from unittest.mock import MagicMock +from hisp.festim_models.new_mb_model import compute_export_times + + +def _make_pulse(ramp_up, steady_state, ramp_down, waiting=0, nb_pulses=1): + """Create a mock pulse with given timing parameters.""" + pulse = MagicMock() + pulse.ramp_up = ramp_up + pulse.steady_state = steady_state + pulse.ramp_down = ramp_down + pulse.waiting = waiting + pulse.total_duration = ramp_up + steady_state + ramp_down + waiting + pulse.nb_pulses = nb_pulses + return pulse + + +def _make_scenario(pulses): + """Create a mock scenario with given pulse list.""" + scenario = MagicMock() + scenario.pulses = pulses + return scenario + + +class TestComputeExportTimes: + def test_single_pulse_3_samples(self): + """3 samples span: start of ramp-up, mid-steady-state, end of pulse.""" + pulse = _make_pulse(10, 100, 10, waiting=0, nb_pulses=1) + scenario = _make_scenario([pulse]) + + times = compute_export_times(scenario, samples_per_pulse=3) + + assert len(times) == 3 + assert times[0] == 0.0 # start of ramp-up + assert times[1] == pytest.approx(10 + 50) # middle of steady-state + assert times[2] == pytest.approx(120) # end of pulse + + def test_multiple_pulses_cumulative_offsets(self): + """Multiple pulse occurrences stack in time (no overlap).""" + pulse1 = _make_pulse(5, 50, 5, waiting=0, nb_pulses=2) + pulse2 = _make_pulse(10, 100, 10, waiting=0, nb_pulses=1) + scenario = _make_scenario([pulse1, pulse2]) + + times = compute_export_times(scenario, samples_per_pulse=3) + + # pulse1 has nb_pulses=2, pulse2 has nb_pulses=1 -> 3 occurrences total + assert len(times) == 9 + + # First occurrence of pulse1: [0, 5+25, 60] + assert times[0] == 0.0 + assert times[1] == pytest.approx(5 + 25) + assert times[2] == pytest.approx(60) + + # Second occurrence of pulse1: starts at 60 + assert times[3] == pytest.approx(60) + assert times[4] == pytest.approx(60 + 5 + 25) + assert times[5] == pytest.approx(120) + + # pulse2: starts at 120 + assert times[6] == pytest.approx(120) + + def test_custom_samples_per_pulse(self): + """Samples are evenly spaced within the pulse duration.""" + pulse = _make_pulse(10, 80, 10, waiting=0, nb_pulses=1) + scenario = _make_scenario([pulse]) + + times = compute_export_times(scenario, samples_per_pulse=5) + + assert len(times) == 5 + # Evenly spaced within pulse duration + duration = 100 + for i, t in enumerate(times): + expected = (i + 0.5) * duration / 5 + assert t == pytest.approx(expected) + + def test_len_equals_nb_pulses_times_samples(self): + """Total number of export times = nb_pulses × samples_per_pulse.""" + pulse = _make_pulse(2, 20, 2, waiting=0, nb_pulses=4) + scenario = _make_scenario([pulse]) + samples = 7 + + times = compute_export_times(scenario, samples_per_pulse=samples) + + assert len(times) == 4 * samples diff --git a/tests/test_helpers/test_flux.py b/tests/test_helpers/test_flux.py new file mode 100644 index 00000000..398cb55c --- /dev/null +++ b/tests/test_helpers/test_flux.py @@ -0,0 +1,133 @@ +"""Tests for compute_flux_values and build_ufl_flux_expression. + +Verifies that particle flux time-profiles are correctly constructed from +scenario/plasma data, including tritium fraction splitting, time stacking +for repeated pulses, and UFL expression generation for FESTIM sources. +""" +import pytest +from unittest.mock import MagicMock +from hisp.festim_models.new_mb_model import compute_flux_values, build_ufl_flux_expression + + +def _make_pulse(ramp_up, steady_state, ramp_down, waiting=0, nb_pulses=1, + tritium_fraction=0.5, pulse_type="FP"): + pulse = MagicMock() + pulse.ramp_up = ramp_up + pulse.steady_state = steady_state + pulse.ramp_down = ramp_down + pulse.waiting = waiting + pulse.total_duration = ramp_up + steady_state + ramp_down + waiting + pulse.nb_pulses = nb_pulses + pulse.tritium_fraction = tritium_fraction + pulse.pulse_type = pulse_type + # GDC attributes (None unless Bake+GDC) + pulse.gdc_ramp_up = None + pulse.gdc_steady_state = None + pulse.gdc_ramp_down = None + return pulse + + +def _make_scenario(pulses): + scenario = MagicMock() + scenario.pulses = pulses + return scenario + + +def _make_plasma_data_handling(ion_flux=1e20, atom_flux=5e19): + pdh = MagicMock() + + def get_pf(pulse, bin_, t_rel, ion): + return ion_flux if ion else atom_flux + + pdh.get_particle_flux = MagicMock(side_effect=get_pf) + return pdh + + +class TestComputeFluxValues: + def test_tritium_fraction_applied(self): + """Tritium fraction correctly splits ion/atom flux into D and T components.""" + pulse = _make_pulse(5, 50, 5, nb_pulses=1, tritium_fraction=0.3) + scenario = _make_scenario([pulse]) + pdh = _make_plasma_data_handling(ion_flux=1e20, atom_flux=5e19) + bin_ = MagicMock() + + occs = compute_flux_values(scenario, pdh, bin_) + + assert len(occs) == 1 + occ = occs[0] + assert occ['T_ion'] == pytest.approx(1e20 * 0.3) + assert occ['D_ion'] == pytest.approx(1e20 * 0.7) + assert occ['T_atom'] == pytest.approx(5e19 * 0.3) + assert occ['D_atom'] == pytest.approx(5e19 * 0.7) + + def test_timing_stacks_correctly(self): + """Multiple pulse occurrences produce non-overlapping time intervals.""" + pulse = _make_pulse(5, 50, 5, nb_pulses=3, tritium_fraction=0.5) + scenario = _make_scenario([pulse]) + pdh = _make_plasma_data_handling() + bin_ = MagicMock() + + occs = compute_flux_values(scenario, pdh, bin_) + + assert len(occs) == 3 + duration = 60.0 + for i, occ in enumerate(occs): + assert occ['start'] == pytest.approx(i * duration) + assert occ['end'] == pytest.approx((i + 1) * duration) + + def test_bake_gdc_uses_gdc_timing(self): + """Bake+GDC pulses sample flux at GDC sub-timing, not main pulse timing.""" + pulse = _make_pulse(10, 100, 10, nb_pulses=1, tritium_fraction=0.5, + pulse_type="Bake+GDC") + # Set GDC sub-timing + pulse.gdc_ramp_up = 2 + pulse.gdc_steady_state = 20 + pulse.gdc_ramp_down = 3 + + scenario = _make_scenario([pulse]) + pdh = _make_plasma_data_handling() + bin_ = MagicMock() + + occs = compute_flux_values(scenario, pdh, bin_) + + # The function should call get_particle_flux with t_rel = gdc_ramp_up + gdc_steady_state / 2 + expected_t_rel = 2 + 20 / 2 # = 12 + # Check the call was made with gdc timing + calls = pdh.get_particle_flux.call_args_list + for call in calls: + _, kwargs = call + if 't_rel' in kwargs: + assert kwargs['t_rel'] == pytest.approx(expected_t_rel) + + +class TestBuildUflFluxExpression: + def test_returns_four_callables(self): + """Returns 4 flux functions: D_ion, D_atom, T_ion, T_atom.""" + occs = [{ + 'start': 0, 'end': 100, + 'pulse': _make_pulse(5, 50, 5), + 'D_ion': 1e20, 'D_atom': 5e19, 'T_ion': 3e19, 'T_atom': 1e19, + }] + result = build_ufl_flux_expression(occs) + assert len(result) == 4 + for fn in result: + assert callable(fn) + + def test_value_zero_outside_time_window(self): + """Flux expression evaluates to zero outside the active pulse window.""" + occs = [{ + 'start': 10, 'end': 50, + 'pulse': _make_pulse(5, 30, 5), + 'D_ion': 1e20, 'D_atom': 5e19, 'T_ion': 3e19, 'T_atom': 1e19, + }] + d_ion_fn, _, _, _ = build_ufl_flux_expression(occs, value_off=0.0) + + # Since these return UFL expressions, verify the function is callable + # and returns a valid UFL expression when given a dolfinx Constant + from dolfinx import mesh as dfx_mesh, fem + from mpi4py import MPI + + domain = dfx_mesh.create_unit_interval(MPI.COMM_WORLD, 10) + t_val = fem.Constant(domain, 5.0) # before window (start=10) + expr = d_ion_fn(t_val) + assert expr is not None diff --git a/tests/test_helpers/test_mesh_vertices.py b/tests/test_helpers/test_mesh_vertices.py new file mode 100644 index 00000000..aa3e7421 --- /dev/null +++ b/tests/test_helpers/test_mesh_vertices.py @@ -0,0 +1,51 @@ +"""Tests for graded_vertices mesh generation. + +Verifies that graded_vertices produces a geometrically-graded 1D mesh +with correct start/end positions, monotonicity, and growth ratio. +""" +import numpy as np +from hisp.festim_models.new_mb_model import graded_vertices + + +class TestGradedVertices: + def test_starts_at_zero(self): + """Mesh always starts at x = 0 (plasma-facing surface).""" + verts = graded_vertices(L=1e-3, h0=1e-10, r=1.05) + assert verts[0] == 0.0 + + def test_ends_at_L(self): + """Mesh ends exactly at the specified thickness L.""" + L = 5e-4 + verts = graded_vertices(L=L, h0=1e-10, r=1.05) + assert np.isclose(verts[-1], L) + + def test_monotonically_increasing(self): + """All vertices are strictly increasing (no duplicates or reversals).""" + verts = graded_vertices(L=1e-3, h0=1e-9, r=1.1) + diffs = np.diff(verts) + assert np.all(diffs > 0) + + def test_growth_ratio_approximately_r(self): + """Consecutive cell sizes grow by the specified ratio r.""" + r = 1.08 + verts = graded_vertices(L=1e-3, h0=1e-10, r=r) + diffs = np.diff(verts) + # Check ratio between consecutive step sizes (skip last step which may be clipped) + ratios = diffs[1:-1] / diffs[:-2] + assert np.allclose(ratios, r, atol=1e-10) + + def test_small_domain(self): + """Works correctly for very thin domains (micrometers).""" + L = 1e-6 + verts = graded_vertices(L=L, h0=1e-10, r=1.05) + assert verts[0] == 0.0 + assert np.isclose(verts[-1], L) + assert len(verts) >= 2 + + def test_h0_larger_than_L(self): + """If initial cell size h0 >= L, mesh degenerates to [0, L] gracefully.""" + """If h0 >= L, mesh should still start at 0 and end at L.""" + L = 1e-10 + verts = graded_vertices(L=L, h0=1e-9, r=1.05) + assert verts[0] == 0.0 + assert np.isclose(verts[-1], L) diff --git a/tests/test_helpers/test_surface_concentration.py b/tests/test_helpers/test_surface_concentration.py new file mode 100644 index 00000000..15021990 --- /dev/null +++ b/tests/test_helpers/test_surface_concentration.py @@ -0,0 +1,113 @@ +"""Tests for make_surface_concentration_time_function. + +Verifies the analytical surface concentration BC formula c_s = φ·Rp/D +used for the 'Dirichlet - Analytical implantation approximation' BC, +including the optional recombination-limited correction term. +""" +import pytest +import numpy as np +from hisp.festim_models.new_mb_model import make_surface_concentration_time_function + + +def _constant_T(x, t): + """Temperature function returning 500 K everywhere.""" + return np.full_like(x[0], 500.0) + + +class TestSurfaceConcentration: + def test_zero_flux_returns_zero(self): + """Zero incoming flux gives zero surface concentration.""" + c_S = make_surface_concentration_time_function( + T_fun=_constant_T, + flux_fun=lambda t: 0.0, + D0=4.1e-7, + E_eV=0.39, + R_p=3e-9, + ) + assert c_S(1.0) == 0.0 + + def test_positive_flux_gives_positive_concentration(self): + """Non-zero flux produces a positive surface concentration.""" + c_S = make_surface_concentration_time_function( + T_fun=_constant_T, + flux_fun=lambda t: 1e20, + D0=4.1e-7, + E_eV=0.39, + R_p=3e-9, + ) + val = c_S(1.0) + assert val > 0.0 + + def test_no_recombination_term_when_Kr0_is_None(self): + """Without recombination (Kr0=None), c_s = φ·Rp / D(T) exactly.""" + flux = 1e20 + D0 = 4.1e-7 + E_eV = 0.39 + R_p = 3e-9 + T = 500.0 + + c_S = make_surface_concentration_time_function( + T_fun=_constant_T, + flux_fun=lambda t: flux, + D0=D0, + E_eV=E_eV, + R_p=R_p, + Kr0=None, + ) + val = c_S(1.0) + + # Manual calculation + kB_J = 1.380649e-23 + eV_to_J = 1.602176634e-19 + D_T = D0 * np.exp(-(E_eV * eV_to_J) / (kB_J * T)) + expected = (flux * R_p) / D_T + assert val == pytest.approx(expected, rel=1e-10) + + def test_with_recombination_term(self): + """Recombination-limited term (sqrt(φ/Kr)) adds to base concentration.""" + flux = 1e20 + D0 = 4.1e-7 + E_eV = 0.39 + R_p = 3e-9 + Kr0 = 7.94e-17 + E_Kr = -2.0 + + c_S_no_kr = make_surface_concentration_time_function( + T_fun=_constant_T, + flux_fun=lambda t: flux, + D0=D0, + E_eV=E_eV, + R_p=R_p, + Kr0=None, + ) + + c_S_with_kr = make_surface_concentration_time_function( + T_fun=_constant_T, + flux_fun=lambda t: flux, + D0=D0, + E_eV=E_eV, + R_p=R_p, + flux_tot_fun=lambda t: flux, + Kr0=Kr0, + E_Kr=E_Kr, + ) + + # With recombination, concentration should be higher (extra term adds) + assert c_S_with_kr(1.0) > c_S_no_kr(1.0) + + def test_zero_total_flux_with_Kr(self): + """If total flux is zero, recombination term is skipped (avoids division by zero).""" + c_S = make_surface_concentration_time_function( + T_fun=_constant_T, + flux_fun=lambda t: 1e20, + D0=4.1e-7, + E_eV=0.39, + R_p=3e-9, + flux_tot_fun=lambda t: 0.0, + Kr0=7.94e-17, + E_Kr=-2.0, + ) + # Should still return finite positive value (just diffusion term) + val = c_S(1.0) + assert val > 0.0 + assert np.isfinite(val) diff --git a/tests/test_helpers/test_temperature.py b/tests/test_helpers/test_temperature.py new file mode 100644 index 00000000..47052dc0 --- /dev/null +++ b/tests/test_helpers/test_temperature.py @@ -0,0 +1,116 @@ +"""Tests for temperature calculation functions. + +Verifies the 1D steady-state temperature profile calculations for: +- Tungsten (W) plasma-facing components with/without Cu interlayer +- Stainless steel (SS) components +- Boron (B) surface temperature correlation +- Underlying tungsten_slab_temperature analytical solution +""" +import pytest +import numpy as np +from hisp.festim_models.new_mb_model import ( + calculate_temperature_W, + calculate_temperature_SS, + calculate_temperature_B, + tungsten_slab_temperature, +) + + +class TestCalculateTemperatureW: + def test_linear_in_x(self): + """W temperature varies linearly through slab thickness (constant k).""" + x = np.linspace(0, 6e-3, 50) + T = calculate_temperature_W(x, heat_flux=5e6, coolant_temp=343.0, + thickness=6e-3, copper_thickness=1e-3) + # Linear fit: residuals should be negligible + coeffs = np.polyfit(x, T, 1) + T_fit = np.polyval(coeffs, x) + assert np.allclose(T, T_fit, atol=1e-6) + + def test_increases_with_heat_flux(self): + """Higher heat flux produces higher surface temperature.""" + x = np.array([0.0]) + T_low = calculate_temperature_W(x, heat_flux=1e6, coolant_temp=343.0, + thickness=6e-3, copper_thickness=1e-3) + T_high = calculate_temperature_W(x, heat_flux=10e6, coolant_temp=343.0, + thickness=6e-3, copper_thickness=1e-3) + assert T_high > T_low + + def test_with_copper_thickness(self): + """With Cu interlayer, uses 2-layer analytical slab model.""" + x = np.array([0.0]) + T = calculate_temperature_W(x, heat_flux=5e6, coolant_temp=343.0, + thickness=6e-3, copper_thickness=1e-3) + assert T > 343.0 + + def test_without_copper_thickness(self): + """Without Cu, uses Remi's linear correlation T = a*q + T_cool.""" + x = np.array([0.0]) + T = calculate_temperature_W(x, heat_flux=5e6, coolant_temp=343.0, + thickness=6e-3, copper_thickness=None) + # Surface temp: 1.1e-4 * 5e6 + 343 = 893 K + assert T == pytest.approx(1.1e-4 * 5e6 + 343.0) + + def test_zero_flux_returns_coolant_temp(self): + """Zero heat flux: surface temperature equals coolant temperature.""" + x = np.array([0.0]) + T = calculate_temperature_W(x, heat_flux=0.0, coolant_temp=400.0, + thickness=6e-3, copper_thickness=None) + assert T == pytest.approx(400.0) + + +class TestCalculateTemperatureSS: + def test_linear_in_x(self): + """SS temperature varies linearly through slab (constant k).""" + x = np.linspace(0, 3e-3, 50) + T = calculate_temperature_SS(x, heat_flux=3.5e5, coolant_temp=343.0, + thickness=3e-3) + coeffs = np.polyfit(x, T, 1) + T_fit = np.polyval(coeffs, x) + assert np.allclose(T, T_fit, atol=1e-6) + + def test_zero_flux_gives_coolant_temp(self): + """Zero heat flux: entire slab at coolant temperature.""" + x = np.linspace(0, 3e-3, 10) + T = calculate_temperature_SS(x, heat_flux=0.0, coolant_temp=343.0, + thickness=3e-3) + assert np.allclose(T, 343.0) + + def test_surface_hotter_than_rear(self): + """Plasma-facing surface (x=0) is hotter than coolant side.""" + thickness = 3e-3 + T_front = calculate_temperature_SS(0.0, heat_flux=3.5e5, coolant_temp=343.0, + thickness=thickness) + T_rear = calculate_temperature_SS(thickness, heat_flux=3.5e5, coolant_temp=343.0, + thickness=thickness) + assert T_front > T_rear + + +class TestCalculateTemperatureB: + def test_increases_with_flux(self): + """Boron surface temperature grows with applied heat flux.""" + T_low = calculate_temperature_B(heat_flux=1e5, coolant_temp=343.0) + T_high = calculate_temperature_B(heat_flux=5e6, coolant_temp=343.0) + assert T_high > T_low + + def test_zero_flux(self): + """Zero flux: boron temperature equals coolant temperature.""" + T = calculate_temperature_B(heat_flux=0.0, coolant_temp=343.0) + assert T == pytest.approx(343.0) + + +class TestTungstenSlabTemperature: + def test_surface_greater_than_interface(self): + """W surface is hotter than W/Cu interface.""" + T_surf, T_interface = tungsten_slab_temperature( + q_front=5e6, D_W=6e-3, D_Cu=1e-3, T_cool=343.0 + ) + assert T_surf > T_interface + + def test_zero_flux(self): + """Zero flux: both surface and interface equal coolant temperature.""" + T_surf, T_interface = tungsten_slab_temperature( + q_front=0.0, D_W=6e-3, D_Cu=1e-3, T_cool=343.0 + ) + assert T_surf == pytest.approx(343.0) + assert T_interface == pytest.approx(343.0) diff --git a/tests/test_integration/__init__.py b/tests/test_integration/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_integration/test_with_real_bin.py b/tests/test_integration/test_with_real_bin.py new file mode 100644 index 00000000..22856f65 --- /dev/null +++ b/tests/test_integration/test_with_real_bin.py @@ -0,0 +1,128 @@ +"""Integration tests using real Bin and Material objects from PFC-Tritium-Transport. + +Verifies that make_dynamic_mb_model works with real PFC-TT classes +(Bin, Material, BinConfiguration, Trap) rather than fake/mock objects. +""" +import pytest +import numpy as np +import tempfile + +# Trigger PFC-TT path resolution so PFC-TT packages become importable +import hisp.bin # noqa: F401 + +from bins_from_csv.csv_bin import Bin, BinConfiguration +from materials.materials import Material, Trap +from hisp.festim_models.new_mb_model import make_dynamic_mb_model + + +def _make_real_bin(): + """Build a real PFC-TT Bin with a W material and 1 trap.""" + material = Material( + name="W", + Mat_density=6.3e28, + D0=4.1e-7, + E_D=0.39, + K_R=7.94e-17, + E_R=-2.0, + N_traps=1, + traps=[Trap(Trap_density=1e-3, k_0=8.96e-17, E_k=0.2, p_0=1e13, E_p=0.87)], + ) + config = BinConfiguration( + rtol=1e-10, + atol=1e10, + fp_max_stepsize=5.0, + max_stepsize_no_fp=100.0, + bc_plasma_facing_surface="Dirichlet - 0 concentration + Implantation", + bc_rear_surface="Dirichlet - 0 concentration", + ) + return Bin( + flux_id=0, + material=material, + thickness=6e-3, + cu_thickness=2e-3, + mode="high_wetted", + parent_bin_surf_area=26.0, + surface_area=0.886, + f_ion_flux_fraction=0.919, + location="FW", + bin_configuration=config, + calculate_implantation_params=False, + ) + + +@pytest.mark.integration +class TestWithRealBin: + """Integration tests using real PFC-TT Bin/Material objects.""" + + def test_real_bin_has_required_attributes(self): + """PFC-TT Bin exposes the attributes make_dynamic_mb_model needs.""" + bin_ = _make_real_bin() + assert hasattr(bin_, "material") + assert hasattr(bin_, "thickness") + assert hasattr(bin_, "bin_configuration") + assert hasattr(bin_, "bin_number") + assert bin_.material.N_traps == 1 + + def test_real_bin_model_construction(self): + """Full model builds from a real PFC-TT Bin without crashing.""" + bin_ = _make_real_bin() + T_fn = lambda x, t: np.full_like(x[0], 500.0) + flux_fn = lambda t: 1e20 + + with tempfile.TemporaryDirectory() as tmpdir: + model, quantities = make_dynamic_mb_model( + bin=bin_, + temperature=T_fn, + deuterium_ion_flux=flux_fn, + tritium_ion_flux=flux_fn, + deuterium_atom_flux=flux_fn, + tritium_atom_flux=flux_fn, + final_time=100.0, + folder=tmpdir, + exports=False, + profile_export=False, + ) + + assert model is not None + assert len(model.species) >= 2 + assert "D" in quantities + assert "T" in quantities + + def test_real_bin_model_initialises(self): + """Model initialises and its internals reflect the bin properties.""" + bin_ = _make_real_bin() + T_fn = lambda x, t: np.full_like(x[0], 500.0) + flux_fn = lambda t: 1e20 + + with tempfile.TemporaryDirectory() as tmpdir: + model, _ = make_dynamic_mb_model( + bin=bin_, + temperature=T_fn, + deuterium_ion_flux=flux_fn, + tritium_ion_flux=flux_fn, + deuterium_atom_flux=flux_fn, + tritium_atom_flux=flux_fn, + final_time=100.0, + folder=tmpdir, + exports=False, + profile_export=False, + ) + + model.initialise() + + # Mesh spans [0, bin.thickness] + assert model.mesh.vertices[0] == 0.0 + assert np.isclose(model.mesh.vertices[-1], bin_.thickness) + + # Material diffusivity matches bin material + vol = model.subdomains[0] + assert vol.material.D_0 == bin_.material.D0 + assert vol.material.E_D == bin_.material.E_D + + # Species count: 2 mobile + 2 × N_traps + n_traps = bin_.material.N_traps + assert len(model.species) == 2 + 2 * n_traps + + # Solver tolerances match bin configuration + assert model.settings.rtol == bin_.bin_configuration.rtol + assert model.settings.atol == bin_.bin_configuration.atol diff --git a/tests/test_model_construction/__init__.py b/tests/test_model_construction/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_model_construction/test_boundary_conditions.py b/tests/test_model_construction/test_boundary_conditions.py new file mode 100644 index 00000000..c30b12e7 --- /dev/null +++ b/tests/test_model_construction/test_boundary_conditions.py @@ -0,0 +1,162 @@ +"""Tests for boundary condition setup in make_dynamic_mb_model. + +Verifies that each combination of plasma-facing and rear-surface BC +options produces the correct FESTIM BC objects (FixedConcentrationBC, +SurfaceReactionBC) with correct parameters on the correct surfaces. +""" +import pytest +import sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +import numpy as np +import festim as F +from hisp.festim_models.new_mb_model import make_dynamic_mb_model +from conftest import FakeBin, FakeBinConfig, FakeMaterial, FakeTrap + + +def _build_model(bc_plasma_facing, bc_rear, material=None): + """Helper to build a model with specified BCs.""" + mat = material or FakeMaterial() + config = FakeBinConfig( + bc_plasma_facing_surface=bc_plasma_facing, + bc_rear_surface=bc_rear, + ) + bin_ = FakeBin(material=mat, bin_configuration=config) + + T_fn = lambda x, t: np.full_like(x[0], 500.0) + flux_fn = lambda t: 1e20 + + model, quantities = make_dynamic_mb_model( + bin=bin_, + temperature=T_fn, + deuterium_ion_flux=flux_fn, + tritium_ion_flux=flux_fn, + deuterium_atom_flux=flux_fn, + tritium_atom_flux=flux_fn, + final_time=100.0, + folder="/tmp/test_bcs", + exports=False, + profile_export=False, + ) + return model, quantities + + +class TestPlasmaFacingBC: + def test_robin_implantation(self): + """Robin PFS: 3 SurfaceReactionBC (DD, DT, TT) + 4 volumetric sources.""" + model, _ = _build_model( + "Robin - Surf. Rec. + Implantation", + "Dirichlet - 0 concentration", + ) + # Should have SurfaceReactionBC objects in boundary_conditions + srbc = [bc for bc in model.boundary_conditions if isinstance(bc, F.SurfaceReactionBC)] + assert len(srbc) == 3 # DD, DT, TT + # Should have volumetric sources + assert hasattr(model, 'sources') and len(model.sources) == 4 + + def test_dirichlet_zero_implantation(self): + """Dirichlet PFS: c=0 at surface + volumetric implantation sources.""" + model, _ = _build_model( + "Dirichlet - 0 concentration + Implantation", + "Dirichlet - 0 concentration", + ) + # Should have FixedConcentrationBC at inlet with value 0 + fixed_bcs = [bc for bc in model.boundary_conditions if isinstance(bc, F.FixedConcentrationBC)] + # At least 2 for inlet (D, T) + 2 for outlet + inlet_zeros = [bc for bc in fixed_bcs if bc.value == 0.0] + assert len(inlet_zeros) >= 2 + # Should have volumetric sources + assert hasattr(model, 'sources') and len(model.sources) == 4 + + def test_dirichlet_analytical(self): + """Analytical PFS: time-dependent c_s(t) Dirichlet BCs, no vol. sources.""" + model, _ = _build_model( + "Dirichlet - Analyttical implantation approximation", + "Dirichlet - 0 concentration", + ) + # Should have FixedConcentrationBC with callable values (not zero) + fixed_bcs = [bc for bc in model.boundary_conditions if isinstance(bc, F.FixedConcentrationBC)] + # Should have at least 2 for inlet + 2 for outlet = 4 + assert len(fixed_bcs) >= 4 + # Inlet BCs should have callable values (time-dependent concentration) + callable_bcs = [bc for bc in fixed_bcs if callable(bc.value)] + assert len(callable_bcs) >= 2 + + def test_unsupported_plasma_facing_raises(self): + """Unsupported PFS BC string raises ValueError.""" + with pytest.raises(ValueError, match="Unsupported plasma-facing BC"): + _build_model("Invalid BC", "Dirichlet - 0 concentration") + + +class TestRearBC: + def test_dirichlet_zero(self): + """Dirichlet rear: c=0 FixedConcentrationBC at rear surface.""" + model, _ = _build_model( + "Dirichlet - 0 concentration + Implantation", + "Dirichlet - 0 concentration", + ) + fixed_bcs = [bc for bc in model.boundary_conditions if isinstance(bc, F.FixedConcentrationBC)] + # Should include zeros at outlet + assert len(fixed_bcs) >= 2 + + def test_neumann_no_flux(self): + """Neumann rear: natural BC (no explicit BC added), model builds OK.""" + model, _ = _build_model( + "Dirichlet - 0 concentration + Implantation", + "Neumann - no flux", + ) + # The Neumann BC currently extends with empty list, so no additional BCs + # compared to just inlet BCs + # Just verify model builds without error + assert model is not None + + def test_robin_surf_rec_rear(self): + """Robin rear: 3 SurfaceReactionBC (DD,DT,TT) at outlet with material K_R/E_R.""" + mat = FakeMaterial(K_R=1.5e-16, E_R=-1.8) + model, _ = _build_model( + "Dirichlet - 0 concentration + Implantation", + "Robin - Surf. Rec.", + material=mat, + ) + srbc = [bc for bc in model.boundary_conditions if isinstance(bc, F.SurfaceReactionBC)] + assert len(srbc) == 3 + + # Verify subdomain is outlet (x = L) + for bc in srbc: + assert bc.subdomain.x == model.mesh.vertices[-1] + + # Verify reactant pairs + reactant_pairs = [] + for bc in srbc: + names = sorted([r.name for r in bc.reactant]) + reactant_pairs.append(tuple(names)) + assert ("D", "D") in reactant_pairs + assert ("T", "T") in reactant_pairs + assert ("D", "T") in reactant_pairs + + # Verify k_r0 and E_kr read from material + for bc in srbc: + assert bc.k_r0 == 1.5e-16 + assert bc.E_kr == -1.8 + + def test_robin_surf_rec_rear_defaults(self): + """Robin rear without K_R/E_R in material falls back to W defaults.""" + mat = FakeMaterial() + # Remove K_R to trigger getattr default + del mat.K_R + del mat.E_R + model, _ = _build_model( + "Dirichlet - 0 concentration + Implantation", + "Robin - Surf. Rec.", + material=mat, + ) + srbc = [bc for bc in model.boundary_conditions if isinstance(bc, F.SurfaceReactionBC)] + for bc in srbc: + assert bc.k_r0 == 7.94e-17 + assert bc.E_kr == -2.0 + + def test_unsupported_rear_raises(self): + """Unsupported rear BC string raises ValueError.""" + with pytest.raises(ValueError, match="Unsupported rear BC"): + _build_model("Dirichlet - 0 concentration + Implantation", "Invalid rear BC") diff --git a/tests/test_model_construction/test_exports.py b/tests/test_model_construction/test_exports.py new file mode 100644 index 00000000..f8e7bbcc --- /dev/null +++ b/tests/test_model_construction/test_exports.py @@ -0,0 +1,81 @@ +"""Tests for model exports and quantities setup. + +Verifies that make_dynamic_mb_model creates the correct TotalVolume +and SurfaceFlux export objects for all species, and that VTX/profile +exports are toggled correctly by the exports/profile_export flags. +""" +import pytest +import sys +from pathlib import Path +sys.path.insert(0, str(Path(__file__).resolve().parents[1])) + +import numpy as np +import festim as F +from hisp.festim_models.new_mb_model import make_dynamic_mb_model +from conftest import FakeBin, FakeBinConfig, FakeMaterial + + +def _build_model(profile_export=True, exports=True): + """Helper to build a model with given export settings.""" + bin_ = FakeBin() + T_fn = lambda x, t: np.full_like(x[0], 500.0) + flux_fn = lambda t: 1e20 + + model, quantities = make_dynamic_mb_model( + bin=bin_, + temperature=T_fn, + deuterium_ion_flux=flux_fn, + tritium_ion_flux=flux_fn, + deuterium_atom_flux=flux_fn, + tritium_atom_flux=flux_fn, + final_time=100.0, + folder="/tmp/test_exports", + exports=exports, + profile_export=profile_export, + ) + return model, quantities + + +class TestModelExports: + def test_quantities_contain_all_species(self): + """Quantities dict has keys for every species (D, T, trap1_D, trap1_T).""" + _, quantities = _build_model() + # Should contain keys for all species (D, T, trap1_D, trap1_T) + assert "D" in quantities + assert "T" in quantities + assert "trap1_D" in quantities + assert "trap1_T" in quantities + + def test_mobile_species_have_flux_entries(self): + """Mobile species get inlet and outlet SurfaceFlux exports.""" + _, quantities = _build_model() + assert "D_inlet_flux" in quantities + assert "D_outlet_flux" in quantities + assert "T_inlet_flux" in quantities + assert "T_outlet_flux" in quantities + + def test_profile_export_present_when_true(self): + """profile_export=True adds XDMFExport for each species.""" + _, quantities = _build_model(profile_export=True) + assert "D_profile" in quantities + assert "T_profile" in quantities + assert "trap1_D_profile" in quantities + assert "trap1_T_profile" in quantities + + def test_profile_export_absent_when_false(self): + """profile_export=False omits profile export quantities.""" + _, quantities = _build_model(profile_export=False) + assert "D_profile" not in quantities + assert "T_profile" not in quantities + + def test_exports_false_no_vtx(self): + """exports=False suppresses VTXSpeciesExport objects.""" + model, _ = _build_model(exports=False) + vtx_exports = [e for e in model.exports if isinstance(e, F.VTXSpeciesExport)] + assert len(vtx_exports) == 0 + + def test_exports_true_has_vtx(self): + """exports=True creates at least one VTXSpeciesExport.""" + model, _ = _build_model(exports=True) + vtx_exports = [e for e in model.exports if isinstance(e, F.VTXSpeciesExport)] + assert len(vtx_exports) >= 1 diff --git a/tests/test_model_construction/test_species.py b/tests/test_model_construction/test_species.py new file mode 100644 index 00000000..bcdf326d --- /dev/null +++ b/tests/test_model_construction/test_species.py @@ -0,0 +1,73 @@ +"""Tests for species and trap creation in make_dynamic_mb_model. + +Verifies that mobile species (D, T) and trapped species (trap1_D, trap1_T, ...) +are created with correct names and counts based on the number of traps +in the material definition. +""" +import pytest +import numpy as np +from hisp.festim_models.new_mb_model import make_dynamic_mb_model + + +@pytest.fixture +def simple_model(fake_bin, T_fn, flux_fn): + """Build a model without running it.""" + model, quantities = make_dynamic_mb_model( + bin=fake_bin, + temperature=T_fn, + deuterium_ion_flux=flux_fn, + tritium_ion_flux=flux_fn, + deuterium_atom_flux=flux_fn, + tritium_atom_flux=flux_fn, + final_time=100.0, + folder="/tmp/test_species", + exports=False, + profile_export=False, + ) + return model, quantities + + +class TestModelSpecies: + def test_species_count(self, simple_model): + """Total species = 2 mobile (D,T) + 2×N_traps (trapped D+T per trap).""" + model, _ = simple_model + n_traps = 1 # default FakeBin has 1 trap + assert len(model.species) == 2 + 2 * n_traps + + def test_mobile_species_names(self, simple_model): + """Mobile species are named 'D' and 'T'.""" + model, _ = simple_model + mobile_names = [s.name for s in model.species if s.mobile] + assert "D" in mobile_names + assert "T" in mobile_names + assert len(mobile_names) == 2 + + def test_trapped_species_naming(self, simple_model): + """Trapped species follow naming convention 'trap{i}_{isotope}'.""" + model, _ = simple_model + trapped = [s for s in model.species if not s.mobile] + names = {s.name for s in trapped} + assert "trap1_D" in names + assert "trap1_T" in names + + def test_multiple_traps(self, fake_bin, T_fn, flux_fn, fake_material_2traps): + """With 2 traps: 2 mobile + 4 trapped = 6 species total.""" + fake_bin.material = fake_material_2traps + model, _ = make_dynamic_mb_model( + bin=fake_bin, + temperature=T_fn, + deuterium_ion_flux=flux_fn, + tritium_ion_flux=flux_fn, + deuterium_atom_flux=flux_fn, + tritium_atom_flux=flux_fn, + final_time=100.0, + folder="/tmp/test_species_2traps", + exports=False, + profile_export=False, + ) + assert len(model.species) == 6 + trapped_names = {s.name for s in model.species if not s.mobile} + assert "trap1_D" in trapped_names + assert "trap1_T" in trapped_names + assert "trap2_D" in trapped_names + assert "trap2_T" in trapped_names