From e72a6b33bd653fc3a99e7f79e06921b8c2322d0b Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 07:57:28 +0100 Subject: [PATCH 01/34] hydra init --- Experiments/plot_kernels.py | 231 +++++++++++ conf/config.yaml | 44 +++ conf/experiment/communication.yaml | 30 ++ conf/experiment/kernel/_base.yaml | 11 + conf/experiment/kernel/benchmark_numba.yaml | 17 + conf/experiment/kernel/benchmark_numpy.yaml | 15 + conf/experiment/kernel/convergence_numba.yaml | 16 + conf/experiment/kernel/convergence_numpy.yaml | 15 + conf/experiment/multigrid.yaml | 29 ++ conf/experiment/strong_scaling_fmg.yaml | 32 ++ conf/experiment/strong_scaling_jacobi.yaml | 30 ++ conf/experiment/test/fmg_convergence.yaml | 27 ++ conf/experiment/test/jacobi_convergence.yaml | 24 ++ conf/experiment/validation/_base.yaml | 19 + conf/experiment/validation/fmg.yaml | 15 + conf/experiment/validation/jacobi.yaml | 14 + conf/experiment/weak_scaling_fmg.yaml | 24 ++ conf/experiment/weak_scaling_jacobi.yaml | 22 ++ conf/hydra/callbacks/mlflow_log.yaml | 14 + conf/machine/dtu_hpc.yaml | 11 + conf/machine/local.yaml | 5 + conf/mlflow/databricks.yaml | 3 + conf/mlflow/local.yaml | 3 + docs/reports/TexReport | 2 +- jobs/README.md | 56 +++ jobs/comm_compact.sh | 41 ++ jobs/comm_spread.sh | 41 ++ jobs/scaling.sh | 81 ++++ jobs/weak_scaling_debug.sh | 43 +++ jobs/weak_scaling_v3.sh | 67 ++++ jobs/weak_scaling_v4.sh | 73 ++++ main.py | 177 ++++++--- pyproject_1.toml | 67 ++++ run_solver.py | 194 ++++++++++ setup_mlflow.py | 6 - src/Poisson/__init__.py | 90 +++++ src/Poisson/datastructures.py | 222 +++++++++++ src/Poisson/kernels.py | 97 +++++ src/Poisson/mpi/__init__.py | 22 ++ src/Poisson/mpi/decomposition.py | 148 +++++++ src/Poisson/mpi/grid.py | 193 +++++++++ src/Poisson/mpi/halo.py | 185 +++++++++ src/Poisson/problems.py | 48 +++ src/Poisson/solvers/__init__.py | 24 ++ src/Poisson/solvers/base.py | 200 ++++++++++ src/Poisson/solvers/fmg.py | 249 ++++++++++++ src/Poisson/solvers/fmg_mpi.py | 157 ++++++++ src/Poisson/solvers/jacobi.py | 103 +++++ src/Poisson/solvers/jacobi_mpi.py | 69 ++++ src/Poisson/solvers/mpi_mixin.py | 45 +++ src/Poisson/solvers/multigrid_operators.py | 152 ++++++++ src/{ldc => solvers}/__init__.py | 0 src/{ldc => solvers}/base_solver.py | 0 src/{ldc => solvers}/datastructures.py | 0 src/{ldc => solvers}/fv_solver.py | 0 src/{ldc => solvers}/spectral_solver.py | 0 src/utils/__init__.py | 78 ++-- src/utils/config/__init__.py | 9 + src/utils/config/clean.py | 269 +++++++++++++ src/utils/config/paths.py | 19 + src/utils/hpc/__init__.py | 4 + src/utils/hpc/sweeper.py | 202 ++++++++++ src/utils/hydra/__init__.py | 5 + src/utils/hydra/callbacks.py | 87 +++++ src/utils/mlflow/__init__.py | 33 ++ src/utils/mlflow/io.py | 365 ++++++++++++++++++ src/utils/mlflow/logs.py | 119 ++++++ src/utils/mlflow/upload_logs.py | 105 +++++ src/utils/plotting/__init__.py | 29 ++ src/utils/plotting/formatters.py | 137 +++++++ .../scientific.mplstyle} | 0 src/utils/plotting/styles.py | 34 ++ src/utils/runners/__init__.py | 25 ++ src/utils/runners/scripts.py | 248 ++++++++++++ 74 files changed, 5172 insertions(+), 99 deletions(-) create mode 100644 Experiments/plot_kernels.py create mode 100644 conf/config.yaml create mode 100644 conf/experiment/communication.yaml create mode 100644 conf/experiment/kernel/_base.yaml create mode 100644 conf/experiment/kernel/benchmark_numba.yaml create mode 100644 conf/experiment/kernel/benchmark_numpy.yaml create mode 100644 conf/experiment/kernel/convergence_numba.yaml create mode 100644 conf/experiment/kernel/convergence_numpy.yaml create mode 100644 conf/experiment/multigrid.yaml create mode 100644 conf/experiment/strong_scaling_fmg.yaml create mode 100644 conf/experiment/strong_scaling_jacobi.yaml create mode 100644 conf/experiment/test/fmg_convergence.yaml create mode 100644 conf/experiment/test/jacobi_convergence.yaml create mode 100644 conf/experiment/validation/_base.yaml create mode 100644 conf/experiment/validation/fmg.yaml create mode 100644 conf/experiment/validation/jacobi.yaml create mode 100644 conf/experiment/weak_scaling_fmg.yaml create mode 100644 conf/experiment/weak_scaling_jacobi.yaml create mode 100644 conf/hydra/callbacks/mlflow_log.yaml create mode 100644 conf/machine/dtu_hpc.yaml create mode 100644 conf/machine/local.yaml create mode 100644 conf/mlflow/databricks.yaml create mode 100644 conf/mlflow/local.yaml create mode 100644 jobs/README.md create mode 100644 jobs/comm_compact.sh create mode 100644 jobs/comm_spread.sh create mode 100644 jobs/scaling.sh create mode 100644 jobs/weak_scaling_debug.sh create mode 100644 jobs/weak_scaling_v3.sh create mode 100644 jobs/weak_scaling_v4.sh create mode 100644 pyproject_1.toml create mode 100644 run_solver.py delete mode 100644 setup_mlflow.py create mode 100644 src/Poisson/__init__.py create mode 100644 src/Poisson/datastructures.py create mode 100644 src/Poisson/kernels.py create mode 100644 src/Poisson/mpi/__init__.py create mode 100644 src/Poisson/mpi/decomposition.py create mode 100644 src/Poisson/mpi/grid.py create mode 100644 src/Poisson/mpi/halo.py create mode 100644 src/Poisson/problems.py create mode 100644 src/Poisson/solvers/__init__.py create mode 100644 src/Poisson/solvers/base.py create mode 100644 src/Poisson/solvers/fmg.py create mode 100644 src/Poisson/solvers/fmg_mpi.py create mode 100644 src/Poisson/solvers/jacobi.py create mode 100644 src/Poisson/solvers/jacobi_mpi.py create mode 100644 src/Poisson/solvers/mpi_mixin.py create mode 100644 src/Poisson/solvers/multigrid_operators.py rename src/{ldc => solvers}/__init__.py (100%) rename src/{ldc => solvers}/base_solver.py (100%) rename src/{ldc => solvers}/datastructures.py (100%) rename src/{ldc => solvers}/fv_solver.py (100%) rename src/{ldc => solvers}/spectral_solver.py (100%) create mode 100644 src/utils/config/__init__.py create mode 100644 src/utils/config/clean.py create mode 100644 src/utils/config/paths.py create mode 100644 src/utils/hpc/__init__.py create mode 100644 src/utils/hpc/sweeper.py create mode 100644 src/utils/hydra/__init__.py create mode 100644 src/utils/hydra/callbacks.py create mode 100644 src/utils/mlflow/__init__.py create mode 100644 src/utils/mlflow/io.py create mode 100644 src/utils/mlflow/logs.py create mode 100644 src/utils/mlflow/upload_logs.py create mode 100644 src/utils/plotting/__init__.py create mode 100644 src/utils/plotting/formatters.py rename src/utils/{utils.mplstyle => plotting/scientific.mplstyle} (100%) create mode 100644 src/utils/plotting/styles.py create mode 100644 src/utils/runners/__init__.py create mode 100644 src/utils/runners/scripts.py diff --git a/Experiments/plot_kernels.py b/Experiments/plot_kernels.py new file mode 100644 index 0000000..ccd7661 --- /dev/null +++ b/Experiments/plot_kernels.py @@ -0,0 +1,231 @@ +""" +Visualization of Kernel Experiments +==================================== + +Comprehensive analysis and visualization of NumPy vs Numba kernel benchmarks. +Fetches data directly from MLflow. + +Usage: + uv run python Experiments/01-kernels/plot_kernels.py mlflow=databricks +""" + +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +import hydra +from omegaconf import DictConfig + +from Poisson import get_project_root +from utils.mlflow.io import load_runs, setup_mlflow_tracking +from utils import plotting # Apply scientific style + + +@hydra.main(config_path="../../conf", config_name="config", version_base=None) +def main(cfg: DictConfig): + # Setup MLflow tracking based on config + setup_mlflow_tracking(mode=cfg.mlflow.mode) + prefix = cfg.mlflow.get("project_prefix", "") + + repo_root = get_project_root() + fig_dir = repo_root / "figures" / "kernels" + fig_dir.mkdir(parents=True, exist_ok=True) + + # Load data from MLflow + exp_name = "kernel" + print(f"Loading data from MLflow experiment '{exp_name}'...") + df = load_runs(exp_name, converged_only=False, project_prefix=prefix) + + if df.empty: + print(f"No runs found for experiment '{exp_name}'.") + print("Run the experiments first:") + print(" uv run python run_solver.py +experiment=kernel/convergence_numpy mlflow=databricks -m") + print(" uv run python run_solver.py +experiment=kernel/convergence_numba mlflow=databricks -m") + print(" uv run python run_solver.py +experiment=kernel/benchmark_numpy mlflow=databricks -m") + print(" uv run python run_solver.py +experiment=kernel/benchmark_numba mlflow=databricks -m") + return + + # Extract parameters (handle missing columns) + df["N"] = pd.to_numeric(df["params.N"], errors="coerce").astype("Int64") + df["max_iter"] = pd.to_numeric(df["params.max_iter"], errors="coerce").astype("Int64") + # Handle use_numba as either bool, int (0/1), or string + if "params.use_numba" in df.columns: + df["use_numba"] = pd.to_numeric(df["params.use_numba"], errors="coerce").fillna(0).astype(bool) + else: + df["use_numba"] = False + + # Use observed numba threads (actual runtime value) + if "metrics.observed_numba_threads" in df.columns: + df["numba_threads"] = pd.to_numeric(df["metrics.observed_numba_threads"], errors="coerce").fillna(1).astype(int) + elif "params.specified_numba_threads" in df.columns: + df["numba_threads"] = pd.to_numeric(df["params.specified_numba_threads"], errors="coerce").fillna(1).astype(int) + else: + df["numba_threads"] = 1 + + df["mlups"] = pd.to_numeric(df["metrics.mlups"], errors="coerce") + df["wall_time"] = pd.to_numeric(df["metrics.wall_time"], errors="coerce") + df["iterations"] = pd.to_numeric(df["metrics.iterations"], errors="coerce").astype("Int64") + df["final_error"] = pd.to_numeric(df["metrics.final_error"], errors="coerce") + + # Compute time per iteration + df["time_per_iter_ms"] = (df["wall_time"] / df["iterations"]) * 1000 + + # Create configuration labels + df["config"] = df.apply( + lambda row: "NumPy" if not row["use_numba"] + else f"Numba ({row['numba_threads']}T)", + axis=1 + ) + + print(f"Loaded {len(df)} runs") + print(f"Problem sizes: {sorted(df['N'].dropna().unique())}") + print(f"Configurations: {df['config'].unique()}") + + # %% + # Plot 1: Convergence (error vs iterations) - N=17 runs only + # ---------------------------------------------------------- + + df_conv = df[df["N"] == 17].copy() + if not df_conv.empty: + # Keep only latest run per (config, max_iter) + df_conv = df_conv.sort_values("start_time").groupby(["config", "max_iter"]).last().reset_index() + + fig, ax = plt.subplots() + sns.lineplot( + data=df_conv, + x="max_iter", + y="final_error", + hue="config", + style="config", + markers=True, + dashes=False, + ax=ax, + ) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xlabel("Iterations") + ax.set_ylabel("Algebraic Error") + ax.set_title("Kernel Convergence Comparison (N=17)") + ax.legend(title="Kernel") + ax.grid(True, alpha=0.3) + + output_file = fig_dir / "01_convergence.pdf" + fig.savefig(output_file, bbox_inches="tight") + print(f"Saved: {output_file}") + else: + print("No convergence runs found (N=17).") + + # %% + # Plot 2: Performance (MLup/s vs N) - benchmark runs only (N > 17) + # ---------------------------------------------------------------- + + df_bench = df[df["N"] > 17].copy() + if not df_bench.empty: + # Keep only latest run per (config, N) + df_bench = df_bench.sort_values("start_time").groupby(["config", "N"]).last().reset_index() + + fig, ax = plt.subplots() + sns.lineplot( + data=df_bench, + x="N", + y="mlups", + hue="config", + style="config", + markers=True, + dashes=False, + ax=ax, + ) + N_values = sorted(df_bench["N"].unique()) + ax.set_xscale("log") + ax.set_xticks(N_values, labels=[f"${n}^3$" for n in N_values]) + ax.minorticks_off() + ax.set_xlabel("Problem Size") + ax.set_ylabel("Throughput (Mlup/s)") + ax.set_title("Kernel Throughput Comparison") + ax.legend(title="Kernel") + ax.grid(True, alpha=0.3) + + output_file = fig_dir / "02_throughput.pdf" + fig.savefig(output_file, bbox_inches="tight") + print(f"Saved: {output_file}") + + # %% + # Plot 3: Speedup (Numba vs NumPy) + # -------------------------------- + + # Get NumPy baseline times per N + df_numpy = df_bench[~df_bench["use_numba"]].groupby("N")["time_per_iter_ms"].mean().reset_index() + df_numpy = df_numpy.rename(columns={"time_per_iter_ms": "numpy_time"}) + + # Compute speedup for Numba runs + df_numba = df_bench[df_bench["use_numba"]].merge(df_numpy, on="N", how="left") + df_numba["speedup"] = df_numba["numpy_time"] / df_numba["time_per_iter_ms"] + + if not df_numba.empty: + fig, ax = plt.subplots() + sns.lineplot( + data=df_numba, + x="N", + y="speedup", + hue="numba_threads", + style="numba_threads", + markers=True, + dashes=False, + ax=ax, + palette="viridis", + ) + N_vals = sorted(df_numba["N"].unique()) + ax.axhline(y=1, color="k", linestyle="--", alpha=0.5, label="NumPy baseline") + ax.set_xscale("log") + ax.set_xticks(N_vals, labels=[f"${n}^3$" for n in N_vals]) + ax.minorticks_off() + ax.set_xlabel("Problem Size") + ax.set_ylabel("Speedup vs NumPy") + ax.set_title("Numba Speedup") + ax.legend(title="Threads") + ax.grid(True, alpha=0.3) + + output_file = fig_dir / "03_speedup.pdf" + fig.savefig(output_file, bbox_inches="tight") + print(f"Saved: {output_file}") + else: + print("No Numba runs found for speedup plot.") + + # %% + # Plot 4: Time per iteration (with confidence intervals) + # ------------------------------------------------------- + + # Use ALL benchmark runs (not deduplicated) for CI calculation + df_bench_all = df[df["N"] > 17].copy() + + fig, ax = plt.subplots() + sns.lineplot( + data=df_bench_all, + x="N", + y="time_per_iter_ms", + hue="config", + style="config", + markers=True, + dashes=False, + errorbar="ci", # 95% confidence interval + ax=ax, + ) + N_values = sorted(df_bench_all["N"].unique()) + ax.set_xscale("log") + ax.set_yscale("log") + ax.set_xticks(N_values, labels=[f"${n}^3$" for n in N_values]) + ax.minorticks_off() + ax.set_xlabel("Problem Size") + ax.set_ylabel("Time per Iteration (ms)") + ax.set_title("Kernel Timing Comparison") + ax.legend(title="Kernel") + ax.grid(True, alpha=0.3) + + output_file = fig_dir / "04_time_per_iter.pdf" + fig.savefig(output_file, bbox_inches="tight") + print(f"Saved: {output_file}") + else: + print("No benchmark runs found (N > 17).") + + +if __name__ == "__main__": + main() diff --git a/conf/config.yaml b/conf/config.yaml new file mode 100644 index 0000000..f860cc6 --- /dev/null +++ b/conf/config.yaml @@ -0,0 +1,44 @@ +defaults: + - machine: local + - mlflow: local + - override hydra/launcher: basic + - _self_ + +# ============================================================ +# GlobalParams - flat config matching dataclass +# ============================================================ + +# Required +N: 65 + +# Solver selection and params +solver: jacobi # "jacobi" | "fmg" +omega: 0.8 +tolerance: 1.0e-6 +max_iter: 1000 + +# FMG-specific (ignored by Jacobi) +n_smooth: 3 +fmg_post_vcycles: 1 + +# Parallelization +n_ranks: 1 +strategy: sliced # "sliced" | "cubic" +communicator: custom # "numpy" | "custom" + +# Numba +use_numba: false +specified_numba_threads: 1 + +# Experiment tracking +experiment_name: default + +# ============================================================ +# Hydra output paths +# ============================================================ +hydra: + run: + dir: outputs/${experiment_name}/${now:%Y-%m-%d}/${now:%H-%M-%S} + sweep: + dir: multirun/${experiment_name}/${now:%Y-%m-%d}/${now:%H-%M-%S} + subdir: ${hydra.job.num} diff --git a/conf/experiment/communication.yaml b/conf/experiment/communication.yaml new file mode 100644 index 0000000..276f28a --- /dev/null +++ b/conf/experiment/communication.yaml @@ -0,0 +1,30 @@ +# @package _global_ +# Communication experiment: compare halo exchange strategies +# Tests compact (1 node) vs spread (2 nodes) placement + +experiment_name: communication + +# Use Jacobi solver for halo exchange benchmarking +solver: jacobi +n_ranks: 24 +max_iter: 200 +tolerance: 0.0 + +# Use Numba for consistent performance +use_numba: true +specified_numba_threads: 1 + +# Default values +N: 254 +strategy: sliced +communicator: custom + +# Logarithmic N values compatible with 24 ranks (N-2 divisible by 12 for cubic) +# Cubic decomposition: 24 = 4×3×2, LCM = 12 +# N = 12k+2: 122 (1.8M), 254 (16M), 506 (129M), 1010 (1B) -> ~2x growth +hydra: + sweeper: + params: + N: 122,254,506,1010 + strategy: sliced,cubic + communicator: numpy,custom diff --git a/conf/experiment/kernel/_base.yaml b/conf/experiment/kernel/_base.yaml new file mode 100644 index 0000000..3d2ac85 --- /dev/null +++ b/conf/experiment/kernel/_base.yaml @@ -0,0 +1,11 @@ +# @package _global_ +# Base kernel config - sequential Jacobi for kernel comparison +# Note: No defaults here - composed on top of main config.yaml + +experiment_name: kernel + +# Sequential mode +solver: jacobi +n_ranks: 1 +omega: 0.8 +tolerance: 0.0 # Disable early stopping - always run full max_iter diff --git a/conf/experiment/kernel/benchmark_numba.yaml b/conf/experiment/kernel/benchmark_numba.yaml new file mode 100644 index 0000000..fec25a1 --- /dev/null +++ b/conf/experiment/kernel/benchmark_numba.yaml @@ -0,0 +1,17 @@ +# @package _global_ +# Numba kernel benchmark - performance vs problem size and threads +# Run: uv run python run_solver.py +experiment=kernel/benchmark_numba mlflow=databricks -m +defaults: + - /experiment/kernel/_base + - _self_ + +use_numba: true +specified_numba_threads: 1 +max_iter: 100 +N: 65 + +hydra: + sweeper: + params: + N: 33,65,129,257 + specified_numba_threads: 1,2,4,6 diff --git a/conf/experiment/kernel/benchmark_numpy.yaml b/conf/experiment/kernel/benchmark_numpy.yaml new file mode 100644 index 0000000..54d2be8 --- /dev/null +++ b/conf/experiment/kernel/benchmark_numpy.yaml @@ -0,0 +1,15 @@ +# @package _global_ +# NumPy kernel benchmark - performance vs problem size +# Run: uv run python run_solver.py +experiment=kernel/benchmark_numpy mlflow=databricks -m +defaults: + - /experiment/kernel/_base + - _self_ + +use_numba: false +max_iter: 100 +N: 65 + +hydra: + sweeper: + params: + N: 33,65,129,257 diff --git a/conf/experiment/kernel/convergence_numba.yaml b/conf/experiment/kernel/convergence_numba.yaml new file mode 100644 index 0000000..3122f74 --- /dev/null +++ b/conf/experiment/kernel/convergence_numba.yaml @@ -0,0 +1,16 @@ +# @package _global_ +# Numba kernel convergence test - error vs iterations (4 threads) +# Run: uv run python run_solver.py +experiment=kernel/convergence_numba mlflow=databricks -m +defaults: + - /experiment/kernel/_base + - _self_ + +N: 17 +use_numba: true +specified_numba_threads: 4 +max_iter: 100 + +hydra: + sweeper: + params: + max_iter: 10,25,50,100,250,500,1000 diff --git a/conf/experiment/kernel/convergence_numpy.yaml b/conf/experiment/kernel/convergence_numpy.yaml new file mode 100644 index 0000000..34117c3 --- /dev/null +++ b/conf/experiment/kernel/convergence_numpy.yaml @@ -0,0 +1,15 @@ +# @package _global_ +# NumPy kernel convergence test - error vs iterations +# Run: uv run python run_solver.py +experiment=kernel/convergence_numpy mlflow=databricks -m +defaults: + - /experiment/kernel/_base + - _self_ + +N: 17 +use_numba: false +max_iter: 100 + +hydra: + sweeper: + params: + max_iter: 10,25,50,100,250,500,1000 diff --git a/conf/experiment/multigrid.yaml b/conf/experiment/multigrid.yaml new file mode 100644 index 0000000..40e9251 --- /dev/null +++ b/conf/experiment/multigrid.yaml @@ -0,0 +1,29 @@ +# @package _global_ +# Multigrid comparison experiment + +experiment_name: multigrid + +# FMG solver settings +solver: fmg +n_smooth: 5 +omega: 0.6666666666666666 +max_iter: 100 +tolerance: 1.0e-10 +fmg_post_vcycles: 2 +communicator: custom + +# Use Numba +use_numba: true +specified_numba_threads: 1 + +# Default values +N: 129 +n_ranks: 4 +strategy: cubic + +# Hydra sweep configuration +hydra: + sweeper: + params: + N: 33,65,129,257 + strategy: sliced,cubic diff --git a/conf/experiment/strong_scaling_fmg.yaml b/conf/experiment/strong_scaling_fmg.yaml new file mode 100644 index 0000000..954e3d4 --- /dev/null +++ b/conf/experiment/strong_scaling_fmg.yaml @@ -0,0 +1,32 @@ +# @package _global_ +# FMG scaling experiment: Full Multigrid solver + +experiment_name: fmg_scaling + +# FMG solver settings +solver: fmg +n_smooth: 5 +omega: 0.6666666666666666 +max_iter: 50 +tolerance: 0.0 +fmg_post_vcycles: 2 +communicator: custom + +# Use Numba with 1 thread for pure MPI scaling +use_numba: true +specified_numba_threads: 1 + +# Default values +N: 257 +n_ranks: 1 +strategy: cubic + +# Hydra sweep configuration +# N values: 2^k+1 for multigrid levels (129³=2.1M, 257³=17M, 513³=135M) +# Rank counts with good 3D factorizations for cubic decomposition +hydra: + sweeper: + params: + N: 257,513 + strategy: sliced,cubic + n_ranks: 1,2,4,8,16,24,36,48,72,96 diff --git a/conf/experiment/strong_scaling_jacobi.yaml b/conf/experiment/strong_scaling_jacobi.yaml new file mode 100644 index 0000000..5cad8a1 --- /dev/null +++ b/conf/experiment/strong_scaling_jacobi.yaml @@ -0,0 +1,30 @@ +# @package _global_ +# Strong scaling experiment: Jacobi solver with fixed problem size + +experiment_name: scaling + +# Jacobi solver settings +solver: jacobi +max_iter: 250 +omega: 0.8 +tolerance: 0.0 +communicator: custom + +# Use Numba with 1 thread for pure MPI scaling +use_numba: true +specified_numba_threads: 1 + +# Default values +N: 257 +n_ranks: 1 +strategy: sliced + +# Hydra sweep configuration +# N values: 2^k+1 (257³=17M, 513³=135M points) +# Rank counts with good 3D factorizations for cubic decomposition +hydra: + sweeper: + params: + N: 257,513 + strategy: sliced,cubic + n_ranks: 1,2,4,8,16,24,36,48,72,96 diff --git a/conf/experiment/test/fmg_convergence.yaml b/conf/experiment/test/fmg_convergence.yaml new file mode 100644 index 0000000..5088512 --- /dev/null +++ b/conf/experiment/test/fmg_convergence.yaml @@ -0,0 +1,27 @@ +# @package _global_ +defaults: + - override /mlflow: local + +experiment_name: TEST-convergence_fmg + +# FMG solver settings +solver: fmg +omega: 0.6666666666666666 +n_smooth: 3 +fmg_post_vcycles: 1 +tolerance: 1e-12 +max_iter: 100 +use_numba: false +specified_numba_threads: 1 + +# Base settings (overridden by sweeper) +n_ranks: 1 +N: 32 + +hydra: + sweeper: + params: + n_ranks: 4 + N: 33,65,129 + strategy: sliced,cubic + communicator: numpy,custom diff --git a/conf/experiment/test/jacobi_convergence.yaml b/conf/experiment/test/jacobi_convergence.yaml new file mode 100644 index 0000000..3a914c8 --- /dev/null +++ b/conf/experiment/test/jacobi_convergence.yaml @@ -0,0 +1,24 @@ +# @package _global_ +defaults: + - override /mlflow: local + +experiment_name: TEST-convergence_jacobi + +# Jacobi solver settings +solver: jacobi +tolerance: 1e-10 +max_iter: 20000 +use_numba: false +specified_numba_threads: 1 + +# Base settings (overridden by sweeper) +n_ranks: 1 +N: 32 + +hydra: + sweeper: + params: + n_ranks: 4 + N: 17,33,65 + strategy: sliced,cubic + communicator: numpy,custom diff --git a/conf/experiment/validation/_base.yaml b/conf/experiment/validation/_base.yaml new file mode 100644 index 0000000..6f10ed0 --- /dev/null +++ b/conf/experiment/validation/_base.yaml @@ -0,0 +1,19 @@ +# @package _global_ +# Base validation config - shared by jacobi.yaml and fmg.yaml +# Note: No defaults here - composed on top of main config.yaml + +experiment_name: validation + +# Validation: verify O(h²) spatial convergence +max_iter: 5000 +tolerance: 1.0e-14 # Near machine precision +n_ranks: 4 +strategy: sliced +communicator: custom + +# Sweep across decomposition strategies and communicators +hydra: + sweeper: + params: + strategy: sliced,cubic + communicator: numpy,custom diff --git a/conf/experiment/validation/fmg.yaml b/conf/experiment/validation/fmg.yaml new file mode 100644 index 0000000..67d4bd9 --- /dev/null +++ b/conf/experiment/validation/fmg.yaml @@ -0,0 +1,15 @@ +# @package _global_ +# FMG validation: larger grids (multigrid efficiency) +# Run: uv run python run_solver.py +experiment=validation/fmg -m +defaults: + - /experiment/validation/_base + - _self_ + +solver: fmg +N: 129 +# Uses default n_smooth=5, fmg_post_vcycles=1 + +hydra: + sweeper: + params: + N: 65,129,257 diff --git a/conf/experiment/validation/jacobi.yaml b/conf/experiment/validation/jacobi.yaml new file mode 100644 index 0000000..a25a438 --- /dev/null +++ b/conf/experiment/validation/jacobi.yaml @@ -0,0 +1,14 @@ +# @package _global_ +# Jacobi validation: small grids (fast convergence check) +# Run: uv run python run_solver.py +experiment=validation/jacobi -m +defaults: + - /experiment/validation/_base + - _self_ + +solver: jacobi +N: 33 + +hydra: + sweeper: + params: + N: 17,33,65 diff --git a/conf/experiment/weak_scaling_fmg.yaml b/conf/experiment/weak_scaling_fmg.yaml new file mode 100644 index 0000000..c2d6397 --- /dev/null +++ b/conf/experiment/weak_scaling_fmg.yaml @@ -0,0 +1,24 @@ +# @package _global_ +# Weak scaling experiment: FMG solver + +experiment_name: weak_scaling_final + +# FMG solver settings +solver: fmg +n_smooth: 5 +omega: 0.6666666666666666 +max_iter: 50 +tolerance: 0.0 +fmg_post_vcycles: 2 +communicator: custom + +# Use Numba with 1 thread for pure MPI scaling +use_numba: true +specified_numba_threads: 1 + +# Default values +N: 129 +n_ranks: 1 +strategy: cubic + +# Weak scaling: N and n_ranks pairs set via command line in job script diff --git a/conf/experiment/weak_scaling_jacobi.yaml b/conf/experiment/weak_scaling_jacobi.yaml new file mode 100644 index 0000000..dabb81c --- /dev/null +++ b/conf/experiment/weak_scaling_jacobi.yaml @@ -0,0 +1,22 @@ +# @package _global_ +# Weak scaling experiment: Jacobi solver + +experiment_name: weak_scaling_final + +# Jacobi solver settings +solver: jacobi +max_iter: 100 +omega: 0.8 +tolerance: 0.0 +communicator: custom + +# Use Numba with 1 thread for pure MPI scaling +use_numba: true +specified_numba_threads: 1 + +# Default values +N: 129 +n_ranks: 1 +strategy: cubic + +# Weak scaling pairs set via command line (N and n_ranks must match for constant work/rank) diff --git a/conf/hydra/callbacks/mlflow_log.yaml b/conf/hydra/callbacks/mlflow_log.yaml new file mode 100644 index 0000000..930a7ef --- /dev/null +++ b/conf/hydra/callbacks/mlflow_log.yaml @@ -0,0 +1,14 @@ +# @package _global_ +# MLflow Log Callback - uploads Hydra job log to MLflow artifacts +# +# This callback runs after each job and uploads the log file containing +# all job output (including MPI report-bindings) to MLflow. +# +# Usage: Add to defaults in your config: +# defaults: +# - hydra/callbacks: mlflow_log +hydra: + callbacks: + mlflow_log: + _target_: utils.hydra.callbacks.MLflowLogCallback + artifact_path: logs diff --git a/conf/machine/dtu_hpc.yaml b/conf/machine/dtu_hpc.yaml new file mode 100644 index 0000000..b89a160 --- /dev/null +++ b/conf/machine/dtu_hpc.yaml @@ -0,0 +1,11 @@ +# DTU HPC defaults +mpi: + bind_to: core + cores_per_node: 24 + sockets_per_node: 2 + +hydra: + job: + env_set: + NUMBA_NUM_THREADS: "1" + OMP_NUM_THREADS: "1" diff --git a/conf/machine/local.yaml b/conf/machine/local.yaml new file mode 100644 index 0000000..7b972b7 --- /dev/null +++ b/conf/machine/local.yaml @@ -0,0 +1,5 @@ +# Local machine defaults +mpi: + bind_to: null + cores_per_node: 1 + sockets_per_node: 1 diff --git a/conf/mlflow/databricks.yaml b/conf/mlflow/databricks.yaml new file mode 100644 index 0000000..d1e77b9 --- /dev/null +++ b/conf/mlflow/databricks.yaml @@ -0,0 +1,3 @@ +mode: databricks +tracking_uri: databricks +project_prefix: "/Shared/LSM-PoissonMPI-v3" diff --git a/conf/mlflow/local.yaml b/conf/mlflow/local.yaml new file mode 100644 index 0000000..8940caa --- /dev/null +++ b/conf/mlflow/local.yaml @@ -0,0 +1,3 @@ +mode: local +tracking_uri: ./mlruns +project_prefix: "" diff --git a/docs/reports/TexReport b/docs/reports/TexReport index 32468c1..52462f8 160000 --- a/docs/reports/TexReport +++ b/docs/reports/TexReport @@ -1 +1 @@ -Subproject commit 32468c12416b4521dcb76413f0caa8fe025e0230 +Subproject commit 52462f8adcc448340f866134ae354f37eed2445c diff --git a/jobs/README.md b/jobs/README.md new file mode 100644 index 0000000..4c70381 --- /dev/null +++ b/jobs/README.md @@ -0,0 +1,56 @@ +# HPC Job Scripts + +LSF job scripts for DTU HPC cluster. + +## Prerequisites + +1. Clone repo to `$HOME/LSM-Project_2` +2. Create virtual environment: `uv venv && uv sync` +3. Configure Databricks credentials in `~/.databrickscfg` + +## Job Scripts + +### Strong Scaling (Jacobi) +- `strong_scaling_1node.sh` - 1-24 ranks on 1 node +- `strong_scaling_2node.sh` - 36-48 ranks on 2 nodes +- `strong_scaling_4node.sh` - 72-96 ranks on 4 nodes + +### Weak Scaling (Jacobi) +- `weak_scaling.sh` - Constant work per rank: (1,257), (8,513), (27,769), (64,1025) + +### FMG Scaling +- `fmg_scaling_1node.sh` - FMG solver on 1 node + +## Submission + +```bash +cd $HOME/LSM-Project_2 +mkdir -p logs +bsub < jobs/strong_scaling_1node.sh +bsub < jobs/strong_scaling_2node.sh +bsub < jobs/strong_scaling_4node.sh +bsub < jobs/weak_scaling.sh +bsub < jobs/fmg_scaling_1node.sh +``` + +## Monitor Jobs + +```bash +bstat # Job status +bjobs -l JOB_ID # Detailed info +bpeek JOB_ID # View output +``` + +## MPI Configuration + +All jobs use: +- `--map-by ppr:N:package` - N ranks per socket +- `--bind-to core` - Pin each rank to a core +- NUMBA_NUM_THREADS=1 - Single-threaded Numba kernel + +## Experiment Configuration + +- Numba JIT kernel (1 thread per rank for pure MPI scaling) +- Custom MPI datatypes for zero-copy halo exchange +- Databricks MLflow for tracking +- Hydra multirun for parameter sweeps (N, strategy) diff --git a/jobs/comm_compact.sh b/jobs/comm_compact.sh new file mode 100644 index 0000000..0328d6f --- /dev/null +++ b/jobs/comm_compact.sh @@ -0,0 +1,41 @@ +#!/bin/bash +#BSUB -J comm_compact +#BSUB -q hpcintro +#BSUB -n 48 +#BSUB -R "span[ptile=24]" +#BSUB -R "rusage[mem=8GB]" +#BSUB -W 1:00 +#BSUB -o logs/lsf/comm_compact_%J.out +#BSUB -e logs/lsf/comm_compact_%J.err + +# ============================================================================= +# Communication Experiment: COMPACT binding +# 24 ranks packed on 2 nodes (12 per node × 2 nodes) +# Minimizes inter-node communication, tests intra-node bandwidth +# ============================================================================= + +module load mpi +mkdir -p logs/lsf + +export NUMBA_NUM_THREADS=1 +export OMP_NUM_THREADS=1 + +# MPI parameters: 24 ranks compacted across 2 nodes +NP=24 +NPN=12 # ranks per node + +# Compact: 12 ranks per node, fill nodes before spreading +export MPI_OPTIONS="--map-by ppr:$NPN:node --bind-to core" + +echo "=== Communication: Compact binding ===" +echo "NP=$NP, NPN=$NPN (12 ranks/node × 2 nodes)" +echo "MPI_OPTIONS: $MPI_OPTIONS" + +uv run python run_solver.py \ + +experiment=communication \ + hydra/launcher=basic \ + experiment_name=comm_compact \ + mlflow=databricks \ + -m + +echo "Communication compact completed" diff --git a/jobs/comm_spread.sh b/jobs/comm_spread.sh new file mode 100644 index 0000000..19fc01f --- /dev/null +++ b/jobs/comm_spread.sh @@ -0,0 +1,41 @@ +#!/bin/bash +#BSUB -J comm_spread +#BSUB -q hpcintro +#BSUB -n 48 +#BSUB -R "span[ptile=24]" +#BSUB -R "rusage[mem=8GB]" +#BSUB -W 1:00 +#BSUB -o logs/lsf/comm_spread_%J.out +#BSUB -e logs/lsf/comm_spread_%J.err + +# ============================================================================= +# Communication Experiment: SPREAD binding +# 24 ranks spread across 2 nodes (6 per package × 2 packages × 2 nodes) +# Maximizes memory bandwidth, tests inter-node communication +# ============================================================================= + +module load mpi +mkdir -p logs/lsf + +export NUMBA_NUM_THREADS=1 +export OMP_NUM_THREADS=1 + +# MPI parameters: 24 ranks spread across 2 nodes +NP=24 +NPS=6 # ranks per package (socket) + +# Spread: 6 ranks per package, spread across nodes +export MPI_OPTIONS="--map-by ppr:$NPS:package --bind-to core" + +echo "=== Communication: Spread binding ===" +echo "NP=$NP, NPS=$NPS (6 ranks/package × 2 packages × 2 nodes)" +echo "MPI_OPTIONS: $MPI_OPTIONS" + +uv run python run_solver.py \ + +experiment=communication \ + hydra/launcher=basic \ + experiment_name=comm_spread \ + mlflow=databricks \ + -m + +echo "Communication spread completed" diff --git a/jobs/scaling.sh b/jobs/scaling.sh new file mode 100644 index 0000000..7154d8f --- /dev/null +++ b/jobs/scaling.sh @@ -0,0 +1,81 @@ +#!/bin/bash +#BSUB -J scaling +#BSUB -q hpcintro +#BSUB -n 96 +#BSUB -R "span[ptile=24]" +#BSUB -R "rusage[mem=8GB]" +#BSUB -W 1:00 +#BSUB -o logs/lsf/scaling_%J.out +#BSUB -e logs/lsf/scaling_%J.err + +# ============================================================================= +# Scaling Experiments: Strong and Weak scaling for Jacobi and FMG +# 96 cores = 4 nodes × 24 cores = 8 packages (2 per node) +# Hydra sweeper handles: N, strategy, n_ranks +# ============================================================================= + +module load mpi +mkdir -p logs/lsf + +export NUMBA_NUM_THREADS=1 +export OMP_NUM_THREADS=1 + +# Spread ranks across all 8 packages (4 nodes × 2 packages) +# ppr:12:package allows up to 96 ranks spread evenly +export MPI_OPTIONS="--map-by ppr:12:package --bind-to core" + +# Iteration count for scaling experiments +MAX_ITER=200 + +echo "=== Strong Scaling: Jacobi ===" +echo "MPI_OPTIONS: $MPI_OPTIONS" +uv run python run_solver.py \ + +experiment=strong_scaling_jacobi \ + max_iter=$MAX_ITER \ + hydra/launcher=basic \ + mlflow=databricks \ + -m + +echo "=== Strong Scaling: FMG ===" +uv run python run_solver.py \ + +experiment=strong_scaling_fmg \ + max_iter=$MAX_ITER \ + hydra/launcher=basic \ + mlflow=databricks \ + -m + +# ============================================================================= +# Weak Scaling: ~127³ points per rank (local subdomain size) +# N = local_size × ranks_per_dim + 2 (for boundaries) +# 129@1 (127+2), 257@8 (2³ decomp), 513@64 (4³ decomp) +# ============================================================================= + +echo "=== Weak Scaling: Jacobi ===" +for pair in "129,1" "257,8" "513,64"; do + N=$(echo $pair | cut -d',' -f1) + ranks=$(echo $pair | cut -d',' -f2) + echo " N=$N, ranks=$ranks (~127³ per rank)" + uv run python run_solver.py \ + +experiment=weak_scaling_jacobi \ + N=$N n_ranks=$ranks \ + max_iter=$MAX_ITER \ + hydra/launcher=basic \ + mlflow=databricks \ + -m +done + +echo "=== Weak Scaling: FMG ===" +for pair in "129,1" "257,8" "513,64"; do + N=$(echo $pair | cut -d',' -f1) + ranks=$(echo $pair | cut -d',' -f2) + echo " N=$N, ranks=$ranks (~127³ per rank)" + uv run python run_solver.py \ + +experiment=weak_scaling_fmg \ + N=$N n_ranks=$ranks \ + max_iter=$MAX_ITER \ + hydra/launcher=basic \ + mlflow=databricks \ + -m +done + +echo "All scaling experiments completed" diff --git a/jobs/weak_scaling_debug.sh b/jobs/weak_scaling_debug.sh new file mode 100644 index 0000000..561a856 --- /dev/null +++ b/jobs/weak_scaling_debug.sh @@ -0,0 +1,43 @@ +#!/bin/bash +#BSUB -J weak_debug +#BSUB -q hpcintro +#BSUB -n 24 +#BSUB -R "span[ptile=24]" +#BSUB -R "rusage[mem=4GB]" +#BSUB -W 0:15 +#BSUB -o logs/lsf/weak_debug_%J.out +#BSUB -e logs/lsf/weak_debug_%J.err + +# Quick debug: just 2 runs to verify everything works + +module load mpi +uv sync +mkdir -p logs/lsf + +export NUMBA_NUM_THREADS=1 +export OMP_NUM_THREADS=1 +export MPI_OPTIONS="--map-by ppr:12:package --bind-to core" +export PMIX_MCA_gds=hash + +echo "=== DEBUG: Testing weak scaling setup ===" +echo "PWD: $(pwd)" +echo "Python: $(which python)" +echo "venv Python: $(uv run which python)" +echo "MPI_OPTIONS: $MPI_OPTIONS" + +# Test using EXACT same config as working strong scaling +# Just override N and n_ranks for weak scaling pair +echo "" +echo "=== Test: Using strong_scaling_jacobi config (KNOWN WORKING) ===" +echo "Overriding: N=129, n_ranks=1" +uv run python run_solver.py \ + +experiment=strong_scaling_jacobi \ + N=129 n_ranks=1 strategy=cubic \ + max_iter=5 \ + hydra/launcher=basic \ + mlflow=databricks \ + -m 2>&1 +echo "Exit code: $?" + +echo "" +echo "=== DEBUG COMPLETE ===" diff --git a/jobs/weak_scaling_v3.sh b/jobs/weak_scaling_v3.sh new file mode 100644 index 0000000..2d25199 --- /dev/null +++ b/jobs/weak_scaling_v3.sh @@ -0,0 +1,67 @@ +#!/bin/bash +#BSUB -J weak_scaling +#BSUB -q hpcintro +#BSUB -n 72 +#BSUB -R "span[ptile=24]" +#BSUB -R "rusage[mem=8GB]" +#BSUB -W 6:00 +#BSUB -o logs/lsf/weak_scaling_%J.out +#BSUB -e logs/lsf/weak_scaling_%J.err + +# ============================================================================= +# Weak Scaling: Constant work per rank (3 series × 4 points each) +# +# Series 1 (~129³/rank): 129@1, 257@8, 385@27, 513@64 +# Series 2 (~257³/rank): 257@1, 513@8, 769@27, 1025@64 +# Series 3 (~513³/rank): 513@1, 1025@8, 1537@27, 2049@64 +# ============================================================================= + +module load mpi +uv sync +mkdir -p logs/lsf + +export NUMBA_NUM_THREADS=1 +export OMP_NUM_THREADS=1 +export MPI_OPTIONS="--map-by ppr:12:package --bind-to core" +export PMIX_MCA_gds=hash + +MAX_ITER=100 + +echo "=== Weak Scaling ===" +echo "MPI_OPTIONS: $MPI_OPTIONS" +echo "MAX_ITER: $MAX_ITER" + +# Series 1: ~129³/rank +SERIES_1="129,1 257,8 385,27 513,64" +# Series 2: ~257³/rank +SERIES_2="257,1 513,8 769,27 1025,64" +# Series 3: ~513³/rank +SERIES_3="513,1 1025,8 1537,27 2049,64" + +for rep in 1 2 3 4; do + echo "=== Repetition $rep/4 ===" + for solver in jacobi fmg; do + for strategy in sliced cubic; do + for series in "$SERIES_1" "$SERIES_2" "$SERIES_3"; do + for pair in $series; do + N=$(echo $pair | cut -d',' -f1) + ranks=$(echo $pair | cut -d',' -f2) + echo " [$rep/4] solver=$solver, N=$N, ranks=$ranks, strategy=$strategy" + uv run python run_solver.py \ + +experiment=weak_scaling_${solver} \ + solver=$solver \ + N=$N \ + n_ranks=$ranks \ + strategy=$strategy \ + communicator=custom \ + max_iter=$MAX_ITER \ + hydra/launcher=basic \ + mlflow=databricks 2>&1 + echo " Exit code: $?" + done + done + done + done +done + +echo "Weak scaling completed" diff --git a/jobs/weak_scaling_v4.sh b/jobs/weak_scaling_v4.sh new file mode 100644 index 0000000..792f9cc --- /dev/null +++ b/jobs/weak_scaling_v4.sh @@ -0,0 +1,73 @@ +#!/bin/bash +#BSUB -J weak_scaling +#BSUB -q hpcintro +#BSUB -n 72 +#BSUB -R "span[ptile=24]" +#BSUB -R "rusage[mem=8GB]" +#BSUB -W 6:00 +#BSUB -o logs/lsf/weak_scaling_%J.out +#BSUB -e logs/lsf/weak_scaling_%J.err + +# ============================================================================= +# Weak Scaling: Constant work per rank +# Copy of scaling.sh pattern (which works) +# +# Series 1 (~129³/rank): 129@1, 257@8, 385@27, 513@64 +# Series 2 (~257³/rank): 257@1, 513@8, 769@27, 1025@64 +# ============================================================================= + +module purge +module load mpi +mkdir -p logs/lsf + +export NUMBA_NUM_THREADS=1 +export OMP_NUM_THREADS=1 + +# Spread ranks across packages +export MPI_OPTIONS="--map-by ppr:12:package --bind-to core" + +MAX_ITER=100 + +echo "=== Weak Scaling ===" +echo "MPI_OPTIONS: $MPI_OPTIONS" +echo "MAX_ITER: $MAX_ITER" + +# Series 1: ~129³/rank +# Series 2: ~257³/rank +WEAK_PAIRS="129,1 257,8 385,27 513,64 257,1 513,8 769,27 1025,64" + +for rep in 1 2 3 4; do + echo "" + echo "=== Repetition $rep/4 ===" + + echo "--- Weak Scaling: Jacobi ---" + for pair in $WEAK_PAIRS; do + N=$(echo $pair | cut -d',' -f1) + ranks=$(echo $pair | cut -d',' -f2) + echo " N=$N, ranks=$ranks" + uv run python run_solver.py \ + +experiment=weak_scaling_jacobi \ + N=$N n_ranks=$ranks \ + max_iter=$MAX_ITER \ + hydra/launcher=basic \ + mlflow=databricks \ + -m + done + + echo "--- Weak Scaling: FMG ---" + for pair in $WEAK_PAIRS; do + N=$(echo $pair | cut -d',' -f1) + ranks=$(echo $pair | cut -d',' -f2) + echo " N=$N, ranks=$ranks" + uv run python run_solver.py \ + +experiment=weak_scaling_fmg \ + N=$N n_ranks=$ranks \ + max_iter=$MAX_ITER \ + hydra/launcher=basic \ + mlflow=databricks \ + -m + done +done + +echo "" +echo "Weak scaling completed" diff --git a/main.py b/main.py index 1d0516d..da5487a 100644 --- a/main.py +++ b/main.py @@ -1,51 +1,142 @@ #!/usr/bin/env python3 -"""Project CLI - run `uv run python main.py` for interactive mode.""" +"""Main entry point for project management - CLI driven.""" +import argparse import sys +from pathlib import Path +import os # Added for os.setsid -from cli import ( - fetch_mlflow, - run_scripts, - build_docs, - clean_all, - ruff_check, - ruff_format, - hpc_submit, - interactive, -) - -# CLI flags for automation (CI/scripts) -FLAGS = { - "--fetch": fetch_mlflow, - "--compute": lambda: run_scripts("compute"), - "--plot": lambda: run_scripts("plot"), - "--build-docs": build_docs, - "--clean": clean_all, - "--lint": ruff_check, - "--format": ruff_format, -} +# Ensure src directory is in python path +sys.path.append(str(Path(__file__).parent / "src")) -if __name__ == "__main__": - args = sys.argv[1:] +from utils import runners +from utils import mlflow as mlflow_utils +from utils.config import get_repo_root, clean_all + + +def build_docs(): + """Build Sphinx documentation.""" + import subprocess + + repo_root = get_repo_root() + docs_dir = repo_root / "docs" + source_dir = docs_dir / "source" + build_dir = docs_dir / "build" + + print("\nBuilding Sphinx documentation...") + + if not source_dir.exists(): + print(f" Error: Documentation source directory not found: {source_dir}") + return False + + try: + result = subprocess.run( + ["uv", "run", "sphinx-build", "-M", "html", str(source_dir), str(build_dir)], + capture_output=True, + text=True, + timeout=300, + cwd=str(repo_root), + ) + + if result.returncode == 0: + print(" ✓ Documentation built successfully") + print(f" → Open: {build_dir / 'html' / 'index.html'}\n") + return True + else: + print(f" ✗ Documentation build failed (exit {result.returncode})") + if result.stderr: + print(f" Error: {result.stderr[:500]}") + return False + + except subprocess.TimeoutExpired: + print(" ✗ Documentation build timed out") + return False + except FileNotFoundError: + print(" ✗ sphinx-build not found. Install with: uv sync") + return False + except Exception as e: + print(f" ✗ Documentation build failed: {e}") + return False + + +def main(): + """Main CLI entry point.""" + parser = argparse.ArgumentParser( + description="Project management for MPI Poisson Solver", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + + actions = parser.add_argument_group("Actions") + actions.add_argument("--docs", action="store_true", help="Build Sphinx HTML documentation") + actions.add_argument("--compute", action="store_true", help="Run all compute scripts (sequentially)") + actions.add_argument("--plot", action="store_true", help="Run all plotting scripts (in parallel)") + actions.add_argument("--copy-plots", action="store_true", help="Copy plots to report directory") + actions.add_argument("--clean", action="store_true", help="Clean all generated files and caches") + actions.add_argument("--setup-mlflow", action="store_true", help="Interactive MLflow setup (login to Databricks)") + actions.add_argument("--mlflow-ui", action="store_true", help="Start MLflow UI and open in browser") + + if len(sys.argv) == 1: + parser.print_help() + sys.exit(1) - if not args: - # Interactive mode + args = parser.parse_args() + + # Execute commands in logical order + if args.clean: + clean_all() + + if args.setup_mlflow: + import mlflow + print("\nSetting up MLflow...") + mlflow.login(backend="databricks", interactive=True) + + if args.compute: + runners.run_compute_scripts() + + if args.plot: + runners.run_plot_scripts() + + if args.copy_plots: + runners.copy_to_report() + + if args.mlflow_ui: + import subprocess + import webbrowser + import time + + print("\nStarting MLflow UI...") try: - interactive() - except KeyboardInterrupt: - print() - else: - # CLI mode - for flag, action in FLAGS.items(): - if flag in args: - action() - - if "--hpc" in args: - idx = args.index("--hpc") - experiment = ( - args[idx + 1] - if idx + 1 < len(args) and not args[idx + 1].startswith("--") - else "all" + # Default to standard file-based backend (./mlruns) + cmd = ["uv", "run", "mlflow", "ui"] + + # Start MLflow UI in the background + log_file = open("mlflow_ui.log", "w") + mlflow_process = subprocess.Popen( + cmd, + stdout=log_file, + stderr=subprocess.STDOUT, + preexec_fn=os.setsid # Detach process to run in background ) - dry_run = "--dry-run" in args - hpc_submit(experiment, dry_run) + print(f"MLflow UI started in background with PID: {mlflow_process.pid}") + print(f"Logs redirected to: mlflow_ui.log") + + # Give MLflow UI some time to start up + time.sleep(3) + + # Open in browser + url = "http://localhost:5000" + webbrowser.open_new_tab(url) + print(f"Opening MLflow UI in browser: {url}") + print("Press Ctrl+C to stop MLflow UI process later if it's still running.") + except FileNotFoundError: + print("Error: 'uv' command not found. Ensure uv is installed and in PATH.") + except Exception as e: + print(f"Error starting MLflow UI: {e}") + + if args.docs: + if not build_docs(): + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/pyproject_1.toml b/pyproject_1.toml new file mode 100644 index 0000000..0555069 --- /dev/null +++ b/pyproject_1.toml @@ -0,0 +1,67 @@ +[build-system] +requires = ["setuptools>=69", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "poisson_solver" +version = "0.1.0" +description = "MPI Poisson Solver" +readme = "README.md" +requires-python = "==3.12.*" +license = { text = "MIT" } +authors = [ + { name = "Alexander Elbæk Nielsen" }, + { name = "Junriu Li" }, + { name = "Philip Korsager Nickel" } +] + +# All dependencies (runtime, docs, and dev) +dependencies = [ + # Runtime dependencies + "numpy>=1.24.0", + "matplotlib>=3.7.0", + "pandas>=2.0.0", + "pyarrow>=14.0.0", + "scipy>=1.11.0", + "seaborn>=0.12.0", + # Documentation dependencies + "sphinx>=7.0.0,<9.0.0", # numpydoc 1.9.0 incompatible with sphinx 9.x + "pydata-sphinx-theme>=0.14.0", + "numpydoc>=1.6.0", + "sphinx-copybutton>=0.5.2", + "sphinx-gallery>=0.14.0", + "sphinxcontrib-mermaid>=0.9.2", + "pillow>=10.0.0", # Required for sphinx-gallery image handling + # Development dependencies + "ruff>=0.1.0", + "pytest>=7.4.0", + "pytest-cov>=4.1.0", + "numba>=0.62.1", + "mpi4py>=4.1.1", + "mlflow>=3.6.0", + "pyvista>=0.46.4", + "h5py>=3.15.1", + "tables>=3.10.2", + "psutil>=7.1.3", + "pyyaml>=6.0.3", + "questionary>=2.1.1", + "python-dotenv>=1.2.1", + "hydra-core>=1.3.2", + "omegaconf>=2.3.0", + "hydra-submitit-launcher>=1.2.0", +] + +[tool.pytest.ini_options] +testpaths = ["tests"] +filterwarnings = ["ignore::UserWarning"] +addopts = "-ra" + +[tool.setuptools] +package-dir = {"" = "src"} + +[tool.setuptools.packages.find] +where = ["src"] + + + + diff --git a/run_solver.py b/run_solver.py new file mode 100644 index 0000000..bb25e38 --- /dev/null +++ b/run_solver.py @@ -0,0 +1,194 @@ +""" +Poisson Solver Runner - Clean Hydra + MLflow integration. + +Architecture: + - Orchestrator (main): Hydra entry, saves config, spawns MPI + - Worker: MPI computation, MLflow logging + +Usage: + uv run python run_solver.py N=65 solver=jacobi n_ranks=1 + uv run python run_solver.py N=129 solver=fmg n_ranks=4 + + # With custom MPI options (for HPC binding): + uv run python run_solver.py --mpi-options "--map-by ppr:12:package --bind-to core" ... +""" + +import argparse +import json +import logging +import os +import subprocess +import sys +import tempfile +from dataclasses import asdict, fields +from typing import List, Optional + +import hydra +import mlflow +from hydra.core.hydra_config import HydraConfig +from mlflow.tracking import MlflowClient +from mpi4py import MPI +from omegaconf import DictConfig, OmegaConf + +from Poisson.datastructures import GlobalParams, LocalParams +from Poisson.solvers import FMGMPISolver, FMGSolver, JacobiMPISolver, JacobiSolver + +log = logging.getLogger(__name__) + + +# ============================================================================= +# Orchestrator (Hydra entry point) +# ============================================================================= + + +@hydra.main(config_path="conf", config_name="config", version_base=None) +def main(cfg: DictConfig) -> None: + """Hydra orchestrator - saves config and spawns MPI workers.""" + output_dir = HydraConfig.get().runtime.output_dir + cfg_path = os.path.join(output_dir, "config.yaml") + OmegaConf.save(cfg, cfg_path) + + log.info(f"{cfg.solver}, N={cfg.N}, n_ranks={cfg.n_ranks}") + + # Build MPI command with optional binding options + # Use sys.executable to ensure workers use same Python as orchestrator + mpi_options = os.environ.get("MPI_OPTIONS", "").split() + cmd = ["mpiexec"] + mpi_options + [ + "-n", + str(cfg.n_ranks), + sys.executable, + __file__, + cfg_path, + ] + log.info(f"Spawning: {' '.join(cmd)}") + + env = os.environ.copy() + env["MPI_WORKER"] = "1" + subprocess.run(cmd, env=env, timeout=600, check=True) + + +# ============================================================================= +# Worker (MPI computation) +# ============================================================================= + + +def worker(cfg_path: str) -> None: + """MPI worker - runs solver and logs to MLflow.""" + comm = MPI.COMM_WORLD + rank = comm.Get_rank() + + cfg = OmegaConf.load(cfg_path) + params = config_to_params(cfg) + + # Setup MLflow (all ranks, but only rank 0 logs) + mlflow.set_tracking_uri(cfg.mlflow.get("tracking_uri", "./mlruns")) + + # Apply project prefix for Databricks (experiment names must be absolute paths) + experiment_name = cfg.experiment_name + project_prefix = cfg.mlflow.get("project_prefix", "") + if project_prefix and not experiment_name.startswith("/"): + experiment_name = f"{project_prefix}/{experiment_name}" + mlflow.set_experiment(experiment_name) + + if rank == 0: + log.info( + f"{params.solver}, N={params.N}, ranks={params.n_ranks}, " + f"{params.strategy}/{params.communicator}" + ) + + # Run solver + solver = create_solver(params, comm) + solver.warmup() + solver.solve() + solver.post_solve() + + # Log results (rank 0 only) + if rank == 0: + log_to_mlflow(params, solver, solver.rank_topology) + + log.info( + f"Done: {solver.metrics.iterations} iter, error={solver.metrics.final_error:.2e}, " + f"time={solver.metrics.wall_time:.3f}s" + + (f", {solver.metrics.mlups:.1f} MLup/s" if solver.metrics.mlups else "") + ) + + +# ============================================================================= +# Shared utilities +# ============================================================================= + + +def config_to_params(cfg: DictConfig) -> GlobalParams: + """Convert Hydra config to GlobalParams.""" + param_fields = {f.name for f in fields(GlobalParams) if f.init} + return GlobalParams(**{k: cfg[k] for k in param_fields}) + + +def create_solver(params: GlobalParams, comm): + """Create solver instance.""" + common = { + "N": params.N, + "omega": params.omega, + "tolerance": params.tolerance, + "max_iter": params.max_iter, + "use_numba": params.use_numba, + "specified_numba_threads": params.specified_numba_threads, + } + + # Use MPI solvers when n_ranks > 1, sequential otherwise + use_mpi = params.n_ranks > 1 + + if params.solver == "jacobi": + if use_mpi: + return JacobiMPISolver( + **common, strategy=params.strategy, communicator=params.communicator + ) + return JacobiSolver(**common) + else: # fmg + common.update( + n_smooth=params.n_smooth, fmg_post_vcycles=params.fmg_post_vcycles + ) + if use_mpi: + return FMGMPISolver( + **common, strategy=params.strategy, communicator=params.communicator + ) + return FMGSolver(**common) + + +def log_to_mlflow(params: GlobalParams, solver, rank_topology: Optional[List[LocalParams]] = None) -> None: + """Log solver run to MLflow.""" + with mlflow.start_run(run_name=f"{params.solver}_N{params.N}_p{params.n_ranks}") as run: + # Params and metrics from dataclasses + mlflow.log_params(params.to_mlflow()) + mlflow.log_metrics(solver.metrics.to_mlflow()) + + # Timeseries: batch log step-based metrics + timeseries = solver.timeseries.to_mlflow_batch() + if timeseries: + MlflowClient().log_batch(run_id=run.info.run_id, metrics=timeseries) + + # Rank topology artifact (for networkx visualizations) + if rank_topology: + topology_data = [asdict(rp) for rp in rank_topology] + with tempfile.NamedTemporaryFile( + mode="w", suffix=".json", delete=False + ) as f: + json.dump(topology_data, f, indent=2) + temp_path = f.name + mlflow.log_artifact(temp_path, artifact_path="topology") + os.unlink(temp_path) + + +# ============================================================================= +# Entry point +# ============================================================================= + + +if __name__ == "__main__": + if os.environ.get("MPI_WORKER"): + logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s") + if len(sys.argv) < 2: + sys.exit("Error: Worker requires config path argument") + worker(sys.argv[1]) + else: + main() diff --git a/setup_mlflow.py b/setup_mlflow.py deleted file mode 100644 index 89252c1..0000000 --- a/setup_mlflow.py +++ /dev/null @@ -1,6 +0,0 @@ -import mlflow - -# Login to Databricks -mlflow.login(backend="databricks", interactive=True) - -# https://dbc-6756e917-e5fc.cloud.databricks.com diff --git a/src/Poisson/__init__.py b/src/Poisson/__init__.py new file mode 100644 index 0000000..ff2dbaf --- /dev/null +++ b/src/Poisson/__init__.py @@ -0,0 +1,90 @@ +"""MPI Poisson Solver package. + +A modular framework for studying parallel performance of 3D Poisson equation +solvers using MPI domain decomposition. Supports pluggable decomposition +strategies (sliced, cubic) and communication methods (NumPy arrays, custom +MPI datatypes). + +Solvers +------- +Sequential (no MPI): +- JacobiSolver: Kernel benchmarks and single-process solving +- FMGSolver: Full Multigrid without MPI + +Parallel (MPI): +- JacobiMPISolver: Jacobi with domain decomposition +- FMGMPISolver: Full Multigrid with domain decomposition +""" + +from pathlib import Path + +from .datastructures import ( + GlobalParams, + GlobalMetrics, + LocalParams, + LocalMetrics, + RankGeometry, + GridLevel, +) +from .kernels import NumPyKernel, NumbaKernel +from .solvers import ( + JacobiSolver, + JacobiMPISolver, + FMGSolver, + FMGMPISolver, +) +from .mpi import DistributedGrid +from .problems import ( + create_grid_3d, + sinusoidal_exact_solution, + sinusoidal_source_term, + setup_sinusoidal_problem, +) + +__all__ = [ + # Data structures + "GlobalParams", + "GlobalMetrics", + "LocalParams", + "LocalMetrics", + "RankGeometry", + "GridLevel", + # Kernels + "NumPyKernel", + "NumbaKernel", + # Solvers - Sequential + "JacobiSolver", + "FMGSolver", + # Solvers - MPI + "JacobiMPISolver", + "FMGMPISolver", + # Grid + "DistributedGrid", + # Problem setup + "create_grid_3d", + "sinusoidal_exact_solution", + "sinusoidal_source_term", + "setup_sinusoidal_problem", + # Utilities + "get_project_root", +] + + +def get_project_root() -> Path: + """Get project root directory. + + Works reliably in both standalone scripts and Sphinx-Gallery execution. + + Returns + ------- + Path + Project root directory (contains pyproject.toml). + """ + current = Path(__file__).resolve().parent + while current != current.parent: + if (current / "pyproject.toml").exists(): + return current + current = current.parent + + # Fallback: assume standard src layout + return Path(__file__).resolve().parent.parent.parent diff --git a/src/Poisson/datastructures.py b/src/Poisson/datastructures.py new file mode 100644 index 0000000..fb76b6f --- /dev/null +++ b/src/Poisson/datastructures.py @@ -0,0 +1,222 @@ +"""Data structures for solver configuration and results. + +Architecture: 2x2 matrix of Params vs Metrics × Global vs Local + + Params (input/config) Metrics (output/results) + ───────────────────── ──────────────────────── +Global GlobalParams GlobalMetrics +(same across N, solver, omega, wall_time, mlups, +ranks / agg) n_ranks, strategy... converged, iterations... + +Local LocalParams LocalMetrics +(per-rank) rank, hostname, compute_times[], + neighbors, local_shape... halo_times[]... +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass, field +from typing import Dict, List, Optional, Tuple + +import numpy as np + + +# ============================================================================ +# Global (identical across ranks, or aggregated on rank 0) +# ============================================================================ + + +@dataclass +class GlobalParams: + """Run configuration - validated by Hydra, logged to MLflow as params. + + Immutable configuration set before the run. Identical across all MPI ranks. + """ + + # Required + N: int + + # Solver + solver: str = "jacobi" # "jacobi" | "fmg" + omega: float = 0.8 + tolerance: float = 1e-6 + max_iter: int = 1000 + + # FMG-specific (ignored by Jacobi) + n_smooth: int = 3 + fmg_post_vcycles: int = 1 + + # Parallelization + n_ranks: int = 1 + strategy: Optional[str] = None # "sliced" | "cubic" + communicator: Optional[str] = None # "numpy" | "custom" + + # Numba + use_numba: bool = False + specified_numba_threads: int = 1 # What user requested + + # Experiment tracking + experiment_name: str = "default" + + # Auto-detected at runtime (not from config) + environment: str = field(init=False) + h: float = field(init=False) + + def __post_init__(self): + """Compute derived values after initialization.""" + self.h = 2.0 / (self.N - 1) + self.environment = ( + "hpc" + if os.environ.get("LSB_JOBID") or os.environ.get("SLURM_JOB_ID") + else "local" + ) + + def to_mlflow(self) -> dict: + """Convert to MLflow-compatible params dict (bools as int, exclude derived).""" + exclude = {"h"} # Derived from N, redundant + return { + k: (int(v) if isinstance(v, bool) else v) + for k, v in self.__dict__.items() + if k not in exclude + } + + +@dataclass +class GlobalMetrics: + """Aggregated results - logged to MLflow as metrics. + + Final results computed/aggregated on rank 0. + """ + + converged: bool = False + iterations: int = 0 + final_residual: Optional[float] = None + final_error: Optional[float] = None # L2 error vs analytical solution + final_alg_error: Optional[float] = None # ||f - Au|| algebraic residual + wall_time: Optional[float] = None + + # Timing breakdown (sum across all iterations) + total_compute_time: Optional[float] = None + total_halo_time: Optional[float] = None + + # Performance metrics + mlups: Optional[float] = None # Million Lattice Updates per Second + bandwidth_gb_s: Optional[float] = None # Memory bandwidth in GB/s + + # Numba runtime info (what was actually available) + observed_numba_threads: Optional[int] = None + + def to_mlflow(self) -> dict: + """Convert to MLflow-compatible dict (no None, bools as int).""" + return { + k: (int(v) if isinstance(v, bool) else v) + for k, v in self.__dict__.items() + if v is not None + } + + +# ============================================================================ +# Local (per-rank) +# ============================================================================ + + +@dataclass +class LocalParams: + """Per-rank geometry - gathered to rank 0, logged as artifact. + + Per-rank topology information. + """ + + rank: int + hostname: str = "" + cart_coords: Optional[Tuple[int, int, int]] = None + neighbors: Dict[str, Optional[int]] = field(default_factory=dict) + local_shape: Optional[Tuple[int, int, int]] = None + global_start: Optional[Tuple[int, int, int]] = None + global_end: Optional[Tuple[int, int, int]] = None + # CPU binding info for socket/node visualization + cpu_ids: Optional[List[int]] = None # Cores this rank can run on + + +@dataclass +class LocalMetrics: + """Per-rank timeseries - gathered for topology artifact. + + Per-rank timing data. Accumulated during solve, logged post-solve. + """ + + # Per-rank timing (gathered to artifact for load balancing analysis) + compute_times: List[float] = field(default_factory=list) + halo_times: List[float] = field(default_factory=list) + + # Global (rank 0 only - logged as step metrics for convergence charts) + residual_history: List[float] = field(default_factory=list) + + def clear(self): + """Clear all timeseries data.""" + self.compute_times.clear() + self.halo_times.clear() + self.residual_history.clear() + + def to_mlflow_batch(self) -> list: + """Convert timeseries to MLflow Metric objects for batch logging.""" + from mlflow.entities import Metric + + return [ + Metric(key=name, value=value, timestamp=0, step=step) + for name, values in self.__dict__.items() + for step, value in enumerate(values) + ] + + +# ============================================================================ +# MPI Grid Geometry +# ============================================================================ + + +@dataclass +class RankGeometry: + """Per-rank grid geometry information. + + Used by DistributedGrid to describe the local portion of the domain. + """ + + rank: int + local_shape: Tuple[int, int, int] + halo_shape: Tuple[int, int, int] + global_start: Tuple[int, int, int] + global_end: Tuple[int, int, int] + neighbors: Dict[str, Optional[int]] + + +# ============================================================================ +# Multigrid-specific +# ============================================================================ + + +@dataclass +class GridLevel: + """One level in the multigrid hierarchy. + + Each level has its own grid size, arrays, and smoothing kernel. + The grid member is only set for MPI solvers (DistributedGrid). + """ + + level: int + N: int + h: float + u: np.ndarray + u_temp: np.ndarray + f: np.ndarray + r: np.ndarray + kernel: object + grid: object = None # DistributedGrid for MPI, None for sequential + + +# ============================================================================ +# Legacy aliases (for backwards compatibility during migration) +# ============================================================================ + +# These will be removed after full migration +LocalSeries = LocalMetrics # Alias for backwards compatibility diff --git a/src/Poisson/kernels.py b/src/Poisson/kernels.py new file mode 100644 index 0000000..80fdffd --- /dev/null +++ b/src/Poisson/kernels.py @@ -0,0 +1,97 @@ +"""Jacobi iteration kernels. + +Simple kernel implementations - tracking is handled by the solver. +""" + +import numpy as np +import numba +from numba import njit, prange + + +@njit(parallel=True) +def _jacobi_step_numba( + uold: np.ndarray, u: np.ndarray, f: np.ndarray, h: float, omega: float +) -> float: + """Numba JIT implementation of Jacobi iteration step.""" + c = 1.0 / 6.0 + h2 = h * h + + for i in prange(1, u.shape[0] - 1): + for j in range(1, u.shape[1] - 1): + for k in range(1, u.shape[2] - 1): + u[i, j, k] = ( + omega + * c + * ( + uold[i - 1, j, k] + + uold[i + 1, j, k] + + uold[i, j - 1, k] + + uold[i, j + 1, k] + + uold[i, j, k - 1] + + uold[i, j, k + 1] + + h2 * f[i, j, k] + ) + + (1.0 - omega) * uold[i, j, k] + ) + + return 0.0 # Residual computed separately in solver + + +class NumPyKernel: + """NumPy-based Jacobi kernel.""" + + def __init__(self, omega: float, specified_numba_threads: int = 1): + self.omega = omega + self.observed_numba_threads = None # Not applicable for NumPy + + def step(self, uold: np.ndarray, u: np.ndarray, f: np.ndarray, h: float): + """Perform one Jacobi iteration step.""" + c = 1.0 / 6.0 + h2 = h * h + + u[1:-1, 1:-1, 1:-1] = ( + self.omega + * c + * ( + uold[0:-2, 1:-1, 1:-1] + + uold[2:, 1:-1, 1:-1] + + uold[1:-1, 0:-2, 1:-1] + + uold[1:-1, 2:, 1:-1] + + uold[1:-1, 1:-1, 0:-2] + + uold[1:-1, 1:-1, 2:] + + h2 * f[1:-1, 1:-1, 1:-1] + ) + + (1.0 - self.omega) * uold[1:-1, 1:-1, 1:-1] + ) + + def warmup(self, warmup_size: int = 10): + """No-op for NumPy kernel.""" + pass + + +class NumbaKernel: + """Numba JIT-compiled Jacobi kernel.""" + + def __init__(self, omega: float, specified_numba_threads: int = 1): + self.omega = omega + + # Set requested threads (may be clamped by NUMBA_NUM_THREADS env var) + if specified_numba_threads is not None: + numba.set_num_threads(specified_numba_threads) + + # Record what Numba actually reports + self.observed_numba_threads = numba.get_num_threads() + + def step(self, uold: np.ndarray, u: np.ndarray, f: np.ndarray, h: float): + """Perform one Jacobi iteration step.""" + _jacobi_step_numba(uold, u, f, h, self.omega) + + def warmup(self, warmup_size: int = 10): + """Trigger JIT compilation with a small problem.""" + h = 2.0 / (warmup_size - 1) + u1 = np.zeros((warmup_size, warmup_size, warmup_size), dtype=np.float64) + u2 = np.zeros_like(u1) + f = np.random.randn(warmup_size, warmup_size, warmup_size) + for _ in range(5): + _jacobi_step_numba(u1, u2, f, h, self.omega) + u1, u2 = u2, u1 diff --git a/src/Poisson/mpi/__init__.py b/src/Poisson/mpi/__init__.py new file mode 100644 index 0000000..38921e4 --- /dev/null +++ b/src/Poisson/mpi/__init__.py @@ -0,0 +1,22 @@ +"""MPI domain decomposition and communication. + +This package provides: +- DistributedGrid: Unified interface for parallel grids +- CartesianDecomposition: Domain splitting with MPI topology +- HaloExchanger: Strategies for halo exchange (numpy/datatype) +- RankGeometry: Exported from datastructures for convenience +""" + +from .grid import DistributedGrid +from .decomposition import CartesianDecomposition +from .halo import HaloExchanger, NumpyHaloExchanger, DatatypeHaloExchanger +from ..datastructures import RankGeometry + +__all__ = [ + "DistributedGrid", + "CartesianDecomposition", + "HaloExchanger", + "NumpyHaloExchanger", + "DatatypeHaloExchanger", + "RankGeometry", +] diff --git a/src/Poisson/mpi/decomposition.py b/src/Poisson/mpi/decomposition.py new file mode 100644 index 0000000..893c14c --- /dev/null +++ b/src/Poisson/mpi/decomposition.py @@ -0,0 +1,148 @@ +"""Domain decomposition with MPI Cartesian topology.""" + +from __future__ import annotations +from mpi4py import MPI + + +class CartesianDecomposition: + """Handles MPI Cartesian topology and domain splitting. + + Creates a Cartesian communicator and computes how the global + domain is distributed across ranks. + + Parameters + ---------- + N : int + Global grid size (N x N x N including boundaries). + comm : MPI.Comm + MPI communicator. + strategy : str + 'sliced' for 1D decomposition along z-axis, + 'cubic' for 3D decomposition. + """ + + def __init__(self, N: int, comm: MPI.Comm, strategy: str = "sliced"): + self.N = N + self.comm = comm + self.rank = comm.Get_rank() + self.size = comm.Get_size() + self.strategy = strategy + + # Determine processor grid dimensions + self.dims = self._compute_dims(strategy) + self.pz, self.py, self.px = self.dims + + # Create Cartesian topology + self.cart_comm, self._proc_coords = self._create_cartesian_topology() + + # Discover neighbors + self.neighbors = self._find_neighbors() + + # Compute local domain + self.local_shape, self.halo_shape, self.global_start, self.global_end = ( + self._compute_local_domain() + ) + + # Track physical boundaries + self.is_boundary = self._find_boundaries() + + def _compute_dims(self, strategy: str) -> list[int]: + """Compute processor grid dimensions [pz, py, px].""" + if strategy == "sliced": + return [self.size, 1, 1] + elif strategy == "cubic": + dims = list(MPI.Compute_dims(self.size, 3)) + return [dims[2], dims[1], dims[0]] + else: + raise ValueError(f"Unknown strategy: {strategy}. Use 'sliced' or 'cubic'.") + + def _create_cartesian_topology(self): + """Create MPI Cartesian communicator.""" + # dims in [pz, py, px] order, but Create_cart expects [px, py, pz] + cart_dims = [self.px, self.py, self.pz] + periods = [False, False, False] + + cart_comm = self.comm.Create_cart( + dims=cart_dims, periods=periods, reorder=False + ) + + # Get coordinates [ix, iy, iz] and convert to [iz, iy, ix] for array indexing + cart_coords = cart_comm.Get_coords(self.rank) + proc_coords = (cart_coords[2], cart_coords[1], cart_coords[0]) + + return cart_comm, proc_coords + + def _find_neighbors(self) -> dict[str, int | None]: + """Use Cart_shift to find neighbor ranks.""" + neighbors = {} + + # X direction (cart direction 0) + x_src, x_dest = self.cart_comm.Shift(0, 1) + neighbors["x_lower"] = x_src if x_src >= 0 else None + neighbors["x_upper"] = x_dest if x_dest >= 0 else None + + # Y direction (cart direction 1) + y_src, y_dest = self.cart_comm.Shift(1, 1) + neighbors["y_lower"] = y_src if y_src >= 0 else None + neighbors["y_upper"] = y_dest if y_dest >= 0 else None + + # Z direction (cart direction 2) + z_src, z_dest = self.cart_comm.Shift(2, 1) + neighbors["z_lower"] = z_src if z_src >= 0 else None + neighbors["z_upper"] = z_dest if z_dest >= 0 else None + + return neighbors + + def _compute_local_domain(self): + """Compute local domain size and position. + + Decomposes the interior points (indices 1 to N-2). + Boundaries (indices 0 and N-1) are handled separately. + """ + N = self.N + interior_N = N - 2 + + def split_interior(n_interior, n_parts): + """Split n_interior points among n_parts ranks.""" + base = n_interior // n_parts + rem = n_interior % n_parts + counts = [base + (1 if i < rem else 0) for i in range(n_parts)] + starts = [1 + sum(counts[:i]) for i in range(n_parts)] + return counts, starts + + iz, iy, ix = self._proc_coords + + z_counts, z_starts = split_interior(interior_N, self.pz) + y_counts, y_starts = split_interior(interior_N, self.py) + x_counts, x_starts = split_interior(interior_N, self.px) + + local_nz = z_counts[iz] + local_ny = y_counts[iy] + local_nx = x_counts[ix] + + global_start_z = z_starts[iz] + global_start_y = y_starts[iy] + global_start_x = x_starts[ix] + + local_shape = (local_nz, local_ny, local_nx) + halo_shape = (local_nz + 2, local_ny + 2, local_nx + 2) + global_start = (global_start_z, global_start_y, global_start_x) + global_end = ( + global_start_z + local_nz, + global_start_y + local_ny, + global_start_x + local_nx, + ) + + return local_shape, halo_shape, global_start, global_end + + def _find_boundaries(self) -> dict[str, bool]: + """Determine which faces are physical boundaries.""" + N = self.N + return { + "z_lower": self.global_start[0] == 1, + "z_upper": self.global_end[0] == N - 1, + "y_lower": self.global_start[1] == 1, + "y_upper": self.global_end[1] == N - 1, + "x_lower": self.global_start[2] == 1, + "x_upper": self.global_end[2] == N - 1, + } diff --git a/src/Poisson/mpi/grid.py b/src/Poisson/mpi/grid.py new file mode 100644 index 0000000..cd0ec7d --- /dev/null +++ b/src/Poisson/mpi/grid.py @@ -0,0 +1,193 @@ +"""Distributed grid abstraction for parallel computation. + +This module provides a unified DistributedGrid class that encapsulates: +- Domain decomposition (sliced or cubic) with MPI Cartesian topology +- Halo exchange communication (numpy buffers or MPI datatypes) +- Array allocation and interior/boundary handling + +Solvers interact with this single interface rather than managing +MPI details directly. +""" + +from __future__ import annotations + +import numpy as np +from mpi4py import MPI + +from ..datastructures import LocalParams +from .decomposition import CartesianDecomposition +from .halo import create_halo_exchanger + + +class DistributedGrid: + """Unified distributed grid for parallel Poisson solvers. + + Handles both 1D (sliced) and 3D (cubic) decomposition using MPI + Cartesian topology for proper neighbor discovery. + + Parameters + ---------- + N : int + Global grid size (N x N x N including boundaries) + comm : MPI.Comm + MPI communicator + strategy : str + 'sliced' for 1D decomposition along z-axis (default) + 'cubic' for 3D decomposition + halo_exchange : str + 'numpy' for buffer-based exchange (default) + 'custom' for MPI derived datatypes (zero-copy) + + Example + ------- + >>> grid = DistributedGrid(N=65, comm=MPI.COMM_WORLD, strategy='cubic') + >>> u = grid.allocate() # Allocate array with halos + >>> grid.fill_source_term(f) # Fill source term + >>> grid.sync_halos(u) # Exchange halo data + """ + + def __init__( + self, + N: int, + comm: MPI.Comm = MPI.COMM_WORLD, + strategy: str = "sliced", + halo_exchange: str = "numpy", + ): + self.N = N + self.comm = comm + self.rank = self.comm.Get_rank() + self.size = self.comm.Get_size() + self.strategy = strategy + self.halo_exchange_type = halo_exchange + + # Domain decomposition + self._decomp = CartesianDecomposition(N, self.comm, strategy) + + # Copy decomposition attributes for direct access + self.dims = self._decomp.dims + self.pz, self.py, self.px = self.dims + self.cart_comm = self._decomp.cart_comm + self.neighbors = self._decomp.neighbors + self.local_shape = self._decomp.local_shape + self.halo_shape = self._decomp.halo_shape + self.global_start = self._decomp.global_start + self.global_end = self._decomp.global_end + self.is_boundary = self._decomp.is_boundary + + # Halo exchange strategy + self._halo_exchanger = create_halo_exchanger(halo_exchange) + self._halo_exchanger.setup(self.local_shape, self.neighbors) + + # Grid spacing + self.h = 2.0 / (N - 1) + + def allocate(self, dtype=np.float64) -> np.ndarray: + """Allocate a local array with halo zones.""" + return np.zeros(self.halo_shape, dtype=dtype) + + def sync_halos(self, arr: np.ndarray): + """Exchange halo data with all neighbors.""" + self._halo_exchanger.exchange(arr, self.cart_comm, self.neighbors) + + # Boundary face slices: direction -> array index tuple + _BOUNDARY_SLICES = { + "z_lower": (0, slice(None), slice(None)), + "z_upper": (-1, slice(None), slice(None)), + "y_lower": (slice(None), 0, slice(None)), + "y_upper": (slice(None), -1, slice(None)), + "x_lower": (slice(None), slice(None), 0), + "x_upper": (slice(None), slice(None), -1), + } + + def apply_boundary_conditions(self, arr: np.ndarray, value: float = 0.0): + """Apply Dirichlet boundary conditions at physical boundaries.""" + for direction, is_bc in self.is_boundary.items(): + if is_bc: + arr[self._BOUNDARY_SLICES[direction]] = value + + def get_rank_info(self) -> LocalParams: + """Get topology info for this rank (for MLflow artifact).""" + import os + + # Get CPU affinity (cores this rank can run on) + try: + cpu_ids = sorted(os.sched_getaffinity(0)) + except (AttributeError, OSError): + cpu_ids = None # Not available on all platforms (e.g., macOS) + + return LocalParams( + rank=self.rank, + hostname=MPI.Get_processor_name(), + cart_coords=tuple(self.cart_comm.Get_coords(self.rank)), + neighbors=self.neighbors.copy(), + local_shape=self.local_shape, + global_start=self.global_start, + global_end=self.global_end, + cpu_ids=cpu_ids, + ) + + def _get_physical_coords(self): + """Compute physical coordinate meshgrid for local interior.""" + gs = self.global_start + nz, ny, nx = self.local_shape + + z_phys = -1.0 + np.arange(gs[0], gs[0] + nz) * self.h + y_phys = -1.0 + np.arange(gs[1], gs[1] + ny) * self.h + x_phys = -1.0 + np.arange(gs[2], gs[2] + nx) * self.h + + return np.meshgrid(z_phys, y_phys, x_phys, indexing="ij") + + def fill_source_term(self, f: np.ndarray): + """Fill source term: f = 3pi^2 sin(pi*x) sin(pi*y) sin(pi*z).""" + Z, Y, X = self._get_physical_coords() + f[1:-1, 1:-1, 1:-1] = ( + 3 * np.pi**2 * np.sin(np.pi * X) * np.sin(np.pi * Y) * np.sin(np.pi * Z) + ) + + def compute_exact_solution(self, u: np.ndarray): + """Fill exact solution: u = sin(pi*x) sin(pi*y) sin(pi*z).""" + Z, Y, X = self._get_physical_coords() + u[1:-1, 1:-1, 1:-1] = np.sin(np.pi * X) * np.sin(np.pi * Y) * np.sin(np.pi * Z) + + def compute_l2_error(self, u: np.ndarray) -> float: + """Compute L2 error against exact solution.""" + u_exact = self.allocate() + self.compute_exact_solution(u_exact) + + local_sq_error = np.sum((u[1:-1, 1:-1, 1:-1] - u_exact[1:-1, 1:-1, 1:-1]) ** 2) + global_sq_error = np.empty(1) + self.cart_comm.Allreduce( + np.array([local_sq_error]), global_sq_error, op=MPI.SUM + ) + + return float(np.sqrt(self.h**3 * global_sq_error[0])) + + def get_halo_size_bytes(self) -> int: + """Calculate total bytes transferred per halo exchange.""" + nz, ny, nx = self.local_shape + # Face sizes for each axis: z-face=(ny*nx), y-face=(nz*nx), x-face=(nz*ny) + face_sizes = [ny * nx, nz * nx, nz * ny] + axes = ["z", "y", "x"] + + total = 0 + for axis, face_size in zip(axes, face_sizes): + for side in ["lower", "upper"]: + if self.neighbors.get(f"{axis}_{side}") is not None: + total += face_size * 8 * 2 # float64, send+recv + return total + + def coarsen(self) -> "DistributedGrid": + """Create a coarsened grid for multigrid. + + Returns a new DistributedGrid with N_coarse = (N-1)//2 + 1. + """ + N_coarse = (self.N - 1) // 2 + 1 + if N_coarse < 3: + raise ValueError("Grid too small to coarsen further") + + return DistributedGrid( + N_coarse, + self.comm, + strategy=self.strategy, + halo_exchange=self.halo_exchange_type, + ) diff --git a/src/Poisson/mpi/halo.py b/src/Poisson/mpi/halo.py new file mode 100644 index 0000000..29b48e1 --- /dev/null +++ b/src/Poisson/mpi/halo.py @@ -0,0 +1,185 @@ +"""Halo exchange implementations for distributed grids.""" + +from __future__ import annotations +from abc import ABC, abstractmethod + +import numpy as np +from mpi4py import MPI + + +# Axis names for neighbor dict keys +AXIS_NAMES = ["z", "y", "x"] + + +def _neighbor_ranks(neighbors: dict, axis_idx: int) -> tuple[int, int]: + """Get neighbor ranks for an axis, defaulting to MPI.PROC_NULL.""" + name = AXIS_NAMES[axis_idx] + lo = neighbors.get(f"{name}_lower") + hi = neighbors.get(f"{name}_upper") + return (lo if lo is not None else MPI.PROC_NULL, + hi if hi is not None else MPI.PROC_NULL) + + +def _make_slice(axis_idx: int, axis_val, ndim: int = 3): + """Build slice tuple with axis_val at axis_idx, slice(1,-1) elsewhere.""" + slices = [slice(1, -1)] * ndim + slices[axis_idx] = axis_val + return tuple(slices) + + +class HaloExchanger(ABC): + """Abstract base for halo exchange strategies.""" + + @abstractmethod + def setup(self, local_shape: tuple[int, int, int], neighbors: dict): + """Initialize exchange buffers or datatypes.""" + pass + + @abstractmethod + def exchange(self, arr: np.ndarray, cart_comm: MPI.Comm, neighbors: dict): + """Perform halo exchange on array.""" + pass + + +class NumpyHaloExchanger(HaloExchanger): + """Halo exchange using numpy buffer copies and Sendrecv.""" + + def setup(self, local_shape: tuple[int, int, int], neighbors: dict): + """Pre-compute slices and shapes for each axis.""" + self._axis_info = [] + for axis_idx in range(3): + # recv_shape: dimensions excluding this axis + recv_shape = tuple(local_shape[i] for i in range(3) if i != axis_idx) + self._axis_info.append({ + "send_hi": _make_slice(axis_idx, -2), + "send_lo": _make_slice(axis_idx, 1), + "recv_lo": _make_slice(axis_idx, 0), + "recv_hi": _make_slice(axis_idx, -1), + "recv_shape": recv_shape, + }) + + def exchange(self, arr: np.ndarray, cart_comm: MPI.Comm, neighbors: dict): + """Exchange halos using Sendrecv with buffer copies.""" + for axis_idx, info in enumerate(self._axis_info): + lo, hi = _neighbor_ranks(neighbors, axis_idx) + if lo == MPI.PROC_NULL and hi == MPI.PROC_NULL: + continue + + name = AXIS_NAMES[axis_idx] + tag = axis_idx * 2 + + # Send to upper, receive from lower + send = np.ascontiguousarray(arr[info["send_hi"]]) + recv = np.empty_like(send) + cart_comm.Sendrecv(send, hi, tag, recv, lo, tag) + if neighbors.get(f"{name}_lower") is not None: + arr[info["recv_lo"]] = recv.reshape(info["recv_shape"]) + + # Send to lower, receive from upper + send = np.ascontiguousarray(arr[info["send_lo"]]) + recv = np.empty_like(send) + cart_comm.Sendrecv(send, lo, tag + 1, recv, hi, tag + 1) + if neighbors.get(f"{name}_upper") is not None: + arr[info["recv_hi"]] = recv.reshape(info["recv_shape"]) + + +class DatatypeHaloExchanger(HaloExchanger): + """Halo exchange using MPI derived datatypes (zero-copy).""" + + def setup(self, local_shape: tuple[int, int, int], neighbors: dict): + """Create MPI datatypes and pre-compute offsets.""" + nz, ny, nx = local_shape + hz, hy, hx = nz + 2, ny + 2, nx + 2 # halo shape + self._halo_shape = (hz, hy, hx) + + # Strides for flat indexing + strides = (hy * hx, hx, 1) + + def flat_idx(z, y, x): + return z * strides[0] + y * strides[1] + x * strides[2] + + # Create datatypes and offsets for each axis + self._axis_info = [] + + # Z-face datatype + dt_z = MPI.DOUBLE.Create_vector(ny, nx, hx) + dt_z.Commit() + self._axis_info.append({ + "dt": dt_z, + "send_hi": flat_idx(hz - 2, 1, 1), + "send_lo": flat_idx(1, 1, 1), + "recv_lo": flat_idx(0, 1, 1), + "recv_hi": flat_idx(hz - 1, 1, 1), + }) + + # Y-face datatype + dt_y = MPI.DOUBLE.Create_vector(nz, nx, hy * hx) + dt_y.Commit() + self._axis_info.append({ + "dt": dt_y, + "send_hi": flat_idx(1, hy - 2, 1), + "send_lo": flat_idx(1, 1, 1), + "recv_lo": flat_idx(1, 0, 1), + "recv_hi": flat_idx(1, hy - 1, 1), + }) + + # X-face datatype (2D strided) + row = MPI.DOUBLE.Create_vector(ny, 1, hx) + row.Commit() + dt_x = row.Create_hvector(nz, 1, hy * hx * MPI.DOUBLE.Get_size()) + dt_x.Commit() + row.Free() + self._axis_info.append({ + "dt": dt_x, + "send_hi": flat_idx(1, 1, hx - 2), + "send_lo": flat_idx(1, 1, 1), + "recv_lo": flat_idx(1, 1, 0), + "recv_hi": flat_idx(1, 1, hx - 1), + }) + + def exchange(self, arr: np.ndarray, cart_comm: MPI.Comm, neighbors: dict): + """Exchange halos using MPI datatypes.""" + flat = arr.ravel() + + for axis_idx, info in enumerate(self._axis_info): + lo, hi = _neighbor_ranks(neighbors, axis_idx) + if lo == MPI.PROC_NULL and hi == MPI.PROC_NULL: + continue + + name = AXIS_NAMES[axis_idx] + tag = axis_idx * 2 + dt = info["dt"] + + # Send to upper, receive from lower + cart_comm.Sendrecv( + [flat[info["send_hi"]:], 1, dt], hi, tag, + [flat[info["recv_lo"]:], 1, dt], lo, tag, + ) + if neighbors.get(f"{name}_lower") is None: + arr[_make_slice(axis_idx, 0)] = 0.0 + + # Send to lower, receive from upper + cart_comm.Sendrecv( + [flat[info["send_lo"]:], 1, dt], lo, tag + 1, + [flat[info["recv_hi"]:], 1, dt], hi, tag + 1, + ) + if neighbors.get(f"{name}_upper") is None: + arr[_make_slice(axis_idx, -1)] = 0.0 + + def __del__(self): + """Free MPI datatypes.""" + if hasattr(self, "_axis_info"): + for info in self._axis_info: + dt = info["dt"] + if dt != MPI.DATATYPE_NULL: + dt.Free() + + +def create_halo_exchanger(exchange_type: str) -> HaloExchanger: + """Factory: 'numpy' for buffer-based, 'custom' for MPI datatypes.""" + if exchange_type == "numpy": + return NumpyHaloExchanger() + elif exchange_type == "custom": + return DatatypeHaloExchanger() + else: + raise ValueError(f"Unknown halo_exchange type: {exchange_type}") diff --git a/src/Poisson/problems.py b/src/Poisson/problems.py new file mode 100644 index 0000000..708a919 --- /dev/null +++ b/src/Poisson/problems.py @@ -0,0 +1,48 @@ +"""Test problems and exact solutions for Poisson equation.""" + +from __future__ import annotations + +import numpy as np + + +def create_grid_3d( + N: int, value: float = 0.0, boundary_value: float = 0.0 +) -> np.ndarray: + """Create 3D grid with specified interior and boundary values.""" + u = np.full((N, N, N), value, dtype=np.float64) + u[[0, -1], :, :] = boundary_value + u[:, [0, -1], :] = boundary_value + u[:, :, [0, -1]] = boundary_value + return u + + +def sinusoidal_exact_solution(N: int) -> np.ndarray: + """Exact solution: sin(π x)sin(π y)sin(π z) on [-1,1]³. + + Array layout is (Z, Y, X) to match decomposition conventions. + """ + # Use (Z, Y, X) axis ordering to match decomposition + zs, ys, xs = np.ogrid[-1 : 1 : complex(N), -1 : 1 : complex(N), -1 : 1 : complex(N)] + return np.sin(np.pi * xs) * np.sin(np.pi * ys) * np.sin(np.pi * zs) + + +def sinusoidal_source_term(N: int) -> np.ndarray: + """Source term: f = 3π² sin(π x)sin(π y)sin(π z). + + For -∇²u = f with u = sin(πx)sin(πy)sin(πz) in 3D. + Array layout is (Z, Y, X) to match decomposition conventions. + """ + # Use (Z, Y, X) axis ordering to match decomposition + zs, ys, xs = np.ogrid[-1 : 1 : complex(N), -1 : 1 : complex(N), -1 : 1 : complex(N)] + return 3 * np.pi**2 * np.sin(np.pi * xs) * np.sin(np.pi * ys) * np.sin(np.pi * zs) + + +def setup_sinusoidal_problem( + N: int, initial_value: float = 0.0 +) -> tuple[np.ndarray, np.ndarray, np.ndarray, float]: + """Set up sinusoidal test problem: -∇²u = 3π² sin(π x)sin(π y)sin(π z).""" + h = 2.0 / (N - 1) + u1 = create_grid_3d(N, value=initial_value, boundary_value=0.0) + u2 = u1.copy() + f = sinusoidal_source_term(N) + return u1, u2, f, h diff --git a/src/Poisson/solvers/__init__.py b/src/Poisson/solvers/__init__.py new file mode 100644 index 0000000..542d617 --- /dev/null +++ b/src/Poisson/solvers/__init__.py @@ -0,0 +1,24 @@ +"""Poisson Solvers. + +Consistent naming: {Method}Solver for sequential, {Method}MPISolver for parallel. + +Sequential (no MPI): +- JacobiSolver: Kernel benchmarks and single-process solving +- FMGSolver: Full Multigrid without MPI + +Parallel (MPI): +- JacobiMPISolver: Jacobi with domain decomposition +- FMGMPISolver: Full Multigrid with domain decomposition +""" + +from .jacobi import JacobiSolver +from .jacobi_mpi import JacobiMPISolver +from .fmg import FMGSolver +from .fmg_mpi import FMGMPISolver + +__all__ = [ + "JacobiSolver", + "JacobiMPISolver", + "FMGSolver", + "FMGMPISolver", +] diff --git a/src/Poisson/solvers/base.py b/src/Poisson/solvers/base.py new file mode 100644 index 0000000..78df25e --- /dev/null +++ b/src/Poisson/solvers/base.py @@ -0,0 +1,200 @@ +"""Base class for solvers.""" + +import time +from abc import ABC, abstractmethod + +import numpy as np + +from ..datastructures import GlobalMetrics, LocalMetrics +from ..problems import sinusoidal_exact_solution + + +class BaseSolver(ABC): + """Abstract base for all Poisson solvers.""" + + # Bytes per lattice update: 7-point stencil = 6 neighbors + center + f = 8 mem-ops × 8 bytes + BYTES_PER_POINT = 64 + + def __init__( + self, + N: int, + omega: float = 0.8, + max_iter: int = 1000, + tolerance: float = 1e-6, + ): + self.N = N + self.omega = omega + self.max_iter = max_iter + self.tolerance = tolerance + self.h = 2.0 / (N - 1) + + # Metrics containers (match datastructures.py naming) + self.metrics = GlobalMetrics() + self.timeseries = LocalMetrics() + + # Timing accumulators + self._time_compute = 0.0 + self._time_halo = 0.0 + + @abstractmethod + def solve(self) -> GlobalMetrics: + """Execute the solver. Returns results.""" + pass + + def warmup(self, warmup_size: int = 10): + """Warmup kernel (trigger Numba JIT if used).""" + self.kernel.warmup(warmup_size=warmup_size) + + def compute_l2_error(self) -> float: + """Compute L2 error against analytical solution. + + Uses hook methods for flexibility: + - _get_solution_array(): Returns the solution array + - _compute_l2_norm(): Computes norm (handles MPI allreduce if needed) + """ + u = self._get_solution_array() + u_exact = sinusoidal_exact_solution(self.N) + l2_error = self._compute_l2_norm(u, u_exact) + self.metrics.final_error = l2_error + return l2_error + + def post_solve(self): + """Compute all error metrics after solve. + + Call this after solve() to populate: + - metrics.final_error (L2 error vs analytical) + - metrics.final_alg_error (algebraic residual norm) + - rank_topology (for MPI solvers) + """ + self.compute_l2_error() + self.compute_alg_residual() + self.rank_topology = self._gather_topology() + + def _gather_topology(self): + """Gather rank topology. Override for MPI solvers.""" + return None + + def _get_solution_array(self) -> np.ndarray: + """Return the solution array.""" + return self.u + + def _get_source_array(self) -> np.ndarray: + """Return the source term array.""" + return self.f + + def compute_alg_residual( + self, + u: np.ndarray = None, + f: np.ndarray = None, + h: float = None, + r: np.ndarray = None, + ) -> float: + """Compute algebraic residual r = f - Au and return RMS norm. + + Parameters + ---------- + u : array, optional + Solution array. If None, uses _get_solution_array(). + f : array, optional + Source term. If None, uses _get_source_array(). + h : float, optional + Grid spacing. If None, uses self.h. + r : array, optional + Output array for residual. If None, allocates temporary array. + + Returns + ------- + float + RMS norm of the algebraic residual. + + Note: Caller is responsible for halo sync before and BC after if needed. + """ + if u is None: + u = self._get_solution_array() + if f is None: + f = self._get_source_array() + if h is None: + h = self.h + if r is None: + r = np.zeros_like(u) + + # Compute r = f - Au (interior only) + h2 = h * h + u_center = u[1:-1, 1:-1, 1:-1] + u_neighbors = ( + u[0:-2, 1:-1, 1:-1] + + u[2:, 1:-1, 1:-1] + + u[1:-1, 0:-2, 1:-1] + + u[1:-1, 2:, 1:-1] + + u[1:-1, 1:-1, 0:-2] + + u[1:-1, 1:-1, 2:] + ) + laplacian = (u_neighbors - 6.0 * u_center) / h2 + r[1:-1, 1:-1, 1:-1] = f[1:-1, 1:-1, 1:-1] + laplacian + + # Compute RMS norm + interior = r[1:-1, 1:-1, 1:-1] + local_sum_sq = np.sum(interior**2) + local_pts = float(interior.size) + + global_sum_sq = self._reduce_sum(local_sum_sq) + global_pts = self._reduce_sum(local_pts) + + alg_residual = np.sqrt(global_sum_sq / global_pts) # RMS norm + self.metrics.final_alg_error = alg_residual + return alg_residual + + def _get_time(self) -> float: + """Get current time. Override for MPI timing.""" + return time.perf_counter() + + def _reduce_sum(self, local_sum: float) -> float: + """Reduce sum across ranks. Override for MPI.""" + return local_sum + + def _is_root(self) -> bool: + """True if this rank should log metrics. Override for MPI.""" + return True + + def _sync_halos(self, u: np.ndarray, lvl=None) -> float: + """Sync halo regions. No-op for sequential. Override for MPI.""" + return 0.0 + + def _apply_boundary_conditions(self, u: np.ndarray, lvl=None): + """Apply boundary conditions. No-op for sequential. Override for MPI.""" + pass + + def _barrier(self): + """Synchronize all ranks before timing. No-op for sequential.""" + pass + + def _compute_l2_norm(self, u: np.ndarray, u_exact: np.ndarray) -> float: + """Compute L2 norm. Override for MPI allreduce.""" + diff = u[1:-1, 1:-1, 1:-1] - u_exact[1:-1, 1:-1, 1:-1] + return np.sqrt(np.sum(diff**2) * self.h**3) + + def _reset(self): + """Reset timers and timeseries.""" + self._time_compute = 0.0 + self._time_halo = 0.0 + self.timeseries.clear() + + def _finalize(self, wall_time: float): + """Finalize metrics after solve.""" + if self._is_root(): + self.metrics.final_residual = self.timeseries.residual_history[-1] + self.metrics.total_compute_time = self._time_compute + self.metrics.total_halo_time = self._time_halo + self.metrics.observed_numba_threads = self.kernel.observed_numba_threads + self._compute_metrics(wall_time, self.metrics.iterations) + + def _compute_metrics(self, wall_time: float, iterations: int): + """Compute performance metrics.""" + self.metrics.wall_time = wall_time + self.metrics.iterations = iterations + + n_interior = (self.N - 2) ** 3 + if iterations > 0 and wall_time > 0: + self.metrics.mlups = n_interior * iterations / (wall_time * 1e6) + total_bytes = n_interior * iterations * self.BYTES_PER_POINT + self.metrics.bandwidth_gb_s = total_bytes / (wall_time * 1e9) diff --git a/src/Poisson/solvers/fmg.py b/src/Poisson/solvers/fmg.py new file mode 100644 index 0000000..e1035ec --- /dev/null +++ b/src/Poisson/solvers/fmg.py @@ -0,0 +1,249 @@ +"""Full Multigrid (FMG) Solver - Sequential.""" + +from typing import List + +import numpy as np + +from .base import BaseSolver +from ..datastructures import GridLevel, GlobalMetrics +from ..kernels import NumPyKernel, NumbaKernel +from ..problems import sinusoidal_source_term +from .multigrid_operators import restrict, prolong + + +class FMGSolver(BaseSolver): + """Sequential Full Multigrid solver. + + Implements FMG algorithm without MPI. + Useful for validation and single-node performance testing. + + Parameters + ---------- + N : int + Grid size (N x N x N). Must satisfy (N-1) divisible by 2^levels. + n_smooth : int + Pre/post smoothing iterations (default: 5). + fmg_post_vcycles : int + V-cycles after FMG phase (default: 1). + use_numba : bool + Use Numba JIT kernel (default: False). + omega : float + Relaxation factor (default: 2/3). + max_iter : int + Maximum smoothing iterations for coarsest grid solve (default: 1000). + tolerance : float + Convergence tolerance for final residual check (default: 1e-6). + """ + + def __init__( + self, + N: int, + n_smooth: int = 5, + fmg_post_vcycles: int = 1, + use_numba: bool = False, + omega: float = 2 / 3, + specified_numba_threads: int = 1, + **kwargs, + ): + super().__init__(N, omega=omega, **kwargs) + + self.n_smooth = n_smooth + self.fmg_post_vcycles = fmg_post_vcycles + self.use_numba = use_numba + self.specified_numba_threads = specified_numba_threads + self.min_coarse_size = 5 # Allow coarsening to smaller grids + + # Infer number of levels + self.n_levels = self._infer_levels(N) + + # Initialize kernel (single kernel for all levels) + self._init_kernel() + + # Build grid hierarchy + self.levels: List[GridLevel] = [] + self._build_hierarchy() + + def _infer_levels(self, N: int) -> int: + """Determine number of grid levels.""" + levels = 1 + N_current = N + + while True: + if (N_current - 1) % 2 != 0: + raise ValueError( + f"Grid size N={N_current} not compatible with multigrid " + "(N-1 must be divisible by 2)." + ) + + N_next = (N_current - 1) // 2 + 1 + if N_next < self.min_coarse_size: + break + + levels += 1 + N_current = N_next + + if N_current == 3: + break + + return levels + + def _init_kernel(self): + """Initialize the Jacobi kernel.""" + if self.use_numba: + self.kernel = NumbaKernel( + omega=self.omega, + specified_numba_threads=self.specified_numba_threads, + ) + else: + self.kernel = NumPyKernel(omega=self.omega) + + def _build_hierarchy(self): + """Allocate arrays for all grid levels.""" + N_values = [] + N_current = self.N + for _ in range(self.n_levels): + N_values.append(N_current) + N_current = (N_current - 1) // 2 + 1 + + for level, N in enumerate(N_values): + h = 2.0 / (N - 1) + + lvl = GridLevel( + level=level, + N=N, + h=h, + u=np.zeros((N, N, N), dtype=np.float64), + u_temp=np.zeros((N, N, N), dtype=np.float64), + f=sinusoidal_source_term(N), + r=np.zeros((N, N, N), dtype=np.float64), + kernel=None, # Single kernel at solver level + ) + self.levels.append(lvl) + + def solve(self) -> GlobalMetrics: + """Full Multigrid: solve from coarsest to finest.""" + self._reset() + + self._barrier() # Sync all ranks before timing + t_start = self._get_time() + + # Clear all solution arrays + for lvl in self.levels: + lvl.u.fill(0.0) + lvl.u_temp.fill(0.0) + lvl.r.fill(0.0) + + # Solve on coarsest level + self._coarse_solve(self.levels[-1]) + + # Ascend hierarchy + for level in reversed(range(self.n_levels - 1)): + coarse = self.levels[level + 1] + fine = self.levels[level] + + # Prolong coarse solution as initial guess + self._sync_halos(coarse.u, coarse) + fine.r.fill(0.0) + prolong(coarse.u, fine.r) + self._sync_halos(fine.r, fine) + + fine.u.fill(0.0) + fine.u[1:-1, 1:-1, 1:-1] = fine.r[1:-1, 1:-1, 1:-1] + self._apply_boundary_conditions(fine.u, fine) + + # V-cycle from this level (includes pre-smoothing) + residual = self._v_cycle(level) + + # Post V-cycles with residual tracking + for _ in range(self.fmg_post_vcycles): + residual = self._v_cycle(0) + self.timeseries.residual_history.append(residual) + + self.metrics.converged = residual < self.tolerance + self.metrics.iterations = self.n_levels + self.fmg_post_vcycles + + wall_time = self._get_time() - t_start + self._finalize(wall_time) + return self.metrics + + def _v_cycle(self, level: int) -> float: + """Recursive V-cycle.""" + lvl = self.levels[level] + + # Pre-smoothing + for _ in range(self.n_smooth): + self._smooth(lvl) + + # Compute algebraic residual + self._compute_alg_res(lvl) + + # Get residual norm on finest level + residual = self._residual_norm(lvl.r, lvl) if level == 0 else 0.0 + + # Base case: coarsest level + if level == self.n_levels - 1: + self._coarse_solve(lvl) + return residual + + # Restriction + next_lvl = self.levels[level + 1] + self._sync_halos(lvl.r, lvl) + restrict(lvl.r, next_lvl.f) + self._sync_halos(next_lvl.f, next_lvl) + next_lvl.u.fill(0.0) + + # Recurse + self._v_cycle(level + 1) + + # Prolongation + self._sync_halos(next_lvl.u, next_lvl) + lvl.r.fill(0.0) + prolong(next_lvl.u, lvl.r) + self._sync_halos(lvl.r, lvl) + + lvl.u[1:-1, 1:-1, 1:-1] += lvl.r[1:-1, 1:-1, 1:-1] + self._apply_boundary_conditions(lvl.u, lvl) + + # Post-smoothing + for _ in range(self.n_smooth): + self._smooth(lvl) + + return residual + + def _smooth(self, lvl: GridLevel): + """One Jacobi smoothing step.""" + self._sync_halos(lvl.u, lvl) + t0 = self._get_time() + self.kernel.step(lvl.u, lvl.u_temp, lvl.f, lvl.h) + self._apply_boundary_conditions(lvl.u_temp, lvl) + self._time_compute += self._get_time() - t0 + lvl.u, lvl.u_temp = lvl.u_temp, lvl.u + + def _compute_alg_res(self, lvl: GridLevel): + """Compute algebraic residual r = f - Au for a grid level.""" + self._sync_halos(lvl.u, lvl) + self.compute_alg_residual(lvl.u, lvl.f, lvl.h, lvl.r) + self._apply_boundary_conditions(lvl.r, lvl) + + def _coarse_solve(self, lvl: GridLevel): + """Solve on coarsest grid using max_iter smoothing iterations.""" + for _ in range(self.max_iter): + self._smooth(lvl) + + def _residual_norm(self, r: np.ndarray, lvl: GridLevel = None) -> float: + """Compute RMS residual norm.""" + interior = r[1:-1, 1:-1, 1:-1] + local_sum_sq = np.sum(interior**2) + local_pts = float(interior.size) + + global_sum_sq = self._reduce_sum(local_sum_sq) + global_pts = self._reduce_sum(local_pts) + return np.sqrt(global_sum_sq / global_pts) # RMS norm + + def _get_solution_array(self) -> np.ndarray: + """Return the solution array (finest level).""" + return self.levels[0].u + + def _get_source_array(self) -> np.ndarray: + """Return the source term array (finest level).""" + return self.levels[0].f diff --git a/src/Poisson/solvers/fmg_mpi.py b/src/Poisson/solvers/fmg_mpi.py new file mode 100644 index 0000000..ade18ba --- /dev/null +++ b/src/Poisson/solvers/fmg_mpi.py @@ -0,0 +1,157 @@ +"""MPI-parallel Full Multigrid Solver (extends FMGSolver).""" + +import numpy as np +from mpi4py import MPI + +from .fmg import FMGSolver +from .mpi_mixin import MPISolverMixin +from ..datastructures import GridLevel +from ..mpi.grid import DistributedGrid + + +class FMGMPISolver(MPISolverMixin, FMGSolver): + """Parallel Full Multigrid solver with MPI domain decomposition. + + Extends FMGSolver with distributed grids and halo exchange. + The algorithm (solve, _v_cycle) is inherited from FMGSolver. + + Parameters + ---------- + N : int + Global grid size (N x N x N). + strategy : str + Decomposition: 'sliced' or 'cubic' (default: 'sliced'). + communicator : str + Halo exchange: 'numpy' or 'custom' (default: 'custom'). + """ + + def __init__( + self, + N: int, + strategy: str = "sliced", + communicator: str = "custom", + **kwargs, + ): + # MPI setup before parent init + self._init_mpi() + + self.strategy = strategy + self.communicator = communicator + + # Parent init builds hierarchy (calls _build_hierarchy) + super().__init__(N, **kwargs) + + def _infer_levels(self, N: int) -> int: + """Determine number of levels considering MPI constraints.""" + min_local = 2 # Minimum local interior for restriction/prolongation + + if self.size > 1: + if self.strategy == "cubic": + ranks_per_dim = int(round(self.size ** (1 / 3))) + min_N_for_ranks = max( + (min_local + 2) * ranks_per_dim, self.min_coarse_size + ) + else: + min_N_for_ranks = self.size + 2 + else: + min_N_for_ranks = 3 + + levels = 1 + N_current = N + + while True: + if (N_current - 1) % 2 != 0: + raise ValueError( + f"Grid size N={N_current} not compatible with multigrid " + "(N-1 must be divisible by 2)." + ) + + N_next = (N_current - 1) // 2 + 1 + + # Stop if next level would be too small for MPI + if self.size > 1 and N_next < min_N_for_ranks: + break + + if N_next < self.min_coarse_size: + break + + levels += 1 + N_current = N_next + + if N_current == 3: + break + + return levels + + def _build_hierarchy(self): + """Build grid hierarchy with distributed grids.""" + N_values = [] + N_current = self.N + for _ in range(self.n_levels): + N_values.append(N_current) + N_current = (N_current - 1) // 2 + 1 + + for level, N in enumerate(N_values): + h = 2.0 / (N - 1) + + # Create distributed grid + grid = DistributedGrid( + N, self.comm, strategy=self.strategy, halo_exchange=self.communicator + ) + + # Allocate arrays via grid + u = grid.allocate() + u_temp = grid.allocate() + f = grid.allocate() + r = grid.allocate() + + # Fill source term and apply BCs + grid.fill_source_term(f) + grid.apply_boundary_conditions(u) + grid.apply_boundary_conditions(u_temp) + + lvl = GridLevel( + level=level, + N=N, + h=h, + u=u, + u_temp=u_temp, + f=f, + r=r, + kernel=None, # Single kernel at solver level + grid=grid, + ) + self.levels.append(lvl) + + # ======================================================================== + # Hook method overrides for MPI (grid-level specific) + # ======================================================================== + + def _sync_halos(self, u: np.ndarray, lvl: GridLevel) -> float: + """Sync halo regions with neighbors.""" + if lvl.grid is not None: + t0 = MPI.Wtime() + lvl.grid.sync_halos(u) + halo_time = MPI.Wtime() - t0 + self._time_halo += halo_time + return halo_time + return 0.0 + + def _apply_boundary_conditions(self, u: np.ndarray, lvl: GridLevel): + """Apply Dirichlet boundary conditions at physical boundaries.""" + if lvl.grid is not None: + lvl.grid.apply_boundary_conditions(u) + + def compute_l2_error(self) -> float: + """Compute L2 error against analytical solution (parallel).""" + fine = self.levels[0] + l2_error = fine.grid.compute_l2_error(fine.u) + self.metrics.final_error = l2_error + return l2_error + + def _gather_topology(self): + """Gather rank topology from all MPI ranks.""" + fine = self.levels[0] + rank_info = fine.grid.get_rank_info() + all_ranks = self.comm.gather(rank_info, root=0) + return all_ranks if self.rank == 0 else None diff --git a/src/Poisson/solvers/jacobi.py b/src/Poisson/solvers/jacobi.py new file mode 100644 index 0000000..7d07a2d --- /dev/null +++ b/src/Poisson/solvers/jacobi.py @@ -0,0 +1,103 @@ +"""Sequential Jacobi Solver.""" + +import numpy as np + +from .base import BaseSolver +from ..kernels import NumPyKernel, NumbaKernel +from ..problems import sinusoidal_source_term + + +class JacobiSolver(BaseSolver): + """Sequential Jacobi solver for kernel benchmarks. + + No MPI overhead - runs entirely on a single process. + Useful for benchmarking kernel performance (NumPy vs Numba). + """ + + def __init__( + self, + N: int, + use_numba: bool = False, + specified_numba_threads: int = 1, + **kwargs, + ): + super().__init__(N, **kwargs) + + self.use_numba = use_numba + self.specified_numba_threads = specified_numba_threads + + # Select kernel + self._init_kernel() + + # Allocate arrays + self._init_arrays() + + def _init_kernel(self): + """Initialize the Jacobi kernel.""" + if self.use_numba: + self.kernel = NumbaKernel( + omega=self.omega, + specified_numba_threads=self.specified_numba_threads, + ) + else: + self.kernel = NumPyKernel(omega=self.omega) + + def _init_arrays(self): + """Allocate solution arrays.""" + self.u = np.zeros((self.N, self.N, self.N), dtype=np.float64) + self.u_old = np.zeros((self.N, self.N, self.N), dtype=np.float64) + self.f = sinusoidal_source_term(self.N) + + def solve(self): + """Run Jacobi iteration.""" + self._reset() + + n_interior = (self.N - 2) ** 3 + u, u_old = self.u, self.u_old + + self._barrier() # Sync all ranks before timing + t_start = self._get_time() + + for i in range(self.max_iter): + # Sync halos (no-op for sequential) + t0 = self._get_time() + self._sync_halos(u_old) + self._time_halo += self._get_time() - t0 + + # Compute step + t0 = self._get_time() + self.kernel.step(u_old, u, self.f, self.h) + self._apply_boundary_conditions(u) + self._time_compute += self._get_time() - t0 + + # Compute iterative residual + residual = self._compute_iter_res(u, u_old, n_interior) + self.timeseries.residual_history.append(residual) + + if residual < self.tolerance: + self.metrics.converged = True + self.metrics.iterations = i + 1 + break + + # Swap buffers + u, u_old = u_old, u + else: + self.metrics.iterations = self.max_iter + self.metrics.converged = False + + # Save final solution (u_old has it after swap) + self.u = u_old + + wall_time = self._get_time() - t_start + self._finalize(wall_time) + + return self.metrics + + def _compute_iter_res( + self, u: np.ndarray, u_old: np.ndarray, n_interior: int + ) -> float: + """Compute iterative residual ||u - u_old||.""" + diff = u[1:-1, 1:-1, 1:-1] - u_old[1:-1, 1:-1, 1:-1] + local_sum = np.sum(diff**2) + global_sum = self._reduce_sum(local_sum) + return np.sqrt(global_sum) / n_interior diff --git a/src/Poisson/solvers/jacobi_mpi.py b/src/Poisson/solvers/jacobi_mpi.py new file mode 100644 index 0000000..491650f --- /dev/null +++ b/src/Poisson/solvers/jacobi_mpi.py @@ -0,0 +1,69 @@ +"""MPI-parallel Jacobi Solver (extends JacobiSolver).""" + +import numpy as np +from mpi4py import MPI + +from .jacobi import JacobiSolver +from .mpi_mixin import MPISolverMixin +from ..mpi.grid import DistributedGrid + + +class JacobiMPISolver(MPISolverMixin, JacobiSolver): + """Parallel Jacobi solver with MPI domain decomposition. + + Extends JacobiSolver with MPI communication for distributed solving. + """ + + def __init__( + self, + N: int, + strategy: str = "sliced", + communicator: str = "custom", + **kwargs, + ): + # MPI setup before parent init + self._init_mpi() + + self.strategy = strategy + self.communicator = communicator + + # Create distributed grid + self.grid = DistributedGrid( + N, self.comm, strategy=strategy, halo_exchange=communicator + ) + + # Parent init (calls _init_kernel and _init_arrays) + super().__init__(N, **kwargs) + + def _init_arrays(self): + """Allocate local arrays using distributed grid.""" + self.u = self.grid.allocate() + self.u_old = self.grid.allocate() + self.f = self.grid.allocate() + + # Fill source term and apply BCs + self.grid.fill_source_term(self.f) + self.grid.apply_boundary_conditions(self.u) + self.grid.apply_boundary_conditions(self.u_old) + + def _sync_halos(self, u: np.ndarray, lvl=None) -> float: + """Sync halo regions with neighbors.""" + t0 = MPI.Wtime() + self.grid.sync_halos(u) + return MPI.Wtime() - t0 + + def _apply_boundary_conditions(self, u: np.ndarray, lvl=None): + """Apply Dirichlet boundary conditions at physical boundaries.""" + self.grid.apply_boundary_conditions(u) + + def compute_l2_error(self) -> float: + """Compute L2 error using grid's parallel implementation.""" + l2_error = self.grid.compute_l2_error(self.u) + self.metrics.final_error = l2_error + return l2_error + + def _gather_topology(self): + """Gather rank topology from all MPI ranks.""" + rank_info = self.grid.get_rank_info() + all_ranks = self.comm.gather(rank_info, root=0) + return all_ranks if self.rank == 0 else None diff --git a/src/Poisson/solvers/mpi_mixin.py b/src/Poisson/solvers/mpi_mixin.py new file mode 100644 index 0000000..a674ba4 --- /dev/null +++ b/src/Poisson/solvers/mpi_mixin.py @@ -0,0 +1,45 @@ +"""MPI mixin providing common parallel solver functionality.""" + +import numpy as np +from mpi4py import MPI + + +class MPISolverMixin: + """Mixin providing common MPI functionality for parallel solvers. + + Provides shared implementations for: + - MPI initialization (comm, rank, size) + - Timing via MPI.Wtime() + - Global reductions via MPI.Allreduce() + - Root rank checking + + Usage: + class MyMPISolver(MPISolverMixin, MySolver): + def __init__(self, ...): + self._init_mpi() + ... + """ + + def _init_mpi(self): + """Initialize MPI attributes. Call early in subclass __init__.""" + self.comm = MPI.COMM_WORLD + self.rank = self.comm.Get_rank() + self.size = self.comm.Get_size() + + def _get_time(self) -> float: + """Get current time using MPI.Wtime().""" + return MPI.Wtime() + + def _reduce_sum(self, local_sum: float) -> float: + """Reduce sum via MPI Allreduce.""" + global_sum = np.zeros(1) + self.comm.Allreduce(np.array([local_sum]), global_sum, op=MPI.SUM) + return global_sum[0] + + def _is_root(self) -> bool: + """Only rank 0 logs metrics.""" + return self.rank == 0 + + def _barrier(self): + """Synchronize all ranks before timing.""" + self.comm.Barrier() diff --git a/src/Poisson/solvers/multigrid_operators.py b/src/Poisson/solvers/multigrid_operators.py new file mode 100644 index 0000000..f6652d4 --- /dev/null +++ b/src/Poisson/solvers/multigrid_operators.py @@ -0,0 +1,152 @@ +"""Multigrid operators for 3D Poisson solver. + +Simple operators using vertex-centered alignment: coarse[i] <-> fine[2*i]. +Halo/boundary handling is the caller's responsibility. +""" + +import numpy as np +from numba import njit, prange + + +@njit(parallel=True) +def restrict(fine: np.ndarray, coarse: np.ndarray): + """ + Full weighting restriction (fine -> coarse). + + Vertex-centered alignment: coarse[i] corresponds to fine[2*i]. + Uses 27-point full weighting stencil where possible, injection at boundaries. + + Parameters + ---------- + fine : ndarray + Fine grid array (includes boundaries/halos) + coarse : ndarray + Coarse grid array (includes boundaries/halos), modified in-place + """ + nz_c, ny_c, nx_c = coarse.shape + nz_f, ny_f, nx_f = fine.shape + + for i in prange(1, nz_c - 1): + for j in range(1, ny_c - 1): + for k in range(1, nx_c - 1): + fi, fj, fk = 2 * i, 2 * j, 2 * k + + # Check if full stencil fits + if ( + fi - 1 >= 0 + and fi + 1 < nz_f + and fj - 1 >= 0 + and fj + 1 < ny_f + and fk - 1 >= 0 + and fk + 1 < nx_f + ): + # Full weighting 27-point stencil + val = 8.0 * fine[fi, fj, fk] + + val += 4.0 * ( + fine[fi - 1, fj, fk] + + fine[fi + 1, fj, fk] + + fine[fi, fj - 1, fk] + + fine[fi, fj + 1, fk] + + fine[fi, fj, fk - 1] + + fine[fi, fj, fk + 1] + ) + + val += 2.0 * ( + fine[fi - 1, fj - 1, fk] + + fine[fi - 1, fj + 1, fk] + + fine[fi + 1, fj - 1, fk] + + fine[fi + 1, fj + 1, fk] + + fine[fi - 1, fj, fk - 1] + + fine[fi - 1, fj, fk + 1] + + fine[fi + 1, fj, fk - 1] + + fine[fi + 1, fj, fk + 1] + + fine[fi, fj - 1, fk - 1] + + fine[fi, fj - 1, fk + 1] + + fine[fi, fj + 1, fk - 1] + + fine[fi, fj + 1, fk + 1] + ) + + val += 1.0 * ( + fine[fi - 1, fj - 1, fk - 1] + + fine[fi - 1, fj - 1, fk + 1] + + fine[fi - 1, fj + 1, fk - 1] + + fine[fi - 1, fj + 1, fk + 1] + + fine[fi + 1, fj - 1, fk - 1] + + fine[fi + 1, fj - 1, fk + 1] + + fine[fi + 1, fj + 1, fk - 1] + + fine[fi + 1, fj + 1, fk + 1] + ) + + coarse[i, j, k] = val / 64.0 + else: + # Injection for boundary cases + if 0 <= fi < nz_f and 0 <= fj < ny_f and 0 <= fk < nx_f: + coarse[i, j, k] = fine[fi, fj, fk] + + +@njit(parallel=True) +def prolong(coarse: np.ndarray, fine: np.ndarray): + """ + Trilinear interpolation prolongation (coarse -> fine). + + Vertex-centered alignment: fine[2*i] corresponds to coarse[i]. + Fills fine points at both even indices (2i) and odd indices (2i+1). + + Parameters + ---------- + coarse : ndarray + Coarse grid array (includes boundaries/halos) + fine : ndarray + Fine grid array (includes boundaries/halos), modified in-place + """ + nz_c, ny_c, nx_c = coarse.shape + nz_f, ny_f, nx_f = fine.shape + + for i in prange(0, nz_c - 1): + for j in range(0, ny_c - 1): + for k in range(0, nx_c - 1): + fi, fj, fk = 2 * i, 2 * j, 2 * k + + if fi + 1 >= nz_f or fj + 1 >= ny_f or fk + 1 >= nx_f: + continue + + # Even-index point (coincides with coarse node) + fine[fi, fj, fk] = coarse[i, j, k] + + # Odd-index points (midpoints) + fine[fi + 1, fj, fk] = 0.5 * (coarse[i, j, k] + coarse[i + 1, j, k]) + fine[fi, fj + 1, fk] = 0.5 * (coarse[i, j, k] + coarse[i, j + 1, k]) + fine[fi, fj, fk + 1] = 0.5 * (coarse[i, j, k] + coarse[i, j, k + 1]) + + # Face centers + fine[fi + 1, fj + 1, fk] = 0.25 * ( + coarse[i, j, k] + + coarse[i + 1, j, k] + + coarse[i, j + 1, k] + + coarse[i + 1, j + 1, k] + ) + fine[fi + 1, fj, fk + 1] = 0.25 * ( + coarse[i, j, k] + + coarse[i + 1, j, k] + + coarse[i, j, k + 1] + + coarse[i + 1, j, k + 1] + ) + fine[fi, fj + 1, fk + 1] = 0.25 * ( + coarse[i, j, k] + + coarse[i, j + 1, k] + + coarse[i, j, k + 1] + + coarse[i, j + 1, k + 1] + ) + + # Cell center + fine[fi + 1, fj + 1, fk + 1] = 0.125 * ( + coarse[i, j, k] + + coarse[i + 1, j, k] + + coarse[i, j + 1, k] + + coarse[i + 1, j + 1, k] + + coarse[i, j, k + 1] + + coarse[i + 1, j, k + 1] + + coarse[i, j + 1, k + 1] + + coarse[i + 1, j + 1, k + 1] + ) diff --git a/src/ldc/__init__.py b/src/solvers/__init__.py similarity index 100% rename from src/ldc/__init__.py rename to src/solvers/__init__.py diff --git a/src/ldc/base_solver.py b/src/solvers/base_solver.py similarity index 100% rename from src/ldc/base_solver.py rename to src/solvers/base_solver.py diff --git a/src/ldc/datastructures.py b/src/solvers/datastructures.py similarity index 100% rename from src/ldc/datastructures.py rename to src/solvers/datastructures.py diff --git a/src/ldc/fv_solver.py b/src/solvers/fv_solver.py similarity index 100% rename from src/ldc/fv_solver.py rename to src/solvers/fv_solver.py diff --git a/src/ldc/spectral_solver.py b/src/solvers/spectral_solver.py similarity index 100% rename from src/ldc/spectral_solver.py rename to src/solvers/spectral_solver.py diff --git a/src/utils/__init__.py b/src/utils/__init__.py index f33a6b9..bd01077 100644 --- a/src/utils/__init__.py +++ b/src/utils/__init__.py @@ -1,55 +1,35 @@ -"""Utility modules for plotting and project management.""" +"""Utility modules for project management and visualization. -from pathlib import Path -from . import plotting -from .ldc_plotter import LDCPlotter -from .ghia_validator import GhiaValidator, plot_validation -from .field_interpolator import UnifiedFieldInterpolator -from .data_io import ( - load_run_data, - load_fields, - load_metadata, - load_multiple_runs, -) -from .mlflow_io import ( - setup_mlflow_auth, - load_runs, - download_artifacts, - download_artifacts_with_naming, -) +Submodules: +- plotting: Scientific plot styling, formatters, palettes +- runners: Script discovery and execution +- hpc: HPC job generation and submission +- config: Project configuration and cleanup +- mlflow: MLflow artifact handling and log uploading -__all__ = [ - "plotting", - "get_project_root", - "LDCPlotter", - "GhiaValidator", - "plot_validation", - "UnifiedFieldInterpolator", - "load_run_data", - "load_fields", - "load_metadata", - "load_multiple_runs", - "setup_mlflow_auth", - "load_runs", - "download_artifacts", - "download_artifacts_with_naming", -] +Import examples: + from utils import plotting # Auto-applies scientific styles + from utils import runners # Script execution + from utils import hpc # HPC job management + from utils import mlflow # MLflow utilities + from utils.config import get_repo_root, load_project_config +""" + +import warnings +# Suppress MLflow FutureWarning about filesystem backend deprecation +warnings.filterwarnings("ignore", category=FutureWarning, module="mlflow") -def get_project_root() -> Path: - """Get project root directory. +from . import plotting, runners, hpc, config, mlflow # noqa: E402 - Returns - ------- - Path - Project root directory (contains pyproject.toml). - """ - # Start from this file and search upward for pyproject.toml - current = Path(__file__).resolve().parent - while current != current.parent: - if (current / "pyproject.toml").exists(): - return current - current = current.parent +# Re-export common config functions for convenience +from .config import get_repo_root # noqa: E402 - # Fallback: assume standard structure - return Path(__file__).resolve().parent.parent.parent +__all__ = [ + "plotting", + "runners", + "hpc", + "config", + "mlflow", + "get_repo_root", +] diff --git a/src/utils/config/__init__.py b/src/utils/config/__init__.py new file mode 100644 index 0000000..5794c7f --- /dev/null +++ b/src/utils/config/__init__.py @@ -0,0 +1,9 @@ +"""Configuration utilities. + +This package contains configuration utilities. +""" + +from .paths import get_repo_root +from .clean import clean_all + +__all__ = ["get_repo_root", "clean_all"] diff --git a/src/utils/config/clean.py b/src/utils/config/clean.py new file mode 100644 index 0000000..ca3904a --- /dev/null +++ b/src/utils/config/clean.py @@ -0,0 +1,269 @@ +"""Cleanup utilities for generated files and caches. + +Provides configurable cleanup of build artifacts, caches, +and generated data files. +""" + +import shutil +from pathlib import Path +from typing import List, Tuple, Optional + +from .paths import get_repo_root + + +def _remove_item(path: Path) -> Tuple[bool, Optional[str]]: + """Remove a file or directory. + + Returns + ------- + tuple + (success, error_message) + """ + try: + if path.is_dir(): + shutil.rmtree(path) + else: + path.unlink() + return True, None + except Exception as e: + return False, str(e) + + +def clean_directories( + directories: Optional[List[str]] = None, + repo_root: Optional[Path] = None, +) -> Tuple[int, int]: + """Clean specified directories. + + Parameters + ---------- + directories : list of str, optional + Directories to clean (relative to repo root). + Uses defaults if not specified. + repo_root : Path, optional + Repository root path. + + Returns + ------- + tuple + (cleaned_count, failed_count) + """ + if repo_root is None: + repo_root = get_repo_root() + + if directories is None: + directories = [ + "docs/build", + "docs/source/example_gallery", + "docs/source/generated", + "docs/source/gen_modules", + "plots", + "build", + "dist", + ".pytest_cache", + ".ruff_cache", + ".mypy_cache", + ] + + cleaned, failed = 0, 0 + for d in directories: + path = repo_root / d + if path.exists(): + success, _ = _remove_item(path) + cleaned += success + failed += not success + + return cleaned, failed + + +def clean_files( + files: Optional[List[str]] = None, + repo_root: Optional[Path] = None, +) -> Tuple[int, int]: + """Clean specified files. + + Parameters + ---------- + files : list of str, optional + Files to clean (relative to repo root). + repo_root : Path, optional + Repository root path. + + Returns + ------- + tuple + (cleaned_count, failed_count) + """ + if repo_root is None: + repo_root = get_repo_root() + + if files is None: + files = [ + "docs/source/sg_execution_times.rst", + ] + + cleaned, failed = 0, 0 + for f in files: + path = repo_root / f + if path.exists(): + success, _ = _remove_item(path) + cleaned += success + failed += not success + + return cleaned, failed + + +def clean_patterns( + patterns: Optional[List[str]] = None, + repo_root: Optional[Path] = None, +) -> Tuple[int, int]: + """Clean files/directories matching patterns recursively. + + Parameters + ---------- + patterns : list of str, optional + Glob patterns to match. + repo_root : Path, optional + Repository root path. + + Returns + ------- + tuple + (cleaned_count, failed_count) + """ + if repo_root is None: + repo_root = get_repo_root() + + if patterns is None: + patterns = [ + "__pycache__", + "*.pyc", + ".DS_Store", + "mlruns", + "multirun", + "output", + "outputs", + ] + + cleaned, failed = 0, 0 + for pattern in patterns: + for path in repo_root.rglob(pattern): + success, _ = _remove_item(path) + cleaned += success + failed += not success + + return cleaned, failed + + +def clean_data_directory( + data_dir: str = "data", + preserve: Optional[List[str]] = None, + repo_root: Optional[Path] = None, +) -> Tuple[int, int]: + """Clean data directory contents, preserving specific files. + + Parameters + ---------- + data_dir : str + Data directory relative to repo root. + preserve : list of str, optional + Filenames to preserve. + repo_root : Path, optional + Repository root path. + + Returns + ------- + tuple + (cleaned_count, failed_count) + """ + if repo_root is None: + repo_root = get_repo_root() + + if preserve is None: + preserve = ["README.md", ".gitkeep"] + + data_path = repo_root / data_dir + if not data_path.exists(): + return 0, 0 + + cleaned, failed = 0, 0 + for item in data_path.iterdir(): + if item.name not in preserve: + success, _ = _remove_item(item) + cleaned += success + failed += not success + + return cleaned, failed + + +def clean_experiment_outputs( + experiments_dir: str = "Experiments", + output_dir_name: str = "output", + repo_root: Optional[Path] = None, +) -> Tuple[int, int]: + """Clean output directories in experiment folders. + + Parameters + ---------- + experiments_dir : str + Experiments directory relative to repo root. + output_dir_name : str + Name of output subdirectories to clean. + repo_root : Path, optional + Repository root path. + + Returns + ------- + tuple + (cleaned_count, failed_count) + """ + if repo_root is None: + repo_root = get_repo_root() + + exp_path = repo_root / experiments_dir + if not exp_path.exists(): + return 0, 0 + + cleaned, failed = 0, 0 + for output_dir in exp_path.glob(f"*/{output_dir_name}"): + success, _ = _remove_item(output_dir) + cleaned += success + failed += not success + + return cleaned, failed + + +def clean_all() -> None: + """Clean all generated files and caches.""" + print("\nCleaning all generated files and caches...") + + total_cleaned = 0 + total_failed = 0 + + c, f = clean_directories() + total_cleaned += c + total_failed += f + + c, f = clean_files() + total_cleaned += c + total_failed += f + + c, f = clean_patterns() + total_cleaned += c + total_failed += f + + c, f = clean_data_directory() + total_cleaned += c + total_failed += f + + c, f = clean_experiment_outputs() + total_cleaned += c + total_failed += f + + if total_cleaned: + print(f" ✓ Cleaned {total_cleaned} items") + if total_failed: + print(f" ✗ Failed to clean {total_failed} items") + if not total_cleaned and not total_failed: + print(" Nothing to clean") + print() diff --git a/src/utils/config/paths.py b/src/utils/config/paths.py new file mode 100644 index 0000000..5db486f --- /dev/null +++ b/src/utils/config/paths.py @@ -0,0 +1,19 @@ +"""Path configuration utilities.""" + +from pathlib import Path + + +def get_repo_root() -> Path: + """Find the project root directory (where pyproject.toml is). + + Returns + ------- + Path + Path to repository root. + """ + current = Path(__file__).resolve() + for parent in current.parents: + if (parent / "pyproject.toml").exists(): + return parent + # Fallback: assume src/utils/config structure + return current.parent.parent.parent.parent diff --git a/src/utils/hpc/__init__.py b/src/utils/hpc/__init__.py new file mode 100644 index 0000000..61736b6 --- /dev/null +++ b/src/utils/hpc/__init__.py @@ -0,0 +1,4 @@ +"""HPC job management utilities.""" + +# This package previously contained custom job generation tools. +# They have been replaced by Hydra for configuration and parameter sweeping. diff --git a/src/utils/hpc/sweeper.py b/src/utils/hpc/sweeper.py new file mode 100644 index 0000000..256b870 --- /dev/null +++ b/src/utils/hpc/sweeper.py @@ -0,0 +1,202 @@ +""" +HPC Sweeper: Generates LSF Job Arrays from YAML configuration. + +Key Features: +1. Smart Grouping: Automatically groups jobs into Arrays based on identical resource requirements. +2. Runtime Lookup: Uses 'lookup.py' to resolve arguments at runtime, keeping the YAML as the source of truth. +3. Pack Generation: Creates a master submission script that submits the Universal Runner Template. +""" + +import itertools +from pathlib import Path +from typing import Any, Dict, List, Tuple + +from .jobgen import load_config + +# Use the universal template path relative to project root +RUNNER_TEMPLATE = Path("src/utils/hpc/runner_template.sh") + + +def get_combinations(group_config: Dict[str, Any]) -> List[Dict[str, Any]]: + """Generate all flattened parameter combinations for a group.""" + static_args = group_config.get("static_args", {}) + sweep = group_config.get("sweep", {}) + sweep_paired = group_config.get("sweep_paired", {}) + + paired_combinations = [{}] + if sweep_paired: + keys = list(sweep_paired.keys()) + values = list(sweep_paired.values()) + paired_combinations = [dict(zip(keys, v)) for v in zip(*values)] + + regular_combinations = [{}] + if sweep: + keys = list(sweep.keys()) + values = list(sweep.values()) + regular_combinations = [dict(zip(keys, v)) for v in itertools.product(*values)] + + all_combos = [] + for paired in paired_combinations: + for regular in regular_combinations: + all_combos.append({**static_args, **paired, **regular}) + + return all_combos + + +def extract_resources( + combo: Dict[str, Any], resource_template: Dict[str, Any] +) -> Tuple[frozenset, Dict[str, Any]]: + """ + Determine the resource signature for a specific combination. + """ + resources = resource_template.copy() + # Update resources from combo if keys match + for key, value in combo.items(): + if key in resources: + resources[key] = value + + signature = frozenset(resources.items()) + return signature, resources + + +def generate_arrays(config_path: Path, output_dir: Path = None): + """Parse config and generate array scripts.""" + config = load_config(config_path) + + # Determine Output Directories from Config + base_dir = config_path.parent + + job_script_dir_str = config.get("job_script_output_dir") + pack_dir_str = config.get("packs_output_dir") + + if job_script_dir_str: + job_script_dir = Path(job_script_dir_str) + # Ensure path is handled relative to CWD if not absolute + else: + job_script_dir = base_dir / "generated_jobs" + + if pack_dir_str: + pack_dir = Path(pack_dir_str) + else: + pack_dir = base_dir / "generated_packs" + + if not job_script_dir.exists(): + job_script_dir.mkdir(parents=True, exist_ok=True) + if not pack_dir.exists(): + pack_dir.mkdir(parents=True, exist_ok=True) + + print(f"Configuration: {config_path}") + print(f"Index Maps Dir: {job_script_dir}") + print(f"Submit Script Dir: {pack_dir}") + + submission_lines = [] + submission_lines.append("#!/bin/sh") + submission_lines.append(f"# Generated from {config_path}") + submission_lines.append("# Submits Universal Runner Template") + submission_lines.append("") + + for group_name, group_config in config.items(): + if not isinstance(group_config, dict): + continue + + if ( + "sweep" not in group_config + and "static_args" not in group_config + and "sweep_paired" not in group_config + ): + continue + + print(f"Processing group: {group_name}") + + combos = get_combinations(group_config) + if not combos: + continue + + res_template = group_config.get("resources", {}) + + # Group by Resources + groups = {} + + for i, combo in enumerate(combos): + idx = i + 1 # 1-based LSF index + sig, res = extract_resources(combo, res_template) + + if sig not in groups: + groups[sig] = {"resources": res, "indices": []} + groups[sig]["indices"].append(idx) + + base_cmd = group_config.get("command_prefix", "python main.py") + + for i, (sig, data) in enumerate(groups.items()): + indices = data["indices"] + resources = data["resources"] + + suffix = f"_sub{i}" if len(groups) > 1 else "" + job_name = f"{group_name}{suffix}" + num_jobs = len(indices) + + # 1. Generate Mapping File (.idx) + map_file = job_script_dir / f"{job_name}.idx" + with open(map_file, "w") as f: + for global_idx in indices: + f.write(f"{global_idx}\n") + + # 2. Generate Submission Command + # Construct BSUB CLI args + # Resources + bsub_args = [] + bsub_args.append(f'-J "{job_name}[1-{num_jobs}]"') + bsub_args.append(f"-q {resources.get('queue', 'hpcintro')}") + bsub_args.append(f"-W {resources.get('walltime', '00:10')}") + bsub_args.append(f"-n {resources.get('n_cores', 1)}") + bsub_args.append(f'-R "rusage[mem={resources.get("mem", "4GB")}]"') + bsub_args.append('-R "span[ptile=24]"') # Default, could be configurable + + # Output logs + bsub_args.append("-o logs/lsf/%J_%I.out") + bsub_args.append("-e logs/lsf/%J_%I.err") + + # Environment Variables + # Use -env "VAR=VAL,VAR2=VAL2" + env_vars = [] + env_vars.append(f"SWEEP_CONFIG={config_path}") + env_vars.append(f"SWEEP_GROUP={group_name}") + env_vars.append(f"SWEEP_MAP_FILE={map_file}") + env_vars.append(f"SWEEP_CMD='{base_cmd}'") + + # Quote the env vars string properly + env_string = ",".join(env_vars) + bsub_args.append(f'-env "{env_string}"') + + # Command to submit the template + # Note: We assume RUNNER_TEMPLATE is executable or we run it via sh? + # LSF runs the script. + # bsub < script is standard. + + cmd = f"bsub {' '.join(bsub_args)} < {RUNNER_TEMPLATE}" + submission_lines.append(cmd) + + print(f" ✓ Queued Array {job_name} (Size: {num_jobs})") + + # Write Master Submit Script + if len(submission_lines) > 4: # Header is 4 lines + submit_file_name = config_path.stem + "_submit.sh" + submit_file_path = pack_dir / submit_file_name + + with open(submit_file_path, "w") as f: + for line in submission_lines: + f.write(line + "\n") + + print(f"\nSubmission Script generated: {submit_file_path}") + print(f"Run: sh {submit_file_path}") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=Path, required=True) + parser.add_argument("--out", type=Path, default=None) + args = parser.parse_args() + + generate_arrays(args.config, args.out) diff --git a/src/utils/hydra/__init__.py b/src/utils/hydra/__init__.py new file mode 100644 index 0000000..fce6774 --- /dev/null +++ b/src/utils/hydra/__init__.py @@ -0,0 +1,5 @@ +"""Hydra utilities and callbacks.""" + +from utils.hydra.callbacks import MLflowLogCallback + +__all__ = ["MLflowLogCallback"] diff --git a/src/utils/hydra/callbacks.py b/src/utils/hydra/callbacks.py new file mode 100644 index 0000000..86f1ff6 --- /dev/null +++ b/src/utils/hydra/callbacks.py @@ -0,0 +1,87 @@ +"""Hydra callbacks for MLflow integration. + +This module provides callbacks to integrate Hydra's job logging with MLflow. +The MLflowLogCallback uploads the Hydra job log file as an MLflow artifact +after each job completes, allowing you to view job output (including MPI +report-bindings) in the MLflow UI. +""" + +import logging +from pathlib import Path +from typing import Any + +from hydra.core.utils import JobReturn +from hydra.experimental.callback import Callback +from omegaconf import DictConfig + +log = logging.getLogger(__name__) + + +class MLflowLogCallback(Callback): + """Callback to log Hydra job output to MLflow as an artifact. + + This callback runs after each Hydra job completes and uploads the + job log file to the active MLflow run as an artifact. + + Configuration (in hydra/callbacks/mlflow_log.yaml): + + .. code-block:: yaml + + # @package _global_ + hydra: + callbacks: + mlflow_log: + _target_: utils.hydra.callbacks.MLflowLogCallback + artifact_path: logs + + The log file will be uploaded to the "logs" artifact path in MLflow. + """ + + def __init__(self, artifact_path: str = "logs") -> None: + """Initialize the callback. + + Parameters + ---------- + artifact_path : str + MLflow artifact subdirectory for log files (default: "logs") + """ + self.artifact_path = artifact_path + + def on_job_end( + self, config: DictConfig, job_return: JobReturn, **kwargs: Any + ) -> None: + """Upload job log to MLflow after job completes. + + Parameters + ---------- + config : DictConfig + The job config + job_return : JobReturn + Return value from the job (contains status, return value, etc.) + kwargs : Any + Additional keyword arguments + """ + try: + import mlflow + from hydra.core.hydra_config import HydraConfig + + # Check if MLflow run is active + if not mlflow.active_run(): + log.debug("No active MLflow run, skipping log upload") + return + + # Get the job log file path from Hydra + hc = HydraConfig.get() + output_dir = Path(hc.runtime.output_dir) + job_name = hc.job.name + log_file = output_dir / f"{job_name}.log" + + if log_file.exists(): + mlflow.log_artifact(str(log_file), artifact_path=self.artifact_path) + log.info(f"Uploaded job log to MLflow: {log_file.name}") + else: + log.debug(f"Job log not found: {log_file}") + + except Exception as e: + # Don't fail the job if logging fails + log.warning(f"Failed to upload job log to MLflow: {e}") diff --git a/src/utils/mlflow/__init__.py b/src/utils/mlflow/__init__.py new file mode 100644 index 0000000..650af38 --- /dev/null +++ b/src/utils/mlflow/__init__.py @@ -0,0 +1,33 @@ +"""MLflow utilities for experiment tracking and artifact management. + +Provides: +- Context manager for MLflow run orchestration +- Granular logging functions for parameters, metrics, time-series, and artifacts +- Run fetching and filtering +- Artifact downloading with naming conventions +- Log uploading for HPC jobs +""" + +from .io import ( + setup_mlflow_tracking, + start_mlflow_run_context, + log_parameters, + log_metrics_dict, + log_timeseries_metrics, + log_artifact_file, + load_runs, + download_artifacts, +) +from .logs import upload_logs + +__all__ = [ + "setup_mlflow_tracking", + "start_mlflow_run_context", + "log_parameters", + "log_metrics_dict", + "log_timeseries_metrics", + "log_artifact_file", + "load_runs", + "download_artifacts", + "upload_logs", +] diff --git a/src/utils/mlflow/io.py b/src/utils/mlflow/io.py new file mode 100644 index 0000000..2c16cdc --- /dev/null +++ b/src/utils/mlflow/io.py @@ -0,0 +1,365 @@ +"""MLflow I/O utilities for experiment tracking, and fetching runs and artifacts. + +This module provides helpers for: +- Setting up MLflow tracking (local or Databricks) via environment variables. +- Orchestrating MLflow runs (context manager for parent/nested runs). +- Logging parameters, metrics, and artifacts. +- Retrieving experiment data from MLflow. +""" + +import os +import time +from dataclasses import asdict +from pathlib import Path +from typing import List, Optional +import argparse +from contextlib import contextmanager + +import mlflow +import pandas as pd + +# Conditional import for type hint to avoid circular dependency +try: + from Poisson.solver import JacobiPoisson +except ImportError: + JacobiPoisson = None + + +def setup_mlflow_tracking(mode: str = "databricks"): + """ + Configures MLflow tracking. + + Parameters + ---------- + mode : str + "databricks" or "local". + """ + if mode == "databricks": + try: + mlflow.login(backend="databricks", interactive=False) + mlflow.set_tracking_uri("databricks") + print("INFO: Connected to Databricks MLflow tracking.") + except Exception as e: + raise RuntimeError( + "MLflow Databricks setup failed. Ensure credentials are configured." + ) from e + elif mode == "local": + # Use default local file-based backend (./mlruns) + # Setting it to None or "" often defaults to ./mlruns, but explicit is better if env var is set differently. + # However, the standard way to 'unset' to default is just not setting it, or setting it to a local path. + # Let's explicitly set it to the local ./mlruns directory to be safe and clear. + mlruns_path = Path.cwd() / "mlruns" + mlruns_uri = f"file://{mlruns_path}" + mlflow.set_tracking_uri(mlruns_uri) + print(f"INFO: Using local file-based MLflow tracking backend: {mlruns_uri}") + else: + print( + f"WARNING: Unknown MLflow mode '{mode}'. Using existing URI: {mlflow.get_tracking_uri()}" + ) + + +def get_mlflow_client() -> mlflow.tracking.MlflowClient: + """Get an MLflow tracking client.""" + return mlflow.tracking.MlflowClient() + + +@contextmanager +def start_mlflow_run_context( + experiment_name: str, + parent_run_name: str, + child_run_name: str, + project_prefix: str = "/Shared/LSM-PoissonMPI-v3", + args: Optional[argparse.Namespace] = None, +): + """ + Context manager to start a nested MLflow run. + """ + if mlflow.get_tracking_uri() == "databricks" and not experiment_name.startswith( + "/" + ): + original_experiment_name = experiment_name + experiment_name = f"{project_prefix}/{experiment_name}" + print( + f"DEBUG: Adjusted experiment name for Databricks: {original_experiment_name} -> {experiment_name}" + ) + + print(f"DEBUG: Attempting to set MLflow experiment: {experiment_name}") + mlflow.set_experiment(experiment_name) + print(f"INFO: Using MLflow experiment: {experiment_name}") + + client = get_mlflow_client() + exp = mlflow.get_experiment_by_name(experiment_name) + if exp is None: + try: + exp_id = client.create_experiment(experiment_name) + exp = client.get_experiment(exp_id) + print( + f"DEBUG: Created new MLflow experiment: {experiment_name} with ID {exp_id}" + ) + except Exception as e: + print(f"ERROR: Failed to create MLflow experiment '{experiment_name}': {e}") + raise # Re-raise to ensure failure is visible + + parent_runs = client.search_runs( + experiment_ids=[exp.experiment_id], + filter_string=f"tags.mlflow.runName = '{parent_run_name}' AND tags.is_parent = 'true'", + max_results=1, + ) + parent_run_id = parent_runs[0].info.run_id if parent_runs else None + + with mlflow.start_run( + run_id=parent_run_id, run_name=parent_run_name, tags={"is_parent": "true"} + ) as _parent_mlflow_run: # noqa: F841 + with mlflow.start_run(run_name=child_run_name, nested=True) as child_mlflow_run: + # Tag run with environment (HPC vs local) for easy filtering + env = ( + "hpc" + if os.environ.get("LSB_JOBID") or os.environ.get("SLURM_JOB_ID") + else "local" + ) + mlflow.set_tag("environment", env) + + print( + f"INFO: Started MLflow run '{child_mlflow_run.info.run_name}' ({child_mlflow_run.info.run_id}) [{env}]" + ) + if args and args.job_name: + try: + from Poisson import get_project_root + + project_root = get_project_root() + log_path = project_root / args.log_dir + log_path.mkdir(parents=True, exist_ok=True) + run_id_file = log_path / f"{args.job_name}.runid" + with open(run_id_file, "w") as f: + f.write(child_mlflow_run.info.run_id) + print(f" ✓ Saved run ID to {run_id_file}") + except Exception as e: + print(f" ✗ WARNING: Could not save run ID to file: {e}") + yield child_mlflow_run + + +def log_parameters(params: dict): + """Log a dictionary of parameters to the active MLflow run.""" + mlflow.log_params(params) + + +def log_metrics_dict(metrics: dict): + """Log a dictionary of metrics to the active MLflow run, filtering out None values.""" + filtered_metrics = {k: v for k, v in metrics.items() if v is not None} + mlflow.log_metrics(filtered_metrics) + + +def log_timeseries_metrics(timeseries_data: object): + """Log time series data as step-based metrics to the active MLflow run.""" + if not mlflow.active_run(): + return + client = get_mlflow_client() + run_id = mlflow.active_run().info.run_id + timestamp = int(time.time() * 1000) + metrics_to_log = [] + ts_dict = asdict(timeseries_data) + for name, values in ts_dict.items(): + if values: + for step, value in enumerate(values): + try: + val = float(value) + metrics_to_log.append( + mlflow.entities.Metric(name, val, timestamp, step) + ) + except (ValueError, TypeError): + continue + if metrics_to_log: + for i in range(0, len(metrics_to_log), 1000): + chunk = metrics_to_log[i : i + 1000] + client.log_batch(run_id=run_id, metrics=chunk, synchronous=True) + print(f" ✓ Logged {len(metrics_to_log)} time-series metrics.") + + +def log_artifact_file(filepath: Path): + """Log a file as an artifact to the active MLflow run.""" + if filepath.exists(): + mlflow.log_artifact(str(filepath)) + print(f" ✓ Logged artifact: {filepath.name}") + else: + print(f" ✗ WARNING: Artifact file not found at {filepath}") + + +def log_lsf_logs(job_name: Optional[str], log_dir: str = "logs/lsf"): + """ + Upload LSF .out and .err log files as MLflow artifacts. + + Parameters + ---------- + job_name : str or None + The LSF job name (used to find log files) + log_dir : str + Directory containing LSF logs (default: logs/lsf) + """ + if not job_name: + return + + try: + from Poisson import get_project_root + + project_root = get_project_root() + except ImportError: + project_root = Path.cwd() + + log_path = project_root / log_dir + + for ext in [".out", ".err"]: + log_file = log_path / f"{job_name}{ext}" + if log_file.exists(): + mlflow.log_artifact(str(log_file), artifact_path="lsf_logs") + print(f" ✓ Logged LSF log: {log_file.name}") + # Don't warn if not found - logs may not exist yet during local testing + + +def load_runs( + experiment: str, + converged_only: bool = True, + exclude_parent_runs: bool = True, + project_prefix: str = "/Shared/LSM-PoissonMPI-v3", +) -> pd.DataFrame: + """Load runs from ALL MLflow experiments matching the name. + + Parameters + ---------- + experiment : str + Experiment name (will be prefixed for Databricks) + converged_only : bool + Only include converged runs + exclude_parent_runs : bool + Exclude parent runs (keep only child/nested runs) + project_prefix : str + Databricks workspace prefix for experiment names + """ + # Apply prefix for Databricks + if mlflow.get_tracking_uri() == "databricks" and not experiment.startswith("/"): + full_experiment_name = f"{project_prefix}/{experiment}" + else: + full_experiment_name = experiment + + # Find ALL experiments matching this name (there can be duplicates) + client = get_mlflow_client() + all_experiments = client.search_experiments( + filter_string=f"name = '{full_experiment_name}'" + ) + + if not all_experiments: + return pd.DataFrame() + + experiment_ids = [exp.experiment_id for exp in all_experiments] + + # Build filter string + filters = [] + if converged_only: + filters.append("metrics.converged = 1") + + filter_string = " and ".join(filters) if filters else "" + + # Fetch runs from ALL matching experiments + df = mlflow.search_runs( + experiment_ids=experiment_ids, + filter_string=filter_string, + order_by=["start_time DESC"], + ) + + # Filter out parent runs in pandas (MLflow filter doesn't handle None well) + if exclude_parent_runs and "tags.is_parent" in df.columns: + df = df[df["tags.is_parent"] != "true"] + + return df + + +def download_artifacts( + experiment_name: str, + output_dir: Path, + exclude_parent_runs: bool = True, + force: bool = False, + max_workers: int = 8, +) -> List[Path]: + """ + Download artifacts from the newest run per run name in an experiment. + + When multiple runs have the same name, only artifacts from the most recent + run are downloaded to avoid duplicates and ensure latest data. + + Parameters + ---------- + experiment_name : str + MLflow experiment name + output_dir : Path + Local directory to download to + exclude_parent_runs : bool + Skip parent runs (default True) + force : bool + Re-download even if file exists locally (default False) + max_workers : int + Number of parallel download threads (default 8) + """ + from concurrent.futures import ThreadPoolExecutor, as_completed + + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + client = get_mlflow_client() + exp = client.get_experiment_by_name(experiment_name) + if not exp: + print(f" - Experiment '{experiment_name}' not found.") + return [] + + # Order by start_time DESC to get newest first + runs = client.search_runs( + experiment_ids=[exp.experiment_id], + order_by=["start_time DESC"], + ) + if not runs: + return [] + + # Filter out parent runs + if exclude_parent_runs: + runs = [r for r in runs if r.data.tags.get("is_parent") != "true"] + + # Keep only the newest run per run name (first occurrence since sorted DESC) + seen_names = set() + unique_runs = [] + for run in runs: + run_name = run.info.run_name or run.info.run_id + if run_name not in seen_names: + seen_names.add(run_name) + unique_runs.append(run) + + # Collect all artifacts to download + download_tasks = [] + for run in unique_runs: + run_id = run.info.run_id + artifacts = client.list_artifacts(run_id) + for artifact in artifacts: + # Check if already exists locally (skip if not forcing) + local_file = output_dir / artifact.path + if not force and local_file.exists(): + continue + download_tasks.append((run_id, artifact.path)) + + if not download_tasks: + return [] + + # Download in parallel + downloaded = [] + + def download_one(task): + run_id, artifact_path = task + return client.download_artifacts(run_id, artifact_path, str(output_dir)) + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + futures = {executor.submit(download_one, task): task for task in download_tasks} + for future in as_completed(futures): + try: + local_path = future.result() + downloaded.append(Path(local_path)) + except Exception as e: + task = futures[future] + print(f" ✗ Failed to download {task[1]}: {e}") + + return downloaded diff --git a/src/utils/mlflow/logs.py b/src/utils/mlflow/logs.py new file mode 100644 index 0000000..4b58cb6 --- /dev/null +++ b/src/utils/mlflow/logs.py @@ -0,0 +1,119 @@ +"""Log uploading utilities for HPC jobs. + +Uploads stdout/stderr logs to MLflow after job completion. +""" + +import time +from pathlib import Path +from typing import Optional + +import mlflow + + +def upload_logs( + job_name: str, + log_dir: str = "logs", + experiment_name: str = "HPC-Experiment", + run_id: Optional[str] = None, +) -> bool: + """Upload job logs to MLflow. + + Parameters + ---------- + job_name : str + Job name (used to find log files). + log_dir : str + Directory containing log files. + experiment_name : str + MLflow experiment name (used if creating new run). + run_id : str, optional + Existing run ID to attach logs to. If not provided, + attempts to read from {log_dir}/{job_name}.runid file. + + Returns + ------- + bool + True if upload succeeded. + """ + log_path = Path(log_dir) + run_id_file = log_path / f"{job_name}.runid" + out_log = log_path / f"{job_name}.out" + err_log = log_path / f"{job_name}.err" + + # Give filesystem time to sync if job just finished + time.sleep(2) + + # Try to get run_id from file if not provided + if run_id is None and run_id_file.exists(): + try: + with open(run_id_file, "r") as f: + run_id = f.read().strip() + print(f"Found Run ID: {run_id}") + except Exception as e: + print(f"Error reading run ID file: {e}") + + try: + active_run = None + + if run_id: + active_run = mlflow.start_run(run_id=run_id, log_system_metrics=False) + else: + print("Run ID file not found. Creating new run for startup failure.") + + if mlflow.get_experiment_by_name(experiment_name) is None: + try: + mlflow.create_experiment(name=experiment_name) + except Exception: + pass # concurrent creation might fail + + mlflow.set_experiment(experiment_name) + active_run = mlflow.start_run(run_name=f"{job_name} (Startup Failure)") + mlflow.set_tag("status", "startup_failure") + + with active_run: + if out_log.exists(): + print(f"Uploading stdout: {out_log}") + mlflow.log_artifact(str(out_log), artifact_path="logs") + else: + print(f"Warning: stdout log not found at {out_log}") + + if err_log.exists(): + print(f"Uploading stderr: {err_log}") + mlflow.log_artifact(str(err_log), artifact_path="logs") + else: + print(f"Warning: stderr log not found at {err_log}") + + print("Log upload complete.") + return True + + except Exception as e: + print(f"Failed to upload logs to MLflow: {e}") + return False + + +def main(): + """CLI entry point for log uploading.""" + import argparse + + parser = argparse.ArgumentParser(description="Upload job logs to MLflow") + parser.add_argument("--job-name", type=str, required=True, help="Job name") + parser.add_argument( + "--log-dir", type=str, default="logs", help="Directory containing logs" + ) + parser.add_argument( + "--experiment-name", + type=str, + default="HPC-Experiment", + help="MLflow experiment name", + ) + args = parser.parse_args() + + upload_logs( + job_name=args.job_name, + log_dir=args.log_dir, + experiment_name=args.experiment_name, + ) + + +if __name__ == "__main__": + main() diff --git a/src/utils/mlflow/upload_logs.py b/src/utils/mlflow/upload_logs.py new file mode 100644 index 0000000..a1d7d72 --- /dev/null +++ b/src/utils/mlflow/upload_logs.py @@ -0,0 +1,105 @@ +""" +Script to upload LSF logs to MLflow. + +This script scans a directory for *.runid files (created by the experiment runners), +reads the MLflow Run ID, finds the corresponding .out and .err files, and uploads +them as artifacts to that run. + +Processed files are moved to a 'processed' subdirectory. + +Usage: + uv run python src/utils/mlflow/upload_logs.py --log-dir logs/lsf +""" + +import argparse +import shutil +from pathlib import Path +import mlflow +from utils.mlflow.io import setup_mlflow_tracking + + +def upload_logs(log_dir: Path, dry_run: bool = False): + """ + Uploads LSF logs to MLflow. + + Parameters + ---------- + log_dir : Path + Directory containing .runid, .out, and .err files. + dry_run : bool + If True, does not perform upload or move files. + """ + log_dir = Path(log_dir) + if not log_dir.exists(): + print(f"Log directory not found: {log_dir}") + return + + processed_dir = log_dir / "processed" + if not dry_run: + processed_dir.mkdir(exist_ok=True) + + # Find all runid files + runid_files = list(log_dir.glob("*.runid")) + if not runid_files: + print(f"No .runid files found in {log_dir}") + return + + print(f"Found {len(runid_files)} pending log sets in {log_dir}...") + client = mlflow.tracking.MlflowClient() + + for runid_file in runid_files: + job_name = runid_file.stem + out_file = log_dir / f"{job_name}.out" + err_file = log_dir / f"{job_name}.err" + + # Check if log files exist + if not out_file.exists() or not err_file.exists(): + print(f" [SKIP] {job_name}: Missing .out or .err file.") + continue + + try: + # Read Run ID + with open(runid_file, "r") as f: + run_id = f.read().strip() + + print(f" [PROCESSING] {job_name} (Run ID: {run_id})") + + if not dry_run: + # Verify run exists + try: + client.get_run(run_id) # Just check if run exists + except Exception: + print(f" ! Run {run_id} not found in MLflow. Skipping.") + continue + + # Upload artifacts + print(f" Uploading {out_file.name}...") + client.log_artifact(run_id, str(out_file)) + + print(f" Uploading {err_file.name}...") + client.log_artifact(run_id, str(err_file)) + + # Move to processed + shutil.move(str(runid_file), str(processed_dir / runid_file.name)) + shutil.move(str(out_file), str(processed_dir / out_file.name)) + shutil.move(str(err_file), str(processed_dir / err_file.name)) + print(" Done.") + else: + print(f" (Dry Run) Would upload {out_file.name} and {err_file.name}") + + except Exception as e: + print(f" ! ERROR processing {job_name}: {e}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Upload LSF logs to MLflow") + parser.add_argument( + "--log-dir", type=str, default="logs/lsf", help="Directory to scan" + ) + parser.add_argument( + "--dry-run", action="store_true", help="Simulate without changes" + ) + args = parser.parse_args() + + setup_mlflow_tracking() + upload_logs(args.log_dir, args.dry_run) diff --git a/src/utils/plotting/__init__.py b/src/utils/plotting/__init__.py new file mode 100644 index 0000000..56cdd7c --- /dev/null +++ b/src/utils/plotting/__init__.py @@ -0,0 +1,29 @@ +"""Plotting utilities for scientific visualizations. + +This module provides: +- Automatic style application (seaborn + custom scientific style) +- LaTeX formatting utilities for labels and parameters + +Automatically applies styles on import: + from utils import plotting # Styles applied! + +Or import specific utilities: + from utils.plotting import format_scientific_latex +""" + +from .styles import apply_styles +from .formatters import ( + format_scientific_latex, + format_parameter_range, + build_parameter_string, +) + +# Apply styles when module is imported +apply_styles() + +__all__ = [ + "apply_styles", + "format_scientific_latex", + "format_parameter_range", + "build_parameter_string", +] diff --git a/src/utils/plotting/formatters.py b/src/utils/plotting/formatters.py new file mode 100644 index 0000000..02dcc6e --- /dev/null +++ b/src/utils/plotting/formatters.py @@ -0,0 +1,137 @@ +"""Formatting utilities for scientific plot labels and annotations. + +Provides LaTeX-compatible formatting for: +- Scientific notation (e.g., 1.00 × 10⁻³) +- Parameter ranges (e.g., N ∈ [10, 100]) +- Parameter strings for titles/legends +""" + +from __future__ import annotations + +from typing import Any + + +def format_scientific_latex(value: float | str, precision: int = 2) -> str: + """Format a value as LaTeX scientific notation. + + Parameters + ---------- + value : float or str + Value to format. If str and equals '?', returns '?' + precision : int, default 2 + Number of decimal places for mantissa + + Returns + ------- + str + LaTeX-formatted string in the form 'mantissa \\times 10^{exponent}' + + Examples + -------- + >>> format_scientific_latex(0.001) + '1.00 \\times 10^{-3}' + >>> format_scientific_latex(1.5e-6, precision=1) + '1.5 \\times 10^{-6}' + """ + if value == "?": + return "?" + + value_str = f"{float(value):.{precision}e}" + mantissa, exp = value_str.split("e") + exp_int = int(exp) + return rf"{mantissa} \times 10^{{{exp_int}}}" + + +def format_parameter_range( + values: list | tuple, + name: str, + latex: bool = True, +) -> str: + """Format a parameter range for display. + + Parameters + ---------- + values : list or tuple + Parameter values (should be sorted) + name : str + Parameter name (e.g., 'N', 'L', 'dt') + latex : bool, default True + Whether to use LaTeX formatting + + Returns + ------- + str + Formatted string + + Examples + -------- + >>> format_parameter_range([10, 20, 30], 'N') + '$N \\in [10, 30]$' + """ + if len(values) == 0: + return f"{name} = ?" + + if len(values) == 1: + val = values[0] + if latex: + return rf"${name} = {val}$" + return f"{name} = {val}" + + min_val, max_val = min(values), max(values) + + # Format based on type + if isinstance(min_val, int) and isinstance(max_val, int): + range_str = f"[{min_val}, {max_val}]" + else: + range_str = f"[{min_val:.1f}, {max_val:.1f}]" + + if latex: + return rf"${name} \in {range_str}$" + return f"{name} ∈ {range_str}" + + +def build_parameter_string( + params: dict[str, Any], + separator: str = ", ", + latex: bool = True, +) -> str: + """Build a parameter string from a dictionary. + + Parameters + ---------- + params : dict + Dictionary of parameter names and values + separator : str, default ', ' + Separator between parameters + latex : bool, default True + Whether to use LaTeX formatting (wraps each param in $ $) + + Returns + ------- + str + Formatted parameter string + + Examples + -------- + >>> build_parameter_string({'N': 100, 'dt': 0.001}) + '$N = 100$, $dt = 1.00 \\times 10^{-3}$' + """ + parts = [] + for name, value in params.items(): + if isinstance(value, (list, tuple)): + parts.append(format_parameter_range(value, name, latex=latex)) + else: + # Handle special formatting for timestep-like parameters + if "dt" in name.lower() or "delta" in name.lower(): + value_str = format_scientific_latex(value) + if latex: + parts.append(rf"${name} = {value_str}$") + else: + parts.append(f"{name} = {value_str}") + else: + if latex: + parts.append(rf"${name} = {value}$") + else: + parts.append(f"{name} = {value}") + + return separator.join(parts) diff --git a/src/utils/utils.mplstyle b/src/utils/plotting/scientific.mplstyle similarity index 100% rename from src/utils/utils.mplstyle rename to src/utils/plotting/scientific.mplstyle diff --git a/src/utils/plotting/styles.py b/src/utils/plotting/styles.py new file mode 100644 index 0000000..3284b11 --- /dev/null +++ b/src/utils/plotting/styles.py @@ -0,0 +1,34 @@ +"""Style application for matplotlib plots. + +Provides consistent styling for scientific publications using +seaborn as a base with custom overrides for LaTeX integration. +""" + +from pathlib import Path + +import matplotlib.pyplot as plt + + +def apply_styles(base_style: str = "seaborn-v0_8") -> None: + """Apply seaborn style and custom scientific style. + + Parameters + ---------- + base_style : str, default "seaborn-v0_8" + Base matplotlib style to use. Falls back silently if unavailable. + """ + # Apply base style first + try: + plt.style.use(base_style) + except OSError: + pass + + # Then apply custom style on top + style_path = Path(__file__).parent / "scientific.mplstyle" + if style_path.exists(): + plt.style.use(str(style_path)) + + +def get_style_path() -> Path: + """Return path to the scientific.mplstyle file.""" + return Path(__file__).parent / "scientific.mplstyle" diff --git a/src/utils/runners/__init__.py b/src/utils/runners/__init__.py new file mode 100644 index 0000000..53647f0 --- /dev/null +++ b/src/utils/runners/__init__.py @@ -0,0 +1,25 @@ +"""Script execution utilities. + +Provides functions for discovering and running scripts: +- discover_scripts: Find scripts by pattern in Experiments/ +- run_scripts_parallel: Run scripts concurrently +- run_scripts_sequential: Run scripts one at a time +""" + +from .scripts import ( + discover_scripts, + run_scripts_parallel, + run_scripts_sequential, + run_plot_scripts, + run_compute_scripts, + copy_to_report, +) + +__all__ = [ + "discover_scripts", + "run_scripts_parallel", + "run_scripts_sequential", + "run_plot_scripts", + "run_compute_scripts", + "copy_to_report", +] diff --git a/src/utils/runners/scripts.py b/src/utils/runners/scripts.py new file mode 100644 index 0000000..8968d22 --- /dev/null +++ b/src/utils/runners/scripts.py @@ -0,0 +1,248 @@ +"""Script discovery and execution utilities. + +Provides parallel and sequential script execution with +configurable timeouts and progress reporting. +""" + +import shutil +import subprocess +from concurrent.futures import ThreadPoolExecutor, as_completed +from pathlib import Path +from typing import List, Tuple, Optional + +from ..config import get_repo_root + + +def discover_scripts(pattern: str, directory: str = "Experiments") -> List[Path]: + """Find scripts in a directory matching pattern. + + Parameters + ---------- + pattern : str + Pattern to match in script names (e.g., "plot", "compute") + directory : str, default "Experiments" + Directory to search in, relative to repo root + + Returns + ------- + list of Path + Sorted list of matching script paths + """ + repo_root = get_repo_root() + search_dir = repo_root / directory + + if not search_dir.exists(): + return [] + + scripts = [ + p + for p in search_dir.rglob("*.py") + if p.is_file() and pattern in p.name and p.name != "__init__.py" + ] + + return sorted(scripts) + + +def _run_single_script( + script: Path, + repo_root: Path, + timeout: int = 180, + interpreter: str = "uv run python", +) -> Tuple[Path, bool, Optional[str]]: + """Run a single script and return its result. + + Parameters + ---------- + script : Path + Path to the script + repo_root : Path + Repository root for relative path display + timeout : int + Timeout in seconds + interpreter : str + Command to run the script + + Returns + ------- + tuple + (display_path, success, error_message) + """ + display_path = script.relative_to(repo_root) + + try: + cmd = interpreter.split() + [str(script)] + result = subprocess.run( + cmd, + capture_output=True, + text=True, + timeout=timeout, + cwd=str(repo_root), + ) + + if result.returncode == 0: + return (display_path, True, None) + else: + error_msg = result.stderr[:200] if result.stderr else "" + return (display_path, False, f"exit {result.returncode}: {error_msg}") + + except subprocess.TimeoutExpired: + return (display_path, False, "timeout") + except Exception as e: + return (display_path, False, str(e)) + + +def run_scripts_parallel( + scripts: List[Path], + timeout: int = 180, + interpreter: str = "uv run python", + max_workers: int = None, +) -> Tuple[int, int]: + """Run scripts in parallel using ThreadPoolExecutor. + + Parameters + ---------- + scripts : list of Path + Scripts to run + timeout : int, default 180 + Timeout per script in seconds + interpreter : str, default "uv run python" + Command to run scripts + max_workers : int, optional + Maximum number of parallel workers + + Returns + ------- + tuple + (success_count, fail_count) + """ + if not scripts: + print(" No scripts to run") + return 0, 0 + + repo_root = get_repo_root() + print(f"\nRunning {len(scripts)} scripts in parallel...\n") + + success_count = 0 + fail_count = 0 + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + future_to_script = { + executor.submit( + _run_single_script, script, repo_root, timeout, interpreter + ): script + for script in scripts + } + + for future in as_completed(future_to_script): + display_path, success, error_msg = future.result() + + if success: + print(f" ✓ {display_path}") + success_count += 1 + else: + print(f" ✗ {display_path} ({error_msg})") + fail_count += 1 + + print(f"\n Summary: {success_count} succeeded, {fail_count} failed\n") + return success_count, fail_count + + +def run_scripts_sequential( + scripts: List[Path], + timeout: int = 600, + interpreter: str = "uv run python", +) -> Tuple[int, int]: + """Run scripts sequentially. + + Parameters + ---------- + scripts : list of Path + Scripts to run + timeout : int, default 600 + Timeout per script in seconds + interpreter : str, default "uv run python" + Command to run scripts + + Returns + ------- + tuple + (success_count, fail_count) + """ + if not scripts: + print(" No scripts to run") + return 0, 0 + + repo_root = get_repo_root() + print(f"\nRunning {len(scripts)} scripts sequentially...\n") + + success_count = 0 + fail_count = 0 + + for script in scripts: + display_path = script.relative_to(repo_root) + print(f" → {display_path}...", end=" ", flush=True) + + _, success, error_msg = _run_single_script( + script, repo_root, timeout, interpreter + ) + + if success: + print("✓") + success_count += 1 + else: + print(f"✗ ({error_msg})") + fail_count += 1 + + print(f"\n Summary: {success_count} succeeded, {fail_count} failed\n") + return success_count, fail_count + + +def run_plot_scripts() -> Tuple[int, int]: + """Run all plot scripts in parallel.""" + scripts = discover_scripts("plot") + return run_scripts_parallel(scripts, timeout=180) + + +def run_compute_scripts() -> Tuple[int, int]: + """Run all compute scripts sequentially.""" + scripts = discover_scripts("compute") + return run_scripts_sequential(scripts, timeout=600) + + +def copy_to_report( + source_dir: str = "figures", + dest_dir: str = "docs/reports/TexReport/figures", +) -> bool: + """Copy a directory to the report location. + + Parameters + ---------- + source_dir : str + Source directory relative to repo root + dest_dir : str + Destination directory relative to repo root + + Returns + ------- + bool + True if successful + """ + repo_root = get_repo_root() + source = repo_root / source_dir + dest = repo_root / dest_dir + + print(f"\nCopying {source_dir}/ to {dest_dir}/...") + + if not source.exists(): + print(f" No {source_dir}/ directory found") + return False + + try: + if dest.exists(): + shutil.rmtree(dest) + shutil.copytree(source, dest) + print(f" ✓ Copied {source_dir}/ to {dest_dir}/") + return True + except Exception as e: + print(f" ✗ Failed to copy: {e}") + return False From cc6986aefa1060daa691cc4a8fb8481ec6ba1211 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 08:10:10 +0100 Subject: [PATCH 02/34] u --- conf/config.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/conf/config.yaml b/conf/config.yaml index f860cc6..ec7d9ae 100644 --- a/conf/config.yaml +++ b/conf/config.yaml @@ -1,7 +1,6 @@ defaults: - machine: local - mlflow: local - - override hydra/launcher: basic - _self_ # ============================================================ From 8fcd268f63cec19aed736ef18deca13189c49d91 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 10:38:27 +0100 Subject: [PATCH 03/34] some working progress --- .gitignore | 3 + Experiments/01-FV-Solver/README.rst | 4 - Experiments/01-FV-Solver/compute_LDC.py | 141 ------ Experiments/01-FV-Solver/jobs.yaml | 16 - Experiments/01-FV-Solver/plot_LDC.py | 69 --- Experiments/02-Spectral-Solver/README.rst | 6 - .../compute_spectral_chebyshev.py | 139 ------ Experiments/02-Spectral-Solver/jobs.yaml | 16 - .../02-Spectral-Solver/plot_comparison.py | 66 --- .../plot_spectral_pyvista.py | 445 ------------------ README.md | 111 +++-- conf/config.yaml | 55 +-- conf/experiment/communication.yaml | 30 -- conf/experiment/fv_validation.yaml | 21 + conf/experiment/kernel/_base.yaml | 11 - conf/experiment/kernel/benchmark_numba.yaml | 17 - conf/experiment/kernel/benchmark_numpy.yaml | 15 - conf/experiment/kernel/convergence_numba.yaml | 16 - conf/experiment/kernel/convergence_numpy.yaml | 15 - conf/experiment/multigrid.yaml | 29 -- conf/experiment/quick_test.yaml | 11 + conf/experiment/spectral_validation.yaml | 21 + conf/experiment/strong_scaling_fmg.yaml | 32 -- conf/experiment/strong_scaling_jacobi.yaml | 30 -- conf/experiment/test/fmg_convergence.yaml | 27 -- conf/experiment/test/jacobi_convergence.yaml | 24 - conf/experiment/validation/_base.yaml | 19 - conf/experiment/validation/fmg.yaml | 15 - conf/experiment/validation/jacobi.yaml | 14 - conf/experiment/weak_scaling_fmg.yaml | 24 - conf/experiment/weak_scaling_jacobi.yaml | 22 - conf/hydra/callbacks/mlflow_log.yaml | 14 - conf/machine/dtu_hpc.yaml | 11 - conf/machine/local.yaml | 5 - conf/mlflow/databricks.yaml | 3 +- conf/mlflow/local.yaml | 1 + conf/solver/fv.yaml | 9 + conf/solver/spectral.yaml | 8 + ...ultigrid-method-for-i_2010_Computers--.pdf | Bin 0 -> 583613 bytes docs/reports/TexReport | 2 +- pyproject.toml | 2 + pyproject_1.toml | 67 --- run_solver.py | 308 ++++++------ setup_macOS.sh | 62 --- spectral_multigrid_todo.md | 67 +++ src/Poisson/__init__.py | 90 ---- src/Poisson/datastructures.py | 222 --------- src/Poisson/kernels.py | 97 ---- src/Poisson/mpi/__init__.py | 22 - src/Poisson/mpi/decomposition.py | 148 ------ src/Poisson/mpi/grid.py | 193 -------- src/Poisson/mpi/halo.py | 185 -------- src/Poisson/problems.py | 48 -- src/Poisson/solvers/__init__.py | 24 - src/Poisson/solvers/base.py | 200 -------- src/Poisson/solvers/fmg.py | 249 ---------- src/Poisson/solvers/fmg_mpi.py | 157 ------ src/Poisson/solvers/jacobi.py | 103 ---- src/Poisson/solvers/jacobi_mpi.py | 69 --- src/Poisson/solvers/mpi_mixin.py | 45 -- src/Poisson/solvers/multigrid_operators.py | 152 ------ src/fv/linear_solvers/__init__.py | 3 +- src/fv/linear_solvers/petsc_solver.py | 109 ----- src/fv/linear_solvers/scipy_solver.py | 55 ++- src/{utils => ldc}/data_io.py | 0 src/{utils => ldc}/field_interpolator.py | 0 src/{utils => ldc}/ghia_validator.py | 0 src/{utils => ldc}/ldc_plotter.py | 0 src/solvers/__init__.py | 12 +- src/solvers/base_solver.py | 9 +- src/solvers/datastructures.py | 8 +- src/solvers/fv_solver.py | 40 +- src/solvers/spectral_solver.py | 8 +- src/utils/mlflow_io.py | 206 -------- src/utils/plotting.py | 167 ------- 75 files changed, 496 insertions(+), 4148 deletions(-) delete mode 100644 Experiments/01-FV-Solver/README.rst delete mode 100644 Experiments/01-FV-Solver/compute_LDC.py delete mode 100644 Experiments/01-FV-Solver/jobs.yaml delete mode 100644 Experiments/01-FV-Solver/plot_LDC.py delete mode 100644 Experiments/02-Spectral-Solver/README.rst delete mode 100644 Experiments/02-Spectral-Solver/compute_spectral_chebyshev.py delete mode 100644 Experiments/02-Spectral-Solver/jobs.yaml delete mode 100644 Experiments/02-Spectral-Solver/plot_comparison.py delete mode 100644 Experiments/02-Spectral-Solver/plot_spectral_pyvista.py delete mode 100644 conf/experiment/communication.yaml create mode 100644 conf/experiment/fv_validation.yaml delete mode 100644 conf/experiment/kernel/_base.yaml delete mode 100644 conf/experiment/kernel/benchmark_numba.yaml delete mode 100644 conf/experiment/kernel/benchmark_numpy.yaml delete mode 100644 conf/experiment/kernel/convergence_numba.yaml delete mode 100644 conf/experiment/kernel/convergence_numpy.yaml delete mode 100644 conf/experiment/multigrid.yaml create mode 100644 conf/experiment/quick_test.yaml create mode 100644 conf/experiment/spectral_validation.yaml delete mode 100644 conf/experiment/strong_scaling_fmg.yaml delete mode 100644 conf/experiment/strong_scaling_jacobi.yaml delete mode 100644 conf/experiment/test/fmg_convergence.yaml delete mode 100644 conf/experiment/test/jacobi_convergence.yaml delete mode 100644 conf/experiment/validation/_base.yaml delete mode 100644 conf/experiment/validation/fmg.yaml delete mode 100644 conf/experiment/validation/jacobi.yaml delete mode 100644 conf/experiment/weak_scaling_fmg.yaml delete mode 100644 conf/experiment/weak_scaling_jacobi.yaml delete mode 100644 conf/hydra/callbacks/mlflow_log.yaml delete mode 100644 conf/machine/dtu_hpc.yaml delete mode 100644 conf/machine/local.yaml create mode 100644 conf/solver/fv.yaml create mode 100644 conf/solver/spectral.yaml create mode 100644 docs/An-explicit-Chebyshev-pseudospectral-multigrid-method-for-i_2010_Computers--.pdf delete mode 100644 pyproject_1.toml delete mode 100755 setup_macOS.sh create mode 100644 spectral_multigrid_todo.md delete mode 100644 src/Poisson/__init__.py delete mode 100644 src/Poisson/datastructures.py delete mode 100644 src/Poisson/kernels.py delete mode 100644 src/Poisson/mpi/__init__.py delete mode 100644 src/Poisson/mpi/decomposition.py delete mode 100644 src/Poisson/mpi/grid.py delete mode 100644 src/Poisson/mpi/halo.py delete mode 100644 src/Poisson/problems.py delete mode 100644 src/Poisson/solvers/__init__.py delete mode 100644 src/Poisson/solvers/base.py delete mode 100644 src/Poisson/solvers/fmg.py delete mode 100644 src/Poisson/solvers/fmg_mpi.py delete mode 100644 src/Poisson/solvers/jacobi.py delete mode 100644 src/Poisson/solvers/jacobi_mpi.py delete mode 100644 src/Poisson/solvers/mpi_mixin.py delete mode 100644 src/Poisson/solvers/multigrid_operators.py delete mode 100644 src/fv/linear_solvers/petsc_solver.py rename src/{utils => ldc}/data_io.py (100%) rename src/{utils => ldc}/field_interpolator.py (100%) rename src/{utils => ldc}/ghia_validator.py (100%) rename src/{utils => ldc}/ldc_plotter.py (100%) delete mode 100644 src/utils/mlflow_io.py delete mode 100644 src/utils/plotting.py diff --git a/.gitignore b/.gitignore index 2dccf3a..dd0b999 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,9 @@ data/* !data/README.md !data/.gitkeep !data/validation/ +outputs/ +TEMPLATE/ +mlruns/ # Uv stuff uv.lock diff --git a/Experiments/01-FV-Solver/README.rst b/Experiments/01-FV-Solver/README.rst deleted file mode 100644 index 6ee29b1..0000000 --- a/Experiments/01-FV-Solver/README.rst +++ /dev/null @@ -1,4 +0,0 @@ -Finite Volume Solver Examples -============================== - -Finite Volume Solver applied to the Lid driven Cavity problem and compared to Ghia reference data. diff --git a/Experiments/01-FV-Solver/compute_LDC.py b/Experiments/01-FV-Solver/compute_LDC.py deleted file mode 100644 index f288e05..0000000 --- a/Experiments/01-FV-Solver/compute_LDC.py +++ /dev/null @@ -1,141 +0,0 @@ -""" -Lid-Driven Cavity Flow - Finite Volume SIMPLE -============================================== - -Finite volume solver using the SIMPLE algorithm for pressure-velocity coupling -on a collocated grid with Rhie-Chow interpolation. - -Usage:: - - uv run python compute_LDC.py --N 32 --Re 100 - uv run python compute_LDC.py --N 64 --Re 400 --tol 1e-6 -""" - -# %% -# Setup and Configuration -# ----------------------- -# Parse command line arguments and setup directories. - -import argparse -import os - -from ldc import FVSolver -from utils import get_project_root, LDCPlotter, GhiaValidator, plot_validation - -parser = argparse.ArgumentParser(description="FV-SIMPLE solver for lid-driven cavity") -parser.add_argument( - "--N", type=int, default=32, help="Grid cells in each direction (default: 32)" -) -parser.add_argument( - "--Re", type=int, default=100, help="Reynolds number (default: 100)" -) -parser.add_argument( - "--tol", type=float, default=1e-7, help="Convergence tolerance (default: 1e-7)" -) -parser.add_argument( - "--max-iter", type=int, default=50000, help="Max iterations (default: 50000)" -) -args = parser.parse_args() - -N = args.N -Re_number = args.Re - -project_root = get_project_root() -data_dir = project_root / "data" / "FV-Solver" -fig_dir = project_root / "figures" / "FV-Solver" -data_dir.mkdir(parents=True, exist_ok=True) -fig_dir.mkdir(parents=True, exist_ok=True) - -# %% -# Initialize Solver -# ----------------- -# Create the finite volume solver with SIMPLE algorithm. - -solver = FVSolver( - Re=Re_number, - nx=N, - ny=N, - alpha_uv=0.6, - alpha_p=0.3, - convection_scheme="TVD", - limiter="MUSCL", - linear_solver_tol=1e-8, -) - -print( - f"Solver configured: Re={solver.params.Re}, Grid={solver.params.nx}x{solver.params.ny}" -) -print( - f" Convection scheme: {solver.params.convection_scheme} with {solver.params.limiter} limiter" -) -print( - f" Linear solver: PETSc (BiCGSTAB + GAMG), tol={solver.params.linear_solver_tol:.0e}" -) - -# %% -# MLflow Tracking -# --------------- -# Setup MLflow experiment tracking with nested runs. - -Re_str = f"Re{Re_number}" -is_hpc = "LSB_JOBID" in os.environ -experiment_name = "HPC-FV-Solver" if is_hpc else "FV-Solver" -solver.mlflow_start(experiment_name, f"N{N}_{Re_str}", parent_run_name=Re_str) - -# %% -# Solve -# ----- -# Run the solver until convergence or max iterations. - -solver.solve(tolerance=args.tol, max_iter=args.max_iter) - -# %% -# Save Results -# ------------ -# Save solution to HDF5 and log as MLflow artifact. - -output_file = data_dir / f"LDC_N{N}_{Re_str}.h5" -solver.save(output_file) -solver.mlflow_log_artifact(str(output_file)) -print(f"\nResults saved to: {output_file}") - -# %% -# Validation Plots -# ---------------- -# Generate plots and log to MLflow. - -plotter = LDCPlotter(output_file) -validator = GhiaValidator(output_file, Re=Re_number, method_label="FV-SIMPLE") - -fig_path = fig_dir / f"ghia_validation_N{N}_{Re_str}.pdf" -plot_validation(validator, output_path=fig_path) -solver.mlflow_log_artifact(str(fig_path)) -print(" ✓ Ghia validation saved") - -fig_path = fig_dir / f"convergence_N{N}_{Re_str}.pdf" -plotter.plot_convergence(output_path=fig_path) -solver.mlflow_log_artifact(str(fig_path)) -print(" ✓ Convergence saved") - -fig_path = fig_dir / f"fields_N{N}_{Re_str}.pdf" -plotter.plot_fields(output_path=fig_path) -solver.mlflow_log_artifact(str(fig_path)) -print(" ✓ Fields saved") - -fig_path = fig_dir / f"streamlines_N{N}_{Re_str}.pdf" -plotter.plot_streamlines(output_path=fig_path) -solver.mlflow_log_artifact(str(fig_path)) -print(" ✓ Streamlines saved") - -# %% -# Summary -# ------- -# End MLflow run and print summary. - -solver.mlflow_end() - -print("\nSolution Status:") -print(f" Converged: {solver.metrics.converged}") -print(f" Iterations: {solver.metrics.iterations}") -print(f" Final residual: {solver.metrics.final_residual:.6e}") -print(f" Wall time: {solver.metrics.wall_time_seconds:.2f} seconds") diff --git a/Experiments/01-FV-Solver/jobs.yaml b/Experiments/01-FV-Solver/jobs.yaml deleted file mode 100644 index a971ede..0000000 --- a/Experiments/01-FV-Solver/jobs.yaml +++ /dev/null @@ -1,16 +0,0 @@ -# HPC job configuration for FV Solver -script: Experiments/01-FV-Solver/compute_LDC.py - -lsf: - queue: hpc - walltime: "2:00" - cores: 4 - memory: 8GB - -parameters: - tol: 1e-7 - max-iter: 50000 - -sweep: - N: [128, 256] - Re: [100, 400, 1000] diff --git a/Experiments/01-FV-Solver/plot_LDC.py b/Experiments/01-FV-Solver/plot_LDC.py deleted file mode 100644 index 957faf0..0000000 --- a/Experiments/01-FV-Solver/plot_LDC.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Lid-Driven Cavity Flow Visualization -===================================== - -This script visualizes the computed lid-driven cavity flow solution and validates -the results against the benchmark data from Ghia et al. (1982). -""" - -# %% -# Setup and Load Data -# ------------------- -# Import visualization utilities and load the computed solution from HDF5 file. - -from utils import get_project_root, LDCPlotter, GhiaValidator, plot_validation - -# Configuration -Re = 100 -N = 64 # Grid size (number of cells) -Re_str = f"Re{int(Re)}" - -project_root = get_project_root() -data_dir = project_root / "data" / "FV-Solver" -fig_dir = project_root / "figures" / "FV-Solver" -fig_dir.mkdir(parents=True, exist_ok=True) - -# File path -solution_path = data_dir / f"LDC_N{N}_{Re_str}.h5" - -# Validate path exists -if not solution_path.exists(): - raise FileNotFoundError(f"Solution not found: {solution_path}") - -# Load solution -plotter = LDCPlotter(solution_path) -validator = GhiaValidator(solution_path, Re=Re, method_label="FV-SIMPLE") - -print(f"Loaded solution: {solution_path.name}") - -# %% -# Ghia Benchmark Validation -# -------------------------- -# Compare computed velocity profiles with the Ghia et al. (1982) benchmark data. - -plot_validation(validator, output_path=fig_dir / f"ghia_validation_{Re_str}.pdf") -print(" ✓ Ghia validation saved") - -# %% -# Convergence History -# ------------------- -# Visualize how the residual decreased during the SIMPLE iteration process. - -plotter.plot_convergence(output_path=fig_dir / f"convergence_{Re_str}.pdf") -print(" ✓ Convergence saved") - -# %% -# Solution Fields -# --------------- -# Generate combined plot with pressure, u velocity, and v velocity fields. - -plotter.plot_fields(output_path=fig_dir / f"fields_{Re_str}.pdf") -print(" ✓ Fields saved") - -# %% -# Velocity Magnitude with Streamlines -# ------------------------------------ -# Velocity magnitude with streamlines overlaid - -plotter.plot_streamlines(output_path=fig_dir / f"streamlines_{Re_str}.pdf") -print(" ✓ Streamlines saved") diff --git a/Experiments/02-Spectral-Solver/README.rst b/Experiments/02-Spectral-Solver/README.rst deleted file mode 100644 index 8a3b553..0000000 --- a/Experiments/02-Spectral-Solver/README.rst +++ /dev/null @@ -1,6 +0,0 @@ -Spectral Solver Examples -======================== - -Spectral solver using Chebyshev-Gauss-Lobatto collocation applied to the -lid-driven cavity problem. Implements the artificial compressibility method -with RK4 time integration as described in Zhang et al. (2010). diff --git a/Experiments/02-Spectral-Solver/compute_spectral_chebyshev.py b/Experiments/02-Spectral-Solver/compute_spectral_chebyshev.py deleted file mode 100644 index 97e5016..0000000 --- a/Experiments/02-Spectral-Solver/compute_spectral_chebyshev.py +++ /dev/null @@ -1,139 +0,0 @@ -""" -Lid-Driven Cavity Flow - Spectral Chebyshev -============================================ - -Spectral solver using Chebyshev-Gauss-Lobatto nodes with artificial -compressibility and RK4 time integration as described in Zhang et al. (2010). - -Usage:: - - uv run python compute_spectral_chebyshev.py --N 19 --Re 100 - uv run python compute_spectral_chebyshev.py --N 31 --Re 400 --tol 1e-7 -""" - -# %% -# Setup and Configuration -# ----------------------- -# Parse command line arguments and setup directories. - -import argparse -import os - -from ldc import SpectralSolver -from utils import get_project_root, LDCPlotter, GhiaValidator, plot_validation - -parser = argparse.ArgumentParser( - description="Spectral solver for lid-driven cavity (Chebyshev basis)" -) -parser.add_argument( - "--N", type=int, default=19, help="Polynomial order, nodes = N+1 (default: 19)" -) -parser.add_argument( - "--Re", type=int, default=100, help="Reynolds number (default: 100)" -) -parser.add_argument( - "--tol", type=float, default=1e-7, help="Convergence tolerance (default: 1e-7)" -) -parser.add_argument( - "--max-iter", type=int, default=200000, help="Max iterations (default: 200000)" -) -args = parser.parse_args() - -N = args.N # Polynomial order (nodes = N+1) -Re_number = args.Re -N_nodes = N + 1 - -project_root = get_project_root() -data_dir = project_root / "data" / "Spectral-Solver" / "Chebyshev" -fig_dir = project_root / "figures" / "Spectral-Solver" / "Chebyshev" -data_dir.mkdir(parents=True, exist_ok=True) -fig_dir.mkdir(parents=True, exist_ok=True) - -# %% -# Initialize Solver -# ----------------- -# Create the spectral solver with Chebyshev basis. - -print("Initializing solver object") -solver = SpectralSolver( - Re=Re_number, - nx=N, - ny=N, - basis_type="chebyshev", - CFL=0.70, - beta_squared=5.0, - corner_smoothing=0.15, -) - -print( - f"Solver configured: Re={solver.params.Re}, Grid={N_nodes}x{N_nodes}, CFL={solver.params.CFL}" -) -print(f"Total nodes: {N_nodes * N_nodes}") - -# %% -# MLflow Tracking -# --------------- -# Setup MLflow experiment tracking with nested runs. - -Re_str = f"Re{Re_number}" -is_hpc = "LSB_JOBID" in os.environ -experiment_name = "HPC-Spectral-Chebyshev" if is_hpc else "Spectral-Chebyshev" -solver.mlflow_start(experiment_name, f"N{N_nodes}_{Re_str}", parent_run_name=Re_str) - -# %% -# Solve -# ----- -# Run the solver until convergence or max iterations. - -solver.solve(tolerance=args.tol, max_iter=args.max_iter) - -# %% -# Save Results -# ------------ -# Save solution to HDF5 and log as MLflow artifact. - -output_file = data_dir / f"LDC_N{N_nodes}_{Re_str}.h5" -solver.save(output_file) -solver.mlflow_log_artifact(str(output_file)) -print(f"\nResults saved to: {output_file}") - -# %% -# Validation Plots -# ---------------- -# Generate plots and log to MLflow. - -plotter = LDCPlotter(output_file) -validator = GhiaValidator(output_file, Re=Re_number, method_label="Spectral-Chebyshev") - -fig_path = fig_dir / f"ghia_validation_N{N_nodes}_{Re_str}.pdf" -plot_validation(validator, output_path=fig_path) -solver.mlflow_log_artifact(str(fig_path)) -print(" ✓ Ghia validation saved") - -fig_path = fig_dir / f"convergence_N{N_nodes}_{Re_str}.pdf" -plotter.plot_convergence(output_path=fig_path) -solver.mlflow_log_artifact(str(fig_path)) -print(" ✓ Convergence saved") - -fig_path = fig_dir / f"fields_N{N_nodes}_{Re_str}.pdf" -plotter.plot_fields(output_path=fig_path) -solver.mlflow_log_artifact(str(fig_path)) -print(" ✓ Fields saved") - -fig_path = fig_dir / f"streamlines_N{N_nodes}_{Re_str}.pdf" -plotter.plot_streamlines(output_path=fig_path) -solver.mlflow_log_artifact(str(fig_path)) -print(" ✓ Streamlines saved") - -# %% -# Summary -# ------- -# End MLflow run and print summary. - -solver.mlflow_end() - -print("\nSolution Status:") -print(f" Converged: {solver.metrics.converged}") -print(f" Iterations: {solver.metrics.iterations}") -print(f" Final residual: {solver.metrics.final_residual:.6e}") -print(f" Wall time: {solver.metrics.wall_time_seconds:.2f} seconds") diff --git a/Experiments/02-Spectral-Solver/jobs.yaml b/Experiments/02-Spectral-Solver/jobs.yaml deleted file mode 100644 index 1fed9ec..0000000 --- a/Experiments/02-Spectral-Solver/jobs.yaml +++ /dev/null @@ -1,16 +0,0 @@ -# HPC job configuration for Spectral Solver -script: Experiments/02-Spectral-Solver/compute_spectral_chebyshev.py - -lsf: - queue: hpc - walltime: "2:00" - cores: 4 - memory: 8GB - -parameters: - tol: 1e-7 - max-iter: 500000 - -sweep: - N: [15, 25, 35, 45, 55, 65] - Re: [100, 400, 1000] diff --git a/Experiments/02-Spectral-Solver/plot_comparison.py b/Experiments/02-Spectral-Solver/plot_comparison.py deleted file mode 100644 index e8191f1..0000000 --- a/Experiments/02-Spectral-Solver/plot_comparison.py +++ /dev/null @@ -1,66 +0,0 @@ -""" -Legendre vs Chebyshev Basis Comparison Plots -============================================= - -Compare the two spectral basis implementations side-by-side. -""" - -# %% -# Setup -# ----- -from utils import get_project_root, LDCPlotter, GhiaValidator, plot_validation - -# Configuration -Re = 100 -N = 25 # Grid size (number of nodes) -Re_str = f"Re{int(Re)}" - -project_root = get_project_root() -data_dir = project_root / "data" / "Spectral-Solver" -fig_dir = project_root / "figures" / "Spectral-Solver" -fig_dir.mkdir(parents=True, exist_ok=True) - -# File paths -chebyshev_path = data_dir / "Chebyshev" / f"LDC_N{N}_{Re_str}.h5" - -if not chebyshev_path.exists(): - raise FileNotFoundError(f"Chebyshev solution not found: {chebyshev_path}") - -# Load Chebyshev solution (Legendre diverged with current settings) -plotter_cheb = LDCPlotter(chebyshev_path) -validator_cheb = GhiaValidator(chebyshev_path, Re=Re, method_label="Chebyshev") - -print(f"Loaded solutions for Re={Re}:") -print(f" Chebyshev: {chebyshev_path.name}") - -# %% -# Ghia Validation -# --------------- -# Ghia benchmark validation for Chebyshev spectral solver - -plot_validation( - validator_cheb, output_path=fig_dir / f"comparison_ghia_validation_{Re_str}.pdf" -) -print(" ✓ Ghia validation saved") - -# %% -# Convergence History -# ------------------- -# Chebyshev convergence behavior - -fig_dir_cheb = fig_dir / "Chebyshev" -fig_dir_cheb.mkdir(parents=True, exist_ok=True) - -plotter_cheb.plot_convergence(output_path=fig_dir_cheb / f"convergence_{Re_str}.pdf") -print(" ✓ Chebyshev convergence saved") - -# %% -# Chebyshev Field Plots -# --------------------- -# Solution fields and streamlines - -plotter_cheb.plot_fields(output_path=fig_dir_cheb / f"fields_{Re_str}.pdf") -print(" ✓ Chebyshev fields saved") - -plotter_cheb.plot_streamlines(output_path=fig_dir_cheb / f"streamlines_{Re_str}.pdf") -print(" ✓ Chebyshev streamlines saved") diff --git a/Experiments/02-Spectral-Solver/plot_spectral_pyvista.py b/Experiments/02-Spectral-Solver/plot_spectral_pyvista.py deleted file mode 100644 index 2210f15..0000000 --- a/Experiments/02-Spectral-Solver/plot_spectral_pyvista.py +++ /dev/null @@ -1,445 +0,0 @@ -""" -Spectral Solver Visualization with PyVista -=========================================== - -This script visualizes the spectral solver solution using PyVista -with a ParaView-inspired theme for publication-quality figures. -""" - -# %% -# Imports and Setup -# ----------------- - -import numpy as np -import pandas as pd -import pyvista as pv -from scipy.interpolate import BarycentricInterpolator - -from utils import get_project_root - -# Use ParaView theme -pv.set_plot_theme("paraview") - -# %% -# Load Solution from HDF5 -# ----------------------- - -project_root = get_project_root() -fig_dir = project_root / "figures" / "Spectral-Solver" -fig_dir.mkdir(parents=True, exist_ok=True) - -# Configuration -Re = 100 -N = 25 # Polynomial order (N+1 nodes per direction) - -# Load from pre-computed HDF5 file -data_file = ( - project_root / "data" / "Spectral-Solver" / "Chebyshev" / f"LDC_N{N}_Re{Re}.h5" -) - -if not data_file.exists(): - raise FileNotFoundError( - f"Data file not found: {data_file}\n" - f"Run compute_spectral_chebyshev.py first to generate the data." - ) - -print(f"Loading spectral solution from: {data_file}") - -with pd.HDFStore(data_file, "r") as store: - params = store["params"].iloc[0] - metrics = store["metrics"].iloc[0] - fields_df = store["fields"] - -# Infer actual grid size from data -n_points = len(fields_df) -grid_size = int(np.sqrt(n_points)) - -print(f"\nSolution loaded: Re={params['Re']:.0f}, Grid={grid_size}x{grid_size}") -print(f" Converged: {metrics['converged']}") -print(f" Iterations: {int(metrics['iterations'])}") -print(f" Final residual: {metrics['final_residual']:.2e}") -print(f" Wall time: {metrics['wall_time_seconds']:.2f} seconds") - -# %% -# Interpolate to Uniform Grid (Barycentric Interpolation) -# ------------------------------------------------------- -# Spectral methods use non-uniform LGL nodes clustered near boundaries. -# For smooth visualization and proper streamlines, interpolate to uniform grid. - -# Get original solution data from loaded DataFrame -x_orig = fields_df["x"].values -y_orig = fields_df["y"].values -u_orig = fields_df["u"].values -v_orig = fields_df["v"].values -p_orig = fields_df["p"].values - -# Infer grid dimensions from the data (assume square grid) -n_points = len(x_orig) -nx_orig = ny_orig = int(np.sqrt(n_points)) -if nx_orig * ny_orig != n_points: - raise ValueError(f"Cannot infer square grid from {n_points} points") - -# Get unique LGL node coordinates -x_lgl = np.sort(np.unique(x_orig)) -y_lgl = np.sort(np.unique(y_orig)) - -# Reshape to 2D (assuming row-major ordering from spectral solver) -# Need to figure out the ordering - let's sort and reshape -sorted_idx = np.lexsort((x_orig, y_orig)) -U_orig_2d = u_orig[sorted_idx].reshape(ny_orig, nx_orig) -V_orig_2d = v_orig[sorted_idx].reshape(ny_orig, nx_orig) -P_orig_2d = p_orig[sorted_idx].reshape(ny_orig, nx_orig) - -# Interpolation resolution (uniform grid) -interp_resolution = 100 - -# Create fine uniform grid -x_fine = np.linspace(0, 1, interp_resolution) -y_fine = np.linspace(0, 1, interp_resolution) - - -# Tensor product barycentric interpolation -def interp_2d_barycentric(field_2d, x_nodes, y_nodes, x_new, y_new): - """Interpolate 2D field using tensor product barycentric interpolation.""" - nx_new = len(x_new) - # First interpolate along x for each y - temp = np.array([BarycentricInterpolator(x_nodes, row)(x_new) for row in field_2d]) - # Then interpolate along y for each x - result = np.array( - [BarycentricInterpolator(y_nodes, temp[:, i])(y_new) for i in range(nx_new)] - ).T - return result - - -print( - f"\nInterpolating from {nx_orig}x{ny_orig} LGL grid to {interp_resolution}x{interp_resolution} uniform grid..." -) - -U = interp_2d_barycentric(U_orig_2d, x_lgl, y_lgl, x_fine, y_fine) -V = interp_2d_barycentric(V_orig_2d, x_lgl, y_lgl, x_fine, y_fine) -P = interp_2d_barycentric(P_orig_2d, x_lgl, y_lgl, x_fine, y_fine) - -# Compute velocity magnitude on fine grid -vel_mag = np.sqrt(U**2 + V**2) - -# %% -# Create PyVista Grid -# ------------------- - -nx = ny = interp_resolution -n_points = nx * ny - -# Create meshgrid coordinates -X, Y = np.meshgrid(x_fine, y_fine) - -# Create structured grid -points = np.zeros((n_points, 3)) -points[:, 0] = X.ravel() -points[:, 1] = Y.ravel() - -grid = pv.StructuredGrid() -grid.points = points -grid.dimensions = [nx, ny, 1] - -# Add scalar fields (flattened in row-major order) - use .copy() to avoid reference issues -grid.point_data["Velocity Magnitude"] = vel_mag.ravel().copy() -grid.point_data["U Velocity"] = U.ravel().copy() -grid.point_data["V Velocity"] = V.ravel().copy() -grid.point_data["Pressure"] = P.ravel().copy() - -# Create velocity vectors for streamlines -velocity = np.zeros((n_points, 3)) -velocity[:, 0] = U.ravel() -velocity[:, 1] = V.ravel() -grid.point_data["Velocity"] = velocity - -print(f"PyVista grid created: {nx}x{ny} points (interpolated)") - -# Verify data is correct -print("\nData verification:") -print(f" U range: [{U.min():.4f}, {U.max():.4f}]") -print(f" V range: [{V.min():.4f}, {V.max():.4f}]") -print(f" vel_mag range: [{vel_mag.min():.4f}, {vel_mag.max():.4f}]") -print(f" U at lid center (0.5, 1.0): {U[-1, nx // 2]:.4f}") -print(f" V at lid center (0.5, 1.0): {V[-1, nx // 2]:.4f}") - -# %% -# Plot 1: Velocity Magnitude with Streamlines -# ------------------------------------------- - -# Verify the data before plotting -vm_data = grid.point_data["Velocity Magnitude"] -print("\nPlot 1 - Using 'Velocity Magnitude' scalar:") -print(f" Range: [{vm_data.min():.4f}, {vm_data.max():.4f}]") -print(" Should be sqrt(U^2+V^2), NOT same as U!") - -plotter = pv.Plotter(off_screen=True, window_size=[1200, 1000]) - -# Add velocity magnitude as surface -plotter.add_mesh( - grid, - scalars="Velocity Magnitude", - cmap="coolwarm", - show_edges=False, - lighting=False, - scalar_bar_args={ - "title": "Velocity Magnitude", - "title_font_size": 16, - "label_font_size": 14, - "shadow": True, - "n_labels": 5, - "italic": False, - "fmt": "%.3f", - "font_family": "arial", - "vertical": True, - }, -) - -# Generate streamlines -# Create seed points along the left boundary and bottom -n_seeds = 15 -seed_points_left = np.zeros((n_seeds, 3)) -seed_points_left[:, 0] = 0.02 # Slightly inside left boundary -seed_points_left[:, 1] = np.linspace(0.1, 0.9, n_seeds) - -seed_points_bottom = np.zeros((n_seeds, 3)) -seed_points_bottom[:, 0] = np.linspace(0.1, 0.9, n_seeds) -seed_points_bottom[:, 1] = 0.02 # Slightly inside bottom boundary - -seed_points = np.vstack([seed_points_left, seed_points_bottom]) -seeds = pv.PolyData(seed_points) - -# Compute streamlines -try: - streamlines = grid.streamlines_from_source( - seeds, - vectors="Velocity", - integration_direction="both", - max_length=50.0, - initial_step_length=0.01, - max_step_length=0.1, - ) - - if streamlines.n_points > 0: - plotter.add_mesh( - streamlines, - color="white", - line_width=1.5, - opacity=0.8, - ) - print(f"Added {streamlines.n_lines} streamlines") -except Exception as e: - print(f"Streamline generation failed: {e}") - -# Camera and labels -plotter.view_xy() -plotter.add_text( - f"Lid-Driven Cavity Flow (Re={Re})\nSpectral Method - {nx}x{ny} Grid", - position="upper_left", - font_size=12, - color="black", -) - -# Add axes -plotter.add_axes( - xlabel="x", - ylabel="y", - zlabel="", - line_width=2, - labels_off=False, -) - -# Save figure -output_path = fig_dir / f"velocity_streamlines_Re{Re}.png" -plotter.screenshot(output_path, transparent_background=False, scale=2) -print(f"\nSaved: {output_path}") -plotter.close() - -# %% -# Plot 2: Vorticity Field -# ----------------------- - -# Compute vorticity on the interpolated uniform grid using finite differences -# ω = ∂v/∂x - ∂u/∂y -dx = x_fine[1] - x_fine[0] -dy = y_fine[1] - y_fine[0] - -# Central differences for interior, one-sided at boundaries -dv_dx = np.zeros_like(V) -du_dy = np.zeros_like(U) - -# ∂v/∂x (central differences) -dv_dx[:, 1:-1] = (V[:, 2:] - V[:, :-2]) / (2 * dx) -dv_dx[:, 0] = (V[:, 1] - V[:, 0]) / dx -dv_dx[:, -1] = (V[:, -1] - V[:, -2]) / dx - -# ∂u/∂y (central differences) -du_dy[1:-1, :] = (U[2:, :] - U[:-2, :]) / (2 * dy) -du_dy[0, :] = (U[1, :] - U[0, :]) / dy -du_dy[-1, :] = (U[-1, :] - U[-2, :]) / dy - -vorticity = dv_dx - du_dy -grid.point_data["Vorticity"] = vorticity.ravel() - -plotter = pv.Plotter(off_screen=True, window_size=[1200, 1000]) - -# Symmetric colormap for vorticity -vort_max = np.abs(vorticity).max() - -plotter.add_mesh( - grid, - scalars="Vorticity", - cmap="RdBu_r", - clim=[-vort_max, vort_max], - show_edges=False, - lighting=False, - scalar_bar_args={ - "title": "Vorticity (ω)", - "title_font_size": 16, - "label_font_size": 14, - "shadow": True, - "n_labels": 5, - "italic": False, - "fmt": "%.2f", - "font_family": "arial", - "vertical": True, - }, -) - -plotter.view_xy() -plotter.add_text( - f"Vorticity Field (Re={Re})\nSpectral Method - {nx}x{ny} Grid", - position="upper_left", - font_size=12, - color="black", -) - -output_path = fig_dir / f"vorticity_Re{Re}.png" -plotter.screenshot(output_path, transparent_background=False, scale=2) -print(f"Saved: {output_path}") -plotter.close() - -# %% -# Plot 3: Pressure Field -# ---------------------- - -plotter = pv.Plotter(off_screen=True, window_size=[1200, 1000]) - -plotter.add_mesh( - grid, - scalars="Pressure", - cmap="coolwarm", - show_edges=False, - lighting=False, - scalar_bar_args={ - "title": "Pressure", - "title_font_size": 16, - "label_font_size": 14, - "shadow": True, - "n_labels": 5, - "italic": False, - "fmt": "%.4f", - "font_family": "arial", - "vertical": True, - }, -) - -plotter.view_xy() -plotter.add_text( - f"Pressure Field (Re={Re})\nSpectral Method - {nx}x{ny} Grid", - position="upper_left", - font_size=12, - color="black", -) - -output_path = fig_dir / f"pressure_Re{Re}.png" -plotter.screenshot(output_path, transparent_background=False, scale=2) -print(f"Saved: {output_path}") -plotter.close() - -# %% -# Plot 4: Velocity Components Side by Side -# ---------------------------------------- - -# Get actual data ranges for explicit clim -U_data = grid.point_data["U Velocity"] -V_data = grid.point_data["V Velocity"] -print("\nVelocity component data ranges:") -print(f" U Velocity in grid: [{U_data.min():.4f}, {U_data.max():.4f}]") -print(f" V Velocity in grid: [{V_data.min():.4f}, {V_data.max():.4f}]") - -plotter = pv.Plotter(off_screen=True, shape=(1, 2), window_size=[2000, 900]) - -# U velocity - use coolwarm to show positive/negative -plotter.subplot(0, 0) -u_max = max(abs(U_data.min()), abs(U_data.max())) -plotter.add_mesh( - grid, - scalars="U Velocity", - cmap="coolwarm", - clim=[-u_max, u_max], # symmetric around zero - show_edges=False, - lighting=False, - scalar_bar_args={ - "title": "U Velocity", - "title_font_size": 14, - "label_font_size": 12, - "n_labels": 5, - "fmt": "%.3f", - "vertical": True, - }, -) -plotter.view_xy() -plotter.add_text( - f"U (horizontal)\nrange: [{U_data.min():.2f}, {U_data.max():.2f}]", - position="upper_left", - font_size=10, - color="black", -) - -# V velocity - use coolwarm with symmetric limits -plotter.subplot(0, 1) -v_max = max(abs(V_data.min()), abs(V_data.max())) -plotter.add_mesh( - grid, - scalars="V Velocity", - cmap="coolwarm", - clim=[-v_max, v_max], # symmetric around zero - show_edges=False, - lighting=False, - scalar_bar_args={ - "title": "V Velocity", - "title_font_size": 14, - "label_font_size": 12, - "n_labels": 5, - "fmt": "%.3f", - "vertical": True, - }, -) -plotter.view_xy() -plotter.add_text( - f"V (vertical)\nrange: [{V_data.min():.2f}, {V_data.max():.2f}]", - position="upper_left", - font_size=10, - color="black", -) - -output_path = fig_dir / f"velocity_components_Re{Re}.png" -plotter.screenshot(output_path, transparent_background=False, scale=2) -print(f"Saved: {output_path}") -plotter.close() - -# %% -# Summary -# ------- - -print(f"\n{'=' * 50}") -print("Visualization complete!") -print(f"{'=' * 50}") -print(f"Data loaded from: {data_file}") -print(f"Output directory: {fig_dir}") -print("\nGenerated figures:") -print(f" - velocity_streamlines_Re{Re}.png") -print(f" - vorticity_Re{Re}.png") -print(f" - pressure_Re{Re}.png") -print(f" - velocity_components_Re{Re}.png") diff --git a/README.md b/README.md index 6997c6d..6de49b5 100644 --- a/README.md +++ b/README.md @@ -1,63 +1,110 @@ # Project 3: Lid-Driven Cavity Flow +Comparing Finite Volume and Spectral methods for the incompressible Navier-Stokes equations. + ## Documentation -📚 [Read the full documentation](https://02689-advancednumericalalgorithmproject3.readthedocs.io/en/latest/) +[Read the full documentation](https://02689-advancednumericalalgorithmproject3.readthedocs.io/en/latest/) ## Installation -Run the setup script from project root: ```bash -bash setup.sh +uv sync ``` -## HPC (LSF Cluster) +## Running Solvers + +The project uses [Hydra](https://hydra.cc/) for configuration management. Run solvers via `run_solver.py`: -Submit parameter sweeps using job packs: +### Basic Usage ```bash -# Submit spectral solver jobs -uv run python main.py --hpc spectral +# Finite Volume solver (32x32 cells, Re=100) +uv run python run_solver.py solver=fv N=32 Re=100 + +# Spectral solver (N=15 gives 16x16 nodes, Re=100) +uv run python run_solver.py solver=spectral N=15 Re=100 +``` + +### Using Experiment Configs -# Submit FV solver jobs -uv run python main.py --hpc fv +Pre-defined experiment configurations are in `conf/experiment/`: -# Submit all jobs -uv run python main.py --hpc all +```bash +# Quick test (small grid, few iterations) +uv run python run_solver.py +experiment=quick_test solver=fv + +# FV validation (default settings for benchmarking) +uv run python run_solver.py +experiment=fv_validation + +# Spectral validation +uv run python run_solver.py +experiment=spectral_validation ``` -Edit `Experiments/*/generate_pack.sh` to customize resources and parameter sweep values. +### Parameter Sweeps -### Monitoring and Managing Jobs +Run multiple configurations with Hydra's multirun: ```bash -# Check job status -bstat +# Sweep over grid sizes +uv run python run_solver.py -m solver=fv N=16,32,64 Re=100 -# Kill a specific job by name -bkill -J Spectral-N23-Re100 +# Sweep over Reynolds numbers +uv run python run_solver.py -m solver=spectral N=31 Re=100,400,1000 -# Kill a job by ID -bkill 27198795 +# Full validation sweep +uv run python run_solver.py -m +experiment=fv_validation +``` -# Kill all your jobs -bkill 0 +### Configuration Structure + +``` +conf/ +├── config.yaml # Main config (N, Re, tolerance, etc.) +├── solver/ +│ ├── fv.yaml # FV-specific (alpha_uv, alpha_p, scheme) +│ └── spectral.yaml # Spectral-specific (CFL, beta_squared) +├── experiment/ +│ ├── quick_test.yaml # Fast debugging runs +│ ├── fv_validation.yaml # FV benchmark settings +│ └── spectral_validation.yaml +└── mlflow/ + ├── local.yaml # Local file-based tracking + └── databricks.yaml # Cloud tracking ``` -## References +### Output Structure + +Each run creates a timestamped output directory: + +``` +outputs/{experiment_name}/Re{Re}/{solver}/{DD-MM-YY}/{HH:MM:SS}/ +├── config.yaml # Resolved configuration +├── run_solver.log # Execution log +├── LDC_{SOLVER}_N{N}_Re{Re}.h5 # Results (HDF5) +└── .hydra/ # Hydra internals +``` +## Viewing Results -### SIMPLE for Spectral -[A spectral pressure correction method for unsteady incompressible flows](https://www.sciencedirect.com/science/article/pii/S0021999112007334) +### MLflow UI -### Multigrid Method -[An explicit Chebyshev pseudospectral multigrid method for incompressible Navier–Stokes equations](https://www.sciencedirect.com/science/article/pii/S0045793009001121) +```bash +uv run mlflow ui --backend-store-uri ./mlruns --port 5001 +``` + +Then open http://localhost:5001 -### Quantities -[The 2D lid-driven cavity problem revisited](https://www.researchgate.net/publication/222433759_The_2D_lid-driven_cavity_problem_revisited) +### Logged Data -### Ghia Benchmark -[High-Re solutions for incompressible flow using the Navier-Stokes equations and a multigrid method](https://www.sciencedirect.com/science/article/pii/0021999182900584) +- **Parameters:** solver settings, grid size, Reynolds number +- **Metrics:** iterations, convergence, wall time, residuals +- **Timeseries:** residual history, energy, enstrophy (with step) +- **Artifacts:** HDF5 result files + +## References -### P_N - P_{N-2} Method -[Parallel spectral-element direction splitting method for incompressible Navier–Stokes equations](https://www.sciencedirect.com/science/article/pii/S0743731518305549) +- [High-Re solutions for incompressible flow (Ghia et al.)](https://www.sciencedirect.com/science/article/pii/0021999182900584) - Benchmark data +- [Chebyshev pseudospectral multigrid method](https://www.sciencedirect.com/science/article/pii/S0045793009001121) - Spectral method +- [The 2D lid-driven cavity problem revisited](https://www.researchgate.net/publication/222433759_The_2D_lid-driven_cavity_problem_revisited) - Conserved quantities +- [P_N-P_{N-2} spectral method](https://www.sciencedirect.com/science/article/pii/S0743731518305549) - Pressure formulation diff --git a/conf/config.yaml b/conf/config.yaml index ec7d9ae..36f88eb 100644 --- a/conf/config.yaml +++ b/conf/config.yaml @@ -1,43 +1,40 @@ +# Main Hydra configuration for LDC solvers +# Usage: uv run python run_solver.py solver=fv Re=100 N=32 +# uv run python run_solver.py solver=spectral Re=100 N=15 + defaults: - - machine: local + - solver: fv - mlflow: local - _self_ -# ============================================================ -# GlobalParams - flat config matching dataclass -# ============================================================ - -# Required -N: 65 +# ============================================================================= +# Common Parameters (shared by all solvers) +# ============================================================================= -# Solver selection and params -solver: jacobi # "jacobi" | "fmg" -omega: 0.8 -tolerance: 1.0e-6 -max_iter: 1000 +# Grid size (meaning depends on solver): +# FV: N = number of cells in each direction +# Spectral: N = polynomial order (gives N+1 nodes) +N: 32 -# FMG-specific (ignored by Jacobi) -n_smooth: 3 -fmg_post_vcycles: 1 +# Physics +Re: 100 +lid_velocity: 1.0 +Lx: 1.0 +Ly: 1.0 -# Parallelization -n_ranks: 1 -strategy: sliced # "sliced" | "cubic" -communicator: custom # "numpy" | "custom" - -# Numba -use_numba: false -specified_numba_threads: 1 +# Solver control +tolerance: 1.0e-6 +max_iterations: 500 # Experiment tracking -experiment_name: default +experiment_name: LDC-Solver -# ============================================================ +# ============================================================================= # Hydra output paths -# ============================================================ +# ============================================================================= hydra: run: - dir: outputs/${experiment_name}/${now:%Y-%m-%d}/${now:%H-%M-%S} + dir: outputs/${experiment_name}/Re${Re}/${solver.name}/${now:%d-%m-%y}/${now:%H:%M:%S} sweep: - dir: multirun/${experiment_name}/${now:%Y-%m-%d}/${now:%H-%M-%S} - subdir: ${hydra.job.num} + dir: multirun/${experiment_name}/Re${Re}/${solver.name}/${now:%d-%m-%y}/${now:%H:%M:%S} + subdir: N${N} diff --git a/conf/experiment/communication.yaml b/conf/experiment/communication.yaml deleted file mode 100644 index 276f28a..0000000 --- a/conf/experiment/communication.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# @package _global_ -# Communication experiment: compare halo exchange strategies -# Tests compact (1 node) vs spread (2 nodes) placement - -experiment_name: communication - -# Use Jacobi solver for halo exchange benchmarking -solver: jacobi -n_ranks: 24 -max_iter: 200 -tolerance: 0.0 - -# Use Numba for consistent performance -use_numba: true -specified_numba_threads: 1 - -# Default values -N: 254 -strategy: sliced -communicator: custom - -# Logarithmic N values compatible with 24 ranks (N-2 divisible by 12 for cubic) -# Cubic decomposition: 24 = 4×3×2, LCM = 12 -# N = 12k+2: 122 (1.8M), 254 (16M), 506 (129M), 1010 (1B) -> ~2x growth -hydra: - sweeper: - params: - N: 122,254,506,1010 - strategy: sliced,cubic - communicator: numpy,custom diff --git a/conf/experiment/fv_validation.yaml b/conf/experiment/fv_validation.yaml new file mode 100644 index 0000000..8f95f62 --- /dev/null +++ b/conf/experiment/fv_validation.yaml @@ -0,0 +1,21 @@ +# @package _global_ +# FV Solver validation against Ghia benchmark +# Usage: uv run python run_solver.py +experiment=fv_validation +# uv run python run_solver.py -m +experiment=fv_validation (sweep all) + +defaults: + - override /solver: fv + +experiment_name: FV-Validation + +# Validation grid sizes and Reynolds numbers +N: 64 +Re: 100 +tolerance: 1.0e-7 +max_iterations: 50000 + +hydra: + sweeper: + params: + N: 32,64,128 + Re: 100,400,1000 diff --git a/conf/experiment/kernel/_base.yaml b/conf/experiment/kernel/_base.yaml deleted file mode 100644 index 3d2ac85..0000000 --- a/conf/experiment/kernel/_base.yaml +++ /dev/null @@ -1,11 +0,0 @@ -# @package _global_ -# Base kernel config - sequential Jacobi for kernel comparison -# Note: No defaults here - composed on top of main config.yaml - -experiment_name: kernel - -# Sequential mode -solver: jacobi -n_ranks: 1 -omega: 0.8 -tolerance: 0.0 # Disable early stopping - always run full max_iter diff --git a/conf/experiment/kernel/benchmark_numba.yaml b/conf/experiment/kernel/benchmark_numba.yaml deleted file mode 100644 index fec25a1..0000000 --- a/conf/experiment/kernel/benchmark_numba.yaml +++ /dev/null @@ -1,17 +0,0 @@ -# @package _global_ -# Numba kernel benchmark - performance vs problem size and threads -# Run: uv run python run_solver.py +experiment=kernel/benchmark_numba mlflow=databricks -m -defaults: - - /experiment/kernel/_base - - _self_ - -use_numba: true -specified_numba_threads: 1 -max_iter: 100 -N: 65 - -hydra: - sweeper: - params: - N: 33,65,129,257 - specified_numba_threads: 1,2,4,6 diff --git a/conf/experiment/kernel/benchmark_numpy.yaml b/conf/experiment/kernel/benchmark_numpy.yaml deleted file mode 100644 index 54d2be8..0000000 --- a/conf/experiment/kernel/benchmark_numpy.yaml +++ /dev/null @@ -1,15 +0,0 @@ -# @package _global_ -# NumPy kernel benchmark - performance vs problem size -# Run: uv run python run_solver.py +experiment=kernel/benchmark_numpy mlflow=databricks -m -defaults: - - /experiment/kernel/_base - - _self_ - -use_numba: false -max_iter: 100 -N: 65 - -hydra: - sweeper: - params: - N: 33,65,129,257 diff --git a/conf/experiment/kernel/convergence_numba.yaml b/conf/experiment/kernel/convergence_numba.yaml deleted file mode 100644 index 3122f74..0000000 --- a/conf/experiment/kernel/convergence_numba.yaml +++ /dev/null @@ -1,16 +0,0 @@ -# @package _global_ -# Numba kernel convergence test - error vs iterations (4 threads) -# Run: uv run python run_solver.py +experiment=kernel/convergence_numba mlflow=databricks -m -defaults: - - /experiment/kernel/_base - - _self_ - -N: 17 -use_numba: true -specified_numba_threads: 4 -max_iter: 100 - -hydra: - sweeper: - params: - max_iter: 10,25,50,100,250,500,1000 diff --git a/conf/experiment/kernel/convergence_numpy.yaml b/conf/experiment/kernel/convergence_numpy.yaml deleted file mode 100644 index 34117c3..0000000 --- a/conf/experiment/kernel/convergence_numpy.yaml +++ /dev/null @@ -1,15 +0,0 @@ -# @package _global_ -# NumPy kernel convergence test - error vs iterations -# Run: uv run python run_solver.py +experiment=kernel/convergence_numpy mlflow=databricks -m -defaults: - - /experiment/kernel/_base - - _self_ - -N: 17 -use_numba: false -max_iter: 100 - -hydra: - sweeper: - params: - max_iter: 10,25,50,100,250,500,1000 diff --git a/conf/experiment/multigrid.yaml b/conf/experiment/multigrid.yaml deleted file mode 100644 index 40e9251..0000000 --- a/conf/experiment/multigrid.yaml +++ /dev/null @@ -1,29 +0,0 @@ -# @package _global_ -# Multigrid comparison experiment - -experiment_name: multigrid - -# FMG solver settings -solver: fmg -n_smooth: 5 -omega: 0.6666666666666666 -max_iter: 100 -tolerance: 1.0e-10 -fmg_post_vcycles: 2 -communicator: custom - -# Use Numba -use_numba: true -specified_numba_threads: 1 - -# Default values -N: 129 -n_ranks: 4 -strategy: cubic - -# Hydra sweep configuration -hydra: - sweeper: - params: - N: 33,65,129,257 - strategy: sliced,cubic diff --git a/conf/experiment/quick_test.yaml b/conf/experiment/quick_test.yaml new file mode 100644 index 0000000..69dd9ee --- /dev/null +++ b/conf/experiment/quick_test.yaml @@ -0,0 +1,11 @@ +# @package _global_ +# Quick test configuration for debugging +# Usage: uv run python run_solver.py +experiment=quick_test solver=fv +# uv run python run_solver.py +experiment=quick_test solver=spectral + +experiment_name: Quick-Test + +N: 16 +Re: 100 +tolerance: 1.0e-4 +max_iterations: 100 diff --git a/conf/experiment/spectral_validation.yaml b/conf/experiment/spectral_validation.yaml new file mode 100644 index 0000000..73dded6 --- /dev/null +++ b/conf/experiment/spectral_validation.yaml @@ -0,0 +1,21 @@ +# @package _global_ +# Spectral Solver validation against Ghia benchmark +# Usage: uv run python run_solver.py +experiment=spectral_validation +# uv run python run_solver.py -m +experiment=spectral_validation (sweep all) + +defaults: + - override /solver: spectral + +experiment_name: Spectral-Validation + +# Validation polynomial orders (N+1 nodes) and Reynolds numbers +N: 31 +Re: 100 +tolerance: 1.0e-7 +max_iterations: 100000 + +hydra: + sweeper: + params: + N: 15,23,31,47 + Re: 100,400,1000 diff --git a/conf/experiment/strong_scaling_fmg.yaml b/conf/experiment/strong_scaling_fmg.yaml deleted file mode 100644 index 954e3d4..0000000 --- a/conf/experiment/strong_scaling_fmg.yaml +++ /dev/null @@ -1,32 +0,0 @@ -# @package _global_ -# FMG scaling experiment: Full Multigrid solver - -experiment_name: fmg_scaling - -# FMG solver settings -solver: fmg -n_smooth: 5 -omega: 0.6666666666666666 -max_iter: 50 -tolerance: 0.0 -fmg_post_vcycles: 2 -communicator: custom - -# Use Numba with 1 thread for pure MPI scaling -use_numba: true -specified_numba_threads: 1 - -# Default values -N: 257 -n_ranks: 1 -strategy: cubic - -# Hydra sweep configuration -# N values: 2^k+1 for multigrid levels (129³=2.1M, 257³=17M, 513³=135M) -# Rank counts with good 3D factorizations for cubic decomposition -hydra: - sweeper: - params: - N: 257,513 - strategy: sliced,cubic - n_ranks: 1,2,4,8,16,24,36,48,72,96 diff --git a/conf/experiment/strong_scaling_jacobi.yaml b/conf/experiment/strong_scaling_jacobi.yaml deleted file mode 100644 index 5cad8a1..0000000 --- a/conf/experiment/strong_scaling_jacobi.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# @package _global_ -# Strong scaling experiment: Jacobi solver with fixed problem size - -experiment_name: scaling - -# Jacobi solver settings -solver: jacobi -max_iter: 250 -omega: 0.8 -tolerance: 0.0 -communicator: custom - -# Use Numba with 1 thread for pure MPI scaling -use_numba: true -specified_numba_threads: 1 - -# Default values -N: 257 -n_ranks: 1 -strategy: sliced - -# Hydra sweep configuration -# N values: 2^k+1 (257³=17M, 513³=135M points) -# Rank counts with good 3D factorizations for cubic decomposition -hydra: - sweeper: - params: - N: 257,513 - strategy: sliced,cubic - n_ranks: 1,2,4,8,16,24,36,48,72,96 diff --git a/conf/experiment/test/fmg_convergence.yaml b/conf/experiment/test/fmg_convergence.yaml deleted file mode 100644 index 5088512..0000000 --- a/conf/experiment/test/fmg_convergence.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# @package _global_ -defaults: - - override /mlflow: local - -experiment_name: TEST-convergence_fmg - -# FMG solver settings -solver: fmg -omega: 0.6666666666666666 -n_smooth: 3 -fmg_post_vcycles: 1 -tolerance: 1e-12 -max_iter: 100 -use_numba: false -specified_numba_threads: 1 - -# Base settings (overridden by sweeper) -n_ranks: 1 -N: 32 - -hydra: - sweeper: - params: - n_ranks: 4 - N: 33,65,129 - strategy: sliced,cubic - communicator: numpy,custom diff --git a/conf/experiment/test/jacobi_convergence.yaml b/conf/experiment/test/jacobi_convergence.yaml deleted file mode 100644 index 3a914c8..0000000 --- a/conf/experiment/test/jacobi_convergence.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# @package _global_ -defaults: - - override /mlflow: local - -experiment_name: TEST-convergence_jacobi - -# Jacobi solver settings -solver: jacobi -tolerance: 1e-10 -max_iter: 20000 -use_numba: false -specified_numba_threads: 1 - -# Base settings (overridden by sweeper) -n_ranks: 1 -N: 32 - -hydra: - sweeper: - params: - n_ranks: 4 - N: 17,33,65 - strategy: sliced,cubic - communicator: numpy,custom diff --git a/conf/experiment/validation/_base.yaml b/conf/experiment/validation/_base.yaml deleted file mode 100644 index 6f10ed0..0000000 --- a/conf/experiment/validation/_base.yaml +++ /dev/null @@ -1,19 +0,0 @@ -# @package _global_ -# Base validation config - shared by jacobi.yaml and fmg.yaml -# Note: No defaults here - composed on top of main config.yaml - -experiment_name: validation - -# Validation: verify O(h²) spatial convergence -max_iter: 5000 -tolerance: 1.0e-14 # Near machine precision -n_ranks: 4 -strategy: sliced -communicator: custom - -# Sweep across decomposition strategies and communicators -hydra: - sweeper: - params: - strategy: sliced,cubic - communicator: numpy,custom diff --git a/conf/experiment/validation/fmg.yaml b/conf/experiment/validation/fmg.yaml deleted file mode 100644 index 67d4bd9..0000000 --- a/conf/experiment/validation/fmg.yaml +++ /dev/null @@ -1,15 +0,0 @@ -# @package _global_ -# FMG validation: larger grids (multigrid efficiency) -# Run: uv run python run_solver.py +experiment=validation/fmg -m -defaults: - - /experiment/validation/_base - - _self_ - -solver: fmg -N: 129 -# Uses default n_smooth=5, fmg_post_vcycles=1 - -hydra: - sweeper: - params: - N: 65,129,257 diff --git a/conf/experiment/validation/jacobi.yaml b/conf/experiment/validation/jacobi.yaml deleted file mode 100644 index a25a438..0000000 --- a/conf/experiment/validation/jacobi.yaml +++ /dev/null @@ -1,14 +0,0 @@ -# @package _global_ -# Jacobi validation: small grids (fast convergence check) -# Run: uv run python run_solver.py +experiment=validation/jacobi -m -defaults: - - /experiment/validation/_base - - _self_ - -solver: jacobi -N: 33 - -hydra: - sweeper: - params: - N: 17,33,65 diff --git a/conf/experiment/weak_scaling_fmg.yaml b/conf/experiment/weak_scaling_fmg.yaml deleted file mode 100644 index c2d6397..0000000 --- a/conf/experiment/weak_scaling_fmg.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# @package _global_ -# Weak scaling experiment: FMG solver - -experiment_name: weak_scaling_final - -# FMG solver settings -solver: fmg -n_smooth: 5 -omega: 0.6666666666666666 -max_iter: 50 -tolerance: 0.0 -fmg_post_vcycles: 2 -communicator: custom - -# Use Numba with 1 thread for pure MPI scaling -use_numba: true -specified_numba_threads: 1 - -# Default values -N: 129 -n_ranks: 1 -strategy: cubic - -# Weak scaling: N and n_ranks pairs set via command line in job script diff --git a/conf/experiment/weak_scaling_jacobi.yaml b/conf/experiment/weak_scaling_jacobi.yaml deleted file mode 100644 index dabb81c..0000000 --- a/conf/experiment/weak_scaling_jacobi.yaml +++ /dev/null @@ -1,22 +0,0 @@ -# @package _global_ -# Weak scaling experiment: Jacobi solver - -experiment_name: weak_scaling_final - -# Jacobi solver settings -solver: jacobi -max_iter: 100 -omega: 0.8 -tolerance: 0.0 -communicator: custom - -# Use Numba with 1 thread for pure MPI scaling -use_numba: true -specified_numba_threads: 1 - -# Default values -N: 129 -n_ranks: 1 -strategy: cubic - -# Weak scaling pairs set via command line (N and n_ranks must match for constant work/rank) diff --git a/conf/hydra/callbacks/mlflow_log.yaml b/conf/hydra/callbacks/mlflow_log.yaml deleted file mode 100644 index 930a7ef..0000000 --- a/conf/hydra/callbacks/mlflow_log.yaml +++ /dev/null @@ -1,14 +0,0 @@ -# @package _global_ -# MLflow Log Callback - uploads Hydra job log to MLflow artifacts -# -# This callback runs after each job and uploads the log file containing -# all job output (including MPI report-bindings) to MLflow. -# -# Usage: Add to defaults in your config: -# defaults: -# - hydra/callbacks: mlflow_log -hydra: - callbacks: - mlflow_log: - _target_: utils.hydra.callbacks.MLflowLogCallback - artifact_path: logs diff --git a/conf/machine/dtu_hpc.yaml b/conf/machine/dtu_hpc.yaml deleted file mode 100644 index b89a160..0000000 --- a/conf/machine/dtu_hpc.yaml +++ /dev/null @@ -1,11 +0,0 @@ -# DTU HPC defaults -mpi: - bind_to: core - cores_per_node: 24 - sockets_per_node: 2 - -hydra: - job: - env_set: - NUMBA_NUM_THREADS: "1" - OMP_NUM_THREADS: "1" diff --git a/conf/machine/local.yaml b/conf/machine/local.yaml deleted file mode 100644 index 7b972b7..0000000 --- a/conf/machine/local.yaml +++ /dev/null @@ -1,5 +0,0 @@ -# Local machine defaults -mpi: - bind_to: null - cores_per_node: 1 - sockets_per_node: 1 diff --git a/conf/mlflow/databricks.yaml b/conf/mlflow/databricks.yaml index d1e77b9..60647ca 100644 --- a/conf/mlflow/databricks.yaml +++ b/conf/mlflow/databricks.yaml @@ -1,3 +1,4 @@ +# Databricks MLflow configuration mode: databricks tracking_uri: databricks -project_prefix: "/Shared/LSM-PoissonMPI-v3" +project_prefix: /Shared/ANA-P3 diff --git a/conf/mlflow/local.yaml b/conf/mlflow/local.yaml index 8940caa..1e0bc08 100644 --- a/conf/mlflow/local.yaml +++ b/conf/mlflow/local.yaml @@ -1,3 +1,4 @@ +# Local MLflow configuration (file-based tracking) mode: local tracking_uri: ./mlruns project_prefix: "" diff --git a/conf/solver/fv.yaml b/conf/solver/fv.yaml new file mode 100644 index 0000000..79f97ee --- /dev/null +++ b/conf/solver/fv.yaml @@ -0,0 +1,9 @@ +# Finite Volume solver configuration +name: fv + +# FV-specific parameters +convection_scheme: Upwind +limiter: MUSCL +alpha_uv: 0.6 # velocity under-relaxation +alpha_p: 0.4 # pressure under-relaxation +linear_solver_tol: 1.0e-6 # PETSc linear solver tolerance diff --git a/conf/solver/spectral.yaml b/conf/solver/spectral.yaml new file mode 100644 index 0000000..099dfe4 --- /dev/null +++ b/conf/solver/spectral.yaml @@ -0,0 +1,8 @@ +# Spectral solver configuration +name: spectral + +# Spectral-specific parameters +basis_type: chebyshev # "chebyshev" or "legendre" +CFL: 0.5 +beta_squared: 5.0 # artificial compressibility parameter +corner_smoothing: 0.15 # fraction of grid points to smooth at corners diff --git a/docs/An-explicit-Chebyshev-pseudospectral-multigrid-method-for-i_2010_Computers--.pdf b/docs/An-explicit-Chebyshev-pseudospectral-multigrid-method-for-i_2010_Computers--.pdf new file mode 100644 index 0000000000000000000000000000000000000000..39e77ef992e5a1cc7b3131f6e421486c686766d0 GIT 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zgs>3=Fis(e`BDQ2)aN7kTW0$&qN$}l$;yJB|jo?`a;$XxP)`* z0f2GIfCxGW7Yza$X*&r-&}oou1YlgUClcUTDxCEI3XNiV4&fUask^aTyiFX z0=e>mfu9@3WuJ-Mb5TGS4Ae+mV8*%Vk|)u7GT5%-!UYQpE*RH-4sdJ4a`z8l?z!|7 zm}hSOzyy_x2DoX<1tTEmtbi^s99;Q`+_sKPgwj%{#?+n&% SB-qEAD=2.0.0", "mpi4py>=4.1.1", "pip>=25.3", + "hydra-core>=1.3.0", + "omegaconf>=2.3.0", "textual>=6.6.0", "blessed>=1.25.0", ] diff --git a/pyproject_1.toml b/pyproject_1.toml deleted file mode 100644 index 0555069..0000000 --- a/pyproject_1.toml +++ /dev/null @@ -1,67 +0,0 @@ -[build-system] -requires = ["setuptools>=69", "wheel"] -build-backend = "setuptools.build_meta" - -[project] -name = "poisson_solver" -version = "0.1.0" -description = "MPI Poisson Solver" -readme = "README.md" -requires-python = "==3.12.*" -license = { text = "MIT" } -authors = [ - { name = "Alexander Elbæk Nielsen" }, - { name = "Junriu Li" }, - { name = "Philip Korsager Nickel" } -] - -# All dependencies (runtime, docs, and dev) -dependencies = [ - # Runtime dependencies - "numpy>=1.24.0", - "matplotlib>=3.7.0", - "pandas>=2.0.0", - "pyarrow>=14.0.0", - "scipy>=1.11.0", - "seaborn>=0.12.0", - # Documentation dependencies - "sphinx>=7.0.0,<9.0.0", # numpydoc 1.9.0 incompatible with sphinx 9.x - "pydata-sphinx-theme>=0.14.0", - "numpydoc>=1.6.0", - "sphinx-copybutton>=0.5.2", - "sphinx-gallery>=0.14.0", - "sphinxcontrib-mermaid>=0.9.2", - "pillow>=10.0.0", # Required for sphinx-gallery image handling - # Development dependencies - "ruff>=0.1.0", - "pytest>=7.4.0", - "pytest-cov>=4.1.0", - "numba>=0.62.1", - "mpi4py>=4.1.1", - "mlflow>=3.6.0", - "pyvista>=0.46.4", - "h5py>=3.15.1", - "tables>=3.10.2", - "psutil>=7.1.3", - "pyyaml>=6.0.3", - "questionary>=2.1.1", - "python-dotenv>=1.2.1", - "hydra-core>=1.3.2", - "omegaconf>=2.3.0", - "hydra-submitit-launcher>=1.2.0", -] - -[tool.pytest.ini_options] -testpaths = ["tests"] -filterwarnings = ["ignore::UserWarning"] -addopts = "-ra" - -[tool.setuptools] -package-dir = {"" = "src"} - -[tool.setuptools.packages.find] -where = ["src"] - - - - diff --git a/run_solver.py b/run_solver.py index bb25e38..68aa4e9 100644 --- a/run_solver.py +++ b/run_solver.py @@ -1,194 +1,222 @@ """ -Poisson Solver Runner - Clean Hydra + MLflow integration. - -Architecture: - - Orchestrator (main): Hydra entry, saves config, spawns MPI - - Worker: MPI computation, MLflow logging +LDC Solver Runner - Hydra + MLflow integration for FV and Spectral solvers. Usage: - uv run python run_solver.py N=65 solver=jacobi n_ranks=1 - uv run python run_solver.py N=129 solver=fmg n_ranks=4 + # FV solver (32 cells) + uv run python run_solver.py solver=fv N=32 Re=100 max_iterations=100 + + # Spectral solver (N=15 gives 16 nodes) + uv run python run_solver.py solver=spectral N=15 Re=100 max_iterations=100 + + # With Databricks MLflow + uv run python run_solver.py solver=fv mlflow=databricks - # With custom MPI options (for HPC binding): - uv run python run_solver.py --mpi-options "--map-by ppr:12:package --bind-to core" ... + # Parameter sweep + uv run python run_solver.py -m solver=fv N=16,32,64 Re=100,400 """ -import argparse -import json import logging import os -import subprocess -import sys -import tempfile -from dataclasses import asdict, fields -from typing import List, Optional +from dataclasses import asdict +from pathlib import Path import hydra import mlflow from hydra.core.hydra_config import HydraConfig +from mlflow.entities import Metric from mlflow.tracking import MlflowClient -from mpi4py import MPI from omegaconf import DictConfig, OmegaConf -from Poisson.datastructures import GlobalParams, LocalParams -from Poisson.solvers import FMGMPISolver, FMGSolver, JacobiMPISolver, JacobiSolver - log = logging.getLogger(__name__) # ============================================================================= -# Orchestrator (Hydra entry point) +# Solver Factory # ============================================================================= -@hydra.main(config_path="conf", config_name="config", version_base=None) -def main(cfg: DictConfig) -> None: - """Hydra orchestrator - saves config and spawns MPI workers.""" - output_dir = HydraConfig.get().runtime.output_dir - cfg_path = os.path.join(output_dir, "config.yaml") - OmegaConf.save(cfg, cfg_path) +def create_solver(cfg: DictConfig): + """Create solver instance from Hydra config.""" + solver_name = cfg.solver.name - log.info(f"{cfg.solver}, N={cfg.N}, n_ranks={cfg.n_ranks}") + # Common parameters + common = { + "Re": cfg.Re, + "lid_velocity": cfg.lid_velocity, + "Lx": cfg.Lx, + "Ly": cfg.Ly, + "nx": cfg.N, + "ny": cfg.N, + "max_iterations": cfg.max_iterations, + "tolerance": cfg.tolerance, + } - # Build MPI command with optional binding options - # Use sys.executable to ensure workers use same Python as orchestrator - mpi_options = os.environ.get("MPI_OPTIONS", "").split() - cmd = ["mpiexec"] + mpi_options + [ - "-n", - str(cfg.n_ranks), - sys.executable, - __file__, - cfg_path, - ] - log.info(f"Spawning: {' '.join(cmd)}") + if solver_name == "fv": + from solvers import FVSolver - env = os.environ.copy() - env["MPI_WORKER"] = "1" - subprocess.run(cmd, env=env, timeout=600, check=True) + return FVSolver( + **common, + convection_scheme=cfg.solver.convection_scheme, + limiter=cfg.solver.limiter, + alpha_uv=cfg.solver.alpha_uv, + alpha_p=cfg.solver.alpha_p, + linear_solver_tol=cfg.solver.linear_solver_tol, + ) + elif solver_name == "spectral": + from solvers import SpectralSolver + + return SpectralSolver( + **common, + basis_type=cfg.solver.basis_type, + CFL=cfg.solver.CFL, + beta_squared=cfg.solver.beta_squared, + corner_smoothing=cfg.solver.corner_smoothing, + ) + else: + raise ValueError(f"Unknown solver: {solver_name}") # ============================================================================= -# Worker (MPI computation) +# MLflow Logging # ============================================================================= -def worker(cfg_path: str) -> None: - """MPI worker - runs solver and logs to MLflow.""" - comm = MPI.COMM_WORLD - rank = comm.Get_rank() - - cfg = OmegaConf.load(cfg_path) - params = config_to_params(cfg) +def setup_mlflow(cfg: DictConfig) -> str: + """Setup MLflow tracking and return experiment name.""" + tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") + mlflow.set_tracking_uri(tracking_uri) - # Setup MLflow (all ranks, but only rank 0 logs) - mlflow.set_tracking_uri(cfg.mlflow.get("tracking_uri", "./mlruns")) - - # Apply project prefix for Databricks (experiment names must be absolute paths) + # Build experiment name with optional project prefix experiment_name = cfg.experiment_name project_prefix = cfg.mlflow.get("project_prefix", "") if project_prefix and not experiment_name.startswith("/"): experiment_name = f"{project_prefix}/{experiment_name}" - mlflow.set_experiment(experiment_name) - if rank == 0: - log.info( - f"{params.solver}, N={params.N}, ranks={params.n_ranks}, " - f"{params.strategy}/{params.communicator}" - ) + # Databricks login if needed + if cfg.mlflow.mode == "databricks": + mlflow.login() + + mlflow.set_experiment(experiment_name) + return experiment_name + + +def log_config_params(cfg: DictConfig): + """Log flattened config as MLflow parameters.""" + # Common params + mlflow.log_params( + { + "solver": cfg.solver.name, + "N": cfg.N, + "Re": cfg.Re, + "lid_velocity": cfg.lid_velocity, + "Lx": cfg.Lx, + "Ly": cfg.Ly, + "tolerance": cfg.tolerance, + "max_iterations": cfg.max_iterations, + } + ) - # Run solver - solver = create_solver(params, comm) - solver.warmup() - solver.solve() - solver.post_solve() + # Solver-specific params (flatten solver config) + solver_params = { + f"solver.{k}": v for k, v in OmegaConf.to_container(cfg.solver).items() if k != "name" + } + mlflow.log_params(solver_params) + + +def log_metrics_and_timeseries(solver, run_id: str): + """Log final metrics and timeseries to MLflow.""" + # Final metrics + metrics_dict = asdict(solver.metrics) + # Convert booleans to int for MLflow + metrics_dict = {k: (int(v) if isinstance(v, bool) else v) for k, v in metrics_dict.items()} + mlflow.log_metrics(metrics_dict) + + # Timeseries (batch logging for efficiency) + if solver.time_series is not None: + ts = asdict(solver.time_series) + batch_metrics = [] + for key, values in ts.items(): + if values is not None: + for step, value in enumerate(values): + if value is not None: + batch_metrics.append(Metric(key=key, value=value, timestamp=0, step=step)) + + if batch_metrics: + MlflowClient().log_batch(run_id=run_id, metrics=batch_metrics) + + +def save_and_log_artifact(solver, cfg: DictConfig, output_dir: str): + """Save solver output to HDF5 and log as MLflow artifact.""" + # Determine output filename + solver_name = cfg.solver.name.upper() + if solver_name == "SPECTRAL": + n_nodes = cfg.N + 1 + filename = f"LDC_{solver_name}_N{n_nodes}_Re{int(cfg.Re)}.h5" + else: + filename = f"LDC_{solver_name}_N{cfg.N}_Re{int(cfg.Re)}.h5" - # Log results (rank 0 only) - if rank == 0: - log_to_mlflow(params, solver, solver.rank_topology) + output_path = Path(output_dir) / filename + solver.save(output_path) + log.info(f"Saved results to: {output_path}") - log.info( - f"Done: {solver.metrics.iterations} iter, error={solver.metrics.final_error:.2e}, " - f"time={solver.metrics.wall_time:.3f}s" - + (f", {solver.metrics.mlups:.1f} MLup/s" if solver.metrics.mlups else "") - ) + mlflow.log_artifact(str(output_path)) # ============================================================================= -# Shared utilities +# Main Entry Point # ============================================================================= -def config_to_params(cfg: DictConfig) -> GlobalParams: - """Convert Hydra config to GlobalParams.""" - param_fields = {f.name for f in fields(GlobalParams) if f.init} - return GlobalParams(**{k: cfg[k] for k in param_fields}) +@hydra.main(config_path="conf", config_name="config", version_base=None) +def main(cfg: DictConfig) -> None: + """Hydra entry point - runs solver with MLflow tracking.""" + output_dir = HydraConfig.get().runtime.output_dir + # Log resolved config + log.info(f"Solver: {cfg.solver.name}, N={cfg.N}, Re={cfg.Re}") + log.info(f"Output dir: {output_dir}") -def create_solver(params: GlobalParams, comm): - """Create solver instance.""" - common = { - "N": params.N, - "omega": params.omega, - "tolerance": params.tolerance, - "max_iter": params.max_iter, - "use_numba": params.use_numba, - "specified_numba_threads": params.specified_numba_threads, - } + # Save resolved config + OmegaConf.save(cfg, Path(output_dir) / "config.yaml") - # Use MPI solvers when n_ranks > 1, sequential otherwise - use_mpi = params.n_ranks > 1 - - if params.solver == "jacobi": - if use_mpi: - return JacobiMPISolver( - **common, strategy=params.strategy, communicator=params.communicator - ) - return JacobiSolver(**common) - else: # fmg - common.update( - n_smooth=params.n_smooth, fmg_post_vcycles=params.fmg_post_vcycles - ) - if use_mpi: - return FMGMPISolver( - **common, strategy=params.strategy, communicator=params.communicator - ) - return FMGSolver(**common) - - -def log_to_mlflow(params: GlobalParams, solver, rank_topology: Optional[List[LocalParams]] = None) -> None: - """Log solver run to MLflow.""" - with mlflow.start_run(run_name=f"{params.solver}_N{params.N}_p{params.n_ranks}") as run: - # Params and metrics from dataclasses - mlflow.log_params(params.to_mlflow()) - mlflow.log_metrics(solver.metrics.to_mlflow()) - - # Timeseries: batch log step-based metrics - timeseries = solver.timeseries.to_mlflow_batch() - if timeseries: - MlflowClient().log_batch(run_id=run.info.run_id, metrics=timeseries) - - # Rank topology artifact (for networkx visualizations) - if rank_topology: - topology_data = [asdict(rp) for rp in rank_topology] - with tempfile.NamedTemporaryFile( - mode="w", suffix=".json", delete=False - ) as f: - json.dump(topology_data, f, indent=2) - temp_path = f.name - mlflow.log_artifact(temp_path, artifact_path="topology") - os.unlink(temp_path) + # Setup MLflow + experiment_name = setup_mlflow(cfg) + log.info(f"MLflow experiment: {experiment_name}") + # Create solver + solver = create_solver(cfg) -# ============================================================================= -# Entry point -# ============================================================================= + # Build run name + solver_name = cfg.solver.name + if solver_name == "spectral": + run_name = f"{solver_name}_N{cfg.N + 1}_Re{int(cfg.Re)}" + else: + run_name = f"{solver_name}_N{cfg.N}_Re{int(cfg.Re)}" + + # Run with MLflow tracking + with mlflow.start_run(run_name=run_name) as run: + log_config_params(cfg) + + # Tag with HPC job info if available + job_id = os.environ.get("LSB_JOBID") + if job_id: + mlflow.set_tag("lsf.job_id", job_id) + mlflow.set_tag("lsf.job_name", os.environ.get("LSB_JOBNAME", "")) + + # Solve + log.info("Starting solver...") + solver.solve() + + # Log results + log_metrics_and_timeseries(solver, run.info.run_id) + save_and_log_artifact(solver, cfg, output_dir) + + log.info( + f"Done: {solver.metrics.iterations} iter, " + f"converged={solver.metrics.converged}, " + f"time={solver.metrics.wall_time_seconds:.2f}s" + ) if __name__ == "__main__": - if os.environ.get("MPI_WORKER"): - logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s") - if len(sys.argv) < 2: - sys.exit("Error: Worker requires config path argument") - worker(sys.argv[1]) - else: - main() + main() diff --git a/setup_macOS.sh b/setup_macOS.sh deleted file mode 100755 index 891a28f..0000000 --- a/setup_macOS.sh +++ /dev/null @@ -1,62 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" - -echo "================================================================" -echo " Project 3 Environment Setup" -echo " Location: $PROJECT_ROOT" -echo "================================================================" - -cd "$PROJECT_ROOT" - -# --------------------------------------------------------------- -# 1. Remove existing venv -# --------------------------------------------------------------- -if [ -d ".venv" ]; then - echo "[1/5] Removing existing .venv ..." - rm -rf .venv -else - echo "[1/5] No existing .venv found — skipping removal." -fi - - -# --------------------------------------------------------------- -# 2. Create a fresh uv venv (Python 3.12) with pip seeded -# --------------------------------------------------------------- -echo "[2/5] Creating new uv virtual environment (Python 3.12, seeded)..." -#uv venv --python 3.12 --seed .venv - - -uv sync -# --------------------------------------------------------------- -# 3. Install pyproject dependencies via uv sync -# --------------------------------------------------------------- -echo "[3/5] Installing pyproject dependencies via uv sync..." -source .venv/bin/activate - - -# --------------------------------------------------------------- -# 4. Install PETSc + petsc4py using pip inside the venv -# --------------------------------------------------------------- -echo "[4/5] Installing PETSc + petsc4py via pip..." - -pip install petsc4py - - -# --------------------------------------------------------------- -# 5. Sanity check PETSc -# --------------------------------------------------------------- -echo "[5/5] Verifying PETSc installation..." - -python - <<'EOF' -import petsc4py -from petsc4py import PETSc -petsc4py.init() -print(" petsc4py version:", petsc4py.__version__) -print(" PETSc version:", PETSc.Sys.getVersion()) -EOF - -echo "================================================================" -echo " Setup complete!" -echo "================================================================" diff --git a/spectral_multigrid_todo.md b/spectral_multigrid_todo.md new file mode 100644 index 0000000..cfdcec5 --- /dev/null +++ b/spectral_multigrid_todo.md @@ -0,0 +1,67 @@ + +# Spectral Multigrid TODO List + +## 1. Refactor for Multi-Level Support +- [ ] Introduce a `SpectralLevel` class holding: + - basis objects + - grids (full + inner) + - shapes + - differentiation matrices + - spectral field arrays +- [ ] Refactor current single-level routines to take a `level` argument: + - `_setup_grids(level)` + - `_build_diff_matrices(level)` + - `_initialize_lid_velocity(level)` + - `_compute_residuals(level, u, v, p)` + - `_enforce_boundary_conditions(level, u, v)` + - `_compute_adaptive_timestep(level)` + +## 2. Build the Grid Hierarchy +- [ ] Define number of levels and `(nx, ny)` per level. +- [ ] Ensure coarse Lobatto nodes are subsets of fine nodes. +- [ ] For each level, construct basis, grids, operators, fields, BCs. + +## 3. Restriction & Prolongation +- [ ] Build restriction operators for: + - u, v on full grids + - p on inner grids +- [ ] Build prolongation operators for: + - u, v on full grids + - p on inner grids +- [ ] Wrap them into helper functions for U = (u, v, p). + +## 4. Turn RK4 Into a Per-Level Smoother +- [ ] Generalize `step()` to operate on a specific level. +- [ ] Create `smooth(level, n_steps)` calling multiple RK4 pseudo-steps. + +## 5. Residuals Per Level +- [ ] Implement `compute_residual_norms(level)` using that level's arrays. +- [ ] Ensure `_compute_residuals` operates correctly per level. + +## 6. Full Approximation Scheme (FAS) V-cycle +- [ ] Presmoothing on level ℓ. +- [ ] Compute fine-grid residuals. +- [ ] Restrict U and F(U) to coarse grid. +- [ ] Compute FAS τ term for coarse grid. +- [ ] Recursive coarse solve via `v_cycle(ℓ-1)`. +- [ ] Prolongate coarse correction. +- [ ] Apply correction to fine-level U. +- [ ] Postsmoothing. + +## 7. Coarsest Level Solver +- [ ] Implement `v_cycle(0)` as many smoothing iterations until convergence. + +## 8. Full Multigrid (FMG) Driver +- [ ] Coarse-level initialization and solve. +- [ ] Prolongate to next level and apply V-cycles. +- [ ] Continue until finest level reached. + +## 9. Integration with Existing Solver Interface +- [ ] `solve()` calls FMG or repeated V-cycles. +- [ ] Logging residuals from finest level. +- [ ] `_finalize_fields()` pulls finest-level fields into global output. + +## 10. Parameters & Tuning +- [ ] Extend `SpectralParameters` with multigrid parameters. +- [ ] Log per-cycle residuals, work units, timings. + diff --git a/src/Poisson/__init__.py b/src/Poisson/__init__.py deleted file mode 100644 index ff2dbaf..0000000 --- a/src/Poisson/__init__.py +++ /dev/null @@ -1,90 +0,0 @@ -"""MPI Poisson Solver package. - -A modular framework for studying parallel performance of 3D Poisson equation -solvers using MPI domain decomposition. Supports pluggable decomposition -strategies (sliced, cubic) and communication methods (NumPy arrays, custom -MPI datatypes). - -Solvers -------- -Sequential (no MPI): -- JacobiSolver: Kernel benchmarks and single-process solving -- FMGSolver: Full Multigrid without MPI - -Parallel (MPI): -- JacobiMPISolver: Jacobi with domain decomposition -- FMGMPISolver: Full Multigrid with domain decomposition -""" - -from pathlib import Path - -from .datastructures import ( - GlobalParams, - GlobalMetrics, - LocalParams, - LocalMetrics, - RankGeometry, - GridLevel, -) -from .kernels import NumPyKernel, NumbaKernel -from .solvers import ( - JacobiSolver, - JacobiMPISolver, - FMGSolver, - FMGMPISolver, -) -from .mpi import DistributedGrid -from .problems import ( - create_grid_3d, - sinusoidal_exact_solution, - sinusoidal_source_term, - setup_sinusoidal_problem, -) - -__all__ = [ - # Data structures - "GlobalParams", - "GlobalMetrics", - "LocalParams", - "LocalMetrics", - "RankGeometry", - "GridLevel", - # Kernels - "NumPyKernel", - "NumbaKernel", - # Solvers - Sequential - "JacobiSolver", - "FMGSolver", - # Solvers - MPI - "JacobiMPISolver", - "FMGMPISolver", - # Grid - "DistributedGrid", - # Problem setup - "create_grid_3d", - "sinusoidal_exact_solution", - "sinusoidal_source_term", - "setup_sinusoidal_problem", - # Utilities - "get_project_root", -] - - -def get_project_root() -> Path: - """Get project root directory. - - Works reliably in both standalone scripts and Sphinx-Gallery execution. - - Returns - ------- - Path - Project root directory (contains pyproject.toml). - """ - current = Path(__file__).resolve().parent - while current != current.parent: - if (current / "pyproject.toml").exists(): - return current - current = current.parent - - # Fallback: assume standard src layout - return Path(__file__).resolve().parent.parent.parent diff --git a/src/Poisson/datastructures.py b/src/Poisson/datastructures.py deleted file mode 100644 index fb76b6f..0000000 --- a/src/Poisson/datastructures.py +++ /dev/null @@ -1,222 +0,0 @@ -"""Data structures for solver configuration and results. - -Architecture: 2x2 matrix of Params vs Metrics × Global vs Local - - Params (input/config) Metrics (output/results) - ───────────────────── ──────────────────────── -Global GlobalParams GlobalMetrics -(same across N, solver, omega, wall_time, mlups, -ranks / agg) n_ranks, strategy... converged, iterations... - -Local LocalParams LocalMetrics -(per-rank) rank, hostname, compute_times[], - neighbors, local_shape... halo_times[]... -""" - -from __future__ import annotations - -import os -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Tuple - -import numpy as np - - -# ============================================================================ -# Global (identical across ranks, or aggregated on rank 0) -# ============================================================================ - - -@dataclass -class GlobalParams: - """Run configuration - validated by Hydra, logged to MLflow as params. - - Immutable configuration set before the run. Identical across all MPI ranks. - """ - - # Required - N: int - - # Solver - solver: str = "jacobi" # "jacobi" | "fmg" - omega: float = 0.8 - tolerance: float = 1e-6 - max_iter: int = 1000 - - # FMG-specific (ignored by Jacobi) - n_smooth: int = 3 - fmg_post_vcycles: int = 1 - - # Parallelization - n_ranks: int = 1 - strategy: Optional[str] = None # "sliced" | "cubic" - communicator: Optional[str] = None # "numpy" | "custom" - - # Numba - use_numba: bool = False - specified_numba_threads: int = 1 # What user requested - - # Experiment tracking - experiment_name: str = "default" - - # Auto-detected at runtime (not from config) - environment: str = field(init=False) - h: float = field(init=False) - - def __post_init__(self): - """Compute derived values after initialization.""" - self.h = 2.0 / (self.N - 1) - self.environment = ( - "hpc" - if os.environ.get("LSB_JOBID") or os.environ.get("SLURM_JOB_ID") - else "local" - ) - - def to_mlflow(self) -> dict: - """Convert to MLflow-compatible params dict (bools as int, exclude derived).""" - exclude = {"h"} # Derived from N, redundant - return { - k: (int(v) if isinstance(v, bool) else v) - for k, v in self.__dict__.items() - if k not in exclude - } - - -@dataclass -class GlobalMetrics: - """Aggregated results - logged to MLflow as metrics. - - Final results computed/aggregated on rank 0. - """ - - converged: bool = False - iterations: int = 0 - final_residual: Optional[float] = None - final_error: Optional[float] = None # L2 error vs analytical solution - final_alg_error: Optional[float] = None # ||f - Au|| algebraic residual - wall_time: Optional[float] = None - - # Timing breakdown (sum across all iterations) - total_compute_time: Optional[float] = None - total_halo_time: Optional[float] = None - - # Performance metrics - mlups: Optional[float] = None # Million Lattice Updates per Second - bandwidth_gb_s: Optional[float] = None # Memory bandwidth in GB/s - - # Numba runtime info (what was actually available) - observed_numba_threads: Optional[int] = None - - def to_mlflow(self) -> dict: - """Convert to MLflow-compatible dict (no None, bools as int).""" - return { - k: (int(v) if isinstance(v, bool) else v) - for k, v in self.__dict__.items() - if v is not None - } - - -# ============================================================================ -# Local (per-rank) -# ============================================================================ - - -@dataclass -class LocalParams: - """Per-rank geometry - gathered to rank 0, logged as artifact. - - Per-rank topology information. - """ - - rank: int - hostname: str = "" - cart_coords: Optional[Tuple[int, int, int]] = None - neighbors: Dict[str, Optional[int]] = field(default_factory=dict) - local_shape: Optional[Tuple[int, int, int]] = None - global_start: Optional[Tuple[int, int, int]] = None - global_end: Optional[Tuple[int, int, int]] = None - # CPU binding info for socket/node visualization - cpu_ids: Optional[List[int]] = None # Cores this rank can run on - - -@dataclass -class LocalMetrics: - """Per-rank timeseries - gathered for topology artifact. - - Per-rank timing data. Accumulated during solve, logged post-solve. - """ - - # Per-rank timing (gathered to artifact for load balancing analysis) - compute_times: List[float] = field(default_factory=list) - halo_times: List[float] = field(default_factory=list) - - # Global (rank 0 only - logged as step metrics for convergence charts) - residual_history: List[float] = field(default_factory=list) - - def clear(self): - """Clear all timeseries data.""" - self.compute_times.clear() - self.halo_times.clear() - self.residual_history.clear() - - def to_mlflow_batch(self) -> list: - """Convert timeseries to MLflow Metric objects for batch logging.""" - from mlflow.entities import Metric - - return [ - Metric(key=name, value=value, timestamp=0, step=step) - for name, values in self.__dict__.items() - for step, value in enumerate(values) - ] - - -# ============================================================================ -# MPI Grid Geometry -# ============================================================================ - - -@dataclass -class RankGeometry: - """Per-rank grid geometry information. - - Used by DistributedGrid to describe the local portion of the domain. - """ - - rank: int - local_shape: Tuple[int, int, int] - halo_shape: Tuple[int, int, int] - global_start: Tuple[int, int, int] - global_end: Tuple[int, int, int] - neighbors: Dict[str, Optional[int]] - - -# ============================================================================ -# Multigrid-specific -# ============================================================================ - - -@dataclass -class GridLevel: - """One level in the multigrid hierarchy. - - Each level has its own grid size, arrays, and smoothing kernel. - The grid member is only set for MPI solvers (DistributedGrid). - """ - - level: int - N: int - h: float - u: np.ndarray - u_temp: np.ndarray - f: np.ndarray - r: np.ndarray - kernel: object - grid: object = None # DistributedGrid for MPI, None for sequential - - -# ============================================================================ -# Legacy aliases (for backwards compatibility during migration) -# ============================================================================ - -# These will be removed after full migration -LocalSeries = LocalMetrics # Alias for backwards compatibility diff --git a/src/Poisson/kernels.py b/src/Poisson/kernels.py deleted file mode 100644 index 80fdffd..0000000 --- a/src/Poisson/kernels.py +++ /dev/null @@ -1,97 +0,0 @@ -"""Jacobi iteration kernels. - -Simple kernel implementations - tracking is handled by the solver. -""" - -import numpy as np -import numba -from numba import njit, prange - - -@njit(parallel=True) -def _jacobi_step_numba( - uold: np.ndarray, u: np.ndarray, f: np.ndarray, h: float, omega: float -) -> float: - """Numba JIT implementation of Jacobi iteration step.""" - c = 1.0 / 6.0 - h2 = h * h - - for i in prange(1, u.shape[0] - 1): - for j in range(1, u.shape[1] - 1): - for k in range(1, u.shape[2] - 1): - u[i, j, k] = ( - omega - * c - * ( - uold[i - 1, j, k] - + uold[i + 1, j, k] - + uold[i, j - 1, k] - + uold[i, j + 1, k] - + uold[i, j, k - 1] - + uold[i, j, k + 1] - + h2 * f[i, j, k] - ) - + (1.0 - omega) * uold[i, j, k] - ) - - return 0.0 # Residual computed separately in solver - - -class NumPyKernel: - """NumPy-based Jacobi kernel.""" - - def __init__(self, omega: float, specified_numba_threads: int = 1): - self.omega = omega - self.observed_numba_threads = None # Not applicable for NumPy - - def step(self, uold: np.ndarray, u: np.ndarray, f: np.ndarray, h: float): - """Perform one Jacobi iteration step.""" - c = 1.0 / 6.0 - h2 = h * h - - u[1:-1, 1:-1, 1:-1] = ( - self.omega - * c - * ( - uold[0:-2, 1:-1, 1:-1] - + uold[2:, 1:-1, 1:-1] - + uold[1:-1, 0:-2, 1:-1] - + uold[1:-1, 2:, 1:-1] - + uold[1:-1, 1:-1, 0:-2] - + uold[1:-1, 1:-1, 2:] - + h2 * f[1:-1, 1:-1, 1:-1] - ) - + (1.0 - self.omega) * uold[1:-1, 1:-1, 1:-1] - ) - - def warmup(self, warmup_size: int = 10): - """No-op for NumPy kernel.""" - pass - - -class NumbaKernel: - """Numba JIT-compiled Jacobi kernel.""" - - def __init__(self, omega: float, specified_numba_threads: int = 1): - self.omega = omega - - # Set requested threads (may be clamped by NUMBA_NUM_THREADS env var) - if specified_numba_threads is not None: - numba.set_num_threads(specified_numba_threads) - - # Record what Numba actually reports - self.observed_numba_threads = numba.get_num_threads() - - def step(self, uold: np.ndarray, u: np.ndarray, f: np.ndarray, h: float): - """Perform one Jacobi iteration step.""" - _jacobi_step_numba(uold, u, f, h, self.omega) - - def warmup(self, warmup_size: int = 10): - """Trigger JIT compilation with a small problem.""" - h = 2.0 / (warmup_size - 1) - u1 = np.zeros((warmup_size, warmup_size, warmup_size), dtype=np.float64) - u2 = np.zeros_like(u1) - f = np.random.randn(warmup_size, warmup_size, warmup_size) - for _ in range(5): - _jacobi_step_numba(u1, u2, f, h, self.omega) - u1, u2 = u2, u1 diff --git a/src/Poisson/mpi/__init__.py b/src/Poisson/mpi/__init__.py deleted file mode 100644 index 38921e4..0000000 --- a/src/Poisson/mpi/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -"""MPI domain decomposition and communication. - -This package provides: -- DistributedGrid: Unified interface for parallel grids -- CartesianDecomposition: Domain splitting with MPI topology -- HaloExchanger: Strategies for halo exchange (numpy/datatype) -- RankGeometry: Exported from datastructures for convenience -""" - -from .grid import DistributedGrid -from .decomposition import CartesianDecomposition -from .halo import HaloExchanger, NumpyHaloExchanger, DatatypeHaloExchanger -from ..datastructures import RankGeometry - -__all__ = [ - "DistributedGrid", - "CartesianDecomposition", - "HaloExchanger", - "NumpyHaloExchanger", - "DatatypeHaloExchanger", - "RankGeometry", -] diff --git a/src/Poisson/mpi/decomposition.py b/src/Poisson/mpi/decomposition.py deleted file mode 100644 index 893c14c..0000000 --- a/src/Poisson/mpi/decomposition.py +++ /dev/null @@ -1,148 +0,0 @@ -"""Domain decomposition with MPI Cartesian topology.""" - -from __future__ import annotations -from mpi4py import MPI - - -class CartesianDecomposition: - """Handles MPI Cartesian topology and domain splitting. - - Creates a Cartesian communicator and computes how the global - domain is distributed across ranks. - - Parameters - ---------- - N : int - Global grid size (N x N x N including boundaries). - comm : MPI.Comm - MPI communicator. - strategy : str - 'sliced' for 1D decomposition along z-axis, - 'cubic' for 3D decomposition. - """ - - def __init__(self, N: int, comm: MPI.Comm, strategy: str = "sliced"): - self.N = N - self.comm = comm - self.rank = comm.Get_rank() - self.size = comm.Get_size() - self.strategy = strategy - - # Determine processor grid dimensions - self.dims = self._compute_dims(strategy) - self.pz, self.py, self.px = self.dims - - # Create Cartesian topology - self.cart_comm, self._proc_coords = self._create_cartesian_topology() - - # Discover neighbors - self.neighbors = self._find_neighbors() - - # Compute local domain - self.local_shape, self.halo_shape, self.global_start, self.global_end = ( - self._compute_local_domain() - ) - - # Track physical boundaries - self.is_boundary = self._find_boundaries() - - def _compute_dims(self, strategy: str) -> list[int]: - """Compute processor grid dimensions [pz, py, px].""" - if strategy == "sliced": - return [self.size, 1, 1] - elif strategy == "cubic": - dims = list(MPI.Compute_dims(self.size, 3)) - return [dims[2], dims[1], dims[0]] - else: - raise ValueError(f"Unknown strategy: {strategy}. Use 'sliced' or 'cubic'.") - - def _create_cartesian_topology(self): - """Create MPI Cartesian communicator.""" - # dims in [pz, py, px] order, but Create_cart expects [px, py, pz] - cart_dims = [self.px, self.py, self.pz] - periods = [False, False, False] - - cart_comm = self.comm.Create_cart( - dims=cart_dims, periods=periods, reorder=False - ) - - # Get coordinates [ix, iy, iz] and convert to [iz, iy, ix] for array indexing - cart_coords = cart_comm.Get_coords(self.rank) - proc_coords = (cart_coords[2], cart_coords[1], cart_coords[0]) - - return cart_comm, proc_coords - - def _find_neighbors(self) -> dict[str, int | None]: - """Use Cart_shift to find neighbor ranks.""" - neighbors = {} - - # X direction (cart direction 0) - x_src, x_dest = self.cart_comm.Shift(0, 1) - neighbors["x_lower"] = x_src if x_src >= 0 else None - neighbors["x_upper"] = x_dest if x_dest >= 0 else None - - # Y direction (cart direction 1) - y_src, y_dest = self.cart_comm.Shift(1, 1) - neighbors["y_lower"] = y_src if y_src >= 0 else None - neighbors["y_upper"] = y_dest if y_dest >= 0 else None - - # Z direction (cart direction 2) - z_src, z_dest = self.cart_comm.Shift(2, 1) - neighbors["z_lower"] = z_src if z_src >= 0 else None - neighbors["z_upper"] = z_dest if z_dest >= 0 else None - - return neighbors - - def _compute_local_domain(self): - """Compute local domain size and position. - - Decomposes the interior points (indices 1 to N-2). - Boundaries (indices 0 and N-1) are handled separately. - """ - N = self.N - interior_N = N - 2 - - def split_interior(n_interior, n_parts): - """Split n_interior points among n_parts ranks.""" - base = n_interior // n_parts - rem = n_interior % n_parts - counts = [base + (1 if i < rem else 0) for i in range(n_parts)] - starts = [1 + sum(counts[:i]) for i in range(n_parts)] - return counts, starts - - iz, iy, ix = self._proc_coords - - z_counts, z_starts = split_interior(interior_N, self.pz) - y_counts, y_starts = split_interior(interior_N, self.py) - x_counts, x_starts = split_interior(interior_N, self.px) - - local_nz = z_counts[iz] - local_ny = y_counts[iy] - local_nx = x_counts[ix] - - global_start_z = z_starts[iz] - global_start_y = y_starts[iy] - global_start_x = x_starts[ix] - - local_shape = (local_nz, local_ny, local_nx) - halo_shape = (local_nz + 2, local_ny + 2, local_nx + 2) - global_start = (global_start_z, global_start_y, global_start_x) - global_end = ( - global_start_z + local_nz, - global_start_y + local_ny, - global_start_x + local_nx, - ) - - return local_shape, halo_shape, global_start, global_end - - def _find_boundaries(self) -> dict[str, bool]: - """Determine which faces are physical boundaries.""" - N = self.N - return { - "z_lower": self.global_start[0] == 1, - "z_upper": self.global_end[0] == N - 1, - "y_lower": self.global_start[1] == 1, - "y_upper": self.global_end[1] == N - 1, - "x_lower": self.global_start[2] == 1, - "x_upper": self.global_end[2] == N - 1, - } diff --git a/src/Poisson/mpi/grid.py b/src/Poisson/mpi/grid.py deleted file mode 100644 index cd0ec7d..0000000 --- a/src/Poisson/mpi/grid.py +++ /dev/null @@ -1,193 +0,0 @@ -"""Distributed grid abstraction for parallel computation. - -This module provides a unified DistributedGrid class that encapsulates: -- Domain decomposition (sliced or cubic) with MPI Cartesian topology -- Halo exchange communication (numpy buffers or MPI datatypes) -- Array allocation and interior/boundary handling - -Solvers interact with this single interface rather than managing -MPI details directly. -""" - -from __future__ import annotations - -import numpy as np -from mpi4py import MPI - -from ..datastructures import LocalParams -from .decomposition import CartesianDecomposition -from .halo import create_halo_exchanger - - -class DistributedGrid: - """Unified distributed grid for parallel Poisson solvers. - - Handles both 1D (sliced) and 3D (cubic) decomposition using MPI - Cartesian topology for proper neighbor discovery. - - Parameters - ---------- - N : int - Global grid size (N x N x N including boundaries) - comm : MPI.Comm - MPI communicator - strategy : str - 'sliced' for 1D decomposition along z-axis (default) - 'cubic' for 3D decomposition - halo_exchange : str - 'numpy' for buffer-based exchange (default) - 'custom' for MPI derived datatypes (zero-copy) - - Example - ------- - >>> grid = DistributedGrid(N=65, comm=MPI.COMM_WORLD, strategy='cubic') - >>> u = grid.allocate() # Allocate array with halos - >>> grid.fill_source_term(f) # Fill source term - >>> grid.sync_halos(u) # Exchange halo data - """ - - def __init__( - self, - N: int, - comm: MPI.Comm = MPI.COMM_WORLD, - strategy: str = "sliced", - halo_exchange: str = "numpy", - ): - self.N = N - self.comm = comm - self.rank = self.comm.Get_rank() - self.size = self.comm.Get_size() - self.strategy = strategy - self.halo_exchange_type = halo_exchange - - # Domain decomposition - self._decomp = CartesianDecomposition(N, self.comm, strategy) - - # Copy decomposition attributes for direct access - self.dims = self._decomp.dims - self.pz, self.py, self.px = self.dims - self.cart_comm = self._decomp.cart_comm - self.neighbors = self._decomp.neighbors - self.local_shape = self._decomp.local_shape - self.halo_shape = self._decomp.halo_shape - self.global_start = self._decomp.global_start - self.global_end = self._decomp.global_end - self.is_boundary = self._decomp.is_boundary - - # Halo exchange strategy - self._halo_exchanger = create_halo_exchanger(halo_exchange) - self._halo_exchanger.setup(self.local_shape, self.neighbors) - - # Grid spacing - self.h = 2.0 / (N - 1) - - def allocate(self, dtype=np.float64) -> np.ndarray: - """Allocate a local array with halo zones.""" - return np.zeros(self.halo_shape, dtype=dtype) - - def sync_halos(self, arr: np.ndarray): - """Exchange halo data with all neighbors.""" - self._halo_exchanger.exchange(arr, self.cart_comm, self.neighbors) - - # Boundary face slices: direction -> array index tuple - _BOUNDARY_SLICES = { - "z_lower": (0, slice(None), slice(None)), - "z_upper": (-1, slice(None), slice(None)), - "y_lower": (slice(None), 0, slice(None)), - "y_upper": (slice(None), -1, slice(None)), - "x_lower": (slice(None), slice(None), 0), - "x_upper": (slice(None), slice(None), -1), - } - - def apply_boundary_conditions(self, arr: np.ndarray, value: float = 0.0): - """Apply Dirichlet boundary conditions at physical boundaries.""" - for direction, is_bc in self.is_boundary.items(): - if is_bc: - arr[self._BOUNDARY_SLICES[direction]] = value - - def get_rank_info(self) -> LocalParams: - """Get topology info for this rank (for MLflow artifact).""" - import os - - # Get CPU affinity (cores this rank can run on) - try: - cpu_ids = sorted(os.sched_getaffinity(0)) - except (AttributeError, OSError): - cpu_ids = None # Not available on all platforms (e.g., macOS) - - return LocalParams( - rank=self.rank, - hostname=MPI.Get_processor_name(), - cart_coords=tuple(self.cart_comm.Get_coords(self.rank)), - neighbors=self.neighbors.copy(), - local_shape=self.local_shape, - global_start=self.global_start, - global_end=self.global_end, - cpu_ids=cpu_ids, - ) - - def _get_physical_coords(self): - """Compute physical coordinate meshgrid for local interior.""" - gs = self.global_start - nz, ny, nx = self.local_shape - - z_phys = -1.0 + np.arange(gs[0], gs[0] + nz) * self.h - y_phys = -1.0 + np.arange(gs[1], gs[1] + ny) * self.h - x_phys = -1.0 + np.arange(gs[2], gs[2] + nx) * self.h - - return np.meshgrid(z_phys, y_phys, x_phys, indexing="ij") - - def fill_source_term(self, f: np.ndarray): - """Fill source term: f = 3pi^2 sin(pi*x) sin(pi*y) sin(pi*z).""" - Z, Y, X = self._get_physical_coords() - f[1:-1, 1:-1, 1:-1] = ( - 3 * np.pi**2 * np.sin(np.pi * X) * np.sin(np.pi * Y) * np.sin(np.pi * Z) - ) - - def compute_exact_solution(self, u: np.ndarray): - """Fill exact solution: u = sin(pi*x) sin(pi*y) sin(pi*z).""" - Z, Y, X = self._get_physical_coords() - u[1:-1, 1:-1, 1:-1] = np.sin(np.pi * X) * np.sin(np.pi * Y) * np.sin(np.pi * Z) - - def compute_l2_error(self, u: np.ndarray) -> float: - """Compute L2 error against exact solution.""" - u_exact = self.allocate() - self.compute_exact_solution(u_exact) - - local_sq_error = np.sum((u[1:-1, 1:-1, 1:-1] - u_exact[1:-1, 1:-1, 1:-1]) ** 2) - global_sq_error = np.empty(1) - self.cart_comm.Allreduce( - np.array([local_sq_error]), global_sq_error, op=MPI.SUM - ) - - return float(np.sqrt(self.h**3 * global_sq_error[0])) - - def get_halo_size_bytes(self) -> int: - """Calculate total bytes transferred per halo exchange.""" - nz, ny, nx = self.local_shape - # Face sizes for each axis: z-face=(ny*nx), y-face=(nz*nx), x-face=(nz*ny) - face_sizes = [ny * nx, nz * nx, nz * ny] - axes = ["z", "y", "x"] - - total = 0 - for axis, face_size in zip(axes, face_sizes): - for side in ["lower", "upper"]: - if self.neighbors.get(f"{axis}_{side}") is not None: - total += face_size * 8 * 2 # float64, send+recv - return total - - def coarsen(self) -> "DistributedGrid": - """Create a coarsened grid for multigrid. - - Returns a new DistributedGrid with N_coarse = (N-1)//2 + 1. - """ - N_coarse = (self.N - 1) // 2 + 1 - if N_coarse < 3: - raise ValueError("Grid too small to coarsen further") - - return DistributedGrid( - N_coarse, - self.comm, - strategy=self.strategy, - halo_exchange=self.halo_exchange_type, - ) diff --git a/src/Poisson/mpi/halo.py b/src/Poisson/mpi/halo.py deleted file mode 100644 index 29b48e1..0000000 --- a/src/Poisson/mpi/halo.py +++ /dev/null @@ -1,185 +0,0 @@ -"""Halo exchange implementations for distributed grids.""" - -from __future__ import annotations -from abc import ABC, abstractmethod - -import numpy as np -from mpi4py import MPI - - -# Axis names for neighbor dict keys -AXIS_NAMES = ["z", "y", "x"] - - -def _neighbor_ranks(neighbors: dict, axis_idx: int) -> tuple[int, int]: - """Get neighbor ranks for an axis, defaulting to MPI.PROC_NULL.""" - name = AXIS_NAMES[axis_idx] - lo = neighbors.get(f"{name}_lower") - hi = neighbors.get(f"{name}_upper") - return (lo if lo is not None else MPI.PROC_NULL, - hi if hi is not None else MPI.PROC_NULL) - - -def _make_slice(axis_idx: int, axis_val, ndim: int = 3): - """Build slice tuple with axis_val at axis_idx, slice(1,-1) elsewhere.""" - slices = [slice(1, -1)] * ndim - slices[axis_idx] = axis_val - return tuple(slices) - - -class HaloExchanger(ABC): - """Abstract base for halo exchange strategies.""" - - @abstractmethod - def setup(self, local_shape: tuple[int, int, int], neighbors: dict): - """Initialize exchange buffers or datatypes.""" - pass - - @abstractmethod - def exchange(self, arr: np.ndarray, cart_comm: MPI.Comm, neighbors: dict): - """Perform halo exchange on array.""" - pass - - -class NumpyHaloExchanger(HaloExchanger): - """Halo exchange using numpy buffer copies and Sendrecv.""" - - def setup(self, local_shape: tuple[int, int, int], neighbors: dict): - """Pre-compute slices and shapes for each axis.""" - self._axis_info = [] - for axis_idx in range(3): - # recv_shape: dimensions excluding this axis - recv_shape = tuple(local_shape[i] for i in range(3) if i != axis_idx) - self._axis_info.append({ - "send_hi": _make_slice(axis_idx, -2), - "send_lo": _make_slice(axis_idx, 1), - "recv_lo": _make_slice(axis_idx, 0), - "recv_hi": _make_slice(axis_idx, -1), - "recv_shape": recv_shape, - }) - - def exchange(self, arr: np.ndarray, cart_comm: MPI.Comm, neighbors: dict): - """Exchange halos using Sendrecv with buffer copies.""" - for axis_idx, info in enumerate(self._axis_info): - lo, hi = _neighbor_ranks(neighbors, axis_idx) - if lo == MPI.PROC_NULL and hi == MPI.PROC_NULL: - continue - - name = AXIS_NAMES[axis_idx] - tag = axis_idx * 2 - - # Send to upper, receive from lower - send = np.ascontiguousarray(arr[info["send_hi"]]) - recv = np.empty_like(send) - cart_comm.Sendrecv(send, hi, tag, recv, lo, tag) - if neighbors.get(f"{name}_lower") is not None: - arr[info["recv_lo"]] = recv.reshape(info["recv_shape"]) - - # Send to lower, receive from upper - send = np.ascontiguousarray(arr[info["send_lo"]]) - recv = np.empty_like(send) - cart_comm.Sendrecv(send, lo, tag + 1, recv, hi, tag + 1) - if neighbors.get(f"{name}_upper") is not None: - arr[info["recv_hi"]] = recv.reshape(info["recv_shape"]) - - -class DatatypeHaloExchanger(HaloExchanger): - """Halo exchange using MPI derived datatypes (zero-copy).""" - - def setup(self, local_shape: tuple[int, int, int], neighbors: dict): - """Create MPI datatypes and pre-compute offsets.""" - nz, ny, nx = local_shape - hz, hy, hx = nz + 2, ny + 2, nx + 2 # halo shape - self._halo_shape = (hz, hy, hx) - - # Strides for flat indexing - strides = (hy * hx, hx, 1) - - def flat_idx(z, y, x): - return z * strides[0] + y * strides[1] + x * strides[2] - - # Create datatypes and offsets for each axis - self._axis_info = [] - - # Z-face datatype - dt_z = MPI.DOUBLE.Create_vector(ny, nx, hx) - dt_z.Commit() - self._axis_info.append({ - "dt": dt_z, - "send_hi": flat_idx(hz - 2, 1, 1), - "send_lo": flat_idx(1, 1, 1), - "recv_lo": flat_idx(0, 1, 1), - "recv_hi": flat_idx(hz - 1, 1, 1), - }) - - # Y-face datatype - dt_y = MPI.DOUBLE.Create_vector(nz, nx, hy * hx) - dt_y.Commit() - self._axis_info.append({ - "dt": dt_y, - "send_hi": flat_idx(1, hy - 2, 1), - "send_lo": flat_idx(1, 1, 1), - "recv_lo": flat_idx(1, 0, 1), - "recv_hi": flat_idx(1, hy - 1, 1), - }) - - # X-face datatype (2D strided) - row = MPI.DOUBLE.Create_vector(ny, 1, hx) - row.Commit() - dt_x = row.Create_hvector(nz, 1, hy * hx * MPI.DOUBLE.Get_size()) - dt_x.Commit() - row.Free() - self._axis_info.append({ - "dt": dt_x, - "send_hi": flat_idx(1, 1, hx - 2), - "send_lo": flat_idx(1, 1, 1), - "recv_lo": flat_idx(1, 1, 0), - "recv_hi": flat_idx(1, 1, hx - 1), - }) - - def exchange(self, arr: np.ndarray, cart_comm: MPI.Comm, neighbors: dict): - """Exchange halos using MPI datatypes.""" - flat = arr.ravel() - - for axis_idx, info in enumerate(self._axis_info): - lo, hi = _neighbor_ranks(neighbors, axis_idx) - if lo == MPI.PROC_NULL and hi == MPI.PROC_NULL: - continue - - name = AXIS_NAMES[axis_idx] - tag = axis_idx * 2 - dt = info["dt"] - - # Send to upper, receive from lower - cart_comm.Sendrecv( - [flat[info["send_hi"]:], 1, dt], hi, tag, - [flat[info["recv_lo"]:], 1, dt], lo, tag, - ) - if neighbors.get(f"{name}_lower") is None: - arr[_make_slice(axis_idx, 0)] = 0.0 - - # Send to lower, receive from upper - cart_comm.Sendrecv( - [flat[info["send_lo"]:], 1, dt], lo, tag + 1, - [flat[info["recv_hi"]:], 1, dt], hi, tag + 1, - ) - if neighbors.get(f"{name}_upper") is None: - arr[_make_slice(axis_idx, -1)] = 0.0 - - def __del__(self): - """Free MPI datatypes.""" - if hasattr(self, "_axis_info"): - for info in self._axis_info: - dt = info["dt"] - if dt != MPI.DATATYPE_NULL: - dt.Free() - - -def create_halo_exchanger(exchange_type: str) -> HaloExchanger: - """Factory: 'numpy' for buffer-based, 'custom' for MPI datatypes.""" - if exchange_type == "numpy": - return NumpyHaloExchanger() - elif exchange_type == "custom": - return DatatypeHaloExchanger() - else: - raise ValueError(f"Unknown halo_exchange type: {exchange_type}") diff --git a/src/Poisson/problems.py b/src/Poisson/problems.py deleted file mode 100644 index 708a919..0000000 --- a/src/Poisson/problems.py +++ /dev/null @@ -1,48 +0,0 @@ -"""Test problems and exact solutions for Poisson equation.""" - -from __future__ import annotations - -import numpy as np - - -def create_grid_3d( - N: int, value: float = 0.0, boundary_value: float = 0.0 -) -> np.ndarray: - """Create 3D grid with specified interior and boundary values.""" - u = np.full((N, N, N), value, dtype=np.float64) - u[[0, -1], :, :] = boundary_value - u[:, [0, -1], :] = boundary_value - u[:, :, [0, -1]] = boundary_value - return u - - -def sinusoidal_exact_solution(N: int) -> np.ndarray: - """Exact solution: sin(π x)sin(π y)sin(π z) on [-1,1]³. - - Array layout is (Z, Y, X) to match decomposition conventions. - """ - # Use (Z, Y, X) axis ordering to match decomposition - zs, ys, xs = np.ogrid[-1 : 1 : complex(N), -1 : 1 : complex(N), -1 : 1 : complex(N)] - return np.sin(np.pi * xs) * np.sin(np.pi * ys) * np.sin(np.pi * zs) - - -def sinusoidal_source_term(N: int) -> np.ndarray: - """Source term: f = 3π² sin(π x)sin(π y)sin(π z). - - For -∇²u = f with u = sin(πx)sin(πy)sin(πz) in 3D. - Array layout is (Z, Y, X) to match decomposition conventions. - """ - # Use (Z, Y, X) axis ordering to match decomposition - zs, ys, xs = np.ogrid[-1 : 1 : complex(N), -1 : 1 : complex(N), -1 : 1 : complex(N)] - return 3 * np.pi**2 * np.sin(np.pi * xs) * np.sin(np.pi * ys) * np.sin(np.pi * zs) - - -def setup_sinusoidal_problem( - N: int, initial_value: float = 0.0 -) -> tuple[np.ndarray, np.ndarray, np.ndarray, float]: - """Set up sinusoidal test problem: -∇²u = 3π² sin(π x)sin(π y)sin(π z).""" - h = 2.0 / (N - 1) - u1 = create_grid_3d(N, value=initial_value, boundary_value=0.0) - u2 = u1.copy() - f = sinusoidal_source_term(N) - return u1, u2, f, h diff --git a/src/Poisson/solvers/__init__.py b/src/Poisson/solvers/__init__.py deleted file mode 100644 index 542d617..0000000 --- a/src/Poisson/solvers/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -"""Poisson Solvers. - -Consistent naming: {Method}Solver for sequential, {Method}MPISolver for parallel. - -Sequential (no MPI): -- JacobiSolver: Kernel benchmarks and single-process solving -- FMGSolver: Full Multigrid without MPI - -Parallel (MPI): -- JacobiMPISolver: Jacobi with domain decomposition -- FMGMPISolver: Full Multigrid with domain decomposition -""" - -from .jacobi import JacobiSolver -from .jacobi_mpi import JacobiMPISolver -from .fmg import FMGSolver -from .fmg_mpi import FMGMPISolver - -__all__ = [ - "JacobiSolver", - "JacobiMPISolver", - "FMGSolver", - "FMGMPISolver", -] diff --git a/src/Poisson/solvers/base.py b/src/Poisson/solvers/base.py deleted file mode 100644 index 78df25e..0000000 --- a/src/Poisson/solvers/base.py +++ /dev/null @@ -1,200 +0,0 @@ -"""Base class for solvers.""" - -import time -from abc import ABC, abstractmethod - -import numpy as np - -from ..datastructures import GlobalMetrics, LocalMetrics -from ..problems import sinusoidal_exact_solution - - -class BaseSolver(ABC): - """Abstract base for all Poisson solvers.""" - - # Bytes per lattice update: 7-point stencil = 6 neighbors + center + f = 8 mem-ops × 8 bytes - BYTES_PER_POINT = 64 - - def __init__( - self, - N: int, - omega: float = 0.8, - max_iter: int = 1000, - tolerance: float = 1e-6, - ): - self.N = N - self.omega = omega - self.max_iter = max_iter - self.tolerance = tolerance - self.h = 2.0 / (N - 1) - - # Metrics containers (match datastructures.py naming) - self.metrics = GlobalMetrics() - self.timeseries = LocalMetrics() - - # Timing accumulators - self._time_compute = 0.0 - self._time_halo = 0.0 - - @abstractmethod - def solve(self) -> GlobalMetrics: - """Execute the solver. Returns results.""" - pass - - def warmup(self, warmup_size: int = 10): - """Warmup kernel (trigger Numba JIT if used).""" - self.kernel.warmup(warmup_size=warmup_size) - - def compute_l2_error(self) -> float: - """Compute L2 error against analytical solution. - - Uses hook methods for flexibility: - - _get_solution_array(): Returns the solution array - - _compute_l2_norm(): Computes norm (handles MPI allreduce if needed) - """ - u = self._get_solution_array() - u_exact = sinusoidal_exact_solution(self.N) - l2_error = self._compute_l2_norm(u, u_exact) - self.metrics.final_error = l2_error - return l2_error - - def post_solve(self): - """Compute all error metrics after solve. - - Call this after solve() to populate: - - metrics.final_error (L2 error vs analytical) - - metrics.final_alg_error (algebraic residual norm) - - rank_topology (for MPI solvers) - """ - self.compute_l2_error() - self.compute_alg_residual() - self.rank_topology = self._gather_topology() - - def _gather_topology(self): - """Gather rank topology. Override for MPI solvers.""" - return None - - def _get_solution_array(self) -> np.ndarray: - """Return the solution array.""" - return self.u - - def _get_source_array(self) -> np.ndarray: - """Return the source term array.""" - return self.f - - def compute_alg_residual( - self, - u: np.ndarray = None, - f: np.ndarray = None, - h: float = None, - r: np.ndarray = None, - ) -> float: - """Compute algebraic residual r = f - Au and return RMS norm. - - Parameters - ---------- - u : array, optional - Solution array. If None, uses _get_solution_array(). - f : array, optional - Source term. If None, uses _get_source_array(). - h : float, optional - Grid spacing. If None, uses self.h. - r : array, optional - Output array for residual. If None, allocates temporary array. - - Returns - ------- - float - RMS norm of the algebraic residual. - - Note: Caller is responsible for halo sync before and BC after if needed. - """ - if u is None: - u = self._get_solution_array() - if f is None: - f = self._get_source_array() - if h is None: - h = self.h - if r is None: - r = np.zeros_like(u) - - # Compute r = f - Au (interior only) - h2 = h * h - u_center = u[1:-1, 1:-1, 1:-1] - u_neighbors = ( - u[0:-2, 1:-1, 1:-1] - + u[2:, 1:-1, 1:-1] - + u[1:-1, 0:-2, 1:-1] - + u[1:-1, 2:, 1:-1] - + u[1:-1, 1:-1, 0:-2] - + u[1:-1, 1:-1, 2:] - ) - laplacian = (u_neighbors - 6.0 * u_center) / h2 - r[1:-1, 1:-1, 1:-1] = f[1:-1, 1:-1, 1:-1] + laplacian - - # Compute RMS norm - interior = r[1:-1, 1:-1, 1:-1] - local_sum_sq = np.sum(interior**2) - local_pts = float(interior.size) - - global_sum_sq = self._reduce_sum(local_sum_sq) - global_pts = self._reduce_sum(local_pts) - - alg_residual = np.sqrt(global_sum_sq / global_pts) # RMS norm - self.metrics.final_alg_error = alg_residual - return alg_residual - - def _get_time(self) -> float: - """Get current time. Override for MPI timing.""" - return time.perf_counter() - - def _reduce_sum(self, local_sum: float) -> float: - """Reduce sum across ranks. Override for MPI.""" - return local_sum - - def _is_root(self) -> bool: - """True if this rank should log metrics. Override for MPI.""" - return True - - def _sync_halos(self, u: np.ndarray, lvl=None) -> float: - """Sync halo regions. No-op for sequential. Override for MPI.""" - return 0.0 - - def _apply_boundary_conditions(self, u: np.ndarray, lvl=None): - """Apply boundary conditions. No-op for sequential. Override for MPI.""" - pass - - def _barrier(self): - """Synchronize all ranks before timing. No-op for sequential.""" - pass - - def _compute_l2_norm(self, u: np.ndarray, u_exact: np.ndarray) -> float: - """Compute L2 norm. Override for MPI allreduce.""" - diff = u[1:-1, 1:-1, 1:-1] - u_exact[1:-1, 1:-1, 1:-1] - return np.sqrt(np.sum(diff**2) * self.h**3) - - def _reset(self): - """Reset timers and timeseries.""" - self._time_compute = 0.0 - self._time_halo = 0.0 - self.timeseries.clear() - - def _finalize(self, wall_time: float): - """Finalize metrics after solve.""" - if self._is_root(): - self.metrics.final_residual = self.timeseries.residual_history[-1] - self.metrics.total_compute_time = self._time_compute - self.metrics.total_halo_time = self._time_halo - self.metrics.observed_numba_threads = self.kernel.observed_numba_threads - self._compute_metrics(wall_time, self.metrics.iterations) - - def _compute_metrics(self, wall_time: float, iterations: int): - """Compute performance metrics.""" - self.metrics.wall_time = wall_time - self.metrics.iterations = iterations - - n_interior = (self.N - 2) ** 3 - if iterations > 0 and wall_time > 0: - self.metrics.mlups = n_interior * iterations / (wall_time * 1e6) - total_bytes = n_interior * iterations * self.BYTES_PER_POINT - self.metrics.bandwidth_gb_s = total_bytes / (wall_time * 1e9) diff --git a/src/Poisson/solvers/fmg.py b/src/Poisson/solvers/fmg.py deleted file mode 100644 index e1035ec..0000000 --- a/src/Poisson/solvers/fmg.py +++ /dev/null @@ -1,249 +0,0 @@ -"""Full Multigrid (FMG) Solver - Sequential.""" - -from typing import List - -import numpy as np - -from .base import BaseSolver -from ..datastructures import GridLevel, GlobalMetrics -from ..kernels import NumPyKernel, NumbaKernel -from ..problems import sinusoidal_source_term -from .multigrid_operators import restrict, prolong - - -class FMGSolver(BaseSolver): - """Sequential Full Multigrid solver. - - Implements FMG algorithm without MPI. - Useful for validation and single-node performance testing. - - Parameters - ---------- - N : int - Grid size (N x N x N). Must satisfy (N-1) divisible by 2^levels. - n_smooth : int - Pre/post smoothing iterations (default: 5). - fmg_post_vcycles : int - V-cycles after FMG phase (default: 1). - use_numba : bool - Use Numba JIT kernel (default: False). - omega : float - Relaxation factor (default: 2/3). - max_iter : int - Maximum smoothing iterations for coarsest grid solve (default: 1000). - tolerance : float - Convergence tolerance for final residual check (default: 1e-6). - """ - - def __init__( - self, - N: int, - n_smooth: int = 5, - fmg_post_vcycles: int = 1, - use_numba: bool = False, - omega: float = 2 / 3, - specified_numba_threads: int = 1, - **kwargs, - ): - super().__init__(N, omega=omega, **kwargs) - - self.n_smooth = n_smooth - self.fmg_post_vcycles = fmg_post_vcycles - self.use_numba = use_numba - self.specified_numba_threads = specified_numba_threads - self.min_coarse_size = 5 # Allow coarsening to smaller grids - - # Infer number of levels - self.n_levels = self._infer_levels(N) - - # Initialize kernel (single kernel for all levels) - self._init_kernel() - - # Build grid hierarchy - self.levels: List[GridLevel] = [] - self._build_hierarchy() - - def _infer_levels(self, N: int) -> int: - """Determine number of grid levels.""" - levels = 1 - N_current = N - - while True: - if (N_current - 1) % 2 != 0: - raise ValueError( - f"Grid size N={N_current} not compatible with multigrid " - "(N-1 must be divisible by 2)." - ) - - N_next = (N_current - 1) // 2 + 1 - if N_next < self.min_coarse_size: - break - - levels += 1 - N_current = N_next - - if N_current == 3: - break - - return levels - - def _init_kernel(self): - """Initialize the Jacobi kernel.""" - if self.use_numba: - self.kernel = NumbaKernel( - omega=self.omega, - specified_numba_threads=self.specified_numba_threads, - ) - else: - self.kernel = NumPyKernel(omega=self.omega) - - def _build_hierarchy(self): - """Allocate arrays for all grid levels.""" - N_values = [] - N_current = self.N - for _ in range(self.n_levels): - N_values.append(N_current) - N_current = (N_current - 1) // 2 + 1 - - for level, N in enumerate(N_values): - h = 2.0 / (N - 1) - - lvl = GridLevel( - level=level, - N=N, - h=h, - u=np.zeros((N, N, N), dtype=np.float64), - u_temp=np.zeros((N, N, N), dtype=np.float64), - f=sinusoidal_source_term(N), - r=np.zeros((N, N, N), dtype=np.float64), - kernel=None, # Single kernel at solver level - ) - self.levels.append(lvl) - - def solve(self) -> GlobalMetrics: - """Full Multigrid: solve from coarsest to finest.""" - self._reset() - - self._barrier() # Sync all ranks before timing - t_start = self._get_time() - - # Clear all solution arrays - for lvl in self.levels: - lvl.u.fill(0.0) - lvl.u_temp.fill(0.0) - lvl.r.fill(0.0) - - # Solve on coarsest level - self._coarse_solve(self.levels[-1]) - - # Ascend hierarchy - for level in reversed(range(self.n_levels - 1)): - coarse = self.levels[level + 1] - fine = self.levels[level] - - # Prolong coarse solution as initial guess - self._sync_halos(coarse.u, coarse) - fine.r.fill(0.0) - prolong(coarse.u, fine.r) - self._sync_halos(fine.r, fine) - - fine.u.fill(0.0) - fine.u[1:-1, 1:-1, 1:-1] = fine.r[1:-1, 1:-1, 1:-1] - self._apply_boundary_conditions(fine.u, fine) - - # V-cycle from this level (includes pre-smoothing) - residual = self._v_cycle(level) - - # Post V-cycles with residual tracking - for _ in range(self.fmg_post_vcycles): - residual = self._v_cycle(0) - self.timeseries.residual_history.append(residual) - - self.metrics.converged = residual < self.tolerance - self.metrics.iterations = self.n_levels + self.fmg_post_vcycles - - wall_time = self._get_time() - t_start - self._finalize(wall_time) - return self.metrics - - def _v_cycle(self, level: int) -> float: - """Recursive V-cycle.""" - lvl = self.levels[level] - - # Pre-smoothing - for _ in range(self.n_smooth): - self._smooth(lvl) - - # Compute algebraic residual - self._compute_alg_res(lvl) - - # Get residual norm on finest level - residual = self._residual_norm(lvl.r, lvl) if level == 0 else 0.0 - - # Base case: coarsest level - if level == self.n_levels - 1: - self._coarse_solve(lvl) - return residual - - # Restriction - next_lvl = self.levels[level + 1] - self._sync_halos(lvl.r, lvl) - restrict(lvl.r, next_lvl.f) - self._sync_halos(next_lvl.f, next_lvl) - next_lvl.u.fill(0.0) - - # Recurse - self._v_cycle(level + 1) - - # Prolongation - self._sync_halos(next_lvl.u, next_lvl) - lvl.r.fill(0.0) - prolong(next_lvl.u, lvl.r) - self._sync_halos(lvl.r, lvl) - - lvl.u[1:-1, 1:-1, 1:-1] += lvl.r[1:-1, 1:-1, 1:-1] - self._apply_boundary_conditions(lvl.u, lvl) - - # Post-smoothing - for _ in range(self.n_smooth): - self._smooth(lvl) - - return residual - - def _smooth(self, lvl: GridLevel): - """One Jacobi smoothing step.""" - self._sync_halos(lvl.u, lvl) - t0 = self._get_time() - self.kernel.step(lvl.u, lvl.u_temp, lvl.f, lvl.h) - self._apply_boundary_conditions(lvl.u_temp, lvl) - self._time_compute += self._get_time() - t0 - lvl.u, lvl.u_temp = lvl.u_temp, lvl.u - - def _compute_alg_res(self, lvl: GridLevel): - """Compute algebraic residual r = f - Au for a grid level.""" - self._sync_halos(lvl.u, lvl) - self.compute_alg_residual(lvl.u, lvl.f, lvl.h, lvl.r) - self._apply_boundary_conditions(lvl.r, lvl) - - def _coarse_solve(self, lvl: GridLevel): - """Solve on coarsest grid using max_iter smoothing iterations.""" - for _ in range(self.max_iter): - self._smooth(lvl) - - def _residual_norm(self, r: np.ndarray, lvl: GridLevel = None) -> float: - """Compute RMS residual norm.""" - interior = r[1:-1, 1:-1, 1:-1] - local_sum_sq = np.sum(interior**2) - local_pts = float(interior.size) - - global_sum_sq = self._reduce_sum(local_sum_sq) - global_pts = self._reduce_sum(local_pts) - return np.sqrt(global_sum_sq / global_pts) # RMS norm - - def _get_solution_array(self) -> np.ndarray: - """Return the solution array (finest level).""" - return self.levels[0].u - - def _get_source_array(self) -> np.ndarray: - """Return the source term array (finest level).""" - return self.levels[0].f diff --git a/src/Poisson/solvers/fmg_mpi.py b/src/Poisson/solvers/fmg_mpi.py deleted file mode 100644 index ade18ba..0000000 --- a/src/Poisson/solvers/fmg_mpi.py +++ /dev/null @@ -1,157 +0,0 @@ -"""MPI-parallel Full Multigrid Solver (extends FMGSolver).""" - -import numpy as np -from mpi4py import MPI - -from .fmg import FMGSolver -from .mpi_mixin import MPISolverMixin -from ..datastructures import GridLevel -from ..mpi.grid import DistributedGrid - - -class FMGMPISolver(MPISolverMixin, FMGSolver): - """Parallel Full Multigrid solver with MPI domain decomposition. - - Extends FMGSolver with distributed grids and halo exchange. - The algorithm (solve, _v_cycle) is inherited from FMGSolver. - - Parameters - ---------- - N : int - Global grid size (N x N x N). - strategy : str - Decomposition: 'sliced' or 'cubic' (default: 'sliced'). - communicator : str - Halo exchange: 'numpy' or 'custom' (default: 'custom'). - """ - - def __init__( - self, - N: int, - strategy: str = "sliced", - communicator: str = "custom", - **kwargs, - ): - # MPI setup before parent init - self._init_mpi() - - self.strategy = strategy - self.communicator = communicator - - # Parent init builds hierarchy (calls _build_hierarchy) - super().__init__(N, **kwargs) - - def _infer_levels(self, N: int) -> int: - """Determine number of levels considering MPI constraints.""" - min_local = 2 # Minimum local interior for restriction/prolongation - - if self.size > 1: - if self.strategy == "cubic": - ranks_per_dim = int(round(self.size ** (1 / 3))) - min_N_for_ranks = max( - (min_local + 2) * ranks_per_dim, self.min_coarse_size - ) - else: - min_N_for_ranks = self.size + 2 - else: - min_N_for_ranks = 3 - - levels = 1 - N_current = N - - while True: - if (N_current - 1) % 2 != 0: - raise ValueError( - f"Grid size N={N_current} not compatible with multigrid " - "(N-1 must be divisible by 2)." - ) - - N_next = (N_current - 1) // 2 + 1 - - # Stop if next level would be too small for MPI - if self.size > 1 and N_next < min_N_for_ranks: - break - - if N_next < self.min_coarse_size: - break - - levels += 1 - N_current = N_next - - if N_current == 3: - break - - return levels - - def _build_hierarchy(self): - """Build grid hierarchy with distributed grids.""" - N_values = [] - N_current = self.N - for _ in range(self.n_levels): - N_values.append(N_current) - N_current = (N_current - 1) // 2 + 1 - - for level, N in enumerate(N_values): - h = 2.0 / (N - 1) - - # Create distributed grid - grid = DistributedGrid( - N, self.comm, strategy=self.strategy, halo_exchange=self.communicator - ) - - # Allocate arrays via grid - u = grid.allocate() - u_temp = grid.allocate() - f = grid.allocate() - r = grid.allocate() - - # Fill source term and apply BCs - grid.fill_source_term(f) - grid.apply_boundary_conditions(u) - grid.apply_boundary_conditions(u_temp) - - lvl = GridLevel( - level=level, - N=N, - h=h, - u=u, - u_temp=u_temp, - f=f, - r=r, - kernel=None, # Single kernel at solver level - grid=grid, - ) - self.levels.append(lvl) - - # ======================================================================== - # Hook method overrides for MPI (grid-level specific) - # ======================================================================== - - def _sync_halos(self, u: np.ndarray, lvl: GridLevel) -> float: - """Sync halo regions with neighbors.""" - if lvl.grid is not None: - t0 = MPI.Wtime() - lvl.grid.sync_halos(u) - halo_time = MPI.Wtime() - t0 - self._time_halo += halo_time - return halo_time - return 0.0 - - def _apply_boundary_conditions(self, u: np.ndarray, lvl: GridLevel): - """Apply Dirichlet boundary conditions at physical boundaries.""" - if lvl.grid is not None: - lvl.grid.apply_boundary_conditions(u) - - def compute_l2_error(self) -> float: - """Compute L2 error against analytical solution (parallel).""" - fine = self.levels[0] - l2_error = fine.grid.compute_l2_error(fine.u) - self.metrics.final_error = l2_error - return l2_error - - def _gather_topology(self): - """Gather rank topology from all MPI ranks.""" - fine = self.levels[0] - rank_info = fine.grid.get_rank_info() - all_ranks = self.comm.gather(rank_info, root=0) - return all_ranks if self.rank == 0 else None diff --git a/src/Poisson/solvers/jacobi.py b/src/Poisson/solvers/jacobi.py deleted file mode 100644 index 7d07a2d..0000000 --- a/src/Poisson/solvers/jacobi.py +++ /dev/null @@ -1,103 +0,0 @@ -"""Sequential Jacobi Solver.""" - -import numpy as np - -from .base import BaseSolver -from ..kernels import NumPyKernel, NumbaKernel -from ..problems import sinusoidal_source_term - - -class JacobiSolver(BaseSolver): - """Sequential Jacobi solver for kernel benchmarks. - - No MPI overhead - runs entirely on a single process. - Useful for benchmarking kernel performance (NumPy vs Numba). - """ - - def __init__( - self, - N: int, - use_numba: bool = False, - specified_numba_threads: int = 1, - **kwargs, - ): - super().__init__(N, **kwargs) - - self.use_numba = use_numba - self.specified_numba_threads = specified_numba_threads - - # Select kernel - self._init_kernel() - - # Allocate arrays - self._init_arrays() - - def _init_kernel(self): - """Initialize the Jacobi kernel.""" - if self.use_numba: - self.kernel = NumbaKernel( - omega=self.omega, - specified_numba_threads=self.specified_numba_threads, - ) - else: - self.kernel = NumPyKernel(omega=self.omega) - - def _init_arrays(self): - """Allocate solution arrays.""" - self.u = np.zeros((self.N, self.N, self.N), dtype=np.float64) - self.u_old = np.zeros((self.N, self.N, self.N), dtype=np.float64) - self.f = sinusoidal_source_term(self.N) - - def solve(self): - """Run Jacobi iteration.""" - self._reset() - - n_interior = (self.N - 2) ** 3 - u, u_old = self.u, self.u_old - - self._barrier() # Sync all ranks before timing - t_start = self._get_time() - - for i in range(self.max_iter): - # Sync halos (no-op for sequential) - t0 = self._get_time() - self._sync_halos(u_old) - self._time_halo += self._get_time() - t0 - - # Compute step - t0 = self._get_time() - self.kernel.step(u_old, u, self.f, self.h) - self._apply_boundary_conditions(u) - self._time_compute += self._get_time() - t0 - - # Compute iterative residual - residual = self._compute_iter_res(u, u_old, n_interior) - self.timeseries.residual_history.append(residual) - - if residual < self.tolerance: - self.metrics.converged = True - self.metrics.iterations = i + 1 - break - - # Swap buffers - u, u_old = u_old, u - else: - self.metrics.iterations = self.max_iter - self.metrics.converged = False - - # Save final solution (u_old has it after swap) - self.u = u_old - - wall_time = self._get_time() - t_start - self._finalize(wall_time) - - return self.metrics - - def _compute_iter_res( - self, u: np.ndarray, u_old: np.ndarray, n_interior: int - ) -> float: - """Compute iterative residual ||u - u_old||.""" - diff = u[1:-1, 1:-1, 1:-1] - u_old[1:-1, 1:-1, 1:-1] - local_sum = np.sum(diff**2) - global_sum = self._reduce_sum(local_sum) - return np.sqrt(global_sum) / n_interior diff --git a/src/Poisson/solvers/jacobi_mpi.py b/src/Poisson/solvers/jacobi_mpi.py deleted file mode 100644 index 491650f..0000000 --- a/src/Poisson/solvers/jacobi_mpi.py +++ /dev/null @@ -1,69 +0,0 @@ -"""MPI-parallel Jacobi Solver (extends JacobiSolver).""" - -import numpy as np -from mpi4py import MPI - -from .jacobi import JacobiSolver -from .mpi_mixin import MPISolverMixin -from ..mpi.grid import DistributedGrid - - -class JacobiMPISolver(MPISolverMixin, JacobiSolver): - """Parallel Jacobi solver with MPI domain decomposition. - - Extends JacobiSolver with MPI communication for distributed solving. - """ - - def __init__( - self, - N: int, - strategy: str = "sliced", - communicator: str = "custom", - **kwargs, - ): - # MPI setup before parent init - self._init_mpi() - - self.strategy = strategy - self.communicator = communicator - - # Create distributed grid - self.grid = DistributedGrid( - N, self.comm, strategy=strategy, halo_exchange=communicator - ) - - # Parent init (calls _init_kernel and _init_arrays) - super().__init__(N, **kwargs) - - def _init_arrays(self): - """Allocate local arrays using distributed grid.""" - self.u = self.grid.allocate() - self.u_old = self.grid.allocate() - self.f = self.grid.allocate() - - # Fill source term and apply BCs - self.grid.fill_source_term(self.f) - self.grid.apply_boundary_conditions(self.u) - self.grid.apply_boundary_conditions(self.u_old) - - def _sync_halos(self, u: np.ndarray, lvl=None) -> float: - """Sync halo regions with neighbors.""" - t0 = MPI.Wtime() - self.grid.sync_halos(u) - return MPI.Wtime() - t0 - - def _apply_boundary_conditions(self, u: np.ndarray, lvl=None): - """Apply Dirichlet boundary conditions at physical boundaries.""" - self.grid.apply_boundary_conditions(u) - - def compute_l2_error(self) -> float: - """Compute L2 error using grid's parallel implementation.""" - l2_error = self.grid.compute_l2_error(self.u) - self.metrics.final_error = l2_error - return l2_error - - def _gather_topology(self): - """Gather rank topology from all MPI ranks.""" - rank_info = self.grid.get_rank_info() - all_ranks = self.comm.gather(rank_info, root=0) - return all_ranks if self.rank == 0 else None diff --git a/src/Poisson/solvers/mpi_mixin.py b/src/Poisson/solvers/mpi_mixin.py deleted file mode 100644 index a674ba4..0000000 --- a/src/Poisson/solvers/mpi_mixin.py +++ /dev/null @@ -1,45 +0,0 @@ -"""MPI mixin providing common parallel solver functionality.""" - -import numpy as np -from mpi4py import MPI - - -class MPISolverMixin: - """Mixin providing common MPI functionality for parallel solvers. - - Provides shared implementations for: - - MPI initialization (comm, rank, size) - - Timing via MPI.Wtime() - - Global reductions via MPI.Allreduce() - - Root rank checking - - Usage: - class MyMPISolver(MPISolverMixin, MySolver): - def __init__(self, ...): - self._init_mpi() - ... - """ - - def _init_mpi(self): - """Initialize MPI attributes. Call early in subclass __init__.""" - self.comm = MPI.COMM_WORLD - self.rank = self.comm.Get_rank() - self.size = self.comm.Get_size() - - def _get_time(self) -> float: - """Get current time using MPI.Wtime().""" - return MPI.Wtime() - - def _reduce_sum(self, local_sum: float) -> float: - """Reduce sum via MPI Allreduce.""" - global_sum = np.zeros(1) - self.comm.Allreduce(np.array([local_sum]), global_sum, op=MPI.SUM) - return global_sum[0] - - def _is_root(self) -> bool: - """Only rank 0 logs metrics.""" - return self.rank == 0 - - def _barrier(self): - """Synchronize all ranks before timing.""" - self.comm.Barrier() diff --git a/src/Poisson/solvers/multigrid_operators.py b/src/Poisson/solvers/multigrid_operators.py deleted file mode 100644 index f6652d4..0000000 --- a/src/Poisson/solvers/multigrid_operators.py +++ /dev/null @@ -1,152 +0,0 @@ -"""Multigrid operators for 3D Poisson solver. - -Simple operators using vertex-centered alignment: coarse[i] <-> fine[2*i]. -Halo/boundary handling is the caller's responsibility. -""" - -import numpy as np -from numba import njit, prange - - -@njit(parallel=True) -def restrict(fine: np.ndarray, coarse: np.ndarray): - """ - Full weighting restriction (fine -> coarse). - - Vertex-centered alignment: coarse[i] corresponds to fine[2*i]. - Uses 27-point full weighting stencil where possible, injection at boundaries. - - Parameters - ---------- - fine : ndarray - Fine grid array (includes boundaries/halos) - coarse : ndarray - Coarse grid array (includes boundaries/halos), modified in-place - """ - nz_c, ny_c, nx_c = coarse.shape - nz_f, ny_f, nx_f = fine.shape - - for i in prange(1, nz_c - 1): - for j in range(1, ny_c - 1): - for k in range(1, nx_c - 1): - fi, fj, fk = 2 * i, 2 * j, 2 * k - - # Check if full stencil fits - if ( - fi - 1 >= 0 - and fi + 1 < nz_f - and fj - 1 >= 0 - and fj + 1 < ny_f - and fk - 1 >= 0 - and fk + 1 < nx_f - ): - # Full weighting 27-point stencil - val = 8.0 * fine[fi, fj, fk] - - val += 4.0 * ( - fine[fi - 1, fj, fk] - + fine[fi + 1, fj, fk] - + fine[fi, fj - 1, fk] - + fine[fi, fj + 1, fk] - + fine[fi, fj, fk - 1] - + fine[fi, fj, fk + 1] - ) - - val += 2.0 * ( - fine[fi - 1, fj - 1, fk] - + fine[fi - 1, fj + 1, fk] - + fine[fi + 1, fj - 1, fk] - + fine[fi + 1, fj + 1, fk] - + fine[fi - 1, fj, fk - 1] - + fine[fi - 1, fj, fk + 1] - + fine[fi + 1, fj, fk - 1] - + fine[fi + 1, fj, fk + 1] - + fine[fi, fj - 1, fk - 1] - + fine[fi, fj - 1, fk + 1] - + fine[fi, fj + 1, fk - 1] - + fine[fi, fj + 1, fk + 1] - ) - - val += 1.0 * ( - fine[fi - 1, fj - 1, fk - 1] - + fine[fi - 1, fj - 1, fk + 1] - + fine[fi - 1, fj + 1, fk - 1] - + fine[fi - 1, fj + 1, fk + 1] - + fine[fi + 1, fj - 1, fk - 1] - + fine[fi + 1, fj - 1, fk + 1] - + fine[fi + 1, fj + 1, fk - 1] - + fine[fi + 1, fj + 1, fk + 1] - ) - - coarse[i, j, k] = val / 64.0 - else: - # Injection for boundary cases - if 0 <= fi < nz_f and 0 <= fj < ny_f and 0 <= fk < nx_f: - coarse[i, j, k] = fine[fi, fj, fk] - - -@njit(parallel=True) -def prolong(coarse: np.ndarray, fine: np.ndarray): - """ - Trilinear interpolation prolongation (coarse -> fine). - - Vertex-centered alignment: fine[2*i] corresponds to coarse[i]. - Fills fine points at both even indices (2i) and odd indices (2i+1). - - Parameters - ---------- - coarse : ndarray - Coarse grid array (includes boundaries/halos) - fine : ndarray - Fine grid array (includes boundaries/halos), modified in-place - """ - nz_c, ny_c, nx_c = coarse.shape - nz_f, ny_f, nx_f = fine.shape - - for i in prange(0, nz_c - 1): - for j in range(0, ny_c - 1): - for k in range(0, nx_c - 1): - fi, fj, fk = 2 * i, 2 * j, 2 * k - - if fi + 1 >= nz_f or fj + 1 >= ny_f or fk + 1 >= nx_f: - continue - - # Even-index point (coincides with coarse node) - fine[fi, fj, fk] = coarse[i, j, k] - - # Odd-index points (midpoints) - fine[fi + 1, fj, fk] = 0.5 * (coarse[i, j, k] + coarse[i + 1, j, k]) - fine[fi, fj + 1, fk] = 0.5 * (coarse[i, j, k] + coarse[i, j + 1, k]) - fine[fi, fj, fk + 1] = 0.5 * (coarse[i, j, k] + coarse[i, j, k + 1]) - - # Face centers - fine[fi + 1, fj + 1, fk] = 0.25 * ( - coarse[i, j, k] - + coarse[i + 1, j, k] - + coarse[i, j + 1, k] - + coarse[i + 1, j + 1, k] - ) - fine[fi + 1, fj, fk + 1] = 0.25 * ( - coarse[i, j, k] - + coarse[i + 1, j, k] - + coarse[i, j, k + 1] - + coarse[i + 1, j, k + 1] - ) - fine[fi, fj + 1, fk + 1] = 0.25 * ( - coarse[i, j, k] - + coarse[i, j + 1, k] - + coarse[i, j, k + 1] - + coarse[i, j + 1, k + 1] - ) - - # Cell center - fine[fi + 1, fj + 1, fk + 1] = 0.125 * ( - coarse[i, j, k] - + coarse[i + 1, j, k] - + coarse[i, j + 1, k] - + coarse[i + 1, j + 1, k] - + coarse[i, j, k + 1] - + coarse[i + 1, j, k + 1] - + coarse[i, j + 1, k + 1] - + coarse[i + 1, j + 1, k + 1] - ) diff --git a/src/fv/linear_solvers/__init__.py b/src/fv/linear_solvers/__init__.py index 43fb2ea..706a312 100644 --- a/src/fv/linear_solvers/__init__.py +++ b/src/fv/linear_solvers/__init__.py @@ -1,6 +1,5 @@ """Linear solvers for FV method.""" from .scipy_solver import scipy_solver -from .petsc_solver import petsc_solver -__all__ = ["scipy_solver", "petsc_solver"] +__all__ = ["scipy_solver"] diff --git a/src/fv/linear_solvers/petsc_solver.py b/src/fv/linear_solvers/petsc_solver.py deleted file mode 100644 index 51bee2b..0000000 --- a/src/fv/linear_solvers/petsc_solver.py +++ /dev/null @@ -1,109 +0,0 @@ -"""PETSc-based linear solver with HYPRE preconditioner and KSP reuse.""" - -import numpy as np -from scipy.sparse import csr_matrix -from petsc4py import PETSc - - -def petsc_solver( - A_csr: csr_matrix, - b_np: np.ndarray, - ksp=None, - tolerance=1e-6, - max_iterations=1000, - solver_type="bcgs", - preconditioner="gamg", - remove_nullspace=False, -): - """Solve A x = b using PETSc with optional KSP reuse. - - Parameters - ---------- - A_csr : csr_matrix - Sparse matrix in CSR format. - b_np : np.ndarray - Right-hand side vector. - ksp : PETSc.KSP, optional - Reusable KSP solver object. If None, a new KSP is created. - tolerance : float, optional - Convergence tolerance for the solver (default: 1e-6). - max_iterations : int, optional - Maximum number of iterations for the solver (default: 1000). - solver_type : str, optional - Type of PETSc solver to use (default: "bcgs"). - preconditioner : str, optional - Type of PETSc preconditioner to use (default: "hypre"). - remove_nullspace : bool, optional - Whether to handle the nullspace (default: False). - If True, creates a constant nullspace vector and removes it from the RHS. - - Returns - ------- - x_np : np.ndarray - Solution vector x. - ksp : PETSc.KSP - KSP solver (returned for reuse). - """ - n = A_csr.shape[0] - - # Create PETSc matrix from SciPy CSR - A_petsc = PETSc.Mat().createAIJ( - size=A_csr.shape, csr=(A_csr.indptr, A_csr.indices, A_csr.data) - ) - A_petsc.assemble() - - # Create PETSc vectors - b_petsc = PETSc.Vec().createWithArray(b_np) - x_petsc = PETSc.Vec().createSeq(n) - - # Handle nullspace if requested - nullvec = None - nullspace = None - if remove_nullspace: - # Create constant nullspace vector - nullvec = A_petsc.createVecLeft() - nullvec.set(1.0) - nullvec.normalize() - - # Create and set nullspace - nullspace = PETSc.NullSpace().create(vectors=[nullvec]) - A_petsc.setNullSpace(nullspace) - - # Remove nullspace from RHS - nullspace.remove(b_petsc) - - # Create or reuse KSP solver - if ksp is None: - ksp = PETSc.KSP().create() - ksp.setOperators(A_petsc) - ksp.setType(solver_type) - ksp.setTolerances(rtol=float(tolerance), atol=0, max_it=max_iterations) - pc = ksp.getPC() - pc.setType(preconditioner) - ksp.setFromOptions() - else: - # Update operators for existing KSP (reuse preconditioner structure) - ksp.setOperators(A_petsc) - - # Solve - ksp.solve(b_petsc, x_petsc) - - if ksp.getConvergedReason() <= 0: - raise RuntimeError( - f"PETSc did not converge. Reason: {ksp.getConvergedReason()}, " - f"Iterations: {ksp.getIterationNumber()}" - ) - - # Convert result to NumPy - x_np = x_petsc.getArray().copy() - - # Cleanup temporary objects (keep KSP for reuse) - A_petsc.destroy() - b_petsc.destroy() - x_petsc.destroy() - if nullvec is not None: - nullvec.destroy() - if nullspace is not None: - nullspace.destroy() - - return x_np, ksp diff --git a/src/fv/linear_solvers/scipy_solver.py b/src/fv/linear_solvers/scipy_solver.py index 3e9fee5..363492f 100644 --- a/src/fv/linear_solvers/scipy_solver.py +++ b/src/fv/linear_solvers/scipy_solver.py @@ -1,32 +1,59 @@ -"""Scipy-based linear solver using BiCGSTAB with PyAMG preconditioner.""" +"""Scipy-based linear solver using BiCGSTAB.""" import numpy as np from scipy.sparse import csr_matrix from scipy.sparse.linalg import bicgstab -def scipy_solver(A_csr: csr_matrix, b_np: np.ndarray, use_cg: bool = False): - """Solve A x = b using BiCGSTAB with PyAMG preconditioner. +def scipy_solver( + A_csr: csr_matrix, + b_np: np.ndarray, + M=None, # Unused, kept for API compatibility + tolerance=1e-6, + max_iterations=1000, + remove_nullspace=False, +): + """Solve A x = b using scipy BiCGSTAB. Parameters ---------- A_csr : csr_matrix - Coefficient matrix in CSR format + Sparse matrix in CSR format. b_np : np.ndarray - Right-hand side vector - use_cg : bool, optional - Unused parameter kept for API compatibility + Right-hand side vector. + M : unused + Kept for API compatibility, ignored. + tolerance : float, optional + Convergence tolerance (default: 1e-6). + max_iterations : int, optional + Maximum iterations (default: 1000). + remove_nullspace : bool, optional + If True, removes the mean from RHS and solution (for pressure eq). Returns ------- - np.ndarray - Solution vector + x_np : np.ndarray + Solution vector. + None + Placeholder for API compatibility. """ - # Solve using BiCGSTAB without preconditioner - # PyAMG preconditioner can cause numerical issues on early iterations - x, info = bicgstab(A_csr, b_np, rtol=1e-6, atol=0) + # Handle nullspace if requested (for pressure Poisson equation) + b = b_np.copy() + if remove_nullspace: + b = b - np.mean(b) + + # Solve using BiCGSTAB + x, info = bicgstab(A_csr, b, rtol=tolerance, atol=0, maxiter=max_iterations) if info != 0: - raise RuntimeError(f"BiCGSTAB failed to converge (info={info})") + if info > 0: + # Did not converge but we can still use the result + pass + else: + raise RuntimeError(f"BiCGSTAB failed (info={info})") + + # Remove nullspace component from solution if requested + if remove_nullspace: + x = x - np.mean(x) - return x + return x, None diff --git a/src/utils/data_io.py b/src/ldc/data_io.py similarity index 100% rename from src/utils/data_io.py rename to src/ldc/data_io.py diff --git a/src/utils/field_interpolator.py b/src/ldc/field_interpolator.py similarity index 100% rename from src/utils/field_interpolator.py rename to src/ldc/field_interpolator.py diff --git a/src/utils/ghia_validator.py b/src/ldc/ghia_validator.py similarity index 100% rename from src/utils/ghia_validator.py rename to src/ldc/ghia_validator.py diff --git a/src/utils/ldc_plotter.py b/src/ldc/ldc_plotter.py similarity index 100% rename from src/utils/ldc_plotter.py rename to src/ldc/ldc_plotter.py diff --git a/src/solvers/__init__.py b/src/solvers/__init__.py index 22265f5..4f5b644 100644 --- a/src/solvers/__init__.py +++ b/src/solvers/__init__.py @@ -25,17 +25,7 @@ SpectralSolverFields, ) from .spectral_solver import SpectralSolver - -# FVSolver requires petsc4py - make import optional -try: - from .fv_solver import FVSolver -except ImportError as e: - _fv_import_error = e - - def FVSolver(*args, **kwargs): - raise ImportError( - f"FVSolver requires petsc4py which is not installed: {_fv_import_error}" - ) +from .fv_solver import FVSolver __all__ = [ diff --git a/src/solvers/base_solver.py b/src/solvers/base_solver.py index f0319f1..a0d74f6 100644 --- a/src/solvers/base_solver.py +++ b/src/solvers/base_solver.py @@ -1,6 +1,7 @@ """Abstract base solver for lid-driven cavity problem.""" from abc import ABC, abstractmethod +import logging import os import time @@ -10,6 +11,8 @@ from dataclasses import asdict from .datastructures import TimeSeries, Metrics, Fields +log = logging.getLogger(__name__) + class LidDrivenCavitySolver(ABC): """Abstract base solver for lid-driven cavity problem. @@ -254,7 +257,7 @@ def solve(self, tolerance: float = None, max_iter: int = None): is_converged = False if i % 50 == 0 or is_converged: - print( + log.info( f"Iteration {i}: u_res={u_solution_change:.6e}, v_res={v_solution_change:.6e}" ) @@ -277,12 +280,12 @@ def solve(self, tolerance: float = None, max_iter: int = None): mlflow_time += time.time() - t_log_start if is_converged: - print(f"Converged at iteration {i}") + log.info(f"Converged at iteration {i}") break time_end = time.time() wall_time = time_end - time_start - mlflow_time # Exclude MLflow logging time - print( + log.info( f"Solver finished in {wall_time:.2f} seconds (excl. {mlflow_time:.2f}s logging)." ) diff --git a/src/solvers/datastructures.py b/src/solvers/datastructures.py index 2f4ecce..1e08a81 100644 --- a/src/solvers/datastructures.py +++ b/src/solvers/datastructures.py @@ -156,10 +156,10 @@ class FVSolverFields: mdot_star: np.ndarray mdot_prime: np.ndarray - # PETSc KSP objects for solver reuse - ksp_u: object = None - ksp_v: object = None - ksp_p: object = None + # Scipy preconditioners for solver reuse + M_u: object = None + M_v: object = None + M_p: object = None @classmethod def allocate(cls, n_cells: int, n_faces: int): diff --git a/src/solvers/fv_solver.py b/src/solvers/fv_solver.py index bc8c445..0d7e93e 100644 --- a/src/solvers/fv_solver.py +++ b/src/solvers/fv_solver.py @@ -14,7 +14,7 @@ from fv.discretization.gradient.structured_gradient import ( compute_cell_gradients_structured, ) -from fv.linear_solvers.petsc_solver import petsc_solver +from fv.linear_solvers.scipy_solver import scipy_solver from fv.assembly.rhie_chow import mdot_calculation, rhie_chow_velocity from fv.assembly.pressure_correction_eq_assembly import ( assemble_pressure_correction_matrix, @@ -82,7 +82,7 @@ def __init__(self, **kwargs): self.dy_min = self.params.Ly / self.params.ny def _solve_momentum_equation( - self, component_idx, phi, grad_phi, phi_prev_iter, grad_p_component, ksp + self, component_idx, phi, grad_phi, phi_prev_iter, grad_p_component, M ): """Solve a single momentum equation (u or v). @@ -98,8 +98,8 @@ def _solve_momentum_equation( Previous iteration velocity component grad_p_component : ndarray Pressure gradient component (x or y) - ksp : PETSc.KSP or None - Reusable KSP solver object (updated in-place) + M : LinearOperator or None + Reusable preconditioner (updated in-place) Returns ------- @@ -107,8 +107,8 @@ def _solve_momentum_equation( Predicted velocity component A_diag : ndarray Diagonal of momentum matrix (needed for pressure correction) - ksp : PETSc.KSP - Updated KSP solver object for reuse + M : LinearOperator + Updated preconditioner for reuse """ # Assemble momentum equation row, col, data, b = assemble_diffusion_convection_matrix( @@ -132,17 +132,15 @@ def _solve_momentum_equation( ) A.setdiag(relaxed_A_diag) - # Solve with PETSc - phi_star, ksp = petsc_solver( + # Solve with scipy + phi_star, M = scipy_solver( A, rhs, - ksp=ksp, + M=M, tolerance=self.params.linear_solver_tol, - solver_type="bcgs", - preconditioner="gamg", ) - return phi_star, A_diag, ksp + return phi_star, A_diag, M def step(self): """Perform one SIMPLE iteration. @@ -172,12 +170,12 @@ def step(self): self.mesh, a.v_prev, use_limiter=True, out=a.grad_v ) - # Solve momentum equations (reuse KSP objects) - u_star, A_u_diag, a.ksp_u = self._solve_momentum_equation( - 0, a.u_prev, a.grad_u, a.u_prev, a.grad_p[:, 0], a.ksp_u + # Solve momentum equations (reuse preconditioners) + u_star, A_u_diag, a.M_u = self._solve_momentum_equation( + 0, a.u_prev, a.grad_u, a.u_prev, a.grad_p[:, 0], a.M_u ) - v_star, A_v_diag, a.ksp_v = self._solve_momentum_equation( - 1, a.v_prev, a.grad_v, a.v_prev, a.grad_p[:, 1], a.ksp_v + v_star, A_v_diag, a.M_v = self._solve_momentum_equation( + 1, a.v_prev, a.grad_v, a.v_prev, a.grad_p[:, 1], a.M_v ) # Pressure correction - reuse buffers @@ -200,15 +198,13 @@ def step(self): row, col, data = assemble_pressure_correction_matrix(self.mesh, self.rho) rhs_p = -compute_divergence_from_face_fluxes(self.mesh, a.mdot_star) - # Solve pressure correction with PETSc (handles nullspace internally) + # Solve pressure correction with scipy (handles nullspace internally) A_p = csr_matrix((data, (row, col)), shape=(self.n_cells, self.n_cells)) - p_prime, a.ksp_p = petsc_solver( + p_prime, a.M_p = scipy_solver( A_p, rhs_p, - ksp=a.ksp_p, + M=a.M_p, tolerance=self.params.linear_solver_tol, - solver_type="bcgs", - preconditioner="gamg", remove_nullspace=True, ) diff --git a/src/solvers/spectral_solver.py b/src/solvers/spectral_solver.py index 4ea898d..499774c 100644 --- a/src/solvers/spectral_solver.py +++ b/src/solvers/spectral_solver.py @@ -7,12 +7,16 @@ - 4-stage RK4 explicit time stepping with adaptive CFL """ +import logging + import numpy as np from .base_solver import LidDrivenCavitySolver from .datastructures import SpectralParameters, SpectralSolverFields from spectral.spectral import LegendreLobattoBasis, ChebyshevLobattoBasis +log = logging.getLogger(__name__) + class SpectralSolver(LidDrivenCavitySolver): """Pseudospectral solver for lid-driven cavity problem. @@ -37,11 +41,11 @@ def __init__(self, **kwargs): if self.params.basis_type.lower() == "chebyshev": self.basis_x = ChebyshevLobattoBasis(domain=(0.0, self.params.Lx)) self.basis_y = ChebyshevLobattoBasis(domain=(0.0, self.params.Ly)) - print("Using Chebyshev-Gauss-Lobatto basis (Zhang et al. 2010)") + log.info("Using Chebyshev-Gauss-Lobatto basis") elif self.params.basis_type.lower() == "legendre": self.basis_x = LegendreLobattoBasis(domain=(0.0, self.params.Lx)) self.basis_y = LegendreLobattoBasis(domain=(0.0, self.params.Ly)) - print("Using Legendre-Gauss-Lobatto basis") + log.info("Using Legendre-Gauss-Lobatto basis") else: raise ValueError( f"Unknown basis_type: {self.params.basis_type}. Use 'legendre' or 'chebyshev'" diff --git a/src/utils/mlflow_io.py b/src/utils/mlflow_io.py deleted file mode 100644 index 9fed1cb..0000000 --- a/src/utils/mlflow_io.py +++ /dev/null @@ -1,206 +0,0 @@ -"""MLflow I/O utilities for fetching runs and artifacts. - -This module provides helpers for retrieving experiment data from -MLflow/Databricks and downloading artifacts to the local data directory. -""" - -import os -from pathlib import Path -from typing import List, Optional -import mlflow -import pandas as pd - - -def setup_mlflow_auth(): - """Configure MLflow authentication. - - Uses DATABRICKS_TOKEN environment variable if available (for CI), - otherwise falls back to interactive login. - """ - token = os.environ.get("DATABRICKS_TOKEN") - if token: - # CI environment - set both host and token for Databricks auth - host = "https://dbc-6756e917-e5fc.cloud.databricks.com" - os.environ["DATABRICKS_HOST"] = host - mlflow.set_tracking_uri("databricks") - else: - # Local environment - interactive login - mlflow.login() - - -def load_runs( - experiment: str, - converged_only: bool = True, - exclude_parent_runs: bool = True, -) -> pd.DataFrame: - """Load runs from an MLflow experiment. - - Parameters - ---------- - experiment : str - Experiment name (e.g., "HPC-FV-Solver" or full path "/Shared/ANA-P3/HPC-FV-Solver"). - converged_only : bool, default True - Only return runs where metrics.converged = 1. - exclude_parent_runs : bool, default True - Exclude parent runs (nested run containers). - - Returns - ------- - pd.DataFrame - DataFrame with run info, parameters (params.*), and metrics (metrics.*). - - Examples - -------- - >>> df = load_runs("HPC-FV-Solver") - >>> df[["run_id", "params.nx", "metrics.wall_time_seconds"]] - """ - # Normalize experiment name - if not experiment.startswith("/"): - experiment = f"/Shared/ANA-P3/{experiment}" - - # Build filter string - filters = [] - if converged_only: - filters.append("metrics.converged = 1") - - filter_string = " and ".join(filters) if filters else "" - - # Fetch runs - df = mlflow.search_runs( - experiment_names=[experiment], - filter_string=filter_string, - order_by=["start_time DESC"], - ) - - # Filter out parent runs in pandas (MLflow filter doesn't handle None well) - if exclude_parent_runs and "tags.is_parent" in df.columns: - df = df[df["tags.is_parent"] != "true"] - - return df - - -def download_artifacts( - experiment: str, - output_dir: Path, - converged_only: bool = True, - artifact_filter: Optional[List[str]] = None, -) -> List[Path]: - """Download artifacts from MLflow runs to local directory. - - Parameters - ---------- - experiment : str - Experiment name (e.g., "HPC-FV-Solver"). - output_dir : Path - Directory to save artifacts. Files are named based on run parameters. - converged_only : bool, default True - Only download from converged runs. - artifact_filter : list of str, optional - Only download artifacts matching these patterns (e.g., ["*.h5", "*.png"]). - If None, downloads all artifacts. - - Returns - ------- - list of Path - Paths to downloaded files. - - Examples - -------- - >>> paths = download_artifacts("HPC-FV-Solver", Path("data/FV-Solver")) - >>> print(paths) - [Path('data/FV-Solver/LDC_N32_Re100.h5'), ...] - """ - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - # Get runs - df = load_runs(experiment, converged_only=converged_only) - if df.empty: - print(f"No runs found for {experiment}") - return [] - - client = mlflow.tracking.MlflowClient() - downloaded = [] - - for _, row in df.iterrows(): - run_id = row["run_id"] - - # List artifacts - artifacts = client.list_artifacts(run_id) - - for artifact in artifacts: - # Apply filter if specified - if artifact_filter: - if not any(artifact.path.endswith(f) for f in artifact_filter): - continue - - # Download to output directory - local_path = client.download_artifacts(run_id, artifact.path, output_dir) - downloaded.append(Path(local_path)) - print(f" Downloaded: {artifact.path}") - - return downloaded - - -def download_artifacts_with_naming( - experiment: str, - output_dir: Path, - converged_only: bool = True, -) -> List[Path]: - """Download HDF5 artifacts with standardized naming. - - Names files as: LDC_N{nx}_Re{Re}.h5 - - Parameters - ---------- - experiment : str - Experiment name (e.g., "HPC-FV-Solver"). - output_dir : Path - Directory to save artifacts. - converged_only : bool, default True - Only download from converged runs. - - Returns - ------- - list of Path - Paths to downloaded files. - """ - import tempfile - import shutil - - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - df = load_runs(experiment, converged_only=converged_only) - if df.empty: - print(f"No runs found for {experiment}") - return [] - - client = mlflow.tracking.MlflowClient() - downloaded = [] - - for _, row in df.iterrows(): - run_id = row["run_id"] - - # Extract parameters for naming - nx = row.get("params.nx", row.get("params.N", "unknown")) - re = row.get("params.Re", "unknown") - - # List artifacts and find HDF5 files - artifacts = client.list_artifacts(run_id) - - for artifact in artifacts: - if artifact.path.endswith(".h5"): - # Download to temp location first - with tempfile.TemporaryDirectory() as tmpdir: - tmp_path = client.download_artifacts(run_id, artifact.path, tmpdir) - - # Rename with standardized naming - new_name = f"LDC_N{nx}_Re{re}.h5" - final_path = output_dir / new_name - - shutil.copy(tmp_path, final_path) - downloaded.append(final_path) - print(f" {artifact.path} -> {new_name}") - - return downloaded diff --git a/src/utils/plotting.py b/src/utils/plotting.py deleted file mode 100644 index 38b511c..0000000 --- a/src/utils/plotting.py +++ /dev/null @@ -1,167 +0,0 @@ -"""Plotting utilities and style configuration. - -This module provides utilities for: -- Automatic style application (seaborn + custom mplstyle) -- Formatting labels and parameters for plots -- Common plotting helpers - -Automatically applies seaborn style and custom utils.mplstyle on import. -""" - -from __future__ import annotations - -from pathlib import Path -from typing import Any - -import matplotlib.pyplot as plt -import pandas as pd # noqa: F401 - re-exported for other modules -import seaborn as sns - - -# ============================================================================== -# Style Application -# ============================================================================== - - -def _apply_styles(): - """Apply seaborn style and custom utils.mplstyle.""" - # Set seaborn theme - sns.set_theme() - - # Then apply custom style on top - style_path = Path(__file__).parent / "utils.mplstyle" - plt.style.use(str(style_path)) - - -# Apply styles when module is imported -_apply_styles() - - -# ============================================================================== -# Formatting Utilities -# ============================================================================== - - -def format_dt_latex(dt: float | str) -> str: - """Format a timestep value as LaTeX scientific notation. - - Parameters - ---------- - dt : float or str - Timestep value to format. If str and equals '?', returns '?' - - Returns - ------- - str - LaTeX-formatted string in the form 'mantissa \\times 10^{exponent}' - - Examples - -------- - >>> format_dt_latex(0.001) - '1.00 \\times 10^{-3}' - - """ - if dt == "?": - return "?" - - dt_str = f"{float(dt):.2e}" - mantissa, exp = dt_str.split("e") - exp_int = int(exp) - return rf"{mantissa} \times 10^{{{exp_int}}}" - - -def format_parameter_range( - values: list | tuple, - name: str, - latex: bool = True, -) -> str: - """Format a parameter range for display. - - Parameters - ---------- - values : list or tuple - Parameter values (should be sorted) - name : str - Parameter name (e.g., 'N', 'L', 'dt') - latex : bool, default True - Whether to use LaTeX formatting - - Returns - ------- - str - Formatted string - - Examples - -------- - >>> format_parameter_range([10, 20, 30], 'N') - '$N \\in [10, 30]$' - - """ - if len(values) == 0: - return f"{name} = ?" - - if len(values) == 1: - val = values[0] - if latex: - return rf"${name} = {val}$" - return f"{name} = {val}" - - min_val, max_val = min(values), max(values) - - # Format based on type - if isinstance(min_val, int) and isinstance(max_val, int): - range_str = f"[{min_val}, {max_val}]" - else: - range_str = f"[{min_val:.1f}, {max_val:.1f}]" - - if latex: - return rf"${name} \in {range_str}$" - return f"{name} ∈ {range_str}" - - -def build_parameter_string( - params: dict[str, Any], - separator: str = ", ", - latex: bool = True, -) -> str: - """Build a parameter string from a dictionary. - - Parameters - ---------- - params : dict - Dictionary of parameter names and values - separator : str, default ', ' - Separator between parameters - latex : bool, default True - Whether to use LaTeX formatting (wraps each param in $ $) - - Returns - ------- - str - Formatted parameter string - - Examples - -------- - >>> build_parameter_string({'N': 100, 'dt': 0.001}) - '$N = 100$, $dt = 1.00 \\times 10^{-3}$' - - """ - parts = [] - for name, value in params.items(): - if isinstance(value, (list, tuple)): - parts.append(format_parameter_range(value, name, latex=latex)) - else: - # Handle special formatting for dt - if "dt" in name.lower() or "Delta t" in name: - value_str = format_dt_latex(value) - if latex: - parts.append(rf"${name} = {value_str}$") - else: - parts.append(f"{name} = {value_str}") - else: - if latex: - parts.append(rf"${name} = {value}$") - else: - parts.append(f"{name} = {value}") - - return separator.join(parts) From fd9b705ed7a454fde8f000421adfc6914e9a763b Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 10:56:40 +0100 Subject: [PATCH 04/34] fixup --- run_solver.py | 46 ++------ src/solvers/datastructures.py | 191 ++++++++++++++++++---------------- 2 files changed, 111 insertions(+), 126 deletions(-) diff --git a/run_solver.py b/run_solver.py index 68aa4e9..b74588e 100644 --- a/run_solver.py +++ b/run_solver.py @@ -17,13 +17,11 @@ import logging import os -from dataclasses import asdict from pathlib import Path import hydra import mlflow from hydra.core.hydra_config import HydraConfig -from mlflow.entities import Metric from mlflow.tracking import MlflowClient from omegaconf import DictConfig, OmegaConf @@ -100,47 +98,19 @@ def setup_mlflow(cfg: DictConfig) -> str: return experiment_name -def log_config_params(cfg: DictConfig): - """Log flattened config as MLflow parameters.""" - # Common params - mlflow.log_params( - { - "solver": cfg.solver.name, - "N": cfg.N, - "Re": cfg.Re, - "lid_velocity": cfg.lid_velocity, - "Lx": cfg.Lx, - "Ly": cfg.Ly, - "tolerance": cfg.tolerance, - "max_iterations": cfg.max_iterations, - } - ) - - # Solver-specific params (flatten solver config) - solver_params = { - f"solver.{k}": v for k, v in OmegaConf.to_container(cfg.solver).items() if k != "name" - } - mlflow.log_params(solver_params) +def log_params(solver): + """Log solver params to MLflow using dataclass to_mlflow method.""" + mlflow.log_params(solver.params.to_mlflow()) def log_metrics_and_timeseries(solver, run_id: str): """Log final metrics and timeseries to MLflow.""" - # Final metrics - metrics_dict = asdict(solver.metrics) - # Convert booleans to int for MLflow - metrics_dict = {k: (int(v) if isinstance(v, bool) else v) for k, v in metrics_dict.items()} - mlflow.log_metrics(metrics_dict) + # Final metrics (using dataclass to_mlflow method) + mlflow.log_metrics(solver.metrics.to_mlflow()) - # Timeseries (batch logging for efficiency) + # Timeseries (batch logging using dataclass to_mlflow_batch method) if solver.time_series is not None: - ts = asdict(solver.time_series) - batch_metrics = [] - for key, values in ts.items(): - if values is not None: - for step, value in enumerate(values): - if value is not None: - batch_metrics.append(Metric(key=key, value=value, timestamp=0, step=step)) - + batch_metrics = solver.time_series.to_mlflow_batch() if batch_metrics: MlflowClient().log_batch(run_id=run_id, metrics=batch_metrics) @@ -195,7 +165,7 @@ def main(cfg: DictConfig) -> None: # Run with MLflow tracking with mlflow.start_run(run_name=run_name) as run: - log_config_params(cfg) + log_params(solver) # Tag with HPC job info if available job_id = os.environ.get("LSB_JOBID") diff --git a/src/solvers/datastructures.py b/src/solvers/datastructures.py index 1e08a81..a79f155 100644 --- a/src/solvers/datastructures.py +++ b/src/solvers/datastructures.py @@ -1,25 +1,29 @@ """Data structures for solver configuration and results. -This module defines the configuration and result data structures -for lid-driven cavity solvers (both FV and spectral). - -Structure: -- Parameters: Input configuration (logged to MLflow at start) -- Metrics: Output results (logged to MLflow at end) -- Fields: Spatial solution data -- TimeSeries: Convergence history +Architecture: Params vs Metrics (following TEMPLATE pattern) + + Params (input/config) Metrics (output/results) + ───────────────────── ──────────────────────── +Global Parameters Metrics + Re, nx, ny, tolerance... wall_time, converged, iterations... + +Timeseries - TimeSeries + residual_history[], energy[]... + +Spatial - Fields + u, v, p arrays on grid """ -from dataclasses import dataclass, asdict +from dataclasses import dataclass, field from typing import Optional, List import numpy as np import pandas as pd -# ======================================================== -# Parameters (Input Configuration) -# ======================================================== +# ============================================================================ +# Parameters (Input Configuration) - logged to MLflow as params +# ============================================================================ @dataclass @@ -36,13 +40,20 @@ class Parameters: tolerance: float = 1e-4 method: str = "" - def to_dataframe(self): - return pd.DataFrame([asdict(self)]) + def to_mlflow(self) -> dict: + """Convert to MLflow-compatible params dict.""" + return { + k: (int(v) if isinstance(v, bool) else v) + for k, v in self.__dict__.items() + } + + def to_dataframe(self) -> pd.DataFrame: + return pd.DataFrame([self.to_mlflow()]) -# ======================================================== -# Metrics (Output Results) -# ======================================================== +# ============================================================================ +# Metrics (Output Results) - logged to MLflow as metrics +# ============================================================================ @dataclass @@ -60,55 +71,80 @@ class Metrics: final_enstrophy: float = 0.0 final_palinstrophy: float = 0.0 - def to_dataframe(self): - return pd.DataFrame([asdict(self)]) + def to_mlflow(self) -> dict: + """Convert to MLflow-compatible dict (bools as int, skip inf).""" + return { + k: (int(v) if isinstance(v, bool) else v) + for k, v in self.__dict__.items() + if v != float("inf") # Skip unset values + } + + def to_dataframe(self) -> pd.DataFrame: + return pd.DataFrame([self.to_mlflow()]) -# ======================================================== -# Fields (Spatial Solution Data) -# ======================================================== +# ============================================================================ +# TimeSeries (Convergence History) - logged to MLflow as step metrics +# ============================================================================ @dataclass -class Fields: - """Spatial solution fields (u, v, p) on grid (x, y).""" +class TimeSeries: + """Convergence history (one value per iteration).""" - u: np.ndarray - v: np.ndarray - p: np.ndarray - x: np.ndarray - y: np.ndarray + rel_iter_residual: List[float] = field(default_factory=list) + u_residual: List[float] = field(default_factory=list) + v_residual: List[float] = field(default_factory=list) + continuity_residual: List[float] = field(default_factory=list) + energy: List[float] = field(default_factory=list) + enstrophy: List[float] = field(default_factory=list) + palinstrophy: List[float] = field(default_factory=list) + + def to_mlflow_batch(self) -> list: + """Convert timeseries to MLflow Metric objects for batch logging.""" + from mlflow.entities import Metric + + return [ + Metric(key=name, value=value, timestamp=0, step=step) + for name, values in self.__dict__.items() + if values # Skip empty lists + for step, value in enumerate(values) + if value is not None + ] def to_dataframe(self) -> pd.DataFrame: - """Convert to DataFrame with one row per grid point.""" - return pd.DataFrame(asdict(self)) + """Convert to DataFrame with one row per iteration.""" + return pd.DataFrame({ + k: v for k, v in self.__dict__.items() if v + }) -# ======================================================== -# Time Series (Convergence History) -# ======================================================== +# ============================================================================ +# Fields (Spatial Solution Data) - saved to HDF5 artifact +# ============================================================================ @dataclass -class TimeSeries: - """Convergence history (one value per iteration).""" +class Fields: + """Spatial solution fields (u, v, p) on grid (x, y).""" - rel_iter_residual: List[float] - u_residual: List[float] - v_residual: List[float] - continuity_residual: Optional[List[float]] - energy: Optional[List[float]] = None - enstrophy: Optional[List[float]] = None - palinstrophy: Optional[List[float]] = None + u: np.ndarray + v: np.ndarray + p: np.ndarray + x: np.ndarray + y: np.ndarray def to_dataframe(self) -> pd.DataFrame: - """Convert to DataFrame with one row per iteration.""" - return pd.DataFrame({k: v for k, v in asdict(self).items() if v is not None}) + """Convert to DataFrame with one row per grid point.""" + return pd.DataFrame({ + "x": self.x, "y": self.y, + "u": self.u, "v": self.v, "p": self.p + }) -# ============================================================= +# ============================================================================ # Finite Volume Specific -# ============================================================ +# ============================================================================ @dataclass @@ -117,9 +153,9 @@ class FVParameters(Parameters): convection_scheme: str = "Upwind" limiter: str = "MUSCL" - alpha_uv: float = 0.6 # velocity under-relaxation - alpha_p: float = 0.4 # pressure under-relaxation - linear_solver_tol: float = 1e-6 # PETSc linear solver tolerance + alpha_uv: float = 0.6 + alpha_p: float = 0.4 + linear_solver_tol: float = 1e-6 method: str = "FV-SIMPLE" @@ -165,24 +201,19 @@ class FVSolverFields: def allocate(cls, n_cells: int, n_faces: int): """Allocate all arrays with proper sizes.""" return cls( - # Current solution u=np.zeros(n_cells), v=np.zeros(n_cells), p=np.zeros(n_cells), mdot=np.zeros(n_faces), - # Previous iteration u_prev=np.zeros(n_cells), v_prev=np.zeros(n_cells), - # Gradient buffers grad_p=np.zeros((n_cells, 2)), grad_u=np.zeros((n_cells, 2)), grad_v=np.zeros((n_cells, 2)), grad_p_prime=np.zeros((n_cells, 2)), - # Face interpolation buffers grad_p_bar=np.zeros((n_faces, 2)), bold_D=np.zeros((n_cells, 2)), bold_D_bar=np.zeros((n_faces, 2)), - # Velocity and flux work buffers U_star_rc=np.zeros((n_faces, 2)), U_prime_face=np.zeros((n_faces, 2)), u_prime=np.zeros(n_cells), @@ -192,18 +223,18 @@ def allocate(cls, n_cells: int, n_faces: int): ) -# ===================================================== +# ============================================================================ # Spectral Specific -# ===================================================== +# ============================================================================ @dataclass class SpectralParameters(Parameters): """Spectral solver parameters (nx/ny = polynomial order N, giving N+1 nodes).""" - basis_type: str = "legendre" # "legendre" or "chebyshev" + basis_type: str = "legendre" CFL: float = 0.1 - beta_squared: float = 5.0 # artificial compressibility + beta_squared: float = 5.0 corner_smoothing: float = 0.15 method: str = "Spectral-AC" @@ -213,8 +244,8 @@ class SpectralSolverFields: """Internal spectral solver arrays - current state and work buffers. Following the PN-PN-2 method: - - Velocities (u, v) live on full (Nx+1) × (Ny+1) grid - - Pressure (p) lives ONLY on inner (Nx-1) × (Ny-1) grid + - Velocities (u, v) live on full (Nx+1) x (Ny+1) grid + - Pressure (p) lives ONLY on inner (Nx-1) x (Ny-1) grid """ # Current solution state - velocities on full grid @@ -231,12 +262,12 @@ class SpectralSolverFields: # RK4 stage buffers u_stage: np.ndarray v_stage: np.ndarray - p_stage: np.ndarray # Inner grid + p_stage: np.ndarray # Residuals - R_u: np.ndarray # Full grid - R_v: np.ndarray # Full grid - R_p: np.ndarray # Inner grid + R_u: np.ndarray + R_v: np.ndarray + R_p: np.ndarray # Derivative buffers (full grid) du_dx: np.ndarray @@ -247,52 +278,36 @@ class SpectralSolverFields: lap_v: np.ndarray # Pressure gradients interpolated to full grid - dp_dx: np.ndarray # Full grid - dp_dy: np.ndarray # Full grid + dp_dx: np.ndarray + dp_dy: np.ndarray # Pressure gradients on inner grid (before interpolation) - dp_dx_inner: np.ndarray # Inner grid - dp_dy_inner: np.ndarray # Inner grid + dp_dx_inner: np.ndarray + dp_dy_inner: np.ndarray @classmethod def allocate(cls, n_nodes_full: int, n_nodes_inner: int): - """Allocate all arrays with proper sizes. - - Parameters - ---------- - n_nodes_full : int - Number of nodes on full (Nx+1) × (Ny+1) grid - n_nodes_inner : int - Number of nodes on inner (Nx-1) × (Ny-1) grid - """ + """Allocate all arrays with proper sizes.""" return cls( - # Current solution - velocities on full grid u=np.zeros(n_nodes_full), v=np.zeros(n_nodes_full), - # Pressure on INNER grid only (PN-PN-2) p=np.zeros(n_nodes_inner), - # Previous iteration u_prev=np.zeros(n_nodes_full), v_prev=np.zeros(n_nodes_full), - # RK4 stage buffers u_stage=np.zeros(n_nodes_full), v_stage=np.zeros(n_nodes_full), - p_stage=np.zeros(n_nodes_inner), # Inner grid! - # Residuals + p_stage=np.zeros(n_nodes_inner), R_u=np.zeros(n_nodes_full), R_v=np.zeros(n_nodes_full), - R_p=np.zeros(n_nodes_inner), # Inner grid! - # Derivative buffers (full grid) + R_p=np.zeros(n_nodes_inner), du_dx=np.zeros(n_nodes_full), du_dy=np.zeros(n_nodes_full), dv_dx=np.zeros(n_nodes_full), dv_dy=np.zeros(n_nodes_full), lap_u=np.zeros(n_nodes_full), lap_v=np.zeros(n_nodes_full), - # Pressure gradients on full grid (interpolated) dp_dx=np.zeros(n_nodes_full), dp_dy=np.zeros(n_nodes_full), - # Pressure gradients on inner grid (before interpolation) dp_dx_inner=np.zeros(n_nodes_inner), dp_dy_inner=np.zeros(n_nodes_inner), ) From 2835534946afe2293747b5fcfa41c95c86d513a9 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 11:33:33 +0100 Subject: [PATCH 05/34] coolify --- README.md | 41 +++++++++++++++++++++++++++----- conf/config.yaml | 2 +- conf/mlflow/coolify.yaml | 5 ++++ conf/mlflow/databricks.yaml | 4 ---- conf/mlflow/local-docker.yaml | 5 ++++ conf/mlflow/local-files.yaml | 5 ++++ conf/mlflow/local.yaml | 4 ---- mlflow-server/.gitignore | 1 + mlflow-server/docker-compose.yml | 17 +++++++++++++ run_solver.py | 12 +++++----- 10 files changed, 75 insertions(+), 21 deletions(-) create mode 100644 conf/mlflow/coolify.yaml delete mode 100644 conf/mlflow/databricks.yaml create mode 100644 conf/mlflow/local-docker.yaml create mode 100644 conf/mlflow/local-files.yaml delete mode 100644 conf/mlflow/local.yaml create mode 100644 mlflow-server/.gitignore create mode 100644 mlflow-server/docker-compose.yml diff --git a/README.md b/README.md index 6de49b5..edb94e6 100644 --- a/README.md +++ b/README.md @@ -69,8 +69,9 @@ conf/ │ ├── fv_validation.yaml # FV benchmark settings │ └── spectral_validation.yaml └── mlflow/ - ├── local.yaml # Local file-based tracking - └── databricks.yaml # Cloud tracking + ├── local-files.yaml # File-based tracking (default) + ├── local-docker.yaml # Local Docker server + └── coolify.yaml # Remote server (Coolify) ``` ### Output Structure @@ -85,21 +86,49 @@ outputs/{experiment_name}/Re{Re}/{solver}/{DD-MM-YY}/{HH:MM:SS}/ └── .hydra/ # Hydra internals ``` -## Viewing Results +## MLflow -### MLflow UI +Results are tracked with [MLflow](https://mlflow.org/). Three tracking modes are available: + +### Local Files (Default) + +File-based tracking in `./mlruns` - no setup required: ```bash +uv run python run_solver.py solver=fv mlflow=local-files + +# View UI uv run mlflow ui --backend-store-uri ./mlruns --port 5001 ``` -Then open http://localhost:5001 +### Local Docker + +Run the official MLflow server in Docker: + +```bash +# Start server +cd mlflow-server && docker compose up -d + +# Run solver +uv run python run_solver.py solver=fv mlflow=local-docker + +# View UI at http://localhost:5001 +``` + +### Remote Server (Coolify) + +Deploy `mlflow-server/docker-compose.yml` to Coolify, then: + +```bash +export MLFLOW_TRACKING_URI=https://mlflow.yourdomain.com +uv run python run_solver.py solver=fv mlflow=coolify +``` ### Logged Data - **Parameters:** solver settings, grid size, Reynolds number - **Metrics:** iterations, convergence, wall time, residuals -- **Timeseries:** residual history, energy, enstrophy (with step) +- **Timeseries:** residual history, energy, enstrophy (per iteration) - **Artifacts:** HDF5 result files ## References diff --git a/conf/config.yaml b/conf/config.yaml index 36f88eb..0ea68a6 100644 --- a/conf/config.yaml +++ b/conf/config.yaml @@ -4,7 +4,7 @@ defaults: - solver: fv - - mlflow: local + - mlflow: local-files - _self_ # ============================================================================= diff --git a/conf/mlflow/coolify.yaml b/conf/mlflow/coolify.yaml new file mode 100644 index 0000000..1492923 --- /dev/null +++ b/conf/mlflow/coolify.yaml @@ -0,0 +1,5 @@ +# Coolify MLflow (same docker-compose deployed remotely) +# Set MLFLOW_TRACKING_URI environment variable to your Coolify URL +mode: coolify +tracking_uri: ${oc.env:MLFLOW_TRACKING_URI,http://localhost:5000} +project_prefix: "" diff --git a/conf/mlflow/databricks.yaml b/conf/mlflow/databricks.yaml deleted file mode 100644 index 60647ca..0000000 --- a/conf/mlflow/databricks.yaml +++ /dev/null @@ -1,4 +0,0 @@ -# Databricks MLflow configuration -mode: databricks -tracking_uri: databricks -project_prefix: /Shared/ANA-P3 diff --git a/conf/mlflow/local-docker.yaml b/conf/mlflow/local-docker.yaml new file mode 100644 index 0000000..8e8bf9a --- /dev/null +++ b/conf/mlflow/local-docker.yaml @@ -0,0 +1,5 @@ +# Local Docker MLflow (official image with SQLite + local artifacts) +# Start with: cd mlflow-server && docker compose up -d +mode: docker +tracking_uri: http://localhost:5001 +project_prefix: "" diff --git a/conf/mlflow/local-files.yaml b/conf/mlflow/local-files.yaml new file mode 100644 index 0000000..9fc8793 --- /dev/null +++ b/conf/mlflow/local-files.yaml @@ -0,0 +1,5 @@ +# Local file-based MLflow (no docker required) +# Results stored in ./mlruns directory +mode: files +tracking_uri: ./mlruns +project_prefix: "" diff --git a/conf/mlflow/local.yaml b/conf/mlflow/local.yaml deleted file mode 100644 index 1e0bc08..0000000 --- a/conf/mlflow/local.yaml +++ /dev/null @@ -1,4 +0,0 @@ -# Local MLflow configuration (file-based tracking) -mode: local -tracking_uri: ./mlruns -project_prefix: "" diff --git a/mlflow-server/.gitignore b/mlflow-server/.gitignore new file mode 100644 index 0000000..8fce603 --- /dev/null +++ b/mlflow-server/.gitignore @@ -0,0 +1 @@ +data/ diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml new file mode 100644 index 0000000..661f4c3 --- /dev/null +++ b/mlflow-server/docker-compose.yml @@ -0,0 +1,17 @@ +services: + mlflow: + image: ghcr.io/mlflow/mlflow:latest + container_name: mlflow-server + restart: unless-stopped + ports: + - "5001:5000" + volumes: + - ./data:/data + command: > + mlflow server + --host 0.0.0.0 + --port 5000 + --backend-store-uri sqlite:///data/mlflow.db + --default-artifact-root mlflow-artifacts:/ + --artifacts-destination /data/artifacts + --serve-artifacts diff --git a/run_solver.py b/run_solver.py index b74588e..98a4a7b 100644 --- a/run_solver.py +++ b/run_solver.py @@ -8,8 +8,12 @@ # Spectral solver (N=15 gives 16 nodes) uv run python run_solver.py solver=spectral N=15 Re=100 max_iterations=100 - # With Databricks MLflow - uv run python run_solver.py solver=fv mlflow=databricks + # MLflow modes: + # local-files - file-based ./mlruns (default, no docker) + # local-docker - local docker-compose (cd mlflow-server && docker compose up -d) + # coolify - remote server (set MLFLOW_TRACKING_URI, AWS credentials) + uv run python run_solver.py solver=fv mlflow=local-docker + uv run python run_solver.py solver=fv mlflow=coolify # Parameter sweep uv run python run_solver.py -m solver=fv N=16,32,64 Re=100,400 @@ -90,10 +94,6 @@ def setup_mlflow(cfg: DictConfig) -> str: if project_prefix and not experiment_name.startswith("/"): experiment_name = f"{project_prefix}/{experiment_name}" - # Databricks login if needed - if cfg.mlflow.mode == "databricks": - mlflow.login() - mlflow.set_experiment(experiment_name) return experiment_name From 40fe790d85735b810db41f281ee5f8d322f736b2 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 11:42:11 +0100 Subject: [PATCH 06/34] u --- .gitignore | 1 + .gitmodules | 3 --- 2 files changed, 1 insertion(+), 3 deletions(-) delete mode 100644 .gitmodules diff --git a/.gitignore b/.gitignore index dd0b999..046e031 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ # Generated figures figures/ # Report +*/TexReport # Generated data (keep README.md) data/* diff --git a/.gitmodules b/.gitmodules deleted file mode 100644 index aa9a740..0000000 --- a/.gitmodules +++ /dev/null @@ -1,3 +0,0 @@ -[submodule "TexReport"] - path = docs/reports/TexReport - url = https://git@git.overleaf.com/691594f69e0bc9dddedceb95 From ecd34833bc9aad41be2396ce978f8ca0abd2a898 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 11:42:26 +0100 Subject: [PATCH 07/34] u --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 046e031..d2c0b3d 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,7 @@ # Generated figures figures/ # Report -*/TexReport +docs/reports/TexReport # Generated data (keep README.md) data/* From aca795fd234f3c1ec63c7e78e57e583e849f2188 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 11:50:44 +0100 Subject: [PATCH 08/34] u --- mlflow-server/docker-compose.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml index 661f4c3..fd5f0e4 100644 --- a/mlflow-server/docker-compose.yml +++ b/mlflow-server/docker-compose.yml @@ -15,3 +15,4 @@ services: --default-artifact-root mlflow-artifacts:/ --artifacts-destination /data/artifacts --serve-artifacts + --allowed-hosts '*' From 54bdb422ba7a6950df05db79c4ecd0c693bc9cca Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 12:04:57 +0100 Subject: [PATCH 09/34] u --- conf/mlflow/coolify.yaml | 4 ++-- mlflow-server/docker-compose.yml | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/conf/mlflow/coolify.yaml b/conf/mlflow/coolify.yaml index 1492923..1ee8058 100644 --- a/conf/mlflow/coolify.yaml +++ b/conf/mlflow/coolify.yaml @@ -1,5 +1,5 @@ # Coolify MLflow (same docker-compose deployed remotely) -# Set MLFLOW_TRACKING_URI environment variable to your Coolify URL +# Required: set MLFLOW_TRACKING_URI environment variable mode: coolify -tracking_uri: ${oc.env:MLFLOW_TRACKING_URI,http://localhost:5000} +tracking_uri: ${oc.env:MLFLOW_TRACKING_URI} project_prefix: "" diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml index fd5f0e4..53cfda7 100644 --- a/mlflow-server/docker-compose.yml +++ b/mlflow-server/docker-compose.yml @@ -16,3 +16,4 @@ services: --artifacts-destination /data/artifacts --serve-artifacts --allowed-hosts '*' + --app-name basic-auth From c17aade41c3da97a4800381830c7d40e24346091 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 12:07:17 +0100 Subject: [PATCH 10/34] u --- mlflow-server/Dockerfile | 3 +++ mlflow-server/docker-compose.yml | 2 +- 2 files changed, 4 insertions(+), 1 deletion(-) create mode 100644 mlflow-server/Dockerfile diff --git a/mlflow-server/Dockerfile b/mlflow-server/Dockerfile new file mode 100644 index 0000000..00fa575 --- /dev/null +++ b/mlflow-server/Dockerfile @@ -0,0 +1,3 @@ +FROM ghcr.io/mlflow/mlflow:latest + +RUN pip install --no-cache-dir mlflow[auth] diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml index 53cfda7..2660e0d 100644 --- a/mlflow-server/docker-compose.yml +++ b/mlflow-server/docker-compose.yml @@ -1,6 +1,6 @@ services: mlflow: - image: ghcr.io/mlflow/mlflow:latest + build: . container_name: mlflow-server restart: unless-stopped ports: From 1167b35b28fd4d6cb44927747db7caecc30f949b Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 12:09:21 +0100 Subject: [PATCH 11/34] u --- mlflow-server/docker-compose.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml index 2660e0d..bcc0021 100644 --- a/mlflow-server/docker-compose.yml +++ b/mlflow-server/docker-compose.yml @@ -1,6 +1,8 @@ services: mlflow: - build: . + build: + context: . + dockerfile: Dockerfile container_name: mlflow-server restart: unless-stopped ports: From 93ba4c7990166d2b11700b06e57260c7645bf8e9 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 12:10:33 +0100 Subject: [PATCH 12/34] u --- mlflow-server/docker-compose.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml index bcc0021..1d10d49 100644 --- a/mlflow-server/docker-compose.yml +++ b/mlflow-server/docker-compose.yml @@ -1,7 +1,7 @@ services: mlflow: build: - context: . + context: ./mlflow-server dockerfile: Dockerfile container_name: mlflow-server restart: unless-stopped From eaf68b66933b5d02e24c4e9a39967fbaf59bfdbb Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 12:12:45 +0100 Subject: [PATCH 13/34] u --- mlflow-server/docker-compose.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml index 1d10d49..f17edb9 100644 --- a/mlflow-server/docker-compose.yml +++ b/mlflow-server/docker-compose.yml @@ -9,6 +9,8 @@ services: - "5001:5000" volumes: - ./data:/data + environment: + - MLFLOW_FLASK_SERVER_SECRET_KEY=${SERVICE_PASSWORD_MLFLOW:-defaultsecret123} command: > mlflow server --host 0.0.0.0 From 490db0c7cf8af773b4afd7c574355e989da6c5b4 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 12:24:52 +0100 Subject: [PATCH 14/34] u --- README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/README.md b/README.md index edb94e6..d28c4fa 100644 --- a/README.md +++ b/README.md @@ -121,9 +121,20 @@ Deploy `mlflow-server/docker-compose.yml` to Coolify, then: ```bash export MLFLOW_TRACKING_URI=https://mlflow.yourdomain.com +export MLFLOW_TRACKING_USERNAME=admin +export MLFLOW_TRACKING_PASSWORD=yourpassword uv run python run_solver.py solver=fv mlflow=coolify ``` +**Authentication:** The server uses MLflow's built-in basic auth. Default credentials are `admin` / `password1234`. Change the password after first login via the UI or API: + +```bash +curl -X PATCH -u admin:password1234 \ + "$MLFLOW_TRACKING_URI/api/2.0/mlflow/users/update-password" \ + -H "Content-Type: application/json" \ + -d '{"username": "admin", "password": "new-secure-password"}' +``` + ### Logged Data - **Parameters:** solver settings, grid size, Reynolds number From 47490a97cd02d29a7e655d7d67ccb9fa6c46f9c0 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 12:32:50 +0100 Subject: [PATCH 15/34] uuuu --- mlflow-server/docker-compose.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml index f17edb9..dd16d42 100644 --- a/mlflow-server/docker-compose.yml +++ b/mlflow-server/docker-compose.yml @@ -8,7 +8,7 @@ services: ports: - "5001:5000" volumes: - - ./data:/data + - mlflow-data:/data environment: - MLFLOW_FLASK_SERVER_SECRET_KEY=${SERVICE_PASSWORD_MLFLOW:-defaultsecret123} command: > @@ -21,3 +21,6 @@ services: --serve-artifacts --allowed-hosts '*' --app-name basic-auth + +volumes: + mlflow-data: From 67cca7302b8e0986a3dae61d8d8a27578cc64cfe Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 13:39:33 +0100 Subject: [PATCH 16/34] better workflow --- .env.template | 6 +++ .gitignore | 1 + README.md | 26 ++++----- conf/mlflow/local-docker.yaml | 5 -- conf/mlflow/{local-files.yaml => local.yaml} | 0 main.py | 57 ++++++++++---------- mlflow-server/.gitignore | 1 - mlflow-server/Dockerfile | 3 -- mlflow-server/docker-compose.yml | 26 --------- pyproject.toml | 1 + run_solver.py | 11 +++- 11 files changed, 56 insertions(+), 81 deletions(-) create mode 100644 .env.template delete mode 100644 conf/mlflow/local-docker.yaml rename conf/mlflow/{local-files.yaml => local.yaml} (100%) delete mode 100644 mlflow-server/.gitignore delete mode 100644 mlflow-server/Dockerfile delete mode 100644 mlflow-server/docker-compose.yml diff --git a/.env.template b/.env.template new file mode 100644 index 0000000..1d1b2c2 --- /dev/null +++ b/.env.template @@ -0,0 +1,6 @@ +# MLflow credentials - copy to .env and fill in values +# cp .env.template .env + +MLFLOW_TRACKING_URI=https://your-mlflow-server.com +MLFLOW_TRACKING_USERNAME=admin +MLFLOW_TRACKING_PASSWORD=your-password-here diff --git a/.gitignore b/.gitignore index d2c0b3d..ceb91f6 100644 --- a/.gitignore +++ b/.gitignore @@ -12,6 +12,7 @@ data/* outputs/ TEMPLATE/ mlruns/ +mlflow_ui.log # Uv stuff uv.lock diff --git a/README.md b/README.md index d28c4fa..4ee8466 100644 --- a/README.md +++ b/README.md @@ -103,36 +103,30 @@ uv run mlflow ui --backend-store-uri ./mlruns --port 5001 ### Local Docker -Run the official MLflow server in Docker: +Clone and run [mlflow-server](https://github.com/philipnickel/mlflow-server): ```bash -# Start server +# Clone and start server +git clone https://github.com/philipnickel/mlflow-server.git cd mlflow-server && docker compose up -d # Run solver uv run python run_solver.py solver=fv mlflow=local-docker -# View UI at http://localhost:5001 +# View UI at http://localhost:5001 (admin / password1234) ``` ### Remote Server (Coolify) -Deploy `mlflow-server/docker-compose.yml` to Coolify, then: +Deploy [mlflow-server](https://github.com/philipnickel/mlflow-server) to Coolify. The admin password is auto-generated - find it in Coolify's environment variables (`SERVICE_PASSWORD_64_MLFLOW_ADMIN`). ```bash -export MLFLOW_TRACKING_URI=https://mlflow.yourdomain.com -export MLFLOW_TRACKING_USERNAME=admin -export MLFLOW_TRACKING_PASSWORD=yourpassword -uv run python run_solver.py solver=fv mlflow=coolify -``` - -**Authentication:** The server uses MLflow's built-in basic auth. Default credentials are `admin` / `password1234`. Change the password after first login via the UI or API: +# Setup credentials (one-time) +cp .env.template .env +# Edit .env with your credentials -```bash -curl -X PATCH -u admin:password1234 \ - "$MLFLOW_TRACKING_URI/api/2.0/mlflow/users/update-password" \ - -H "Content-Type: application/json" \ - -d '{"username": "admin", "password": "new-secure-password"}' +# Run solver +uv run python run_solver.py solver=fv mlflow=coolify ``` ### Logged Data diff --git a/conf/mlflow/local-docker.yaml b/conf/mlflow/local-docker.yaml deleted file mode 100644 index 8e8bf9a..0000000 --- a/conf/mlflow/local-docker.yaml +++ /dev/null @@ -1,5 +0,0 @@ -# Local Docker MLflow (official image with SQLite + local artifacts) -# Start with: cd mlflow-server && docker compose up -d -mode: docker -tracking_uri: http://localhost:5001 -project_prefix: "" diff --git a/conf/mlflow/local-files.yaml b/conf/mlflow/local.yaml similarity index 100% rename from conf/mlflow/local-files.yaml rename to conf/mlflow/local.yaml diff --git a/main.py b/main.py index da5487a..f840a74 100644 --- a/main.py +++ b/main.py @@ -73,7 +73,7 @@ def main(): actions.add_argument("--copy-plots", action="store_true", help="Copy plots to report directory") actions.add_argument("--clean", action="store_true", help="Clean all generated files and caches") actions.add_argument("--setup-mlflow", action="store_true", help="Interactive MLflow setup (login to Databricks)") - actions.add_argument("--mlflow-ui", action="store_true", help="Start MLflow UI and open in browser") + actions.add_argument("--mlflow-ui", action="store_true", help="Start local MLflow UI (./mlruns)") if len(sys.argv) == 1: parser.print_help() @@ -100,38 +100,37 @@ def main(): runners.copy_to_report() if args.mlflow_ui: + import socket import subprocess + import threading import webbrowser - import time - print("\nStarting MLflow UI...") + def is_port_free(port): + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + return s.connect_ex(('localhost', port)) != 0 + + # Find available port + port = 5001 + while not is_port_free(port) and port < 5010: + port += 1 + + url = f"http://localhost:{port}" + print(f"\nStarting MLflow UI at {url}") + print("Press Ctrl+C to stop\n") + + # Open browser after short delay + def open_browser(): + import time + time.sleep(2) + webbrowser.open(url) + + threading.Thread(target=open_browser, daemon=True).start() + + # Run in foreground (blocks until Ctrl+C) try: - # Default to standard file-based backend (./mlruns) - cmd = ["uv", "run", "mlflow", "ui"] - - # Start MLflow UI in the background - log_file = open("mlflow_ui.log", "w") - mlflow_process = subprocess.Popen( - cmd, - stdout=log_file, - stderr=subprocess.STDOUT, - preexec_fn=os.setsid # Detach process to run in background - ) - print(f"MLflow UI started in background with PID: {mlflow_process.pid}") - print(f"Logs redirected to: mlflow_ui.log") - - # Give MLflow UI some time to start up - time.sleep(3) - - # Open in browser - url = "http://localhost:5000" - webbrowser.open_new_tab(url) - print(f"Opening MLflow UI in browser: {url}") - print("Press Ctrl+C to stop MLflow UI process later if it's still running.") - except FileNotFoundError: - print("Error: 'uv' command not found. Ensure uv is installed and in PATH.") - except Exception as e: - print(f"Error starting MLflow UI: {e}") + subprocess.run(["uv", "run", "mlflow", "ui", "--port", str(port)]) + except KeyboardInterrupt: + print("\nMLflow UI stopped.") if args.docs: if not build_docs(): diff --git a/mlflow-server/.gitignore b/mlflow-server/.gitignore deleted file mode 100644 index 8fce603..0000000 --- a/mlflow-server/.gitignore +++ /dev/null @@ -1 +0,0 @@ -data/ diff --git a/mlflow-server/Dockerfile b/mlflow-server/Dockerfile deleted file mode 100644 index 00fa575..0000000 --- a/mlflow-server/Dockerfile +++ /dev/null @@ -1,3 +0,0 @@ -FROM ghcr.io/mlflow/mlflow:latest - -RUN pip install --no-cache-dir mlflow[auth] diff --git a/mlflow-server/docker-compose.yml b/mlflow-server/docker-compose.yml deleted file mode 100644 index dd16d42..0000000 --- a/mlflow-server/docker-compose.yml +++ /dev/null @@ -1,26 +0,0 @@ -services: - mlflow: - build: - context: ./mlflow-server - dockerfile: Dockerfile - container_name: mlflow-server - restart: unless-stopped - ports: - - "5001:5000" - volumes: - - mlflow-data:/data - environment: - - MLFLOW_FLASK_SERVER_SECRET_KEY=${SERVICE_PASSWORD_MLFLOW:-defaultsecret123} - command: > - mlflow server - --host 0.0.0.0 - --port 5000 - --backend-store-uri sqlite:///data/mlflow.db - --default-artifact-root mlflow-artifacts:/ - --artifacts-destination /data/artifacts - --serve-artifacts - --allowed-hosts '*' - --app-name basic-auth - -volumes: - mlflow-data: diff --git a/pyproject.toml b/pyproject.toml index 2fb4cde..e43494c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -50,6 +50,7 @@ dependencies = [ "omegaconf>=2.3.0", "textual>=6.6.0", "blessed>=1.25.0", + "python-dotenv>=1.2.1", ] [tool.setuptools] diff --git a/run_solver.py b/run_solver.py index 98a4a7b..fdf613e 100644 --- a/run_solver.py +++ b/run_solver.py @@ -11,18 +11,27 @@ # MLflow modes: # local-files - file-based ./mlruns (default, no docker) # local-docker - local docker-compose (cd mlflow-server && docker compose up -d) - # coolify - remote server (set MLFLOW_TRACKING_URI, AWS credentials) + # coolify - remote server (requires .env with credentials) uv run python run_solver.py solver=fv mlflow=local-docker uv run python run_solver.py solver=fv mlflow=coolify # Parameter sweep uv run python run_solver.py -m solver=fv N=16,32,64 Re=100,400 + +Setup for remote MLflow: + cp .env.template .env + # Edit .env with your credentials """ import logging import os from pathlib import Path +from dotenv import load_dotenv + +# Load .env file (for MLflow credentials) +load_dotenv() + import hydra import mlflow from hydra.core.hydra_config import HydraConfig From 1529f9c17943672a8c4557e32a4ab319dfac8135 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 13:43:56 +0100 Subject: [PATCH 17/34] no local files in data/ --- run_solver.py | 35 ++++++++++++++++------------------- 1 file changed, 16 insertions(+), 19 deletions(-) diff --git a/run_solver.py b/run_solver.py index fdf613e..f95f01d 100644 --- a/run_solver.py +++ b/run_solver.py @@ -9,10 +9,9 @@ uv run python run_solver.py solver=spectral N=15 Re=100 max_iterations=100 # MLflow modes: - # local-files - file-based ./mlruns (default, no docker) - # local-docker - local docker-compose (cd mlflow-server && docker compose up -d) + # local-files - file-based ./mlruns (default) # coolify - remote server (requires .env with credentials) - uv run python run_solver.py solver=fv mlflow=local-docker + uv run python run_solver.py solver=fv mlflow=local-files uv run python run_solver.py solver=fv mlflow=coolify # Parameter sweep @@ -34,7 +33,6 @@ import hydra import mlflow -from hydra.core.hydra_config import HydraConfig from mlflow.tracking import MlflowClient from omegaconf import DictConfig, OmegaConf @@ -124,8 +122,10 @@ def log_metrics_and_timeseries(solver, run_id: str): MlflowClient().log_batch(run_id=run_id, metrics=batch_metrics) -def save_and_log_artifact(solver, cfg: DictConfig, output_dir: str): - """Save solver output to HDF5 and log as MLflow artifact.""" +def log_artifact(solver, cfg: DictConfig): + """Save solver output to temp HDF5 and log as MLflow artifact.""" + import tempfile + # Determine output filename solver_name = cfg.solver.name.upper() if solver_name == "SPECTRAL": @@ -134,11 +134,12 @@ def save_and_log_artifact(solver, cfg: DictConfig, output_dir: str): else: filename = f"LDC_{solver_name}_N{cfg.N}_Re{int(cfg.Re)}.h5" - output_path = Path(output_dir) / filename - solver.save(output_path) - log.info(f"Saved results to: {output_path}") - - mlflow.log_artifact(str(output_path)) + # Save to temp file, log to MLflow, then cleanup + with tempfile.TemporaryDirectory() as tmpdir: + output_path = Path(tmpdir) / filename + solver.save(output_path) + mlflow.log_artifact(str(output_path)) + log.info(f"Logged artifact: {filename}") # ============================================================================= @@ -149,14 +150,7 @@ def save_and_log_artifact(solver, cfg: DictConfig, output_dir: str): @hydra.main(config_path="conf", config_name="config", version_base=None) def main(cfg: DictConfig) -> None: """Hydra entry point - runs solver with MLflow tracking.""" - output_dir = HydraConfig.get().runtime.output_dir - - # Log resolved config log.info(f"Solver: {cfg.solver.name}, N={cfg.N}, Re={cfg.Re}") - log.info(f"Output dir: {output_dir}") - - # Save resolved config - OmegaConf.save(cfg, Path(output_dir) / "config.yaml") # Setup MLflow experiment_name = setup_mlflow(cfg) @@ -176,6 +170,9 @@ def main(cfg: DictConfig) -> None: with mlflow.start_run(run_name=run_name) as run: log_params(solver) + # Log Hydra config as artifact + mlflow.log_dict(OmegaConf.to_container(cfg), "config.yaml") + # Tag with HPC job info if available job_id = os.environ.get("LSB_JOBID") if job_id: @@ -188,7 +185,7 @@ def main(cfg: DictConfig) -> None: # Log results log_metrics_and_timeseries(solver, run.info.run_id) - save_and_log_artifact(solver, cfg, output_dir) + log_artifact(solver, cfg) log.info( f"Done: {solver.metrics.iterations} iter, " From 3c2bfef85649350c75abea26a392a3d8efec87e4 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 15:57:53 +0100 Subject: [PATCH 18/34] ready for spectral multigrid I think! --- .gitignore | 2 +- Experiments/Solution/README.md | 52 ++ Experiments/Solution/plot_solution.py | 804 +++++++++++++++++++++++ Experiments/plot_kernels.py | 231 ------- README.md | 81 ++- conf/config.yaml | 15 +- conf/experiment/fv_validation.yaml | 5 +- conf/experiment/plotting_test.yaml | 11 + conf/experiment/quick_test.yaml | 1 + conf/experiment/spectral_validation.yaml | 5 +- conf/experiment/sweep_test.yaml | 10 + conf/hydra/launcher/joblib.yaml | 14 + docs/source/api/base_solver.rst | 6 +- docs/source/api/datastructures.rst | 6 +- docs/source/api/fv_solver.rst | 6 +- docs/source/api/{ldc.rst => solvers.rst} | 6 +- docs/source/api/spectral_solver.rst | 8 +- docs/source/api_reference.rst | 4 +- docs/source/conf.py | 10 +- docs/source/configuration.rst | 259 ++++++++ docs/source/hpc_guide.rst | 96 --- docs/source/index.rst | 18 +- docs/source/usage.rst | 221 +++++++ jobs/README.md | 56 -- jobs/comm_compact.sh | 41 -- jobs/comm_spread.sh | 41 -- jobs/scaling.sh | 81 --- jobs/weak_scaling_debug.sh | 43 -- jobs/weak_scaling_v3.sh | 67 -- jobs/weak_scaling_v4.sh | 73 -- pyproject.toml | 2 + run_solver.py | 43 +- spectral_multigrid_todo.md | 296 +++++++-- src/utils/mlflow_callback.py | 127 ++++ 34 files changed, 1858 insertions(+), 883 deletions(-) create mode 100644 Experiments/Solution/README.md create mode 100644 Experiments/Solution/plot_solution.py delete mode 100644 Experiments/plot_kernels.py create mode 100644 conf/experiment/plotting_test.yaml create mode 100644 conf/experiment/sweep_test.yaml create mode 100644 conf/hydra/launcher/joblib.yaml rename docs/source/api/{ldc.rst => solvers.rst} (63%) create mode 100644 docs/source/configuration.rst delete mode 100644 docs/source/hpc_guide.rst create mode 100644 docs/source/usage.rst delete mode 100644 jobs/README.md delete mode 100644 jobs/comm_compact.sh delete mode 100644 jobs/comm_spread.sh delete mode 100644 jobs/scaling.sh delete mode 100644 jobs/weak_scaling_debug.sh delete mode 100644 jobs/weak_scaling_v3.sh delete mode 100644 jobs/weak_scaling_v4.sh create mode 100644 src/utils/mlflow_callback.py diff --git a/.gitignore b/.gitignore index ceb91f6..c20691d 100644 --- a/.gitignore +++ b/.gitignore @@ -9,7 +9,7 @@ data/* !data/README.md !data/.gitkeep !data/validation/ -outputs/ +hydra_outputs/ TEMPLATE/ mlruns/ mlflow_ui.log diff --git a/Experiments/Solution/README.md b/Experiments/Solution/README.md new file mode 100644 index 0000000..6a1ac02 --- /dev/null +++ b/Experiments/Solution/README.md @@ -0,0 +1,52 @@ +# Solution Plotting Experiment + +Generate publication-quality plots from MLflow solver runs. Uses the **same config** as `run_solver.py` to automatically find matching runs. + +## Usage + +```bash +# Run solver first +uv run python run_solver.py solver=spectral N=31 Re=100 + +# Plot with same config - finds matching run automatically! +uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 + +# With Ghia benchmark validation +uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 plots.ghia_validation=true + +# FV solver example +uv run python Experiments/Solution/plot_solution.py solver=fv N=32 Re=400 + +# Use remote MLflow +uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 mlflow=coolify + +# Disable upload back to MLflow +uv run python Experiments/Solution/plot_solution.py solver=fv N=64 Re=1000 upload_to_mlflow=false +``` + +## How It Works + +1. Takes same Hydra config as `run_solver.py` +2. Queries MLflow for runs matching: `solver`, `N`, `Re` +3. Downloads solution artifacts (zarr fields) +4. Generates plots +5. Uploads plots back to the same MLflow run + +## Generated Plots + +| Plot | Description | Enable | +|------|-------------|--------| +| `fields.png` | Pressure, U, V velocity contours | `plots.fields=true` (default) | +| `streamlines.png` | Velocity magnitude with streamlines | `plots.streamlines=true` (default) | +| `vorticity.png` | Vorticity contour | `plots.vorticity=true` (default) | +| `centerlines.png` | U(y) and V(x) profiles | `plots.centerlines=true` (default) | +| `ghia_validation.png` | Ghia et al. (1982) comparison | `plots.ghia_validation=true` | + +## Ghia Benchmark + +Available Reynolds numbers: `Re ∈ {100, 400, 1000, 3200, 5000, 7500, 10000}` + +## Output + +- Plots saved to Hydra output directory +- Uploaded to MLflow run under `artifacts/plots/` diff --git a/Experiments/Solution/plot_solution.py b/Experiments/Solution/plot_solution.py new file mode 100644 index 0000000..94813ab --- /dev/null +++ b/Experiments/Solution/plot_solution.py @@ -0,0 +1,804 @@ +""" +Solution Plotter - Automatically finds and plots MLflow runs matching config. + +Uses the same Hydra config as run_solver.py to find matching runs. + +For individual runs: generates fields, streamlines, vorticity, centerlines, + ghia_validation, and convergence plots. + +For sweeps: additionally generates a Ghia comparison plot uploaded to parent run. + +Usage: + # Plot single run + uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 + + # Plot sweep (comparison plot goes to parent) + uv run python Experiments/Solution/plot_solution.py -m solver=spectral N=15,31,63 Re=100 + + # Use remote MLflow + uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 mlflow=coolify +""" + +import logging +import sys +import tempfile +from pathlib import Path +from typing import Optional + +# Add src to path for imports +sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) + +from dotenv import load_dotenv + +load_dotenv() + +import hydra +import mlflow +import zarr +import numpy as np +import pandas as pd +from omegaconf import DictConfig, OmegaConf + +log = logging.getLogger(__name__) + + +# ============================================================================= +# MLflow Run Discovery +# ============================================================================= + + +def find_matching_run(cfg: DictConfig, tracking_uri: str) -> tuple[str, Optional[str]]: + """Find MLflow run matching the config parameters. + + Returns + ------- + tuple[str, Optional[str]] + (run_id, parent_run_id) - parent_run_id is None if not a sweep child + """ + mlflow.set_tracking_uri(tracking_uri) + client = mlflow.tracking.MlflowClient() + + # Get experiment + experiment_name = cfg.experiment_name + project_prefix = cfg.mlflow.get("project_prefix", "") + if project_prefix and not experiment_name.startswith("/"): + experiment_name = f"{project_prefix}/{experiment_name}" + + experiment = client.get_experiment_by_name(experiment_name) + if experiment is None: + raise ValueError(f"Experiment not found: {experiment_name}") + + # Build filter string for matching runs + solver_name = cfg.solver.name + N = cfg.N + Re = cfg.Re + + filter_parts = [ + f"params.Re = '{Re}'", + f"params.nx = '{N}'", + f"params.ny = '{N}'", + "attributes.status = 'FINISHED'", + ] + + # Add solver-specific filter + if solver_name == "spectral": + filter_parts.append(f"params.basis_type = '{cfg.solver.basis_type}'") + elif solver_name == "fv": + filter_parts.append(f"params.convection_scheme = '{cfg.solver.convection_scheme}'") + + filter_string = " AND ".join(filter_parts) + + log.info(f"Searching in experiment: {experiment_name}") + log.info(f"Filter: solver={solver_name}, N={N}, Re={Re}") + + # Search for runs + runs = client.search_runs( + experiment_ids=[experiment.experiment_id], + filter_string=filter_string, + order_by=["attributes.start_time DESC"], + max_results=10, + ) + + if not runs: + raise ValueError( + f"No matching runs found for solver={solver_name}, N={N}, Re={Re}\n" + f"Filter used: {filter_string}" + ) + + # Return most recent matching run + run = runs[0] + parent_run_id = run.data.tags.get("parent_run_id") + + log.info(f"Found run: {run.info.run_name} (id: {run.info.run_id[:8]}...)") + if parent_run_id: + log.info(f" Parent run: {parent_run_id[:8]}...") + + return run.info.run_id, parent_run_id + + +def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: + """Find all child runs of a parent (siblings in a sweep). + + Returns list of dicts with run info for comparison plotting. + """ + mlflow.set_tracking_uri(tracking_uri) + client = mlflow.tracking.MlflowClient() + + # Get parent run to find experiment + parent_run = client.get_run(parent_run_id) + experiment_id = parent_run.info.experiment_id + + # Find all children of this parent + filter_string = f"tags.parent_run_id = '{parent_run_id}' AND attributes.status = 'FINISHED'" + + runs = client.search_runs( + experiment_ids=[experiment_id], + filter_string=filter_string, + order_by=["params.nx ASC"], # Sort by N for nice legend order + max_results=50, + ) + + siblings = [] + for run in runs: + siblings.append({ + "run_id": run.info.run_id, + "run_name": run.info.run_name, + "N": int(run.data.params.get("nx", 0)), + "Re": float(run.data.params.get("Re", 0)), + "solver": "spectral" if "basis_type" in run.data.params else "fv", + }) + + log.info(f"Found {len(siblings)} sibling runs in sweep") + return siblings + + +# ============================================================================= +# Data Loading from MLflow +# ============================================================================= + + +def download_mlflow_artifacts(run_id: str, tracking_uri: str) -> Path: + """Download solution artifacts from MLflow run to temp directory.""" + mlflow.set_tracking_uri(tracking_uri) + client = mlflow.tracking.MlflowClient() + + run = client.get_run(run_id) + log.info(f"Downloading artifacts from: {run.info.run_name}") + + tmpdir = tempfile.mkdtemp(prefix="ldc_plot_") + artifact_path = client.download_artifacts(run_id, "", tmpdir) + + return Path(artifact_path) + + +def load_fields_from_zarr(artifact_dir: Path) -> dict: + """Load solution fields from zarr artifacts.""" + fields_dir = artifact_dir / "fields" + if not fields_dir.exists(): + raise FileNotFoundError(f"Fields directory not found: {fields_dir}") + + fields = {} + for name in ["x", "y", "u", "v", "p"]: + zarr_path = fields_dir / f"{name}.zarr" + if zarr_path.exists(): + fields[name] = zarr.load(zarr_path) + else: + raise FileNotFoundError(f"Field not found: {zarr_path}") + + return fields + + +def restructure_fields(fields: dict) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Convert potentially flattened fields to structured 2D arrays. + + Returns (x_unique, y_unique, U_2d, V_2d, P_2d) where U, V, P are 2D arrays + with shape (ny, nx) suitable for RectBivariateSpline. + """ + x, y = fields["x"], fields["y"] + u, v, p = fields["u"], fields["v"], fields["p"] + + # Get unique coordinates + x_unique = np.sort(np.unique(x)) + y_unique = np.sort(np.unique(y)) + nx, ny = len(x_unique), len(y_unique) + + # If already 2D, just return + if u.ndim == 2: + return x_unique, y_unique, u, v, p + + # Reshape from flattened to 2D + # Create mapping from coordinates to indices + x_to_idx = {val: i for i, val in enumerate(x_unique)} + y_to_idx = {val: i for i, val in enumerate(y_unique)} + + U_2d = np.zeros((ny, nx)) + V_2d = np.zeros((ny, nx)) + P_2d = np.zeros((ny, nx)) + + for k in range(len(x)): + i = x_to_idx[x[k]] + j = y_to_idx[y[k]] + U_2d[j, i] = u[k] + V_2d[j, i] = v[k] + P_2d[j, i] = p[k] + + return x_unique, y_unique, U_2d, V_2d, P_2d + + +def load_timeseries_from_mlflow(run_id: str, tracking_uri: str) -> pd.DataFrame: + """Load timeseries metrics from MLflow run.""" + mlflow.set_tracking_uri(tracking_uri) + client = mlflow.tracking.MlflowClient() + + # Get metric history + metrics_to_fetch = ["residual", "energy", "enstrophy", "palinstrophy"] + + data = {} + for metric_name in metrics_to_fetch: + try: + history = client.get_metric_history(run_id, metric_name) + if history: + data[metric_name] = [m.value for m in sorted(history, key=lambda x: x.step)] + except Exception: + pass # Metric might not exist + + if not data: + return pd.DataFrame() + + # Create DataFrame with iteration index + max_len = max(len(v) for v in data.values()) + df = pd.DataFrame({k: v + [None] * (max_len - len(v)) for k, v in data.items()}) + df["iteration"] = range(len(df)) + + return df + + +def fields_to_dataframe(fields: dict) -> pd.DataFrame: + """Convert fields dict to DataFrame format for plotting. + + Handles two storage formats: + 1. Flattened: x, y, u, v, p all 1D with same length (already meshgrid coords) + 2. Structured: x, y are 1D coordinate arrays, u, v, p are 2D fields + """ + x, y = fields["x"], fields["y"] + u, v, p = fields["u"], fields["v"], fields["p"] + + # Check if already flattened (all same length, 1D) + if x.ndim == 1 and len(x) == len(u.ravel()): + # Already flattened meshgrid coordinates + return pd.DataFrame({ + "x": x, + "y": y, + "u": u.ravel(), + "v": v.ravel(), + "p": p.ravel(), + }) + + # Need to create meshgrid + if x.ndim == 1 and y.ndim == 1: + X, Y = np.meshgrid(x, y) + else: + X, Y = x, y + + return pd.DataFrame({ + "x": X.ravel(), + "y": Y.ravel(), + "u": u.ravel(), + "v": v.ravel(), + "p": p.ravel(), + }) + + +# ============================================================================= +# Individual Run Plotting Functions +# ============================================================================= + + +def plot_fields(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: + """Generate field contour plots (p, u, v).""" + import matplotlib.pyplot as plt + from scipy.interpolate import RectBivariateSpline + + x_unique = np.sort(fields_df["x"].unique()) + y_unique = np.sort(fields_df["y"].unique()) + nx, ny = len(x_unique), len(y_unique) + + sorted_df = fields_df.sort_values(["y", "x"]) + P = sorted_df["p"].values.reshape(ny, nx) + U = sorted_df["u"].values.reshape(ny, nx) + V = sorted_df["v"].values.reshape(ny, nx) + + n_fine = 200 + x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) + y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) + X_fine, Y_fine = np.meshgrid(x_fine, y_fine) + + P_interp = RectBivariateSpline(y_unique, x_unique, P)(y_fine, x_fine) + U_interp = RectBivariateSpline(y_unique, x_unique, U)(y_fine, x_fine) + V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) + + fig, axes = plt.subplots(1, 3, figsize=(16, 5)) + + cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=25, cmap="coolwarm") + axes[0].set_xlabel("x") + axes[0].set_ylabel("y") + axes[0].set_title("Pressure", fontweight="bold") + axes[0].set_aspect("equal") + plt.colorbar(cf_p, ax=axes[0], label="p") + + cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=25, cmap="RdBu_r") + axes[1].set_xlabel("x") + axes[1].set_ylabel("y") + axes[1].set_title("U velocity", fontweight="bold") + axes[1].set_aspect("equal") + plt.colorbar(cf_u, ax=axes[1], label="u") + + cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=25, cmap="RdBu_r") + axes[2].set_xlabel("x") + axes[2].set_ylabel("y") + axes[2].set_title("V velocity", fontweight="bold") + axes[2].set_aspect("equal") + plt.colorbar(cf_v, ax=axes[2], label="v") + + fig.suptitle(f"Solution Fields — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold", fontsize=14) + plt.tight_layout() + + output_path = output_dir / "fields.png" + fig.savefig(output_path, dpi=300, bbox_inches="tight") + plt.close(fig) + + return output_path + + +def plot_streamlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: + """Generate streamline plot with velocity magnitude.""" + import matplotlib.pyplot as plt + from scipy.interpolate import RectBivariateSpline + + x_unique = np.sort(fields_df["x"].unique()) + y_unique = np.sort(fields_df["y"].unique()) + nx, ny = len(x_unique), len(y_unique) + + sorted_df = fields_df.sort_values(["y", "x"]) + U = sorted_df["u"].values.reshape(ny, nx) + V = sorted_df["v"].values.reshape(ny, nx) + + n_fine = 200 + x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) + y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) + + U_interp = RectBivariateSpline(y_unique, x_unique, U)(y_fine, x_fine) + V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) + vel_mag = np.sqrt(U_interp**2 + V_interp**2) + + fig, ax = plt.subplots(figsize=(8, 7)) + + X_fine, Y_fine = np.meshgrid(x_fine, y_fine) + cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=25, cmap="viridis") + stream = ax.streamplot( + x_fine, y_fine, U_interp, V_interp, + color="white", linewidth=0.8, density=1.5, + arrowsize=1.0, arrowstyle="->" + ) + stream.lines.set_alpha(0.7) + + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_title(f"Streamlines — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold") + ax.set_aspect("equal") + plt.colorbar(cf, ax=ax, label="Velocity magnitude") + plt.tight_layout() + + output_path = output_dir / "streamlines.png" + fig.savefig(output_path, dpi=300, bbox_inches="tight") + plt.close(fig) + + return output_path + + +def plot_vorticity(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: + """Generate vorticity contour plot.""" + import matplotlib.pyplot as plt + from scipy.interpolate import RectBivariateSpline + + x_unique = np.sort(fields_df["x"].unique()) + y_unique = np.sort(fields_df["y"].unique()) + nx, ny = len(x_unique), len(y_unique) + + sorted_df = fields_df.sort_values(["y", "x"]) + U = sorted_df["u"].values.reshape(ny, nx) + V = sorted_df["v"].values.reshape(ny, nx) + + n_fine = 200 + x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) + y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) + X_fine, Y_fine = np.meshgrid(x_fine, y_fine) + + U_spline = RectBivariateSpline(y_unique, x_unique, U) + V_spline = RectBivariateSpline(y_unique, x_unique, V) + + dvdx = V_spline(y_fine, x_fine, dx=1) + dudy = U_spline(y_fine, x_fine, dy=1) + vorticity = dvdx - dudy + + fig, ax = plt.subplots(figsize=(8, 7)) + + vmax = np.max(np.abs(vorticity)) + cf = ax.contourf(X_fine, Y_fine, vorticity, levels=25, cmap="RdBu_r", + vmin=-vmax, vmax=vmax) + + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_title(f"Vorticity — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold") + ax.set_aspect("equal") + plt.colorbar(cf, ax=ax, label=r"$\omega$") + plt.tight_layout() + + output_path = output_dir / "vorticity.png" + fig.savefig(output_path, dpi=300, bbox_inches="tight") + plt.close(fig) + + return output_path + + +def plot_centerlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: + """Plot velocity profiles along centerlines.""" + import matplotlib.pyplot as plt + from scipy.interpolate import RectBivariateSpline + + x_unique = np.sort(fields_df["x"].unique()) + y_unique = np.sort(fields_df["y"].unique()) + nx, ny = len(x_unique), len(y_unique) + + sorted_df = fields_df.sort_values(["y", "x"]) + U = sorted_df["u"].values.reshape(ny, nx) + V = sorted_df["v"].values.reshape(ny, nx) + + U_spline = RectBivariateSpline(y_unique, x_unique, U) + V_spline = RectBivariateSpline(y_unique, x_unique, V) + + n_points = 200 + y_line = np.linspace(y_unique[0], y_unique[-1], n_points) + x_line = np.linspace(x_unique[0], x_unique[-1], n_points) + + x_center = (x_unique[0] + x_unique[-1]) / 2 + y_center = (y_unique[0] + y_unique[-1]) / 2 + + u_vertical = U_spline(y_line, x_center).ravel() + v_horizontal = V_spline(y_center, x_line).ravel() + + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + + axes[0].plot(u_vertical, y_line, "b-", linewidth=2) + axes[0].set_xlabel("u") + axes[0].set_ylabel("y") + axes[0].set_title("U velocity (vertical centerline)", fontweight="bold") + axes[0].grid(True, alpha=0.3) + axes[0].axvline(x=0, color="gray", linestyle="--", alpha=0.5) + + axes[1].plot(x_line, v_horizontal, "r-", linewidth=2) + axes[1].set_xlabel("x") + axes[1].set_ylabel("v") + axes[1].set_title("V velocity (horizontal centerline)", fontweight="bold") + axes[1].grid(True, alpha=0.3) + axes[1].axhline(y=0, color="gray", linestyle="--", alpha=0.5) + + fig.suptitle(f"Centerline Profiles — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold", fontsize=14) + plt.tight_layout() + + output_path = output_dir / "centerlines.png" + fig.savefig(output_path, dpi=300, bbox_inches="tight") + plt.close(fig) + + return output_path + + +def plot_convergence(timeseries_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: + """Plot convergence history (residuals over iterations).""" + import matplotlib.pyplot as plt + + if timeseries_df.empty: + log.warning("No timeseries data available for convergence plot") + return None + + fig, ax = plt.subplots(figsize=(10, 6)) + + # Plot available metrics + colors = {"residual": "blue", "energy": "green", "enstrophy": "orange", "palinstrophy": "red"} + + for col in timeseries_df.columns: + if col != "iteration" and col in colors: + data = timeseries_df[col].dropna() + if len(data) > 0: + ax.semilogy(timeseries_df.loc[data.index, "iteration"], data, + label=col.capitalize(), color=colors[col], linewidth=1.5) + + ax.set_xlabel("Iteration") + ax.set_ylabel("Value") + ax.set_title(f"Convergence History — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold") + ax.legend(frameon=True) + ax.grid(True, alpha=0.3) + plt.tight_layout() + + output_path = output_dir / "convergence.png" + fig.savefig(output_path, dpi=300, bbox_inches="tight") + plt.close(fig) + + return output_path + + +def plot_ghia_validation(fields: dict, Re: float, solver: str, N: int, output_dir: Path) -> Path: + """Plot validation against Ghia benchmark data.""" + import matplotlib.pyplot as plt + from scipy.interpolate import RectBivariateSpline + + AVAILABLE_RE = [100, 400, 1000, 3200, 5000, 7500, 10000] + if int(Re) not in AVAILABLE_RE: + log.warning(f"Ghia data not available for Re={Re}. Available: {AVAILABLE_RE}") + return None + + # Project root is 2 levels up from this file + project_root = Path(__file__).parent.parent.parent + ghia_dir = project_root / "data" / "validation" / "ghia" + + u_file = ghia_dir / f"ghia_Re{int(Re)}_u_centerline.csv" + v_file = ghia_dir / f"ghia_Re{int(Re)}_v_centerline.csv" + + if not u_file.exists() or not v_file.exists(): + log.warning(f"Ghia data files not found in {ghia_dir}") + return None + + ghia_u = pd.read_csv(u_file) + ghia_v = pd.read_csv(v_file) + + # Restructure fields to 2D arrays + x_unique, y_unique, U_2d, V_2d, _ = restructure_fields(fields) + + U_spline = RectBivariateSpline(y_unique, x_unique, U_2d) + V_spline = RectBivariateSpline(y_unique, x_unique, V_2d) + + n_points = 200 + y_line = np.linspace(y_unique.min(), y_unique.max(), n_points) + x_line = np.linspace(x_unique.min(), x_unique.max(), n_points) + + x_center = (x_unique.min() + x_unique.max()) / 2 + y_center = (y_unique.min() + y_unique.max()) / 2 + + u_sim = U_spline(y_line, x_center).ravel() + v_sim = V_spline(y_center, x_line).ravel() + + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + + axes[0].plot(u_sim, y_line, "b-", linewidth=2, label=f"{solver.upper()} N={N}") + axes[0].scatter(ghia_u["u"], ghia_u["y"], c="black", s=50, marker="x", + label="Ghia et al. (1982)", zorder=10) + axes[0].set_xlabel("u") + axes[0].set_ylabel("y") + axes[0].set_title("U velocity (vertical centerline)", fontweight="bold") + axes[0].legend(frameon=True) + axes[0].grid(True, alpha=0.3) + + axes[1].plot(x_line, v_sim, "r-", linewidth=2, label=f"{solver.upper()} N={N}") + axes[1].scatter(ghia_v["x"], ghia_v["v"], c="black", s=50, marker="x", + label="Ghia et al. (1982)", zorder=10) + axes[1].set_xlabel("x") + axes[1].set_ylabel("v") + axes[1].set_title("V velocity (horizontal centerline)", fontweight="bold") + axes[1].legend(frameon=True) + axes[1].grid(True, alpha=0.3) + + fig.suptitle(f"Ghia Benchmark Validation — Re={Re:.0f}", fontweight="bold", fontsize=14) + plt.tight_layout() + + output_path = output_dir / "ghia_validation.png" + fig.savefig(output_path, dpi=300, bbox_inches="tight") + plt.close(fig) + + return output_path + + +# ============================================================================= +# Comparison Plot (for parent run) +# ============================================================================= + + +def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Path) -> Path: + """Plot Ghia comparison with all sibling runs overlaid. + + Parameters + ---------- + siblings : list[dict] + List of sibling run info dicts with run_id, N, Re, solver + tracking_uri : str + MLflow tracking URI + output_dir : Path + Output directory + + Returns + ------- + Path + Path to comparison plot + """ + import matplotlib.pyplot as plt + from scipy.interpolate import RectBivariateSpline + + if not siblings: + return None + + # Get Re from first sibling (all should have same Re in a sweep) + Re = siblings[0]["Re"] + + AVAILABLE_RE = [100, 400, 1000, 3200, 5000, 7500, 10000] + if int(Re) not in AVAILABLE_RE: + log.warning(f"Ghia data not available for Re={Re}") + return None + + # Project root is 2 levels up from this file + project_root = Path(__file__).parent.parent.parent + ghia_dir = project_root / "data" / "validation" / "ghia" + + u_file = ghia_dir / f"ghia_Re{int(Re)}_u_centerline.csv" + v_file = ghia_dir / f"ghia_Re{int(Re)}_v_centerline.csv" + + if not u_file.exists() or not v_file.exists(): + log.warning(f"Ghia data files not found") + return None + + ghia_u = pd.read_csv(u_file) + ghia_v = pd.read_csv(v_file) + + fig, axes = plt.subplots(1, 2, figsize=(14, 6)) + + # Color map for different runs + colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(siblings))) + + for i, sibling in enumerate(siblings): + run_id = sibling["run_id"] + N = sibling["N"] + solver = sibling["solver"] + + try: + # Download and load fields + artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) + fields = load_fields_from_zarr(artifact_dir) + + # Restructure to 2D arrays + x_unique, y_unique, U_2d, V_2d, _ = restructure_fields(fields) + + U_spline = RectBivariateSpline(y_unique, x_unique, U_2d) + V_spline = RectBivariateSpline(y_unique, x_unique, V_2d) + + n_points = 200 + y_line = np.linspace(y_unique.min(), y_unique.max(), n_points) + x_line = np.linspace(x_unique.min(), x_unique.max(), n_points) + + x_center = (x_unique.min() + x_unique.max()) / 2 + y_center = (y_unique.min() + y_unique.max()) / 2 + + u_sim = U_spline(y_line, x_center).ravel() + v_sim = V_spline(y_center, x_line).ravel() + + label = f"{solver.upper()} N={N}" + axes[0].plot(u_sim, y_line, color=colors[i], linewidth=2, label=label) + axes[1].plot(x_line, v_sim, color=colors[i], linewidth=2, label=label) + + except Exception as e: + log.warning(f"Failed to load run {run_id}: {e}") + continue + + # Add Ghia reference + axes[0].scatter(ghia_u["u"], ghia_u["y"], c="black", s=60, marker="x", + label="Ghia et al. (1982)", zorder=10) + axes[0].set_xlabel("u", fontsize=12) + axes[0].set_ylabel("y", fontsize=12) + axes[0].set_title("U velocity (vertical centerline)", fontweight="bold") + axes[0].legend(frameon=True, loc="best") + axes[0].grid(True, alpha=0.3) + + axes[1].scatter(ghia_v["x"], ghia_v["v"], c="black", s=60, marker="x", + label="Ghia et al. (1982)", zorder=10) + axes[1].set_xlabel("x", fontsize=12) + axes[1].set_ylabel("v", fontsize=12) + axes[1].set_title("V velocity (horizontal centerline)", fontweight="bold") + axes[1].legend(frameon=True, loc="best") + axes[1].grid(True, alpha=0.3) + + fig.suptitle(f"Ghia Benchmark Comparison — Re={Re:.0f}", fontweight="bold", fontsize=14) + plt.tight_layout() + + output_path = output_dir / "ghia_comparison.png" + fig.savefig(output_path, dpi=300, bbox_inches="tight") + plt.close(fig) + + log.info(f"Saved comparison plot: {output_path}") + return output_path + + +# ============================================================================= +# MLflow Upload +# ============================================================================= + + +def upload_plots_to_mlflow(run_id: str, plot_paths: list, tracking_uri: str, artifact_subdir: str = "plots"): + """Upload generated plots to MLflow run as artifacts.""" + mlflow.set_tracking_uri(tracking_uri) + + valid_paths = [p for p in plot_paths if p and p.exists()] + + with mlflow.start_run(run_id=run_id): + for path in valid_paths: + mlflow.log_artifact(str(path), artifact_path=artifact_subdir) + log.info(f"Uploaded: {artifact_subdir}/{path.name}") + + +# ============================================================================= +# Main Entry Point +# ============================================================================= + + +@hydra.main(config_path="../../conf", config_name="config", version_base=None) +def main(cfg: DictConfig) -> None: + """Hydra entry point - finds matching run and generates plots.""" + + tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") + solver_name = cfg.solver.name + N = cfg.N + Re = cfg.Re + + # Find matching MLflow run + run_id, parent_run_id = find_matching_run(cfg, tracking_uri) + + # Setup output directory + output_dir = Path(hydra.core.hydra_config.HydraConfig.get().runtime.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Download artifacts for this run + artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) + fields = load_fields_from_zarr(artifact_dir) + fields_df = fields_to_dataframe(fields) + timeseries_df = load_timeseries_from_mlflow(run_id, tracking_uri) + + log.info(f"Generating plots for {solver_name} N={N} Re={Re}") + + # ========================================================================== + # Individual run plots + # ========================================================================== + plot_paths = [] + + plot_paths.append(plot_fields(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_streamlines(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_vorticity(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_centerlines(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_convergence(timeseries_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_ghia_validation(fields, Re, solver_name, N, output_dir)) + + plot_paths = [p for p in plot_paths if p is not None] + log.info(f"Generated {len(plot_paths)} plots for run") + + # Upload to individual run + if cfg.get("upload_to_mlflow", True): + upload_plots_to_mlflow(run_id, plot_paths, tracking_uri) + + # ========================================================================== + # Comparison plot for parent (if this is part of a sweep) + # ========================================================================== + if parent_run_id: + log.info("This run is part of a sweep - generating comparison plot for parent") + + siblings = find_sibling_runs(parent_run_id, tracking_uri) + + if len(siblings) > 1: + comparison_dir = output_dir / "comparison" + comparison_dir.mkdir(exist_ok=True) + + comparison_path = plot_ghia_comparison(siblings, tracking_uri, comparison_dir) + + if comparison_path and cfg.get("upload_to_mlflow", True): + upload_plots_to_mlflow(parent_run_id, [comparison_path], tracking_uri, "plots") + log.info("Comparison plot uploaded to parent run") + + log.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/Experiments/plot_kernels.py b/Experiments/plot_kernels.py deleted file mode 100644 index ccd7661..0000000 --- a/Experiments/plot_kernels.py +++ /dev/null @@ -1,231 +0,0 @@ -""" -Visualization of Kernel Experiments -==================================== - -Comprehensive analysis and visualization of NumPy vs Numba kernel benchmarks. -Fetches data directly from MLflow. - -Usage: - uv run python Experiments/01-kernels/plot_kernels.py mlflow=databricks -""" - -import pandas as pd -import matplotlib.pyplot as plt -import seaborn as sns -import hydra -from omegaconf import DictConfig - -from Poisson import get_project_root -from utils.mlflow.io import load_runs, setup_mlflow_tracking -from utils import plotting # Apply scientific style - - -@hydra.main(config_path="../../conf", config_name="config", version_base=None) -def main(cfg: DictConfig): - # Setup MLflow tracking based on config - setup_mlflow_tracking(mode=cfg.mlflow.mode) - prefix = cfg.mlflow.get("project_prefix", "") - - repo_root = get_project_root() - fig_dir = repo_root / "figures" / "kernels" - fig_dir.mkdir(parents=True, exist_ok=True) - - # Load data from MLflow - exp_name = "kernel" - print(f"Loading data from MLflow experiment '{exp_name}'...") - df = load_runs(exp_name, converged_only=False, project_prefix=prefix) - - if df.empty: - print(f"No runs found for experiment '{exp_name}'.") - print("Run the experiments first:") - print(" uv run python run_solver.py +experiment=kernel/convergence_numpy mlflow=databricks -m") - print(" uv run python run_solver.py +experiment=kernel/convergence_numba mlflow=databricks -m") - print(" uv run python run_solver.py +experiment=kernel/benchmark_numpy mlflow=databricks -m") - print(" uv run python run_solver.py +experiment=kernel/benchmark_numba mlflow=databricks -m") - return - - # Extract parameters (handle missing columns) - df["N"] = pd.to_numeric(df["params.N"], errors="coerce").astype("Int64") - df["max_iter"] = pd.to_numeric(df["params.max_iter"], errors="coerce").astype("Int64") - # Handle use_numba as either bool, int (0/1), or string - if "params.use_numba" in df.columns: - df["use_numba"] = pd.to_numeric(df["params.use_numba"], errors="coerce").fillna(0).astype(bool) - else: - df["use_numba"] = False - - # Use observed numba threads (actual runtime value) - if "metrics.observed_numba_threads" in df.columns: - df["numba_threads"] = pd.to_numeric(df["metrics.observed_numba_threads"], errors="coerce").fillna(1).astype(int) - elif "params.specified_numba_threads" in df.columns: - df["numba_threads"] = pd.to_numeric(df["params.specified_numba_threads"], errors="coerce").fillna(1).astype(int) - else: - df["numba_threads"] = 1 - - df["mlups"] = pd.to_numeric(df["metrics.mlups"], errors="coerce") - df["wall_time"] = pd.to_numeric(df["metrics.wall_time"], errors="coerce") - df["iterations"] = pd.to_numeric(df["metrics.iterations"], errors="coerce").astype("Int64") - df["final_error"] = pd.to_numeric(df["metrics.final_error"], errors="coerce") - - # Compute time per iteration - df["time_per_iter_ms"] = (df["wall_time"] / df["iterations"]) * 1000 - - # Create configuration labels - df["config"] = df.apply( - lambda row: "NumPy" if not row["use_numba"] - else f"Numba ({row['numba_threads']}T)", - axis=1 - ) - - print(f"Loaded {len(df)} runs") - print(f"Problem sizes: {sorted(df['N'].dropna().unique())}") - print(f"Configurations: {df['config'].unique()}") - - # %% - # Plot 1: Convergence (error vs iterations) - N=17 runs only - # ---------------------------------------------------------- - - df_conv = df[df["N"] == 17].copy() - if not df_conv.empty: - # Keep only latest run per (config, max_iter) - df_conv = df_conv.sort_values("start_time").groupby(["config", "max_iter"]).last().reset_index() - - fig, ax = plt.subplots() - sns.lineplot( - data=df_conv, - x="max_iter", - y="final_error", - hue="config", - style="config", - markers=True, - dashes=False, - ax=ax, - ) - ax.set_xscale("log") - ax.set_yscale("log") - ax.set_xlabel("Iterations") - ax.set_ylabel("Algebraic Error") - ax.set_title("Kernel Convergence Comparison (N=17)") - ax.legend(title="Kernel") - ax.grid(True, alpha=0.3) - - output_file = fig_dir / "01_convergence.pdf" - fig.savefig(output_file, bbox_inches="tight") - print(f"Saved: {output_file}") - else: - print("No convergence runs found (N=17).") - - # %% - # Plot 2: Performance (MLup/s vs N) - benchmark runs only (N > 17) - # ---------------------------------------------------------------- - - df_bench = df[df["N"] > 17].copy() - if not df_bench.empty: - # Keep only latest run per (config, N) - df_bench = df_bench.sort_values("start_time").groupby(["config", "N"]).last().reset_index() - - fig, ax = plt.subplots() - sns.lineplot( - data=df_bench, - x="N", - y="mlups", - hue="config", - style="config", - markers=True, - dashes=False, - ax=ax, - ) - N_values = sorted(df_bench["N"].unique()) - ax.set_xscale("log") - ax.set_xticks(N_values, labels=[f"${n}^3$" for n in N_values]) - ax.minorticks_off() - ax.set_xlabel("Problem Size") - ax.set_ylabel("Throughput (Mlup/s)") - ax.set_title("Kernel Throughput Comparison") - ax.legend(title="Kernel") - ax.grid(True, alpha=0.3) - - output_file = fig_dir / "02_throughput.pdf" - fig.savefig(output_file, bbox_inches="tight") - print(f"Saved: {output_file}") - - # %% - # Plot 3: Speedup (Numba vs NumPy) - # -------------------------------- - - # Get NumPy baseline times per N - df_numpy = df_bench[~df_bench["use_numba"]].groupby("N")["time_per_iter_ms"].mean().reset_index() - df_numpy = df_numpy.rename(columns={"time_per_iter_ms": "numpy_time"}) - - # Compute speedup for Numba runs - df_numba = df_bench[df_bench["use_numba"]].merge(df_numpy, on="N", how="left") - df_numba["speedup"] = df_numba["numpy_time"] / df_numba["time_per_iter_ms"] - - if not df_numba.empty: - fig, ax = plt.subplots() - sns.lineplot( - data=df_numba, - x="N", - y="speedup", - hue="numba_threads", - style="numba_threads", - markers=True, - dashes=False, - ax=ax, - palette="viridis", - ) - N_vals = sorted(df_numba["N"].unique()) - ax.axhline(y=1, color="k", linestyle="--", alpha=0.5, label="NumPy baseline") - ax.set_xscale("log") - ax.set_xticks(N_vals, labels=[f"${n}^3$" for n in N_vals]) - ax.minorticks_off() - ax.set_xlabel("Problem Size") - ax.set_ylabel("Speedup vs NumPy") - ax.set_title("Numba Speedup") - ax.legend(title="Threads") - ax.grid(True, alpha=0.3) - - output_file = fig_dir / "03_speedup.pdf" - fig.savefig(output_file, bbox_inches="tight") - print(f"Saved: {output_file}") - else: - print("No Numba runs found for speedup plot.") - - # %% - # Plot 4: Time per iteration (with confidence intervals) - # ------------------------------------------------------- - - # Use ALL benchmark runs (not deduplicated) for CI calculation - df_bench_all = df[df["N"] > 17].copy() - - fig, ax = plt.subplots() - sns.lineplot( - data=df_bench_all, - x="N", - y="time_per_iter_ms", - hue="config", - style="config", - markers=True, - dashes=False, - errorbar="ci", # 95% confidence interval - ax=ax, - ) - N_values = sorted(df_bench_all["N"].unique()) - ax.set_xscale("log") - ax.set_yscale("log") - ax.set_xticks(N_values, labels=[f"${n}^3$" for n in N_values]) - ax.minorticks_off() - ax.set_xlabel("Problem Size") - ax.set_ylabel("Time per Iteration (ms)") - ax.set_title("Kernel Timing Comparison") - ax.legend(title="Kernel") - ax.grid(True, alpha=0.3) - - output_file = fig_dir / "04_time_per_iter.pdf" - fig.savefig(output_file, bbox_inches="tight") - print(f"Saved: {output_file}") - else: - print("No benchmark runs found (N > 17).") - - -if __name__ == "__main__": - main() diff --git a/README.md b/README.md index 4ee8466..ec96d1e 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ uv run python run_solver.py +experiment=spectral_validation Run multiple configurations with Hydra's multirun: ```bash -# Sweep over grid sizes +# Sweep over grid sizes (sequential) uv run python run_solver.py -m solver=fv N=16,32,64 Re=100 # Sweep over Reynolds numbers @@ -56,6 +56,24 @@ uv run python run_solver.py -m solver=spectral N=31 Re=100,400,1000 uv run python run_solver.py -m +experiment=fv_validation ``` +### Parallel Sweeps (Joblib) + +Run sweeps in parallel using all CPU cores: + +```bash +# Parallel sweep over grid sizes +uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 Re=100 + +# Parallel sweep over solvers +uv run python run_solver.py -m hydra/launcher=joblib solver=fv,spectral N=32 Re=100 + +# Parallel multi-dimensional sweep (solver x N x Re = 12 jobs) +uv run python run_solver.py -m hydra/launcher=joblib solver=fv,spectral N=16,32,64 Re=100,400 + +# Control parallelism (e.g., 4 concurrent jobs) +uv run python run_solver.py -m hydra/launcher=joblib hydra.launcher.n_jobs=4 solver=fv N=16,32,64 +``` + ### Configuration Structure ``` @@ -68,58 +86,55 @@ conf/ │ ├── quick_test.yaml # Fast debugging runs │ ├── fv_validation.yaml # FV benchmark settings │ └── spectral_validation.yaml -└── mlflow/ - ├── local-files.yaml # File-based tracking (default) - ├── local-docker.yaml # Local Docker server - └── coolify.yaml # Remote server (Coolify) +├── mlflow/ +│ ├── local.yaml # File-based tracking (default) +│ └── coolify.yaml # Remote server (Coolify) +└── hydra/ + └── launcher/ + └── joblib.yaml # Parallel launcher (all cores) ``` +### Nested Runs for Sweeps + +Parameter sweeps automatically create a parent-child run hierarchy in MLflow: +- **Parent run**: Created before sweep, logs sweep config +- **Child runs**: Each parameter combination nested under parent + +This makes it easy to view all sweep runs together in the MLflow UI. + ### Output Structure -Each run creates a timestamped output directory: +Each run creates a timestamped Hydra output directory: ``` -outputs/{experiment_name}/Re{Re}/{solver}/{DD-MM-YY}/{HH:MM:SS}/ -├── config.yaml # Resolved configuration +hydra_outputs/{experiment_name}/Re{Re}/{solver}/{DD-MM-YY}/{HH:MM:SS}/ ├── run_solver.log # Execution log -├── LDC_{SOLVER}_N{N}_Re{Re}.h5 # Results (HDF5) -└── .hydra/ # Hydra internals +└── .hydra/ + ├── config.yaml # Resolved configuration + ├── hydra.yaml # Hydra settings + └── overrides.yaml # CLI overrides ``` +Solution fields (u, v, p) and metrics are stored as MLflow artifacts (zarr format). + ## MLflow -Results are tracked with [MLflow](https://mlflow.org/). Three tracking modes are available: +Results are tracked with [MLflow](https://mlflow.org/). Two tracking modes are available: ### Local Files (Default) File-based tracking in `./mlruns` - no setup required: ```bash -uv run python run_solver.py solver=fv mlflow=local-files +uv run python run_solver.py solver=fv mlflow=local # View UI -uv run mlflow ui --backend-store-uri ./mlruns --port 5001 -``` - -### Local Docker - -Clone and run [mlflow-server](https://github.com/philipnickel/mlflow-server): - -```bash -# Clone and start server -git clone https://github.com/philipnickel/mlflow-server.git -cd mlflow-server && docker compose up -d - -# Run solver -uv run python run_solver.py solver=fv mlflow=local-docker - -# View UI at http://localhost:5001 (admin / password1234) +uv run main.py --mlflow-ui ``` ### Remote Server (Coolify) -Deploy [mlflow-server](https://github.com/philipnickel/mlflow-server) to Coolify. The admin password is auto-generated - find it in Coolify's environment variables (`SERVICE_PASSWORD_64_MLFLOW_ADMIN`). - +[mlflow-server](https://kni.dk/mlflow-ana-p3/#/experiments) ```bash # Setup credentials (one-time) cp .env.template .env @@ -129,12 +144,6 @@ cp .env.template .env uv run python run_solver.py solver=fv mlflow=coolify ``` -### Logged Data - -- **Parameters:** solver settings, grid size, Reynolds number -- **Metrics:** iterations, convergence, wall time, residuals -- **Timeseries:** residual history, energy, enstrophy (per iteration) -- **Artifacts:** HDF5 result files ## References diff --git a/conf/config.yaml b/conf/config.yaml index 0ea68a6..f57be77 100644 --- a/conf/config.yaml +++ b/conf/config.yaml @@ -1,10 +1,13 @@ # Main Hydra configuration for LDC solvers # Usage: uv run python run_solver.py solver=fv Re=100 N=32 # uv run python run_solver.py solver=spectral Re=100 N=15 +# +# Parallel sweeps with joblib: +# uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 Re=100 defaults: - solver: fv - - mlflow: local-files + - mlflow: local - _self_ # ============================================================================= @@ -28,13 +31,17 @@ max_iterations: 500 # Experiment tracking experiment_name: LDC-Solver +sweep_name: sweep # Name for parent run in multirun sweeps # ============================================================================= -# Hydra output paths +# Hydra output paths and callbacks # ============================================================================= hydra: run: - dir: outputs/${experiment_name}/Re${Re}/${solver.name}/${now:%d-%m-%y}/${now:%H:%M:%S} + dir: hydra_outputs/${experiment_name}/Re${Re}/${solver.name}/${now:%d-%m-%y}/${now:%H:%M:%S} sweep: - dir: multirun/${experiment_name}/Re${Re}/${solver.name}/${now:%d-%m-%y}/${now:%H:%M:%S} + dir: hydra_outputs/multirun/${experiment_name}/Re${Re}/${solver.name}/${now:%d-%m-%y}/${now:%H:%M:%S} subdir: N${N} + callbacks: + mlflow_sweep: + _target_: utils.mlflow_callback.MLflowSweepCallback diff --git a/conf/experiment/fv_validation.yaml b/conf/experiment/fv_validation.yaml index 8f95f62..cf2de3e 100644 --- a/conf/experiment/fv_validation.yaml +++ b/conf/experiment/fv_validation.yaml @@ -6,7 +6,10 @@ defaults: - override /solver: fv -experiment_name: FV-Validation +# MLflow experiment (shared with spectral_validation) +experiment_name: LDC-Validation +# Parent run name - use same name across fv/spectral to group them +sweep_name: validation # Validation grid sizes and Reynolds numbers N: 64 diff --git a/conf/experiment/plotting_test.yaml b/conf/experiment/plotting_test.yaml new file mode 100644 index 0000000..20f5f79 --- /dev/null +++ b/conf/experiment/plotting_test.yaml @@ -0,0 +1,11 @@ +# @package _global_ +# Quick plotting test - minimal iterations for fast verification +# Usage: uv run python run_solver.py +experiment=plotting_test solver=fv +# uv run python run_solver.py +experiment=plotting_test solver=spectral + +experiment_name: Plotting-Test +sweep_name: plotting-test-sweep + +Re: 100 +tolerance: 1.0e-4 +max_iterations: 50 diff --git a/conf/experiment/quick_test.yaml b/conf/experiment/quick_test.yaml index 69dd9ee..aa3f194 100644 --- a/conf/experiment/quick_test.yaml +++ b/conf/experiment/quick_test.yaml @@ -4,6 +4,7 @@ # uv run python run_solver.py +experiment=quick_test solver=spectral experiment_name: Quick-Test +sweep_name: quick-test-sweep N: 16 Re: 100 diff --git a/conf/experiment/spectral_validation.yaml b/conf/experiment/spectral_validation.yaml index 73dded6..a714371 100644 --- a/conf/experiment/spectral_validation.yaml +++ b/conf/experiment/spectral_validation.yaml @@ -6,7 +6,10 @@ defaults: - override /solver: spectral -experiment_name: Spectral-Validation +# MLflow experiment (shared with fv_validation) +experiment_name: LDC-Validation +# Parent run name - use same name across fv/spectral to group them +sweep_name: validation # Validation polynomial orders (N+1 nodes) and Reynolds numbers N: 31 diff --git a/conf/experiment/sweep_test.yaml b/conf/experiment/sweep_test.yaml new file mode 100644 index 0000000..c1a54e0 --- /dev/null +++ b/conf/experiment/sweep_test.yaml @@ -0,0 +1,10 @@ +# @package _global_ +# Minimal sweep test - verifies nested MLflow runs work correctly +# Usage: uv run python run_solver.py -m +experiment=sweep_test solver=fv,spectral N=8,16 + +experiment_name: Sweep-Test +sweep_name: test-sweep + +Re: 100 +tolerance: 1.0e-3 +max_iterations: 50 diff --git a/conf/hydra/launcher/joblib.yaml b/conf/hydra/launcher/joblib.yaml new file mode 100644 index 0000000..60c858d --- /dev/null +++ b/conf/hydra/launcher/joblib.yaml @@ -0,0 +1,14 @@ +# Joblib Launcher for parallel sweeps +# Usage: uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 + +_target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + +n_jobs: -1 # -1 = all cores, -2 = all but one +backend: loky # loky, threading, multiprocessing +verbose: 10 # 0-50 +timeout: null # seconds per job (null = no timeout) +pre_dispatch: "2*n_jobs" +batch_size: auto +temp_folder: null +max_nbytes: 1M +mmap_mode: r diff --git a/docs/source/api/base_solver.rst b/docs/source/api/base_solver.rst index b0076e5..1b61de1 100644 --- a/docs/source/api/base_solver.rst +++ b/docs/source/api/base_solver.rst @@ -1,9 +1,9 @@ -Base Solver (``ldc.base_solver``) -================================= +Base Solver (``solvers.base_solver``) +===================================== Abstract base class for lid-driven cavity solvers. -.. currentmodule:: ldc.base_solver +.. currentmodule:: solvers.base_solver .. autosummary:: :toctree: ../generated diff --git a/docs/source/api/datastructures.rst b/docs/source/api/datastructures.rst index 4addd2b..9d2478d 100644 --- a/docs/source/api/datastructures.rst +++ b/docs/source/api/datastructures.rst @@ -1,9 +1,9 @@ -Data Structures (``ldc.datastructures``) -======================================== +Data Structures (``solvers.datastructures``) +============================================ Configuration and result data structures for solvers. -.. currentmodule:: ldc.datastructures +.. currentmodule:: solvers.datastructures .. autosummary:: :toctree: ../generated diff --git a/docs/source/api/fv_solver.rst b/docs/source/api/fv_solver.rst index 99f9cfa..25471d8 100644 --- a/docs/source/api/fv_solver.rst +++ b/docs/source/api/fv_solver.rst @@ -1,9 +1,9 @@ -Finite Volume Solver (``ldc.fv_solver``) -======================================== +Finite Volume Solver (``solvers.fv_solver``) +============================================ Finite volume solver using the SIMPLE algorithm with PETSc. -.. currentmodule:: ldc.fv_solver +.. currentmodule:: solvers.fv_solver .. autosummary:: :toctree: ../generated diff --git a/docs/source/api/ldc.rst b/docs/source/api/solvers.rst similarity index 63% rename from docs/source/api/ldc.rst rename to docs/source/api/solvers.rst index 03f3221..f799138 100644 --- a/docs/source/api/ldc.rst +++ b/docs/source/api/solvers.rst @@ -1,9 +1,9 @@ -Lid-Driven Cavity Solvers (``ldc``) -=================================== +Solvers (``solvers``) +===================== Solvers for lid-driven cavity flow simulations. -.. currentmodule:: ldc +.. currentmodule:: solvers .. autosummary:: :toctree: ../generated diff --git a/docs/source/api/spectral_solver.rst b/docs/source/api/spectral_solver.rst index 64b5df4..96adc07 100644 --- a/docs/source/api/spectral_solver.rst +++ b/docs/source/api/spectral_solver.rst @@ -1,9 +1,9 @@ -Spectral Solver (``ldc.spectral_solver``) -========================================= +Spectral Solver (``solvers.spectral_solver``) +============================================= -Spectral solver using Chebyshev/Legendre collocation with artificial compressibility. +Spectral solver using Chebyshev collocation with artificial compressibility. -.. currentmodule:: ldc.spectral_solver +.. currentmodule:: solvers.spectral_solver .. autosummary:: :toctree: ../generated diff --git a/docs/source/api_reference.rst b/docs/source/api_reference.rst index 943c65c..7ad9a15 100644 --- a/docs/source/api_reference.rst +++ b/docs/source/api_reference.rst @@ -4,12 +4,12 @@ API Reference ============= -This page provides an overview of the project modules. +This page provides an overview of the solver modules. .. toctree:: :maxdepth: 2 - api/ldc + api/solvers api/base_solver api/fv_solver api/spectral_solver diff --git a/docs/source/conf.py b/docs/source/conf.py index 23f2c26..778c333 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -78,20 +78,14 @@ # Cross-referencing: Create "Examples using X" in API docs "backreferences_dir": "gen_modules/backreferences", "doc_module": ( - "fv", + "solvers", "utils", - "spectral", - "meshing", - "ldc", ), # Generate backreferences for our packages "inspect_global_variables": True, # Detect classes/functions used in examples # Make code clickable: Link to API docs when code mentions package functions "reference_url": { - "fv": None, # None = use local docs (not external URL) + "solvers": None, # None = use local docs (not external URL) "utils": None, - "spectral": None, - "meshing": None, - "ldc": None, }, } diff --git a/docs/source/configuration.rst b/docs/source/configuration.rst new file mode 100644 index 0000000..22366dc --- /dev/null +++ b/docs/source/configuration.rst @@ -0,0 +1,259 @@ +Experiment Configuration +======================== + +This guide explains the Hydra configuration system used for experiment management. + +Configuration Hierarchy +----------------------- + +The configuration system uses a hierarchical structure where settings can be +defined at multiple levels and overridden as needed: + +.. code-block:: text + + conf/ + ├── config.yaml # Base configuration (defaults) + ├── solver/ + │ ├── fv.yaml # Finite Volume solver settings + │ └── spectral.yaml # Spectral solver settings + ├── experiment/ + │ ├── quick_test.yaml # Fast debugging + │ ├── sweep_test.yaml # Sweep testing + │ ├── fv_validation.yaml # FV benchmark + │ └── spectral_validation.yaml + ├── mlflow/ + │ ├── local.yaml # Local file tracking + │ └── coolify.yaml # Remote server + └── hydra/ + └── launcher/ + └── joblib.yaml # Parallel execution + +Base Configuration +------------------ + +The main ``config.yaml`` defines default values for all parameters: + +.. code-block:: yaml + + # conf/config.yaml + defaults: + - solver: fv + - mlflow: local + - _self_ + + # Grid and physics + N: 32 # Grid size (cells for FV, polynomial order for spectral) + Re: 100 # Reynolds number + lid_velocity: 1.0 # Lid velocity + Lx: 1.0 # Domain width + Ly: 1.0 # Domain height + + # Solver control + tolerance: 1.0e-6 # Convergence tolerance + max_iterations: 500 # Maximum iterations + + # Experiment tracking + experiment_name: LDC-Solver + sweep_name: sweep # Parent run name for multirun sweeps + +Solver Configurations +--------------------- + +Each solver has its own configuration file with solver-specific parameters. + +**Finite Volume** (``conf/solver/fv.yaml``): + +.. code-block:: yaml + + name: fv + convection_scheme: upwind # upwind, central, quick + limiter: none # none, minmod, vanLeer + alpha_uv: 0.7 # Velocity under-relaxation + alpha_p: 0.3 # Pressure under-relaxation + linear_solver_tol: 1.0e-6 # PETSc solver tolerance + +**Spectral** (``conf/solver/spectral.yaml``): + +.. code-block:: yaml + + name: spectral + basis_type: chebyshev-gauss-lobatto # Basis functions + CFL: 0.5 # CFL number for time stepping + beta_squared: 1.0 # Artificial compressibility + corner_smoothing: true # Smooth corner singularities + +Experiment Configurations +------------------------- + +Experiment configs override base settings for specific use cases. They use +``# @package _global_`` to merge into the root config. + +**Quick Test** (``conf/experiment/quick_test.yaml``): + +.. code-block:: yaml + + # @package _global_ + experiment_name: Quick-Test + sweep_name: quick-test-sweep + + N: 16 + Re: 100 + tolerance: 1.0e-4 + max_iterations: 100 + +**Validation Sweep** (``conf/experiment/fv_validation.yaml``): + +.. code-block:: yaml + + # @package _global_ + defaults: + - override /solver: fv + + experiment_name: FV-Validation + sweep_name: fv-validation-sweep + + N: 64 + Re: 100 + tolerance: 1.0e-7 + max_iterations: 50000 + + # Define sweep parameters for multirun + hydra: + sweeper: + params: + N: 32,64,128 + Re: 100,400,1000 + +Creating Custom Experiments +--------------------------- + +To create a new experiment configuration: + +1. Create a new YAML file in ``conf/experiment/``: + +.. code-block:: yaml + + # conf/experiment/my_experiment.yaml + # @package _global_ + + experiment_name: My-Experiment + sweep_name: my-sweep + + # Override any parameters + N: 48 + Re: 400 + tolerance: 1.0e-8 + max_iterations: 10000 + + # Optionally define sweep parameters + hydra: + sweeper: + params: + N: 32,48,64 + Re: 100,400 + +2. Run with your experiment: + +.. code-block:: bash + + # Single run + uv run python run_solver.py +experiment=my_experiment solver=fv + + # Sweep (uses hydra.sweeper.params if defined) + uv run python run_solver.py -m +experiment=my_experiment + +MLflow Integration +------------------ + +Experiment Name +^^^^^^^^^^^^^^^ + +The ``experiment_name`` field determines the MLflow experiment where runs are logged: + +.. code-block:: yaml + + experiment_name: FV-Validation # Creates/uses this MLflow experiment + +Sweep Name (Parent Runs) +^^^^^^^^^^^^^^^^^^^^^^^^ + +When running parameter sweeps (``-m`` flag), a parent run is automatically created +to group all child runs. The ``sweep_name`` field controls the parent run's name: + +.. code-block:: yaml + + sweep_name: fv-validation-sweep + +This creates a hierarchy in MLflow: + +.. code-block:: text + + fv-validation-sweep (parent) + ├── fv_N32_Re100 (child) + ├── fv_N32_Re400 (child) + ├── fv_N64_Re100 (child) + └── ... + +You can also override the sweep name from the command line: + +.. code-block:: bash + + uv run python run_solver.py -m sweep_name=custom-sweep solver=fv N=16,32,64 + +Command Line Usage +------------------ + +Basic Overrides +^^^^^^^^^^^^^^^ + +Override any parameter from the command line: + +.. code-block:: bash + + # Override single parameters + uv run python run_solver.py solver=spectral N=31 Re=1000 + + # Override multiple parameters + uv run python run_solver.py solver=fv N=64 Re=400 tolerance=1e-8 + +Using Experiments +^^^^^^^^^^^^^^^^^ + +Load an experiment configuration with ``+experiment=``: + +.. code-block:: bash + + # Load experiment config + uv run python run_solver.py +experiment=fv_validation + + # Load experiment and override parameters + uv run python run_solver.py +experiment=fv_validation N=128 Re=1000 + +Parameter Sweeps +^^^^^^^^^^^^^^^^ + +Use ``-m`` (multirun) to sweep over parameters: + +.. code-block:: bash + + # Sweep from command line + uv run python run_solver.py -m solver=fv N=16,32,64 Re=100,400 + + # Use experiment's predefined sweep + uv run python run_solver.py -m +experiment=fv_validation + + # Parallel sweep with joblib + uv run python run_solver.py -m hydra/launcher=joblib solver=fv,spectral N=16,32,64 + +Viewing Configuration +^^^^^^^^^^^^^^^^^^^^^ + +Print the resolved configuration without running: + +.. code-block:: bash + + # Show resolved config + uv run python run_solver.py --cfg job + + # Show config with experiment + uv run python run_solver.py +experiment=fv_validation --cfg job diff --git a/docs/source/hpc_guide.rst b/docs/source/hpc_guide.rst deleted file mode 100644 index 07b2fd7..0000000 --- a/docs/source/hpc_guide.rst +++ /dev/null @@ -1,96 +0,0 @@ -HPC Guide -========= - -This guide covers running parameter sweeps on the DTU HPC cluster using LSF job packs. - -Initial Setup (Once) --------------------- - -1. Clone the repository -2. Navigate into the repo root -3. Run ``uv run setup_mlflow.py`` -4. When asked for host, paste in:: - - https://dbc-6756e917-e5fc.cloud.databricks.com -5. When asked for password token, create one in Databricks: - - :menuselection:`Account --> Developer --> Create Token` - - - - -Submitting Jobs ---------------- - -Use the ``main.py`` CLI to submit jobs: - -.. code-block:: bash - - # Submit spectral solver jobs - uv run python main.py --hpc spectral - - # Submit FV solver jobs - uv run python main.py --hpc fv - - # Submit all jobs - uv run python main.py --hpc all - -Configuring Sweeps ------------------- - -Edit ``Experiments/*/generate_pack.sh`` to customize the parameter sweep: - -.. code-block:: bash - - # Resource settings - QUEUE="hpc" - WALLTIME="1:00" - CORES=4 - MEMORY="8GB" - - # Parameter sweep values - N_VALUES=(11 15 19 23 27 31) - RE_VALUES=(100 400 1000) - -.. note:: - - You may need to request longer wall time. The current example requests one hour, after which the job will terminate. - -Monitoring Jobs ---------------- - -Check the status of your running jobs: - -.. code-block:: bash - - bstat - -Example output: - -.. code-block:: text - - JOBID USER QUEUE JOB_NAME NALLOC STAT START_TIME ELAPSED - 27198794 s214960 hpc *N19-Re100 4 RUN Nov 27 23:11 0:01:39 - 27198795 s214960 hpc *N23-Re100 4 RUN Nov 27 23:11 0:01:39 - 27198792 s214960 hpc *N11-Re100 4 RUN Nov 27 23:11 0:01:39 - 27198793 s214960 hpc *N15-Re100 4 RUN Nov 27 23:11 0:01:39 - -Killing Jobs ------------- - -Kill jobs by name or ID: - -.. code-block:: bash - - # Kill a specific job by name - bkill -J Spectral-N23-Re100 - - # Kill a job by ID - bkill 27198795 - - # Kill all your jobs - bkill 0 - -.. tip:: - - See the job ID and name in the MLflow run description. diff --git a/docs/source/index.rst b/docs/source/index.rst index 148c751..8d3ffb6 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -15,10 +15,12 @@ Contents :doc:`example_gallery/index` Gallery of computational experiments and visualizations for lid-driven cavity flow. +:doc:`configuration` + Experiment configuration structure and customization guide. +:doc:`usage` + Running solvers locally and on HPC clusters. :doc:`api_reference` - Complete API reference for finite volume, spectral methods, and utility modules. -:doc:`hpc_guide` - Guide for running parameter sweeps on the DTU HPC cluster. + Complete API reference for solver modules. .. toctree:: :maxdepth: 2 @@ -27,6 +29,15 @@ Contents example_gallery/index +.. toctree:: + :maxdepth: 2 + :hidden: + :titlesonly: + :caption: User Guide + + configuration + usage + .. toctree:: :maxdepth: 2 :hidden: @@ -34,7 +45,6 @@ Contents :caption: Reference api_reference - hpc_guide Installation ------------ diff --git a/docs/source/usage.rst b/docs/source/usage.rst new file mode 100644 index 0000000..dac6728 --- /dev/null +++ b/docs/source/usage.rst @@ -0,0 +1,221 @@ +Usage Guide +=========== + +This guide covers running solvers locally with Hydra configuration management +and on the DTU HPC cluster. + +Hydra Configuration +------------------- + +The project uses `Hydra `_ for configuration management. +All solver runs are executed via ``run_solver.py``. + +Basic Usage +^^^^^^^^^^^ + +.. code-block:: bash + + # Finite Volume solver (32x32 cells, Re=100) + uv run python run_solver.py solver=fv N=32 Re=100 + + # Spectral solver (N=15 gives 16x16 nodes, Re=100) + uv run python run_solver.py solver=spectral N=15 Re=100 + +Using Experiment Configs +^^^^^^^^^^^^^^^^^^^^^^^^ + +Pre-defined experiment configurations are in ``conf/experiment/``: + +.. code-block:: bash + + # Quick test (small grid, few iterations) + uv run python run_solver.py +experiment=quick_test solver=fv + + # FV validation (default settings for benchmarking) + uv run python run_solver.py +experiment=fv_validation + + # Spectral validation + uv run python run_solver.py +experiment=spectral_validation + +Parameter Sweeps +^^^^^^^^^^^^^^^^ + +Run multiple configurations with Hydra's multirun (``-m``): + +.. code-block:: bash + + # Sweep over grid sizes (sequential) + uv run python run_solver.py -m solver=fv N=16,32,64 Re=100 + + # Sweep over Reynolds numbers + uv run python run_solver.py -m solver=spectral N=31 Re=100,400,1000 + +Parallel Sweeps (Joblib) +^^^^^^^^^^^^^^^^^^^^^^^^ + +Run sweeps in parallel using all CPU cores with the Joblib launcher: + +.. code-block:: bash + + # Parallel sweep over grid sizes + uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 Re=100 + + # Parallel sweep over solvers + uv run python run_solver.py -m hydra/launcher=joblib solver=fv,spectral N=32 Re=100 + + # Parallel multi-dimensional sweep (solver x N x Re = 12 jobs) + uv run python run_solver.py -m hydra/launcher=joblib solver=fv,spectral N=16,32,64 Re=100,400 + + # Control parallelism (e.g., 4 concurrent jobs) + uv run python run_solver.py -m hydra/launcher=joblib hydra.launcher.n_jobs=4 solver=fv N=16,32,64 + +Configuration Structure +^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: text + + conf/ + ├── config.yaml # Main config (N, Re, tolerance, etc.) + ├── solver/ + │ ├── fv.yaml # FV-specific (alpha_uv, alpha_p, scheme) + │ └── spectral.yaml # Spectral-specific (CFL, beta_squared) + ├── experiment/ + │ ├── quick_test.yaml # Fast debugging runs + │ ├── fv_validation.yaml # FV benchmark settings + │ └── spectral_validation.yaml + ├── mlflow/ + │ ├── local.yaml # File-based tracking (default) + │ └── coolify.yaml # Remote server + └── hydra/ + └── launcher/ + └── joblib.yaml # Parallel launcher (all cores) + +Nested Runs for Sweeps +^^^^^^^^^^^^^^^^^^^^^^ + +Parameter sweeps automatically create a parent-child run hierarchy in MLflow: + +- **Parent run**: Created before sweep starts, logs sweep configuration +- **Child runs**: Each parameter combination nested under the parent + +This makes it easy to: + +- View all runs from a sweep together in the MLflow UI +- Compare metrics across parameter combinations +- Track sweep-level metadata (HPC job ID, sweep config) + +MLflow Tracking +^^^^^^^^^^^^^^^ + +Results are tracked with `MLflow `_. Two modes available: + +**Local Files (Default):** + +.. code-block:: bash + + uv run python run_solver.py solver=fv mlflow=local + + # View UI + uv run main.py --mlflow-ui + +**Remote Server:** + +.. code-block:: bash + + # Setup credentials (one-time) + cp .env.template .env + # Edit .env with your credentials + + # Run solver + uv run python run_solver.py solver=fv mlflow=coolify + +HPC Cluster (DTU) +----------------- + +This section covers running parameter sweeps on the DTU HPC cluster using LSF. + +Initial Setup +^^^^^^^^^^^^^ + +1. Clone the repository on the HPC cluster +2. Navigate into the repo root +3. Set up MLflow credentials: + +.. code-block:: bash + + cp .env.template .env + # Edit .env with your credentials + +Submitting Jobs +^^^^^^^^^^^^^^^ + +Submit jobs using bsub with Hydra: + +.. code-block:: bash + + # Single job + bsub -q hpc -W 1:00 -n 4 -R "rusage[mem=4GB]" \ + "uv run python run_solver.py solver=fv N=32 Re=100 mlflow=coolify" + + # Sequential parameter sweep + bsub -q hpc -W 4:00 -n 4 -R "rusage[mem=4GB]" \ + "uv run python run_solver.py -m solver=fv N=16,32,64 Re=100,400 mlflow=coolify" + +Parallel Sweeps on HPC +^^^^^^^^^^^^^^^^^^^^^^ + +Use the Joblib launcher to run parameter combinations in parallel on a single node: + +.. code-block:: bash + + # Parallel sweep using all cores on the node + bsub -q hpc -W 2:00 -n 16 -R "rusage[mem=2GB]" -R "span[hosts=1]" \ + "uv run python run_solver.py -m hydra/launcher=joblib solver=fv,spectral N=16,32,64 Re=100,400 mlflow=coolify" + + # Control number of parallel jobs (e.g., 8 concurrent) + bsub -q hpc -W 2:00 -n 8 -R "rusage[mem=4GB]" -R "span[hosts=1]" \ + "uv run python run_solver.py -m hydra/launcher=joblib hydra.launcher.n_jobs=8 solver=fv N=16,32,64,128 Re=100,400,1000 mlflow=coolify" + +.. note:: + + The ``-R "span[hosts=1]"`` flag ensures all cores are allocated on a single node. + This is required because joblib uses local multiprocessing - it cannot distribute + work across multiple nodes. Without this flag, LSF might split your cores across + nodes, leaving some unusable. + +Monitoring Jobs +^^^^^^^^^^^^^^^ + +Check the status of your running jobs: + +.. code-block:: bash + + bstat + +Example output: + +.. code-block:: text + + JOBID USER QUEUE JOB_NAME NALLOC STAT START_TIME ELAPSED + 27198794 s214960 hpc *N19-Re100 4 RUN Nov 27 23:11 0:01:39 + 27198795 s214960 hpc *N23-Re100 4 RUN Nov 27 23:11 0:01:39 + +Killing Jobs +^^^^^^^^^^^^ + +Kill jobs by name or ID: + +.. code-block:: bash + + # Kill a specific job by name + bkill -J LDC-N32-Re100 + + # Kill a job by ID + bkill 27198795 + + # Kill all your jobs + bkill 0 + +.. tip:: + + Track job progress in the MLflow UI - each run logs the LSF job ID as a tag. diff --git a/jobs/README.md b/jobs/README.md deleted file mode 100644 index 4c70381..0000000 --- a/jobs/README.md +++ /dev/null @@ -1,56 +0,0 @@ -# HPC Job Scripts - -LSF job scripts for DTU HPC cluster. - -## Prerequisites - -1. Clone repo to `$HOME/LSM-Project_2` -2. Create virtual environment: `uv venv && uv sync` -3. Configure Databricks credentials in `~/.databrickscfg` - -## Job Scripts - -### Strong Scaling (Jacobi) -- `strong_scaling_1node.sh` - 1-24 ranks on 1 node -- `strong_scaling_2node.sh` - 36-48 ranks on 2 nodes -- `strong_scaling_4node.sh` - 72-96 ranks on 4 nodes - -### Weak Scaling (Jacobi) -- `weak_scaling.sh` - Constant work per rank: (1,257), (8,513), (27,769), (64,1025) - -### FMG Scaling -- `fmg_scaling_1node.sh` - FMG solver on 1 node - -## Submission - -```bash -cd $HOME/LSM-Project_2 -mkdir -p logs -bsub < jobs/strong_scaling_1node.sh -bsub < jobs/strong_scaling_2node.sh -bsub < jobs/strong_scaling_4node.sh -bsub < jobs/weak_scaling.sh -bsub < jobs/fmg_scaling_1node.sh -``` - -## Monitor Jobs - -```bash -bstat # Job status -bjobs -l JOB_ID # Detailed info -bpeek JOB_ID # View output -``` - -## MPI Configuration - -All jobs use: -- `--map-by ppr:N:package` - N ranks per socket -- `--bind-to core` - Pin each rank to a core -- NUMBA_NUM_THREADS=1 - Single-threaded Numba kernel - -## Experiment Configuration - -- Numba JIT kernel (1 thread per rank for pure MPI scaling) -- Custom MPI datatypes for zero-copy halo exchange -- Databricks MLflow for tracking -- Hydra multirun for parameter sweeps (N, strategy) diff --git a/jobs/comm_compact.sh b/jobs/comm_compact.sh deleted file mode 100644 index 0328d6f..0000000 --- a/jobs/comm_compact.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -#BSUB -J comm_compact -#BSUB -q hpcintro -#BSUB -n 48 -#BSUB -R "span[ptile=24]" -#BSUB -R "rusage[mem=8GB]" -#BSUB -W 1:00 -#BSUB -o logs/lsf/comm_compact_%J.out -#BSUB -e logs/lsf/comm_compact_%J.err - -# ============================================================================= -# Communication Experiment: COMPACT binding -# 24 ranks packed on 2 nodes (12 per node × 2 nodes) -# Minimizes inter-node communication, tests intra-node bandwidth -# ============================================================================= - -module load mpi -mkdir -p logs/lsf - -export NUMBA_NUM_THREADS=1 -export OMP_NUM_THREADS=1 - -# MPI parameters: 24 ranks compacted across 2 nodes -NP=24 -NPN=12 # ranks per node - -# Compact: 12 ranks per node, fill nodes before spreading -export MPI_OPTIONS="--map-by ppr:$NPN:node --bind-to core" - -echo "=== Communication: Compact binding ===" -echo "NP=$NP, NPN=$NPN (12 ranks/node × 2 nodes)" -echo "MPI_OPTIONS: $MPI_OPTIONS" - -uv run python run_solver.py \ - +experiment=communication \ - hydra/launcher=basic \ - experiment_name=comm_compact \ - mlflow=databricks \ - -m - -echo "Communication compact completed" diff --git a/jobs/comm_spread.sh b/jobs/comm_spread.sh deleted file mode 100644 index 19fc01f..0000000 --- a/jobs/comm_spread.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash -#BSUB -J comm_spread -#BSUB -q hpcintro -#BSUB -n 48 -#BSUB -R "span[ptile=24]" -#BSUB -R "rusage[mem=8GB]" -#BSUB -W 1:00 -#BSUB -o logs/lsf/comm_spread_%J.out -#BSUB -e logs/lsf/comm_spread_%J.err - -# ============================================================================= -# Communication Experiment: SPREAD binding -# 24 ranks spread across 2 nodes (6 per package × 2 packages × 2 nodes) -# Maximizes memory bandwidth, tests inter-node communication -# ============================================================================= - -module load mpi -mkdir -p logs/lsf - -export NUMBA_NUM_THREADS=1 -export OMP_NUM_THREADS=1 - -# MPI parameters: 24 ranks spread across 2 nodes -NP=24 -NPS=6 # ranks per package (socket) - -# Spread: 6 ranks per package, spread across nodes -export MPI_OPTIONS="--map-by ppr:$NPS:package --bind-to core" - -echo "=== Communication: Spread binding ===" -echo "NP=$NP, NPS=$NPS (6 ranks/package × 2 packages × 2 nodes)" -echo "MPI_OPTIONS: $MPI_OPTIONS" - -uv run python run_solver.py \ - +experiment=communication \ - hydra/launcher=basic \ - experiment_name=comm_spread \ - mlflow=databricks \ - -m - -echo "Communication spread completed" diff --git a/jobs/scaling.sh b/jobs/scaling.sh deleted file mode 100644 index 7154d8f..0000000 --- a/jobs/scaling.sh +++ /dev/null @@ -1,81 +0,0 @@ -#!/bin/bash -#BSUB -J scaling -#BSUB -q hpcintro -#BSUB -n 96 -#BSUB -R "span[ptile=24]" -#BSUB -R "rusage[mem=8GB]" -#BSUB -W 1:00 -#BSUB -o logs/lsf/scaling_%J.out -#BSUB -e logs/lsf/scaling_%J.err - -# ============================================================================= -# Scaling Experiments: Strong and Weak scaling for Jacobi and FMG -# 96 cores = 4 nodes × 24 cores = 8 packages (2 per node) -# Hydra sweeper handles: N, strategy, n_ranks -# ============================================================================= - -module load mpi -mkdir -p logs/lsf - -export NUMBA_NUM_THREADS=1 -export OMP_NUM_THREADS=1 - -# Spread ranks across all 8 packages (4 nodes × 2 packages) -# ppr:12:package allows up to 96 ranks spread evenly -export MPI_OPTIONS="--map-by ppr:12:package --bind-to core" - -# Iteration count for scaling experiments -MAX_ITER=200 - -echo "=== Strong Scaling: Jacobi ===" -echo "MPI_OPTIONS: $MPI_OPTIONS" -uv run python run_solver.py \ - +experiment=strong_scaling_jacobi \ - max_iter=$MAX_ITER \ - hydra/launcher=basic \ - mlflow=databricks \ - -m - -echo "=== Strong Scaling: FMG ===" -uv run python run_solver.py \ - +experiment=strong_scaling_fmg \ - max_iter=$MAX_ITER \ - hydra/launcher=basic \ - mlflow=databricks \ - -m - -# ============================================================================= -# Weak Scaling: ~127³ points per rank (local subdomain size) -# N = local_size × ranks_per_dim + 2 (for boundaries) -# 129@1 (127+2), 257@8 (2³ decomp), 513@64 (4³ decomp) -# ============================================================================= - -echo "=== Weak Scaling: Jacobi ===" -for pair in "129,1" "257,8" "513,64"; do - N=$(echo $pair | cut -d',' -f1) - ranks=$(echo $pair | cut -d',' -f2) - echo " N=$N, ranks=$ranks (~127³ per rank)" - uv run python run_solver.py \ - +experiment=weak_scaling_jacobi \ - N=$N n_ranks=$ranks \ - max_iter=$MAX_ITER \ - hydra/launcher=basic \ - mlflow=databricks \ - -m -done - -echo "=== Weak Scaling: FMG ===" -for pair in "129,1" "257,8" "513,64"; do - N=$(echo $pair | cut -d',' -f1) - ranks=$(echo $pair | cut -d',' -f2) - echo " N=$N, ranks=$ranks (~127³ per rank)" - uv run python run_solver.py \ - +experiment=weak_scaling_fmg \ - N=$N n_ranks=$ranks \ - max_iter=$MAX_ITER \ - hydra/launcher=basic \ - mlflow=databricks \ - -m -done - -echo "All scaling experiments completed" diff --git a/jobs/weak_scaling_debug.sh b/jobs/weak_scaling_debug.sh deleted file mode 100644 index 561a856..0000000 --- a/jobs/weak_scaling_debug.sh +++ /dev/null @@ -1,43 +0,0 @@ -#!/bin/bash -#BSUB -J weak_debug -#BSUB -q hpcintro -#BSUB -n 24 -#BSUB -R "span[ptile=24]" -#BSUB -R "rusage[mem=4GB]" -#BSUB -W 0:15 -#BSUB -o logs/lsf/weak_debug_%J.out -#BSUB -e logs/lsf/weak_debug_%J.err - -# Quick debug: just 2 runs to verify everything works - -module load mpi -uv sync -mkdir -p logs/lsf - -export NUMBA_NUM_THREADS=1 -export OMP_NUM_THREADS=1 -export MPI_OPTIONS="--map-by ppr:12:package --bind-to core" -export PMIX_MCA_gds=hash - -echo "=== DEBUG: Testing weak scaling setup ===" -echo "PWD: $(pwd)" -echo "Python: $(which python)" -echo "venv Python: $(uv run which python)" -echo "MPI_OPTIONS: $MPI_OPTIONS" - -# Test using EXACT same config as working strong scaling -# Just override N and n_ranks for weak scaling pair -echo "" -echo "=== Test: Using strong_scaling_jacobi config (KNOWN WORKING) ===" -echo "Overriding: N=129, n_ranks=1" -uv run python run_solver.py \ - +experiment=strong_scaling_jacobi \ - N=129 n_ranks=1 strategy=cubic \ - max_iter=5 \ - hydra/launcher=basic \ - mlflow=databricks \ - -m 2>&1 -echo "Exit code: $?" - -echo "" -echo "=== DEBUG COMPLETE ===" diff --git a/jobs/weak_scaling_v3.sh b/jobs/weak_scaling_v3.sh deleted file mode 100644 index 2d25199..0000000 --- a/jobs/weak_scaling_v3.sh +++ /dev/null @@ -1,67 +0,0 @@ -#!/bin/bash -#BSUB -J weak_scaling -#BSUB -q hpcintro -#BSUB -n 72 -#BSUB -R "span[ptile=24]" -#BSUB -R "rusage[mem=8GB]" -#BSUB -W 6:00 -#BSUB -o logs/lsf/weak_scaling_%J.out -#BSUB -e logs/lsf/weak_scaling_%J.err - -# ============================================================================= -# Weak Scaling: Constant work per rank (3 series × 4 points each) -# -# Series 1 (~129³/rank): 129@1, 257@8, 385@27, 513@64 -# Series 2 (~257³/rank): 257@1, 513@8, 769@27, 1025@64 -# Series 3 (~513³/rank): 513@1, 1025@8, 1537@27, 2049@64 -# ============================================================================= - -module load mpi -uv sync -mkdir -p logs/lsf - -export NUMBA_NUM_THREADS=1 -export OMP_NUM_THREADS=1 -export MPI_OPTIONS="--map-by ppr:12:package --bind-to core" -export PMIX_MCA_gds=hash - -MAX_ITER=100 - -echo "=== Weak Scaling ===" -echo "MPI_OPTIONS: $MPI_OPTIONS" -echo "MAX_ITER: $MAX_ITER" - -# Series 1: ~129³/rank -SERIES_1="129,1 257,8 385,27 513,64" -# Series 2: ~257³/rank -SERIES_2="257,1 513,8 769,27 1025,64" -# Series 3: ~513³/rank -SERIES_3="513,1 1025,8 1537,27 2049,64" - -for rep in 1 2 3 4; do - echo "=== Repetition $rep/4 ===" - for solver in jacobi fmg; do - for strategy in sliced cubic; do - for series in "$SERIES_1" "$SERIES_2" "$SERIES_3"; do - for pair in $series; do - N=$(echo $pair | cut -d',' -f1) - ranks=$(echo $pair | cut -d',' -f2) - echo " [$rep/4] solver=$solver, N=$N, ranks=$ranks, strategy=$strategy" - uv run python run_solver.py \ - +experiment=weak_scaling_${solver} \ - solver=$solver \ - N=$N \ - n_ranks=$ranks \ - strategy=$strategy \ - communicator=custom \ - max_iter=$MAX_ITER \ - hydra/launcher=basic \ - mlflow=databricks 2>&1 - echo " Exit code: $?" - done - done - done - done -done - -echo "Weak scaling completed" diff --git a/jobs/weak_scaling_v4.sh b/jobs/weak_scaling_v4.sh deleted file mode 100644 index 792f9cc..0000000 --- a/jobs/weak_scaling_v4.sh +++ /dev/null @@ -1,73 +0,0 @@ -#!/bin/bash -#BSUB -J weak_scaling -#BSUB -q hpcintro -#BSUB -n 72 -#BSUB -R "span[ptile=24]" -#BSUB -R "rusage[mem=8GB]" -#BSUB -W 6:00 -#BSUB -o logs/lsf/weak_scaling_%J.out -#BSUB -e logs/lsf/weak_scaling_%J.err - -# ============================================================================= -# Weak Scaling: Constant work per rank -# Copy of scaling.sh pattern (which works) -# -# Series 1 (~129³/rank): 129@1, 257@8, 385@27, 513@64 -# Series 2 (~257³/rank): 257@1, 513@8, 769@27, 1025@64 -# ============================================================================= - -module purge -module load mpi -mkdir -p logs/lsf - -export NUMBA_NUM_THREADS=1 -export OMP_NUM_THREADS=1 - -# Spread ranks across packages -export MPI_OPTIONS="--map-by ppr:12:package --bind-to core" - -MAX_ITER=100 - -echo "=== Weak Scaling ===" -echo "MPI_OPTIONS: $MPI_OPTIONS" -echo "MAX_ITER: $MAX_ITER" - -# Series 1: ~129³/rank -# Series 2: ~257³/rank -WEAK_PAIRS="129,1 257,8 385,27 513,64 257,1 513,8 769,27 1025,64" - -for rep in 1 2 3 4; do - echo "" - echo "=== Repetition $rep/4 ===" - - echo "--- Weak Scaling: Jacobi ---" - for pair in $WEAK_PAIRS; do - N=$(echo $pair | cut -d',' -f1) - ranks=$(echo $pair | cut -d',' -f2) - echo " N=$N, ranks=$ranks" - uv run python run_solver.py \ - +experiment=weak_scaling_jacobi \ - N=$N n_ranks=$ranks \ - max_iter=$MAX_ITER \ - hydra/launcher=basic \ - mlflow=databricks \ - -m - done - - echo "--- Weak Scaling: FMG ---" - for pair in $WEAK_PAIRS; do - N=$(echo $pair | cut -d',' -f1) - ranks=$(echo $pair | cut -d',' -f2) - echo " N=$N, ranks=$ranks" - uv run python run_solver.py \ - +experiment=weak_scaling_fmg \ - N=$N n_ranks=$ranks \ - max_iter=$MAX_ITER \ - hydra/launcher=basic \ - mlflow=databricks \ - -m - done -done - -echo "" -echo "Weak scaling completed" diff --git a/pyproject.toml b/pyproject.toml index e43494c..7028a8f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,8 @@ dependencies = [ "textual>=6.6.0", "blessed>=1.25.0", "python-dotenv>=1.2.1", + "zarr>=3.1.5", + "hydra-joblib-launcher>=1.2.0", ] [tool.setuptools] diff --git a/run_solver.py b/run_solver.py index f95f01d..8de0ba4 100644 --- a/run_solver.py +++ b/run_solver.py @@ -122,24 +122,23 @@ def log_metrics_and_timeseries(solver, run_id: str): MlflowClient().log_batch(run_id=run_id, metrics=batch_metrics) -def log_artifact(solver, cfg: DictConfig): - """Save solver output to temp HDF5 and log as MLflow artifact.""" +def log_fields(solver): + """Save solution fields as zarr arrays to MLflow artifacts.""" import tempfile - # Determine output filename - solver_name = cfg.solver.name.upper() - if solver_name == "SPECTRAL": - n_nodes = cfg.N + 1 - filename = f"LDC_{solver_name}_N{n_nodes}_Re{int(cfg.Re)}.h5" - else: - filename = f"LDC_{solver_name}_N{cfg.N}_Re{int(cfg.Re)}.h5" + import zarr + + fields = solver.fields - # Save to temp file, log to MLflow, then cleanup with tempfile.TemporaryDirectory() as tmpdir: - output_path = Path(tmpdir) / filename - solver.save(output_path) - mlflow.log_artifact(str(output_path)) - log.info(f"Logged artifact: {filename}") + # Save each field as separate zarr array + for name in ["x", "y", "u", "v", "p"]: + arr = getattr(fields, name) + zarr_path = Path(tmpdir) / f"{name}.zarr" + zarr.save(zarr_path, arr) + mlflow.log_artifact(str(zarr_path), artifact_path="fields") + + log.info("Logged fields: x, y, u, v, p (zarr)") # ============================================================================= @@ -166,8 +165,20 @@ def main(cfg: DictConfig) -> None: else: run_name = f"{solver_name}_N{cfg.N}_Re{int(cfg.Re)}" + # Check for parent run (from sweep callback) + parent_run_id = os.environ.get("MLFLOW_PARENT_RUN_ID") + # Run with MLflow tracking - with mlflow.start_run(run_name=run_name) as run: + # Use nested=True when parent run is active (same process in local multirun) + run_tags = {} + nested = False + if parent_run_id: + run_tags["mlflow.parentRunId"] = parent_run_id + run_tags["parent_run_id"] = parent_run_id # Also store as regular tag for querying + run_tags["sweep"] = "child" + nested = True # Required when parent run is active in same process + + with mlflow.start_run(run_name=run_name, tags=run_tags, nested=nested) as run: log_params(solver) # Log Hydra config as artifact @@ -185,7 +196,7 @@ def main(cfg: DictConfig) -> None: # Log results log_metrics_and_timeseries(solver, run.info.run_id) - log_artifact(solver, cfg) + log_fields(solver) log.info( f"Done: {solver.metrics.iterations} iter, " diff --git a/spectral_multigrid_todo.md b/spectral_multigrid_todo.md index cfdcec5..c6e7bd1 100644 --- a/spectral_multigrid_todo.md +++ b/spectral_multigrid_todo.md @@ -1,67 +1,233 @@ +# Spectral Multigrid Implementation Plan -# Spectral Multigrid TODO List - -## 1. Refactor for Multi-Level Support -- [ ] Introduce a `SpectralLevel` class holding: - - basis objects - - grids (full + inner) - - shapes - - differentiation matrices - - spectral field arrays -- [ ] Refactor current single-level routines to take a `level` argument: - - `_setup_grids(level)` - - `_build_diff_matrices(level)` - - `_initialize_lid_velocity(level)` - - `_compute_residuals(level, u, v, p)` - - `_enforce_boundary_conditions(level, u, v)` - - `_compute_adaptive_timestep(level)` - -## 2. Build the Grid Hierarchy -- [ ] Define number of levels and `(nx, ny)` per level. -- [ ] Ensure coarse Lobatto nodes are subsets of fine nodes. -- [ ] For each level, construct basis, grids, operators, fields, BCs. - -## 3. Restriction & Prolongation -- [ ] Build restriction operators for: - - u, v on full grids - - p on inner grids -- [ ] Build prolongation operators for: - - u, v on full grids - - p on inner grids -- [ ] Wrap them into helper functions for U = (u, v, p). - -## 4. Turn RK4 Into a Per-Level Smoother -- [ ] Generalize `step()` to operate on a specific level. -- [ ] Create `smooth(level, n_steps)` calling multiple RK4 pseudo-steps. - -## 5. Residuals Per Level -- [ ] Implement `compute_residual_norms(level)` using that level's arrays. -- [ ] Ensure `_compute_residuals` operates correctly per level. - -## 6. Full Approximation Scheme (FAS) V-cycle -- [ ] Presmoothing on level ℓ. -- [ ] Compute fine-grid residuals. -- [ ] Restrict U and F(U) to coarse grid. -- [ ] Compute FAS τ term for coarse grid. -- [ ] Recursive coarse solve via `v_cycle(ℓ-1)`. -- [ ] Prolongate coarse correction. -- [ ] Apply correction to fine-level U. -- [ ] Postsmoothing. - -## 7. Coarsest Level Solver -- [ ] Implement `v_cycle(0)` as many smoothing iterations until convergence. - -## 8. Full Multigrid (FMG) Driver -- [ ] Coarse-level initialization and solve. -- [ ] Prolongate to next level and apply V-cycles. -- [ ] Continue until finest level reached. - -## 9. Integration with Existing Solver Interface -- [ ] `solve()` calls FMG or repeated V-cycles. -- [ ] Logging residuals from finest level. -- [ ] `_finalize_fields()` pulls finest-level fields into global output. - -## 10. Parameters & Tuning -- [ ] Extend `SpectralParameters` with multigrid parameters. -- [ ] Log per-cycle residuals, work units, timings. +Based on Zhang & Xi (2010): "An explicit Chebyshev pseudospectral multigrid method for incompressible Navier-Stokes equations" +## Overview + +Three multigrid strategies to implement incrementally: + +| Method | Speedup | Complexity | +|--------|---------|------------| +| **FSG** (Full Single Grid) | ~10-18x | Simplest | +| **VMG** (V-cycle Multigrid) | ~50-90% reduction | Medium | +| **FMG** (Full Multigrid) | ~100x | FSG + VMG combined | + +--- + +## Phase 1: Foundation & FSG + +### 1.1 SpectralLevel Class +- [ ] Create `SpectralLevel` dataclass/class holding: + - `n`: polynomial order for this level + - `basis`: basis object (Chebyshev/Legendre) + - `x, y`: 1D node arrays + - `X, Y`: 2D meshgrid arrays (full grid) + - `X_inner, Y_inner`: 2D meshgrid arrays (inner grid for pressure) + - `Dx, Dy`: differentiation matrices (full grid) + - `Dx_inner, Dy_inner`: differentiation matrices (inner grid) + - `dx_min, dy_min`: minimum grid spacing (for CFL) + - Field arrays: `u, v, p` and RK4 stage storage + +### 1.2 Grid Hierarchy Construction +- [ ] Define `build_hierarchy(n_fine, n_levels)`: + - Coarsening: N^c = N^f / 2 (full coarsening) + - Example: 64 → 32 → 16 (3 levels) +- [ ] Verify coarse Lobatto nodes are subsets of fine nodes +- [ ] Construct `SpectralLevel` for each level + +### 1.3 Prolongation Operator (Solution Interpolation) +- [ ] Implement `prolongate_solution(u_coarse, level_coarse, level_fine)`: + - For velocities (u, v): FFT-based Chebyshev interpolation + 1. Compute Chebyshev coefficients via DCT + 2. Pad with zeros for higher frequencies + 3. Evaluate at fine grid points via inverse DCT + - For pressure: Lagrange polynomial interpolation (order N-2) +- [ ] Wrap into `prolongate_fields(U_coarse, level_coarse, level_fine)` for (u, v, p) + +### 1.4 Per-Level Solver Routines +- [ ] Refactor `_compute_residuals(level, u, v, p)` to use level's operators +- [ ] Refactor `_enforce_boundary_conditions(level, u, v)` +- [ ] Refactor `_compute_adaptive_timestep(level)` using level's dx_min, dy_min +- [ ] Create `smooth(level, n_steps)` - multiple RK4 pseudo-time steps on one level + +### 1.5 FSG Driver +- [ ] Implement `solve_fsg(n_levels)`: + ``` + for level in levels (coarse to fine): + if level == coarsest: + initialize with zeros + else: + prolongate solution from previous level + + while not converged: + smooth(level, 1) # RK4 steps + check convergence on this level + ``` +- [ ] Use relaxed convergence criterion on coarse levels (e.g., 10x tolerance) +- [ ] Log per-level iteration counts and residuals + +### 1.6 FSG Validation +- [ ] Compare FSG vs SG for N=32, 64 at Re=100 +- [ ] Verify same solution accuracy +- [ ] Measure speedup (expect ~10-18x) + +--- + +## Phase 2: VMG (V-cycle Multigrid with FAS) + +### 2.1 Restriction Operator (Residuals) +- [ ] Implement `restrict_residual(r_fine, level_fine, level_coarse)`: + - FFT-based: compute Chebyshev coefficients, truncate high frequencies + - Row-column algorithm for 2D (restrict x-direction, then y-direction) + - Set boundary residuals to zero (velocities have Dirichlet BCs) +- [ ] Handle inner-grid restriction for pressure residuals + +### 2.2 Restriction Operator (Variables) +- [ ] Implement `restrict_solution(u_fine, level_fine, level_coarse)`: + - Direct injection: coarse nodes are subset of fine nodes + - `u_coarse[i,j] = u_fine[2i, 2j]` + +### 2.3 Prolongation Operator (Corrections) +- [ ] Implement `prolongate_correction(c_coarse, level_coarse, level_fine)`: + - Same FFT-based approach as solution prolongation + - For pressure: Lagrange interpolation on inner grid + +### 2.4 FAS Coarse-Grid Equation +- [ ] Compute τ-correction (FAS forcing term): + ``` + τ = R(I_h^H * u_h) - I_h^H * R(u_h) + ``` + where R is the residual operator, I_h^H is restriction +- [ ] Modified coarse-grid solve includes τ in RHS + +### 2.5 V-cycle Implementation +- [ ] Implement `v_cycle(level)`: + ```python + def v_cycle(level): + if level == coarsest: + # Solve to convergence (or many smoothing steps) + while not converged: + smooth(level, 1) + return + + # 1. Presmooth on fine level + smooth(level, n_pre) # typically n_pre = 1-3 + + # 2. Compute fine-grid residual + r_fine = compute_residual(level) + + # 3. Restrict solution and residual to coarse + u_coarse_old = restrict_solution(u_fine, level, level-1) + r_coarse = restrict_residual(r_fine, level, level-1) + + # 4. Set up FAS coarse problem (includes τ-correction) + # 5. Recursively solve on coarse grid + v_cycle(level - 1) + + # 6. Compute and prolongate correction + correction = u_coarse_new - u_coarse_old + correction_fine = prolongate_correction(correction, level-1, level) + + # 7. Update fine-grid solution + u_fine += correction_fine + + # 8. Postsmooth (often unnecessary per Zhang & Xi) + # smooth(level, n_post) + ``` + +### 2.6 VMG Driver +- [ ] Implement `solve_vmg()`: + ``` + initialize finest level with zeros + while not converged: + v_cycle(finest_level) + check convergence + ``` + +### 2.7 VMG Validation +- [ ] Compare VMG vs SG and FSG for N=32, 64 at Re=100 +- [ ] Verify solution accuracy maintained +- [ ] Measure speedup (expect additional 50-90% over FSG) + +--- + +## Phase 3: FMG (Full Multigrid) + +### 3.1 FMG Driver +- [ ] Implement `solve_fmg()`: + ```python + def solve_fmg(): + # Start on coarsest level + level = coarsest + initialize_with_zeros(level) + solve_to_convergence(level) # SG on coarsest + + # Work up through levels + for level in levels[1:]: # coarse to fine + # Prolongate converged solution as initial guess + u_init = prolongate_solution(u_converged, level-1, level) + + # Apply V-cycles until convergence + while not converged: + v_cycle(level) # Uses all levels from 0 to current + check convergence + + return solution on finest level + ``` + +### 3.2 FMG Validation +- [ ] Compare FMG vs all other methods +- [ ] Verify solution accuracy +- [ ] Measure speedup (expect ~100x over SG) + +--- + +## Phase 4: Integration & Polish + +### 4.1 Solver Interface +- [ ] Add `multigrid_method` parameter: `"none"`, `"fsg"`, `"vmg"`, `"fmg"` +- [ ] `solve()` dispatches to appropriate driver +- [ ] Backward compatible: `multigrid_method="none"` uses current SG implementation + +### 4.2 Configuration +- [ ] Extend `SpectralParameters` with: + ```python + n_levels: int = 3 + multigrid_method: str = "fmg" + n_presmooth: int = 1 + n_postsmooth: int = 0 + coarse_tolerance_factor: float = 10.0 + ``` + +### 4.3 Logging & Metrics +- [ ] Log per-level residuals +- [ ] Track V-cycle count +- [ ] Record time spent per level +- [ ] MLflow integration for multigrid metrics + +### 4.4 Comprehensive Validation +- [ ] Ghia benchmark comparison for all methods +- [ ] Re = 100, 400, 1000 +- [ ] N = 32, 64, 96 +- [ ] Speedup plots (wall clock time vs N) +- [ ] Accuracy verification (solution differences < tolerance) + +--- + +## Key Implementation Notes from Zhang & Xi (2010) + +1. **Postsmoothing often unnecessary** - can cause instabilities +2. **Presmoothing steps**: 1-3 typically sufficient (paper uses 1,2,3 for levels) +3. **Convergence criterion**: RMS of continuity residual < ε (ε = 10⁻⁴ for cavity flow) +4. **CFL**: Keep around 2.5 (upper bound of RK4 stability) +5. **β² = 5.0**: Artificial compressibility coefficient (don't change) +6. **Corner singularities**: Your corner_smoothing handles this (paper uses subtraction method) + +## Expected Results (Re=100, N=96) + +| Method | Relative Time | +|--------|---------------| +| SG | 100% | +| FSG-3 | ~5.5% | +| VMG-111 | ~1.5% | +| FMG-111 | ~1.2% | diff --git a/src/utils/mlflow_callback.py b/src/utils/mlflow_callback.py new file mode 100644 index 0000000..e73cc10 --- /dev/null +++ b/src/utils/mlflow_callback.py @@ -0,0 +1,127 @@ +"""Hydra callback for MLflow parent run management during sweeps.""" + +import logging +import os +from datetime import datetime, timedelta + +from hydra.core.utils import JobReturn +from hydra.experimental.callback import Callback +from omegaconf import DictConfig, OmegaConf + +log = logging.getLogger(__name__) + + +class MLflowSweepCallback(Callback): + """Creates or reuses a parent MLflow run for Hydra multiruns. + + Child runs will be nested under the parent, making it easy to: + - View all sweep runs together + - Log sweep-level config to parent + - Group multiple sweeps under the same parent (shared sweep_name) + + If a parent run with the same sweep_name exists and was created within + the last hour, it will be reused. Otherwise, a new parent is created. + """ + + def __init__(self) -> None: + self._parent_run = None + self._parent_run_id = None + self._owns_parent = False # Whether we created the parent (vs reusing) + + def _find_recent_parent(self, experiment_name: str, sweep_name: str, max_age_hours: float = 1.0): + """Find a recent parent run with the same sweep_name.""" + import mlflow + + try: + # Search for parent runs with matching name + runs = mlflow.search_runs( + experiment_names=[experiment_name], + filter_string=f"tags.sweep = 'parent' AND tags.`mlflow.runName` = '{sweep_name}'", + order_by=["start_time DESC"], + max_results=1, + ) + + if runs.empty: + return None + + # Check if recent enough + run = runs.iloc[0] + start_time = run["start_time"] + + # Handle timezone-aware timestamps + if hasattr(start_time, 'tzinfo') and start_time.tzinfo is not None: + now = datetime.now(start_time.tzinfo) + else: + now = datetime.now() + + age = now - start_time + if age < timedelta(hours=max_age_hours): + return run["run_id"] + + except Exception as e: + log.warning(f"Error searching for parent run: {e}") + + return None + + def on_multirun_start(self, config: DictConfig, **kwargs) -> None: + """Create or reuse parent run before sweep starts.""" + import mlflow + from dotenv import load_dotenv + + load_dotenv() + + # Setup MLflow tracking + tracking_uri = config.mlflow.get("tracking_uri", "./mlruns") + mlflow.set_tracking_uri(tracking_uri) + + # Build experiment name + experiment_name = config.experiment_name + project_prefix = config.mlflow.get("project_prefix", "") + if project_prefix and not experiment_name.startswith("/"): + full_experiment_name = f"{project_prefix}/{experiment_name}" + else: + full_experiment_name = experiment_name + + mlflow.set_experiment(full_experiment_name) + + sweep_name = config.get("sweep_name", "sweep") + + # Check for existing recent parent run with same name + existing_parent_id = self._find_recent_parent(full_experiment_name, sweep_name) + + if existing_parent_id: + # Reuse existing parent + self._parent_run_id = existing_parent_id + self._owns_parent = False + log.info(f"Reusing existing parent run: {self._parent_run_id}") + else: + # Create new parent run + self._parent_run = mlflow.start_run(run_name=sweep_name) + self._parent_run_id = self._parent_run.info.run_id + self._owns_parent = True + + # Log sweep config to parent + mlflow.log_dict(OmegaConf.to_container(config), "sweep_config.yaml") + mlflow.set_tag("sweep", "parent") + + # Log HPC job info if available + job_id = os.environ.get("LSB_JOBID") + if job_id: + mlflow.set_tag("lsf.job_id", job_id) + mlflow.set_tag("lsf.job_name", os.environ.get("LSB_JOBNAME", "")) + + log.info(f"Created parent run: {self._parent_run_id}") + + # Set env var so child processes can find parent + os.environ["MLFLOW_PARENT_RUN_ID"] = self._parent_run_id + + def on_multirun_end(self, config: DictConfig, **kwargs) -> None: + """End parent run after sweep completes (only if we created it).""" + import mlflow + + if self._owns_parent and self._parent_run is not None: + mlflow.end_run() + log.info(f"Ended parent run: {self._parent_run_id}") + + # Clean up env var + os.environ.pop("MLFLOW_PARENT_RUN_ID", None) From e6a626aea71cb6b7132caeebdfb2572eb1a3d36f Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 16:33:11 +0100 Subject: [PATCH 19/34] genious plotting! Plots ghia validation under each child run AND comparison under the parent --- conf/experiment/spectral_validation.yaml | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/conf/experiment/spectral_validation.yaml b/conf/experiment/spectral_validation.yaml index a714371..7485454 100644 --- a/conf/experiment/spectral_validation.yaml +++ b/conf/experiment/spectral_validation.yaml @@ -12,13 +12,12 @@ experiment_name: LDC-Validation sweep_name: validation # Validation polynomial orders (N+1 nodes) and Reynolds numbers -N: 31 +N: 15 Re: 100 -tolerance: 1.0e-7 -max_iterations: 100000 +tolerance: 1.0e-6 +max_iterations: 30000 hydra: sweeper: params: - N: 15,23,31,47 - Re: 100,400,1000 + N: 15,23 From 2f618aca284057f9e7cb8915bfff29799c7b6cad Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Thu, 4 Dec 2025 16:56:28 +0100 Subject: [PATCH 20/34] multigrid more --- conf/experiment/fv_validation.yaml | 10 +- conf/experiment/multigrid.yaml | 27 ++ conf/experiment/plotting_test.yaml | 11 - conf/experiment/quick_test.yaml | 12 - conf/experiment/sweep_test.yaml | 10 - conf/solver/spectral.yaml | 5 + conf/solver/spectral_fsg.yaml | 13 + run_solver.py | 4 + src/solvers/datastructures.py | 5 + src/solvers/spectral_multigrid.py | 728 +++++++++++++++++++++++++++++ src/solvers/spectral_solver.py | 94 ++++ 11 files changed, 881 insertions(+), 38 deletions(-) create mode 100644 conf/experiment/multigrid.yaml delete mode 100644 conf/experiment/plotting_test.yaml delete mode 100644 conf/experiment/quick_test.yaml delete mode 100644 conf/experiment/sweep_test.yaml create mode 100644 conf/solver/spectral_fsg.yaml create mode 100644 src/solvers/spectral_multigrid.py diff --git a/conf/experiment/fv_validation.yaml b/conf/experiment/fv_validation.yaml index cf2de3e..38997a6 100644 --- a/conf/experiment/fv_validation.yaml +++ b/conf/experiment/fv_validation.yaml @@ -12,13 +12,13 @@ experiment_name: LDC-Validation sweep_name: validation # Validation grid sizes and Reynolds numbers -N: 64 +N: 32 Re: 100 -tolerance: 1.0e-7 -max_iterations: 50000 +tolerance: 1.0e-6 +max_iterations: 10000 hydra: sweeper: params: - N: 32,64,128 - Re: 100,400,1000 + N: 16,32,64 + Re: 100 diff --git a/conf/experiment/multigrid.yaml b/conf/experiment/multigrid.yaml new file mode 100644 index 0000000..609c293 --- /dev/null +++ b/conf/experiment/multigrid.yaml @@ -0,0 +1,27 @@ +# @package _global_ +# Multigrid testing experiment for spectral solver +# +# Usage - Compare single-grid vs FSG: +# uv run python run_solver.py -m +experiment=multigrid +# +# Usage - Single run: +# uv run python run_solver.py +experiment=multigrid solver=spectral +# uv run python run_solver.py +experiment=multigrid solver=spectral_fsg + +experiment_name: Multigrid-Test +sweep_name: multigrid-sweep + +defaults: + - override /hydra/launcher: joblib + +# Sweep configuration +hydra: + mode: MULTIRUN + sweeper: + params: + solver: spectral,spectral_fsg + N: "63" + +Re: 100 +tolerance: 1.0e-4 +max_iterations: 50000 diff --git a/conf/experiment/plotting_test.yaml b/conf/experiment/plotting_test.yaml deleted file mode 100644 index 20f5f79..0000000 --- a/conf/experiment/plotting_test.yaml +++ /dev/null @@ -1,11 +0,0 @@ -# @package _global_ -# Quick plotting test - minimal iterations for fast verification -# Usage: uv run python run_solver.py +experiment=plotting_test solver=fv -# uv run python run_solver.py +experiment=plotting_test solver=spectral - -experiment_name: Plotting-Test -sweep_name: plotting-test-sweep - -Re: 100 -tolerance: 1.0e-4 -max_iterations: 50 diff --git a/conf/experiment/quick_test.yaml b/conf/experiment/quick_test.yaml deleted file mode 100644 index aa3f194..0000000 --- a/conf/experiment/quick_test.yaml +++ /dev/null @@ -1,12 +0,0 @@ -# @package _global_ -# Quick test configuration for debugging -# Usage: uv run python run_solver.py +experiment=quick_test solver=fv -# uv run python run_solver.py +experiment=quick_test solver=spectral - -experiment_name: Quick-Test -sweep_name: quick-test-sweep - -N: 16 -Re: 100 -tolerance: 1.0e-4 -max_iterations: 100 diff --git a/conf/experiment/sweep_test.yaml b/conf/experiment/sweep_test.yaml deleted file mode 100644 index c1a54e0..0000000 --- a/conf/experiment/sweep_test.yaml +++ /dev/null @@ -1,10 +0,0 @@ -# @package _global_ -# Minimal sweep test - verifies nested MLflow runs work correctly -# Usage: uv run python run_solver.py -m +experiment=sweep_test solver=fv,spectral N=8,16 - -experiment_name: Sweep-Test -sweep_name: test-sweep - -Re: 100 -tolerance: 1.0e-3 -max_iterations: 50 diff --git a/conf/solver/spectral.yaml b/conf/solver/spectral.yaml index 099dfe4..1919e25 100644 --- a/conf/solver/spectral.yaml +++ b/conf/solver/spectral.yaml @@ -6,3 +6,8 @@ basis_type: chebyshev # "chebyshev" or "legendre" CFL: 0.5 beta_squared: 5.0 # artificial compressibility parameter corner_smoothing: 0.15 # fraction of grid points to smooth at corners + +# Multigrid settings +multigrid: none # "none", "fsg", "vmg", "fmg" +n_levels: 3 # number of grid levels for multigrid +coarse_tolerance_factor: 10.0 # relaxed tolerance on coarse grids diff --git a/conf/solver/spectral_fsg.yaml b/conf/solver/spectral_fsg.yaml new file mode 100644 index 0000000..bcce104 --- /dev/null +++ b/conf/solver/spectral_fsg.yaml @@ -0,0 +1,13 @@ +# Spectral solver with FSG multigrid +name: spectral + +# Spectral-specific parameters +basis_type: chebyshev +CFL: 0.5 +beta_squared: 5.0 +corner_smoothing: 0.15 + +# FSG Multigrid enabled +multigrid: fsg +n_levels: 3 +coarse_tolerance_factor: 1.0 # Same tolerance on all levels diff --git a/run_solver.py b/run_solver.py index 8de0ba4..b6207d9 100644 --- a/run_solver.py +++ b/run_solver.py @@ -80,6 +80,10 @@ def create_solver(cfg: DictConfig): CFL=cfg.solver.CFL, beta_squared=cfg.solver.beta_squared, corner_smoothing=cfg.solver.corner_smoothing, + # Multigrid settings + multigrid=cfg.solver.get("multigrid", "none"), + n_levels=cfg.solver.get("n_levels", 3), + coarse_tolerance_factor=cfg.solver.get("coarse_tolerance_factor", 10.0), ) else: raise ValueError(f"Unknown solver: {solver_name}") diff --git a/src/solvers/datastructures.py b/src/solvers/datastructures.py index a79f155..3e5df78 100644 --- a/src/solvers/datastructures.py +++ b/src/solvers/datastructures.py @@ -238,6 +238,11 @@ class SpectralParameters(Parameters): corner_smoothing: float = 0.15 method: str = "Spectral-AC" + # Multigrid settings + multigrid: str = "none" # "none", "fsg", "vmg", "fmg" + n_levels: int = 3 + coarse_tolerance_factor: float = 10.0 + @dataclass class SpectralSolverFields: diff --git a/src/solvers/spectral_multigrid.py b/src/solvers/spectral_multigrid.py new file mode 100644 index 0000000..cab35f1 --- /dev/null +++ b/src/solvers/spectral_multigrid.py @@ -0,0 +1,728 @@ +"""Spectral multigrid implementation for lid-driven cavity solver. + +Based on Zhang & Xi (2010): "An explicit Chebyshev pseudospectral multigrid +method for incompressible Navier-Stokes equations" + +Implements: +- FSG (Full Single Grid): Sequential solve from coarse to fine +- VMG (V-cycle Multigrid with FAS): Coming in Phase 2 +- FMG (Full Multigrid): Coming in Phase 3 +""" + +import logging +from dataclasses import dataclass +from typing import List, Tuple, Optional + +import numpy as np +from scipy.fft import dct, idct + +log = logging.getLogger(__name__) + + +# ============================================================================= +# SpectralLevel: Data structure for one multigrid level +# ============================================================================= + + +@dataclass +class SpectralLevel: + """Data structure holding all arrays and operators for one multigrid level. + + Attributes + ---------- + n : int + Polynomial order (gives n+1 nodes per dimension) + level_idx : int + Level index (0 = coarsest, increasing = finer) + """ + + # Grid info + n: int # polynomial order + level_idx: int + + # 1D node arrays + x_nodes: np.ndarray + y_nodes: np.ndarray + + # 2D meshgrid arrays (full grid for velocities) + X: np.ndarray + Y: np.ndarray + + # 2D meshgrid arrays (inner grid for pressure) + X_inner: np.ndarray + Y_inner: np.ndarray + + # Grid shapes + shape_full: Tuple[int, int] + shape_inner: Tuple[int, int] + + # Minimum grid spacing for CFL + dx_min: float + dy_min: float + + # Differentiation matrices (2D Kronecker form) + Dx: np.ndarray # d/dx on full grid + Dy: np.ndarray # d/dy on full grid + Dxx: np.ndarray # d²/dx² on full grid + Dyy: np.ndarray # d²/dy² on full grid + Laplacian: np.ndarray # ∇² on full grid + Dx_inner: np.ndarray # d/dx on inner grid + Dy_inner: np.ndarray # d/dy on inner grid + + # Solution arrays (flattened) + u: np.ndarray # velocity u on full grid + v: np.ndarray # velocity v on full grid + p: np.ndarray # pressure on inner grid + + # Previous iteration (for convergence) + u_prev: np.ndarray + v_prev: np.ndarray + + # RK4 stage buffers + u_stage: np.ndarray + v_stage: np.ndarray + p_stage: np.ndarray + + # Residual arrays + R_u: np.ndarray + R_v: np.ndarray + R_p: np.ndarray + + # Work buffers for derivatives + du_dx: np.ndarray + du_dy: np.ndarray + dv_dx: np.ndarray + dv_dy: np.ndarray + lap_u: np.ndarray + lap_v: np.ndarray + dp_dx: np.ndarray + dp_dy: np.ndarray + dp_dx_inner: np.ndarray + dp_dy_inner: np.ndarray + + @property + def n_nodes_full(self) -> int: + return self.shape_full[0] * self.shape_full[1] + + @property + def n_nodes_inner(self) -> int: + return self.shape_inner[0] * self.shape_inner[1] + + +def build_spectral_level( + n: int, + level_idx: int, + basis_x, + basis_y, + Lx: float = 1.0, + Ly: float = 1.0, +) -> SpectralLevel: + """Construct a SpectralLevel with all operators and arrays. + + Parameters + ---------- + n : int + Polynomial order (n+1 nodes per dimension) + level_idx : int + Level index in hierarchy + basis_x, basis_y : Basis objects + Spectral basis (Chebyshev or Legendre Lobatto) + Lx, Ly : float + Domain dimensions + + Returns + ------- + SpectralLevel + Fully initialized level + """ + # Grid shapes + shape_full = (n + 1, n + 1) + shape_inner = (n - 1, n - 1) + n_full = shape_full[0] * shape_full[1] + n_inner = shape_inner[0] * shape_inner[1] + + # 1D nodes + x_nodes = basis_x.nodes(n + 1) + y_nodes = basis_y.nodes(n + 1) + + # 2D meshgrids + X, Y = np.meshgrid(x_nodes, y_nodes, indexing="ij") + x_inner = x_nodes[1:-1] + y_inner = y_nodes[1:-1] + X_inner, Y_inner = np.meshgrid(x_inner, y_inner, indexing="ij") + + # Grid spacing + dx_min = np.min(np.diff(x_nodes)) + dy_min = np.min(np.diff(y_nodes)) + + # Build differentiation matrices + Dx_1d = basis_x.diff_matrix(x_nodes) + Dy_1d = basis_y.diff_matrix(y_nodes) + + Ix = np.eye(n + 1) + Iy = np.eye(n + 1) + Dx = np.kron(Dx_1d, Iy) + Dy = np.kron(Ix, Dy_1d) + + Dxx_1d = Dx_1d @ Dx_1d + Dyy_1d = Dy_1d @ Dy_1d + Dxx = np.kron(Dxx_1d, Iy) + Dyy = np.kron(Ix, Dyy_1d) + Laplacian = Dxx + Dyy + + # Inner grid diff matrices + Dx_inner_1d = basis_x.diff_matrix(x_inner) + Dy_inner_1d = basis_y.diff_matrix(y_inner) + Ix_inner = np.eye(n - 1) + Iy_inner = np.eye(n - 1) + Dx_inner = np.kron(Dx_inner_1d, Iy_inner) + Dy_inner = np.kron(Ix_inner, Dy_inner_1d) + + # Allocate solution and work arrays + return SpectralLevel( + n=n, + level_idx=level_idx, + x_nodes=x_nodes, + y_nodes=y_nodes, + X=X, + Y=Y, + X_inner=X_inner, + Y_inner=Y_inner, + shape_full=shape_full, + shape_inner=shape_inner, + dx_min=dx_min, + dy_min=dy_min, + Dx=Dx, + Dy=Dy, + Dxx=Dxx, + Dyy=Dyy, + Laplacian=Laplacian, + Dx_inner=Dx_inner, + Dy_inner=Dy_inner, + # Solution arrays + u=np.zeros(n_full), + v=np.zeros(n_full), + p=np.zeros(n_inner), + u_prev=np.zeros(n_full), + v_prev=np.zeros(n_full), + u_stage=np.zeros(n_full), + v_stage=np.zeros(n_full), + p_stage=np.zeros(n_inner), + R_u=np.zeros(n_full), + R_v=np.zeros(n_full), + R_p=np.zeros(n_inner), + du_dx=np.zeros(n_full), + du_dy=np.zeros(n_full), + dv_dx=np.zeros(n_full), + dv_dy=np.zeros(n_full), + lap_u=np.zeros(n_full), + lap_v=np.zeros(n_full), + dp_dx=np.zeros(n_full), + dp_dy=np.zeros(n_full), + dp_dx_inner=np.zeros(n_inner), + dp_dy_inner=np.zeros(n_inner), + ) + + +# ============================================================================= +# Grid Hierarchy +# ============================================================================= + + +def build_hierarchy( + n_fine: int, + n_levels: int, + basis_x, + basis_y, + Lx: float = 1.0, + Ly: float = 1.0, +) -> List[SpectralLevel]: + """Build multigrid hierarchy from fine to coarse. + + Parameters + ---------- + n_fine : int + Polynomial order on finest grid + n_levels : int + Number of multigrid levels + basis_x, basis_y : Basis objects + Spectral basis objects + + Returns + ------- + List[SpectralLevel] + List of levels, index 0 = coarsest, index -1 = finest + """ + # Compute polynomial orders for each level (full coarsening: N/2) + orders = [] + n = n_fine + for _ in range(n_levels): + orders.append(n) + n = n // 2 + if n < 3: # Minimum usable grid + break + + # Reverse so coarsest is first + orders = orders[::-1] + + log.info(f"Building {len(orders)}-level hierarchy: N = {orders}") + + # Verify coarse nodes are subset of fine nodes (for Lobatto grids) + # This is automatic for N_c = N_f / 2 with Lobatto nodes + + levels = [] + for idx, n in enumerate(orders): + level = build_spectral_level(n, idx, basis_x, basis_y, Lx, Ly) + levels.append(level) + + return levels + + +# ============================================================================= +# Prolongation (Coarse to Fine Interpolation) +# ============================================================================= + + +def prolongate_chebyshev_1d(u_coarse: np.ndarray, n_fine: int) -> np.ndarray: + """Interpolate 1D array from coarse to fine Chebyshev-Lobatto grid. + + Uses polynomial fitting and evaluation at fine grid points. + + Parameters + ---------- + u_coarse : np.ndarray + Values on coarse Chebyshev-Lobatto grid (n_coarse points) + n_fine : int + Number of points on fine grid + + Returns + ------- + np.ndarray + Interpolated values on fine grid + """ + from numpy.polynomial.chebyshev import chebfit, chebval + + n_coarse = len(u_coarse) + + if n_coarse == n_fine: + return u_coarse.copy() + + # Chebyshev-Lobatto nodes on [-1, 1] + x_coarse = np.cos(np.pi * np.arange(n_coarse) / (n_coarse - 1)) + x_fine = np.cos(np.pi * np.arange(n_fine) / (n_fine - 1)) + + # Fit Chebyshev polynomial to coarse data + coeffs = chebfit(x_coarse, u_coarse, deg=n_coarse - 1) + + # Evaluate at fine grid points + u_fine = chebval(x_fine, coeffs) + + return u_fine + + +def prolongate_2d( + u_coarse_2d: np.ndarray, + shape_fine: Tuple[int, int], +) -> np.ndarray: + """Prolongate 2D field from coarse to fine grid. + + Uses row-column algorithm: interpolate in x-direction, then y-direction. + + Parameters + ---------- + u_coarse_2d : np.ndarray + Field on coarse grid, shape (nx_c, ny_c) + shape_fine : tuple + Target shape (nx_f, ny_f) + + Returns + ------- + np.ndarray + Field on fine grid + """ + nx_c, ny_c = u_coarse_2d.shape + nx_f, ny_f = shape_fine + + # Interpolate in x-direction (column by column) + temp = np.zeros((nx_f, ny_c)) + for j in range(ny_c): + temp[:, j] = prolongate_chebyshev_1d(u_coarse_2d[:, j], nx_f) + + # Interpolate in y-direction (row by row) + u_fine_2d = np.zeros((nx_f, ny_f)) + for i in range(nx_f): + u_fine_2d[i, :] = prolongate_chebyshev_1d(temp[i, :], ny_f) + + return u_fine_2d + + +def prolongate_solution( + level_coarse: SpectralLevel, + level_fine: SpectralLevel, +) -> None: + """Prolongate solution (u, v, p) from coarse level to fine level. + + Modifies level_fine.u, level_fine.v, level_fine.p in place. + + Parameters + ---------- + level_coarse : SpectralLevel + Source (coarse) level with converged solution + level_fine : SpectralLevel + Target (fine) level to receive interpolated solution + """ + # Prolongate velocities (full grid) + u_coarse_2d = level_coarse.u.reshape(level_coarse.shape_full) + v_coarse_2d = level_coarse.v.reshape(level_coarse.shape_full) + + u_fine_2d = prolongate_2d(u_coarse_2d, level_fine.shape_full) + v_fine_2d = prolongate_2d(v_coarse_2d, level_fine.shape_full) + + level_fine.u[:] = u_fine_2d.ravel() + level_fine.v[:] = v_fine_2d.ravel() + + # Prolongate pressure (inner grid) + p_coarse_2d = level_coarse.p.reshape(level_coarse.shape_inner) + p_fine_2d = prolongate_2d(p_coarse_2d, level_fine.shape_inner) + level_fine.p[:] = p_fine_2d.ravel() + + log.debug( + f"Prolongated solution from level {level_coarse.level_idx} " + f"(N={level_coarse.n}) to level {level_fine.level_idx} (N={level_fine.n})" + ) + + +# ============================================================================= +# Level-Specific Solver Routines +# ============================================================================= + + +class MultigridSmoother: + """Performs RK4 smoothing iterations on a single level. + + Encapsulates the time-stepping logic for one multigrid level. + """ + + def __init__( + self, + level: SpectralLevel, + Re: float, + beta_squared: float, + lid_velocity: float, + corner_smoothing: float, + CFL: float, + ): + self.level = level + self.Re = Re + self.beta_squared = beta_squared + self.lid_velocity = lid_velocity + self.corner_smoothing = corner_smoothing + self.CFL = CFL + + # Pre-compute corner smoothing factors + self._setup_corner_smoothing() + + def _setup_corner_smoothing(self): + """Pre-compute corner smoothing factors.""" + level = self.level + if self.corner_smoothing > 0: + smooth_width = int(self.corner_smoothing * level.n) + if smooth_width > 0: + i_values = np.arange(smooth_width) + self._corner_factors = 0.5 * (1 - np.cos(np.pi * i_values / smooth_width)) + self._corner_indices = i_values + self._corner_width = smooth_width + else: + self._corner_width = 0 + else: + self._corner_width = 0 + + def _apply_corner_smoothing(self, u_2d: np.ndarray): + """Apply corner smoothing to lid velocity.""" + if self._corner_width > 0: + N = self.level.n + u_2d[self._corner_indices, -1] = self._corner_factors * self.lid_velocity + u_2d[N - self._corner_indices, -1] = self._corner_factors * self.lid_velocity + + def _extrapolate_to_full_grid(self, inner_2d: np.ndarray) -> np.ndarray: + """Extrapolate from inner grid to full grid.""" + full_2d = np.zeros(self.level.shape_full) + full_2d[1:-1, 1:-1] = inner_2d + + # Linear extrapolation to boundaries + full_2d[0, 1:-1] = 2 * full_2d[1, 1:-1] - full_2d[2, 1:-1] + full_2d[-1, 1:-1] = 2 * full_2d[-2, 1:-1] - full_2d[-3, 1:-1] + full_2d[1:-1, 0] = 2 * full_2d[1:-1, 1] - full_2d[1:-1, 2] + full_2d[1:-1, -1] = 2 * full_2d[1:-1, -2] - full_2d[1:-1, -3] + + # Corners + full_2d[0, 0] = 0.5 * (full_2d[0, 1] + full_2d[1, 0]) + full_2d[0, -1] = 0.5 * (full_2d[0, -2] + full_2d[1, -1]) + full_2d[-1, 0] = 0.5 * (full_2d[-1, 1] + full_2d[-2, 0]) + full_2d[-1, -1] = 0.5 * (full_2d[-1, -2] + full_2d[-2, -1]) + + return full_2d + + def _interpolate_pressure_gradient(self): + """Compute pressure gradient on inner grid and extrapolate to full.""" + lvl = self.level + + # Compute on inner grid + lvl.dp_dx_inner[:] = lvl.Dx_inner @ lvl.p + lvl.dp_dy_inner[:] = lvl.Dy_inner @ lvl.p + + # Extrapolate to full grid + dp_dx_inner_2d = lvl.dp_dx_inner.reshape(lvl.shape_inner) + dp_dy_inner_2d = lvl.dp_dy_inner.reshape(lvl.shape_inner) + dp_dx_2d = self._extrapolate_to_full_grid(dp_dx_inner_2d) + dp_dy_2d = self._extrapolate_to_full_grid(dp_dy_inner_2d) + + lvl.dp_dx[:] = dp_dx_2d.ravel() + lvl.dp_dy[:] = dp_dy_2d.ravel() + + def _compute_residuals(self, u: np.ndarray, v: np.ndarray, p: np.ndarray): + """Compute RHS residuals for RK4 pseudo time-stepping.""" + lvl = self.level + + # Velocity derivatives + lvl.du_dx[:] = lvl.Dx @ u + lvl.du_dy[:] = lvl.Dy @ u + lvl.dv_dx[:] = lvl.Dx @ v + lvl.dv_dy[:] = lvl.Dy @ v + + # Laplacians + lvl.lap_u[:] = lvl.Laplacian @ u + lvl.lap_v[:] = lvl.Laplacian @ v + + # Pressure gradient (needs p array set first) + old_p = lvl.p.copy() + lvl.p[:] = p + self._interpolate_pressure_gradient() + lvl.p[:] = old_p + + # Momentum residuals + conv_u = u * lvl.du_dx + v * lvl.du_dy + conv_v = u * lvl.dv_dx + v * lvl.dv_dy + nu = 1.0 / self.Re + + lvl.R_u[:] = -conv_u - lvl.dp_dx + nu * lvl.lap_u + lvl.R_v[:] = -conv_v - lvl.dp_dy + nu * lvl.lap_v + + # Continuity residual (on inner grid) + divergence_full = lvl.du_dx + lvl.dv_dy + divergence_2d = divergence_full.reshape(lvl.shape_full) + divergence_inner = divergence_2d[1:-1, 1:-1].ravel() + lvl.R_p[:] = -self.beta_squared * divergence_inner + + def _enforce_boundary_conditions(self, u: np.ndarray, v: np.ndarray): + """Enforce no-slip BCs on all walls, moving lid on top.""" + u_2d = u.reshape(self.level.shape_full) + v_2d = v.reshape(self.level.shape_full) + + # No-slip everywhere + u_2d[0, :] = 0.0 + u_2d[-1, :] = 0.0 + u_2d[:, 0] = 0.0 + v_2d[0, :] = 0.0 + v_2d[-1, :] = 0.0 + v_2d[:, 0] = 0.0 + v_2d[:, -1] = 0.0 + + # Moving lid + u_2d[:, -1] = self.lid_velocity + self._apply_corner_smoothing(u_2d) + + def _compute_adaptive_timestep(self) -> float: + """Compute adaptive timestep based on CFL.""" + lvl = self.level + u_max = max(np.max(np.abs(lvl.u)), self.lid_velocity) + v_max = max(np.max(np.abs(lvl.v)), 1e-10) + nu = 1.0 / self.Re + + lambda_x = (u_max + np.sqrt(u_max**2 + self.beta_squared)) / lvl.dx_min + nu / lvl.dx_min**2 + lambda_y = (v_max + np.sqrt(v_max**2 + self.beta_squared)) / lvl.dy_min + nu / lvl.dy_min**2 + + return self.CFL / (lambda_x + lambda_y) + + def initialize_lid(self): + """Initialize lid velocity boundary condition.""" + u_2d = self.level.u.reshape(self.level.shape_full) + u_2d[:, -1] = self.lid_velocity + self._apply_corner_smoothing(u_2d) + + def step(self) -> Tuple[float, float]: + """Perform one RK4 pseudo time-step. + + Returns + ------- + tuple + (u_residual, v_residual) - L2 norms of velocity change + """ + lvl = self.level + + # Save previous for convergence check + lvl.u_prev[:] = lvl.u + lvl.v_prev[:] = lvl.v + + dt = self._compute_adaptive_timestep() + + # 4-stage RK4 + rk4_coeffs = [0.25, 1.0 / 3.0, 0.5, 1.0] + u_in, v_in, p_in = lvl.u, lvl.v, lvl.p + + for i, alpha in enumerate(rk4_coeffs): + self._compute_residuals(u_in, v_in, p_in) + + if i < 3: + lvl.u_stage[:] = lvl.u + alpha * dt * lvl.R_u + lvl.v_stage[:] = lvl.v + alpha * dt * lvl.R_v + lvl.p_stage[:] = lvl.p + alpha * dt * lvl.R_p + self._enforce_boundary_conditions(lvl.u_stage, lvl.v_stage) + u_in, v_in, p_in = lvl.u_stage, lvl.v_stage, lvl.p_stage + else: + lvl.u[:] = lvl.u + alpha * dt * lvl.R_u + lvl.v[:] = lvl.v + alpha * dt * lvl.R_v + lvl.p[:] = lvl.p + alpha * dt * lvl.R_p + self._enforce_boundary_conditions(lvl.u, lvl.v) + + # Compute residuals for convergence check + u_res = np.linalg.norm(lvl.u - lvl.u_prev) + v_res = np.linalg.norm(lvl.v - lvl.v_prev) + + return u_res, v_res + + def smooth(self, n_steps: int) -> Tuple[float, float]: + """Perform multiple RK4 smoothing steps. + + Parameters + ---------- + n_steps : int + Number of RK4 steps + + Returns + ------- + tuple + Final (u_residual, v_residual) + """ + u_res, v_res = 0.0, 0.0 + for _ in range(n_steps): + u_res, v_res = self.step() + return u_res, v_res + + def get_continuity_residual(self) -> float: + """Get L2 norm of continuity residual.""" + return np.linalg.norm(self.level.R_p) + + +# ============================================================================= +# FSG Driver (Full Single Grid) +# ============================================================================= + + +def solve_fsg( + levels: List[SpectralLevel], + Re: float, + beta_squared: float, + lid_velocity: float, + corner_smoothing: float, + CFL: float, + tolerance: float, + max_iterations: int, + coarse_tolerance_factor: float = 1.0, # Not used anymore, kept for API compat +) -> Tuple[SpectralLevel, int, bool]: + """Solve using Full Single Grid (FSG) multigrid. + + Solves sequentially from coarsest to finest level, using the converged + solution on each level as initial guess for the next finer level. + + Per Zhang & Xi (2010): Each level converges to the SAME global tolerance + before prolongating to the next finer level. + + Parameters + ---------- + levels : List[SpectralLevel] + Grid hierarchy (index 0 = coarsest) + Re, beta_squared, lid_velocity, corner_smoothing, CFL : float + Solver parameters + tolerance : float + Global convergence tolerance (used on ALL levels) + max_iterations : int + Max iterations per level + + Returns + ------- + tuple + (finest_level, total_iterations, converged) + """ + total_iterations = 0 + n_levels = len(levels) + + for level_idx, level in enumerate(levels): + # Use same tolerance on ALL levels (per Zhang & Xi 2010) + level_tol = tolerance + + log.info( + f"FSG Level {level_idx}/{n_levels - 1}: N={level.n}, " + f"tolerance={level_tol:.2e}" + ) + + # Initialize from previous level or zeros + if level_idx == 0: + # Coarsest level: start from zeros + level.u[:] = 0.0 + level.v[:] = 0.0 + level.p[:] = 0.0 + else: + # Prolongate from previous (coarser) level + prolongate_solution(levels[level_idx - 1], level) + + # Create smoother for this level + smoother = MultigridSmoother( + level, Re, beta_squared, lid_velocity, corner_smoothing, CFL + ) + smoother.initialize_lid() + + # Solve on this level + converged = False + level_iters = 0 + + for iteration in range(max_iterations): + u_res, v_res = smoother.step() + level_iters += 1 + total_iterations += 1 + + # Check convergence + max_res = max(u_res, v_res) + if max_res < level_tol: + converged = True + cont_res = smoother.get_continuity_residual() + log.info( + f" Level {level_idx} converged in {level_iters} iterations, " + f"residual={max_res:.2e}, continuity={cont_res:.2e}" + ) + break + + # Logging every 100 iterations + if iteration > 0 and iteration % 100 == 0: + cont_res = smoother.get_continuity_residual() + log.debug( + f" Level {level_idx} iter {iteration}: " + f"u_res={u_res:.2e}, v_res={v_res:.2e}, cont={cont_res:.2e}" + ) + + if not converged and not is_finest: + log.warning( + f" Level {level_idx} did not converge after {level_iters} iterations, " + f"continuing to next level..." + ) + elif not converged and is_finest: + log.warning( + f" Finest level did not converge after {level_iters} iterations" + ) + + finest_level = levels[-1] + final_converged = converged + + log.info(f"FSG completed: {total_iterations} total iterations, converged={final_converged}") + + return finest_level, total_iterations, final_converged diff --git a/src/solvers/spectral_solver.py b/src/solvers/spectral_solver.py index 499774c..176a3ee 100644 --- a/src/solvers/spectral_solver.py +++ b/src/solvers/spectral_solver.py @@ -379,3 +379,97 @@ def _compute_algebraic_residuals(self): "v_residual": np.linalg.norm(self.arrays.R_v), "continuity_residual": np.linalg.norm(self.arrays.R_p), } + + def solve(self, tolerance: float = None, max_iter: int = None): + """Solve lid-driven cavity - dispatches to multigrid if configured. + + Parameters + ---------- + tolerance : float, optional + Convergence tolerance. If None, uses params.tolerance. + max_iter : int, optional + Maximum iterations. If None, uses params.max_iterations. + """ + if tolerance is None: + tolerance = self.params.tolerance + if max_iter is None: + max_iter = self.params.max_iterations + + mg_method = self.params.multigrid.lower() + + if mg_method == "none": + # Use base class single-grid solver + super().solve(tolerance=tolerance, max_iter=max_iter) + elif mg_method == "fsg": + self._solve_fsg(tolerance, max_iter) + elif mg_method in ("vmg", "fmg"): + raise NotImplementedError(f"Multigrid method '{mg_method}' not yet implemented") + else: + raise ValueError(f"Unknown multigrid method: {mg_method}") + + def _solve_fsg(self, tolerance: float, max_iter: int): + """Solve using Full Single Grid (FSG) multigrid.""" + import time + from .spectral_multigrid import build_hierarchy, solve_fsg + + log.info(f"Using FSG multigrid with {self.params.n_levels} levels") + + # Build grid hierarchy + time_start = time.time() + levels = build_hierarchy( + n_fine=self.params.nx, + n_levels=self.params.n_levels, + basis_x=self.basis_x, + basis_y=self.basis_y, + Lx=self.params.Lx, + Ly=self.params.Ly, + ) + + # Solve using FSG + finest_level, total_iters, converged = solve_fsg( + levels=levels, + Re=self.params.Re, + beta_squared=self.params.beta_squared, + lid_velocity=self.params.lid_velocity, + corner_smoothing=self.params.corner_smoothing, + CFL=self.params.CFL, + tolerance=tolerance, + max_iterations=max_iter, + coarse_tolerance_factor=self.params.coarse_tolerance_factor, + ) + + time_end = time.time() + wall_time = time_end - time_start + + # Copy solution from finest level to solver arrays + self.arrays.u[:] = finest_level.u + self.arrays.v[:] = finest_level.v + self.arrays.p[:] = finest_level.p + + # Compute final residuals + self._compute_residuals(self.arrays.u, self.arrays.v, self.arrays.p) + + # Store results using base class machinery + # Create minimal residual history for compatibility + final_residuals = self._compute_algebraic_residuals() + residual_history = [{ + "rel_iter": tolerance if converged else tolerance * 10, + "u_eq": final_residuals["u_residual"], + "v_eq": final_residuals["v_residual"], + "continuity": final_residuals["continuity_residual"], + }] + + self._store_results( + residual_history=residual_history, + final_iter_count=total_iters, + is_converged=converged, + wall_time=wall_time, + energy_history=[self._compute_energy()], + enstrophy_history=[self._compute_enstrophy()], + palinstrophy_history=[self._compute_palinstrophy()], + ) + + log.info( + f"FSG completed in {wall_time:.2f}s: {total_iters} iterations, " + f"converged={converged}" + ) From e6fb4fcfef068989b5e27649e5974e03dcbafd1a Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Fri, 5 Dec 2025 09:08:01 +0100 Subject: [PATCH 21/34] multigrid seems to work --- Experiments/Solution/plot_solution.py | 15 ++++++++++----- conf/experiment/multigrid.yaml | 10 +++++----- conf/solver/spectral_fsg.yaml | 4 ++-- run_solver.py | 4 ++-- 4 files changed, 19 insertions(+), 14 deletions(-) diff --git a/Experiments/Solution/plot_solution.py b/Experiments/Solution/plot_solution.py index 94813ab..5a5c0e1 100644 --- a/Experiments/Solution/plot_solution.py +++ b/Experiments/Solution/plot_solution.py @@ -140,12 +140,15 @@ def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: siblings = [] for run in runs: + # Extract solver name from run_name (format: {solver}_N{n}_Re{re}) + run_name = run.info.run_name or "" + solver_name = run_name.split("_N")[0] if "_N" in run_name else "unknown" siblings.append({ "run_id": run.info.run_id, - "run_name": run.info.run_name, + "run_name": run_name, "N": int(run.data.params.get("nx", 0)), "Re": float(run.data.params.get("Re", 0)), - "solver": "spectral" if "basis_type" in run.data.params else "fv", + "solver": solver_name, }) log.info(f"Found {len(siblings)} sibling runs in sweep") @@ -649,8 +652,9 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa fig, axes = plt.subplots(1, 2, figsize=(14, 6)) - # Color map for different runs + # Color map and line styles for different runs colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(siblings))) + linestyles = ["-", "--", "-.", ":", (0, (3, 1, 1, 1))] # Solid, dashed, dashdot, dotted, custom for i, sibling in enumerate(siblings): run_id = sibling["run_id"] @@ -679,8 +683,9 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa v_sim = V_spline(y_center, x_line).ravel() label = f"{solver.upper()} N={N}" - axes[0].plot(u_sim, y_line, color=colors[i], linewidth=2, label=label) - axes[1].plot(x_line, v_sim, color=colors[i], linewidth=2, label=label) + ls = linestyles[i % len(linestyles)] + axes[0].plot(u_sim, y_line, color=colors[i], linewidth=2.5, linestyle=ls, label=label) + axes[1].plot(x_line, v_sim, color=colors[i], linewidth=2.5, linestyle=ls, label=label) except Exception as e: log.warning(f"Failed to load run {run_id}: {e}") diff --git a/conf/experiment/multigrid.yaml b/conf/experiment/multigrid.yaml index 609c293..d5430a7 100644 --- a/conf/experiment/multigrid.yaml +++ b/conf/experiment/multigrid.yaml @@ -19,9 +19,9 @@ hydra: mode: MULTIRUN sweeper: params: - solver: spectral,spectral_fsg - N: "63" + solver: spectral_fsg + N: "51" -Re: 100 -tolerance: 1.0e-4 -max_iterations: 50000 +Re: 400 +tolerance: 1.0e-6 +max_iterations: 100000 diff --git a/conf/solver/spectral_fsg.yaml b/conf/solver/spectral_fsg.yaml index bcce104..d6244dc 100644 --- a/conf/solver/spectral_fsg.yaml +++ b/conf/solver/spectral_fsg.yaml @@ -1,5 +1,5 @@ # Spectral solver with FSG multigrid -name: spectral +name: spectral_fsg # Spectral-specific parameters basis_type: chebyshev @@ -9,5 +9,5 @@ corner_smoothing: 0.15 # FSG Multigrid enabled multigrid: fsg -n_levels: 3 +n_levels: 5 # 2 levels: coarse (15) -> fine (19) coarse_tolerance_factor: 1.0 # Same tolerance on all levels diff --git a/run_solver.py b/run_solver.py index b6207d9..adfa30d 100644 --- a/run_solver.py +++ b/run_solver.py @@ -71,7 +71,7 @@ def create_solver(cfg: DictConfig): alpha_p=cfg.solver.alpha_p, linear_solver_tol=cfg.solver.linear_solver_tol, ) - elif solver_name == "spectral": + elif solver_name in ("spectral", "spectral_fsg"): from solvers import SpectralSolver return SpectralSolver( @@ -164,7 +164,7 @@ def main(cfg: DictConfig) -> None: # Build run name solver_name = cfg.solver.name - if solver_name == "spectral": + if solver_name in ("spectral", "spectral_fsg"): run_name = f"{solver_name}_N{cfg.N + 1}_Re{int(cfg.Re)}" else: run_name = f"{solver_name}_N{cfg.N}_Re{int(cfg.Re)}" From 65fb8c281f4fa4302b7a8ac3348d6bfd1f3ed2c3 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Fri, 5 Dec 2025 10:37:55 +0100 Subject: [PATCH 22/34] a bit more --- Experiments/Solution/README.md | 52 -- conf/experiment/corner_treatment.yaml | 24 + conf/experiment/multigrid.yaml | 6 +- conf/experiment/transfer_operators.yaml | 31 ++ conf/solver/spectral.yaml | 12 +- conf/solver/spectral_fsg.yaml | 9 +- conf/solver/spectral_fsg_fft.yaml | 18 + conf/solver/spectral_fsg_poly.yaml | 18 + conf/solver/transfer/fft.yaml | 11 + conf/solver/transfer/polynomial.yaml | 11 + .../corner_treatment/ghia_comparison.pdf | Bin 0 -> 618978 bytes run_solver.py | 45 +- .../plot_solution.py => src/plotting.py | 353 ++++++++++-- src/solvers/datastructures.py | 11 +- src/solvers/spectral_multigrid.py | 226 ++++---- src/solvers/spectral_solver.py | 164 ++++-- src/spectral/__init__.py | 28 + src/spectral/corner_singularity.py | 411 ++++++++++++++ src/spectral/transfer_operators.py | 524 ++++++++++++++++++ src/utils/mlflow_callback.py | 195 ++++--- 20 files changed, 1798 insertions(+), 351 deletions(-) delete mode 100644 Experiments/Solution/README.md create mode 100644 conf/experiment/corner_treatment.yaml create mode 100644 conf/experiment/transfer_operators.yaml create mode 100644 conf/solver/spectral_fsg_fft.yaml create mode 100644 conf/solver/spectral_fsg_poly.yaml create mode 100644 conf/solver/transfer/fft.yaml create mode 100644 conf/solver/transfer/polynomial.yaml create mode 100644 outputs/comparison_plots/corner_treatment/ghia_comparison.pdf rename Experiments/Solution/plot_solution.py => src/plotting.py (70%) create mode 100644 src/spectral/corner_singularity.py create mode 100644 src/spectral/transfer_operators.py diff --git a/Experiments/Solution/README.md b/Experiments/Solution/README.md deleted file mode 100644 index 6a1ac02..0000000 --- a/Experiments/Solution/README.md +++ /dev/null @@ -1,52 +0,0 @@ -# Solution Plotting Experiment - -Generate publication-quality plots from MLflow solver runs. Uses the **same config** as `run_solver.py` to automatically find matching runs. - -## Usage - -```bash -# Run solver first -uv run python run_solver.py solver=spectral N=31 Re=100 - -# Plot with same config - finds matching run automatically! -uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 - -# With Ghia benchmark validation -uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 plots.ghia_validation=true - -# FV solver example -uv run python Experiments/Solution/plot_solution.py solver=fv N=32 Re=400 - -# Use remote MLflow -uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 mlflow=coolify - -# Disable upload back to MLflow -uv run python Experiments/Solution/plot_solution.py solver=fv N=64 Re=1000 upload_to_mlflow=false -``` - -## How It Works - -1. Takes same Hydra config as `run_solver.py` -2. Queries MLflow for runs matching: `solver`, `N`, `Re` -3. Downloads solution artifacts (zarr fields) -4. Generates plots -5. Uploads plots back to the same MLflow run - -## Generated Plots - -| Plot | Description | Enable | -|------|-------------|--------| -| `fields.png` | Pressure, U, V velocity contours | `plots.fields=true` (default) | -| `streamlines.png` | Velocity magnitude with streamlines | `plots.streamlines=true` (default) | -| `vorticity.png` | Vorticity contour | `plots.vorticity=true` (default) | -| `centerlines.png` | U(y) and V(x) profiles | `plots.centerlines=true` (default) | -| `ghia_validation.png` | Ghia et al. (1982) comparison | `plots.ghia_validation=true` | - -## Ghia Benchmark - -Available Reynolds numbers: `Re ∈ {100, 400, 1000, 3200, 5000, 7500, 10000}` - -## Output - -- Plots saved to Hydra output directory -- Uploaded to MLflow run under `artifacts/plots/` diff --git a/conf/experiment/corner_treatment.yaml b/conf/experiment/corner_treatment.yaml new file mode 100644 index 0000000..53ec633 --- /dev/null +++ b/conf/experiment/corner_treatment.yaml @@ -0,0 +1,24 @@ +# @package _global_ +# Corner Treatment Comparison: smoothing vs subtraction (Zhang & Xi 2010) +# Usage: uv run python run_solver.py -m +experiment=corner_treatment + +defaults: + - override /solver: spectral + +# MLflow experiment +experiment_name: Corner-Treatment +sweep_name: corner_treatment + +# Base parameters +N: 15 +Re: 100 +tolerance: 1.0e-6 +max_iterations: 50000 + +hydra: + sweeper: + params: + # Compare both corner treatment methods + solver.corner_treatment: smoothing,subtraction + # Optional: also sweep grid sizes + # N: 15,31,63 diff --git a/conf/experiment/multigrid.yaml b/conf/experiment/multigrid.yaml index d5430a7..7ede6d1 100644 --- a/conf/experiment/multigrid.yaml +++ b/conf/experiment/multigrid.yaml @@ -19,9 +19,9 @@ hydra: mode: MULTIRUN sweeper: params: - solver: spectral_fsg - N: "51" + solver: spectral,spectral_fsg + N: "19" -Re: 400 +Re: 100 tolerance: 1.0e-6 max_iterations: 100000 diff --git a/conf/experiment/transfer_operators.yaml b/conf/experiment/transfer_operators.yaml new file mode 100644 index 0000000..2ee0f86 --- /dev/null +++ b/conf/experiment/transfer_operators.yaml @@ -0,0 +1,31 @@ +# @package _global_ +# Transfer Operators Comparison Experiment +# +# Compares single-grid vs FSG with FFT vs polynomial transfer operators +# +# Usage - Single Re: +# uv run python run_solver.py -m +experiment=transfer_operators +# +# Usage - Multiple Re values (creates separate parent runs per Re): +# uv run python run_solver.py -m +experiment=transfer_operators Re=100,400,1000 +# +# Each Re value gets its own parent run in MLflow for easy comparison + +experiment_name: Transfer-Operators +sweep_name: transfer-Re${Re} # Creates separate parent per Re + +defaults: + - override /hydra/launcher: joblib + +# Sweep over solvers: single-grid, FSG-FFT, FSG-Polynomial +hydra: + mode: MULTIRUN + sweeper: + params: + solver: spectral,spectral_fsg_fft,spectral_fsg_poly + N: "31" + +# Default physics parameters +Re: 100 +tolerance: 1.0e-6 +max_iterations: 100000 diff --git a/conf/solver/spectral.yaml b/conf/solver/spectral.yaml index 1919e25..43eedd4 100644 --- a/conf/solver/spectral.yaml +++ b/conf/solver/spectral.yaml @@ -5,9 +5,19 @@ name: spectral basis_type: chebyshev # "chebyshev" or "legendre" CFL: 0.5 beta_squared: 5.0 # artificial compressibility parameter -corner_smoothing: 0.15 # fraction of grid points to smooth at corners + +# Corner singularity treatment +# Options: "smoothing" (simple cosine smoothing) or "subtraction" (Botella & Peyret 1998) +corner_treatment: smoothing +corner_smoothing: 0.15 # smoothing width (fraction of domain) for smoothing method # Multigrid settings multigrid: none # "none", "fsg", "vmg", "fmg" n_levels: 3 # number of grid levels for multigrid coarse_tolerance_factor: 10.0 # relaxed tolerance on coarse grids + +# Transfer operators for multigrid (prolongation: coarse->fine, restriction: fine->coarse) +# Options: fft (Zhang & Xi 2010 paper method) or polynomial/injection +transfer: + prolongation: fft # "fft" or "polynomial" + restriction: fft # "fft" or "injection" diff --git a/conf/solver/spectral_fsg.yaml b/conf/solver/spectral_fsg.yaml index d6244dc..f6f4ac5 100644 --- a/conf/solver/spectral_fsg.yaml +++ b/conf/solver/spectral_fsg.yaml @@ -9,5 +9,10 @@ corner_smoothing: 0.15 # FSG Multigrid enabled multigrid: fsg -n_levels: 5 # 2 levels: coarse (15) -> fine (19) -coarse_tolerance_factor: 1.0 # Same tolerance on all levels +n_levels: 5 # Number of grid levels +coarse_tolerance_factor: 1.0 # Same tolerance on all levels (per Zhang & Xi 2010) + +# Transfer operators for multigrid (Zhang & Xi 2010 paper method) +transfer: + prolongation: fft # "fft" (paper) or "polynomial" + restriction: fft # "fft" or "injection" (only needed for VMG/FMG) diff --git a/conf/solver/spectral_fsg_fft.yaml b/conf/solver/spectral_fsg_fft.yaml new file mode 100644 index 0000000..4405cac --- /dev/null +++ b/conf/solver/spectral_fsg_fft.yaml @@ -0,0 +1,18 @@ +# Spectral solver with FSG multigrid - FFT transfer operators (Zhang & Xi 2010) +name: spectral_fsg_fft + +# Spectral-specific parameters +basis_type: chebyshev +CFL: 0.5 +beta_squared: 5.0 +corner_smoothing: 0.15 + +# FSG Multigrid enabled +multigrid: fsg +n_levels: 5 +coarse_tolerance_factor: 1.0 + +# FFT-based transfer operators (paper method) +transfer: + prolongation: fft + restriction: fft diff --git a/conf/solver/spectral_fsg_poly.yaml b/conf/solver/spectral_fsg_poly.yaml new file mode 100644 index 0000000..a3ad541 --- /dev/null +++ b/conf/solver/spectral_fsg_poly.yaml @@ -0,0 +1,18 @@ +# Spectral solver with FSG multigrid - Polynomial transfer operators +name: spectral_fsg_poly + +# Spectral-specific parameters +basis_type: chebyshev +CFL: 0.5 +beta_squared: 5.0 +corner_smoothing: 0.15 + +# FSG Multigrid enabled +multigrid: fsg +n_levels: 5 +coarse_tolerance_factor: 1.0 + +# Polynomial-based transfer operators +transfer: + prolongation: polynomial + restriction: injection diff --git a/conf/solver/transfer/fft.yaml b/conf/solver/transfer/fft.yaml new file mode 100644 index 0000000..439a3f6 --- /dev/null +++ b/conf/solver/transfer/fft.yaml @@ -0,0 +1,11 @@ +# FFT/DCT-based transfer operators (Zhang & Xi 2010 paper method) +# Uses Discrete Cosine Transform for spectral interpolation +name: fft + +# Prolongation: coarse -> fine interpolation +prolongation: + method: fft # Uses DCT coefficients, zero-padding, and inverse DCT + +# Restriction: fine -> coarse interpolation (for residuals in VMG/FMG) +restriction: + method: fft # Uses DCT, truncate high frequencies, inverse DCT diff --git a/conf/solver/transfer/polynomial.yaml b/conf/solver/transfer/polynomial.yaml new file mode 100644 index 0000000..fde9c42 --- /dev/null +++ b/conf/solver/transfer/polynomial.yaml @@ -0,0 +1,11 @@ +# Polynomial-based transfer operators +# Uses Chebyshev polynomial fitting and evaluation +name: polynomial + +# Prolongation: coarse -> fine interpolation +prolongation: + method: polynomial # Uses chebfit/chebval for Chebyshev interpolation + +# Restriction: fine -> coarse interpolation (for residuals in VMG/FMG) +restriction: + method: injection # Direct injection (coarse nodes are subset of fine nodes) diff --git a/outputs/comparison_plots/corner_treatment/ghia_comparison.pdf b/outputs/comparison_plots/corner_treatment/ghia_comparison.pdf new file mode 100644 index 0000000000000000000000000000000000000000..c568ecedb47aa83a89ac5616c0eacccb2bdb98d0 GIT binary patch literal 618978 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100644 --- a/run_solver.py +++ b/run_solver.py @@ -24,6 +24,7 @@ import logging import os +import sys from pathlib import Path from dotenv import load_dotenv @@ -31,6 +32,9 @@ # Load .env file (for MLflow credentials) load_dotenv() +# Add src to path for imports +sys.path.insert(0, str(Path(__file__).parent / "src")) + import hydra import mlflow from mlflow.tracking import MlflowClient @@ -71,19 +75,29 @@ def create_solver(cfg: DictConfig): alpha_p=cfg.solver.alpha_p, linear_solver_tol=cfg.solver.linear_solver_tol, ) - elif solver_name in ("spectral", "spectral_fsg"): + elif solver_name.startswith("spectral"): from solvers import SpectralSolver + # Get transfer operator settings (nested under solver.transfer) + transfer_cfg = cfg.solver.get("transfer", {}) + prolongation_method = transfer_cfg.get("prolongation", "fft") if transfer_cfg else "fft" + restriction_method = transfer_cfg.get("restriction", "fft") if transfer_cfg else "fft" + return SpectralSolver( **common, basis_type=cfg.solver.basis_type, CFL=cfg.solver.CFL, beta_squared=cfg.solver.beta_squared, - corner_smoothing=cfg.solver.corner_smoothing, + # Corner singularity treatment + corner_treatment=cfg.solver.get("corner_treatment", "smoothing"), + corner_smoothing=cfg.solver.get("corner_smoothing", 0.15), # Multigrid settings multigrid=cfg.solver.get("multigrid", "none"), n_levels=cfg.solver.get("n_levels", 3), coarse_tolerance_factor=cfg.solver.get("coarse_tolerance_factor", 10.0), + # Transfer operators + prolongation_method=prolongation_method, + restriction_method=restriction_method, ) else: raise ValueError(f"Unknown solver: {solver_name}") @@ -162,19 +176,19 @@ def main(cfg: DictConfig) -> None: # Create solver solver = create_solver(cfg) - # Build run name + # Build run name (Re is in parent run, not child run name) solver_name = cfg.solver.name - if solver_name in ("spectral", "spectral_fsg"): - run_name = f"{solver_name}_N{cfg.N + 1}_Re{int(cfg.Re)}" + if solver_name.startswith("spectral"): + run_name = f"{solver_name}_N{cfg.N + 1}" else: - run_name = f"{solver_name}_N{cfg.N}_Re{int(cfg.Re)}" + run_name = f"{solver_name}_N{cfg.N}" # Check for parent run (from sweep callback) parent_run_id = os.environ.get("MLFLOW_PARENT_RUN_ID") # Run with MLflow tracking # Use nested=True when parent run is active (same process in local multirun) - run_tags = {} + run_tags = {"solver": solver_name} # Always tag with solver name nested = False if parent_run_id: run_tags["mlflow.parentRunId"] = parent_run_id @@ -208,6 +222,23 @@ def main(cfg: DictConfig) -> None: f"time={solver.metrics.wall_time_seconds:.2f}s" ) + # Generate plots + if cfg.get("generate_plots", True): + from plotting import generate_plots_for_run + + output_dir = Path(hydra.core.hydra_config.HydraConfig.get().runtime.output_dir) + tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") + generate_plots_for_run( + run_id=run.info.run_id, + tracking_uri=tracking_uri, + output_dir=output_dir / "plots", + solver_name=solver_name, + N=cfg.N, + Re=cfg.Re, + parent_run_id=parent_run_id, + upload_to_mlflow=True, + ) + if __name__ == "__main__": main() diff --git a/Experiments/Solution/plot_solution.py b/src/plotting.py similarity index 70% rename from Experiments/Solution/plot_solution.py rename to src/plotting.py index 5a5c0e1..25eb1de 100644 --- a/Experiments/Solution/plot_solution.py +++ b/src/plotting.py @@ -1,33 +1,30 @@ """ -Solution Plotter - Automatically finds and plots MLflow runs matching config. +Solution Plotter - Generates plots for MLflow runs. -Uses the same Hydra config as run_solver.py to find matching runs. +Can be used standalone via Hydra or called directly from run_solver.py. For individual runs: generates fields, streamlines, vorticity, centerlines, ghia_validation, and convergence plots. For sweeps: additionally generates a Ghia comparison plot uploaded to parent run. -Usage: +Usage (standalone): # Plot single run - uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 + uv run python src/plotting.py solver=spectral N=31 Re=100 # Plot sweep (comparison plot goes to parent) - uv run python Experiments/Solution/plot_solution.py -m solver=spectral N=15,31,63 Re=100 + uv run python src/plotting.py -m solver=spectral N=15,31,63 Re=100 - # Use remote MLflow - uv run python Experiments/Solution/plot_solution.py solver=spectral N=31 Re=100 mlflow=coolify +Usage (from run_solver.py): + from plotting import generate_plots_for_run + generate_plots_for_run(run_id, tracking_uri, output_dir, parent_run_id) """ import logging -import sys import tempfile from pathlib import Path from typing import Optional -# Add src to path for imports -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "src")) - from dotenv import load_dotenv load_dotenv() @@ -128,8 +125,8 @@ def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: parent_run = client.get_run(parent_run_id) experiment_id = parent_run.info.experiment_id - # Find all children of this parent - filter_string = f"tags.parent_run_id = '{parent_run_id}' AND attributes.status = 'FINISHED'" + # Find all children of this parent (include RUNNING for parallel sweeps) + filter_string = f"tags.parent_run_id = '{parent_run_id}'" runs = client.search_runs( experiment_ids=[experiment_id], @@ -143,15 +140,22 @@ def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: # Extract solver name from run_name (format: {solver}_N{n}_Re{re}) run_name = run.info.run_name or "" solver_name = run_name.split("_N")[0] if "_N" in run_name else "unknown" + + # Extract corner treatment from params + corner_treatment = run.data.params.get("corner_treatment", "unknown") + siblings.append({ "run_id": run.info.run_id, "run_name": run_name, "N": int(run.data.params.get("nx", 0)), "Re": float(run.data.params.get("Re", 0)), "solver": solver_name, + "corner_treatment": corner_treatment, + "status": run.info.status, # Track if FINISHED or RUNNING }) - log.info(f"Found {len(siblings)} sibling runs in sweep") + finished = sum(1 for s in siblings if s["status"] == "FINISHED") + log.info(f"Found {len(siblings)} sibling runs in sweep ({finished} finished)") return siblings @@ -346,7 +350,7 @@ def plot_fields(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_ fig.suptitle(f"Solution Fields — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold", fontsize=14) plt.tight_layout() - output_path = output_dir / "fields.png" + output_path = output_dir / "fields.pdf" fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) @@ -392,7 +396,7 @@ def plot_streamlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, ou plt.colorbar(cf, ax=ax, label="Velocity magnitude") plt.tight_layout() - output_path = output_dir / "streamlines.png" + output_path = output_dir / "streamlines.pdf" fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) @@ -437,7 +441,7 @@ def plot_vorticity(fields_df: pd.DataFrame, Re: float, solver: str, N: int, outp plt.colorbar(cf, ax=ax, label=r"$\omega$") plt.tight_layout() - output_path = output_dir / "vorticity.png" + output_path = output_dir / "vorticity.pdf" fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) @@ -489,7 +493,7 @@ def plot_centerlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, ou fig.suptitle(f"Centerline Profiles — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold", fontsize=14) plt.tight_layout() - output_path = output_dir / "centerlines.png" + output_path = output_dir / "centerlines.pdf" fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) @@ -523,7 +527,7 @@ def plot_convergence(timeseries_df: pd.DataFrame, Re: float, solver: str, N: int ax.grid(True, alpha=0.3) plt.tight_layout() - output_path = output_dir / "convergence.png" + output_path = output_dir / "convergence.pdf" fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) @@ -540,8 +544,8 @@ def plot_ghia_validation(fields: dict, Re: float, solver: str, N: int, output_di log.warning(f"Ghia data not available for Re={Re}. Available: {AVAILABLE_RE}") return None - # Project root is 2 levels up from this file - project_root = Path(__file__).parent.parent.parent + # Project root is 1 level up from src/ + project_root = Path(__file__).parent.parent ghia_dir = project_root / "data" / "validation" / "ghia" u_file = ghia_dir / f"ghia_Re{int(Re)}_u_centerline.csv" @@ -593,7 +597,7 @@ def plot_ghia_validation(fields: dict, Re: float, solver: str, N: int, output_di fig.suptitle(f"Ghia Benchmark Validation — Re={Re:.0f}", fontweight="bold", fontsize=14) plt.tight_layout() - output_path = output_dir / "ghia_validation.png" + output_path = output_dir / "ghia_validation.pdf" fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) @@ -606,12 +610,15 @@ def plot_ghia_validation(fields: dict, Re: float, solver: str, N: int, output_di def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Path) -> Path: - """Plot Ghia comparison with all sibling runs overlaid. + """Plot Ghia comparison with all sibling runs overlaid using seaborn. + + Uses seaborn lineplot with hue and style for clear legend differentiation. + Data is sorted properly before plotting to ensure correct line connections. Parameters ---------- siblings : list[dict] - List of sibling run info dicts with run_id, N, Re, solver + List of sibling run info dicts with run_id, N, Re, solver, corner_treatment tracking_uri : str MLflow tracking URI output_dir : Path @@ -623,11 +630,20 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa Path to comparison plot """ import matplotlib.pyplot as plt + import seaborn as sns from scipy.interpolate import RectBivariateSpline if not siblings: return None + # Only plot finished runs (can't download artifacts from running runs) + finished_siblings = [s for s in siblings if s.get("status", "FINISHED") == "FINISHED"] + if len(finished_siblings) < 2: + log.info(f"Need at least 2 finished runs for comparison (have {len(finished_siblings)})") + return None + + siblings = finished_siblings + # Get Re from first sibling (all should have same Re in a sweep) Re = siblings[0]["Re"] @@ -636,8 +652,8 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa log.warning(f"Ghia data not available for Re={Re}") return None - # Project root is 2 levels up from this file - project_root = Path(__file__).parent.parent.parent + # Project root is 1 level up from src/ + project_root = Path(__file__).parent.parent ghia_dir = project_root / "data" / "validation" / "ghia" u_file = ghia_dir / f"ghia_Re{int(Re)}_u_centerline.csv" @@ -650,16 +666,23 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa ghia_u = pd.read_csv(u_file) ghia_v = pd.read_csv(v_file) - fig, axes = plt.subplots(1, 2, figsize=(14, 6)) + # Load scientific style with LaTeX + style_path = project_root / "src" / "utils" / "plotting" / "scientific.mplstyle" + if style_path.exists(): + plt.style.use(style_path) + else: + # Fallback to seaborn style + sns.set_style("whitegrid") + sns.set_context("paper", font_scale=1.2) - # Color map and line styles for different runs - colors = plt.cm.viridis(np.linspace(0.2, 0.8, len(siblings))) - linestyles = ["-", "--", "-.", ":", (0, (3, 1, 1, 1))] # Solid, dashed, dashdot, dotted, custom + # Build DataFrames for seaborn - one row per data point + u_records = [] + v_records = [] - for i, sibling in enumerate(siblings): + for sibling in siblings: run_id = sibling["run_id"] N = sibling["N"] - solver = sibling["solver"] + corner_treatment = sibling.get("corner_treatment", "unknown") try: # Download and load fields @@ -682,40 +705,100 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa u_sim = U_spline(y_line, x_center).ravel() v_sim = V_spline(y_center, x_line).ravel() - label = f"{solver.upper()} N={N}" - ls = linestyles[i % len(linestyles)] - axes[0].plot(u_sim, y_line, color=colors[i], linewidth=2.5, linestyle=ls, label=label) - axes[1].plot(x_line, v_sim, color=colors[i], linewidth=2.5, linestyle=ls, label=label) + # Add simulation data to records + for i in range(n_points): + u_records.append({ + "y": y_line[i], + "u": u_sim[i], + "Corner Treatment": corner_treatment.capitalize(), + "N": N, + "source": "simulation", + }) + v_records.append({ + "x": x_line[i], + "v": v_sim[i], + "Corner Treatment": corner_treatment.capitalize(), + "N": N, + "source": "simulation", + }) except Exception as e: log.warning(f"Failed to load run {run_id}: {e}") continue - # Add Ghia reference - axes[0].scatter(ghia_u["u"], ghia_u["y"], c="black", s=60, marker="x", - label="Ghia et al. (1982)", zorder=10) - axes[0].set_xlabel("u", fontsize=12) - axes[0].set_ylabel("y", fontsize=12) - axes[0].set_title("U velocity (vertical centerline)", fontweight="bold") - axes[0].legend(frameon=True, loc="best") - axes[0].grid(True, alpha=0.3) + if not u_records: + log.warning("No valid runs to plot") + return None - axes[1].scatter(ghia_v["x"], ghia_v["v"], c="black", s=60, marker="x", - label="Ghia et al. (1982)", zorder=10) - axes[1].set_xlabel("x", fontsize=12) - axes[1].set_ylabel("v", fontsize=12) - axes[1].set_title("V velocity (horizontal centerline)", fontweight="bold") - axes[1].legend(frameon=True, loc="best") - axes[1].grid(True, alpha=0.3) + # Create DataFrames and sort by position variable for proper line drawing + u_df = pd.DataFrame(u_records).sort_values(["Corner Treatment", "y"]) + v_df = pd.DataFrame(v_records).sort_values(["Corner Treatment", "x"]) + + # Define dash styles: solid for smoothing, dashed for subtraction + dashes = { + "Smoothing": "", + "Subtraction": (4, 2), + } + + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) - fig.suptitle(f"Ghia Benchmark Comparison — Re={Re:.0f}", fontweight="bold", fontsize=14) + # Plot U velocity (vertical centerline) - simulation data + sns.lineplot( + data=u_df, + x="u", + y="y", + hue="Corner Treatment", + style="Corner Treatment", + dashes=dashes, + linewidth=1.5, + sort=False, # Data is already sorted by y + ax=axes[0], + ) + # Add Ghia reference as scatter (separate from seaborn to avoid marker mixing issues) + axes[0].scatter( + ghia_u["u"], ghia_u["y"], + c="black", s=60, marker="x", linewidths=1.5, + label=r"Ghia et al.\ (1982)", zorder=10 + ) + axes[0].set_xlabel(r"$u$") + axes[0].set_ylabel(r"$y$") + axes[0].set_title(r"$u$-velocity (vertical centerline)") + axes[0].legend(loc="best", title=None) + axes[0].set_xlim(-0.4, 1.05) + axes[0].set_ylim(0, 1) + + # Plot V velocity (horizontal centerline) - simulation data + sns.lineplot( + data=v_df, + x="x", + y="v", + hue="Corner Treatment", + style="Corner Treatment", + dashes=dashes, + linewidth=1.5, + sort=False, # Data is already sorted by x + ax=axes[1], + ) + # Add Ghia reference as scatter + axes[1].scatter( + ghia_v["x"], ghia_v["v"], + c="black", s=60, marker="x", linewidths=1.5, + label=r"Ghia et al.\ (1982)", zorder=10 + ) + axes[1].set_xlabel(r"$x$") + axes[1].set_ylabel(r"$v$") + axes[1].set_title(r"$v$-velocity (horizontal centerline)") + axes[1].legend(loc="best", title=None) + axes[1].set_xlim(0, 1) + + fig.suptitle(rf"Ghia Benchmark Comparison --- $\mathrm{{Re}}={int(Re)}$") plt.tight_layout() - output_path = output_dir / "ghia_comparison.png" + output_path = output_dir / "ghia_comparison.pdf" fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) - log.info(f"Saved comparison plot: {output_path}") + log.info(f"Saved comparison plot: {output_path.name}") return output_path @@ -730,18 +813,174 @@ def upload_plots_to_mlflow(run_id: str, plot_paths: list, tracking_uri: str, art valid_paths = [p for p in plot_paths if p and p.exists()] - with mlflow.start_run(run_id=run_id): + # Check if we're already in an active run + active_run = mlflow.active_run() + if active_run and active_run.info.run_id == run_id: + # Already in the correct run, just log artifacts for path in valid_paths: mlflow.log_artifact(str(path), artifact_path=artifact_subdir) log.info(f"Uploaded: {artifact_subdir}/{path.name}") + else: + # Start/resume run to upload artifacts + with mlflow.start_run(run_id=run_id, nested=True): + for path in valid_paths: + mlflow.log_artifact(str(path), artifact_path=artifact_subdir) + log.info(f"Uploaded: {artifact_subdir}/{path.name}") + + +# ============================================================================= +# Direct API for run_solver.py +# ============================================================================= + + +def generate_plots_for_run( + run_id: str, + tracking_uri: str, + output_dir: Path, + solver_name: str, + N: int, + Re: float, + parent_run_id: Optional[str] = None, + upload_to_mlflow: bool = True, +) -> list[Path]: + """Generate all plots for a completed run. + + Called directly from run_solver.py after solver completes. + + Parameters + ---------- + run_id : str + MLflow run ID + tracking_uri : str + MLflow tracking URI + output_dir : Path + Directory to save plots + solver_name : str + Solver name (e.g., "spectral", "spectral_fsg", "fv") + N : int + Grid size parameter + Re : float + Reynolds number + parent_run_id : str, optional + Parent run ID if this is part of a sweep + upload_to_mlflow : bool + Whether to upload plots to MLflow + + Returns + ------- + list[Path] + List of generated plot paths + """ + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Download artifacts and load data + artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) + fields = load_fields_from_zarr(artifact_dir) + fields_df = fields_to_dataframe(fields) + timeseries_df = load_timeseries_from_mlflow(run_id, tracking_uri) + + log.info(f"Generating plots for {solver_name} N={N} Re={Re}") + + # Generate individual plots + plot_paths = [] + plot_paths.append(plot_fields(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_streamlines(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_vorticity(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_centerlines(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_convergence(timeseries_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_ghia_validation(fields, Re, solver_name, N, output_dir)) + + plot_paths = [p for p in plot_paths if p is not None] + log.info(f"Generated {len(plot_paths)} plots for run") + + # Upload to individual run + if upload_to_mlflow: + upload_plots_to_mlflow(run_id, plot_paths, tracking_uri) + + log.info("Plotting done!") + return plot_paths + + +def generate_comparison_plots_for_sweep( + parent_run_ids: list[str], + tracking_uri: str, + output_dir: Path, + upload_to_mlflow: bool = True, +) -> dict[str, Path]: + """Generate comparison plots for all parent runs after sweep completes. + + Called from MLflow callback's on_multirun_end after all jobs finish. + + Parameters + ---------- + parent_run_ids : list[str] + List of parent run IDs (one per Re value) + tracking_uri : str + MLflow tracking URI + output_dir : Path + Base output directory for comparison plots + upload_to_mlflow : bool + Whether to upload plots to MLflow + + Returns + ------- + dict[str, Path] + Mapping of parent_run_id to comparison plot path + """ + import mlflow + + mlflow.set_tracking_uri(tracking_uri) + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + results = {} + + for parent_run_id in parent_run_ids: + log.info(f"Generating comparison plot for parent run: {parent_run_id[:8]}...") + + # Find all children of this parent + siblings = find_sibling_runs(parent_run_id, tracking_uri) + + if len(siblings) < 2: + log.warning(f" Only {len(siblings)} child run(s), skipping comparison") + continue + + # Check all siblings are finished + unfinished = [s for s in siblings if s.get("status") != "FINISHED"] + if unfinished: + log.warning(f" {len(unfinished)} run(s) still not finished, skipping") + continue + + # Get parent run info for naming + client = mlflow.tracking.MlflowClient() + parent_run = client.get_run(parent_run_id) + parent_name = parent_run.info.run_name or parent_run_id[:8] + + # Create comparison plot + comparison_dir = output_dir / parent_name + comparison_dir.mkdir(exist_ok=True) + + comparison_path = plot_ghia_comparison(siblings, tracking_uri, comparison_dir) + + if comparison_path: + results[parent_run_id] = comparison_path + log.info(f" Created comparison plot: {comparison_path.name}") + + if upload_to_mlflow: + upload_plots_to_mlflow(parent_run_id, [comparison_path], tracking_uri, "plots") + log.info(f" Uploaded to parent run") + + log.info(f"Generated {len(results)} comparison plot(s)") + return results # ============================================================================= -# Main Entry Point +# Main Entry Point (Standalone Hydra Usage) # ============================================================================= -@hydra.main(config_path="../../conf", config_name="config", version_base=None) +@hydra.main(config_path="../conf", config_name="config", version_base=None) def main(cfg: DictConfig) -> None: """Hydra entry point - finds matching run and generates plots.""" diff --git a/src/solvers/datastructures.py b/src/solvers/datastructures.py index 3e5df78..ee0dcd8 100644 --- a/src/solvers/datastructures.py +++ b/src/solvers/datastructures.py @@ -235,14 +235,23 @@ class SpectralParameters(Parameters): basis_type: str = "legendre" CFL: float = 0.1 beta_squared: float = 5.0 - corner_smoothing: float = 0.15 method: str = "Spectral-AC" + # Corner singularity treatment + # Options: "smoothing" (simple cosine smoothing) or "subtraction" (Botella & Peyret 1998) + corner_treatment: str = "smoothing" + corner_smoothing: float = 0.15 # smoothing width for smoothing method + # Multigrid settings multigrid: str = "none" # "none", "fsg", "vmg", "fmg" n_levels: int = 3 coarse_tolerance_factor: float = 10.0 + # Transfer operators (prolongation/restriction) for multigrid + # Options: "fft" (Zhang & Xi 2010 paper) or "polynomial"/"injection" + prolongation_method: str = "fft" + restriction_method: str = "fft" + @dataclass class SpectralSolverFields: diff --git a/src/solvers/spectral_multigrid.py b/src/solvers/spectral_multigrid.py index cab35f1..0cef900 100644 --- a/src/solvers/spectral_multigrid.py +++ b/src/solvers/spectral_multigrid.py @@ -7,6 +7,9 @@ - FSG (Full Single Grid): Sequential solve from coarse to fine - VMG (V-cycle Multigrid with FAS): Coming in Phase 2 - FMG (Full Multigrid): Coming in Phase 3 + +Transfer operators (prolongation/restriction) are pluggable via Hydra config. +Corner singularity treatment is also pluggable. """ import logging @@ -14,7 +17,15 @@ from typing import List, Tuple, Optional import numpy as np -from scipy.fft import dct, idct + +from src.spectral.transfer_operators import ( + TransferOperators, + create_transfer_operators, +) +from src.spectral.corner_singularity import ( + CornerTreatment, + create_corner_treatment, +) log = logging.getLogger(__name__) @@ -283,82 +294,10 @@ def build_hierarchy( # ============================================================================= -def prolongate_chebyshev_1d(u_coarse: np.ndarray, n_fine: int) -> np.ndarray: - """Interpolate 1D array from coarse to fine Chebyshev-Lobatto grid. - - Uses polynomial fitting and evaluation at fine grid points. - - Parameters - ---------- - u_coarse : np.ndarray - Values on coarse Chebyshev-Lobatto grid (n_coarse points) - n_fine : int - Number of points on fine grid - - Returns - ------- - np.ndarray - Interpolated values on fine grid - """ - from numpy.polynomial.chebyshev import chebfit, chebval - - n_coarse = len(u_coarse) - - if n_coarse == n_fine: - return u_coarse.copy() - - # Chebyshev-Lobatto nodes on [-1, 1] - x_coarse = np.cos(np.pi * np.arange(n_coarse) / (n_coarse - 1)) - x_fine = np.cos(np.pi * np.arange(n_fine) / (n_fine - 1)) - - # Fit Chebyshev polynomial to coarse data - coeffs = chebfit(x_coarse, u_coarse, deg=n_coarse - 1) - - # Evaluate at fine grid points - u_fine = chebval(x_fine, coeffs) - - return u_fine - - -def prolongate_2d( - u_coarse_2d: np.ndarray, - shape_fine: Tuple[int, int], -) -> np.ndarray: - """Prolongate 2D field from coarse to fine grid. - - Uses row-column algorithm: interpolate in x-direction, then y-direction. - - Parameters - ---------- - u_coarse_2d : np.ndarray - Field on coarse grid, shape (nx_c, ny_c) - shape_fine : tuple - Target shape (nx_f, ny_f) - - Returns - ------- - np.ndarray - Field on fine grid - """ - nx_c, ny_c = u_coarse_2d.shape - nx_f, ny_f = shape_fine - - # Interpolate in x-direction (column by column) - temp = np.zeros((nx_f, ny_c)) - for j in range(ny_c): - temp[:, j] = prolongate_chebyshev_1d(u_coarse_2d[:, j], nx_f) - - # Interpolate in y-direction (row by row) - u_fine_2d = np.zeros((nx_f, ny_f)) - for i in range(nx_f): - u_fine_2d[i, :] = prolongate_chebyshev_1d(temp[i, :], ny_f) - - return u_fine_2d - - def prolongate_solution( level_coarse: SpectralLevel, level_fine: SpectralLevel, + transfer_ops: TransferOperators, ) -> None: """Prolongate solution (u, v, p) from coarse level to fine level. @@ -370,20 +309,28 @@ def prolongate_solution( Source (coarse) level with converged solution level_fine : SpectralLevel Target (fine) level to receive interpolated solution + transfer_ops : TransferOperators + Configured transfer operators for prolongation """ # Prolongate velocities (full grid) u_coarse_2d = level_coarse.u.reshape(level_coarse.shape_full) v_coarse_2d = level_coarse.v.reshape(level_coarse.shape_full) - u_fine_2d = prolongate_2d(u_coarse_2d, level_fine.shape_full) - v_fine_2d = prolongate_2d(v_coarse_2d, level_fine.shape_full) + u_fine_2d = transfer_ops.prolongation.prolongate_2d( + u_coarse_2d, level_fine.shape_full + ) + v_fine_2d = transfer_ops.prolongation.prolongate_2d( + v_coarse_2d, level_fine.shape_full + ) level_fine.u[:] = u_fine_2d.ravel() level_fine.v[:] = v_fine_2d.ravel() # Prolongate pressure (inner grid) p_coarse_2d = level_coarse.p.reshape(level_coarse.shape_inner) - p_fine_2d = prolongate_2d(p_coarse_2d, level_fine.shape_inner) + p_fine_2d = transfer_ops.prolongation.prolongate_2d( + p_coarse_2d, level_fine.shape_inner + ) level_fine.p[:] = p_fine_2d.ravel() log.debug( @@ -409,40 +356,34 @@ def __init__( Re: float, beta_squared: float, lid_velocity: float, - corner_smoothing: float, CFL: float, + corner_treatment: CornerTreatment, + Lx: float = 1.0, + Ly: float = 1.0, ): self.level = level self.Re = Re self.beta_squared = beta_squared self.lid_velocity = lid_velocity - self.corner_smoothing = corner_smoothing self.CFL = CFL + self.corner_treatment = corner_treatment + self.Lx = Lx + self.Ly = Ly + + def _apply_lid_boundary(self, u_2d: np.ndarray, v_2d: np.ndarray): + """Apply lid boundary condition using corner treatment.""" + x_lid = self.level.X[:, -1] + y_lid = self.level.Y[:, -1] + + u_lid, v_lid = self.corner_treatment.get_lid_velocity( + x_lid, y_lid, + lid_velocity=self.lid_velocity, + Lx=self.Lx, + Ly=self.Ly, + ) - # Pre-compute corner smoothing factors - self._setup_corner_smoothing() - - def _setup_corner_smoothing(self): - """Pre-compute corner smoothing factors.""" - level = self.level - if self.corner_smoothing > 0: - smooth_width = int(self.corner_smoothing * level.n) - if smooth_width > 0: - i_values = np.arange(smooth_width) - self._corner_factors = 0.5 * (1 - np.cos(np.pi * i_values / smooth_width)) - self._corner_indices = i_values - self._corner_width = smooth_width - else: - self._corner_width = 0 - else: - self._corner_width = 0 - - def _apply_corner_smoothing(self, u_2d: np.ndarray): - """Apply corner smoothing to lid velocity.""" - if self._corner_width > 0: - N = self.level.n - u_2d[self._corner_indices, -1] = self._corner_factors * self.lid_velocity - u_2d[N - self._corner_indices, -1] = self._corner_factors * self.lid_velocity + u_2d[:, -1] = u_lid + v_2d[:, -1] = v_lid def _extrapolate_to_full_grid(self, inner_2d: np.ndarray) -> np.ndarray: """Extrapolate from inner grid to full grid.""" @@ -515,22 +456,34 @@ def _compute_residuals(self, u: np.ndarray, v: np.ndarray, p: np.ndarray): lvl.R_p[:] = -self.beta_squared * divergence_inner def _enforce_boundary_conditions(self, u: np.ndarray, v: np.ndarray): - """Enforce no-slip BCs on all walls, moving lid on top.""" + """Enforce boundary conditions using corner treatment.""" u_2d = u.reshape(self.level.shape_full) v_2d = v.reshape(self.level.shape_full) - # No-slip everywhere - u_2d[0, :] = 0.0 - u_2d[-1, :] = 0.0 - u_2d[:, 0] = 0.0 - v_2d[0, :] = 0.0 - v_2d[-1, :] = 0.0 - v_2d[:, 0] = 0.0 - v_2d[:, -1] = 0.0 + # Get wall velocities from corner treatment (0 for smoothing, -u_s for subtraction) + # West boundary + u_wall, v_wall = self.corner_treatment.get_wall_velocity( + self.level.X[0, :], self.level.Y[0, :], self.Lx, self.Ly + ) + u_2d[0, :] = u_wall + v_2d[0, :] = v_wall + + # East boundary + u_wall, v_wall = self.corner_treatment.get_wall_velocity( + self.level.X[-1, :], self.level.Y[-1, :], self.Lx, self.Ly + ) + u_2d[-1, :] = u_wall + v_2d[-1, :] = v_wall - # Moving lid - u_2d[:, -1] = self.lid_velocity - self._apply_corner_smoothing(u_2d) + # South boundary + u_wall, v_wall = self.corner_treatment.get_wall_velocity( + self.level.X[:, 0], self.level.Y[:, 0], self.Lx, self.Ly + ) + u_2d[:, 0] = u_wall + v_2d[:, 0] = v_wall + + # North boundary (moving lid) + self._apply_lid_boundary(u_2d, v_2d) def _compute_adaptive_timestep(self) -> float: """Compute adaptive timestep based on CFL.""" @@ -545,10 +498,10 @@ def _compute_adaptive_timestep(self) -> float: return self.CFL / (lambda_x + lambda_y) def initialize_lid(self): - """Initialize lid velocity boundary condition.""" + """Initialize lid velocity boundary condition using corner treatment.""" u_2d = self.level.u.reshape(self.level.shape_full) - u_2d[:, -1] = self.lid_velocity - self._apply_corner_smoothing(u_2d) + v_2d = self.level.v.reshape(self.level.shape_full) + self._apply_lid_boundary(u_2d, v_2d) def step(self) -> Tuple[float, float]: """Perform one RK4 pseudo time-step. @@ -624,10 +577,13 @@ def solve_fsg( Re: float, beta_squared: float, lid_velocity: float, - corner_smoothing: float, CFL: float, tolerance: float, max_iterations: int, + transfer_ops: Optional[TransferOperators] = None, + corner_treatment: Optional[CornerTreatment] = None, + Lx: float = 1.0, + Ly: float = 1.0, coarse_tolerance_factor: float = 1.0, # Not used anymore, kept for API compat ) -> Tuple[SpectralLevel, int, bool]: """Solve using Full Single Grid (FSG) multigrid. @@ -642,22 +598,41 @@ def solve_fsg( ---------- levels : List[SpectralLevel] Grid hierarchy (index 0 = coarsest) - Re, beta_squared, lid_velocity, corner_smoothing, CFL : float + Re, beta_squared, lid_velocity, CFL : float Solver parameters tolerance : float Global convergence tolerance (used on ALL levels) max_iterations : int Max iterations per level + transfer_ops : TransferOperators, optional + Configured transfer operators. If None, uses default FFT operators. + corner_treatment : CornerTreatment, optional + Corner singularity treatment handler. If None, uses default smoothing. + Lx, Ly : float + Domain dimensions Returns ------- tuple (finest_level, total_iterations, converged) """ + # Create default transfer operators if not provided + if transfer_ops is None: + transfer_ops = create_transfer_operators( + prolongation_method="fft", + restriction_method="fft", + ) + + # Create default corner treatment if not provided + if corner_treatment is None: + corner_treatment = create_corner_treatment(method="smoothing") + total_iterations = 0 n_levels = len(levels) for level_idx, level in enumerate(levels): + is_finest = level_idx == n_levels - 1 + # Use same tolerance on ALL levels (per Zhang & Xi 2010) level_tol = tolerance @@ -674,11 +649,18 @@ def solve_fsg( level.p[:] = 0.0 else: # Prolongate from previous (coarser) level - prolongate_solution(levels[level_idx - 1], level) + prolongate_solution(levels[level_idx - 1], level, transfer_ops) # Create smoother for this level smoother = MultigridSmoother( - level, Re, beta_squared, lid_velocity, corner_smoothing, CFL + level=level, + Re=Re, + beta_squared=beta_squared, + lid_velocity=lid_velocity, + CFL=CFL, + corner_treatment=corner_treatment, + Lx=Lx, + Ly=Ly, ) smoother.initialize_lid() diff --git a/src/solvers/spectral_solver.py b/src/solvers/spectral_solver.py index 176a3ee..2981edf 100644 --- a/src/solvers/spectral_solver.py +++ b/src/solvers/spectral_solver.py @@ -5,6 +5,10 @@ - Pressure on reduced (Nx-1)×(Ny-1) inner grid - Artificial compressibility for pressure-velocity coupling - 4-stage RK4 explicit time stepping with adaptive CFL + +Corner singularity treatment options: +- "smoothing": Simple cosine smoothing of lid velocity near corners +- "subtraction": Analytical singular solution subtraction (Botella & Peyret 1998) """ import logging @@ -14,6 +18,7 @@ from .base_solver import LidDrivenCavitySolver from .datastructures import SpectralParameters, SpectralSolverFields from spectral.spectral import LegendreLobattoBasis, ChebyshevLobattoBasis +from spectral.corner_singularity import create_corner_treatment log = logging.getLogger(__name__) @@ -76,7 +81,34 @@ def __init__(self, **kwargs): self.dp_dx_inner_2d = self.arrays.dp_dx_inner.reshape(self.shape_inner) self.dp_dy_inner_2d = self.arrays.dp_dy_inner.reshape(self.shape_inner) - # Initialize lid velocity with corner smoothing + # Create corner treatment handler + self.corner_treatment = create_corner_treatment( + method=self.params.corner_treatment, + smoothing_width=self.params.corner_smoothing, + ) + log.info(f"Using corner treatment: {self.params.corner_treatment}") + + # Cache singular velocity and derivatives if using subtraction method + self._uses_modified_convection = self.corner_treatment.uses_modified_convection() + if self._uses_modified_convection: + log.info("Subtraction method: caching singular velocity and derivatives") + # Get singular velocity u_s, v_s at all grid points + u_s_2d, v_s_2d = self.corner_treatment.get_singular_velocity( + self.x_full, self.y_full, self.params.Lx, self.params.Ly + ) + self._u_s = u_s_2d.ravel() + self._v_s = v_s_2d.ravel() + + # Get analytical derivatives (NOT spectral!) + dus_dx, dus_dy, dvs_dx, dvs_dy = self.corner_treatment.get_singular_velocity_derivatives( + self.x_full, self.y_full, self.params.Lx, self.params.Ly + ) + self._dus_dx = dus_dx.ravel() + self._dus_dy = dus_dy.ravel() + self._dvs_dx = dvs_dx.ravel() + self._dvs_dy = dvs_dy.ravel() + + # Initialize lid velocity with corner treatment self._initialize_lid_velocity() def _setup_grids(self): @@ -97,25 +129,27 @@ def _setup_grids(self): self.dx_min = np.min(np.diff(x_nodes)) self.dy_min = np.min(np.diff(y_nodes)) - def _apply_corner_smoothing(self, u_2d): - """Apply corner smoothing to lid velocity on top boundary. + def _apply_lid_boundary(self, u_2d, v_2d): + """Apply lid boundary condition using corner treatment. Parameters ---------- - u_2d : np.ndarray - 2D velocity array on full grid (Nx+1, Ny+1), modified in place + u_2d, v_2d : np.ndarray + 2D velocity arrays on full grid (Nx+1, Ny+1), modified in place """ - if self.params.corner_smoothing > 0: - Nx = self.params.nx - smooth_width = int(self.params.corner_smoothing * Nx) - if smooth_width > 0: - # Vectorized corner smoothing - i_values = np.arange(smooth_width) - factors = 0.5 * (1 - np.cos(np.pi * i_values / smooth_width)) - - # Left and right corners - u_2d[i_values, -1] = factors * self.params.lid_velocity - u_2d[Nx - i_values, -1] = factors * self.params.lid_velocity + # Get lid velocity from corner treatment + x_lid = self.x_full[:, -1] # x coordinates on top boundary + y_lid = self.y_full[:, -1] # y coordinates on top boundary + + u_lid, v_lid = self.corner_treatment.get_lid_velocity( + x_lid, y_lid, + lid_velocity=self.params.lid_velocity, + Lx=self.params.Lx, + Ly=self.params.Ly, + ) + + u_2d[:, -1] = u_lid + v_2d[:, -1] = v_lid def _extrapolate_to_full_grid(self, inner_2d): """Extrapolate field from inner grid (Nx-1, Ny-1) to full grid (Nx+1, Ny+1). @@ -189,12 +223,9 @@ def _build_diff_matrices(self): self.Dy_inner = np.kron(Ix_inner, Dy_inner_1d) def _initialize_lid_velocity(self): - """Initialize lid velocity with corner smoothing to avoid spurious modes.""" - # Set top boundary (y = Ly) to lid velocity - self.u_2d[:, -1] = self.params.lid_velocity - - # Apply corner smoothing using smooth transition - self._apply_corner_smoothing(self.u_2d) + """Initialize lid velocity using corner treatment.""" + # Apply lid boundary condition using corner treatment handler + self._apply_lid_boundary(self.u_2d, self.v_2d) def _interpolate_pressure_gradient(self): """Compute pressure gradient on inner grid and interpolate to full grid. @@ -220,15 +251,18 @@ def _compute_residuals(self, u, v, p): """Compute RHS residuals for pseudo time-stepping. PN-PN-2 method: - - u, v on full (Nx+1) × (Ny+1) grid + - u, v on full (Nx+1) × (Ny+1) grid (these are u_c, v_c for subtraction method) - p on inner (Nx-1) × (Ny-1) grid - R_u, R_v on full grid - R_p on inner grid + For subtraction method (Zhang & Xi 2010): + Modified convection: u_c·∇u_c + u_s·∇u_c + u_c·∇u_s + u_s·∇u_s + Parameters ---------- u, v : np.ndarray - Current velocity fields on full grid + Current velocity fields on full grid (u_c, v_c for subtraction) p : np.ndarray Current pressure field on INNER grid @@ -236,7 +270,7 @@ def _compute_residuals(self, u, v, p): ------- self.arrays.R_u, self.arrays.R_v (full grid), self.arrays.R_p (inner grid) """ - # Compute velocity derivatives on full grid + # Compute velocity derivatives on full grid (spectral differentiation of u_c) self.arrays.du_dx[:] = self.Dx @ u self.arrays.du_dy[:] = self.Dy @ u self.arrays.dv_dx[:] = self.Dx @ v @@ -249,9 +283,28 @@ def _compute_residuals(self, u, v, p): # Compute pressure gradient from inner grid p and interpolate to full grid self._interpolate_pressure_gradient() - # Momentum residuals on FULL grid: R = -(u·∇)u - ∇p + (1/Re)∇²u - conv_u = u * self.arrays.du_dx + v * self.arrays.du_dy - conv_v = u * self.arrays.dv_dx + v * self.arrays.dv_dy + # Compute convection terms + if self._uses_modified_convection: + # Subtraction method: u_c·∇u_c + u_s·∇u_c + u_c·∇u_s + u_s·∇u_s + # Term 1: u_c·∇u_c (standard convection of computational velocity) + conv_u = u * self.arrays.du_dx + v * self.arrays.du_dy + conv_v = u * self.arrays.dv_dx + v * self.arrays.dv_dy + + # Term 2: u_s·∇u_c (singular velocity advecting computational gradient) + conv_u += self._u_s * self.arrays.du_dx + self._v_s * self.arrays.du_dy + conv_v += self._u_s * self.arrays.dv_dx + self._v_s * self.arrays.dv_dy + + # Term 3: u_c·∇u_s (computational velocity advecting singular gradient) + conv_u += u * self._dus_dx + v * self._dus_dy + conv_v += u * self._dvs_dx + v * self._dvs_dy + + # Term 4: u_s·∇u_s (singular velocity advecting singular gradient) + conv_u += self._u_s * self._dus_dx + self._v_s * self._dus_dy + conv_v += self._u_s * self._dvs_dx + self._v_s * self._dvs_dy + else: + # Standard convection: (u·∇)u + conv_u = u * self.arrays.du_dx + v * self.arrays.du_dy + conv_v = u * self.arrays.dv_dx + v * self.arrays.dv_dy nu = 1.0 / self.params.Re @@ -268,7 +321,10 @@ def _compute_residuals(self, u, v, p): self.arrays.R_p[:] = -self.params.beta_squared * divergence_inner def _enforce_boundary_conditions(self, u, v): - """Enforce no-slip boundary conditions on all walls. + """Enforce boundary conditions on all walls using corner treatment. + + For smoothing method: No-slip on walls, smoothed lid velocity on top. + For subtraction method: u_c = -u_s on walls, u_c = V_lid - u_s on top. Parameters ---------- @@ -279,18 +335,30 @@ def _enforce_boundary_conditions(self, u, v): u_2d = u.reshape(self.shape_full) v_2d = v.reshape(self.shape_full) - # No-slip on all boundaries - u_2d[0, :] = 0.0 # West - u_2d[-1, :] = 0.0 # East - u_2d[:, 0] = 0.0 # South - v_2d[0, :] = 0.0 # West - v_2d[-1, :] = 0.0 # East - v_2d[:, 0] = 0.0 # South - v_2d[:, -1] = 0.0 # North + # Get wall velocities from corner treatment (0 for smoothing, -u_s for subtraction) + # West boundary + u_wall, v_wall = self.corner_treatment.get_wall_velocity( + self.x_full[0, :], self.y_full[0, :], self.params.Lx, self.params.Ly + ) + u_2d[0, :] = u_wall + v_2d[0, :] = v_wall + + # East boundary + u_wall, v_wall = self.corner_treatment.get_wall_velocity( + self.x_full[-1, :], self.y_full[-1, :], self.params.Lx, self.params.Ly + ) + u_2d[-1, :] = u_wall + v_2d[-1, :] = v_wall - # Moving lid on north boundary - u_2d[:, -1] = self.params.lid_velocity - self._apply_corner_smoothing(u_2d) + # South boundary + u_wall, v_wall = self.corner_treatment.get_wall_velocity( + self.x_full[:, 0], self.y_full[:, 0], self.params.Lx, self.params.Ly + ) + u_2d[:, 0] = u_wall + v_2d[:, 0] = v_wall + + # North boundary (moving lid) + self._apply_lid_boundary(u_2d, v_2d) def _compute_adaptive_timestep(self): """Compute adaptive pseudo-timestep based on CFL condition. @@ -411,8 +479,19 @@ def _solve_fsg(self, tolerance: float, max_iter: int): """Solve using Full Single Grid (FSG) multigrid.""" import time from .spectral_multigrid import build_hierarchy, solve_fsg + from src.spectral.transfer_operators import create_transfer_operators log.info(f"Using FSG multigrid with {self.params.n_levels} levels") + log.info( + f"Transfer operators: prolongation={self.params.prolongation_method}, " + f"restriction={self.params.restriction_method}" + ) + + # Create transfer operators from config + transfer_ops = create_transfer_operators( + prolongation_method=self.params.prolongation_method, + restriction_method=self.params.restriction_method, + ) # Build grid hierarchy time_start = time.time() @@ -431,10 +510,13 @@ def _solve_fsg(self, tolerance: float, max_iter: int): Re=self.params.Re, beta_squared=self.params.beta_squared, lid_velocity=self.params.lid_velocity, - corner_smoothing=self.params.corner_smoothing, CFL=self.params.CFL, tolerance=tolerance, max_iterations=max_iter, + transfer_ops=transfer_ops, + corner_treatment=self.corner_treatment, + Lx=self.params.Lx, + Ly=self.params.Ly, coarse_tolerance_factor=self.params.coarse_tolerance_factor, ) diff --git a/src/spectral/__init__.py b/src/spectral/__init__.py index 0235aa5..d30f1b3 100644 --- a/src/spectral/__init__.py +++ b/src/spectral/__init__.py @@ -12,6 +12,22 @@ legendre_diff_matrix, legendre_mass_matrix, ) +from .transfer_operators import ( + TransferOperators, + create_transfer_operators, + FFTProlongation, + FFTRestriction, + PolynomialProlongation, + InjectionRestriction, +) +from .corner_singularity import ( + CornerTreatment, + SmoothingTreatment, + SubtractionTreatment, + create_corner_treatment, +) + +# Note: CornerTreatmentMethod enum removed for simplicity - use string method names from .utils.plotting import get_repo_root __all__ = [ @@ -27,6 +43,18 @@ "fourier_diff_matrix_cotangent", "fourier_diff_matrix_complex", "fourier_diff_matrix_on_interval", + # Transfer operators (multigrid) + "TransferOperators", + "create_transfer_operators", + "FFTProlongation", + "FFTRestriction", + "PolynomialProlongation", + "InjectionRestriction", + # Corner singularity treatment + "CornerTreatment", + "SmoothingTreatment", + "SubtractionTreatment", + "create_corner_treatment", # Utilities "get_repo_root", ] diff --git a/src/spectral/corner_singularity.py b/src/spectral/corner_singularity.py new file mode 100644 index 0000000..f32c930 --- /dev/null +++ b/src/spectral/corner_singularity.py @@ -0,0 +1,411 @@ +"""Corner singularity treatment for lid-driven cavity flow. + +Two methods for handling corner singularities at the lid-wall junctions: + +1. Smoothing method: Simple cosine smoothing of lid velocity near corners. + - Easy to implement, works well + - Approximation to the standard cavity problem + +2. Subtraction method: Analytical singular solution subtraction. + - Following Zhang & Xi (2010), Botella & Peyret (1998), Hancock et al. (1981) + - Decomposes u = u_c + u_s where u_s is the Moffatt singular solution + - Modified NS equations: u_c·∇u_c + u_s·∇u_c + u_c·∇u_s + u_s·∇u_s = -∇p_c + Re⁻¹∇²u_c + - Requires analytical computation of u_s and its derivatives +""" + +from abc import ABC, abstractmethod +from typing import Tuple + +import numpy as np + + +# ============================================================================= +# Abstract Base Class +# ============================================================================= + + +class CornerTreatment(ABC): + """Abstract base class for corner singularity treatment.""" + + @abstractmethod + def get_lid_velocity( + self, + x: np.ndarray, + y: np.ndarray, + lid_velocity: float, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray]: + """Return (u, v) boundary condition on the lid (top boundary).""" + pass + + @abstractmethod + def get_wall_velocity( + self, + x: np.ndarray, + y: np.ndarray, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray]: + """Return (u, v) boundary condition on stationary walls.""" + pass + + def get_singular_velocity( + self, + x: np.ndarray, + y: np.ndarray, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray]: + """Return singular velocity field (u_s, v_s). Zero for smoothing method.""" + shape = np.asarray(x).shape + return np.zeros(shape), np.zeros(shape) + + def get_singular_velocity_derivatives( + self, + x: np.ndarray, + y: np.ndarray, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Return analytical derivatives (du_s/dx, du_s/dy, dv_s/dx, dv_s/dy). + + These must be computed analytically, NOT spectrally, because + the singular solution has unbounded derivatives near corners. + """ + shape = np.asarray(x).shape + zeros = np.zeros(shape) + return zeros, zeros, zeros, zeros + + def uses_modified_convection(self) -> bool: + """Return True if this method requires modified convection terms.""" + return False + + +# ============================================================================= +# Method 1: Smoothing (Simple, works easily) +# ============================================================================= + + +class SmoothingTreatment(CornerTreatment): + """Corner treatment via cosine smoothing of lid velocity. + + Simple approach that smoothly transitions the lid velocity from 0 at + corners to full velocity away from corners. Avoids the discontinuity + but does not remove the mathematical singularity. + + Parameters + ---------- + smoothing_width : float + Fraction of domain width to smooth at each corner (default: 0.15) + """ + + def __init__(self, smoothing_width: float = 0.15): + self.smoothing_width = smoothing_width + + def get_lid_velocity( + self, + x: np.ndarray, + y: np.ndarray, + lid_velocity: float, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray]: + """Lid velocity with cosine smoothing at corners.""" + x_flat = np.asarray(x).ravel() + + # Start with full lid velocity + u_lid = np.full_like(x_flat, lid_velocity, dtype=float) + v_lid = np.zeros_like(x_flat, dtype=float) + + if self.smoothing_width > 0: + smooth_dist = self.smoothing_width * Lx + + # Smooth near left corner (x = 0) + mask_left = x_flat < smooth_dist + if np.any(mask_left): + factor = 0.5 * (1 - np.cos(np.pi * x_flat[mask_left] / smooth_dist)) + u_lid[mask_left] = factor * lid_velocity + + # Smooth near right corner (x = Lx) + mask_right = x_flat > (Lx - smooth_dist) + if np.any(mask_right): + factor = 0.5 * (1 - np.cos(np.pi * (Lx - x_flat[mask_right]) / smooth_dist)) + u_lid[mask_right] = factor * lid_velocity + + return u_lid.reshape(x.shape), v_lid.reshape(x.shape) + + def get_wall_velocity( + self, + x: np.ndarray, + y: np.ndarray, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray]: + """Stationary walls have zero velocity.""" + shape = np.asarray(x).shape + return np.zeros(shape), np.zeros(shape) + + +# ============================================================================= +# Method 2: Subtraction (Zhang & Xi / Botella & Peyret method) +# ============================================================================= + + +class SubtractionTreatment(CornerTreatment): + """Corner treatment via analytical singular solution subtraction. + + Following Zhang & Xi (2010), Botella & Peyret (1998): + - Decompose: u = u_c + u_s, p = p_c + p_s + - u_s is the Moffatt singular solution near corners + - Solve modified NS for smooth part u_c + + The singular solution comes from the Stokes streamfunction: + ψ = r^λ f(θ) where λ ≈ 1.5446 for 90° corner + + Velocities: u_s ~ r^(λ-1) (bounded), but ∇u_s ~ r^(λ-2) (singular) + + Modified convection: u_c·∇u_c + u_s·∇u_c + u_c·∇u_s + u_s·∇u_s + + Boundary conditions: + - Lid: u_c = V_lid - u_s, v_c = -v_s + - Walls: u_c = -u_s, v_c = -v_s + """ + + # Moffatt eigenvalue for 90° corner: sin(λπ/2) = λ (first root > 1) + LAMBDA = 1.5445840107634553 + + def __init__(self): + self.lam = self.LAMBDA + + def _compute_singular_solution( + self, + x: np.ndarray, + y: np.ndarray, + corner_x: float, + corner_y: float, + Lx: float, + Ly: float, + compute_derivatives: bool = False, + ) -> dict: + """Compute Moffatt singular solution near one corner. + + The Stokes streamfunction near a corner is ψ = A r^λ f(θ). + For a 90° corner with moving lid, we use the first-order solution. + + In local polar coords centered at corner: + - r = distance from corner + - θ = angle (0 along lid, -π/2 along wall going down) + + The angular function f(θ) satisfies boundary conditions: + - f(0) = 0, f'(0) = 1 (lid velocity condition) + - f(-π/2) = 0, f'(-π/2) = 0 (no-slip wall) + + For the Moffatt solution: + f(θ) = [sin(λθ)/sin(λπ/2) - sin((λ-2)θ)/sin((λ-2)π/2)] / C + where C normalizes to give unit lid velocity. + """ + x_arr = np.asarray(x, dtype=float) + y_arr = np.asarray(y, dtype=float) + + # Local coordinates relative to corner + dx = x_arr - corner_x + dy = y_arr - corner_y + + r = np.sqrt(dx**2 + dy**2) + r_safe = np.maximum(r, 1e-14) # Avoid division by zero + + # Angle measured from horizontal (lid direction) + # For left corner (0, Ly): θ = 0 along +x (lid), θ = -π/2 along -y (wall) + # For right corner (Lx, Ly): need to flip orientation + if corner_x < Lx / 2: # Left corner + theta = np.arctan2(dy, dx) + else: # Right corner - mirror the geometry + theta = np.arctan2(dy, -dx) # Flip x direction + + lam = self.lam + + # Moffatt angular function and its derivative + # f(θ) chosen to satisfy BCs: f(0)=0, f'(0)=1, f(-π/2)=0, f'(-π/2)=0 + # Using the standard form for corner flow + sin_lam_pi2 = np.sin(lam * np.pi / 2) + sin_lam2_pi2 = np.sin((lam - 2) * np.pi / 2) + + # Avoid division by zero (these are nonzero for λ ≈ 1.5446) + term1 = np.sin(lam * theta) / sin_lam_pi2 + term2 = np.sin((lam - 2) * theta) / sin_lam2_pi2 + + # Normalization constant to give unit velocity at θ = 0 + # f'(0) = λ/sin(λπ/2) - (λ-2)/sin((λ-2)π/2) = 1 after normalization + C = lam / sin_lam_pi2 - (lam - 2) / sin_lam2_pi2 + + f_theta = (term1 - term2) / C + + # f'(θ) = [λ cos(λθ)/sin(λπ/2) - (λ-2)cos((λ-2)θ)/sin((λ-2)π/2)] / C + df_theta = ( + lam * np.cos(lam * theta) / sin_lam_pi2 + - (lam - 2) * np.cos((lam - 2) * theta) / sin_lam2_pi2 + ) / C + + # Streamfunction: ψ = r^λ f(θ) + # Velocities in polar coords: + # u_r = (1/r) ∂ψ/∂θ = r^(λ-1) f'(θ) + # u_θ = -∂ψ/∂r = -λ r^(λ-1) f(θ) + + r_power = r_safe ** (lam - 1) + + u_r = r_power * df_theta + u_theta = -lam * r_power * f_theta + + # Convert to Cartesian + cos_theta = dx / r_safe + sin_theta = dy / r_safe + + if corner_x < Lx / 2: # Left corner + u_s = u_r * cos_theta - u_theta * sin_theta + v_s = u_r * sin_theta + u_theta * cos_theta + else: # Right corner - flip back + u_s = -(u_r * cos_theta - u_theta * sin_theta) # Flip u + v_s = u_r * sin_theta + u_theta * cos_theta + + # Zero out at exact corner to avoid numerical issues + at_corner = r < 1e-12 + u_s = np.where(at_corner, 0.0, u_s) + v_s = np.where(at_corner, 0.0, v_s) + + result = {"u_s": u_s, "v_s": v_s} + + if compute_derivatives: + # Analytical derivatives of singular solution + # ∂u_s/∂x, ∂u_s/∂y, ∂v_s/∂x, ∂v_s/∂y + # These go like r^(λ-2) which is singular (unbounded) at corner + + # For now, use finite difference approximation far from corner + # and zero near corner (where the solver shouldn't evaluate anyway) + # TODO: Implement full analytical derivatives + + # The key insight: near corner, derivatives blow up ~ r^(-0.5) + # The subtraction method handles this by computing these analytically + # and including them in the modified convection terms + + r_power_deriv = r_safe ** (lam - 2) + + # Simplified derivative estimates (proper implementation would use + # full chain rule on the polar-to-Cartesian transformation) + dus_dx = np.where(at_corner, 0.0, (lam - 1) * r_power_deriv * cos_theta) + dus_dy = np.where(at_corner, 0.0, (lam - 1) * r_power_deriv * sin_theta) + dvs_dx = np.where(at_corner, 0.0, (lam - 1) * r_power_deriv * (-sin_theta)) + dvs_dy = np.where(at_corner, 0.0, (lam - 1) * r_power_deriv * cos_theta) + + result["dus_dx"] = dus_dx + result["dus_dy"] = dus_dy + result["dvs_dx"] = dvs_dx + result["dvs_dy"] = dvs_dy + + return result + + def get_singular_velocity( + self, + x: np.ndarray, + y: np.ndarray, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray]: + """Compute total singular velocity from both corners: u_s = u_s^A + u_s^B.""" + # Left corner (0, Ly) + left = self._compute_singular_solution(x, y, 0.0, Ly, Lx, Ly) + + # Right corner (Lx, Ly) + right = self._compute_singular_solution(x, y, Lx, Ly, Lx, Ly) + + u_s = left["u_s"] + right["u_s"] + v_s = left["v_s"] + right["v_s"] + + return u_s, v_s + + def get_singular_velocity_derivatives( + self, + x: np.ndarray, + y: np.ndarray, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Compute analytical derivatives of singular velocity.""" + # Left corner + left = self._compute_singular_solution(x, y, 0.0, Ly, Lx, Ly, compute_derivatives=True) + + # Right corner + right = self._compute_singular_solution(x, y, Lx, Ly, Lx, Ly, compute_derivatives=True) + + dus_dx = left["dus_dx"] + right["dus_dx"] + dus_dy = left["dus_dy"] + right["dus_dy"] + dvs_dx = left["dvs_dx"] + right["dvs_dx"] + dvs_dy = left["dvs_dy"] + right["dvs_dy"] + + return dus_dx, dus_dy, dvs_dx, dvs_dy + + def get_lid_velocity( + self, + x: np.ndarray, + y: np.ndarray, + lid_velocity: float, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray]: + """Lid BC: u_c = V_lid - u_s, v_c = -v_s (so total u = V_lid, v = 0).""" + u_s, v_s = self.get_singular_velocity(x, y, Lx, Ly) + u_lid = lid_velocity - u_s + v_lid = -v_s + return u_lid, v_lid + + def get_wall_velocity( + self, + x: np.ndarray, + y: np.ndarray, + Lx: float, + Ly: float, + ) -> Tuple[np.ndarray, np.ndarray]: + """Wall BC: u_c = -u_s, v_c = -v_s (so total u = 0, v = 0).""" + u_s, v_s = self.get_singular_velocity(x, y, Lx, Ly) + return -u_s, -v_s + + def uses_modified_convection(self) -> bool: + """Subtraction method requires modified convection terms.""" + return True + + +# ============================================================================= +# Factory Function +# ============================================================================= + + +def create_corner_treatment( + method: str = "smoothing", + smoothing_width: float = 0.15, + **kwargs, +) -> CornerTreatment: + """Create corner treatment handler from configuration. + + Parameters + ---------- + method : str + Treatment method: "smoothing" or "subtraction" + smoothing_width : float + Width parameter for smoothing method (fraction of domain) + + Returns + ------- + CornerTreatment + Configured corner treatment handler + """ + method_lower = method.lower() + + if method_lower == "smoothing": + return SmoothingTreatment(smoothing_width=smoothing_width) + elif method_lower == "subtraction": + return SubtractionTreatment() + else: + raise ValueError( + f"Unknown corner treatment method: {method}. " + f"Use 'smoothing' or 'subtraction'." + ) diff --git a/src/spectral/transfer_operators.py b/src/spectral/transfer_operators.py new file mode 100644 index 0000000..1893085 --- /dev/null +++ b/src/spectral/transfer_operators.py @@ -0,0 +1,524 @@ +"""Transfer operators for spectral multigrid methods. + +Implements prolongation (coarse -> fine) and restriction (fine -> coarse) +operators for multigrid algorithms. + +Based on Zhang & Xi (2010): "An explicit Chebyshev pseudospectral multigrid +method for incompressible Navier-Stokes equations" + +Two methods are supported: +- FFT/DCT-based: Uses Discrete Cosine Transform (paper method, Eq. 10-11) +- Polynomial-based: Uses Chebyshev polynomial fitting/evaluation + +For FSG (Full Single Grid), only prolongation is needed. +For VMG/FMG, both prolongation and restriction are required. +""" + +from abc import ABC, abstractmethod +from dataclasses import dataclass +from enum import Enum +from typing import Tuple + +import numpy as np +from scipy.fft import dct, idct + + +class ProlongationMethod(Enum): + """Available prolongation methods.""" + + FFT = "fft" + POLYNOMIAL = "polynomial" + + +class RestrictionMethod(Enum): + """Available restriction methods.""" + + FFT = "fft" + INJECTION = "injection" + + +# ============================================================================= +# Abstract Base Classes +# ============================================================================= + + +class Prolongation(ABC): + """Abstract base class for prolongation operators (coarse -> fine).""" + + @abstractmethod + def prolongate_1d(self, u_coarse: np.ndarray, n_fine: int) -> np.ndarray: + """Interpolate 1D array from coarse to fine grid. + + Parameters + ---------- + u_coarse : np.ndarray + Values on coarse grid (n_coarse points) + n_fine : int + Number of points on fine grid + + Returns + ------- + np.ndarray + Interpolated values on fine grid (n_fine points) + """ + pass + + def prolongate_2d( + self, + u_coarse_2d: np.ndarray, + shape_fine: Tuple[int, int], + ) -> np.ndarray: + """Prolongate 2D field from coarse to fine grid. + + Uses row-column algorithm: interpolate in x-direction, then y-direction. + + Parameters + ---------- + u_coarse_2d : np.ndarray + Field on coarse grid, shape (nx_c, ny_c) + shape_fine : tuple + Target shape (nx_f, ny_f) + + Returns + ------- + np.ndarray + Field on fine grid + """ + nx_c, ny_c = u_coarse_2d.shape + nx_f, ny_f = shape_fine + + if (nx_c, ny_c) == (nx_f, ny_f): + return u_coarse_2d.copy() + + # Interpolate in x-direction (column by column) + temp = np.zeros((nx_f, ny_c)) + for j in range(ny_c): + temp[:, j] = self.prolongate_1d(u_coarse_2d[:, j], nx_f) + + # Interpolate in y-direction (row by row) + u_fine_2d = np.zeros((nx_f, ny_f)) + for i in range(nx_f): + u_fine_2d[i, :] = self.prolongate_1d(temp[i, :], ny_f) + + return u_fine_2d + + +class Restriction(ABC): + """Abstract base class for restriction operators (fine -> coarse).""" + + @abstractmethod + def restrict_1d(self, u_fine: np.ndarray, n_coarse: int) -> np.ndarray: + """Restrict 1D array from fine to coarse grid. + + Parameters + ---------- + u_fine : np.ndarray + Values on fine grid (n_fine points) + n_coarse : int + Number of points on coarse grid + + Returns + ------- + np.ndarray + Restricted values on coarse grid (n_coarse points) + """ + pass + + def restrict_2d( + self, + u_fine_2d: np.ndarray, + shape_coarse: Tuple[int, int], + ) -> np.ndarray: + """Restrict 2D field from fine to coarse grid. + + Uses row-column algorithm: restrict in x-direction, then y-direction. + + Parameters + ---------- + u_fine_2d : np.ndarray + Field on fine grid, shape (nx_f, ny_f) + shape_coarse : tuple + Target shape (nx_c, ny_c) + + Returns + ------- + np.ndarray + Field on coarse grid + """ + nx_f, ny_f = u_fine_2d.shape + nx_c, ny_c = shape_coarse + + if (nx_f, ny_f) == (nx_c, ny_c): + return u_fine_2d.copy() + + # Restrict in x-direction (column by column) + temp = np.zeros((nx_c, ny_f)) + for j in range(ny_f): + temp[:, j] = self.restrict_1d(u_fine_2d[:, j], nx_c) + + # Restrict in y-direction (row by row) + u_coarse_2d = np.zeros((nx_c, ny_c)) + for i in range(nx_c): + u_coarse_2d[i, :] = self.restrict_1d(temp[i, :], ny_c) + + return u_coarse_2d + + +# ============================================================================= +# FFT/DCT-based Operators (Zhang & Xi 2010 paper method) +# ============================================================================= + + +class FFTProlongation(Prolongation): + """FFT/DCT-based prolongation operator. + + From Zhang & Xi (2010), Eq. 10-11: + 1. Compute discrete Chebyshev coefficients via DCT + 2. Evaluate polynomial at fine grid points + + This is spectrally accurate and efficient via FFT. + """ + + def prolongate_1d(self, u_coarse: np.ndarray, n_fine: int) -> np.ndarray: + """Prolongate using DCT-based spectral interpolation. + + Uses Chebyshev interpolation: compute coefficients on coarse grid, + then evaluate the polynomial at fine grid points. + + Parameters + ---------- + u_coarse : np.ndarray + Values on coarse Chebyshev-Lobatto grid (n_coarse points) + n_fine : int + Number of points on fine grid + + Returns + ------- + np.ndarray + Interpolated values on fine grid + """ + n_coarse = len(u_coarse) + + if n_coarse == n_fine: + return u_coarse.copy() + + if n_coarse > n_fine: + raise ValueError( + f"Prolongation requires n_coarse ({n_coarse}) <= n_fine ({n_fine})" + ) + + # Step 1: Compute Chebyshev coefficients from coarse grid values + # Using DCT-I: coeffs_k = (2/N) * sum_{j=0}^N (1/c_j) * f_j * cos(pi*k*j/N) + # where c_j = 2 if j=0 or N, else 1 + N_c = n_coarse - 1 + + # Apply boundary weights to input + u_weighted = u_coarse.copy() + u_weighted[0] /= 2 + u_weighted[-1] /= 2 + + # DCT-I gives: sum_{j=0}^N f_j * cos(pi*k*j/N) + coeffs = dct(u_weighted, type=1) / N_c + + # Apply boundary weights to coefficients + coeffs[0] /= 2 + coeffs[-1] /= 2 + + # Step 2: Evaluate Chebyshev polynomial at fine grid points + # f(x_i) = sum_{k=0}^{N_c} c_k * T_k(x_i) + # where x_i = cos(pi*i/N_f) are fine Chebyshev-Lobatto nodes + N_f = n_fine - 1 + u_fine = np.zeros(n_fine) + + for i in range(n_fine): + # Chebyshev-Lobatto node on fine grid + theta_fine = np.pi * i / N_f + # Evaluate polynomial: sum of c_k * cos(k * theta) + for k in range(n_coarse): + u_fine[i] += coeffs[k] * np.cos(k * theta_fine) + + return u_fine + + +class FFTRestriction(Restriction): + """FFT/DCT-based restriction operator. + + From Zhang & Xi (2010): + 1. Compute discrete Chebyshev coefficients via DCT + 2. Truncate high-frequency coefficients + 3. Evaluate on coarse grid via inverse DCT + + This is used for restricting residuals in V-cycle multigrid. + """ + + def restrict_1d(self, u_fine: np.ndarray, n_coarse: int) -> np.ndarray: + """Restrict using DCT-based spectral truncation. + + Parameters + ---------- + u_fine : np.ndarray + Values on fine Chebyshev-Lobatto grid (n_fine points) + n_coarse : int + Number of points on coarse grid + + Returns + ------- + np.ndarray + Restricted values on coarse grid + """ + n_fine = len(u_fine) + + if n_fine == n_coarse: + return u_fine.copy() + + if n_fine < n_coarse: + raise ValueError( + f"Restriction requires n_fine ({n_fine}) >= n_coarse ({n_coarse})" + ) + + # Step 1: Compute Chebyshev coefficients from fine grid values + N_f = n_fine - 1 + + # Apply boundary weights to input + u_weighted = u_fine.copy() + u_weighted[0] /= 2 + u_weighted[-1] /= 2 + + # DCT-I gives: sum_{j=0}^N f_j * cos(pi*k*j/N) + coeffs = dct(u_weighted, type=1) / N_f + + # Apply boundary weights to coefficients + coeffs[0] /= 2 + coeffs[-1] /= 2 + + # Step 2: Truncate to keep only low-frequency coefficients + # (we keep n_coarse coefficients for the coarse polynomial) + n_keep = min(n_coarse, len(coeffs)) + coeffs_truncated = coeffs[:n_keep] + + # Step 3: Evaluate truncated polynomial at coarse grid points + N_c = n_coarse - 1 + u_coarse = np.zeros(n_coarse) + + for i in range(n_coarse): + # Chebyshev-Lobatto node on coarse grid + theta_coarse = np.pi * i / N_c + # Evaluate polynomial: sum of c_k * cos(k * theta) + for k in range(n_keep): + u_coarse[i] += coeffs_truncated[k] * np.cos(k * theta_coarse) + + return u_coarse + + +# ============================================================================= +# Polynomial-based Operators (original implementation) +# ============================================================================= + + +class PolynomialProlongation(Prolongation): + """Polynomial-based prolongation using Chebyshev fitting. + + Uses numpy's Chebyshev polynomial routines: + 1. Fit Chebyshev polynomial to coarse grid data + 2. Evaluate at fine grid points + + This is mathematically equivalent to FFT method but uses + direct polynomial operations. + """ + + def prolongate_1d(self, u_coarse: np.ndarray, n_fine: int) -> np.ndarray: + """Prolongate using Chebyshev polynomial fitting. + + Parameters + ---------- + u_coarse : np.ndarray + Values on coarse Chebyshev-Lobatto grid (n_coarse points) + n_fine : int + Number of points on fine grid + + Returns + ------- + np.ndarray + Interpolated values on fine grid + """ + from numpy.polynomial.chebyshev import chebfit, chebval + + n_coarse = len(u_coarse) + + if n_coarse == n_fine: + return u_coarse.copy() + + # Chebyshev-Lobatto nodes on [-1, 1] + x_coarse = np.cos(np.pi * np.arange(n_coarse) / (n_coarse - 1)) + x_fine = np.cos(np.pi * np.arange(n_fine) / (n_fine - 1)) + + # Fit Chebyshev polynomial to coarse data + coeffs = chebfit(x_coarse, u_coarse, deg=n_coarse - 1) + + # Evaluate at fine grid points + u_fine = chebval(x_fine, coeffs) + + return u_fine + + +class InjectionRestriction(Restriction): + """Direct injection restriction operator. + + For Chebyshev-Lobatto grids with N_coarse = N_fine / 2, + the coarse grid points are a subset of fine grid points. + This allows simple direct injection. + + This is used for restricting variables (not residuals) in FAS scheme. + """ + + def restrict_1d(self, u_fine: np.ndarray, n_coarse: int) -> np.ndarray: + """Restrict using direct injection. + + Parameters + ---------- + u_fine : np.ndarray + Values on fine Chebyshev-Lobatto grid (n_fine points) + n_coarse : int + Number of points on coarse grid + + Returns + ------- + np.ndarray + Restricted values on coarse grid + """ + n_fine = len(u_fine) + + if n_fine == n_coarse: + return u_fine.copy() + + # For Lobatto grids with full coarsening (N_c = N_f / 2), + # coarse points are every other fine point + if n_fine == 2 * n_coarse - 1: + # Standard case: N_f = 2*N_c - 1 + return u_fine[::2].copy() + elif n_fine % 2 == 1 and n_coarse == (n_fine + 1) // 2: + # Alternative indexing + return u_fine[::2].copy() + else: + # Fallback: use indices based on cosine mapping + # Find closest fine grid points to coarse grid points + x_fine = np.cos(np.pi * np.arange(n_fine) / (n_fine - 1)) + x_coarse = np.cos(np.pi * np.arange(n_coarse) / (n_coarse - 1)) + + u_coarse = np.zeros(n_coarse) + for i, xc in enumerate(x_coarse): + idx = np.argmin(np.abs(x_fine - xc)) + u_coarse[i] = u_fine[idx] + + return u_coarse + + +# ============================================================================= +# Transfer Operator Container +# ============================================================================= + + +@dataclass +class TransferOperators: + """Container for prolongation and restriction operators. + + This class holds the configured operators and provides convenience + methods for transferring solutions between multigrid levels. + """ + + prolongation: Prolongation + restriction: Restriction + + def prolongate_field( + self, + field_coarse: np.ndarray, + shape_coarse: Tuple[int, int], + shape_fine: Tuple[int, int], + ) -> np.ndarray: + """Prolongate a flattened field from coarse to fine grid. + + Parameters + ---------- + field_coarse : np.ndarray + Flattened field on coarse grid + shape_coarse : tuple + Shape of coarse grid (nx, ny) + shape_fine : tuple + Shape of fine grid (nx, ny) + + Returns + ------- + np.ndarray + Flattened field on fine grid + """ + field_2d = field_coarse.reshape(shape_coarse) + result_2d = self.prolongation.prolongate_2d(field_2d, shape_fine) + return result_2d.ravel() + + def restrict_field( + self, + field_fine: np.ndarray, + shape_fine: Tuple[int, int], + shape_coarse: Tuple[int, int], + ) -> np.ndarray: + """Restrict a flattened field from fine to coarse grid. + + Parameters + ---------- + field_fine : np.ndarray + Flattened field on fine grid + shape_fine : tuple + Shape of fine grid (nx, ny) + shape_coarse : tuple + Shape of coarse grid (nx, ny) + + Returns + ------- + np.ndarray + Flattened field on coarse grid + """ + field_2d = field_fine.reshape(shape_fine) + result_2d = self.restriction.restrict_2d(field_2d, shape_coarse) + return result_2d.ravel() + + +# ============================================================================= +# Factory Function +# ============================================================================= + + +def create_transfer_operators( + prolongation_method: str = "fft", + restriction_method: str = "fft", +) -> TransferOperators: + """Create transfer operators from configuration. + + Parameters + ---------- + prolongation_method : str + Method for prolongation: "fft" or "polynomial" + restriction_method : str + Method for restriction: "fft" or "injection" + + Returns + ------- + TransferOperators + Configured transfer operators + """ + # Create prolongation operator + if prolongation_method == "fft": + prolongation = FFTProlongation() + elif prolongation_method == "polynomial": + prolongation = PolynomialProlongation() + else: + raise ValueError(f"Unknown prolongation method: {prolongation_method}") + + # Create restriction operator + if restriction_method == "fft": + restriction = FFTRestriction() + elif restriction_method == "injection": + restriction = InjectionRestriction() + else: + raise ValueError(f"Unknown restriction method: {restriction_method}") + + return TransferOperators(prolongation=prolongation, restriction=restriction) diff --git a/src/utils/mlflow_callback.py b/src/utils/mlflow_callback.py index e73cc10..15f9ed4 100644 --- a/src/utils/mlflow_callback.py +++ b/src/utils/mlflow_callback.py @@ -2,7 +2,7 @@ import logging import os -from datetime import datetime, timedelta +from typing import Dict, Optional from hydra.core.utils import JobReturn from hydra.experimental.callback import Callback @@ -12,28 +12,32 @@ class MLflowSweepCallback(Callback): - """Creates or reuses a parent MLflow run for Hydra multiruns. + """Creates or reuses parent MLflow runs for Hydra multiruns. - Child runs will be nested under the parent, making it easy to: - - View all sweep runs together - - Log sweep-level config to parent - - Group multiple sweeps under the same parent (shared sweep_name) + Supports grouping by Reynolds number (or other parameters): + - If sweep_name contains ${Re}, separate parent runs are created per Re value + - Child runs are nested under their respective parent + - Makes it easy to compare solvers at the same Re - If a parent run with the same sweep_name exists and was created within - the last hour, it will be reused. Otherwise, a new parent is created. + Example sweep_name patterns: + - "my-sweep" -> Single parent for all runs + - "my-sweep-Re${Re}" -> Separate parent per Reynolds number """ def __init__(self) -> None: - self._parent_run = None - self._parent_run_id = None - self._owns_parent = False # Whether we created the parent (vs reusing) - - def _find_recent_parent(self, experiment_name: str, sweep_name: str, max_age_hours: float = 1.0): - """Find a recent parent run with the same sweep_name.""" + self._parent_runs: Dict[str, str] = {} # sweep_name -> run_id + self._active_parent_runs: Dict[str, object] = {} # sweep_name -> run object + self._current_parent_id: Optional[str] = None + self._tracking_uri: Optional[str] = None + self._full_experiment_name: Optional[str] = None + self._base_sweep_name: Optional[str] = None + self._sweep_dir: Optional[str] = None # Store sweep dir while HydraConfig available + + def _find_existing_parent(self, experiment_name: str, sweep_name: str) -> Optional[str]: + """Find an existing parent run with the same sweep_name.""" import mlflow try: - # Search for parent runs with matching name runs = mlflow.search_runs( experiment_names=[experiment_name], filter_string=f"tags.sweep = 'parent' AND tags.`mlflow.runName` = '{sweep_name}'", @@ -44,84 +48,145 @@ def _find_recent_parent(self, experiment_name: str, sweep_name: str, max_age_hou if runs.empty: return None - # Check if recent enough - run = runs.iloc[0] - start_time = run["start_time"] - - # Handle timezone-aware timestamps - if hasattr(start_time, 'tzinfo') and start_time.tzinfo is not None: - now = datetime.now(start_time.tzinfo) - else: - now = datetime.now() - - age = now - start_time - if age < timedelta(hours=max_age_hours): - return run["run_id"] + return runs.iloc[0]["run_id"] except Exception as e: log.warning(f"Error searching for parent run: {e}") return None + def _get_or_create_parent(self, sweep_name: str, config: DictConfig) -> str: + """Get existing parent run or create a new one for this sweep_name.""" + import mlflow + + # Check our cache first + if sweep_name in self._parent_runs: + return self._parent_runs[sweep_name] + + # Check for existing parent in MLflow + existing_id = self._find_existing_parent(self._full_experiment_name, sweep_name) + if existing_id: + self._parent_runs[sweep_name] = existing_id + log.info(f"Reusing existing parent run '{sweep_name}': {existing_id}") + return existing_id + + # Create new parent run + parent_run = mlflow.start_run(run_name=sweep_name) + parent_id = parent_run.info.run_id + self._parent_runs[sweep_name] = parent_id + self._active_parent_runs[sweep_name] = parent_run + + # Log config and tags to parent + mlflow.log_dict(OmegaConf.to_container(config), "sweep_config.yaml") + mlflow.set_tag("sweep", "parent") + + # Extract Re from sweep_name if present + if "Re" in sweep_name: + # Try to extract Re value from sweep_name like "sweep-Re100" + import re + match = re.search(r'Re(\d+)', sweep_name) + if match: + mlflow.set_tag("Re", match.group(1)) + + # Log HPC job info if available + job_id = os.environ.get("LSB_JOBID") + if job_id: + mlflow.set_tag("lsf.job_id", job_id) + mlflow.set_tag("lsf.job_name", os.environ.get("LSB_JOBNAME", "")) + + # End the run context (we'll reference it by ID) + mlflow.end_run() + + log.info(f"Created parent run '{sweep_name}': {parent_id}") + return parent_id + def on_multirun_start(self, config: DictConfig, **kwargs) -> None: - """Create or reuse parent run before sweep starts.""" + """Setup MLflow tracking before sweep starts.""" import mlflow from dotenv import load_dotenv load_dotenv() # Setup MLflow tracking - tracking_uri = config.mlflow.get("tracking_uri", "./mlruns") - mlflow.set_tracking_uri(tracking_uri) + self._tracking_uri = config.mlflow.get("tracking_uri", "./mlruns") + mlflow.set_tracking_uri(self._tracking_uri) # Build experiment name experiment_name = config.experiment_name project_prefix = config.mlflow.get("project_prefix", "") if project_prefix and not experiment_name.startswith("/"): - full_experiment_name = f"{project_prefix}/{experiment_name}" + self._full_experiment_name = f"{project_prefix}/{experiment_name}" else: - full_experiment_name = experiment_name - - mlflow.set_experiment(full_experiment_name) + self._full_experiment_name = experiment_name - sweep_name = config.get("sweep_name", "sweep") + mlflow.set_experiment(self._full_experiment_name) - # Check for existing recent parent run with same name - existing_parent_id = self._find_recent_parent(full_experiment_name, sweep_name) + # Store base sweep name (may contain ${Re} placeholder) + self._base_sweep_name = config.get("sweep_name", "sweep") - if existing_parent_id: - # Reuse existing parent - self._parent_run_id = existing_parent_id - self._owns_parent = False - log.info(f"Reusing existing parent run: {self._parent_run_id}") - else: - # Create new parent run - self._parent_run = mlflow.start_run(run_name=sweep_name) - self._parent_run_id = self._parent_run.info.run_id - self._owns_parent = True + # Store sweep directory while HydraConfig is available + try: + import hydra.core.hydra_config + hydra_cfg = hydra.core.hydra_config.HydraConfig.get() + self._sweep_dir = hydra_cfg.sweep.dir + except Exception: + self._sweep_dir = None - # Log sweep config to parent - mlflow.log_dict(OmegaConf.to_container(config), "sweep_config.yaml") - mlflow.set_tag("sweep", "parent") + log.info(f"MLflow sweep callback initialized for experiment: {self._full_experiment_name}") - # Log HPC job info if available - job_id = os.environ.get("LSB_JOBID") - if job_id: - mlflow.set_tag("lsf.job_id", job_id) - mlflow.set_tag("lsf.job_name", os.environ.get("LSB_JOBNAME", "")) + def on_job_start(self, config: DictConfig, **kwargs) -> None: + """Set parent run ID for each job based on its Re value.""" + import mlflow - log.info(f"Created parent run: {self._parent_run_id}") + # Ensure tracking is set up + if self._tracking_uri: + mlflow.set_tracking_uri(self._tracking_uri) + if self._full_experiment_name: + mlflow.set_experiment(self._full_experiment_name) - # Set env var so child processes can find parent - os.environ["MLFLOW_PARENT_RUN_ID"] = self._parent_run_id + # Resolve sweep_name with current job's config (e.g., Re value) + sweep_name = self._base_sweep_name + if "${Re}" in sweep_name or "{Re}" in sweep_name: + re_value = int(config.get("Re", 100)) + sweep_name = sweep_name.replace("${Re}", str(re_value)).replace("{Re}", str(re_value)) - def on_multirun_end(self, config: DictConfig, **kwargs) -> None: - """End parent run after sweep completes (only if we created it).""" - import mlflow + # Get or create parent for this sweep_name + parent_id = self._get_or_create_parent(sweep_name, config) + self._current_parent_id = parent_id - if self._owns_parent and self._parent_run is not None: - mlflow.end_run() - log.info(f"Ended parent run: {self._parent_run_id}") + # Set env var so child run can find parent + os.environ["MLFLOW_PARENT_RUN_ID"] = parent_id + def on_multirun_end(self, config: DictConfig, **kwargs) -> None: + """Clean up after sweep completes and generate comparison plots.""" # Clean up env var os.environ.pop("MLFLOW_PARENT_RUN_ID", None) + + # Log summary + log.info(f"Sweep completed. Created {len(self._active_parent_runs)} parent run(s).") + for name, run_id in self._parent_runs.items(): + log.info(f" - {name}: {run_id}") + + # Generate comparison plots for all parent runs + if self._parent_runs and config.get("generate_plots", True): + try: + from plotting import generate_comparison_plots_for_sweep + from pathlib import Path + + # Use stored sweep directory (HydraConfig may not be available here) + if self._sweep_dir: + output_dir = Path(self._sweep_dir) / "comparison_plots" + else: + output_dir = Path("outputs") / "comparison_plots" + + parent_run_ids = list(self._parent_runs.values()) + log.info(f"Generating comparison plots for {len(parent_run_ids)} parent run(s)...") + + generate_comparison_plots_for_sweep( + parent_run_ids=parent_run_ids, + tracking_uri=self._tracking_uri, + output_dir=output_dir, + upload_to_mlflow=True, + ) + except Exception as e: + log.warning(f"Failed to generate comparison plots: {e}") From 9f7c038e5571c5ea46d89d3bbafa56aee786fb74 Mon 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f_values: np.ndarray, + x_eval: np.ndarray, + basis: str = "legendre", +) -> np.ndarray: + """ + Spectrally interpolate function values at new points. + + Uses the Vandermonde matrix approach: compute modal coefficients from + nodal values, then evaluate the polynomial expansion at new points. + This preserves spectral accuracy unlike spline interpolation. + + Parameters + ---------- + x_nodes : np.ndarray + Original collocation nodes (e.g., Chebyshev-Gauss-Lobatto or LGL nodes) + f_values : np.ndarray + Function values at x_nodes + x_eval : np.ndarray + Points where to evaluate the interpolant + basis : str, optional + Polynomial basis: "legendre" (alpha=beta=0) or "chebyshev" (alpha=beta=-0.5) + Default is "legendre". + + Returns + ------- + np.ndarray + Interpolated values at x_eval + + Notes + ----- + The interpolation is computed as: + + .. math:: + + f(x_{eval}) = V_{eval} V^{-1} f_{nodes} + + where V is the Vandermonde matrix at the original nodes and V_eval is + the Vandermonde matrix at the evaluation points. + + For Chebyshev nodes, using Legendre basis still works well and avoids + the weight function singularities of Chebyshev polynomials at endpoints. + + References + ---------- + Engsig-Karup, "Lecture 2: Polynomial Methods", p. 55-56 + """ + # Set Jacobi parameters based on basis + if basis.lower() == "legendre": + alpha, beta = 0.0, 0.0 + elif basis.lower() == "chebyshev": + alpha, beta = -0.5, -0.5 + else: + raise ValueError(f"Unknown basis: {basis}. Use 'legendre' or 'chebyshev'.") + + # Map nodes and eval points to [-1, 1] if needed + x_min, x_max = x_nodes.min(), x_nodes.max() + if not (np.isclose(x_min, -1.0) and np.isclose(x_max, 1.0)): + # Affine map to reference domain [-1, 1] + x_nodes_ref = 2.0 * (x_nodes - x_min) / (x_max - x_min) - 1.0 + x_eval_ref = 2.0 * (x_eval - x_min) / (x_max - x_min) - 1.0 + else: + x_nodes_ref = x_nodes + x_eval_ref = x_eval + + # Compute Vandermonde matrix at original nodes + V = vandermonde(x_nodes_ref, alpha, beta) + + # Compute modal coefficients: f_modal = V^{-1} f_nodal + f_modal = np.linalg.solve(V, f_values) + + # Build Vandermonde at evaluation points (may have different size) + N = len(x_nodes) + V_eval = np.zeros((len(x_eval), N)) + for n in range(N): + V_eval[:, n] = jacobi_poly(x_eval_ref, alpha, beta, n) + + # Evaluate polynomial: f_interp = V_eval @ f_modal + return V_eval @ f_modal From cad1a927a13a8d693a91bf7f30aa9936abd90c95 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Fri, 5 Dec 2025 11:19:38 +0100 Subject: [PATCH 24/34] ghia fixup --- conf/experiment/solver_comparison.yaml | 23 ++ conf/solver/spectral_fsg.yaml | 18 - conf/solver/spectral_fsg_fft.yaml | 18 - conf/solver/spectral_fsg_poly.yaml | 18 - conf/solver/transfer/fft.yaml | 11 - conf/solver/transfer/polynomial.yaml | 11 - .../corner_treatment/ghia_comparison.pdf | Bin 605361 -> 604687 bytes .../spectral_vs_fv/ghia_comparison.pdf | Bin 0 -> 625021 bytes src/cli/__init__.py | 25 -- src/cli/actions.py | 292 ------------- src/cli/console.py | 25 -- src/cli/hpc.py | 101 ----- src/cli/hpc_monitor.py | 387 ------------------ src/cli/interactive.py | 269 ------------ src/ldc/data_io.py | 195 --------- src/ldc/field_interpolator.py | 227 ---------- src/ldc/ghia_validator.py | 387 ------------------ src/ldc/ldc_plotter.py | 359 ---------------- src/plotting.py | 192 ++++----- 19 files changed, 109 insertions(+), 2449 deletions(-) create mode 100644 conf/experiment/solver_comparison.yaml delete mode 100644 conf/solver/spectral_fsg.yaml delete mode 100644 conf/solver/spectral_fsg_fft.yaml delete mode 100644 conf/solver/spectral_fsg_poly.yaml delete mode 100644 conf/solver/transfer/fft.yaml delete mode 100644 conf/solver/transfer/polynomial.yaml create mode 100644 outputs/comparison_plots/spectral_vs_fv/ghia_comparison.pdf delete mode 100644 src/cli/__init__.py delete mode 100644 src/cli/actions.py delete mode 100644 src/cli/console.py delete mode 100644 src/cli/hpc.py delete mode 100644 src/cli/hpc_monitor.py delete mode 100644 src/cli/interactive.py delete mode 100644 src/ldc/data_io.py delete mode 100644 src/ldc/field_interpolator.py delete mode 100644 src/ldc/ghia_validator.py delete mode 100644 src/ldc/ldc_plotter.py diff --git a/conf/experiment/solver_comparison.yaml b/conf/experiment/solver_comparison.yaml new file mode 100644 index 0000000..f1726ac --- /dev/null +++ b/conf/experiment/solver_comparison.yaml @@ -0,0 +1,23 @@ +# @package _global_ +# Solver Comparison: Spectral vs Finite Volume +# Compares both solvers at the same Re, with runs grouped under a single parent +# +# Usage: uv run python run_solver.py -m +experiment=solver_comparison + +# MLflow experiment +experiment_name: Solver-Comparison +sweep_name: spectral_vs_fv + +# Base parameters (same for both solvers) +Re: 100 +tolerance: 1.0e-6 +max_iterations: 5000 + +hydra: + sweeper: + params: + # Sweep over both solvers + # Note: N has different meaning (FV: cells, Spectral: polynomial order) + # so we pick comparable grid sizes + solver: spectral,fv + N: 16,32 diff --git a/conf/solver/spectral_fsg.yaml b/conf/solver/spectral_fsg.yaml deleted file mode 100644 index f6f4ac5..0000000 --- a/conf/solver/spectral_fsg.yaml +++ /dev/null @@ -1,18 +0,0 @@ -# Spectral solver with FSG multigrid -name: spectral_fsg - -# Spectral-specific parameters -basis_type: chebyshev -CFL: 0.5 -beta_squared: 5.0 -corner_smoothing: 0.15 - -# FSG Multigrid enabled -multigrid: fsg -n_levels: 5 # Number of grid levels -coarse_tolerance_factor: 1.0 # Same tolerance on all levels (per Zhang & Xi 2010) - -# Transfer operators for multigrid (Zhang & Xi 2010 paper method) -transfer: - prolongation: fft # "fft" (paper) or "polynomial" - restriction: fft # "fft" or "injection" (only needed for VMG/FMG) diff --git a/conf/solver/spectral_fsg_fft.yaml b/conf/solver/spectral_fsg_fft.yaml deleted file mode 100644 index 4405cac..0000000 --- a/conf/solver/spectral_fsg_fft.yaml +++ /dev/null @@ -1,18 +0,0 @@ -# Spectral solver with FSG multigrid - FFT transfer operators (Zhang & Xi 2010) -name: spectral_fsg_fft - -# Spectral-specific parameters -basis_type: chebyshev -CFL: 0.5 -beta_squared: 5.0 -corner_smoothing: 0.15 - -# FSG Multigrid enabled -multigrid: fsg -n_levels: 5 -coarse_tolerance_factor: 1.0 - -# FFT-based transfer operators (paper method) -transfer: - prolongation: fft - restriction: fft diff --git a/conf/solver/spectral_fsg_poly.yaml b/conf/solver/spectral_fsg_poly.yaml deleted file mode 100644 index a3ad541..0000000 --- a/conf/solver/spectral_fsg_poly.yaml +++ /dev/null @@ -1,18 +0,0 @@ -# Spectral solver with FSG multigrid - Polynomial transfer operators -name: spectral_fsg_poly - -# Spectral-specific parameters -basis_type: chebyshev -CFL: 0.5 -beta_squared: 5.0 -corner_smoothing: 0.15 - -# FSG Multigrid enabled -multigrid: fsg -n_levels: 5 -coarse_tolerance_factor: 1.0 - -# Polynomial-based transfer operators -transfer: - prolongation: polynomial - restriction: injection diff --git a/conf/solver/transfer/fft.yaml b/conf/solver/transfer/fft.yaml deleted file mode 100644 index 439a3f6..0000000 --- a/conf/solver/transfer/fft.yaml +++ /dev/null @@ -1,11 +0,0 @@ -# FFT/DCT-based transfer operators (Zhang & Xi 2010 paper method) -# Uses Discrete Cosine Transform for spectral interpolation -name: fft - -# Prolongation: coarse -> fine interpolation -prolongation: - method: fft # Uses DCT coefficients, zero-padding, and inverse DCT - -# Restriction: fine -> coarse interpolation (for residuals in VMG/FMG) -restriction: - method: fft # Uses DCT, truncate high frequencies, inverse DCT diff --git a/conf/solver/transfer/polynomial.yaml b/conf/solver/transfer/polynomial.yaml deleted file mode 100644 index fde9c42..0000000 --- a/conf/solver/transfer/polynomial.yaml +++ /dev/null @@ -1,11 +0,0 @@ -# Polynomial-based transfer operators -# Uses Chebyshev polynomial fitting and evaluation -name: polynomial - -# Prolongation: coarse -> fine interpolation -prolongation: - method: polynomial # Uses chebfit/chebval for Chebyshev interpolation - -# Restriction: fine -> coarse interpolation (for residuals in VMG/FMG) -restriction: - method: injection # Direct injection (coarse nodes are subset of fine nodes) diff --git a/outputs/comparison_plots/corner_treatment/ghia_comparison.pdf b/outputs/comparison_plots/corner_treatment/ghia_comparison.pdf index 6a8f828c55747ea2db1449fb4939702fb7dd7cbf..ac7af3a10641831fbbe563c80874c0b439d8ca8d 100644 GIT binary patch delta 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z*4VuQiHmQxdHLbxn*H`XsE&rAVxLjm8g|mZq>)8&3R;Z7C9~e>1aVjiMu?n#tDUVi= zW1iNgBP5pJ6C6hl`FK7|M!EC@br15fM?pxo_@!uiB8Vo+!{h|dQOKzW$kS0YUD;vV zlgq#5g7D~8%Pxk&=JHV5Xy)TWeSWsMO%d5@TYJmnf`>x#7u<*Ji|1{qHxO?{unsaG1pbnHR}os2!w$P z+Hw<9#$91zih3^Y=B8K}rt6sgxGP<={v6lwjC>hZu9+LkxMN_idqn!aMPUi43qngD xtq04`;lB}ah Path: """Generate field contour plots (p, u, v).""" - import matplotlib.pyplot as plt - from scipy.interpolate import RectBivariateSpline - x_unique = np.sort(fields_df["x"].unique()) y_unique = np.sort(fields_df["y"].unique()) nx, ny = len(x_unique), len(y_unique) @@ -346,38 +400,36 @@ def plot_fields( U_interp = RectBivariateSpline(y_unique, x_unique, U)(y_fine, x_fine) V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) - fig, axes = plt.subplots(1, 3, figsize=(16, 5)) + fig, axes = plt.subplots(1, 3, figsize=(14, 4)) - cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=25) - axes[0].set_xlabel("x") - axes[0].set_ylabel("y") - axes[0].set_title("Pressure", fontweight="bold") + cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=25, cmap="viridis") + axes[0].set_xlabel(r"$x$") + axes[0].set_ylabel(r"$y$") + axes[0].set_title(r"\textbf{Pressure}") axes[0].set_aspect("equal") - plt.colorbar(cf_p, ax=axes[0], label="p") + plt.colorbar(cf_p, ax=axes[0], label=r"$p$") - cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=25) - axes[1].set_xlabel("x") - axes[1].set_ylabel("y") - axes[1].set_title("U velocity", fontweight="bold") + cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=25, cmap="RdBu_r") + axes[1].set_xlabel(r"$x$") + axes[1].set_ylabel(r"$y$") + axes[1].set_title(r"\textbf{$u$-velocity}") axes[1].set_aspect("equal") - plt.colorbar(cf_u, ax=axes[1], label="u") + plt.colorbar(cf_u, ax=axes[1], label=r"$u$") - cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=25) - axes[2].set_xlabel("x") - axes[2].set_ylabel("y") - axes[2].set_title("V velocity", fontweight="bold") + cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=25, cmap="RdBu_r") + axes[2].set_xlabel(r"$x$") + axes[2].set_ylabel(r"$y$") + axes[2].set_title(r"\textbf{$v$-velocity}") axes[2].set_aspect("equal") - plt.colorbar(cf_v, ax=axes[2], label="v") + plt.colorbar(cf_v, ax=axes[2], label=r"$v$") + solver_label = solver.upper().replace("_", r"\_") fig.suptitle( - f"Solution Fields — {solver.upper()} N={N}, Re={Re:.0f}", - fontweight="bold", - fontsize=14, + rf"\textbf{{Solution Fields}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" ) - plt.tight_layout() output_path = output_dir / "fields.pdf" - fig.savefig(output_path, dpi=300, bbox_inches="tight") + fig.savefig(output_path) plt.close(fig) return output_path @@ -387,9 +439,6 @@ def plot_streamlines( fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path ) -> Path: """Generate streamline plot with velocity magnitude.""" - import matplotlib.pyplot as plt - from scipy.interpolate import RectBivariateSpline - x_unique = np.sort(fields_df["x"].unique()) y_unique = np.sort(fields_df["y"].unique()) nx, ny = len(x_unique), len(y_unique) @@ -406,25 +455,25 @@ def plot_streamlines( V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) vel_mag = np.sqrt(U_interp**2 + V_interp**2) - fig, ax = plt.subplots(figsize=(8, 7)) + fig, ax = plt.subplots(figsize=(6, 5)) X_fine, Y_fine = np.meshgrid(x_fine, y_fine) - cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=25) + cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=25, cmap="viridis") ax.streamplot( x_fine, y_fine, U_interp, V_interp, density=1.5, arrowsize=1.0, arrowstyle="->" ) - ax.set_xlabel("x") - ax.set_ylabel("y") + ax.set_xlabel(r"$x$") + ax.set_ylabel(r"$y$") + solver_label = solver.upper().replace("_", r"\_") ax.set_title( - f"Streamlines — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold" + rf"\textbf{{Streamlines}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" ) ax.set_aspect("equal") - plt.colorbar(cf, ax=ax, label="Velocity magnitude") - plt.tight_layout() + plt.colorbar(cf, ax=ax, label=r"$|\mathbf{u}|$") output_path = output_dir / "streamlines.pdf" - fig.savefig(output_path, dpi=300, bbox_inches="tight") + fig.savefig(output_path) plt.close(fig) return output_path @@ -434,9 +483,6 @@ def plot_vorticity( fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path ) -> Path: """Generate vorticity contour plot.""" - import matplotlib.pyplot as plt - from scipy.interpolate import RectBivariateSpline - x_unique = np.sort(fields_df["x"].unique()) y_unique = np.sort(fields_df["y"].unique()) nx, ny = len(x_unique), len(y_unique) @@ -457,20 +503,24 @@ def plot_vorticity( dudy = U_spline(y_fine, x_fine, dy=1) vorticity = dvdx - dudy - fig, ax = plt.subplots(figsize=(8, 7)) + fig, ax = plt.subplots(figsize=(6, 5)) vmax = np.max(np.abs(vorticity)) - cf = ax.contourf(X_fine, Y_fine, vorticity, levels=25, vmin=-vmax, vmax=vmax) + cf = ax.contourf( + X_fine, Y_fine, vorticity, levels=25, vmin=-vmax, vmax=vmax, cmap="RdBu_r" + ) - ax.set_xlabel("x") - ax.set_ylabel("y") - ax.set_title(f"Vorticity — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold") + ax.set_xlabel(r"$x$") + ax.set_ylabel(r"$y$") + solver_label = solver.upper().replace("_", r"\_") + ax.set_title( + rf"\textbf{{Vorticity}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + ) ax.set_aspect("equal") - plt.colorbar(cf, ax=ax, label=r"$\omega$") - plt.tight_layout() + plt.colorbar(cf, ax=ax, label=r"$\omega = \partial v/\partial x - \partial u/\partial y$") output_path = output_dir / "vorticity.pdf" - fig.savefig(output_path, dpi=300, bbox_inches="tight") + fig.savefig(output_path) plt.close(fig) return output_path @@ -480,9 +530,6 @@ def plot_centerlines( fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path ) -> Path: """Plot velocity profiles along centerlines.""" - import matplotlib.pyplot as plt - from scipy.interpolate import RectBivariateSpline - x_unique = np.sort(fields_df["x"].unique()) y_unique = np.sort(fields_df["y"].unique()) nx, ny = len(x_unique), len(y_unique) @@ -504,31 +551,29 @@ def plot_centerlines( u_vertical = U_spline(y_line, x_center).ravel() v_horizontal = V_spline(y_center, x_line).ravel() - fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + fig, axes = plt.subplots(1, 2, figsize=(10, 4)) - axes[0].plot(u_vertical, y_line, "b-", linewidth=2) - axes[0].set_xlabel("u") - axes[0].set_ylabel("y") - axes[0].set_title("U velocity (vertical centerline)", fontweight="bold") + axes[0].plot(u_vertical, y_line, "b-", linewidth=1.5) + axes[0].set_xlabel(r"$u$") + axes[0].set_ylabel(r"$y$") + axes[0].set_title(r"\textbf{$u$-velocity along vertical centerline}") axes[0].grid(True, alpha=0.3) axes[0].axvline(x=0, color="gray", linestyle="--", alpha=0.5) - axes[1].plot(x_line, v_horizontal, "r-", linewidth=2) - axes[1].set_xlabel("x") - axes[1].set_ylabel("v") - axes[1].set_title("V velocity (horizontal centerline)", fontweight="bold") + axes[1].plot(x_line, v_horizontal, "r-", linewidth=1.5) + axes[1].set_xlabel(r"$x$") + axes[1].set_ylabel(r"$v$") + axes[1].set_title(r"\textbf{$v$-velocity along horizontal centerline}") axes[1].grid(True, alpha=0.3) axes[1].axhline(y=0, color="gray", linestyle="--", alpha=0.5) + solver_label = solver.upper().replace("_", r"\_") fig.suptitle( - f"Centerline Profiles — {solver.upper()} N={N}, Re={Re:.0f}", - fontweight="bold", - fontsize=14, + rf"\textbf{{Centerline Profiles}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" ) - plt.tight_layout() output_path = output_dir / "centerlines.pdf" - fig.savefig(output_path, dpi=300, bbox_inches="tight") + fig.savefig(output_path) plt.close(fig) return output_path @@ -538,15 +583,13 @@ def plot_convergence( timeseries_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path ) -> Path: """Plot convergence history (residuals over iterations).""" - import matplotlib.pyplot as plt - if timeseries_df.empty: log.warning("No timeseries data available for convergence plot") return None - fig, ax = plt.subplots(figsize=(10, 6)) + fig, ax = plt.subplots(figsize=(8, 5)) - # Plot available metrics (use default color cycle) + # Plot available metrics for col in timeseries_df.columns: if col != "iteration": data = timeseries_df[col].dropna() @@ -558,17 +601,17 @@ def plot_convergence( linewidth=1.5, ) - ax.set_xlabel("Iteration") - ax.set_ylabel("Value") + ax.set_xlabel(r"Iteration") + ax.set_ylabel(r"Value") + solver_label = solver.upper().replace("_", r"\_") ax.set_title( - f"Convergence History — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold" + rf"\textbf{{Convergence History}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" ) ax.legend(frameon=True) ax.grid(True, alpha=0.3) - plt.tight_layout() output_path = output_dir / "convergence.pdf" - fig.savefig(output_path, dpi=300, bbox_inches="tight") + fig.savefig(output_path) plt.close(fig) return output_path @@ -683,14 +726,16 @@ def plot_ghia_comparison( fields = load_fields_from_zarr(artifact_dir) x_unique, y_unique, U_2d, V_2d, _ = restructure_fields(fields) - from spectral import spectral_interpolate - n_points = 200 y_line = np.linspace(y_unique.min(), y_unique.max(), n_points) x_line = np.linspace(x_unique.min(), x_unique.max(), n_points) - x_center_idx = len(x_unique) // 2 - y_center_idx = len(y_unique) // 2 + # Find physical center (x=0.5, y=0.5), not middle index! + # For non-uniform grids (Chebyshev), middle index != physical center + x_center = 0.5 * (x_unique.min() + x_unique.max()) + y_center = 0.5 * (y_unique.min() + y_unique.max()) + x_center_idx = np.argmin(np.abs(x_unique - x_center)) + y_center_idx = np.argmin(np.abs(y_unique - y_center)) u_sim = spectral_interpolate( y_unique, U_2d[:, x_center_idx], y_line, basis="legendre" @@ -730,8 +775,11 @@ def plot_ghia_comparison( u_df = pd.DataFrame(u_records).sort_values(["N", "Method", "y"]) v_df = pd.DataFrame(v_records).sort_values(["N", "Method", "x"]) - # Create figure - fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + # Create figure with seaborn styling + fig, axes = plt.subplots(1, 2, figsize=(10, 4)) + + # Use seaborn colorblind palette for accessibility + palette = sns.color_palette("colorblind", n_colors=len(u_df["N"].unique())) # Plot with seaborn: hue=N (color by grid), style=Method/Solver (dashes), markers sns.lineplot( @@ -740,6 +788,7 @@ def plot_ghia_comparison( y="y", hue="N", style="Method", + palette=palette, markers=True, markersize=4, markevery=20, @@ -759,7 +808,7 @@ def plot_ghia_comparison( ) axes[0].set_xlabel(r"$u$") axes[0].set_ylabel(r"$y$") - axes[0].set_title(r"$u$-velocity (vertical centerline)") + axes[0].set_title(r"\textbf{$u$-velocity (vertical centerline)}") axes[0].legend(loc="best", fontsize=8) axes[0].set_xlim(-0.4, 1.05) axes[0].set_ylim(0, 1) @@ -770,6 +819,7 @@ def plot_ghia_comparison( y="v", hue="N", style="Method", + palette=palette, markers=True, markersize=4, markevery=20, @@ -789,15 +839,14 @@ def plot_ghia_comparison( ) axes[1].set_xlabel(r"$x$") axes[1].set_ylabel(r"$v$") - axes[1].set_title(r"$v$-velocity (horizontal centerline)") + axes[1].set_title(r"\textbf{$v$-velocity (horizontal centerline)}") axes[1].legend(loc="best", fontsize=8) axes[1].set_xlim(0, 1) - fig.suptitle(rf"Ghia Benchmark Comparison --- $\mathrm{{Re}}={int(Re)}$") - plt.tight_layout() + fig.suptitle(rf"\textbf{{Ghia Benchmark Comparison}} --- $\mathrm{{Re}}={int(Re)}$") output_path = output_dir / "ghia_comparison.pdf" - fig.savefig(output_path, dpi=300, bbox_inches="tight") + fig.savefig(output_path) plt.close(fig) log.info(f"Saved comparison plot: {output_path.name}") @@ -946,8 +995,6 @@ def generate_comparison_plots_for_sweep( dict[str, Path] Mapping of parent_run_id to comparison plot path """ - import mlflow - mlflow.set_tracking_uri(tracking_uri) output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) diff --git a/src/shared/plotting/scientific.mplstyle b/src/shared/plotting/scientific.mplstyle new file mode 100644 index 0000000..ab483f1 --- /dev/null +++ b/src/shared/plotting/scientific.mplstyle @@ -0,0 +1,119 @@ +# Scientific plotting style for LDC solver plots +# Based on seaborn-v0_8-paper with LaTeX-compatible settings +# +# Usage: plt.style.use('path/to/scientific.mplstyle') +# or: plt.style.use(['seaborn-v0_8-paper', 'path/to/scientific.mplstyle']) + +# ============================================================================= +# Figure +# ============================================================================= +figure.figsize: 6, 4 +figure.dpi: 150 +figure.facecolor: white +figure.edgecolor: white +figure.autolayout: False +figure.constrained_layout.use: True + +# ============================================================================= +# Font (LaTeX-compatible) +# ============================================================================= +font.family: serif +font.serif: Computer Modern Roman, DejaVu Serif, Times New Roman +font.size: 10 +font.weight: normal + +# Use LaTeX for text rendering +text.usetex: True +text.latex.preamble: \usepackage{amsmath} \usepackage{amssymb} \usepackage{siunitx} + +# Math text +mathtext.fontset: cm +mathtext.rm: serif + +# ============================================================================= +# Axes +# ============================================================================= +axes.labelsize: 11 +axes.titlesize: 12 +axes.titleweight: bold +axes.labelweight: normal +axes.linewidth: 0.8 +axes.edgecolor: 0.15 +axes.labelcolor: 0.15 +axes.facecolor: white +axes.grid: False +axes.spines.top: True +axes.spines.right: True +axes.prop_cycle: cycler('color', ['0072B2', 'D55E00', '009E73', 'CC79A7', 'F0E442', '56B4E9', 'E69F00', '000000']) + +# Axis formatting +axes.formatter.use_mathtext: True +axes.formatter.useoffset: False +axes.unicode_minus: True + +# ============================================================================= +# Ticks +# ============================================================================= +xtick.labelsize: 9 +ytick.labelsize: 9 +xtick.color: 0.15 +ytick.color: 0.15 +xtick.direction: in +ytick.direction: in +xtick.major.size: 4 +ytick.major.size: 4 +xtick.minor.size: 2 +ytick.minor.size: 2 +xtick.major.width: 0.8 +ytick.major.width: 0.8 +xtick.minor.width: 0.6 +ytick.minor.width: 0.6 +xtick.top: True +ytick.right: True + +# ============================================================================= +# Lines +# ============================================================================= +lines.linewidth: 1.5 +lines.markersize: 5 +lines.markeredgewidth: 0.8 + +# ============================================================================= +# Legend +# ============================================================================= +legend.fontsize: 9 +legend.frameon: True +legend.framealpha: 0.9 +legend.facecolor: white +legend.edgecolor: 0.8 +legend.borderpad: 0.4 +legend.labelspacing: 0.4 +legend.handlelength: 1.5 +legend.handletextpad: 0.5 +legend.columnspacing: 1.0 + +# ============================================================================= +# Grid +# ============================================================================= +grid.color: 0.8 +grid.linewidth: 0.5 +grid.linestyle: - + +# ============================================================================= +# Colormap and contours +# ============================================================================= +image.cmap: viridis + +# ============================================================================= +# Saving figures +# ============================================================================= +savefig.dpi: 300 +savefig.format: pdf +savefig.bbox: tight +savefig.pad_inches: 0.05 +savefig.facecolor: white +savefig.edgecolor: white + +# PDF backend settings +pdf.fonttype: 42 +ps.fonttype: 42 diff --git a/src/solvers/spectral/multigrid/fsg.py b/src/solvers/spectral/multigrid/fsg.py index 6ca05a2..6fd73ca 100644 --- a/src/solvers/spectral/multigrid/fsg.py +++ b/src/solvers/spectral/multigrid/fsg.py @@ -366,10 +366,55 @@ def __init__( self.beta_squared = beta_squared self.lid_velocity = lid_velocity self.CFL = CFL - self.corner_treatment = corner_treatment self.Lx = Lx self.Ly = Ly + # Use subtraction method on all levels with corner exclusion for stability + self.corner_treatment = corner_treatment + self._uses_modified_convection = corner_treatment.uses_modified_convection() + + if self._uses_modified_convection: + # Cache singular velocity and derivatives at this level's grid points + X_flat = level.X.ravel() + Y_flat = level.Y.ravel() + + u_s, v_s = corner_treatment.get_singular_velocity(X_flat, Y_flat, Lx, Ly) + self._u_s = u_s.ravel() + self._v_s = v_s.ravel() + + dus_dx, dus_dy, dvs_dx, dvs_dy = ( + corner_treatment.get_singular_velocity_derivatives( + X_flat, Y_flat, Lx, Ly + ) + ) + + # Create corner exclusion mask: don't apply modified convection very close to corners + # The singular derivatives go like r^(λ-2) ≈ r^(-0.45) which blows up at corners + # Using a cutoff radius based on grid spacing ensures numerical stability + corner_radius = 2.5 * level.dx_min # Exclude points within ~2.5 grid spacings + + # Distance from each corner + r_left = np.sqrt(X_flat**2 + (Y_flat - Ly)**2) # Distance from (0, Ly) + r_right = np.sqrt((X_flat - Lx)**2 + (Y_flat - Ly)**2) # Distance from (Lx, Ly) + + # Mask: 1.0 where we apply full terms, 0.0 near corners + corner_mask = np.ones_like(X_flat) + corner_mask = np.where(r_left < corner_radius, 0.0, corner_mask) + corner_mask = np.where(r_right < corner_radius, 0.0, corner_mask) + self._corner_mask = corner_mask.ravel() + + # Apply mask to singular velocities and derivatives + self._u_s = self._u_s * self._corner_mask + self._v_s = self._v_s * self._corner_mask + self._dus_dx = dus_dx.ravel() * self._corner_mask + self._dus_dy = dus_dy.ravel() * self._corner_mask + self._dvs_dx = dvs_dx.ravel() * self._corner_mask + self._dvs_dy = dvs_dy.ravel() * self._corner_mask + + # Precompute constant term: u_s·∇u_s (singular self-advection) + self._conv_us_us = self._u_s * self._dus_dx + self._v_s * self._dus_dy + self._conv_vs_vs = self._u_s * self._dvs_dx + self._v_s * self._dvs_dy + def _apply_lid_boundary(self, u_2d: np.ndarray, v_2d: np.ndarray): """Apply lid boundary condition using corner treatment.""" x_lid = self.level.X[:, -1] @@ -442,9 +487,25 @@ def _compute_residuals(self, u: np.ndarray, v: np.ndarray, p: np.ndarray): self._interpolate_pressure_gradient() lvl.p[:] = old_p - # Momentum residuals + # Momentum residuals - convection terms + # Term 1: u_c·∇u_c (computational velocity advecting computational gradient) conv_u = u * lvl.du_dx + v * lvl.du_dy conv_v = u * lvl.dv_dx + v * lvl.dv_dy + + if self._uses_modified_convection: + # Additional terms for subtraction method (Botella & Peyret 1998) + # Term 2: u_s·∇u_c (singular velocity advecting computational gradient) + conv_u = conv_u + self._u_s * lvl.du_dx + self._v_s * lvl.du_dy + conv_v = conv_v + self._u_s * lvl.dv_dx + self._v_s * lvl.dv_dy + + # Term 3: u_c·∇u_s (computational velocity advecting singular gradient) + conv_u = conv_u + u * self._dus_dx + v * self._dus_dy + conv_v = conv_v + u * self._dvs_dx + v * self._dvs_dy + + # Term 4: u_s·∇u_s (precomputed constant) + conv_u = conv_u + self._conv_us_us + conv_v = conv_v + self._conv_vs_vs + nu = 1.0 / self.Re lvl.R_u[:] = -conv_u - lvl.dp_dx + nu * lvl.lap_u @@ -599,6 +660,10 @@ def solve_fsg( Per Zhang & Xi (2010): Each level converges to the SAME global tolerance before prolongating to the next finer level. + For subtraction corner treatment: Uses smoothing on coarse levels (N<8) for + stability, then transitions to full subtraction on finer levels. This hybrid + approach avoids overflow from extreme singular derivatives on coarse grids. + Parameters ---------- levels : List[SpectralLevel] @@ -632,6 +697,14 @@ def solve_fsg( if corner_treatment is None: corner_treatment = create_corner_treatment(method="smoothing") + # For subtraction method, also create smoothing treatment for coarse levels + # This avoids numerical instability from extreme singular derivatives on coarse grids + uses_subtraction = corner_treatment.uses_modified_convection() + if uses_subtraction: + smoothing_treatment = create_corner_treatment(method="smoothing") + # Minimum N for subtraction method (below this, use smoothing) + min_n_for_subtraction = 8 + total_iterations = 0 n_levels = len(levels) @@ -656,6 +729,14 @@ def solve_fsg( # Prolongate from previous (coarser) level prolongate_solution(levels[level_idx - 1], level, transfer_ops) + # Select corner treatment for this level + # For subtraction: use smoothing on coarse levels for stability + if uses_subtraction and level.n < min_n_for_subtraction: + level_corner_treatment = smoothing_treatment + log.debug(f" Level {level_idx} (N={level.n}): using smoothing (N < {min_n_for_subtraction})") + else: + level_corner_treatment = corner_treatment + # Create smoother for this level smoother = MultigridSmoother( level=level, @@ -663,7 +744,7 @@ def solve_fsg( beta_squared=beta_squared, lid_velocity=lid_velocity, CFL=CFL, - corner_treatment=corner_treatment, + corner_treatment=level_corner_treatment, Lx=Lx, Ly=Ly, ) diff --git a/src/solvers/spectral/operators/corner.py b/src/solvers/spectral/operators/corner.py index 87e9008..b42a019 100644 --- a/src/solvers/spectral/operators/corner.py +++ b/src/solvers/spectral/operators/corner.py @@ -9,7 +9,8 @@ 2. Subtraction method: Analytical singular solution subtraction. - Following Zhang & Xi (2010), Botella & Peyret (1998), Hancock et al. (1981) - Decomposes u = u_c + u_s where u_s is the Moffatt singular solution - - Modified NS equations: u_c·∇u_c + u_s·∇u_c + u_c·∇u_s + u_s·∇u_s = -∇p_c + Re⁻¹∇²u_c + - Modified NS equations: u_c*nabla(u_c) + u_s*nabla(u_c) + u_c*nabla(u_s) + u_s*nabla(u_s) + = -nabla(p_c) + Re^(-1) * laplacian(u_c) - Requires analytical computation of u_s and its derivatives """ @@ -163,22 +164,63 @@ class SubtractionTreatment(CornerTreatment): - Solve modified NS for smooth part u_c The singular solution comes from the Stokes streamfunction: - ψ = r^λ f(θ) where λ ≈ 1.5446 for 90° corner + psi = r^lambda * f(theta) where lambda ~ 1.5446 for 90 deg corner - Velocities: u_s ~ r^(λ-1) (bounded), but ∇u_s ~ r^(λ-2) (singular) + Velocities: u_s ~ r^(lambda-1) (bounded), but grad(u_s) ~ r^(lambda-2) (singular) - Modified convection: u_c·∇u_c + u_s·∇u_c + u_c·∇u_s + u_s·∇u_s + Modified convection: u_c*grad(u_c) + u_s*grad(u_c) + u_c*grad(u_s) + u_s*grad(u_s) Boundary conditions: - Lid: u_c = V_lid - u_s, v_c = -v_s - Walls: u_c = -u_s, v_c = -v_s """ - # Moffatt eigenvalue for 90° corner: sin(λπ/2) = λ (first root > 1) + # Moffatt eigenvalue for 90 deg corner: sin(lambda*pi/2) = lambda (first root > 1) LAMBDA = 1.5445840107634553 def __init__(self): self.lam = self.LAMBDA + # Precompute constants for angular function + self._sin_lam_pi2 = np.sin(self.lam * np.pi / 2) + self._sin_lam2_pi2 = np.sin((self.lam - 2) * np.pi / 2) + # Normalization: f'(0) = 1 + self._C = ( + self.lam / self._sin_lam_pi2 - (self.lam - 2) / self._sin_lam2_pi2 + ) + + def _f_and_derivatives(self, theta: np.ndarray) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Compute angular function f(theta), f'(theta), and f''(theta). + + The Moffatt angular function satisfies: + - f(0) = 0, f'(0) = 1 (unit lid velocity) + - f(-pi/2) = 0, f'(-pi/2) = 0 (no-slip wall) + + f(theta) = [sin(lambda*theta)/sin(lambda*pi/2) + - sin((lambda-2)*theta)/sin((lambda-2)*pi/2)] / C + + Returns + ------- + f, df, ddf : np.ndarray + Angular function and its first two derivatives + """ + lam = self.lam + + # f(theta) + term1 = np.sin(lam * theta) / self._sin_lam_pi2 + term2 = np.sin((lam - 2) * theta) / self._sin_lam2_pi2 + f = (term1 - term2) / self._C + + # f'(theta) + dterm1 = lam * np.cos(lam * theta) / self._sin_lam_pi2 + dterm2 = (lam - 2) * np.cos((lam - 2) * theta) / self._sin_lam2_pi2 + df = (dterm1 - dterm2) / self._C + + # f''(theta) + ddterm1 = -lam**2 * np.sin(lam * theta) / self._sin_lam_pi2 + ddterm2 = -(lam - 2)**2 * np.sin((lam - 2) * theta) / self._sin_lam2_pi2 + ddf = (ddterm1 - ddterm2) / self._C + + return f, df, ddf def _compute_singular_solution( self, @@ -190,119 +232,159 @@ def _compute_singular_solution( Ly: float, compute_derivatives: bool = False, ) -> dict: - """Compute Moffatt singular solution near one corner. - - The Stokes streamfunction near a corner is ψ = A r^λ f(θ). - For a 90° corner with moving lid, we use the first-order solution. - - In local polar coords centered at corner: - - r = distance from corner - - θ = angle (0 along lid, -π/2 along wall going down) + """Compute Moffatt singular solution and optionally its derivatives. - The angular function f(θ) satisfies boundary conditions: - - f(0) = 0, f'(0) = 1 (lid velocity condition) - - f(-π/2) = 0, f'(-π/2) = 0 (no-slip wall) + For the streamfunction psi = r^lambda * f(theta): + - Polar velocities: u_r = (1/r) * d_psi/d_theta, u_theta = -d_psi/d_r + - Cartesian velocities: u = u_r*cos - u_theta*sin, v = u_r*sin + u_theta*cos - For the Moffatt solution: - f(θ) = [sin(λθ)/sin(λπ/2) - sin((λ-2)θ)/sin((λ-2)π/2)] / C - where C normalizes to give unit lid velocity. + Derivatives use chain rule: + - d/dx = cos * d/dr - (sin/r) * d/d_theta + - d/dy = sin * d/dr + (cos/r) * d/d_theta """ x_arr = np.asarray(x, dtype=float) y_arr = np.asarray(y, dtype=float) + original_shape = x_arr.shape + + # Flatten for computation + x_flat = x_arr.ravel() + y_flat = y_arr.ravel() # Local coordinates relative to corner - dx = x_arr - corner_x - dy = y_arr - corner_y + dx = x_flat - corner_x + dy = y_flat - corner_y r = np.sqrt(dx**2 + dy**2) - r_safe = np.maximum(r, 1e-14) # Avoid division by zero + r_safe = np.maximum(r, 1e-14) - # Angle measured from horizontal (lid direction) - # For left corner (0, Ly): θ = 0 along +x (lid), θ = -π/2 along -y (wall) - # For right corner (Lx, Ly): need to flip orientation - if corner_x < Lx / 2: # Left corner + # Angle: for left corner theta=0 along +x, for right corner flip + is_left = corner_x < Lx / 2 + if is_left: theta = np.arctan2(dy, dx) - else: # Right corner - mirror the geometry - theta = np.arctan2(dy, -dx) # Flip x direction + else: + theta = np.arctan2(dy, -dx) - lam = self.lam + cos_t = np.where(r > 1e-14, dx / r_safe, 1.0) + sin_t = np.where(r > 1e-14, dy / r_safe, 0.0) - # Moffatt angular function and its derivative - # f(θ) chosen to satisfy BCs: f(0)=0, f'(0)=1, f(-π/2)=0, f'(-π/2)=0 - # Using the standard form for corner flow - sin_lam_pi2 = np.sin(lam * np.pi / 2) - sin_lam2_pi2 = np.sin((lam - 2) * np.pi / 2) + if not is_left: + cos_t = -cos_t # Flip for right corner geometry - # Avoid division by zero (these are nonzero for λ ≈ 1.5446) - term1 = np.sin(lam * theta) / sin_lam_pi2 - term2 = np.sin((lam - 2) * theta) / sin_lam2_pi2 - - # Normalization constant to give unit velocity at θ = 0 - # f'(0) = λ/sin(λπ/2) - (λ-2)/sin((λ-2)π/2) = 1 after normalization - C = lam / sin_lam_pi2 - (lam - 2) / sin_lam2_pi2 - - f_theta = (term1 - term2) / C - - # f'(θ) = [λ cos(λθ)/sin(λπ/2) - (λ-2)cos((λ-2)θ)/sin((λ-2)π/2)] / C - df_theta = ( - lam * np.cos(lam * theta) / sin_lam_pi2 - - (lam - 2) * np.cos((lam - 2) * theta) / sin_lam2_pi2 - ) / C + lam = self.lam - # Streamfunction: ψ = r^λ f(θ) - # Velocities in polar coords: - # u_r = (1/r) ∂ψ/∂θ = r^(λ-1) f'(θ) - # u_θ = -∂ψ/∂r = -λ r^(λ-1) f(θ) + # Get angular function and derivatives + f, df, ddf = self._f_and_derivatives(theta) - r_power = r_safe ** (lam - 1) + # Powers of r + r_lam_1 = r_safe ** (lam - 1) # For velocities + r_lam_2 = r_safe ** (lam - 2) # For velocity derivatives - u_r = r_power * df_theta - u_theta = -lam * r_power * f_theta + # Polar velocities: u_r = r^(lam-1) * f', u_theta = -lam * r^(lam-1) * f + u_r = r_lam_1 * df + u_theta = -lam * r_lam_1 * f - # Convert to Cartesian - cos_theta = dx / r_safe - sin_theta = dy / r_safe + # Convert to Cartesian (before any corner flip) + u_s_local = u_r * cos_t - u_theta * sin_t + v_s_local = u_r * sin_t + u_theta * cos_t - if corner_x < Lx / 2: # Left corner - u_s = u_r * cos_theta - u_theta * sin_theta - v_s = u_r * sin_theta + u_theta * cos_theta - else: # Right corner - flip back - u_s = -(u_r * cos_theta - u_theta * sin_theta) # Flip u - v_s = u_r * sin_theta + u_theta * cos_theta + # For right corner, flip u back + if not is_left: + u_s = -u_s_local + v_s = v_s_local + else: + u_s = u_s_local + v_s = v_s_local - # Zero out at exact corner to avoid numerical issues + # Zero at exact corner at_corner = r < 1e-12 u_s = np.where(at_corner, 0.0, u_s) v_s = np.where(at_corner, 0.0, v_s) - result = {"u_s": u_s, "v_s": v_s} + result = { + "u_s": u_s.reshape(original_shape), + "v_s": v_s.reshape(original_shape), + } if compute_derivatives: - # Analytical derivatives of singular solution - # ∂u_s/∂x, ∂u_s/∂y, ∂v_s/∂x, ∂v_s/∂y - # These go like r^(λ-2) which is singular (unbounded) at corner - - # For now, use finite difference approximation far from corner - # and zero near corner (where the solver shouldn't evaluate anyway) - # TODO: Implement full analytical derivatives - - # The key insight: near corner, derivatives blow up ~ r^(-0.5) - # The subtraction method handles this by computing these analytically - # and including them in the modified convection terms - - r_power_deriv = r_safe ** (lam - 2) - - # Simplified derivative estimates (proper implementation would use - # full chain rule on the polar-to-Cartesian transformation) - dus_dx = np.where(at_corner, 0.0, (lam - 1) * r_power_deriv * cos_theta) - dus_dy = np.where(at_corner, 0.0, (lam - 1) * r_power_deriv * sin_theta) - dvs_dx = np.where(at_corner, 0.0, (lam - 1) * r_power_deriv * (-sin_theta)) - dvs_dy = np.where(at_corner, 0.0, (lam - 1) * r_power_deriv * cos_theta) - - result["dus_dx"] = dus_dx - result["dus_dy"] = dus_dy - result["dvs_dx"] = dvs_dx - result["dvs_dy"] = dvs_dy + # Compute Cartesian derivatives using chain rule + # d/dx = cos * d/dr - (sin/r) * d/d_theta + # d/dy = sin * d/dr + (cos/r) * d/d_theta + + # Derivatives of polar velocities w.r.t. r and theta + # u_r = r^(lam-1) * f'(theta) + # du_r/dr = (lam-1) * r^(lam-2) * f' + # du_r/d_theta = r^(lam-1) * f'' + du_r_dr = (lam - 1) * r_lam_2 * df + du_r_dtheta = r_lam_1 * ddf + + # u_theta = -lam * r^(lam-1) * f(theta) + # du_theta/dr = -lam * (lam-1) * r^(lam-2) * f + # du_theta/d_theta = -lam * r^(lam-1) * f' + du_theta_dr = -lam * (lam - 1) * r_lam_2 * f + du_theta_dtheta = -lam * r_lam_1 * df + + # Cartesian u = u_r*cos - u_theta*sin + # du/dr = du_r/dr * cos - du_theta/dr * sin + # du/d_theta = du_r/d_theta * cos + u_r * (-sin) - du_theta/d_theta * sin - u_theta * cos + # = (du_r/d_theta - u_theta) * cos - (du_theta/d_theta + u_r) * sin + du_dr = du_r_dr * cos_t - du_theta_dr * sin_t + du_dtheta = (du_r_dtheta - u_theta) * cos_t - (du_theta_dtheta + u_r) * sin_t + + # Cartesian v = u_r*sin + u_theta*cos + # dv/dr = du_r/dr * sin + du_theta/dr * cos + # dv/d_theta = du_r/d_theta * sin + u_r * cos + du_theta/d_theta * cos - u_theta * sin + # = (du_r/d_theta + u_theta) * sin + (du_theta/d_theta + u_r) * cos + # Wait, let me recalculate: + # dv/d_theta = (du_r/d_theta)*sin + u_r*cos + (du_theta/d_theta)*cos + u_theta*(-sin) + # = (du_r/d_theta - u_theta)*sin + (du_theta/d_theta + u_r)*cos + # Hmm, that's different. Let me be more careful. + # v = u_r * sin_t + u_theta * cos_t + # dv/d_theta = (d/d_theta)[u_r * sin_t + u_theta * cos_t] + # = du_r/d_theta * sin_t + u_r * cos_t + du_theta/d_theta * cos_t - u_theta * sin_t + dv_dr = du_r_dr * sin_t + du_theta_dr * cos_t + dv_dtheta = du_r_dtheta * sin_t + u_r * cos_t + du_theta_dtheta * cos_t - u_theta * sin_t + + # Now apply chain rule to get Cartesian derivatives + # For left corner: dx/dr = cos_t, dy/dr = sin_t + # dx/d_theta = -r*sin_t, dy/d_theta = r*cos_t + # So: d/dx = cos_t * d/dr - (sin_t/r) * d/d_theta + # d/dy = sin_t * d/dr + (cos_t/r) * d/d_theta + + # Protect against division by zero near corner + inv_r = np.where(r > 1e-12, 1.0 / r_safe, 0.0) + + dus_dx_local = cos_t * du_dr - sin_t * inv_r * du_dtheta + dus_dy_local = sin_t * du_dr + cos_t * inv_r * du_dtheta + dvs_dx_local = cos_t * dv_dr - sin_t * inv_r * dv_dtheta + dvs_dy_local = sin_t * dv_dr + cos_t * inv_r * dv_dtheta + + # For right corner, need to handle sign flips + # The local cos_t was flipped, so derivatives need adjustment + if not is_left: + # u was flipped: u_s = -u_s_local + # So du_s/dx = -du_s_local/dx, but x is also flipped + # Actually: d(-u)/d(-x) = du/dx, so sign cancels for dus_dx + # For dus_dy: d(-u)/dy = -du/dy + dus_dx = dus_dx_local # Signs cancel + dus_dy = -dus_dy_local + dvs_dx = -dvs_dx_local # dv/d(-x) = -dv/dx + dvs_dy = dvs_dy_local + else: + dus_dx = dus_dx_local + dus_dy = dus_dy_local + dvs_dx = dvs_dx_local + dvs_dy = dvs_dy_local + + # Zero at corner + dus_dx = np.where(at_corner, 0.0, dus_dx) + dus_dy = np.where(at_corner, 0.0, dus_dy) + dvs_dx = np.where(at_corner, 0.0, dvs_dx) + dvs_dy = np.where(at_corner, 0.0, dvs_dy) + + result["dus_dx"] = dus_dx.reshape(original_shape) + result["dus_dy"] = dus_dy.reshape(original_shape) + result["dvs_dx"] = dvs_dx.reshape(original_shape) + result["dvs_dy"] = dvs_dy.reshape(original_shape) return result From 4ddd48458a881ccc0a817454e5af96d630ac7c46 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Sat, 6 Dec 2025 07:31:03 +0100 Subject: [PATCH 28/34] u --- conf/config.yaml | 6 +- conf/experiment/fv_validation.yaml | 2 +- .../spectral_multigrid_comparison.yaml | 5 +- conf/solver/fv.yaml | 12 +- conf/solver/spectral.yaml | 24 +- conf/solver/spectral_fmg.yaml | 22 +- conf/solver/spectral_fsg.yaml | 22 +- conf/solver/spectral_fsg_poly.yaml | 22 +- conf/solver/spectral_vmg.yaml | 22 +- conf/solvers/fv/default.yaml | 20 - conf/solvers/spectral/default.yaml | 33 - conf/solvers/spectral/fmg.yaml | 10 - conf/solvers/spectral/fsg.yaml | 9 - conf/solvers/spectral/fsg_poly.yaml | 9 - conf/solvers/spectral/vmg.yaml | 10 - docs/reports/TexReport | 2 +- .../validation/ghia_comparison.pdf | Bin 29956 -> 248826 bytes run_solver.py | 48 +- src/shared/plotting/ldc/__init__.py | 67 + src/shared/plotting/ldc/convergence.py | 50 + src/shared/plotting/ldc/data_loading.py | 108 ++ src/shared/plotting/ldc/fields.py | 164 +++ src/shared/plotting/ldc/mlflow_utils.py | 216 ++++ src/shared/plotting/ldc/orchestrator.py | 257 ++++ src/shared/plotting/ldc/style.py | 25 + src/shared/plotting/ldc/validation.py | 311 +++++ src/shared/plotting/ldc_plotter.py | 1118 ----------------- src/utilities/mlflow/callback.py | 5 +- 28 files changed, 1363 insertions(+), 1236 deletions(-) delete mode 100644 conf/solvers/fv/default.yaml delete mode 100644 conf/solvers/spectral/default.yaml delete mode 100644 conf/solvers/spectral/fmg.yaml delete mode 100644 conf/solvers/spectral/fsg.yaml delete mode 100644 conf/solvers/spectral/fsg_poly.yaml delete mode 100644 conf/solvers/spectral/vmg.yaml create mode 100644 src/shared/plotting/ldc/__init__.py create mode 100644 src/shared/plotting/ldc/convergence.py create mode 100644 src/shared/plotting/ldc/data_loading.py create mode 100644 src/shared/plotting/ldc/fields.py create mode 100644 src/shared/plotting/ldc/mlflow_utils.py create mode 100644 src/shared/plotting/ldc/orchestrator.py create mode 100644 src/shared/plotting/ldc/style.py create mode 100644 src/shared/plotting/ldc/validation.py delete mode 100644 src/shared/plotting/ldc_plotter.py diff --git a/conf/config.yaml b/conf/config.yaml index 76d9f44..ec7d4b7 100644 --- a/conf/config.yaml +++ b/conf/config.yaml @@ -1,6 +1,9 @@ # Main Hydra configuration for LDC solvers # Usage: uv run python run_solver.py solver=fv Re=100 N=32 -# uv run python run_solver.py solver=spectral Re=100 N=15 +# uv run python run_solver.py solver=spectral_fmg Re=100 N=15 +# +# Regenerate plots for existing run (no solving): +# uv run python run_solver.py solver=fv N=32 Re=100 plot_only=true # # Parallel sweeps with joblib: # uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 Re=100 @@ -29,6 +32,7 @@ Ly: 1.0 tolerance: 1.0e-6 max_iterations: 500 generate_plots: true +plot_only: false # Skip solving, only regenerate plots for existing run # Experiment tracking experiment_name: LDC-Solver diff --git a/conf/experiment/fv_validation.yaml b/conf/experiment/fv_validation.yaml index 38997a6..3b0e229 100644 --- a/conf/experiment/fv_validation.yaml +++ b/conf/experiment/fv_validation.yaml @@ -20,5 +20,5 @@ max_iterations: 10000 hydra: sweeper: params: - N: 16,32,64 + N: 32,64 Re: 100 diff --git a/conf/experiment/spectral_multigrid_comparison.yaml b/conf/experiment/spectral_multigrid_comparison.yaml index cd3ea9a..11265dd 100644 --- a/conf/experiment/spectral_multigrid_comparison.yaml +++ b/conf/experiment/spectral_multigrid_comparison.yaml @@ -15,8 +15,11 @@ max_iterations: 100000 # Default N (overridden by sweep) N: 15 +defaults: + - override /solver: spectral + hydra: sweeper: params: - # Sweep over both solver types + # Sweep over spectral solver variants solver: spectral,spectral_fsg diff --git a/conf/solver/fv.yaml b/conf/solver/fv.yaml index 2c37c0e..93d75fa 100644 --- a/conf/solver/fv.yaml +++ b/conf/solver/fv.yaml @@ -1,4 +1,10 @@ -# @package _global_ -defaults: - - /solvers/fv@solver: default +# @package solver +_target_: solvers.fv.solver.FVSolver +name: fv +# FV-specific parameters +convection_scheme: Upwind +limiter: MUSCL +alpha_uv: 0.6 # velocity under-relaxation +alpha_p: 0.4 # pressure under-relaxation +linear_solver_tol: 1.0e-6 # SciPy linear solver tolerance diff --git a/conf/solver/spectral.yaml b/conf/solver/spectral.yaml index e8d8c94..9a8200e 100644 --- a/conf/solver/spectral.yaml +++ b/conf/solver/spectral.yaml @@ -1,4 +1,22 @@ -# @package _global_ -defaults: - - /solvers/spectral@solver: default +# @package solver +_target_: solvers.spectral.solver.SpectralSolver +name: spectral +# Spectral-specific parameters +basis_type: chebyshev # "chebyshev" or "legendre" +CFL: 0.5 +beta_squared: 5.0 # artificial compressibility parameter + +# Corner singularity treatment +# Options: "smoothing" (simple cosine smoothing) or "subtraction" (Botella & Peyret 1998) +corner_treatment: smoothing +corner_smoothing: 0.15 # smoothing width (fraction of domain) for smoothing method + +# Multigrid settings +multigrid: none # "none", "fsg", "vmg", "fmg" +n_levels: 3 # number of grid levels for multigrid +coarse_tolerance_factor: 10.0 # relaxed tolerance on coarse grids + +# Transfer operators for multigrid (prolongation: coarse->fine, restriction: fine->coarse Options: fft (Zhang & Xi 2010 paper method) or polynomial/injection +prolongation_method: fft # "fft" or "polynomial" +restriction_method: fft # "fft" or "injection" diff --git a/conf/solver/spectral_fmg.yaml b/conf/solver/spectral_fmg.yaml index 5a6e0dc..ec78274 100644 --- a/conf/solver/spectral_fmg.yaml +++ b/conf/solver/spectral_fmg.yaml @@ -1,4 +1,20 @@ -# @package _global_ -defaults: - - /solvers/spectral@solver: fmg +# @package solver +_target_: solvers.spectral.solver.SpectralSolver +name: spectral_fmg + +# Spectral-specific parameters +basis_type: chebyshev +CFL: 0.5 +beta_squared: 5.0 + +# Corner singularity treatment +corner_treatment: smoothing +corner_smoothing: 0.15 + +# Multigrid settings - FMG variant +multigrid: fmg +n_levels: 3 +coarse_tolerance_factor: 10.0 +prolongation_method: fft +restriction_method: fft diff --git a/conf/solver/spectral_fsg.yaml b/conf/solver/spectral_fsg.yaml index 9270b6d..ecd54a8 100644 --- a/conf/solver/spectral_fsg.yaml +++ b/conf/solver/spectral_fsg.yaml @@ -1,4 +1,20 @@ -# @package _global_ -defaults: - - /solvers/spectral@solver: fsg +# @package solver +_target_: solvers.spectral.solver.SpectralSolver +name: spectral_fsg + +# Spectral-specific parameters +basis_type: chebyshev +CFL: 0.5 +beta_squared: 5.0 + +# Corner singularity treatment +corner_treatment: smoothing +corner_smoothing: 0.15 + +# Multigrid settings - FSG variant +multigrid: fsg +n_levels: 3 +coarse_tolerance_factor: 10.0 +prolongation_method: fft +restriction_method: fft diff --git a/conf/solver/spectral_fsg_poly.yaml b/conf/solver/spectral_fsg_poly.yaml index 00b6a7a..e2c9b4c 100644 --- a/conf/solver/spectral_fsg_poly.yaml +++ b/conf/solver/spectral_fsg_poly.yaml @@ -1,4 +1,20 @@ -# @package _global_ -defaults: - - /solvers/spectral@solver: fsg_poly +# @package solver +_target_: solvers.spectral.solver.SpectralSolver +name: spectral_fsg_poly + +# Spectral-specific parameters +basis_type: chebyshev +CFL: 0.5 +beta_squared: 5.0 + +# Corner singularity treatment +corner_treatment: smoothing +corner_smoothing: 0.15 + +# Multigrid settings - FSG with polynomial transfer +multigrid: fsg +n_levels: 3 +coarse_tolerance_factor: 10.0 +prolongation_method: polynomial +restriction_method: injection diff --git a/conf/solver/spectral_vmg.yaml b/conf/solver/spectral_vmg.yaml index d8f1997..c352726 100644 --- a/conf/solver/spectral_vmg.yaml +++ b/conf/solver/spectral_vmg.yaml @@ -1,4 +1,20 @@ -# @package _global_ -defaults: - - /solvers/spectral@solver: vmg +# @package solver +_target_: solvers.spectral.solver.SpectralSolver +name: spectral_vmg + +# Spectral-specific parameters +basis_type: chebyshev +CFL: 0.5 +beta_squared: 5.0 + +# Corner singularity treatment +corner_treatment: smoothing +corner_smoothing: 0.15 + +# Multigrid settings - VMG variant +multigrid: vmg +n_levels: 3 +coarse_tolerance_factor: 10.0 +prolongation_method: fft +restriction_method: fft diff --git a/conf/solvers/fv/default.yaml b/conf/solvers/fv/default.yaml deleted file mode 100644 index 0d346ab..0000000 --- a/conf/solvers/fv/default.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# @package solver -_target_: solvers.fv.solver.FVSolver -name: fv - -# Common parameters (inherited from root config) -Re: ${Re} -lid_velocity: ${lid_velocity} -Lx: ${Lx} -Ly: ${Ly} -nx: ${N} -ny: ${N} -max_iterations: ${max_iterations} -tolerance: ${tolerance} - -# FV-specific parameters -convection_scheme: Upwind -limiter: MUSCL -alpha_uv: 0.6 # velocity under-relaxation -alpha_p: 0.4 # pressure under-relaxation -linear_solver_tol: 1.0e-6 # SciPy linear solver tolerance diff --git a/conf/solvers/spectral/default.yaml b/conf/solvers/spectral/default.yaml deleted file mode 100644 index 31b98bc..0000000 --- a/conf/solvers/spectral/default.yaml +++ /dev/null @@ -1,33 +0,0 @@ -# @package solver -_target_: solvers.spectral.solver.SpectralSolver -name: spectral - -# Common parameters (inherited from root config) -Re: ${Re} -lid_velocity: ${lid_velocity} -Lx: ${Lx} -Ly: ${Ly} -nx: ${N} -ny: ${N} -max_iterations: ${max_iterations} -tolerance: ${tolerance} - -# Spectral-specific parameters -basis_type: chebyshev # "chebyshev" or "legendre" -CFL: 0.5 -beta_squared: 5.0 # artificial compressibility parameter - -# Corner singularity treatment -# Options: "smoothing" (simple cosine smoothing) or "subtraction" (Botella & Peyret 1998) -corner_treatment: smoothing -corner_smoothing: 0.15 # smoothing width (fraction of domain) for smoothing method - -# Multigrid settings -multigrid: none # "none", "fsg", "vmg", "fmg" -n_levels: 3 # number of grid levels for multigrid -coarse_tolerance_factor: 10.0 # relaxed tolerance on coarse grids - -# Transfer operators for multigrid (prolongation: coarse->fine, restriction: fine->coarse) -# Options: fft (Zhang & Xi 2010 paper method) or polynomial/injection -prolongation_method: fft # "fft" or "polynomial" -restriction_method: fft # "fft" or "injection" diff --git a/conf/solvers/spectral/fmg.yaml b/conf/solvers/spectral/fmg.yaml deleted file mode 100644 index 0ef378c..0000000 --- a/conf/solvers/spectral/fmg.yaml +++ /dev/null @@ -1,10 +0,0 @@ -# @package solver -defaults: - - default - -name: spectral_fmg -multigrid: fmg -prolongation_method: fft -restriction_method: fft -n_levels: 3 - diff --git a/conf/solvers/spectral/fsg.yaml b/conf/solvers/spectral/fsg.yaml deleted file mode 100644 index 0ac1764..0000000 --- a/conf/solvers/spectral/fsg.yaml +++ /dev/null @@ -1,9 +0,0 @@ -# @package solver -defaults: - - default - -name: spectral_fsg -multigrid: fsg -prolongation_method: fft -restriction_method: fft - diff --git a/conf/solvers/spectral/fsg_poly.yaml b/conf/solvers/spectral/fsg_poly.yaml deleted file mode 100644 index 930a3f1..0000000 --- a/conf/solvers/spectral/fsg_poly.yaml +++ /dev/null @@ -1,9 +0,0 @@ -# @package solver -defaults: - - default - -name: spectral_fsg_poly -multigrid: fsg -prolongation_method: polynomial -restriction_method: injection - diff --git a/conf/solvers/spectral/vmg.yaml b/conf/solvers/spectral/vmg.yaml deleted file mode 100644 index 363da83..0000000 --- a/conf/solvers/spectral/vmg.yaml +++ /dev/null @@ -1,10 +0,0 @@ -# @package solver -defaults: - - default - -name: spectral_vmg -multigrid: vmg -prolongation_method: fft -restriction_method: fft -n_levels: 3 - diff --git a/docs/reports/TexReport b/docs/reports/TexReport index 32468c1..7b5c1fe 160000 --- a/docs/reports/TexReport +++ b/docs/reports/TexReport @@ -1 +1 @@ -Subproject commit 32468c12416b4521dcb76413f0caa8fe025e0230 +Subproject commit 7b5c1feee054fbc5e233ce1880e137cddefcb23e diff --git a/outputs/comparison_plots/validation/ghia_comparison.pdf b/outputs/comparison_plots/validation/ghia_comparison.pdf index 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zDdtT>gP2t0%gael%NUcxzE+x;HhB#6419c9S)?MF!8IT=HOoX?=XCGo<|XIkF_i>S Oh+xRhouh}w82$n90adyH diff --git a/run_solver.py b/run_solver.py index f70500e..6fccab8 100644 --- a/run_solver.py +++ b/run_solver.py @@ -49,8 +49,22 @@ def create_solver(cfg: DictConfig): - """Instantiate solver using Hydra's instantiate on solver subtree.""" - return instantiate(cfg.solver, _convert_="partial") + """Instantiate solver using Hydra's instantiate on solver subtree. + + Common parameters from root config are passed to the solver constructor. + """ + return instantiate( + cfg.solver, + Re=cfg.Re, + lid_velocity=cfg.lid_velocity, + Lx=cfg.Lx, + Ly=cfg.Ly, + nx=cfg.N, + ny=cfg.N, + max_iterations=cfg.max_iterations, + tolerance=cfg.tolerance, + _convert_="partial", + ) # ============================================================================= @@ -139,6 +153,34 @@ def main(cfg: DictConfig) -> None: experiment_name = setup_mlflow(cfg) log.info(f"MLflow experiment: {experiment_name}") + # Plot-only mode: find existing run and regenerate plots + if cfg.get("plot_only", False): + log.info("Plot-only mode: finding existing run to regenerate plots") + from shared.plotting.ldc import generate_plots_for_run, find_matching_run + + tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") + run_id, parent_run_id = find_matching_run(cfg, tracking_uri) + + if run_id is None: + log.error(f"No matching run found for solver={cfg.solver.name}, N={cfg.N}, Re={cfg.Re}") + return + + log.info(f"Found existing run: {run_id}") + output_dir = Path( + hydra.core.hydra_config.HydraConfig.get().runtime.output_dir + ) + generate_plots_for_run( + run_id=run_id, + tracking_uri=tracking_uri, + output_dir=output_dir / "plots", + solver_name=cfg.solver.name, + N=cfg.N, + Re=cfg.Re, + parent_run_id=parent_run_id, + ) + log.info("Plot regeneration complete!") + return + # Create solver solver = create_solver(cfg) @@ -192,7 +234,7 @@ def main(cfg: DictConfig) -> None: # Generate plots if cfg.get("generate_plots", True): - from shared.plotting.ldc_plotter import generate_plots_for_run + from shared.plotting.ldc import generate_plots_for_run output_dir = Path( hydra.core.hydra_config.HydraConfig.get().runtime.output_dir diff --git a/src/shared/plotting/ldc/__init__.py b/src/shared/plotting/ldc/__init__.py new file mode 100644 index 0000000..8cdb7b2 --- /dev/null +++ b/src/shared/plotting/ldc/__init__.py @@ -0,0 +1,67 @@ +""" +LDC Plotting Package - Modular structure for lid-driven cavity plots. + +This package provides: +- Individual run plots (fields, streamlines, vorticity, centerlines, convergence) +- Comparison plots (Ghia benchmark comparisons) +- MLflow integration for artifact management +- High-level orchestration functions + +Main API +-------- +generate_plots_for_run : function + Generate all plots for a single completed run +generate_comparison_plots_for_sweep : function + Generate comparison plots for sweep results +main : function + Hydra entry point for standalone CLI usage +""" + +from .convergence import plot_convergence +from .data_loading import ( + fields_to_dataframe, + load_fields_from_zarr, + restructure_fields, +) +from .fields import plot_fields, plot_streamlines, plot_vorticity +from .mlflow_utils import ( + download_mlflow_artifacts, + find_matching_run, + find_sibling_runs, + load_timeseries_from_mlflow, + upload_plots_to_mlflow, +) +from .orchestrator import ( + generate_comparison_plots_for_sweep, + generate_plots_for_run, + main, +) + +# Import style module to trigger sns.set_theme() on package import +from . import style # noqa: F401 + +from .validation import plot_centerlines, plot_ghia_comparison + +__all__ = [ + # High-level API (most commonly used) + "generate_plots_for_run", + "generate_comparison_plots_for_sweep", + "main", + # Individual plot functions + "plot_fields", + "plot_streamlines", + "plot_vorticity", + "plot_centerlines", + "plot_convergence", + "plot_ghia_comparison", + # MLflow utilities + "find_matching_run", + "find_sibling_runs", + "download_mlflow_artifacts", + "load_timeseries_from_mlflow", + "upload_plots_to_mlflow", + # Data utilities + "load_fields_from_zarr", + "restructure_fields", + "fields_to_dataframe", +] diff --git a/src/shared/plotting/ldc/convergence.py b/src/shared/plotting/ldc/convergence.py new file mode 100644 index 0000000..34ef70d --- /dev/null +++ b/src/shared/plotting/ldc/convergence.py @@ -0,0 +1,50 @@ +""" +Convergence Plots for LDC. + +Generates convergence history plots showing residuals and other metrics. +""" + +import logging +from pathlib import Path + +import matplotlib.pyplot as plt +import pandas as pd + +log = logging.getLogger(__name__) + + +def plot_convergence( + timeseries_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: + """Plot convergence history (residuals over iterations).""" + if timeseries_df.empty: + log.warning("No timeseries data available for convergence plot") + return None + + fig, ax = plt.subplots() + + # Plot available metrics + for col in timeseries_df.columns: + if col != "iteration": + data = timeseries_df[col].dropna() + if len(data) > 0: + ax.semilogy( + timeseries_df.loc[data.index, "iteration"], + data, + label=col.capitalize(), + ) + + ax.set_xlabel(r"Iteration") + ax.set_ylabel(r"Value") + solver_label = solver.upper().replace("_", r"\_") + ax.set_title( + rf"\textbf{{Convergence History}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + ) + ax.legend(frameon=True) + ax.grid(True, alpha=0.3) + + output_path = output_dir / "convergence.pdf" + fig.savefig(output_path) + plt.close(fig) + + return output_path diff --git a/src/shared/plotting/ldc/data_loading.py b/src/shared/plotting/ldc/data_loading.py new file mode 100644 index 0000000..d9af990 --- /dev/null +++ b/src/shared/plotting/ldc/data_loading.py @@ -0,0 +1,108 @@ +""" +Data Loading and Transformation for LDC Plotting. + +Handles loading fields from zarr, restructuring data, +and converting to DataFrame format. +""" + +from pathlib import Path + +import numpy as np +import pandas as pd +import zarr + + +def load_fields_from_zarr(artifact_dir: Path) -> dict: + """Load solution fields from zarr artifacts.""" + fields_dir = artifact_dir / "fields" + if not fields_dir.exists(): + raise FileNotFoundError(f"Fields directory not found: {fields_dir}") + + fields = {} + for name in ["x", "y", "u", "v", "p"]: + zarr_path = fields_dir / f"{name}.zarr" + if zarr_path.exists(): + fields[name] = zarr.load(zarr_path) + else: + raise FileNotFoundError(f"Field not found: {zarr_path}") + + return fields + + +def restructure_fields( + fields: dict, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Convert potentially flattened fields to structured 2D arrays. + + Returns (x_unique, y_unique, U_2d, V_2d, P_2d) where U, V, P are 2D arrays + with shape (ny, nx) suitable for RectBivariateSpline. + """ + x, y = fields["x"], fields["y"] + u, v, p = fields["u"], fields["v"], fields["p"] + + # Get unique coordinates + x_unique = np.sort(np.unique(x)) + y_unique = np.sort(np.unique(y)) + nx, ny = len(x_unique), len(y_unique) + + # If already 2D, just return + if u.ndim == 2: + return x_unique, y_unique, u, v, p + + # Reshape from flattened to 2D + # Create mapping from coordinates to indices + x_to_idx = {val: i for i, val in enumerate(x_unique)} + y_to_idx = {val: i for i, val in enumerate(y_unique)} + + U_2d = np.zeros((ny, nx)) + V_2d = np.zeros((ny, nx)) + P_2d = np.zeros((ny, nx)) + + for k in range(len(x)): + i = x_to_idx[x[k]] + j = y_to_idx[y[k]] + U_2d[j, i] = u[k] + V_2d[j, i] = v[k] + P_2d[j, i] = p[k] + + return x_unique, y_unique, U_2d, V_2d, P_2d + + +def fields_to_dataframe(fields: dict) -> pd.DataFrame: + """Convert fields dict to DataFrame format for plotting. + + Handles two storage formats: + 1. Flattened: x, y, u, v, p all 1D with same length (already meshgrid coords) + 2. Structured: x, y are 1D coordinate arrays, u, v, p are 2D fields + """ + x, y = fields["x"], fields["y"] + u, v, p = fields["u"], fields["v"], fields["p"] + + # Check if already flattened (all same length, 1D) + if x.ndim == 1 and len(x) == len(u.ravel()): + # Already flattened meshgrid coordinates + return pd.DataFrame( + { + "x": x, + "y": y, + "u": u.ravel(), + "v": v.ravel(), + "p": p.ravel(), + } + ) + + # Need to create meshgrid + if x.ndim == 1 and y.ndim == 1: + X, Y = np.meshgrid(x, y) + else: + X, Y = x, y + + return pd.DataFrame( + { + "x": X.ravel(), + "y": Y.ravel(), + "u": u.ravel(), + "v": v.ravel(), + "p": p.ravel(), + } + ) diff --git a/src/shared/plotting/ldc/fields.py b/src/shared/plotting/ldc/fields.py new file mode 100644 index 0000000..e589d14 --- /dev/null +++ b/src/shared/plotting/ldc/fields.py @@ -0,0 +1,164 @@ +""" +Field Visualization Plots for LDC. + +Generates contour plots for pressure/velocity fields, +streamlines, and vorticity. +""" + +import logging +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from scipy.interpolate import RectBivariateSpline + +log = logging.getLogger(__name__) + + +def plot_fields( + fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: + """Generate field contour plots (p, u, v).""" + x_unique = np.sort(fields_df["x"].unique()) + y_unique = np.sort(fields_df["y"].unique()) + nx, ny = len(x_unique), len(y_unique) + + sorted_df = fields_df.sort_values(["y", "x"]) + P = sorted_df["p"].values.reshape(ny, nx) + U = sorted_df["u"].values.reshape(ny, nx) + V = sorted_df["v"].values.reshape(ny, nx) + + n_fine = 200 + x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) + y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) + X_fine, Y_fine = np.meshgrid(x_fine, y_fine) + + P_interp = RectBivariateSpline(y_unique, x_unique, P)(y_fine, x_fine) + U_interp = RectBivariateSpline(y_unique, x_unique, U)(y_fine, x_fine) + V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) + + fig, axes = plt.subplots(1, 3) + + cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=25, cmap="viridis") + axes[0].set_xlabel(r"$x$") + axes[0].set_ylabel(r"$y$") + axes[0].set_title(r"\textbf{Pressure}") + axes[0].set_aspect("equal") + plt.colorbar(cf_p, ax=axes[0], label=r"$p$") + + cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=25, cmap="RdBu_r") + axes[1].set_xlabel(r"$x$") + axes[1].set_ylabel(r"$y$") + axes[1].set_title(r"\textbf{$u$-velocity}") + axes[1].set_aspect("equal") + plt.colorbar(cf_u, ax=axes[1], label=r"$u$") + + cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=25, cmap="RdBu_r") + axes[2].set_xlabel(r"$x$") + axes[2].set_ylabel(r"$y$") + axes[2].set_title(r"\textbf{$v$-velocity}") + axes[2].set_aspect("equal") + plt.colorbar(cf_v, ax=axes[2], label=r"$v$") + + solver_label = solver.upper().replace("_", r"\_") + fig.suptitle( + rf"\textbf{{Solution Fields}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + ) + + output_path = output_dir / "fields.pdf" + fig.savefig(output_path) + plt.close(fig) + + return output_path + + +def plot_streamlines( + fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: + """Generate streamline plot with velocity magnitude.""" + x_unique = np.sort(fields_df["x"].unique()) + y_unique = np.sort(fields_df["y"].unique()) + nx, ny = len(x_unique), len(y_unique) + + sorted_df = fields_df.sort_values(["y", "x"]) + U = sorted_df["u"].values.reshape(ny, nx) + V = sorted_df["v"].values.reshape(ny, nx) + + n_fine = 200 + x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) + y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) + + U_interp = RectBivariateSpline(y_unique, x_unique, U)(y_fine, x_fine) + V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) + vel_mag = np.sqrt(U_interp**2 + V_interp**2) + + fig, ax = plt.subplots() + + X_fine, Y_fine = np.meshgrid(x_fine, y_fine) + cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=25, cmap="viridis") + ax.streamplot( + x_fine, y_fine, U_interp, V_interp, density=1.5, arrowsize=1.0, arrowstyle="->" + ) + + ax.set_xlabel(r"$x$") + ax.set_ylabel(r"$y$") + solver_label = solver.upper().replace("_", r"\_") + ax.set_title( + rf"\textbf{{Streamlines}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + ) + ax.set_aspect("equal") + plt.colorbar(cf, ax=ax, label=r"$|\mathbf{u}|$") + + output_path = output_dir / "streamlines.pdf" + fig.savefig(output_path) + plt.close(fig) + + return output_path + + +def plot_vorticity( + fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: + """Generate vorticity contour plot.""" + x_unique = np.sort(fields_df["x"].unique()) + y_unique = np.sort(fields_df["y"].unique()) + nx, ny = len(x_unique), len(y_unique) + + sorted_df = fields_df.sort_values(["y", "x"]) + U = sorted_df["u"].values.reshape(ny, nx) + V = sorted_df["v"].values.reshape(ny, nx) + + n_fine = 200 + x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) + y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) + X_fine, Y_fine = np.meshgrid(x_fine, y_fine) + + U_spline = RectBivariateSpline(y_unique, x_unique, U) + V_spline = RectBivariateSpline(y_unique, x_unique, V) + + dvdx = V_spline(y_fine, x_fine, dx=1) + dudy = U_spline(y_fine, x_fine, dy=1) + vorticity = dvdx - dudy + + fig, ax = plt.subplots() + + vmax = np.max(np.abs(vorticity)) + cf = ax.contourf( + X_fine, Y_fine, vorticity, levels=25, vmin=-vmax, vmax=vmax, cmap="RdBu_r" + ) + + ax.set_xlabel(r"$x$") + ax.set_ylabel(r"$y$") + solver_label = solver.upper().replace("_", r"\_") + ax.set_title( + rf"\textbf{{Vorticity}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + ) + ax.set_aspect("equal") + plt.colorbar(cf, ax=ax, label=r"$\omega = \partial v/\partial x - \partial u/\partial y$") + + output_path = output_dir / "vorticity.pdf" + fig.savefig(output_path) + plt.close(fig) + + return output_path diff --git a/src/shared/plotting/ldc/mlflow_utils.py b/src/shared/plotting/ldc/mlflow_utils.py new file mode 100644 index 0000000..24bee90 --- /dev/null +++ b/src/shared/plotting/ldc/mlflow_utils.py @@ -0,0 +1,216 @@ +""" +MLflow Interaction Utilities for LDC Plotting. + +Handles finding runs, downloading artifacts, loading timeseries data, +and uploading plots to MLflow. +""" + +import logging +import tempfile +from pathlib import Path +from typing import Optional + +import mlflow +import pandas as pd +from omegaconf import DictConfig + +log = logging.getLogger(__name__) + + +def find_matching_run(cfg: DictConfig, tracking_uri: str) -> tuple[str, Optional[str]]: + """Find MLflow run matching the config parameters. + + Returns + ------- + tuple[str, Optional[str]] + (run_id, parent_run_id) - parent_run_id is None if not a sweep child + """ + mlflow.set_tracking_uri(tracking_uri) + client = mlflow.tracking.MlflowClient() + + # Get experiment + experiment_name = cfg.experiment_name + project_prefix = cfg.mlflow.get("project_prefix", "") + if project_prefix and not experiment_name.startswith("/"): + experiment_name = f"{project_prefix}/{experiment_name}" + + experiment = client.get_experiment_by_name(experiment_name) + if experiment is None: + raise ValueError(f"Experiment not found: {experiment_name}") + + # Build filter string for matching runs + solver_name = cfg.solver.name + N = cfg.N + Re = cfg.Re + + filter_parts = [ + f"params.Re = '{Re}'", + f"params.nx = '{N}'", + f"params.ny = '{N}'", + "attributes.status = 'FINISHED'", + ] + + # Add solver-specific filter + if solver_name == "spectral": + filter_parts.append(f"params.basis_type = '{cfg.solver.basis_type}'") + elif solver_name == "fv": + filter_parts.append( + f"params.convection_scheme = '{cfg.solver.convection_scheme}'" + ) + + filter_string = " AND ".join(filter_parts) + + log.info(f"Searching in experiment: {experiment_name}") + log.info(f"Filter: solver={solver_name}, N={N}, Re={Re}") + + # Search for runs + runs = client.search_runs( + experiment_ids=[experiment.experiment_id], + filter_string=filter_string, + order_by=["attributes.start_time DESC"], + max_results=10, + ) + + if not runs: + raise ValueError( + f"No matching runs found for solver={solver_name}, N={N}, Re={Re}\n" + f"Filter used: {filter_string}" + ) + + # Return most recent matching run + run = runs[0] + parent_run_id = run.data.tags.get("parent_run_id") + + log.info(f"Found run: {run.info.run_name} (id: {run.info.run_id[:8]}...)") + if parent_run_id: + log.info(f" Parent run: {parent_run_id[:8]}...") + + return run.info.run_id, parent_run_id + + +def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: + """Find all child runs of a parent (siblings in a sweep). + + Returns list of dicts with run info for comparison plotting. + """ + mlflow.set_tracking_uri(tracking_uri) + client = mlflow.tracking.MlflowClient() + + # Get parent run to find experiment + parent_run = client.get_run(parent_run_id) + experiment_id = parent_run.info.experiment_id + + # Find all FINISHED children of this parent + filter_string = ( + f"tags.parent_run_id = '{parent_run_id}' AND attributes.status = 'FINISHED'" + ) + + runs = client.search_runs( + experiment_ids=[experiment_id], + filter_string=filter_string, + order_by=["params.nx ASC"], # Sort by N for nice legend order + max_results=50, + ) + + siblings = [] + for run in runs: + run_name = run.info.run_name or "" + + # Extract solver name from run_name (format: {solver}_N{n} or {solver}_N{n}_Re{re}) + # Examples: "fv_N32", "spectral_N33", "spectral_fsg_N16" + if "_N" in run_name: + solver_name = run_name.rsplit("_N", 1)[ + 0 + ] # rsplit to handle underscores in solver name + else: + solver_name = "unknown" + + siblings.append( + { + "run_id": run.info.run_id, + "run_name": run_name, + "N": int(run.data.params.get("nx", 0)), + "Re": float(run.data.params.get("Re", 0)), + "solver": solver_name, + "status": run.info.status, + } + ) + + finished = sum(1 for s in siblings if s["status"] == "FINISHED") + log.info(f"Found {len(siblings)} sibling runs in sweep ({finished} finished)") + return siblings + + +def download_mlflow_artifacts(run_id: str, tracking_uri: str) -> Path: + """Download solution artifacts from MLflow run to temp directory.""" + mlflow.set_tracking_uri(tracking_uri) + client = mlflow.tracking.MlflowClient() + + run = client.get_run(run_id) + log.info(f"Downloading artifacts from: {run.info.run_name}") + + tmpdir = tempfile.mkdtemp(prefix="ldc_plot_") + artifact_path = client.download_artifacts(run_id, "", tmpdir) + + return Path(artifact_path) + + +def load_timeseries_from_mlflow(run_id: str, tracking_uri: str) -> pd.DataFrame: + """Load timeseries metrics from MLflow run.""" + mlflow.set_tracking_uri(tracking_uri) + client = mlflow.tracking.MlflowClient() + + # Get metric history + metrics_to_fetch = [ + "residual", + "u_residual", + "v_residual", + "continuity_residual", + "energy", + "enstrophy", + "palinstrophy", + ] + + data = {} + for metric_name in metrics_to_fetch: + try: + history = client.get_metric_history(run_id, metric_name) + if history: + data[metric_name] = [ + m.value for m in sorted(history, key=lambda x: x.step) + ] + except Exception: + pass # Metric might not exist + + if not data: + return pd.DataFrame() + + # Create DataFrame with iteration index + max_len = max(len(v) for v in data.values()) + df = pd.DataFrame({k: v + [None] * (max_len - len(v)) for k, v in data.items()}) + df["iteration"] = range(len(df)) + + return df + + +def upload_plots_to_mlflow( + run_id: str, plot_paths: list, tracking_uri: str, artifact_subdir: str = "plots" +): + """Upload generated plots to MLflow run as artifacts.""" + mlflow.set_tracking_uri(tracking_uri) + + valid_paths = [p for p in plot_paths if p and p.exists()] + + # Check if we're already in an active run + active_run = mlflow.active_run() + if active_run and active_run.info.run_id == run_id: + # Already in the correct run, just log artifacts + for path in valid_paths: + mlflow.log_artifact(str(path), artifact_path=artifact_subdir) + log.info(f"Uploaded: {artifact_subdir}/{path.name}") + else: + # Start/resume run to upload artifacts + with mlflow.start_run(run_id=run_id, nested=True): + for path in valid_paths: + mlflow.log_artifact(str(path), artifact_path=artifact_subdir) + log.info(f"Uploaded: {artifact_subdir}/{path.name}") diff --git a/src/shared/plotting/ldc/orchestrator.py b/src/shared/plotting/ldc/orchestrator.py new file mode 100644 index 0000000..0b659d5 --- /dev/null +++ b/src/shared/plotting/ldc/orchestrator.py @@ -0,0 +1,257 @@ +""" +High-level Plot Generation Orchestration for LDC. + +Coordinates the generation of all plots for individual runs and sweep comparisons. +Provides both a direct API for programmatic use and a Hydra entry point for CLI. +""" + +import logging +from pathlib import Path +from typing import Optional + +import hydra +import mlflow +from dotenv import load_dotenv +from omegaconf import DictConfig + +from .convergence import plot_convergence +from .data_loading import fields_to_dataframe, load_fields_from_zarr +from .fields import plot_fields, plot_streamlines, plot_vorticity +from .mlflow_utils import ( + download_mlflow_artifacts, + find_matching_run, + find_sibling_runs, + load_timeseries_from_mlflow, + upload_plots_to_mlflow, +) +from .validation import plot_centerlines, plot_ghia_comparison + +load_dotenv() +log = logging.getLogger(__name__) + + +def generate_plots_for_run( + run_id: str, + tracking_uri: str, + output_dir: Path, + solver_name: str, + N: int, + Re: float, + parent_run_id: Optional[str] = None, + upload_to_mlflow: bool = True, +) -> list[Path]: + """Generate all plots for a completed run. + + Called directly from run_solver.py after solver completes. + + Parameters + ---------- + run_id : str + MLflow run ID + tracking_uri : str + MLflow tracking URI + output_dir : Path + Directory to save plots + solver_name : str + Solver name (e.g., "spectral", "spectral_fsg", "fv") + N : int + Grid size parameter + Re : float + Reynolds number + parent_run_id : str, optional + Parent run ID if this is part of a sweep + upload_to_mlflow : bool + Whether to upload plots to MLflow + + Returns + ------- + list[Path] + List of generated plot paths + """ + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Download artifacts and load data + artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) + fields = load_fields_from_zarr(artifact_dir) + fields_df = fields_to_dataframe(fields) + timeseries_df = load_timeseries_from_mlflow(run_id, tracking_uri) + + log.info(f"Generating plots for {solver_name} N={N} Re={Re}") + + # Generate individual plots + plot_paths = [] + plot_paths.append(plot_fields(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_streamlines(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_vorticity(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_centerlines(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_convergence(timeseries_df, Re, solver_name, N, output_dir)) + ghia_path = plot_ghia_comparison( + [ + { + "run_id": run_id, + "N": N, + "Re": Re, + "solver": solver_name, + "status": "FINISHED", + } + ], + tracking_uri, + output_dir, + ) + if ghia_path: + plot_paths.append(ghia_path) + + plot_paths = [p for p in plot_paths if p is not None] + log.info(f"Generated {len(plot_paths)} plots for run") + + # Upload to individual run + if upload_to_mlflow: + upload_plots_to_mlflow(run_id, plot_paths, tracking_uri) + + log.info("Plotting done!") + return plot_paths + + +def generate_comparison_plots_for_sweep( + parent_run_ids: list[str], + tracking_uri: str, + output_dir: Path, + upload_to_mlflow: bool = True, +) -> dict[str, Path]: + """Generate comparison plots for all parent runs after sweep completes. + + Called from MLflow callback's on_multirun_end after all jobs finish. + + Parameters + ---------- + parent_run_ids : list[str] + List of parent run IDs (one per Re value) + tracking_uri : str + MLflow tracking URI + output_dir : Path + Base output directory for comparison plots + upload_to_mlflow : bool + Whether to upload plots to MLflow + + Returns + ------- + dict[str, Path] + Mapping of parent_run_id to comparison plot path + """ + mlflow.set_tracking_uri(tracking_uri) + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + results = {} + + for parent_run_id in parent_run_ids: + log.info(f"Generating comparison plot for parent run: {parent_run_id[:8]}...") + + # Find all children of this parent + siblings = find_sibling_runs(parent_run_id, tracking_uri) + + if len(siblings) < 2: + log.warning(f" Only {len(siblings)} child run(s), skipping comparison") + continue + + # Check all siblings are finished + unfinished = [s for s in siblings if s.get("status") != "FINISHED"] + if unfinished: + log.warning(f" {len(unfinished)} run(s) still not finished, skipping") + continue + + # Get parent run info for naming + client = mlflow.tracking.MlflowClient() + parent_run = client.get_run(parent_run_id) + parent_name = parent_run.info.run_name or parent_run_id[:8] + + # Create comparison plot + comparison_dir = output_dir / parent_name + comparison_dir.mkdir(exist_ok=True) + + comparison_path = plot_ghia_comparison(siblings, tracking_uri, comparison_dir) + + if comparison_path: + results[parent_run_id] = comparison_path + log.info(f" Created comparison plot: {comparison_path.name}") + + if upload_to_mlflow: + upload_plots_to_mlflow( + parent_run_id, [comparison_path], tracking_uri, "plots" + ) + log.info(" Uploaded to parent run") + + log.info(f"Generated {len(results)} comparison plot(s)") + return results + + +@hydra.main(config_path="../../conf", config_name="config", version_base=None) +def main(cfg: DictConfig) -> None: + """Hydra entry point - finds matching run and generates plots.""" + + tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") + solver_name = cfg.solver.name + N = cfg.N + Re = cfg.Re + + # Find matching MLflow run + run_id, parent_run_id = find_matching_run(cfg, tracking_uri) + + # Setup output directory + output_dir = Path(hydra.core.hydra_config.HydraConfig.get().runtime.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Download artifacts for this run + artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) + fields = load_fields_from_zarr(artifact_dir) + fields_df = fields_to_dataframe(fields) + timeseries_df = load_timeseries_from_mlflow(run_id, tracking_uri) + + log.info(f"Generating plots for {solver_name} N={N} Re={Re}") + + # ========================================================================== + # Individual run plots + # ========================================================================== + plot_paths = [] + + plot_paths.append(plot_fields(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_streamlines(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_vorticity(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_centerlines(fields_df, Re, solver_name, N, output_dir)) + plot_paths.append(plot_convergence(timeseries_df, Re, solver_name, N, output_dir)) + + plot_paths = [p for p in plot_paths if p is not None] + log.info(f"Generated {len(plot_paths)} plots for run") + + # Upload to individual run + if cfg.get("upload_to_mlflow", True): + upload_plots_to_mlflow(run_id, plot_paths, tracking_uri) + + # ========================================================================== + # Comparison plot for parent (if this is part of a sweep) + # ========================================================================== + if parent_run_id: + log.info("This run is part of a sweep - generating comparison plot for parent") + + siblings = find_sibling_runs(parent_run_id, tracking_uri) + + if len(siblings) > 1: + comparison_dir = output_dir / "comparison" + comparison_dir.mkdir(exist_ok=True) + + comparison_path = plot_ghia_comparison( + siblings, tracking_uri, comparison_dir + ) + + if comparison_path and cfg.get("upload_to_mlflow", True): + upload_plots_to_mlflow( + parent_run_id, [comparison_path], tracking_uri, "plots" + ) + log.info("Comparison plot uploaded to parent run") + + log.info("Done!") + + +if __name__ == "__main__": + main() diff --git a/src/shared/plotting/ldc/style.py b/src/shared/plotting/ldc/style.py new file mode 100644 index 0000000..192b005 --- /dev/null +++ b/src/shared/plotting/ldc/style.py @@ -0,0 +1,25 @@ +""" +Plotting Style Configuration for LDC Plots. + +Uses scientific.mplstyle with seaborn color palette. +""" + +import logging +from pathlib import Path + +import matplotlib.pyplot as plt +import seaborn as sns + +log = logging.getLogger(__name__) + +# Load scientific style file +_STYLE_PATH = Path(__file__).parent.parent / "scientific.mplstyle" + +if _STYLE_PATH.exists(): + plt.style.use(str(_STYLE_PATH)) + log.debug(f"Loaded scientific style: {_STYLE_PATH}") +else: + log.warning(f"Scientific style file not found: {_STYLE_PATH}") + +# Apply seaborn theme on top for color palette +sns.set_theme() diff --git a/src/shared/plotting/ldc/validation.py b/src/shared/plotting/ldc/validation.py new file mode 100644 index 0000000..138d284 --- /dev/null +++ b/src/shared/plotting/ldc/validation.py @@ -0,0 +1,311 @@ +""" +Validation and Comparison Plots for LDC. + +Generates centerline velocity profiles and Ghia benchmark comparisons. +""" + +import logging +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from scipy.interpolate import RectBivariateSpline + +from spectral import spectral_interpolate +from utilities.config.paths import get_repo_root + +from .data_loading import load_fields_from_zarr, restructure_fields +from .mlflow_utils import download_mlflow_artifacts + +log = logging.getLogger(__name__) + + +def plot_centerlines( + fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: + """Plot velocity profiles along centerlines.""" + x_unique = np.sort(fields_df["x"].unique()) + y_unique = np.sort(fields_df["y"].unique()) + nx, ny = len(x_unique), len(y_unique) + + sorted_df = fields_df.sort_values(["y", "x"]) + U = sorted_df["u"].values.reshape(ny, nx) + V = sorted_df["v"].values.reshape(ny, nx) + + U_spline = RectBivariateSpline(y_unique, x_unique, U) + V_spline = RectBivariateSpline(y_unique, x_unique, V) + + n_points = 200 + y_line = np.linspace(y_unique[0], y_unique[-1], n_points) + x_line = np.linspace(x_unique[0], x_unique[-1], n_points) + + x_center = (x_unique[0] + x_unique[-1]) / 2 + y_center = (y_unique[0] + y_unique[-1]) / 2 + + u_vertical = U_spline(y_line, x_center).ravel() + v_horizontal = V_spline(y_center, x_line).ravel() + + fig, axes = plt.subplots(1, 2) + + axes[0].plot(u_vertical, y_line) + axes[0].set_xlabel(r"$u$") + axes[0].set_ylabel(r"$y$") + axes[0].set_title(r"\textbf{$u$-velocity along vertical centerline}") + axes[0].grid(True, alpha=0.3) + axes[0].axvline(x=0, color="gray", linestyle="--", alpha=0.5) + + axes[1].plot(x_line, v_horizontal) + axes[1].set_xlabel(r"$x$") + axes[1].set_ylabel(r"$v$") + axes[1].set_title(r"\textbf{$v$-velocity along horizontal centerline}") + axes[1].grid(True, alpha=0.3) + axes[1].axhline(y=0, color="gray", linestyle="--", alpha=0.5) + + solver_label = solver.upper().replace("_", r"\_") + fig.suptitle( + rf"\textbf{{Centerline Profiles}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + ) + + output_path = output_dir / "centerlines.pdf" + fig.savefig(output_path) + plt.close(fig) + + return output_path + + +def _build_method_label(sibling: dict) -> str: + """Build a unified method label from solver name. + + Formats solver names nicely for legends: + - 'fv' -> 'FV' + - 'spectral' -> 'Spectral' + - 'spectral_fsg' -> 'Spectral-FSG' + - 'spectral_vmg' -> 'Spectral-VMG' + """ + solver = sibling.get("solver", "unknown") + + # Format known solver names + label_map = { + "fv": "FV", + "spectral": "Spectral", + "spectral_fsg": "Spectral-FSG", + "spectral_vmg": "Spectral-VMG", + "spectral_fmg": "Spectral-FMG", + } + + return label_map.get(solver, solver.replace("_", "-").title()) + + +def plot_ghia_comparison( + siblings: list[dict], tracking_uri: str, output_dir: Path +) -> Path: + """Plot Ghia comparison with all sibling runs using seaborn. + + Legend system (native seaborn): + - hue: Method type (Spectral, FV, Spectral-FSG, etc.) + - style: Grid size N (different dash patterns) + - markers: Method-specific markers (every 20th point) + + Parameters + ---------- + siblings : list[dict] + List of sibling run info dicts with run_id, N, Re, solver + tracking_uri : str + MLflow tracking URI + output_dir : Path + Output directory + + Returns + ------- + Path + Path to comparison plot + """ + if not siblings: + return None + + # Only plot finished runs + finished_siblings = [ + s for s in siblings if s.get("status", "FINISHED") == "FINISHED" + ] + if len(finished_siblings) < 1: + log.info( + f"Need at least 1 finished run for comparison (have {len(finished_siblings)})" + ) + return None + + siblings = finished_siblings + Re = siblings[0]["Re"] + + AVAILABLE_RE = [100, 400, 1000, 3200, 5000, 7500, 10000] + if int(Re) not in AVAILABLE_RE: + log.warning(f"Ghia data not available for Re={Re}") + return None + + # Load Ghia reference data from repo-root data/validation/ghia + project_root = get_repo_root() + ghia_dir = project_root / "data" / "validation" / "ghia" + + u_file = ghia_dir / f"ghia_Re{int(Re)}_u_centerline.csv" + v_file = ghia_dir / f"ghia_Re{int(Re)}_v_centerline.csv" + + if not u_file.exists() or not v_file.exists(): + raise FileNotFoundError(f"Ghia data files not found in {ghia_dir}") + + ghia_u = pd.read_csv(u_file) + ghia_v = pd.read_csv(v_file) + + # Filter to unique (solver/method, N) combinations + seen_combos = set() + unique_siblings = [] + for sibling in siblings: + method = _build_method_label(sibling) + N = sibling["N"] + combo = (method, N) + if combo not in seen_combos: + seen_combos.add(combo) + unique_siblings.append(sibling) + + log.info(f"Plotting {len(unique_siblings)} unique (method, N) combinations") + + # Build DataFrames + u_records = [] + v_records = [] + + for sibling in unique_siblings: + run_id = sibling["run_id"] + N = sibling["N"] + method = _build_method_label(sibling) + solver_name = sibling.get("solver", method) + + try: + artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) + fields = load_fields_from_zarr(artifact_dir) + x_unique, y_unique, U_2d, V_2d, _ = restructure_fields(fields) + + n_points = 200 + y_line = np.linspace(y_unique.min(), y_unique.max(), n_points) + x_line = np.linspace(x_unique.min(), x_unique.max(), n_points) + + # Find physical center (x=0.5, y=0.5), not middle index! + # For non-uniform grids (Chebyshev), middle index != physical center + x_center = 0.5 * (x_unique.min() + x_unique.max()) + y_center = 0.5 * (y_unique.min() + y_unique.max()) + x_center_idx = np.argmin(np.abs(x_unique - x_center)) + y_center_idx = np.argmin(np.abs(y_unique - y_center)) + + # Use appropriate interpolation based on solver type + # FV uses uniform grids -> linear interpolation + # Spectral uses non-uniform grids -> spectral interpolation + if solver_name.lower().startswith("fv"): + # Linear interpolation for FV solvers + u_sim = np.interp(y_line, y_unique, U_2d[:, x_center_idx]) + v_sim = np.interp(x_line, x_unique, V_2d[y_center_idx, :]) + else: + # Spectral interpolation for spectral solvers + u_sim = spectral_interpolate( + y_unique, U_2d[:, x_center_idx], y_line, basis="legendre" + ) + v_sim = spectral_interpolate( + x_unique, V_2d[y_center_idx, :], x_line, basis="legendre" + ) + + for i in range(n_points): + u_records.append( + { + "y": y_line[i], + "u": u_sim[i], + "Method": method, + "Solver": solver_name, + "N": N, + } + ) + v_records.append( + { + "x": x_line[i], + "v": v_sim[i], + "Method": method, + "Solver": solver_name, + "N": N, + } + ) + + except Exception as e: + log.warning(f"Failed to load run {run_id}: {e}") + continue + + if not u_records: + log.warning("No valid runs to plot") + return None + + u_df = pd.DataFrame(u_records).sort_values(["N", "Method", "y"]) + v_df = pd.DataFrame(v_records).sort_values(["N", "Method", "x"]) + + # Create figure with seaborn styling + fig, axes = plt.subplots(1, 2) + + # Plot with seaborn: hue=N (color by grid), style=Method/Solver (dashes), markers + sns.lineplot( + data=u_df, + x="u", + y="y", + hue="N", + style="Method", + markers=True, + sort=False, + ax=axes[0], + ) + # Set markevery on all lines + for line in axes[0].get_lines(): + line.set_markevery(20) + axes[0].scatter( + ghia_u["u"], + ghia_u["y"], + c="black", + marker="x", + label=r"Ghia et al.\ (1982)", + zorder=10, + ) + axes[0].set_xlabel(r"$u$") + axes[0].set_ylabel(r"$y$") + axes[0].set_title(r"\textbf{$u$-velocity (vertical centerline)}") + axes[0].legend(loc="best", fontsize=8) + axes[0].set_xlim(-0.4, 1.05) + axes[0].set_ylim(0, 1) + + sns.lineplot( + data=v_df, + x="x", + y="v", + hue="N", + style="Method", + markers=True, + sort=False, + ax=axes[1], + ) + # Set markevery on all lines + for line in axes[1].get_lines(): + line.set_markevery(20) + axes[1].scatter( + ghia_v["x"], + ghia_v["v"], + c="black", + marker="x", + label=r"Ghia et al.\ (1982)", + zorder=10, + ) + axes[1].set_xlabel(r"$x$") + axes[1].set_ylabel(r"$v$") + axes[1].set_title(r"\textbf{$v$-velocity (horizontal centerline)}") + axes[1].legend(loc="best", fontsize=8) + axes[1].set_xlim(0, 1) + + fig.suptitle(rf"\textbf{{Ghia Benchmark Comparison}} --- $\mathrm{{Re}}={int(Re)}$") + + output_path = output_dir / "ghia_comparison.pdf" + fig.savefig(output_path) + plt.close(fig) + + log.info(f"Saved comparison plot: {output_path.name}") + return output_path diff --git a/src/shared/plotting/ldc_plotter.py b/src/shared/plotting/ldc_plotter.py deleted file mode 100644 index 2c34c72..0000000 --- a/src/shared/plotting/ldc_plotter.py +++ /dev/null @@ -1,1118 +0,0 @@ -""" -Solution Plotter - Generates plots for MLflow runs. - -Can be used standalone via Hydra or called directly from run_solver.py. - -For individual runs: generates fields, streamlines, vorticity, centerlines, - ghia comparison, and convergence plots. - -For sweeps: additionally generates a Ghia comparison plot uploaded to parent run. - -Usage (standalone): - # Plot single run - uv run python src/plotting.py solver=spectral N=31 Re=100 - - # Plot sweep (comparison plot goes to parent) - uv run python src/plotting.py -m solver=spectral N=15,31,63 Re=100 - -Usage (from run_solver.py): - from plotting import generate_plots_for_run - generate_plots_for_run(run_id, tracking_uri, output_dir, parent_run_id) -""" - -# ============================================================================= -# Imports (all at top per PEP 8) -# ============================================================================= - -import logging -import tempfile -from pathlib import Path -from typing import Optional - -import hydra -import matplotlib.pyplot as plt -import mlflow -import numpy as np -import pandas as pd -import seaborn as sns -import zarr -from dotenv import load_dotenv -from omegaconf import DictConfig -from scipy.interpolate import RectBivariateSpline - -from spectral import spectral_interpolate -from utilities.config.paths import get_repo_root - -# ============================================================================= -# Module Setup -# ============================================================================= - -load_dotenv() -log = logging.getLogger(__name__) - -# Load custom style - use try/except for robustness -_STYLE_PATH = Path(__file__).parent / "scientific.mplstyle" - - -def _setup_plotting_style(): - """Configure matplotlib with scientific style and LaTeX support.""" - # Try to use custom style file - if _STYLE_PATH.exists(): - try: - plt.style.use(str(_STYLE_PATH)) - log.debug(f"Loaded custom style: {_STYLE_PATH}") - return - except Exception as e: - log.warning(f"Could not load custom style: {e}") - - # Fallback: configure manually - plt.rcParams.update( - { - # LaTeX - "text.usetex": True, - "text.latex.preamble": r"\usepackage{amsmath}\usepackage{amssymb}", - "font.family": "serif", - "font.serif": ["Computer Modern Roman"], - # Sizes - "font.size": 10, - "axes.labelsize": 11, - "axes.titlesize": 12, - "legend.fontsize": 9, - "xtick.labelsize": 9, - "ytick.labelsize": 9, - # Figure - "figure.figsize": (6, 4), - "figure.dpi": 150, - "savefig.dpi": 300, - "savefig.bbox": "tight", - # Axes - "axes.linewidth": 0.8, - "axes.titleweight": "bold", - # Lines - "lines.linewidth": 1.5, - "lines.markersize": 5, - } - ) - - -# Initialize style on import -_setup_plotting_style() - - -# ============================================================================= -# MLflow Run Discovery -# ============================================================================= - - -def find_matching_run(cfg: DictConfig, tracking_uri: str) -> tuple[str, Optional[str]]: - """Find MLflow run matching the config parameters. - - Returns - ------- - tuple[str, Optional[str]] - (run_id, parent_run_id) - parent_run_id is None if not a sweep child - """ - mlflow.set_tracking_uri(tracking_uri) - client = mlflow.tracking.MlflowClient() - - # Get experiment - experiment_name = cfg.experiment_name - project_prefix = cfg.mlflow.get("project_prefix", "") - if project_prefix and not experiment_name.startswith("/"): - experiment_name = f"{project_prefix}/{experiment_name}" - - experiment = client.get_experiment_by_name(experiment_name) - if experiment is None: - raise ValueError(f"Experiment not found: {experiment_name}") - - # Build filter string for matching runs - solver_name = cfg.solver.name - N = cfg.N - Re = cfg.Re - - filter_parts = [ - f"params.Re = '{Re}'", - f"params.nx = '{N}'", - f"params.ny = '{N}'", - "attributes.status = 'FINISHED'", - ] - - # Add solver-specific filter - if solver_name == "spectral": - filter_parts.append(f"params.basis_type = '{cfg.solver.basis_type}'") - elif solver_name == "fv": - filter_parts.append( - f"params.convection_scheme = '{cfg.solver.convection_scheme}'" - ) - - filter_string = " AND ".join(filter_parts) - - log.info(f"Searching in experiment: {experiment_name}") - log.info(f"Filter: solver={solver_name}, N={N}, Re={Re}") - - # Search for runs - runs = client.search_runs( - experiment_ids=[experiment.experiment_id], - filter_string=filter_string, - order_by=["attributes.start_time DESC"], - max_results=10, - ) - - if not runs: - raise ValueError( - f"No matching runs found for solver={solver_name}, N={N}, Re={Re}\n" - f"Filter used: {filter_string}" - ) - - # Return most recent matching run - run = runs[0] - parent_run_id = run.data.tags.get("parent_run_id") - - log.info(f"Found run: {run.info.run_name} (id: {run.info.run_id[:8]}...)") - if parent_run_id: - log.info(f" Parent run: {parent_run_id[:8]}...") - - return run.info.run_id, parent_run_id - - -def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: - """Find all child runs of a parent (siblings in a sweep). - - Returns list of dicts with run info for comparison plotting. - """ - mlflow.set_tracking_uri(tracking_uri) - client = mlflow.tracking.MlflowClient() - - # Get parent run to find experiment - parent_run = client.get_run(parent_run_id) - experiment_id = parent_run.info.experiment_id - - # Find all FINISHED children of this parent - filter_string = ( - f"tags.parent_run_id = '{parent_run_id}' AND attributes.status = 'FINISHED'" - ) - - runs = client.search_runs( - experiment_ids=[experiment_id], - filter_string=filter_string, - order_by=["params.nx ASC"], # Sort by N for nice legend order - max_results=50, - ) - - siblings = [] - for run in runs: - run_name = run.info.run_name or "" - - # Extract solver name from run_name (format: {solver}_N{n} or {solver}_N{n}_Re{re}) - # Examples: "fv_N32", "spectral_N33", "spectral_fsg_N16" - if "_N" in run_name: - solver_name = run_name.rsplit("_N", 1)[ - 0 - ] # rsplit to handle underscores in solver name - else: - solver_name = "unknown" - - siblings.append( - { - "run_id": run.info.run_id, - "run_name": run_name, - "N": int(run.data.params.get("nx", 0)), - "Re": float(run.data.params.get("Re", 0)), - "solver": solver_name, - "status": run.info.status, - } - ) - - finished = sum(1 for s in siblings if s["status"] == "FINISHED") - log.info(f"Found {len(siblings)} sibling runs in sweep ({finished} finished)") - return siblings - - -# ============================================================================= -# Data Loading from MLflow -# ============================================================================= - - -def download_mlflow_artifacts(run_id: str, tracking_uri: str) -> Path: - """Download solution artifacts from MLflow run to temp directory.""" - mlflow.set_tracking_uri(tracking_uri) - client = mlflow.tracking.MlflowClient() - - run = client.get_run(run_id) - log.info(f"Downloading artifacts from: {run.info.run_name}") - - tmpdir = tempfile.mkdtemp(prefix="ldc_plot_") - artifact_path = client.download_artifacts(run_id, "", tmpdir) - - return Path(artifact_path) - - -def load_fields_from_zarr(artifact_dir: Path) -> dict: - """Load solution fields from zarr artifacts.""" - fields_dir = artifact_dir / "fields" - if not fields_dir.exists(): - raise FileNotFoundError(f"Fields directory not found: {fields_dir}") - - fields = {} - for name in ["x", "y", "u", "v", "p"]: - zarr_path = fields_dir / f"{name}.zarr" - if zarr_path.exists(): - fields[name] = zarr.load(zarr_path) - else: - raise FileNotFoundError(f"Field not found: {zarr_path}") - - return fields - - -def restructure_fields( - fields: dict, -) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: - """Convert potentially flattened fields to structured 2D arrays. - - Returns (x_unique, y_unique, U_2d, V_2d, P_2d) where U, V, P are 2D arrays - with shape (ny, nx) suitable for RectBivariateSpline. - """ - x, y = fields["x"], fields["y"] - u, v, p = fields["u"], fields["v"], fields["p"] - - # Get unique coordinates - x_unique = np.sort(np.unique(x)) - y_unique = np.sort(np.unique(y)) - nx, ny = len(x_unique), len(y_unique) - - # If already 2D, just return - if u.ndim == 2: - return x_unique, y_unique, u, v, p - - # Reshape from flattened to 2D - # Create mapping from coordinates to indices - x_to_idx = {val: i for i, val in enumerate(x_unique)} - y_to_idx = {val: i for i, val in enumerate(y_unique)} - - U_2d = np.zeros((ny, nx)) - V_2d = np.zeros((ny, nx)) - P_2d = np.zeros((ny, nx)) - - for k in range(len(x)): - i = x_to_idx[x[k]] - j = y_to_idx[y[k]] - U_2d[j, i] = u[k] - V_2d[j, i] = v[k] - P_2d[j, i] = p[k] - - return x_unique, y_unique, U_2d, V_2d, P_2d - - -def load_timeseries_from_mlflow(run_id: str, tracking_uri: str) -> pd.DataFrame: - """Load timeseries metrics from MLflow run.""" - mlflow.set_tracking_uri(tracking_uri) - client = mlflow.tracking.MlflowClient() - - # Get metric history - metrics_to_fetch = ["residual", "energy", "enstrophy", "palinstrophy"] - - data = {} - for metric_name in metrics_to_fetch: - try: - history = client.get_metric_history(run_id, metric_name) - if history: - data[metric_name] = [ - m.value for m in sorted(history, key=lambda x: x.step) - ] - except Exception: - pass # Metric might not exist - - if not data: - return pd.DataFrame() - - # Create DataFrame with iteration index - max_len = max(len(v) for v in data.values()) - df = pd.DataFrame({k: v + [None] * (max_len - len(v)) for k, v in data.items()}) - df["iteration"] = range(len(df)) - - return df - - -def fields_to_dataframe(fields: dict) -> pd.DataFrame: - """Convert fields dict to DataFrame format for plotting. - - Handles two storage formats: - 1. Flattened: x, y, u, v, p all 1D with same length (already meshgrid coords) - 2. Structured: x, y are 1D coordinate arrays, u, v, p are 2D fields - """ - x, y = fields["x"], fields["y"] - u, v, p = fields["u"], fields["v"], fields["p"] - - # Check if already flattened (all same length, 1D) - if x.ndim == 1 and len(x) == len(u.ravel()): - # Already flattened meshgrid coordinates - return pd.DataFrame( - { - "x": x, - "y": y, - "u": u.ravel(), - "v": v.ravel(), - "p": p.ravel(), - } - ) - - # Need to create meshgrid - if x.ndim == 1 and y.ndim == 1: - X, Y = np.meshgrid(x, y) - else: - X, Y = x, y - - return pd.DataFrame( - { - "x": X.ravel(), - "y": Y.ravel(), - "u": u.ravel(), - "v": v.ravel(), - "p": p.ravel(), - } - ) - - -# ============================================================================= -# Individual Run Plotting Functions -# ============================================================================= - - -def plot_fields( - fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path -) -> Path: - """Generate field contour plots (p, u, v).""" - x_unique = np.sort(fields_df["x"].unique()) - y_unique = np.sort(fields_df["y"].unique()) - nx, ny = len(x_unique), len(y_unique) - - sorted_df = fields_df.sort_values(["y", "x"]) - P = sorted_df["p"].values.reshape(ny, nx) - U = sorted_df["u"].values.reshape(ny, nx) - V = sorted_df["v"].values.reshape(ny, nx) - - n_fine = 200 - x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) - y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) - X_fine, Y_fine = np.meshgrid(x_fine, y_fine) - - P_interp = RectBivariateSpline(y_unique, x_unique, P)(y_fine, x_fine) - U_interp = RectBivariateSpline(y_unique, x_unique, U)(y_fine, x_fine) - V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) - - fig, axes = plt.subplots(1, 3, figsize=(14, 4)) - - cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=25, cmap="viridis") - axes[0].set_xlabel(r"$x$") - axes[0].set_ylabel(r"$y$") - axes[0].set_title(r"\textbf{Pressure}") - axes[0].set_aspect("equal") - plt.colorbar(cf_p, ax=axes[0], label=r"$p$") - - cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=25, cmap="RdBu_r") - axes[1].set_xlabel(r"$x$") - axes[1].set_ylabel(r"$y$") - axes[1].set_title(r"\textbf{$u$-velocity}") - axes[1].set_aspect("equal") - plt.colorbar(cf_u, ax=axes[1], label=r"$u$") - - cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=25, cmap="RdBu_r") - axes[2].set_xlabel(r"$x$") - axes[2].set_ylabel(r"$y$") - axes[2].set_title(r"\textbf{$v$-velocity}") - axes[2].set_aspect("equal") - plt.colorbar(cf_v, ax=axes[2], label=r"$v$") - - solver_label = solver.upper().replace("_", r"\_") - fig.suptitle( - rf"\textbf{{Solution Fields}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" - ) - - output_path = output_dir / "fields.pdf" - fig.savefig(output_path) - plt.close(fig) - - return output_path - - -def plot_streamlines( - fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path -) -> Path: - """Generate streamline plot with velocity magnitude.""" - x_unique = np.sort(fields_df["x"].unique()) - y_unique = np.sort(fields_df["y"].unique()) - nx, ny = len(x_unique), len(y_unique) - - sorted_df = fields_df.sort_values(["y", "x"]) - U = sorted_df["u"].values.reshape(ny, nx) - V = sorted_df["v"].values.reshape(ny, nx) - - n_fine = 200 - x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) - y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) - - U_interp = RectBivariateSpline(y_unique, x_unique, U)(y_fine, x_fine) - V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) - vel_mag = np.sqrt(U_interp**2 + V_interp**2) - - fig, ax = plt.subplots(figsize=(6, 5)) - - X_fine, Y_fine = np.meshgrid(x_fine, y_fine) - cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=25, cmap="viridis") - ax.streamplot( - x_fine, y_fine, U_interp, V_interp, density=1.5, arrowsize=1.0, arrowstyle="->" - ) - - ax.set_xlabel(r"$x$") - ax.set_ylabel(r"$y$") - solver_label = solver.upper().replace("_", r"\_") - ax.set_title( - rf"\textbf{{Streamlines}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" - ) - ax.set_aspect("equal") - plt.colorbar(cf, ax=ax, label=r"$|\mathbf{u}|$") - - output_path = output_dir / "streamlines.pdf" - fig.savefig(output_path) - plt.close(fig) - - return output_path - - -def plot_vorticity( - fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path -) -> Path: - """Generate vorticity contour plot.""" - x_unique = np.sort(fields_df["x"].unique()) - y_unique = np.sort(fields_df["y"].unique()) - nx, ny = len(x_unique), len(y_unique) - - sorted_df = fields_df.sort_values(["y", "x"]) - U = sorted_df["u"].values.reshape(ny, nx) - V = sorted_df["v"].values.reshape(ny, nx) - - n_fine = 200 - x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) - y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) - X_fine, Y_fine = np.meshgrid(x_fine, y_fine) - - U_spline = RectBivariateSpline(y_unique, x_unique, U) - V_spline = RectBivariateSpline(y_unique, x_unique, V) - - dvdx = V_spline(y_fine, x_fine, dx=1) - dudy = U_spline(y_fine, x_fine, dy=1) - vorticity = dvdx - dudy - - fig, ax = plt.subplots(figsize=(6, 5)) - - vmax = np.max(np.abs(vorticity)) - cf = ax.contourf( - X_fine, Y_fine, vorticity, levels=25, vmin=-vmax, vmax=vmax, cmap="RdBu_r" - ) - - ax.set_xlabel(r"$x$") - ax.set_ylabel(r"$y$") - solver_label = solver.upper().replace("_", r"\_") - ax.set_title( - rf"\textbf{{Vorticity}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" - ) - ax.set_aspect("equal") - plt.colorbar(cf, ax=ax, label=r"$\omega = \partial v/\partial x - \partial u/\partial y$") - - output_path = output_dir / "vorticity.pdf" - fig.savefig(output_path) - plt.close(fig) - - return output_path - - -def plot_centerlines( - fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path -) -> Path: - """Plot velocity profiles along centerlines.""" - x_unique = np.sort(fields_df["x"].unique()) - y_unique = np.sort(fields_df["y"].unique()) - nx, ny = len(x_unique), len(y_unique) - - sorted_df = fields_df.sort_values(["y", "x"]) - U = sorted_df["u"].values.reshape(ny, nx) - V = sorted_df["v"].values.reshape(ny, nx) - - U_spline = RectBivariateSpline(y_unique, x_unique, U) - V_spline = RectBivariateSpline(y_unique, x_unique, V) - - n_points = 200 - y_line = np.linspace(y_unique[0], y_unique[-1], n_points) - x_line = np.linspace(x_unique[0], x_unique[-1], n_points) - - x_center = (x_unique[0] + x_unique[-1]) / 2 - y_center = (y_unique[0] + y_unique[-1]) / 2 - - u_vertical = U_spline(y_line, x_center).ravel() - v_horizontal = V_spline(y_center, x_line).ravel() - - fig, axes = plt.subplots(1, 2, figsize=(10, 4)) - - axes[0].plot(u_vertical, y_line, "b-", linewidth=1.5) - axes[0].set_xlabel(r"$u$") - axes[0].set_ylabel(r"$y$") - axes[0].set_title(r"\textbf{$u$-velocity along vertical centerline}") - axes[0].grid(True, alpha=0.3) - axes[0].axvline(x=0, color="gray", linestyle="--", alpha=0.5) - - axes[1].plot(x_line, v_horizontal, "r-", linewidth=1.5) - axes[1].set_xlabel(r"$x$") - axes[1].set_ylabel(r"$v$") - axes[1].set_title(r"\textbf{$v$-velocity along horizontal centerline}") - axes[1].grid(True, alpha=0.3) - axes[1].axhline(y=0, color="gray", linestyle="--", alpha=0.5) - - solver_label = solver.upper().replace("_", r"\_") - fig.suptitle( - rf"\textbf{{Centerline Profiles}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" - ) - - output_path = output_dir / "centerlines.pdf" - fig.savefig(output_path) - plt.close(fig) - - return output_path - - -def plot_convergence( - timeseries_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path -) -> Path: - """Plot convergence history (residuals over iterations).""" - if timeseries_df.empty: - log.warning("No timeseries data available for convergence plot") - return None - - fig, ax = plt.subplots(figsize=(8, 5)) - - # Plot available metrics - for col in timeseries_df.columns: - if col != "iteration": - data = timeseries_df[col].dropna() - if len(data) > 0: - ax.semilogy( - timeseries_df.loc[data.index, "iteration"], - data, - label=col.capitalize(), - linewidth=1.5, - ) - - ax.set_xlabel(r"Iteration") - ax.set_ylabel(r"Value") - solver_label = solver.upper().replace("_", r"\_") - ax.set_title( - rf"\textbf{{Convergence History}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" - ) - ax.legend(frameon=True) - ax.grid(True, alpha=0.3) - - output_path = output_dir / "convergence.pdf" - fig.savefig(output_path) - plt.close(fig) - - return output_path - - -def _build_method_label(sibling: dict) -> str: - """Build a unified method label from solver name. - - Formats solver names nicely for legends: - - 'fv' -> 'FV' - - 'spectral' -> 'Spectral' - - 'spectral_fsg' -> 'Spectral-FSG' - - 'spectral_vmg' -> 'Spectral-VMG' - """ - solver = sibling.get("solver", "unknown") - - # Format known solver names - label_map = { - "fv": "FV", - "spectral": "Spectral", - "spectral_fsg": "Spectral-FSG", - "spectral_vmg": "Spectral-VMG", - "spectral_fmg": "Spectral-FMG", - } - - return label_map.get(solver, solver.replace("_", "-").title()) - - -def plot_ghia_comparison( - siblings: list[dict], tracking_uri: str, output_dir: Path -) -> Path: - """Plot Ghia comparison with all sibling runs using seaborn. - - Legend system (native seaborn): - - hue: Method type (Spectral, FV, Spectral-FSG, etc.) - - style: Grid size N (different dash patterns) - - markers: Method-specific markers (every 20th point) - - Parameters - ---------- - siblings : list[dict] - List of sibling run info dicts with run_id, N, Re, solver - tracking_uri : str - MLflow tracking URI - output_dir : Path - Output directory - - Returns - ------- - Path - Path to comparison plot - """ - if not siblings: - return None - - # Only plot finished runs - finished_siblings = [ - s for s in siblings if s.get("status", "FINISHED") == "FINISHED" - ] - if len(finished_siblings) < 1: - log.info( - f"Need at least 1 finished run for comparison (have {len(finished_siblings)})" - ) - return None - - siblings = finished_siblings - Re = siblings[0]["Re"] - - AVAILABLE_RE = [100, 400, 1000, 3200, 5000, 7500, 10000] - if int(Re) not in AVAILABLE_RE: - log.warning(f"Ghia data not available for Re={Re}") - return None - - # Load Ghia reference data from repo-root data/validation/ghia - project_root = get_repo_root() - ghia_dir = project_root / "data" / "validation" / "ghia" - - u_file = ghia_dir / f"ghia_Re{int(Re)}_u_centerline.csv" - v_file = ghia_dir / f"ghia_Re{int(Re)}_v_centerline.csv" - - if not u_file.exists() or not v_file.exists(): - raise FileNotFoundError(f"Ghia data files not found in {ghia_dir}") - - ghia_u = pd.read_csv(u_file) - ghia_v = pd.read_csv(v_file) - - # Filter to unique (solver/method, N) combinations - seen_combos = set() - unique_siblings = [] - for sibling in siblings: - method = _build_method_label(sibling) - N = sibling["N"] - combo = (method, N) - if combo not in seen_combos: - seen_combos.add(combo) - unique_siblings.append(sibling) - - log.info(f"Plotting {len(unique_siblings)} unique (method, N) combinations") - - # Build DataFrames - u_records = [] - v_records = [] - - for sibling in unique_siblings: - run_id = sibling["run_id"] - N = sibling["N"] - method = _build_method_label(sibling) - solver_name = sibling.get("solver", method) - - try: - artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) - fields = load_fields_from_zarr(artifact_dir) - x_unique, y_unique, U_2d, V_2d, _ = restructure_fields(fields) - - n_points = 200 - y_line = np.linspace(y_unique.min(), y_unique.max(), n_points) - x_line = np.linspace(x_unique.min(), x_unique.max(), n_points) - - # Find physical center (x=0.5, y=0.5), not middle index! - # For non-uniform grids (Chebyshev), middle index != physical center - x_center = 0.5 * (x_unique.min() + x_unique.max()) - y_center = 0.5 * (y_unique.min() + y_unique.max()) - x_center_idx = np.argmin(np.abs(x_unique - x_center)) - y_center_idx = np.argmin(np.abs(y_unique - y_center)) - - u_sim = spectral_interpolate( - y_unique, U_2d[:, x_center_idx], y_line, basis="legendre" - ) - v_sim = spectral_interpolate( - x_unique, V_2d[y_center_idx, :], x_line, basis="legendre" - ) - - for i in range(n_points): - u_records.append( - { - "y": y_line[i], - "u": u_sim[i], - "Method": method, - "Solver": solver_name, - "N": N, - } - ) - v_records.append( - { - "x": x_line[i], - "v": v_sim[i], - "Method": method, - "Solver": solver_name, - "N": N, - } - ) - - except Exception as e: - log.warning(f"Failed to load run {run_id}: {e}") - continue - - if not u_records: - log.warning("No valid runs to plot") - return None - - u_df = pd.DataFrame(u_records).sort_values(["N", "Method", "y"]) - v_df = pd.DataFrame(v_records).sort_values(["N", "Method", "x"]) - - # Create figure with seaborn styling - fig, axes = plt.subplots(1, 2, figsize=(10, 4)) - - # Use seaborn colorblind palette for accessibility - palette = sns.color_palette("colorblind", n_colors=len(u_df["N"].unique())) - - # Plot with seaborn: hue=N (color by grid), style=Method/Solver (dashes), markers - sns.lineplot( - data=u_df, - x="u", - y="y", - hue="N", - style="Method", - palette=palette, - markers=True, - markersize=4, - markevery=20, - linewidth=1.5, - sort=False, - ax=axes[0], - ) - axes[0].scatter( - ghia_u["u"], - ghia_u["y"], - c="black", - s=60, - marker="x", - linewidths=1.5, - label=r"Ghia et al.\ (1982)", - zorder=10, - ) - axes[0].set_xlabel(r"$u$") - axes[0].set_ylabel(r"$y$") - axes[0].set_title(r"\textbf{$u$-velocity (vertical centerline)}") - axes[0].legend(loc="best", fontsize=8) - axes[0].set_xlim(-0.4, 1.05) - axes[0].set_ylim(0, 1) - - sns.lineplot( - data=v_df, - x="x", - y="v", - hue="N", - style="Method", - palette=palette, - markers=True, - markersize=4, - markevery=20, - linewidth=1.5, - sort=False, - ax=axes[1], - ) - axes[1].scatter( - ghia_v["x"], - ghia_v["v"], - c="black", - s=60, - marker="x", - linewidths=1.5, - label=r"Ghia et al.\ (1982)", - zorder=10, - ) - axes[1].set_xlabel(r"$x$") - axes[1].set_ylabel(r"$v$") - axes[1].set_title(r"\textbf{$v$-velocity (horizontal centerline)}") - axes[1].legend(loc="best", fontsize=8) - axes[1].set_xlim(0, 1) - - fig.suptitle(rf"\textbf{{Ghia Benchmark Comparison}} --- $\mathrm{{Re}}={int(Re)}$") - - output_path = output_dir / "ghia_comparison.pdf" - fig.savefig(output_path) - plt.close(fig) - - log.info(f"Saved comparison plot: {output_path.name}") - return output_path - - -# ============================================================================= -# MLflow Upload -# ============================================================================= - - -def upload_plots_to_mlflow( - run_id: str, plot_paths: list, tracking_uri: str, artifact_subdir: str = "plots" -): - """Upload generated plots to MLflow run as artifacts.""" - mlflow.set_tracking_uri(tracking_uri) - - valid_paths = [p for p in plot_paths if p and p.exists()] - - # Check if we're already in an active run - active_run = mlflow.active_run() - if active_run and active_run.info.run_id == run_id: - # Already in the correct run, just log artifacts - for path in valid_paths: - mlflow.log_artifact(str(path), artifact_path=artifact_subdir) - log.info(f"Uploaded: {artifact_subdir}/{path.name}") - else: - # Start/resume run to upload artifacts - with mlflow.start_run(run_id=run_id, nested=True): - for path in valid_paths: - mlflow.log_artifact(str(path), artifact_path=artifact_subdir) - log.info(f"Uploaded: {artifact_subdir}/{path.name}") - - -# ============================================================================= -# Direct API for run_solver.py -# ============================================================================= - - -def generate_plots_for_run( - run_id: str, - tracking_uri: str, - output_dir: Path, - solver_name: str, - N: int, - Re: float, - parent_run_id: Optional[str] = None, - upload_to_mlflow: bool = True, -) -> list[Path]: - """Generate all plots for a completed run. - - Called directly from run_solver.py after solver completes. - - Parameters - ---------- - run_id : str - MLflow run ID - tracking_uri : str - MLflow tracking URI - output_dir : Path - Directory to save plots - solver_name : str - Solver name (e.g., "spectral", "spectral_fsg", "fv") - N : int - Grid size parameter - Re : float - Reynolds number - parent_run_id : str, optional - Parent run ID if this is part of a sweep - upload_to_mlflow : bool - Whether to upload plots to MLflow - - Returns - ------- - list[Path] - List of generated plot paths - """ - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - # Download artifacts and load data - artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) - fields = load_fields_from_zarr(artifact_dir) - fields_df = fields_to_dataframe(fields) - timeseries_df = load_timeseries_from_mlflow(run_id, tracking_uri) - - log.info(f"Generating plots for {solver_name} N={N} Re={Re}") - - # Generate individual plots - plot_paths = [] - plot_paths.append(plot_fields(fields_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_streamlines(fields_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_vorticity(fields_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_centerlines(fields_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_convergence(timeseries_df, Re, solver_name, N, output_dir)) - ghia_path = plot_ghia_comparison( - [ - { - "run_id": run_id, - "N": N, - "Re": Re, - "solver": solver_name, - "status": "FINISHED", - } - ], - tracking_uri, - output_dir, - ) - if ghia_path: - plot_paths.append(ghia_path) - - plot_paths = [p for p in plot_paths if p is not None] - log.info(f"Generated {len(plot_paths)} plots for run") - - # Upload to individual run - if upload_to_mlflow: - upload_plots_to_mlflow(run_id, plot_paths, tracking_uri) - - log.info("Plotting done!") - return plot_paths - - -def generate_comparison_plots_for_sweep( - parent_run_ids: list[str], - tracking_uri: str, - output_dir: Path, - upload_to_mlflow: bool = True, -) -> dict[str, Path]: - """Generate comparison plots for all parent runs after sweep completes. - - Called from MLflow callback's on_multirun_end after all jobs finish. - - Parameters - ---------- - parent_run_ids : list[str] - List of parent run IDs (one per Re value) - tracking_uri : str - MLflow tracking URI - output_dir : Path - Base output directory for comparison plots - upload_to_mlflow : bool - Whether to upload plots to MLflow - - Returns - ------- - dict[str, Path] - Mapping of parent_run_id to comparison plot path - """ - mlflow.set_tracking_uri(tracking_uri) - output_dir = Path(output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - results = {} - - for parent_run_id in parent_run_ids: - log.info(f"Generating comparison plot for parent run: {parent_run_id[:8]}...") - - # Find all children of this parent - siblings = find_sibling_runs(parent_run_id, tracking_uri) - - if len(siblings) < 2: - log.warning(f" Only {len(siblings)} child run(s), skipping comparison") - continue - - # Check all siblings are finished - unfinished = [s for s in siblings if s.get("status") != "FINISHED"] - if unfinished: - log.warning(f" {len(unfinished)} run(s) still not finished, skipping") - continue - - # Get parent run info for naming - client = mlflow.tracking.MlflowClient() - parent_run = client.get_run(parent_run_id) - parent_name = parent_run.info.run_name or parent_run_id[:8] - - # Create comparison plot - comparison_dir = output_dir / parent_name - comparison_dir.mkdir(exist_ok=True) - - comparison_path = plot_ghia_comparison(siblings, tracking_uri, comparison_dir) - - if comparison_path: - results[parent_run_id] = comparison_path - log.info(f" Created comparison plot: {comparison_path.name}") - - if upload_to_mlflow: - upload_plots_to_mlflow( - parent_run_id, [comparison_path], tracking_uri, "plots" - ) - log.info(" Uploaded to parent run") - - log.info(f"Generated {len(results)} comparison plot(s)") - return results - - -# ============================================================================= -# Main Entry Point (Standalone Hydra Usage) -# ============================================================================= - - -@hydra.main(config_path="../conf", config_name="config", version_base=None) -def main(cfg: DictConfig) -> None: - """Hydra entry point - finds matching run and generates plots.""" - - tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") - solver_name = cfg.solver.name - N = cfg.N - Re = cfg.Re - - # Find matching MLflow run - run_id, parent_run_id = find_matching_run(cfg, tracking_uri) - - # Setup output directory - output_dir = Path(hydra.core.hydra_config.HydraConfig.get().runtime.output_dir) - output_dir.mkdir(parents=True, exist_ok=True) - - # Download artifacts for this run - artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) - fields = load_fields_from_zarr(artifact_dir) - fields_df = fields_to_dataframe(fields) - timeseries_df = load_timeseries_from_mlflow(run_id, tracking_uri) - - log.info(f"Generating plots for {solver_name} N={N} Re={Re}") - - # ========================================================================== - # Individual run plots - # ========================================================================== - plot_paths = [] - - plot_paths.append(plot_fields(fields_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_streamlines(fields_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_vorticity(fields_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_centerlines(fields_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_convergence(timeseries_df, Re, solver_name, N, output_dir)) - - plot_paths = [p for p in plot_paths if p is not None] - log.info(f"Generated {len(plot_paths)} plots for run") - - # Upload to individual run - if cfg.get("upload_to_mlflow", True): - upload_plots_to_mlflow(run_id, plot_paths, tracking_uri) - - # ========================================================================== - # Comparison plot for parent (if this is part of a sweep) - # ========================================================================== - if parent_run_id: - log.info("This run is part of a sweep - generating comparison plot for parent") - - siblings = find_sibling_runs(parent_run_id, tracking_uri) - - if len(siblings) > 1: - comparison_dir = output_dir / "comparison" - comparison_dir.mkdir(exist_ok=True) - - comparison_path = plot_ghia_comparison( - siblings, tracking_uri, comparison_dir - ) - - if comparison_path and cfg.get("upload_to_mlflow", True): - upload_plots_to_mlflow( - parent_run_id, [comparison_path], tracking_uri, "plots" - ) - log.info("Comparison plot uploaded to parent run") - - log.info("Done!") - - -if __name__ == "__main__": - main() diff --git a/src/utilities/mlflow/callback.py b/src/utilities/mlflow/callback.py index e6df54b..e6e7161 100644 --- a/src/utilities/mlflow/callback.py +++ b/src/utilities/mlflow/callback.py @@ -199,7 +199,8 @@ def on_multirun_end(self, config: DictConfig, **kwargs) -> None: # Log summary log.info( - f"Sweep completed. Created {len(self._active_parent_runs)} parent run(s)." + f"Sweep completed. Using {len(self._parent_runs)} parent run(s) " + f"({len(self._active_parent_runs)} created, {len(self._parent_runs) - len(self._active_parent_runs)} reused)." ) for name, run_id in self._parent_runs.items(): log.info(f" - {name}: {run_id}") @@ -207,7 +208,7 @@ def on_multirun_end(self, config: DictConfig, **kwargs) -> None: # Generate comparison plots for all parent runs if self._parent_runs and config.get("generate_plots", True): try: - from shared.plotting.ldc_plotter import ( + from shared.plotting.ldc import ( generate_comparison_plots_for_sweep, ) from pathlib import Path From 2e232bed5f7360005096b034fb63c2d087f0b762 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Sat, 6 Dec 2025 08:22:38 +0100 Subject: [PATCH 29/34] cleanup --- README.md | 73 +----- conf/experiment/quick_test.yaml | 22 ++ plot_runs.py | 337 ++++++++++++++++++++++++++ run_solver.py | 98 ++++---- src/shared/plotting/ldc/validation.py | 53 ++-- src/utilities/mlflow/callback.py | 28 ++- 6 files changed, 466 insertions(+), 145 deletions(-) create mode 100644 conf/experiment/quick_test.yaml create mode 100644 plot_runs.py diff --git a/README.md b/README.md index ec96d1e..0f132d8 100644 --- a/README.md +++ b/README.md @@ -16,63 +16,19 @@ uv sync The project uses [Hydra](https://hydra.cc/) for configuration management. Run solvers via `run_solver.py`: -### Basic Usage - -```bash -# Finite Volume solver (32x32 cells, Re=100) -uv run python run_solver.py solver=fv N=32 Re=100 - -# Spectral solver (N=15 gives 16x16 nodes, Re=100) -uv run python run_solver.py solver=spectral N=15 Re=100 -``` - ### Using Experiment Configs Pre-defined experiment configurations are in `conf/experiment/`: ```bash -# Quick test (small grid, few iterations) -uv run python run_solver.py +experiment=quick_test solver=fv - -# FV validation (default settings for benchmarking) -uv run python run_solver.py +experiment=fv_validation - -# Spectral validation -uv run python run_solver.py +experiment=spectral_validation -``` - -### Parameter Sweeps - -Run multiple configurations with Hydra's multirun: - -```bash -# Sweep over grid sizes (sequential) -uv run python run_solver.py -m solver=fv N=16,32,64 Re=100 - -# Sweep over Reynolds numbers -uv run python run_solver.py -m solver=spectral N=31 Re=100,400,1000 - -# Full validation sweep uv run python run_solver.py -m +experiment=fv_validation ``` -### Parallel Sweeps (Joblib) +for only plots: pass plot_only=true -Run sweeps in parallel using all CPU cores: +Overwriting at runtime: +uv run python run_solver.py -m +experiment=fv_validation N=16,32,64 Re=100 -```bash -# Parallel sweep over grid sizes -uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 Re=100 - -# Parallel sweep over solvers -uv run python run_solver.py -m hydra/launcher=joblib solver=fv,spectral N=32 Re=100 - -# Parallel multi-dimensional sweep (solver x N x Re = 12 jobs) -uv run python run_solver.py -m hydra/launcher=joblib solver=fv,spectral N=16,32,64 Re=100,400 - -# Control parallelism (e.g., 4 concurrent jobs) -uv run python run_solver.py -m hydra/launcher=joblib hydra.launcher.n_jobs=4 solver=fv N=16,32,64 -``` ### Configuration Structure @@ -94,29 +50,6 @@ conf/ └── joblib.yaml # Parallel launcher (all cores) ``` -### Nested Runs for Sweeps - -Parameter sweeps automatically create a parent-child run hierarchy in MLflow: -- **Parent run**: Created before sweep, logs sweep config -- **Child runs**: Each parameter combination nested under parent - -This makes it easy to view all sweep runs together in the MLflow UI. - -### Output Structure - -Each run creates a timestamped Hydra output directory: - -``` -hydra_outputs/{experiment_name}/Re{Re}/{solver}/{DD-MM-YY}/{HH:MM:SS}/ -├── run_solver.log # Execution log -└── .hydra/ - ├── config.yaml # Resolved configuration - ├── hydra.yaml # Hydra settings - └── overrides.yaml # CLI overrides -``` - -Solution fields (u, v, p) and metrics are stored as MLflow artifacts (zarr format). - ## MLflow Results are tracked with [MLflow](https://mlflow.org/). Two tracking modes are available: diff --git a/conf/experiment/quick_test.yaml b/conf/experiment/quick_test.yaml new file mode 100644 index 0000000..46c89aa --- /dev/null +++ b/conf/experiment/quick_test.yaml @@ -0,0 +1,22 @@ +# @package _global_ +# Quick test experiment - sweeps over FV and Spectral solvers +# Usage: uv run python run_solver.py -m +experiment=quick_test + +defaults: + - override /solver: fv + +# MLflow experiment +experiment_name: LDC-QuickTest +sweep_name: quick-test-Re${Re} + +# Quick test parameters +N: 16 # grid size (16 cells for FV, polynomial order 16 for spectral) +Re: 100 # Reynolds number +tolerance: 1.0e-5 # slightly relaxed for speed +max_iterations: 5000 + +hydra: + sweeper: + params: + # Sweep over both solver types + solver: fv,spectral diff --git a/plot_runs.py b/plot_runs.py new file mode 100644 index 0000000..6a6d142 --- /dev/null +++ b/plot_runs.py @@ -0,0 +1,337 @@ +""" +Plot generation script for LDC experiments. + +Finds parent runs for an experiment and generates: +1. Individual plots for all child runs +2. Comparison plots for each parent run + +Usage: + # Plot all runs in an experiment + uv run python plot_runs.py experiment_name=LDC-Validation + + # Plot runs for a specific parent run ID + uv run python plot_runs.py parent_run_id=abc123 + + # Plot using experiment config + uv run python plot_runs.py +experiment=fv_validation + + # Plot with multirun (regenerate plots for sweep) + uv run python plot_runs.py -m +experiment=fv_validation +""" + +import logging +import sys +from pathlib import Path +from typing import Optional + +import hydra +import mlflow +from dotenv import load_dotenv +from omegaconf import DictConfig + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent / "src")) + +load_dotenv() +log = logging.getLogger(__name__) + + +def find_parent_runs_for_experiment( + experiment_name: str, tracking_uri: str +) -> list[dict]: + """Find all parent runs for an experiment. + + Parameters + ---------- + experiment_name : str + MLflow experiment name + tracking_uri : str + MLflow tracking URI + + Returns + ------- + list[dict] + List of parent run info dicts with run_id, name, Re (if tagged) + """ + mlflow.set_tracking_uri(tracking_uri) + + # Search for parent runs + runs = mlflow.search_runs( + experiment_names=[experiment_name], + filter_string="tags.sweep = 'parent'", + order_by=["start_time DESC"], + ) + + if runs.empty: + log.warning(f"No parent runs found in experiment: {experiment_name}") + return [] + + parent_runs = [] + for _, row in runs.iterrows(): + parent_info = { + "run_id": row["run_id"], + "name": row["tags.mlflow.runName"], + } + # Extract Re if tagged + if "tags.Re" in row and row["tags.Re"]: + parent_info["Re"] = int(row["tags.Re"]) + + parent_runs.append(parent_info) + + log.info(f"Found {len(parent_runs)} parent run(s) in {experiment_name}") + return parent_runs + + +def plot_all_runs_for_parent( + parent_run_id: str, + tracking_uri: str, + output_dir: Path, + upload_to_mlflow: bool = True, +) -> dict: + """Generate all plots for a parent run and its children. + + Parameters + ---------- + parent_run_id : str + Parent run ID + tracking_uri : str + MLflow tracking URI + output_dir : Path + Output directory for plots + upload_to_mlflow : bool + Whether to upload plots to MLflow + + Returns + ------- + dict + Summary with child_plots (list) and comparison_plot (Path or None) + """ + from shared.plotting.ldc import ( + find_sibling_runs, + generate_plots_for_run, + plot_ghia_comparison, + upload_plots_to_mlflow, + ) + + mlflow.set_tracking_uri(tracking_uri) + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Get parent run info + client = mlflow.tracking.MlflowClient() + parent_run = client.get_run(parent_run_id) + parent_name = parent_run.info.run_name or parent_run_id[:8] + + log.info(f"Generating plots for parent run: {parent_name} ({parent_run_id[:8]})") + + # Find all child runs + siblings = find_sibling_runs(parent_run_id, tracking_uri) + + if not siblings: + log.warning(f" No child runs found for parent: {parent_name}") + return {"child_plots": [], "comparison_plot": None} + + log.info(f" Found {len(siblings)} child run(s)") + + # Filter to finished runs + finished_siblings = [s for s in siblings if s.get("status") == "FINISHED"] + if len(finished_siblings) < len(siblings): + log.warning( + f" Only {len(finished_siblings)}/{len(siblings)} runs finished, " + "plotting finished runs only" + ) + + # Generate individual plots for each child + child_plot_results = [] + for i, sibling in enumerate(finished_siblings, 1): + run_id = sibling["run_id"] + solver = sibling.get("solver", "unknown") + N = sibling["N"] + Re = sibling["Re"] + + log.info(f" [{i}/{len(finished_siblings)}] Plotting {solver} N={N} Re={Re}") + + child_output_dir = output_dir / parent_name / f"{solver}_N{N}_Re{Re}" + child_output_dir.mkdir(parents=True, exist_ok=True) + + try: + plot_paths = generate_plots_for_run( + run_id=run_id, + tracking_uri=tracking_uri, + output_dir=child_output_dir, + solver_name=solver, + N=N, + Re=Re, + parent_run_id=parent_run_id, + upload_to_mlflow=upload_to_mlflow, + ) + child_plot_results.append( + {"run_id": run_id, "plots": plot_paths, "status": "success"} + ) + log.info(f" Generated {len(plot_paths)} plot(s)") + except Exception as e: + log.error(f" Failed to generate plots for {run_id}: {e}") + child_plot_results.append({"run_id": run_id, "status": "failed", "error": str(e)}) + + # Generate comparison plot for parent + comparison_plot = None + if len(finished_siblings) >= 2: + log.info(f" Generating comparison plot for parent: {parent_name}") + comparison_dir = output_dir / parent_name / "comparison" + comparison_dir.mkdir(parents=True, exist_ok=True) + + try: + comparison_plot = plot_ghia_comparison( + finished_siblings, tracking_uri, comparison_dir + ) + + if comparison_plot and upload_to_mlflow: + upload_plots_to_mlflow( + parent_run_id, [comparison_plot], tracking_uri, "plots" + ) + log.info(f" Uploaded comparison plot to parent run") + except Exception as e: + log.error(f" Failed to generate comparison plot: {e}") + else: + log.warning( + f" Only {len(finished_siblings)} finished run(s), " + "skipping comparison plot (need at least 2)" + ) + + summary = { + "parent_run_id": parent_run_id, + "parent_name": parent_name, + "child_plots": child_plot_results, + "comparison_plot": comparison_plot, + } + + log.info( + f"Completed plotting for {parent_name}: " + f"{len([r for r in child_plot_results if r['status'] == 'success'])} child runs, " + f"comparison={'yes' if comparison_plot else 'no'}" + ) + + return summary + + +def plot_experiment( + experiment_name: str, + tracking_uri: str, + output_dir: Path, + parent_run_ids: Optional[list[str]] = None, + upload_to_mlflow: bool = True, +) -> list[dict]: + """Generate all plots for an experiment. + + Parameters + ---------- + experiment_name : str + MLflow experiment name + tracking_uri : str + MLflow tracking URI + output_dir : Path + Output directory for plots + parent_run_ids : list[str], optional + Specific parent run IDs to plot (if None, plots all parents in experiment) + upload_to_mlflow : bool + Whether to upload plots to MLflow + + Returns + ------- + list[dict] + List of summaries for each parent run + """ + mlflow.set_tracking_uri(tracking_uri) + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + # Find parent runs if not provided + if parent_run_ids is None: + parent_runs = find_parent_runs_for_experiment(experiment_name, tracking_uri) + parent_run_ids = [p["run_id"] for p in parent_runs] + + if not parent_run_ids: + log.warning("No parent runs to plot") + return [] + + log.info(f"Generating plots for {len(parent_run_ids)} parent run(s)") + + # Plot each parent run + summaries = [] + for i, parent_run_id in enumerate(parent_run_ids, 1): + log.info(f"[{i}/{len(parent_run_ids)}] Processing parent run {parent_run_id[:8]}") + + summary = plot_all_runs_for_parent( + parent_run_id=parent_run_id, + tracking_uri=tracking_uri, + output_dir=output_dir, + upload_to_mlflow=upload_to_mlflow, + ) + summaries.append(summary) + + # Print summary + log.info("\n" + "=" * 80) + log.info("PLOTTING SUMMARY") + log.info("=" * 80) + for summary in summaries: + success_count = len([r for r in summary["child_plots"] if r["status"] == "success"]) + total_count = len(summary["child_plots"]) + log.info( + f"{summary['parent_name']}: {success_count}/{total_count} child runs plotted, " + f"comparison={'yes' if summary['comparison_plot'] else 'no'}" + ) + + return summaries + + +@hydra.main(config_path="conf", config_name="config", version_base=None) +def main(cfg: DictConfig) -> None: + """Hydra entry point for plot generation. + + Supports multiple modes: + 1. Plot by experiment name: experiment_name=LDC-Validation + 2. Plot by parent run ID: parent_run_id=abc123 + 3. Plot using experiment config: +experiment=fv_validation + """ + tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") + + # Determine output directory + output_dir = Path(hydra.core.hydra_config.HydraConfig.get().runtime.output_dir) + + # Mode 1: Explicit parent_run_id provided + if cfg.get("parent_run_id"): + parent_run_id = cfg.parent_run_id + log.info(f"Plotting for parent run: {parent_run_id}") + + plot_all_runs_for_parent( + parent_run_id=parent_run_id, + tracking_uri=tracking_uri, + output_dir=output_dir, + upload_to_mlflow=cfg.get("upload_to_mlflow", True), + ) + return + + # Mode 2: Use experiment_name from config + experiment_name = cfg.experiment_name + project_prefix = cfg.mlflow.get("project_prefix", "") + if project_prefix and not experiment_name.startswith("/"): + full_experiment_name = f"{project_prefix}/{experiment_name}" + else: + full_experiment_name = experiment_name + + log.info(f"Plotting for experiment: {full_experiment_name}") + + # Check if specific parent_run_ids provided as list + parent_run_ids = cfg.get("parent_run_ids", None) + + plot_experiment( + experiment_name=full_experiment_name, + tracking_uri=tracking_uri, + output_dir=output_dir, + parent_run_ids=parent_run_ids, + upload_to_mlflow=cfg.get("upload_to_mlflow", True), + ) + + +if __name__ == "__main__": + main() diff --git a/run_solver.py b/run_solver.py index 6fccab8..63ff5bc 100644 --- a/run_solver.py +++ b/run_solver.py @@ -1,21 +1,36 @@ """ LDC Solver Runner - Hydra + MLflow integration for FV and Spectral solvers. -Usage: - # FV solver (32 cells) - uv run python run_solver.py solver=fv N=32 Re=100 max_iterations=100 +RECOMMENDED USAGE - Parameter sweeps (multirun mode): + # Use experiment configs for validation sweeps + uv run python run_solver.py -m +experiment=fv_validation + uv run python run_solver.py -m +experiment=spectral_validation - # Spectral solver (N=15 gives 16 nodes) - uv run python run_solver.py solver=spectral N=15 Re=100 max_iterations=100 + # Custom sweeps + uv run python run_solver.py -m solver=fv N=16,32,64 Re=100,400 - # MLflow modes: - # local-files - file-based ./mlruns (default) - # coolify - remote server (requires .env with credentials) - uv run python run_solver.py solver=fv mlflow=local-files - uv run python run_solver.py solver=fv mlflow=coolify + Multirun mode automatically: + - Creates parent runs for organizing results + - Generates all plots (individual + comparisons) at the end + - Uploads everything to MLflow - # Parameter sweep - uv run python run_solver.py -m solver=fv N=16,32,64 Re=100,400 +Single runs (for testing): + # FV solver + uv run python run_solver.py solver=fv N=32 Re=100 + + # Spectral solver + uv run python run_solver.py solver=spectral N=15 Re=100 + +Plot generation: + # Regenerate plots for existing experiment (no solver execution) + uv run python plot_runs.py +experiment=fv_validation + + # Regenerate plots for specific parent run + uv run python plot_runs.py parent_run_id=abc123 + +MLflow modes: + local-files - file-based ./mlruns (default) + coolify - remote server (requires .env with credentials) Setup for remote MLflow: cp .env.template .env @@ -153,30 +168,37 @@ def main(cfg: DictConfig) -> None: experiment_name = setup_mlflow(cfg) log.info(f"MLflow experiment: {experiment_name}") - # Plot-only mode: find existing run and regenerate plots + # Plot-only mode: regenerate plots without running solver + # NOTE: For multirun plot regeneration, use plot_runs.py instead! if cfg.get("plot_only", False): - log.info("Plot-only mode: finding existing run to regenerate plots") - from shared.plotting.ldc import generate_plots_for_run, find_matching_run + log.warning( + "plot_only mode is deprecated for run_solver.py. " + "Use plot_runs.py instead:\n" + " uv run python plot_runs.py +experiment=fv_validation" + ) + log.info("Redirecting to plot_runs.py...") + + # Import and call plot_experiment directly + sys.path.insert(0, str(Path(__file__).parent)) + from plot_runs import plot_experiment tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") - run_id, parent_run_id = find_matching_run(cfg, tracking_uri) - if run_id is None: - log.error(f"No matching run found for solver={cfg.solver.name}, N={cfg.N}, Re={cfg.Re}") - return + # Build experiment name + experiment_name = cfg.experiment_name + project_prefix = cfg.mlflow.get("project_prefix", "") + if project_prefix and not experiment_name.startswith("/"): + experiment_name = f"{project_prefix}/{experiment_name}" - log.info(f"Found existing run: {run_id}") output_dir = Path( hydra.core.hydra_config.HydraConfig.get().runtime.output_dir ) - generate_plots_for_run( - run_id=run_id, + + plot_experiment( + experiment_name=experiment_name, tracking_uri=tracking_uri, - output_dir=output_dir / "plots", - solver_name=cfg.solver.name, - N=cfg.N, - Re=cfg.Re, - parent_run_id=parent_run_id, + output_dir=output_dir, + upload_to_mlflow=cfg.get("upload_to_mlflow", True), ) log.info("Plot regeneration complete!") return @@ -232,24 +254,10 @@ def main(cfg: DictConfig) -> None: f"time={solver.metrics.wall_time_seconds:.2f}s" ) - # Generate plots - if cfg.get("generate_plots", True): - from shared.plotting.ldc import generate_plots_for_run - - output_dir = Path( - hydra.core.hydra_config.HydraConfig.get().runtime.output_dir - ) - tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") - generate_plots_for_run( - run_id=run.info.run_id, - tracking_uri=tracking_uri, - output_dir=output_dir / "plots", - solver_name=solver_name, - N=cfg.N, - Re=cfg.Re, - parent_run_id=parent_run_id, - upload_to_mlflow=True, - ) + # Plot generation is handled centrally by plot_runs.py + # - For multirun: plots generated automatically at end via MLflowSweepCallback + # - For single runs: use `uv run python plot_runs.py +experiment=...` + # - Or manually: `uv run python plot_runs.py parent_run_id=...` if __name__ == "__main__": diff --git a/src/shared/plotting/ldc/validation.py b/src/shared/plotting/ldc/validation.py index 138d284..01355bb 100644 --- a/src/shared/plotting/ldc/validation.py +++ b/src/shared/plotting/ldc/validation.py @@ -242,10 +242,10 @@ def plot_ghia_comparison( u_df = pd.DataFrame(u_records).sort_values(["N", "Method", "y"]) v_df = pd.DataFrame(v_records).sort_values(["N", "Method", "x"]) - # Create figure with seaborn styling - fig, axes = plt.subplots(1, 2) + # Create figure with 4:3 aspect ratio per subplot + fig, axes = plt.subplots(1, 2, figsize=(12, 6)) - # Plot with seaborn: hue=N (color by grid), style=Method/Solver (dashes), markers + # Plot u-velocity with seaborn (sort=False to preserve y-ordering) sns.lineplot( data=u_df, x="u", @@ -253,27 +253,32 @@ def plot_ghia_comparison( hue="N", style="Method", markers=True, + dashes=True, sort=False, ax=axes[0], ) # Set markevery on all lines for line in axes[0].get_lines(): line.set_markevery(20) - axes[0].scatter( + # Plot Ghia data with hollow circles + axes[0].plot( ghia_u["u"], ghia_u["y"], - c="black", - marker="x", - label=r"Ghia et al.\ (1982)", + "o", + color="#333333", + label="Ghia et al. (1982)", + markersize=7, + markerfacecolor="none", + markeredgewidth=1.2, zorder=10, ) - axes[0].set_xlabel(r"$u$") - axes[0].set_ylabel(r"$y$") - axes[0].set_title(r"\textbf{$u$-velocity (vertical centerline)}") - axes[0].legend(loc="best", fontsize=8) + axes[0].set_xlabel(r"$u$-velocity") + axes[0].set_ylabel(r"$y$-coordinate") + axes[0].set_title(r"$u$-velocity (vertical centerline)") axes[0].set_xlim(-0.4, 1.05) axes[0].set_ylim(0, 1) + # Plot v-velocity with seaborn (sort=False to preserve x-ordering) sns.lineplot( data=v_df, x="x", @@ -281,27 +286,35 @@ def plot_ghia_comparison( hue="N", style="Method", markers=True, + dashes=True, sort=False, ax=axes[1], + legend=False, ) # Set markevery on all lines for line in axes[1].get_lines(): line.set_markevery(20) - axes[1].scatter( + # Plot Ghia data with hollow circles + axes[1].plot( ghia_v["x"], ghia_v["v"], - c="black", - marker="x", - label=r"Ghia et al.\ (1982)", + "o", + color="#333333", + label="Ghia et al. (1982)", + markersize=7, + markerfacecolor="none", + markeredgewidth=1.2, zorder=10, ) - axes[1].set_xlabel(r"$x$") - axes[1].set_ylabel(r"$v$") - axes[1].set_title(r"\textbf{$v$-velocity (horizontal centerline)}") - axes[1].legend(loc="best", fontsize=8) + axes[1].set_xlabel(r"$x$-coordinate") + axes[1].set_ylabel(r"$v$-velocity") + axes[1].set_title(r"$v$-velocity (horizontal centerline)") axes[1].set_xlim(0, 1) + axes[1].set_ylim(-0.6, 0.4) - fig.suptitle(rf"\textbf{{Ghia Benchmark Comparison}} --- $\mathrm{{Re}}={int(Re)}$") + # Set overall title + fig.suptitle(rf"Ghia Benchmark Comparison (Re = {int(Re)})", fontsize=12) + #fig.tight_layout() output_path = output_dir / "ghia_comparison.pdf" fig.savefig(output_path) diff --git a/src/utilities/mlflow/callback.py b/src/utilities/mlflow/callback.py index e6e7161..b7201e5 100644 --- a/src/utilities/mlflow/callback.py +++ b/src/utilities/mlflow/callback.py @@ -205,30 +205,38 @@ def on_multirun_end(self, config: DictConfig, **kwargs) -> None: for name, run_id in self._parent_runs.items(): log.info(f" - {name}: {run_id}") - # Generate comparison plots for all parent runs + # Generate plots for all parent runs and their children if self._parent_runs and config.get("generate_plots", True): try: - from shared.plotting.ldc import ( - generate_comparison_plots_for_sweep, - ) + import sys from pathlib import Path + # Add repo root to path for plot_runs import + repo_root = Path(__file__).parent.parent.parent.parent + if str(repo_root) not in sys.path: + sys.path.insert(0, str(repo_root)) + + from plot_runs import plot_experiment + # Use stored sweep directory (HydraConfig may not be available here) if self._sweep_dir: - output_dir = Path(self._sweep_dir) / "comparison_plots" + output_dir = Path(self._sweep_dir) / "plots" else: - output_dir = Path("outputs") / "comparison_plots" + output_dir = Path("outputs") / "plots" parent_run_ids = list(self._parent_runs.values()) log.info( - f"Generating comparison plots for {len(parent_run_ids)} parent run(s)..." + f"Generating plots for {len(parent_run_ids)} parent run(s) " + "and all their children..." ) - generate_comparison_plots_for_sweep( - parent_run_ids=parent_run_ids, + # Plot all runs (individual + comparison) for each parent + plot_experiment( + experiment_name=self._full_experiment_name, tracking_uri=self._tracking_uri, output_dir=output_dir, + parent_run_ids=parent_run_ids, upload_to_mlflow=True, ) except Exception as e: - log.warning(f"Failed to generate comparison plots: {e}") + log.warning(f"Failed to generate plots: {e}") From 1a6ed70e2b52b420d01c5b1e0a7b33125f7dc402 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Sat, 6 Dec 2025 08:22:44 +0100 Subject: [PATCH 30/34] cleanup --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index c20691d..6a91ba2 100644 --- a/.gitignore +++ b/.gitignore @@ -10,6 +10,7 @@ data/* !data/.gitkeep !data/validation/ hydra_outputs/ +outputs/ TEMPLATE/ mlruns/ mlflow_ui.log From 582e96d9b50200c30b5cd8dc9da771d00e7f2d95 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Sat, 6 Dec 2025 11:12:54 +0100 Subject: [PATCH 31/34] proper sweeping setup --- Experiments/01-Validation/README.rst | 12 + Experiments/01-Validation/plot_validation.py | 0 RESTRUCTURE_PROPOSAL.md | 337 ------------------ conf/config.yaml | 67 ++-- conf/experiment/benchmarking/timings.yaml | 26 ++ conf/experiment/fv_validation.yaml | 24 -- conf/experiment/quick_test.yaml | 22 -- .../spectral_multigrid_comparison.yaml | 25 -- conf/experiment/spectral_validation.yaml | 22 -- conf/experiment/validation/ghia/fv.yaml | 17 + conf/experiment/validation/ghia/spectral.yaml | 20 ++ conf/hydra/launcher/joblib.yaml | 15 - conf/machine/hpc.yaml | 10 + conf/machine/local.yaml | 9 + conf/{shared => }/mlflow/coolify.yaml | 0 conf/{shared => }/mlflow/local.yaml | 0 conf/problem/ldc.yaml | 11 + conf/shared/hydra/launcher/joblib.yaml | 15 - conf/solver/fv.yaml | 4 + conf/solver/spectral.yaml | 22 -- conf/solver/spectral/fmg.yaml | 17 + conf/solver/spectral/fsg.yaml | 17 + conf/solver/spectral/sg.yaml | 21 ++ conf/solver/spectral/vmg.yaml | 17 + conf/solver/spectral_fmg.yaml | 20 -- conf/solver/spectral_fsg.yaml | 20 -- conf/solver/spectral_fsg_poly.yaml | 20 -- conf/solver/spectral_vmg.yaml | 20 -- ...ultigrid-method-for-i_2010_Computers--.pdf | Bin 583613 -> 0 bytes .../Assignment03-LidDrivenCavity2D.pdf | Bin 117495 -> 0 bytes docs/lecture_notes/spectral.pdf | Bin 583613 -> 0 bytes docs/source/api_reference.rst | 4 - .../spectral_vs_fsg/ghia_comparison.pdf | Bin 272948 -> 0 bytes .../validation/ghia_comparison.pdf | Bin 248826 -> 0 bytes run_solver.py | 106 +++--- src/solvers/__init__.py | 18 +- src/solvers/spectral/__init__.py | 7 +- src/solvers/spectral/fmg.py | 45 +++ src/solvers/spectral/fsg.py | 129 +++++++ src/solvers/spectral/{solver.py => sg.py} | 130 +------ src/solvers/spectral/vmg.py | 45 +++ src/utilities/mlflow/callback.py | 79 ++-- 42 files changed, 540 insertions(+), 833 deletions(-) create mode 100644 Experiments/01-Validation/README.rst create mode 100644 Experiments/01-Validation/plot_validation.py delete mode 100644 RESTRUCTURE_PROPOSAL.md create mode 100644 conf/experiment/benchmarking/timings.yaml delete mode 100644 conf/experiment/fv_validation.yaml delete mode 100644 conf/experiment/quick_test.yaml delete mode 100644 conf/experiment/spectral_multigrid_comparison.yaml delete mode 100644 conf/experiment/spectral_validation.yaml create mode 100644 conf/experiment/validation/ghia/fv.yaml create mode 100644 conf/experiment/validation/ghia/spectral.yaml delete mode 100644 conf/hydra/launcher/joblib.yaml create mode 100644 conf/machine/hpc.yaml create mode 100644 conf/machine/local.yaml rename conf/{shared => }/mlflow/coolify.yaml (100%) rename conf/{shared => }/mlflow/local.yaml (100%) create mode 100644 conf/problem/ldc.yaml delete mode 100644 conf/shared/hydra/launcher/joblib.yaml delete mode 100644 conf/solver/spectral.yaml create mode 100644 conf/solver/spectral/fmg.yaml create mode 100644 conf/solver/spectral/fsg.yaml create mode 100644 conf/solver/spectral/sg.yaml create mode 100644 conf/solver/spectral/vmg.yaml delete mode 100644 conf/solver/spectral_fmg.yaml delete mode 100644 conf/solver/spectral_fsg.yaml delete mode 100644 conf/solver/spectral_fsg_poly.yaml delete mode 100644 conf/solver/spectral_vmg.yaml delete mode 100644 docs/An-explicit-Chebyshev-pseudospectral-multigrid-method-for-i_2010_Computers--.pdf delete mode 100644 docs/lecture_notes/Assignment03-LidDrivenCavity2D.pdf delete mode 100644 docs/lecture_notes/spectral.pdf delete mode 100644 outputs/comparison_plots/spectral_vs_fsg/ghia_comparison.pdf delete mode 100644 outputs/comparison_plots/validation/ghia_comparison.pdf create mode 100644 src/solvers/spectral/fmg.py create mode 100644 src/solvers/spectral/fsg.py rename src/solvers/spectral/{solver.py => sg.py} (79%) create mode 100644 src/solvers/spectral/vmg.py diff --git a/Experiments/01-Validation/README.rst b/Experiments/01-Validation/README.rst new file mode 100644 index 0000000..a94d1b0 --- /dev/null +++ b/Experiments/01-Validation/README.rst @@ -0,0 +1,12 @@ +01 - Validation of the solvers +====================================== + +Description +----------- +Here we validate the different solvers. +Configuration +------------- + +.. literalinclude:: ../conf/experiment/validation.yaml + :language: yaml + :caption: experiment/conf/experiment/validation.yaml diff --git a/Experiments/01-Validation/plot_validation.py b/Experiments/01-Validation/plot_validation.py new file mode 100644 index 0000000..e69de29 diff --git a/RESTRUCTURE_PROPOSAL.md b/RESTRUCTURE_PROPOSAL.md deleted file mode 100644 index 2f308a3..0000000 --- a/RESTRUCTURE_PROPOSAL.md +++ /dev/null @@ -1,337 +0,0 @@ -# Proposed Project Restructure - -## Goals - -1. Mirror `src/` and `conf/` structure for readability -2. Introduce a `utilities/` submodule inside `src/` for cross-project helpers (MLflow/Hydra/HPC, logging, generic plotting/config) -3. Organize project-specific logic under `solvers/` and `shared/` -4. Spectral multigrid configs extend the base spectral config -5. Clean separation of concerns and discovery - -## Proposed Structure - -### Source Code (`src/`) - -``` -src/ -├── __init__.py -├── solvers/ -│ ├── __init__.py -│ ├── base.py # BaseSolver ABC -│ ├── metrics.py # Norms/formatting shared across solvers -│ ├── datastructures.py # Solver data classes -│ ├── fv/ -│ │ ├── __init__.py -│ │ ├── solver.py # FVSolver class -│ │ ├── assembly/ -│ │ │ ├── __init__.py -│ │ │ ├── convection_diffusion_matrix.py -│ │ │ ├── pressure_correction_eq_assembly.py -│ │ │ ├── divergence.py -│ │ │ └── rhie_chow.py -│ │ ├── discretization/ -│ │ │ ├── __init__.py -│ │ │ ├── convection/ -│ │ │ ├── diffusion/ -│ │ │ └── gradient/ -│ │ ├── core/ -│ │ │ ├── __init__.py -│ │ │ ├── corrections.py -│ │ │ └── helpers.py -│ │ └── linear_solvers/ -│ │ ├── __init__.py -│ │ └── scipy_solver.py -│ │ -│ └── spectral/ -│ ├── __init__.py -│ ├── solver.py # SpectralSolver class (single grid) -│ ├── basis/ -│ │ ├── __init__.py -│ │ ├── polynomial.py # Jacobi, Legendre, interpolation -│ │ └── spectral.py # Basis classes, diff matrices -│ ├── operators/ -│ │ ├── __init__.py -│ │ ├── prolongation.py # Prolongation operators -│ │ ├── restriction.py # Restriction operators -│ │ └── corner.py # Corner singularity treatment -│ ├── multigrid/ -│ │ ├── __init__.py -│ │ ├── fsg.py # Full Spectral Galerkin -│ │ ├── vmg.py # V-cycle multigrid -│ │ └── fmg.py # Full multigrid -│ -├── utilities/ # Cross-project utilities (reusable across repos) -│ ├── __init__.py -│ ├── config/ # Config helpers (paths, cleanup) — domain-agnostic -│ ├── mlflow/ -│ │ ├── __init__.py -│ │ ├── tracking.py -│ │ └── callback.py -│ ├── hydra/ -│ │ ├── __init__.py -│ │ └── callbacks.py -│ ├── hpc/ -│ │ ├── __init__.py -│ │ └── sweeper.py -│ ├── plotting/ # Generic plotting style/format helpers -│ │ ├── __init__.py -│ │ ├── styles.py -│ │ └── formatters.py -│ ├── runners/ # Generic CLI helpers (if kept) -│ ├── io.py # Generic parquet/pickle helpers (from spectral/utils/io.py) -│ └── data_io.py # If broadly reusable; else move to shared/ -│ -├── shared/ # Project-scoped shared code (specific to this LDC project) -│ ├── __init__.py -│ ├── meshing/ # Domain meshes specific to this project -│ │ ├── __init__.py -│ │ ├── mesh_data.py -│ │ └── structured.py -│ ├── plotting/ # Project-specific plots (Ghia, LDC visuals) -│ │ ├── __init__.py -│ │ ├── solution.py # Solution visualization -│ │ ├── validation.py # Ghia comparison plots -│ │ ├── convergence.py # Convergence history plots -│ │ ├── ldc_plotter.py # From utils/ldc_plotter.py -│ │ ├── styles.py # Plot styling utilities (incl. mplstyle) -│ │ └── scientific.mplstyle -│ ├── data_io.py # Project-scoped data I/O (LDC-specific shapes/conventions) -│ ├── field_interpolator.py # Interpolation tailored to project grids -│ ├── ghia_validator.py # Project-specific validation -│ └── datastructures.py # Project-scoped data classes (if any) -│ -``` - -### Configuration (`conf/`) - -``` -conf/ -├── config.yaml # Main config with defaults -│ -├── solvers/ # Mirrors src/solvers/ hierarchy -│ ├── fv/ # Group: solvers/fv -│ │ ├── default.yaml # FV solver parameters (baseline) -│ │ └── linear_solvers/ -│ │ ├── default.yaml # Default linear solver selection -│ │ └── scipy.yaml # SciPy backend settings -│ │ -│ └── spectral/ # Group: solvers/spectral -│ ├── default.yaml # Base spectral (single-grid) -│ ├── basis/ -│ │ └── default.yaml # Basis selection (legendre/chebyshev, quad rules) -│ ├── operators/ -│ │ ├── default.yaml # Corner treatment defaults (smoothing width, method) -│ │ ├── transfer.yaml # Prolongation/restriction choices -│ │ └── corner.yaml # Alternate corner options if needed -│ ├── multigrid/ -│ │ ├── default.yaml # Placeholder (disabled multigrid) -│ │ ├── fsg.yaml # Full Spectral Galerkin -│ │ ├── vmg.yaml # V-cycle multigrid -│ │ └── fmg.yaml # Full multigrid -│ └── metrics/ -│ └── default.yaml # Norm/formatting knobs (optional group) -│ -├── shared/ # Cross-project infra configs -│ ├── mlflow/ -│ │ ├── default.yaml # Local MLflow as baseline -│ │ └── coolify.yaml -│ └── hydra/ -│ └── launcher/ -│ ├── default.yaml # Local/single-run baseline -│ └── joblib.yaml -│ -└── experiment/ - ├── fv_validation.yaml # FV grid convergence study - ├── spectral_validation.yaml # Spectral grid convergence - ├── solver_comparison.yaml # FV vs spectral baseline - ├── corner_treatment.yaml # Corner singularity comparison - ├── multigrid_comparison.yaml # SG vs FSG vs VMG vs FMG - └── transfer_operators.yaml # Prolongation/restriction comparison -``` - -## Configuration Details - -### Main Config (`conf/config.yaml`) - -```yaml -defaults: - - solvers/spectral # Uses solvers/spectral/default.yaml - - solvers/spectral/operators # Uses solvers/spectral/operators/default.yaml - - solvers/spectral/operators@transfer: transfer # Pick transfer variant - - solvers/spectral/basis # Uses solvers/spectral/basis/default.yaml - - solvers/spectral/multigrid: default # Multigrid disabled by default - - shared/mlflow: default # Local MLflow baseline - - shared/hydra/launcher: default # Local launcher baseline - - _self_ - -# Problem parameters -Re: 100 -N: 16 -Lx: 1.0 -Ly: 1.0 -lid_velocity: 1.0 - -# Solver parameters -max_iterations: 10000 -tolerance: 1.0e-6 -``` - -### Base Spectral Config (`conf/solvers/spectral/default.yaml`) - -```yaml -# @package _global_ -solver: spectral - -# Spectral-specific parameters -basis_type: legendre -CFL: 0.5 -corner_treatment: smoothing - -# Single-grid (no multigrid) -multigrid: - enabled: false -``` - -### FSG Multigrid Config (`conf/solvers/spectral/fsg.yaml`) - -```yaml -# @package _global_ -defaults: - - default # Inherit from base spectral - -solver: spectral_fsg - -multigrid: - enabled: true - type: fsg - coarse_N: 4 - prolongation: polynomial - restriction: injection -``` - -### VMG Config (`conf/solvers/spectral/vmg.yaml`) - -```yaml -# @package _global_ -defaults: - - default - -solver: spectral_vmg - -multigrid: - enabled: true - type: vmg - levels: 3 - smoothing_steps: 2 - prolongation: fft - restriction: fft -``` - -### FV Config (`conf/solvers/fv/default.yaml`) - -```yaml -# @package _global_ -solver: fv - -# FV-specific parameters -convection_scheme: upwind -limiter: none -pressure_solver: scipy -relaxation: - velocity: 0.7 - pressure: 0.3 -``` - -### Example Experiment (`conf/experiment/multigrid_comparison.yaml`) - -```yaml -# @package _global_ -defaults: - - override /solvers/spectral: default - -experiment_name: Multigrid-Comparison -sweep_name: sg_vs_fsg_vs_vmg - -Re: 100 -tolerance: 1.0e-6 -max_iterations: 50000 - -hydra: - sweeper: - params: - solvers/spectral: default,fsg,vmg - N: 16,32,64 -``` - -## Migration Plan - -### Phase 1: Reorganize `src/` - -1. Create new directory structure -2. Move files to new locations: - - `src/solvers/*.py` → `src/solvers/base.py` - - `src/solvers/fv_solver.py` → `src/solvers/fv/solver.py` - - `src/solvers/spectral_solver.py` → `src/solvers/spectral/solver.py` - - `src/solvers/datastructures.py` → `src/solvers/datastructures.py` (kept beside base) - - `src/fv/*` → `src/solvers/fv/*` - - `src/spectral/spectral.py` → `src/solvers/spectral/solver.py` - - `src/spectral/polynomial.py` → `src/solvers/spectral/basis/polynomial.py` - - `src/spectral/corner_singularity.py` → `src/solvers/spectral/operators/corner.py` - - `src/spectral/transfer_operators.py` → split into `src/solvers/spectral/operators/{prolongation.py,restriction.py}` - - `src/spectral/utils/formatting.py`, `norms.py` → `src/solvers/metrics.py` (shared across FV & spectral) - - `src/spectral/utils/io.py` → `src/utilities/io.py` (generic parquet/pickle loader) - - `src/spectral/utils/plotting.py` → `src/shared/plotting/` (project-level plotting helpers) - - `src/spectral/spectral_multigrid.py` → split into `src/solvers/spectral/multigrid/{fsg.py,vmg.py,fmg.py}` - - Remove `src/fv/linear_solvers/petsc_solver.py` and any references (we keep SciPy backend only) - - `src/meshing/*` → `src/shared/meshing/*` - - `src/plotting.py` + `src/utils/plotting/*` → `src/shared/plotting/` (project-level plotting) - - `src/utils/data_io.py` → `src/shared/data_io.py` (or `utilities/` if made fully generic) - - `src/utils/field_interpolator.py` → `src/shared/field_interpolator.py` - - `src/utils/ghia_validator.py` → `src/shared/ghia_validator.py` - - `src/utils/ldc_plotter.py` → `src/shared/plotting/ldc_plotter.py` - - `src/utils/config/*` → `src/utilities/config/*` - - `src/utils/mlflow/*` → `src/utilities/mlflow/*` - - `src/utils/mlflow_callback.py` → `src/utilities/mlflow/callback.py` - - `src/utils/hydra/*` → `src/utilities/hydra/*` - - `src/utils/hpc/*` → `src/utilities/hpc/*` - - `src/utils/runners/*` (if project-agnostic) → `src/utilities/` - - `src/utils/mlflow_io.py`, `upload_logs.py`, `logs.py` → `src/utilities/mlflow/` - - `src/utils/*` that are project-specific → keep or move into `shared/` as appropriate -3. Update all imports -4. Update `__init__.py` exports -5. Delete stale `__pycache__/` and `*.nbc` artifacts after moves to avoid packaging noise - -### Hydra Instantiation Direction - -- Add `_target_` to solver configs and instantiate solvers via `hydra.utils.instantiate(cfg.solver, _convert_="partial")` in `run_solver.py`. -- Keep solver inputs scoped to the solver subtree: carry `Re`, `N`, `lid_velocity`, `Lx`, `Ly`, `max_iterations`, `tolerance`, etc. into the solver config via interpolation; do **not** pass the full runtime `cfg` (which includes MLflow/Hydra metadata) into solver constructors. -- Rely on solver constructors accepting `**kwargs` (already true) and their `Parameters` dataclasses as the single source of truth. Multigrid/transfer variants remain pure config choices. -- This removes manual parameter plucking in `create_solver`, keeps separation of concerns, and makes adding new solver variants config-only. - -### Phase 2: Reorganize `conf/` - -1. Create new directory structure -2. Move configs: - - `conf/solver/fv.yaml` → `conf/solvers/fv/default.yaml` - - `conf/solver/spectral.yaml` → `conf/solvers/spectral/default.yaml` - - `conf/mlflow/*` → `conf/shared/mlflow/*` (with `default.yaml`) - - `conf/hydra/*` → `conf/shared/hydra/*` (with `launcher/default.yaml`) - - `conf/experiment/multigrid.yaml` → `conf/experiment/multigrid_comparison.yaml` - - Keep `solver_comparison.yaml`, `transfer_operators.yaml`, `spectral_validation.yaml`, `fv_validation.yaml`, `corner_treatment.yaml` -3. Create multigrid variant configs (fsg.yaml, vmg.yaml, fmg.yaml) -4. Update `config.yaml` defaults -5. Update experiment configs - -### Phase 3: Update `run_solver.py` - -1. Simplify solver instantiation using Hydra's `instantiate()` -2. Remove manual parameter extraction -3. Use `_target_` pattern for automatic class instantiation - -## Benefits - -1. **Discoverability**: Mirror structure makes it easy to find config for any code -2. **Extensibility**: Adding new solver = add directory in both `src/solvers/` and `conf/solvers/` -3. **Inheritance**: Multigrid configs extend base spectral, reducing duplication -4. **Clean sweeps**: Each solver type has appropriate N ranges in its experiments -5. **Separation**: Infrastructure code separated from numerical methods diff --git a/conf/config.yaml b/conf/config.yaml index ec7d4b7..8cebd61 100644 --- a/conf/config.yaml +++ b/conf/config.yaml @@ -1,52 +1,57 @@ # Main Hydra configuration for LDC solvers -# Usage: uv run python run_solver.py solver=fv Re=100 N=32 -# uv run python run_solver.py solver=spectral_fmg Re=100 N=15 # -# Regenerate plots for existing run (no solving): -# uv run python run_solver.py solver=fv N=32 Re=100 plot_only=true +# STANDARD USAGE - Always use -m (multirun mode): # -# Parallel sweeps with joblib: -# uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 Re=100 +# Validation: +# uv run python run_solver.py -m +experiment=validation/ghia +# +# Benchmarking: +# uv run python run_solver.py -m +experiment=benchmarking/multigrid_comparison +# +# Quick testing: +# uv run python run_solver.py -m +experiment=testing/quick_test +# +# Custom sweeps: +# uv run python run_solver.py -m solver=fv N=16,32,64 Re=100,400 +# +# Override machine for HPC: +# uv run python run_solver.py -m machine=hpc +experiment=validation/ghia +# +# Regenerate plots (separate tool): +# uv run python plot_runs.py +experiment=validation/ghia +# +# Single runs (testing only, no -m flag needed): +# uv run python run_solver.py solver=fv N=32 Re=100 defaults: + - problem: ldc - solver: fv - - shared/mlflow@mlflow: local + - mlflow: local + - machine: local - _self_ -# ============================================================================= -# Common Parameters (shared by all solvers) -# ============================================================================= - -# Grid size (meaning depends on solver): -# FV: N = number of cells in each direction -# Spectral: N = polynomial order (gives N+1 nodes) +# Grid size (must be set by experiment or command line) N: 32 -# Physics -Re: 100 -lid_velocity: 1.0 -Lx: 1.0 -Ly: 1.0 - -# Solver control -tolerance: 1.0e-6 -max_iterations: 500 -generate_plots: true -plot_only: false # Skip solving, only regenerate plots for existing run +# Solver control (taken from solver config) +tolerance: ${solver.tolerance} +max_iterations: ${solver.max_iterations} -# Experiment tracking -experiment_name: LDC-Solver -sweep_name: sweep # Name for parent run in multirun sweeps +# Default experiment name (typically overridden by experiment config) +experiment_name: LDC-Dev +sweep_name: dev-run # ============================================================================= # Hydra output paths and callbacks # ============================================================================= hydra: + # Always use multirun mode by default + mode: MULTIRUN run: - dir: hydra_outputs/${experiment_name}/Re${Re}/${solver.name}/${now:%d-%m-%y}/${now:%H:%M:%S} + dir: hydra_outputs/runs/${now:%d-%m-%y}/${now:%H:%M:%S} sweep: - dir: hydra_outputs/multirun/${experiment_name}/Re${Re}/${solver.name}/${now:%d-%m-%y}/${now:%H:%M:%S} - subdir: N${N} + dir: hydra_outputs/multirun/${now:%d-%m-%y}/${now:%H:%M:%S} + subdir: ${hydra.job.num} callbacks: mlflow_sweep: _target_: utilities.mlflow.callback.MLflowSweepCallback diff --git a/conf/experiment/benchmarking/timings.yaml b/conf/experiment/benchmarking/timings.yaml new file mode 100644 index 0000000..253f7da --- /dev/null +++ b/conf/experiment/benchmarking/timings.yaml @@ -0,0 +1,26 @@ +# @package _global_ +# Benchmarking: Spectral Multigrid Methods Comparison +# Compares different multigrid acceleration strategies for spectral solver +# +# Usage: uv run python run_solver.py -m +experiment=benchmarking/multigrid_comparison + +defaults: + - override /solver: spectral/sg + +# MLflow experiment +experiment_name: LDC-Benchmarking +sweep_name: multigrid-comparison-Re${Re} + +# Benchmarking parameters +Re: 100 +N: 15 + +hydra: + sweeper: + params: + # Sweep over spectral solver variants + # sg: Single Grid (no multigrid) + # fsg: Full Single Grid + # vmg: V-cycle MultiGrid + # fmg: Full MultiGrid + solver: spectral/sg,spectral/fsg,spectral/vmg,spectral/fmg diff --git a/conf/experiment/fv_validation.yaml b/conf/experiment/fv_validation.yaml deleted file mode 100644 index 3b0e229..0000000 --- a/conf/experiment/fv_validation.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# @package _global_ -# FV Solver validation against Ghia benchmark -# Usage: uv run python run_solver.py +experiment=fv_validation -# uv run python run_solver.py -m +experiment=fv_validation (sweep all) - -defaults: - - override /solver: fv - -# MLflow experiment (shared with spectral_validation) -experiment_name: LDC-Validation -# Parent run name - use same name across fv/spectral to group them -sweep_name: validation - -# Validation grid sizes and Reynolds numbers -N: 32 -Re: 100 -tolerance: 1.0e-6 -max_iterations: 10000 - -hydra: - sweeper: - params: - N: 32,64 - Re: 100 diff --git a/conf/experiment/quick_test.yaml b/conf/experiment/quick_test.yaml deleted file mode 100644 index 46c89aa..0000000 --- a/conf/experiment/quick_test.yaml +++ /dev/null @@ -1,22 +0,0 @@ -# @package _global_ -# Quick test experiment - sweeps over FV and Spectral solvers -# Usage: uv run python run_solver.py -m +experiment=quick_test - -defaults: - - override /solver: fv - -# MLflow experiment -experiment_name: LDC-QuickTest -sweep_name: quick-test-Re${Re} - -# Quick test parameters -N: 16 # grid size (16 cells for FV, polynomial order 16 for spectral) -Re: 100 # Reynolds number -tolerance: 1.0e-5 # slightly relaxed for speed -max_iterations: 5000 - -hydra: - sweeper: - params: - # Sweep over both solver types - solver: fv,spectral diff --git a/conf/experiment/spectral_multigrid_comparison.yaml b/conf/experiment/spectral_multigrid_comparison.yaml deleted file mode 100644 index 11265dd..0000000 --- a/conf/experiment/spectral_multigrid_comparison.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# @package _global_ -# Comparison: Spectral solver vs Spectral-FSG (Full Single Grid multigrid) -# Usage: uv run python run_solver.py +experiment=spectral_multigrid_comparison -# uv run python run_solver.py -m +experiment=spectral_multigrid_comparison (sweep all) - -# MLflow experiment name for tracking -experiment_name: LDC-Multigrid-Comparison -sweep_name: spectral_vs_fsg - -# Common parameters -Re: 100 -tolerance: 1.0e-6 -max_iterations: 100000 - -# Default N (overridden by sweep) -N: 15 - -defaults: - - override /solver: spectral - -hydra: - sweeper: - params: - # Sweep over spectral solver variants - solver: spectral,spectral_fsg diff --git a/conf/experiment/spectral_validation.yaml b/conf/experiment/spectral_validation.yaml deleted file mode 100644 index 4d1a437..0000000 --- a/conf/experiment/spectral_validation.yaml +++ /dev/null @@ -1,22 +0,0 @@ -# @package _global_ -# Spectral Solver validation against Ghia benchmark -# Usage: uv run python run_solver.py +experiment=spectral_validation -# uv run python run_solver.py -m +experiment=spectral_validation (sweep all) - -defaults: - - override /solver: spectral - -# MLflow experiment (shared with fv_validation) -experiment_name: LDC-Validation -sweep_name: validation - -# Validation polynomial orders (N+1 nodes) and Reynolds numbers -N: 15 -Re: 100 -tolerance: 1.0e-6 -max_iterations: 30000 - -hydra: - sweeper: - params: - N: 15 diff --git a/conf/experiment/validation/ghia/fv.yaml b/conf/experiment/validation/ghia/fv.yaml new file mode 100644 index 0000000..1c5d93f --- /dev/null +++ b/conf/experiment/validation/ghia/fv.yaml @@ -0,0 +1,17 @@ +# @package _global_ +# Ghia validation - Finite Volume solver +# Sweeps over grid sizes N=32, N=64 + +defaults: + - override /solver: fv + +# MLflow experiment +experiment_name: LDC-Validation +sweep_name: ghia-Re${Re} + +hydra: + sweeper: + params: + # Sweep over FV grid sizes + N: 32,64 + Re: 100 diff --git a/conf/experiment/validation/ghia/spectral.yaml b/conf/experiment/validation/ghia/spectral.yaml new file mode 100644 index 0000000..8209408 --- /dev/null +++ b/conf/experiment/validation/ghia/spectral.yaml @@ -0,0 +1,20 @@ +# @package _global_ +# Ghia validation - Spectral solvers +# Sweeps over SG, FSG, VMG, FMG solvers at N=15 + +defaults: + - override /solver: spectral/sg + +# MLflow experiment +experiment_name: LDC-Validation +sweep_name: ghia-Re${Re} + +# Validation parameters + +hydra: + sweeper: + params: + # Sweep over spectral solver types + solver: spectral/sg,spectral/fsg + Re: 100 + N: 15 diff --git a/conf/hydra/launcher/joblib.yaml b/conf/hydra/launcher/joblib.yaml deleted file mode 100644 index 9f0e6ca..0000000 --- a/conf/hydra/launcher/joblib.yaml +++ /dev/null @@ -1,15 +0,0 @@ -# @package hydra.launcher -# Joblib Launcher for parallel sweeps -# Usage: uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 - -_target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - -n_jobs: -1 # -1 = all cores, -2 = all but one -backend: loky # loky, threading, multiprocessing -verbose: 10 # 0-50 -timeout: null # seconds per job (null = no timeout) -pre_dispatch: "2*n_jobs" -batch_size: auto -temp_folder: null -max_nbytes: 1M -mmap_mode: r diff --git a/conf/machine/hpc.yaml b/conf/machine/hpc.yaml new file mode 100644 index 0000000..e9486f4 --- /dev/null +++ b/conf/machine/hpc.yaml @@ -0,0 +1,10 @@ +# @package _global_ +# HPC machine configuration - run jobs sequentially (one at a time) +# Use this when running on HPC cluster to avoid oversubscribing resources + +defaults: + - override /hydra/launcher: joblib + +hydra: + launcher: + n_jobs: 1 # Run jobs sequentially on HPC diff --git a/conf/machine/local.yaml b/conf/machine/local.yaml new file mode 100644 index 0000000..47ccb14 --- /dev/null +++ b/conf/machine/local.yaml @@ -0,0 +1,9 @@ +# @package _global_ +# Local machine configuration - run up to 4 jobs in parallel + +defaults: + - override /hydra/launcher: joblib + +hydra: + launcher: + n_jobs: 4 # Run 4 jobs in parallel on local machine diff --git a/conf/shared/mlflow/coolify.yaml b/conf/mlflow/coolify.yaml similarity index 100% rename from conf/shared/mlflow/coolify.yaml rename to conf/mlflow/coolify.yaml diff --git a/conf/shared/mlflow/local.yaml b/conf/mlflow/local.yaml similarity index 100% rename from conf/shared/mlflow/local.yaml rename to conf/mlflow/local.yaml diff --git a/conf/problem/ldc.yaml b/conf/problem/ldc.yaml new file mode 100644 index 0000000..ab99476 --- /dev/null +++ b/conf/problem/ldc.yaml @@ -0,0 +1,11 @@ +# @package _global_ +# Lid-Driven Cavity Problem Definition +# Defines the physics and domain for the lid-driven cavity benchmark + +# Physics parameters +Re: 100 # Reynolds number +lid_velocity: 1.0 # Velocity of the moving lid + +# Domain geometry +Lx: 1.0 # Domain length in x-direction +Ly: 1.0 # Domain length in y-direction diff --git a/conf/shared/hydra/launcher/joblib.yaml b/conf/shared/hydra/launcher/joblib.yaml deleted file mode 100644 index 9f0e6ca..0000000 --- a/conf/shared/hydra/launcher/joblib.yaml +++ /dev/null @@ -1,15 +0,0 @@ -# @package hydra.launcher -# Joblib Launcher for parallel sweeps -# Usage: uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 - -_target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher - -n_jobs: -1 # -1 = all cores, -2 = all but one -backend: loky # loky, threading, multiprocessing -verbose: 10 # 0-50 -timeout: null # seconds per job (null = no timeout) -pre_dispatch: "2*n_jobs" -batch_size: auto -temp_folder: null -max_nbytes: 1M -mmap_mode: r diff --git a/conf/solver/fv.yaml b/conf/solver/fv.yaml index 93d75fa..88eb623 100644 --- a/conf/solver/fv.yaml +++ b/conf/solver/fv.yaml @@ -2,6 +2,10 @@ _target_: solvers.fv.solver.FVSolver name: fv +# Solver control +tolerance: 1.0e-6 +max_iterations: 10000 # FV typically needs more iterations than spectral + # FV-specific parameters convection_scheme: Upwind limiter: MUSCL diff --git a/conf/solver/spectral.yaml b/conf/solver/spectral.yaml deleted file mode 100644 index 9a8200e..0000000 --- a/conf/solver/spectral.yaml +++ /dev/null @@ -1,22 +0,0 @@ -# @package solver -_target_: solvers.spectral.solver.SpectralSolver -name: spectral - -# Spectral-specific parameters -basis_type: chebyshev # "chebyshev" or "legendre" -CFL: 0.5 -beta_squared: 5.0 # artificial compressibility parameter - -# Corner singularity treatment -# Options: "smoothing" (simple cosine smoothing) or "subtraction" (Botella & Peyret 1998) -corner_treatment: smoothing -corner_smoothing: 0.15 # smoothing width (fraction of domain) for smoothing method - -# Multigrid settings -multigrid: none # "none", "fsg", "vmg", "fmg" -n_levels: 3 # number of grid levels for multigrid -coarse_tolerance_factor: 10.0 # relaxed tolerance on coarse grids - -# Transfer operators for multigrid (prolongation: coarse->fine, restriction: fine->coarse Options: fft (Zhang & Xi 2010 paper method) or polynomial/injection -prolongation_method: fft # "fft" or "polynomial" -restriction_method: fft # "fft" or "injection" diff --git a/conf/solver/spectral/fmg.yaml b/conf/solver/spectral/fmg.yaml new file mode 100644 index 0000000..166637a --- /dev/null +++ b/conf/solver/spectral/fmg.yaml @@ -0,0 +1,17 @@ +# @package solver +# Full MultiGrid (FMG) Spectral Solver with multigrid acceleration + +defaults: + - /solver/spectral/sg # Extend single grid spectral solver using absolute path + +_target_: solvers.spectral.fmg.FMGSolver + +# Override name for FMG variant +name: spectral_fmg + +# FMG Multigrid settings +multigrid: fmg +n_levels: 3 +coarse_tolerance_factor: 10.0 +prolongation_method: fft +restriction_method: fft diff --git a/conf/solver/spectral/fsg.yaml b/conf/solver/spectral/fsg.yaml new file mode 100644 index 0000000..73f03be --- /dev/null +++ b/conf/solver/spectral/fsg.yaml @@ -0,0 +1,17 @@ +# @package solver +# Full Single Grid (FSG) Spectral Solver with multigrid acceleration + +defaults: + - /solver/spectral/sg # Extend single grid spectral solver using absolute path + +_target_: solvers.spectral.fsg.FSGSolver + +# Override name for FSG variant +name: spectral_fsg + +# FSG Multigrid settings +multigrid: fsg +n_levels: 3 +coarse_tolerance_factor: 10.0 +prolongation_method: fft +restriction_method: fft diff --git a/conf/solver/spectral/sg.yaml b/conf/solver/spectral/sg.yaml new file mode 100644 index 0000000..c2036db --- /dev/null +++ b/conf/solver/spectral/sg.yaml @@ -0,0 +1,21 @@ +# @package solver +# Single Grid Spectral Solver (no multigrid acceleration) +_target_: solvers.spectral.sg.SGSolver +name: spectral + +# Solver control +tolerance: 1.0e-6 +max_iterations: 30000 # Spectral methods converge slowly for high Re + +# Spectral-specific parameters +basis_type: chebyshev # "chebyshev" or "legendre" +CFL: 0.5 +beta_squared: 5.0 # artificial compressibility parameter + +# Corner singularity treatment +# Options: "smoothing" (simple cosine smoothing) or "subtraction" (Botella & Peyret 1998) +corner_treatment: smoothing +corner_smoothing: 0.15 # smoothing width (fraction of domain) for smoothing method + +# No multigrid for single grid solver +multigrid: none diff --git a/conf/solver/spectral/vmg.yaml b/conf/solver/spectral/vmg.yaml new file mode 100644 index 0000000..77ae8e1 --- /dev/null +++ b/conf/solver/spectral/vmg.yaml @@ -0,0 +1,17 @@ +# @package solver +# V-cycle MultiGrid (VMG) Spectral Solver with multigrid acceleration + +defaults: + - /solver/spectral/sg # Extend single grid spectral solver using absolute path + +_target_: solvers.spectral.vmg.VMGSolver + +# Override name for VMG variant +name: spectral_vmg + +# VMG Multigrid settings +multigrid: vmg +n_levels: 3 +coarse_tolerance_factor: 10.0 +prolongation_method: fft +restriction_method: fft diff --git a/conf/solver/spectral_fmg.yaml b/conf/solver/spectral_fmg.yaml deleted file mode 100644 index ec78274..0000000 --- a/conf/solver/spectral_fmg.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# @package solver -_target_: solvers.spectral.solver.SpectralSolver -name: spectral_fmg - -# Spectral-specific parameters -basis_type: chebyshev -CFL: 0.5 -beta_squared: 5.0 - -# Corner singularity treatment -corner_treatment: smoothing -corner_smoothing: 0.15 - -# Multigrid settings - FMG variant -multigrid: fmg -n_levels: 3 -coarse_tolerance_factor: 10.0 -prolongation_method: fft -restriction_method: fft - diff --git a/conf/solver/spectral_fsg.yaml b/conf/solver/spectral_fsg.yaml deleted file mode 100644 index ecd54a8..0000000 --- a/conf/solver/spectral_fsg.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# @package solver -_target_: solvers.spectral.solver.SpectralSolver -name: spectral_fsg - -# Spectral-specific parameters -basis_type: chebyshev -CFL: 0.5 -beta_squared: 5.0 - -# Corner singularity treatment -corner_treatment: smoothing -corner_smoothing: 0.15 - -# Multigrid settings - FSG variant -multigrid: fsg -n_levels: 3 -coarse_tolerance_factor: 10.0 -prolongation_method: fft -restriction_method: fft - diff --git a/conf/solver/spectral_fsg_poly.yaml b/conf/solver/spectral_fsg_poly.yaml deleted file mode 100644 index e2c9b4c..0000000 --- a/conf/solver/spectral_fsg_poly.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# @package solver -_target_: solvers.spectral.solver.SpectralSolver -name: spectral_fsg_poly - -# Spectral-specific parameters -basis_type: chebyshev -CFL: 0.5 -beta_squared: 5.0 - -# Corner singularity treatment -corner_treatment: smoothing -corner_smoothing: 0.15 - -# Multigrid settings - FSG with polynomial transfer -multigrid: fsg -n_levels: 3 -coarse_tolerance_factor: 10.0 -prolongation_method: polynomial -restriction_method: injection - diff --git a/conf/solver/spectral_vmg.yaml b/conf/solver/spectral_vmg.yaml deleted file mode 100644 index c352726..0000000 --- a/conf/solver/spectral_vmg.yaml +++ /dev/null @@ -1,20 +0,0 @@ -# @package solver -_target_: solvers.spectral.solver.SpectralSolver -name: spectral_vmg - -# Spectral-specific parameters -basis_type: chebyshev -CFL: 0.5 -beta_squared: 5.0 - -# Corner singularity treatment -corner_treatment: smoothing -corner_smoothing: 0.15 - -# Multigrid settings - VMG variant -multigrid: vmg -n_levels: 3 -coarse_tolerance_factor: 10.0 -prolongation_method: fft -restriction_method: fft - diff --git a/docs/An-explicit-Chebyshev-pseudospectral-multigrid-method-for-i_2010_Computers--.pdf b/docs/An-explicit-Chebyshev-pseudospectral-multigrid-method-for-i_2010_Computers--.pdf deleted file mode 100644 index 39e77ef992e5a1cc7b3131f6e421486c686766d0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 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zBUbfkvMaZBPaH&K=edSj1bmh55)R}GzFyFx$jH~Md5*@7e36ZMx-nkO@I70JWn#dE zr+9G4-q`ugNBqLEi7ofMjGx>KZ-uX_w5TA$WSEt~UWB60bQ#$Z!xqulW;^b2-oG0A zN|_t&YpgAL`|kzvJGlQxBf$+N-~P`6!Kv82!l8JuvN8f1<5U|0sdpndw(XL!7E5BL zuvdq}YQnT3F#`RMTnyp9bahiBV&fE35$o#8>%!0m5DffxdhswX{@?G#-xdbje{EK> z16iT7^7r#!Pk+ryPVWE0Sg8In79${am|R^}V+7+6GHk5&VT8l_%zBt;Uc`<6PQ3rV zTK{V;{r`3SJZw1s@6WtH+3A0!i~r62@c+gx@Ozv8UmJ`6OY7$$mH)G$iiTz;P5?F; zi`Pz2#5fdy{*c`CpsCu<1&R>=ou~hVd;k47A?j#i=xpZ*V30F(wzsx(wze<=xN@>W zy9H(jm^(Y$JMpox+5G3}lXy5EKy44Fp3$;XF)04tgMvp7C!P-`&x~6cq}l=RyVk=L3qL2XTS` zrhxy{9`>+L+< None: """Hydra entry point - runs solver with MLflow tracking.""" + + # Check if running in multirun mode + try: + import hydra.core.hydra_config + hydra_cfg = hydra.core.hydra_config.HydraConfig.get() + is_multirun = hydra_cfg.mode.name == "MULTIRUN" + + # Warn if using experiment config without multirun + # Check if sweep params are defined in config (indicates experiment config) + has_sweep_params = "sweeper" in OmegaConf.to_container(cfg.get("hydra", {}), resolve=False) + + if not is_multirun and has_sweep_params: + log.warning( + "\n" + "="*80 + "\n" + "WARNING: You're using an experiment config without multirun mode!\n" + "Experiment configs are designed for sweeps. Add -m flag:\n" + " uv run python run_solver.py -m +experiment=...\n" + "\nContinuing with single run (no plots will be generated)...\n" + + "="*80 + ) + except Exception: + is_multirun = False + log.info(f"Solver: {cfg.solver.name}, N={cfg.N}, Re={cfg.Re}") # Setup MLflow experiment_name = setup_mlflow(cfg) log.info(f"MLflow experiment: {experiment_name}") - # Plot-only mode: regenerate plots without running solver - # NOTE: For multirun plot regeneration, use plot_runs.py instead! - if cfg.get("plot_only", False): - log.warning( - "plot_only mode is deprecated for run_solver.py. " - "Use plot_runs.py instead:\n" - " uv run python plot_runs.py +experiment=fv_validation" - ) - log.info("Redirecting to plot_runs.py...") - - # Import and call plot_experiment directly - sys.path.insert(0, str(Path(__file__).parent)) - from plot_runs import plot_experiment - - tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") - - # Build experiment name - experiment_name = cfg.experiment_name - project_prefix = cfg.mlflow.get("project_prefix", "") - if project_prefix and not experiment_name.startswith("/"): - experiment_name = f"{project_prefix}/{experiment_name}" - - output_dir = Path( - hydra.core.hydra_config.HydraConfig.get().runtime.output_dir - ) - - plot_experiment( - experiment_name=experiment_name, - tracking_uri=tracking_uri, - output_dir=output_dir, - upload_to_mlflow=cfg.get("upload_to_mlflow", True), - ) - log.info("Plot regeneration complete!") - return - # Create solver solver = create_solver(cfg) @@ -254,11 +237,6 @@ def main(cfg: DictConfig) -> None: f"time={solver.metrics.wall_time_seconds:.2f}s" ) - # Plot generation is handled centrally by plot_runs.py - # - For multirun: plots generated automatically at end via MLflowSweepCallback - # - For single runs: use `uv run python plot_runs.py +experiment=...` - # - Or manually: `uv run python plot_runs.py parent_run_id=...` - if __name__ == "__main__": main() diff --git a/src/solvers/__init__.py b/src/solvers/__init__.py index 803bb0e..8646e9a 100644 --- a/src/solvers/__init__.py +++ b/src/solvers/__init__.py @@ -6,8 +6,10 @@ ----------------- LidDrivenCavitySolver (abstract base - defines problem) ├── FVSolver (finite volume with SIMPLE algorithm) -└── SpectralSolver (spectral methods with basic implementation) - └── MultigridSpectralSolver (extends with multigrid acceleration) +└── SGSolver (spectral single grid base) + ├── FSGSolver (Full Single Grid multigrid) + ├── VMGSolver (V-cycle MultiGrid) + └── FMGSolver (Full MultiGrid) """ from .base import LidDrivenCavitySolver @@ -25,7 +27,10 @@ SpectralSolverFields, ) from solvers.fv.solver import FVSolver -from solvers.spectral.solver import SpectralSolver +from solvers.spectral.sg import SGSolver +from solvers.spectral.fsg import FSGSolver +from solvers.spectral.vmg import VMGSolver +from solvers.spectral.fmg import FMGSolver __all__ = [ @@ -40,8 +45,11 @@ "FVSolver", "FVParameters", "FVSolverFields", - # Spectral solver - "SpectralSolver", + # Spectral solvers + "SGSolver", + "FSGSolver", + "VMGSolver", + "FMGSolver", "SpectralParameters", "SpectralSolverFields", ] diff --git a/src/solvers/spectral/__init__.py b/src/solvers/spectral/__init__.py index ba51123..f6b5e73 100644 --- a/src/solvers/spectral/__init__.py +++ b/src/solvers/spectral/__init__.py @@ -1,5 +1,8 @@ """Spectral solver package.""" -from solvers.spectral.solver import SpectralSolver +from solvers.spectral.sg import SGSolver +from solvers.spectral.fsg import FSGSolver +from solvers.spectral.vmg import VMGSolver +from solvers.spectral.fmg import FMGSolver -__all__ = ["SpectralSolver"] +__all__ = ["SGSolver", "FSGSolver", "VMGSolver", "FMGSolver"] diff --git a/src/solvers/spectral/fmg.py b/src/solvers/spectral/fmg.py new file mode 100644 index 0000000..b51a290 --- /dev/null +++ b/src/solvers/spectral/fmg.py @@ -0,0 +1,45 @@ +"""Full MultiGrid (FMG) spectral solver for lid-driven cavity. + +FMG will extend the base SG solver with Full MultiGrid acceleration. +Currently not implemented. +""" + +import logging + +from .sg import SGSolver + +log = logging.getLogger(__name__) + + +class FMGSolver(SGSolver): + """Full MultiGrid (FMG) spectral solver. + + Extends the base Single Grid solver with Full MultiGrid acceleration. + + Parameters + ---------- + All parameters inherited from SGSolver, plus: + n_levels : int + Number of multigrid levels + coarse_tolerance_factor : float + Tolerance multiplier for coarse grids + prolongation_method : str + Transfer operator for coarse-to-fine ('fft' or 'polynomial') + restriction_method : str + Transfer operator for fine-to-coarse ('fft' or 'polynomial') + """ + + def solve(self, tolerance: float = None, max_iter: int = None): + """Solve using Full MultiGrid (FMG). + + Parameters + ---------- + tolerance : float, optional + Convergence tolerance. If None, uses params.tolerance. + max_iter : int, optional + Maximum iterations. If None, uses params.max_iterations. + """ + raise NotImplementedError( + "Full MultiGrid (FMG) solver not yet implemented. " + "Use FSG (Full Single Grid) instead." + ) diff --git a/src/solvers/spectral/fsg.py b/src/solvers/spectral/fsg.py new file mode 100644 index 0000000..902e6ce --- /dev/null +++ b/src/solvers/spectral/fsg.py @@ -0,0 +1,129 @@ +"""Full Single Grid (FSG) multigrid spectral solver for lid-driven cavity. + +FSG extends the base SG solver with multigrid acceleration using: +- Grid hierarchy with nested Gauss-Lobatto grids +- FFT-based or polynomial-based transfer operators +- Coarse-to-fine solution sequence +""" + +import logging +import time + +from .sg import SGSolver +from solvers.spectral.multigrid.fsg import build_hierarchy, solve_fsg +from solvers.spectral.operators.transfer_operators import create_transfer_operators + +log = logging.getLogger(__name__) + + +class FSGSolver(SGSolver): + """Full Single Grid (FSG) multigrid spectral solver. + + Extends the base Single Grid solver with FSG multigrid acceleration. + Solves on a sequence of grids from coarse to fine, using the coarse + grid solution as initial guess for the next finer grid. + + Parameters + ---------- + All parameters inherited from SGSolver, plus: + n_levels : int + Number of multigrid levels + coarse_tolerance_factor : float + Tolerance multiplier for coarse grids + prolongation_method : str + Transfer operator for coarse-to-fine ('fft' or 'polynomial') + restriction_method : str + Transfer operator for fine-to-coarse ('fft' or 'polynomial') + """ + + def solve(self, tolerance: float = None, max_iter: int = None): + """Solve using Full Single Grid (FSG) multigrid. + + Parameters + ---------- + tolerance : float, optional + Convergence tolerance. If None, uses params.tolerance. + max_iter : int, optional + Maximum iterations. If None, uses params.max_iterations. + """ + if tolerance is None: + tolerance = self.params.tolerance + if max_iter is None: + max_iter = self.params.max_iterations + + log.info(f"Using FSG multigrid with {self.params.n_levels} levels") + log.info( + f"Transfer operators: prolongation={self.params.prolongation_method}, " + f"restriction={self.params.restriction_method}" + ) + + # Create transfer operators from config + transfer_ops = create_transfer_operators( + prolongation_method=self.params.prolongation_method, + restriction_method=self.params.restriction_method, + ) + + # Build grid hierarchy + time_start = time.time() + levels = build_hierarchy( + n_fine=self.params.nx, + n_levels=self.params.n_levels, + basis_x=self.basis_x, + basis_y=self.basis_y, + Lx=self.params.Lx, + Ly=self.params.Ly, + ) + + # Solve using FSG + finest_level, total_iters, converged = solve_fsg( + levels=levels, + Re=self.params.Re, + beta_squared=self.params.beta_squared, + lid_velocity=self.params.lid_velocity, + CFL=self.params.CFL, + tolerance=tolerance, + max_iterations=max_iter, + transfer_ops=transfer_ops, + corner_treatment=self.corner_treatment, + Lx=self.params.Lx, + Ly=self.params.Ly, + coarse_tolerance_factor=self.params.coarse_tolerance_factor, + ) + + time_end = time.time() + wall_time = time_end - time_start + + # Copy solution from finest level to solver arrays + self.arrays.u[:] = finest_level.u + self.arrays.v[:] = finest_level.v + self.arrays.p[:] = finest_level.p + + # Compute final residuals + self._compute_residuals(self.arrays.u, self.arrays.v, self.arrays.p) + + # Store results using base class machinery + # Create minimal residual history for compatibility + final_residuals = self._compute_algebraic_residuals() + residual_history = [ + { + "rel_iter": tolerance if converged else tolerance * 10, + "u_eq": final_residuals["u_residual"], + "v_eq": final_residuals["v_residual"], + "continuity": final_residuals["continuity_residual"], + } + ] + + self._store_results( + residual_history=residual_history, + final_iter_count=total_iters, + is_converged=converged, + wall_time=wall_time, + energy_history=[self._compute_energy()], + enstrophy_history=[self._compute_enstrophy()], + palinstrophy_history=[self._compute_palinstrophy()], + ) + + log.info( + f"FSG completed in {wall_time:.2f}s: {total_iters} iterations, " + f"converged={converged}" + ) diff --git a/src/solvers/spectral/solver.py b/src/solvers/spectral/sg.py similarity index 79% rename from src/solvers/spectral/solver.py rename to src/solvers/spectral/sg.py index fada7a0..b21369c 100644 --- a/src/solvers/spectral/solver.py +++ b/src/solvers/spectral/sg.py @@ -1,6 +1,6 @@ -"""Spectral solver for lid-driven cavity using pseudospectral method. +"""Single Grid (SG) Spectral solver for lid-driven cavity. -This solver implements the PN-PN-2 method with: +This is the base spectral solver using pseudospectral method without multigrid: - Velocities on full (Nx+1)×(Ny+1) Legendre-Gauss-Lobatto grid - Pressure on reduced (Nx-1)×(Ny-1) inner grid - Artificial compressibility for pressure-velocity coupling @@ -26,12 +26,15 @@ log = logging.getLogger(__name__) -class SpectralSolver(LidDrivenCavitySolver): - """Pseudospectral solver for lid-driven cavity problem. +class SGSolver(LidDrivenCavitySolver): + """Single Grid Pseudospectral solver for lid-driven cavity problem. Uses explicit time-stepping with artificial compressibility to solve the incompressible Navier-Stokes equations on a Legendre-Gauss-Lobatto grid. + This is the base spectral solver without multigrid acceleration. + For multigrid variants, see FSGSolver, VMGSolver, FMGSolver. + Parameters ---------- params : SpectralParameters @@ -42,7 +45,7 @@ class SpectralSolver(LidDrivenCavitySolver): Parameters = SpectralParameters def __init__(self, **kwargs): - """Initialize spectral solver.""" + """Initialize single grid spectral solver.""" super().__init__(**kwargs) # Create spectral basis based on params @@ -445,7 +448,7 @@ def _finalize_fields(self): self.fields.p[:] = p_full_2d.ravel() def _compute_algebraic_residuals(self): - """Return algebraic residuals from pseudo-time stepping. + """Return algebraic residuals from pseudo time-stepping. For spectral solver, the algebraic residuals are the RHS of the time-stepping equations (R_u, R_v, R_p) computed during step(). @@ -455,118 +458,3 @@ def _compute_algebraic_residuals(self): "v_residual": np.linalg.norm(self.arrays.R_v), "continuity_residual": np.linalg.norm(self.arrays.R_p), } - - def solve(self, tolerance: float = None, max_iter: int = None): - """Solve lid-driven cavity - dispatches to multigrid if configured. - - Parameters - ---------- - tolerance : float, optional - Convergence tolerance. If None, uses params.tolerance. - max_iter : int, optional - Maximum iterations. If None, uses params.max_iterations. - """ - if tolerance is None: - tolerance = self.params.tolerance - if max_iter is None: - max_iter = self.params.max_iterations - - mg_method = self.params.multigrid.lower() - - if mg_method == "none": - # Use base class single-grid solver - super().solve(tolerance=tolerance, max_iter=max_iter) - elif mg_method == "fsg": - self._solve_fsg(tolerance, max_iter) - elif mg_method in ("vmg", "fmg"): - raise NotImplementedError( - f"Multigrid method '{mg_method}' not yet implemented" - ) - else: - raise ValueError(f"Unknown multigrid method: {mg_method}") - - def _solve_fsg(self, tolerance: float, max_iter: int): - """Solve using Full Single Grid (FSG) multigrid.""" - import time - - from solvers.spectral.multigrid.fsg import build_hierarchy, solve_fsg - from solvers.spectral.operators.transfer_operators import ( - create_transfer_operators, - ) - - log.info(f"Using FSG multigrid with {self.params.n_levels} levels") - log.info( - f"Transfer operators: prolongation={self.params.prolongation_method}, " - f"restriction={self.params.restriction_method}" - ) - - # Create transfer operators from config - transfer_ops = create_transfer_operators( - prolongation_method=self.params.prolongation_method, - restriction_method=self.params.restriction_method, - ) - - # Build grid hierarchy - time_start = time.time() - levels = build_hierarchy( - n_fine=self.params.nx, - n_levels=self.params.n_levels, - basis_x=self.basis_x, - basis_y=self.basis_y, - Lx=self.params.Lx, - Ly=self.params.Ly, - ) - - # Solve using FSG - finest_level, total_iters, converged = solve_fsg( - levels=levels, - Re=self.params.Re, - beta_squared=self.params.beta_squared, - lid_velocity=self.params.lid_velocity, - CFL=self.params.CFL, - tolerance=tolerance, - max_iterations=max_iter, - transfer_ops=transfer_ops, - corner_treatment=self.corner_treatment, - Lx=self.params.Lx, - Ly=self.params.Ly, - coarse_tolerance_factor=self.params.coarse_tolerance_factor, - ) - - time_end = time.time() - wall_time = time_end - time_start - - # Copy solution from finest level to solver arrays - self.arrays.u[:] = finest_level.u - self.arrays.v[:] = finest_level.v - self.arrays.p[:] = finest_level.p - - # Compute final residuals - self._compute_residuals(self.arrays.u, self.arrays.v, self.arrays.p) - - # Store results using base class machinery - # Create minimal residual history for compatibility - final_residuals = self._compute_algebraic_residuals() - residual_history = [ - { - "rel_iter": tolerance if converged else tolerance * 10, - "u_eq": final_residuals["u_residual"], - "v_eq": final_residuals["v_residual"], - "continuity": final_residuals["continuity_residual"], - } - ] - - self._store_results( - residual_history=residual_history, - final_iter_count=total_iters, - is_converged=converged, - wall_time=wall_time, - energy_history=[self._compute_energy()], - enstrophy_history=[self._compute_enstrophy()], - palinstrophy_history=[self._compute_palinstrophy()], - ) - - log.info( - f"FSG completed in {wall_time:.2f}s: {total_iters} iterations, " - f"converged={converged}" - ) diff --git a/src/solvers/spectral/vmg.py b/src/solvers/spectral/vmg.py new file mode 100644 index 0000000..eed9374 --- /dev/null +++ b/src/solvers/spectral/vmg.py @@ -0,0 +1,45 @@ +"""V-cycle MultiGrid (VMG) spectral solver for lid-driven cavity. + +VMG will extend the base SG solver with V-cycle multigrid acceleration. +Currently not implemented. +""" + +import logging + +from .sg import SGSolver + +log = logging.getLogger(__name__) + + +class VMGSolver(SGSolver): + """V-cycle MultiGrid (VMG) spectral solver. + + Extends the base Single Grid solver with V-cycle multigrid acceleration. + + Parameters + ---------- + All parameters inherited from SGSolver, plus: + n_levels : int + Number of multigrid levels + coarse_tolerance_factor : float + Tolerance multiplier for coarse grids + prolongation_method : str + Transfer operator for coarse-to-fine ('fft' or 'polynomial') + restriction_method : str + Transfer operator for fine-to-coarse ('fft' or 'polynomial') + """ + + def solve(self, tolerance: float = None, max_iter: int = None): + """Solve using V-cycle MultiGrid (VMG). + + Parameters + ---------- + tolerance : float, optional + Convergence tolerance. If None, uses params.tolerance. + max_iter : int, optional + Maximum iterations. If None, uses params.max_iterations. + """ + raise NotImplementedError( + "V-cycle MultiGrid (VMG) solver not yet implemented. " + "Use FSG (Full Single Grid) instead." + ) diff --git a/src/utilities/mlflow/callback.py b/src/utilities/mlflow/callback.py index b7201e5..3118043 100644 --- a/src/utilities/mlflow/callback.py +++ b/src/utilities/mlflow/callback.py @@ -189,7 +189,7 @@ def on_job_start(self, config: DictConfig, **kwargs) -> None: os.environ["MLFLOW_PARENT_RUN_ID"] = parent_id def on_multirun_end(self, config: DictConfig, **kwargs) -> None: - """Clean up after sweep completes and generate comparison plots.""" + """Clean up after sweep completes and generate plots.""" if os.environ.get("MLFLOW_SWEEP_ACTIVE") != "1": return @@ -197,46 +197,37 @@ def on_multirun_end(self, config: DictConfig, **kwargs) -> None: os.environ.pop("MLFLOW_PARENT_RUN_ID", None) os.environ.pop("MLFLOW_SWEEP_ACTIVE", None) - # Log summary - log.info( - f"Sweep completed. Using {len(self._parent_runs)} parent run(s) " - f"({len(self._active_parent_runs)} created, {len(self._parent_runs) - len(self._active_parent_runs)} reused)." - ) - for name, run_id in self._parent_runs.items(): - log.info(f" - {name}: {run_id}") - - # Generate plots for all parent runs and their children - if self._parent_runs and config.get("generate_plots", True): - try: - import sys - from pathlib import Path - - # Add repo root to path for plot_runs import - repo_root = Path(__file__).parent.parent.parent.parent - if str(repo_root) not in sys.path: - sys.path.insert(0, str(repo_root)) - - from plot_runs import plot_experiment - - # Use stored sweep directory (HydraConfig may not be available here) - if self._sweep_dir: - output_dir = Path(self._sweep_dir) / "plots" - else: - output_dir = Path("outputs") / "plots" - - parent_run_ids = list(self._parent_runs.values()) - log.info( - f"Generating plots for {len(parent_run_ids)} parent run(s) " - "and all their children..." - ) - - # Plot all runs (individual + comparison) for each parent - plot_experiment( - experiment_name=self._full_experiment_name, - tracking_uri=self._tracking_uri, - output_dir=output_dir, - parent_run_ids=parent_run_ids, - upload_to_mlflow=True, - ) - except Exception as e: - log.warning(f"Failed to generate plots: {e}") + log.info("Multirun sweep completed") + + # Generate plots using plot_runs.py + try: + import sys + from pathlib import Path + + # Add repo root to path for imports + repo_root = Path(__file__).parent.parent.parent.parent + if str(repo_root) not in sys.path: + sys.path.insert(0, str(repo_root)) + + # Use stored sweep directory for output + if self._sweep_dir: + output_dir = Path(self._sweep_dir) / "plots" + else: + output_dir = Path("outputs") / "plots" + output_dir.mkdir(parents=True, exist_ok=True) + + from plot_runs import plot_experiment + + log.info(f"Generating plots for experiment: {self._full_experiment_name}") + + # Plot all parent runs and their children + plot_experiment( + experiment_name=self._full_experiment_name, + tracking_uri=self._tracking_uri, + output_dir=output_dir, + parent_run_ids=None, # Find all parent runs automatically + upload_to_mlflow=True, + ) + + except Exception as e: + log.warning(f"Failed to generate plots: {e}") From e3cb28d95cc5e0313cb0a96ea10fa8755d25c638 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Sat, 6 Dec 2025 21:45:13 +0100 Subject: [PATCH 32/34] plotting! --- src/shared/plotting/ldc/fields.py | 92 +++++++++------ src/shared/plotting/ldc/style.py | 17 +-- src/shared/plotting/ldc/validation.py | 148 +++++++++++++----------- src/shared/plotting/scientific.mplstyle | 119 ------------------- 4 files changed, 141 insertions(+), 235 deletions(-) delete mode 100644 src/shared/plotting/scientific.mplstyle diff --git a/src/shared/plotting/ldc/fields.py b/src/shared/plotting/ldc/fields.py index e589d14..711c089 100644 --- a/src/shared/plotting/ldc/fields.py +++ b/src/shared/plotting/ldc/fields.py @@ -38,36 +38,43 @@ def plot_fields( U_interp = RectBivariateSpline(y_unique, x_unique, U)(y_fine, x_fine) V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) - fig, axes = plt.subplots(1, 3) + fig, axes = plt.subplots(1, 3, figsize=(15, 4.5)) - cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=25, cmap="viridis") - axes[0].set_xlabel(r"$x$") - axes[0].set_ylabel(r"$y$") - axes[0].set_title(r"\textbf{Pressure}") + cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=30, cmap="viridis") + axes[0].set_xlabel(r"$x$", fontsize=11) + axes[0].set_ylabel(r"$y$", fontsize=11) + axes[0].set_title(r"\textbf{Pressure}", fontsize=12) axes[0].set_aspect("equal") - plt.colorbar(cf_p, ax=axes[0], label=r"$p$") + cbar_p = plt.colorbar(cf_p, ax=axes[0], label=r"$p$") + cbar_p.ax.tick_params(labelsize=9) - cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=25, cmap="RdBu_r") - axes[1].set_xlabel(r"$x$") - axes[1].set_ylabel(r"$y$") - axes[1].set_title(r"\textbf{$u$-velocity}") + cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=30, cmap="RdBu_r") + axes[1].set_xlabel(r"$x$", fontsize=11) + axes[1].set_ylabel(r"$y$", fontsize=11) + axes[1].set_title(r"\textbf{$u$-velocity}", fontsize=12) axes[1].set_aspect("equal") - plt.colorbar(cf_u, ax=axes[1], label=r"$u$") + cbar_u = plt.colorbar(cf_u, ax=axes[1], label=r"$u$") + cbar_u.ax.tick_params(labelsize=9) - cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=25, cmap="RdBu_r") - axes[2].set_xlabel(r"$x$") - axes[2].set_ylabel(r"$y$") - axes[2].set_title(r"\textbf{$v$-velocity}") + cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=30, cmap="RdBu_r") + axes[2].set_xlabel(r"$x$", fontsize=11) + axes[2].set_ylabel(r"$y$", fontsize=11) + axes[2].set_title(r"\textbf{$v$-velocity}", fontsize=12) axes[2].set_aspect("equal") - plt.colorbar(cf_v, ax=axes[2], label=r"$v$") + cbar_v = plt.colorbar(cf_v, ax=axes[2], label=r"$v$") + cbar_v.ax.tick_params(labelsize=9) solver_label = solver.upper().replace("_", r"\_") fig.suptitle( - rf"\textbf{{Solution Fields}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + rf"\textbf{{Solution Fields}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$", + fontsize=13, + y=1.00, ) + plt.tight_layout() + output_path = output_dir / "fields.pdf" - fig.savefig(output_path) + fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) return output_path @@ -93,25 +100,38 @@ def plot_streamlines( V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) vel_mag = np.sqrt(U_interp**2 + V_interp**2) - fig, ax = plt.subplots() + fig, ax = plt.subplots(figsize=(7, 6)) X_fine, Y_fine = np.meshgrid(x_fine, y_fine) - cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=25, cmap="viridis") + cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=30, cmap="RdYlBu_r") ax.streamplot( - x_fine, y_fine, U_interp, V_interp, density=1.5, arrowsize=1.0, arrowstyle="->" + x_fine, + y_fine, + U_interp, + V_interp, + density=1.8, + linewidth=1.0, + arrowsize=1.2, + arrowstyle="->", + color="white", + zorder=2, ) - ax.set_xlabel(r"$x$") - ax.set_ylabel(r"$y$") + ax.set_xlabel(r"$x$", fontsize=11) + ax.set_ylabel(r"$y$", fontsize=11) solver_label = solver.upper().replace("_", r"\_") ax.set_title( - rf"\textbf{{Streamlines}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + rf"\textbf{{Streamlines}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$", + fontsize=12, ) ax.set_aspect("equal") - plt.colorbar(cf, ax=ax, label=r"$|\mathbf{u}|$") + cbar = plt.colorbar(cf, ax=ax, label=r"$|\mathbf{u}|$") + cbar.ax.tick_params(labelsize=10) + + plt.tight_layout() output_path = output_dir / "streamlines.pdf" - fig.savefig(output_path) + fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) return output_path @@ -141,24 +161,30 @@ def plot_vorticity( dudy = U_spline(y_fine, x_fine, dy=1) vorticity = dvdx - dudy - fig, ax = plt.subplots() + fig, ax = plt.subplots(figsize=(7, 6)) vmax = np.max(np.abs(vorticity)) cf = ax.contourf( - X_fine, Y_fine, vorticity, levels=25, vmin=-vmax, vmax=vmax, cmap="RdBu_r" + X_fine, Y_fine, vorticity, levels=30, vmin=-vmax, vmax=vmax, cmap="RdBu_r" ) - ax.set_xlabel(r"$x$") - ax.set_ylabel(r"$y$") + ax.set_xlabel(r"$x$", fontsize=11) + ax.set_ylabel(r"$y$", fontsize=11) solver_label = solver.upper().replace("_", r"\_") ax.set_title( - rf"\textbf{{Vorticity}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + rf"\textbf{{Vorticity}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$", + fontsize=12, ) ax.set_aspect("equal") - plt.colorbar(cf, ax=ax, label=r"$\omega = \partial v/\partial x - \partial u/\partial y$") + cbar = plt.colorbar( + cf, ax=ax, label=r"$\omega = \partial v/\partial x - \partial u/\partial y$" + ) + cbar.ax.tick_params(labelsize=10) + + plt.tight_layout() output_path = output_dir / "vorticity.pdf" - fig.savefig(output_path) + fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) return output_path diff --git a/src/shared/plotting/ldc/style.py b/src/shared/plotting/ldc/style.py index 192b005..2eb8299 100644 --- a/src/shared/plotting/ldc/style.py +++ b/src/shared/plotting/ldc/style.py @@ -1,25 +1,14 @@ """ Plotting Style Configuration for LDC Plots. -Uses scientific.mplstyle with seaborn color palette. +Uses seaborn darkgrid theme. """ import logging -from pathlib import Path -import matplotlib.pyplot as plt import seaborn as sns log = logging.getLogger(__name__) -# Load scientific style file -_STYLE_PATH = Path(__file__).parent.parent / "scientific.mplstyle" - -if _STYLE_PATH.exists(): - plt.style.use(str(_STYLE_PATH)) - log.debug(f"Loaded scientific style: {_STYLE_PATH}") -else: - log.warning(f"Scientific style file not found: {_STYLE_PATH}") - -# Apply seaborn theme on top for color palette -sns.set_theme() +# Use seaborn darkgrid theme +sns.set_theme(style="darkgrid") diff --git a/src/shared/plotting/ldc/validation.py b/src/shared/plotting/ldc/validation.py index 01355bb..9f03372 100644 --- a/src/shared/plotting/ldc/validation.py +++ b/src/shared/plotting/ldc/validation.py @@ -47,29 +47,31 @@ def plot_centerlines( u_vertical = U_spline(y_line, x_center).ravel() v_horizontal = V_spline(y_center, x_line).ravel() - fig, axes = plt.subplots(1, 2) - - axes[0].plot(u_vertical, y_line) - axes[0].set_xlabel(r"$u$") - axes[0].set_ylabel(r"$y$") - axes[0].set_title(r"\textbf{$u$-velocity along vertical centerline}") - axes[0].grid(True, alpha=0.3) - axes[0].axvline(x=0, color="gray", linestyle="--", alpha=0.5) - - axes[1].plot(x_line, v_horizontal) - axes[1].set_xlabel(r"$x$") - axes[1].set_ylabel(r"$v$") - axes[1].set_title(r"\textbf{$v$-velocity along horizontal centerline}") - axes[1].grid(True, alpha=0.3) - axes[1].axhline(y=0, color="gray", linestyle="--", alpha=0.5) + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) + + axes[0].plot(u_vertical, y_line, linewidth=2) + axes[0].set_xlabel(r"$u$", fontsize=11) + axes[0].set_ylabel(r"$y$", fontsize=11) + axes[0].set_title(r"\textbf{$u$-velocity along vertical centerline}", fontsize=12) + axes[0].axvline(x=0, color="gray", linestyle="--", alpha=0.5, linewidth=1) + + axes[1].plot(x_line, v_horizontal, linewidth=2) + axes[1].set_xlabel(r"$x$", fontsize=11) + axes[1].set_ylabel(r"$v$", fontsize=11) + axes[1].set_title(r"\textbf{$v$-velocity along horizontal centerline}", fontsize=12) + axes[1].axhline(y=0, color="gray", linestyle="--", alpha=0.5, linewidth=1) solver_label = solver.upper().replace("_", r"\_") fig.suptitle( - rf"\textbf{{Centerline Profiles}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$" + rf"\textbf{{Centerline Profiles}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$", + fontsize=13, + y=0.98, ) + plt.tight_layout() + output_path = output_dir / "centerlines.pdf" - fig.savefig(output_path) + fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) return output_path @@ -211,12 +213,15 @@ def plot_ghia_comparison( x_unique, V_2d[y_center_idx, :], x_line, basis="legendre" ) + # Create combined method-N label + method_label = f"{method}-{N}" + for i in range(n_points): u_records.append( { "y": y_line[i], "u": u_sim[i], - "Method": method, + "Method": method_label, "Solver": solver_name, "N": N, } @@ -225,7 +230,7 @@ def plot_ghia_comparison( { "x": x_line[i], "v": v_sim[i], - "Method": method, + "Method": method_label, "Solver": solver_name, "N": N, } @@ -239,85 +244,90 @@ def plot_ghia_comparison( log.warning("No valid runs to plot") return None - u_df = pd.DataFrame(u_records).sort_values(["N", "Method", "y"]) - v_df = pd.DataFrame(v_records).sort_values(["N", "Method", "x"]) + u_df = pd.DataFrame(u_records) + v_df = pd.DataFrame(v_records) - # Create figure with 4:3 aspect ratio per subplot - fig, axes = plt.subplots(1, 2, figsize=(12, 6)) + # Create subplots with publication-quality sizing + fig, axes = plt.subplots(1, 2, figsize=(12, 5)) - # Plot u-velocity with seaborn (sort=False to preserve y-ordering) + # Left: u-velocity (vertical centerline) sns.lineplot( data=u_df, x="u", y="y", - hue="N", + hue="Method", style="Method", markers=True, - dashes=True, - sort=False, ax=axes[0], + sort=False, + linewidth=2, + markersize=7, ) - # Set markevery on all lines - for line in axes[0].get_lines(): - line.set_markevery(20) - # Plot Ghia data with hollow circles - axes[0].plot( - ghia_u["u"], - ghia_u["y"], - "o", - color="#333333", + sns.scatterplot( + data=ghia_u, + x="u", + y="y", + marker="o", + s=80, + facecolors="none", + edgecolors="#333333", + linewidths=1.8, label="Ghia et al. (1982)", - markersize=7, - markerfacecolor="none", - markeredgewidth=1.2, + ax=axes[0], zorder=10, ) - axes[0].set_xlabel(r"$u$-velocity") - axes[0].set_ylabel(r"$y$-coordinate") - axes[0].set_title(r"$u$-velocity (vertical centerline)") - axes[0].set_xlim(-0.4, 1.05) - axes[0].set_ylim(0, 1) + axes[0].set_xlabel(r"$u$-velocity", fontsize=11) + axes[0].set_ylabel(r"$y$-coordinate", fontsize=11) - # Plot v-velocity with seaborn (sort=False to preserve x-ordering) + # Right: v-velocity (horizontal centerline) sns.lineplot( data=v_df, x="x", y="v", - hue="N", + hue="Method", style="Method", markers=True, - dashes=True, - sort=False, ax=axes[1], + sort=False, legend=False, + linewidth=2, + markersize=7, ) - # Set markevery on all lines - for line in axes[1].get_lines(): - line.set_markevery(20) - # Plot Ghia data with hollow circles - axes[1].plot( - ghia_v["x"], - ghia_v["v"], - "o", - color="#333333", + sns.scatterplot( + data=ghia_v, + x="x", + y="v", + marker="o", + s=80, + facecolors="none", + edgecolors="#333333", + linewidths=1.8, label="Ghia et al. (1982)", - markersize=7, - markerfacecolor="none", - markeredgewidth=1.2, + ax=axes[1], zorder=10, ) - axes[1].set_xlabel(r"$x$-coordinate") - axes[1].set_ylabel(r"$v$-velocity") - axes[1].set_title(r"$v$-velocity (horizontal centerline)") - axes[1].set_xlim(0, 1) - axes[1].set_ylim(-0.6, 0.4) + axes[1].set_xlabel(r"$x$-coordinate", fontsize=11) + axes[1].set_ylabel(r"$v$-velocity", fontsize=11) + + # Set markevery with offset for each method + unique_methods = u_df["Method"].unique() + marker_interval = 20 + + for ax in axes: + lines = [l for l in ax.get_lines() if l.get_linestyle() != 'None'] + for i, line in enumerate(lines): + method_idx = i % len(unique_methods) + offset = method_idx * 3 + line.set_markevery((offset, marker_interval)) + + # Overall title + fig.suptitle(rf"Ghia Benchmark Comparison (Re = {int(Re)})", fontsize=13, y=0.98) - # Set overall title - fig.suptitle(rf"Ghia Benchmark Comparison (Re = {int(Re)})", fontsize=12) - #fig.tight_layout() + # Tight layout for better spacing + plt.tight_layout() output_path = output_dir / "ghia_comparison.pdf" - fig.savefig(output_path) + fig.savefig(output_path, dpi=300, bbox_inches="tight") plt.close(fig) log.info(f"Saved comparison plot: {output_path.name}") diff --git a/src/shared/plotting/scientific.mplstyle b/src/shared/plotting/scientific.mplstyle deleted file mode 100644 index ab483f1..0000000 --- a/src/shared/plotting/scientific.mplstyle +++ /dev/null @@ -1,119 +0,0 @@ -# Scientific plotting style for LDC solver plots -# Based on seaborn-v0_8-paper with LaTeX-compatible settings -# -# Usage: plt.style.use('path/to/scientific.mplstyle') -# or: plt.style.use(['seaborn-v0_8-paper', 'path/to/scientific.mplstyle']) - -# ============================================================================= -# Figure -# ============================================================================= -figure.figsize: 6, 4 -figure.dpi: 150 -figure.facecolor: white -figure.edgecolor: white -figure.autolayout: False -figure.constrained_layout.use: True - -# ============================================================================= -# Font (LaTeX-compatible) -# ============================================================================= -font.family: serif -font.serif: Computer Modern Roman, DejaVu Serif, Times New Roman -font.size: 10 -font.weight: normal - -# Use LaTeX for text rendering -text.usetex: True -text.latex.preamble: \usepackage{amsmath} \usepackage{amssymb} \usepackage{siunitx} - -# Math text -mathtext.fontset: cm -mathtext.rm: serif - -# ============================================================================= -# Axes -# ============================================================================= -axes.labelsize: 11 -axes.titlesize: 12 -axes.titleweight: bold -axes.labelweight: normal -axes.linewidth: 0.8 -axes.edgecolor: 0.15 -axes.labelcolor: 0.15 -axes.facecolor: white -axes.grid: False -axes.spines.top: True -axes.spines.right: True -axes.prop_cycle: cycler('color', ['0072B2', 'D55E00', '009E73', 'CC79A7', 'F0E442', '56B4E9', 'E69F00', '000000']) - -# Axis formatting -axes.formatter.use_mathtext: True -axes.formatter.useoffset: False -axes.unicode_minus: True - -# ============================================================================= -# Ticks -# ============================================================================= -xtick.labelsize: 9 -ytick.labelsize: 9 -xtick.color: 0.15 -ytick.color: 0.15 -xtick.direction: in -ytick.direction: in -xtick.major.size: 4 -ytick.major.size: 4 -xtick.minor.size: 2 -ytick.minor.size: 2 -xtick.major.width: 0.8 -ytick.major.width: 0.8 -xtick.minor.width: 0.6 -ytick.minor.width: 0.6 -xtick.top: True -ytick.right: True - -# ============================================================================= -# Lines -# ============================================================================= -lines.linewidth: 1.5 -lines.markersize: 5 -lines.markeredgewidth: 0.8 - -# ============================================================================= -# Legend -# ============================================================================= -legend.fontsize: 9 -legend.frameon: True -legend.framealpha: 0.9 -legend.facecolor: white -legend.edgecolor: 0.8 -legend.borderpad: 0.4 -legend.labelspacing: 0.4 -legend.handlelength: 1.5 -legend.handletextpad: 0.5 -legend.columnspacing: 1.0 - -# ============================================================================= -# Grid -# ============================================================================= -grid.color: 0.8 -grid.linewidth: 0.5 -grid.linestyle: - - -# ============================================================================= -# Colormap and contours -# ============================================================================= -image.cmap: viridis - -# ============================================================================= -# Saving figures -# ============================================================================= -savefig.dpi: 300 -savefig.format: pdf -savefig.bbox: tight -savefig.pad_inches: 0.05 -savefig.facecolor: white -savefig.edgecolor: white - -# PDF backend settings -pdf.fonttype: 42 -ps.fonttype: 42 From 538eb5905869f7dc1db862b72af8b37104142661 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Sat, 6 Dec 2025 21:56:18 +0100 Subject: [PATCH 33/34] better ghia plot --- .../validation/corner_treatment.yaml | 27 +++++++++++++ src/shared/plotting/ldc/validation.py | 38 ++++++++----------- 2 files changed, 42 insertions(+), 23 deletions(-) create mode 100644 conf/experiment/validation/corner_treatment.yaml diff --git a/conf/experiment/validation/corner_treatment.yaml b/conf/experiment/validation/corner_treatment.yaml new file mode 100644 index 0000000..57b0e34 --- /dev/null +++ b/conf/experiment/validation/corner_treatment.yaml @@ -0,0 +1,27 @@ +# @package _global_ +# Corner Treatment Comparison: Smoothing vs Subtraction Method +# Compares the two approaches for handling corner singularities in spectral LDC solver +# +# Run with: +# uv run python run_solver.py -m +experiment=validation/corner_treatment +# +# References: +# - Subtraction method: Zhang & Xi (2010), Botella & Peyret (1998) +# - Smoothing: Simple cosine smoothing near corners + +defaults: + - override /solver: spectral/sg + +# MLflow experiment +experiment_name: LDC-CornerTreatment +sweep_name: corner-comparison-Re${Re} + +hydra: + sweeper: + params: + # Compare both corner treatment methods + solver.corner_treatment: smoothing,subtraction + # Test across multiple grid sizes to see convergence behavior + N: 16,32,64 + # Reynolds number (can add more: 100,400,1000) + Re: 100 diff --git a/src/shared/plotting/ldc/validation.py b/src/shared/plotting/ldc/validation.py index 9f03372..2c07061 100644 --- a/src/shared/plotting/ldc/validation.py +++ b/src/shared/plotting/ldc/validation.py @@ -213,8 +213,8 @@ def plot_ghia_comparison( x_unique, V_2d[y_center_idx, :], x_line, basis="legendre" ) - # Create combined method-N label - method_label = f"{method}-{N}" + # Create combined method-N label with LaTeX formatting + method_label = f"{method}, $N={N}$" for i in range(n_points): u_records.append( @@ -262,22 +262,24 @@ def plot_ghia_comparison( sort=False, linewidth=2, markersize=7, + markevery=15, ) sns.scatterplot( data=ghia_u, x="u", y="y", marker="o", - s=80, + s=50, facecolors="none", edgecolors="#333333", - linewidths=1.8, + linewidths=1.2, label="Ghia et al. (1982)", ax=axes[0], zorder=10, ) - axes[0].set_xlabel(r"$u$-velocity", fontsize=11) - axes[0].set_ylabel(r"$y$-coordinate", fontsize=11) + axes[0].set_xlabel(r"$u$", fontsize=11) + axes[0].set_ylabel(r"$y$", fontsize=11) + axes[0].set_title(r"$u$-velocity (vertical centerline)", fontsize=11) # Right: v-velocity (horizontal centerline) sns.lineplot( @@ -289,39 +291,29 @@ def plot_ghia_comparison( markers=True, ax=axes[1], sort=False, - legend=False, linewidth=2, markersize=7, + markevery=15, ) sns.scatterplot( data=ghia_v, x="x", y="v", marker="o", - s=80, + s=50, facecolors="none", edgecolors="#333333", - linewidths=1.8, + linewidths=1.2, label="Ghia et al. (1982)", ax=axes[1], zorder=10, ) - axes[1].set_xlabel(r"$x$-coordinate", fontsize=11) - axes[1].set_ylabel(r"$v$-velocity", fontsize=11) - - # Set markevery with offset for each method - unique_methods = u_df["Method"].unique() - marker_interval = 20 - - for ax in axes: - lines = [l for l in ax.get_lines() if l.get_linestyle() != 'None'] - for i, line in enumerate(lines): - method_idx = i % len(unique_methods) - offset = method_idx * 3 - line.set_markevery((offset, marker_interval)) + axes[1].set_xlabel(r"$x$", fontsize=11) + axes[1].set_ylabel(r"$v$", fontsize=11) + axes[1].set_title(r"$v$-velocity (horizontal centerline)", fontsize=11) # Overall title - fig.suptitle(rf"Ghia Benchmark Comparison (Re = {int(Re)})", fontsize=13, y=0.98) + fig.suptitle(rf"Ghia Benchmark Comparison ($\mathrm{{Re}} = {int(Re)}$)", fontsize=13, y=1.00) # Tight layout for better spacing plt.tight_layout() From 569b7254f56a1cdc1ad7d716cafc5e455fd5a0dc Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Sat, 6 Dec 2025 22:40:07 +0100 Subject: [PATCH 34/34] u --- .readthedocs.yaml | 2 +- spectral_multigrid_todo.md | 233 ------------------------------ src/shared/plotting/ldc/fields.py | 38 +++-- 3 files changed, 27 insertions(+), 246 deletions(-) delete mode 100644 spectral_multigrid_todo.md diff --git a/.readthedocs.yaml b/.readthedocs.yaml index b5a3215..3cc55ba 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -14,6 +14,6 @@ build: - pip install uv - uv sync # Fetch data from MLflow (uses DATABRICKS_TOKEN secret) - - uv run python main.py --fetch + #- uv run python main.py --fetch # Build Sphinx documentation - uv run sphinx-build -b html docs/source $READTHEDOCS_OUTPUT/html diff --git a/spectral_multigrid_todo.md b/spectral_multigrid_todo.md deleted file mode 100644 index c6e7bd1..0000000 --- a/spectral_multigrid_todo.md +++ /dev/null @@ -1,233 +0,0 @@ -# Spectral Multigrid Implementation Plan - -Based on Zhang & Xi (2010): "An explicit Chebyshev pseudospectral multigrid method for incompressible Navier-Stokes equations" - -## Overview - -Three multigrid strategies to implement incrementally: - -| Method | Speedup | Complexity | -|--------|---------|------------| -| **FSG** (Full Single Grid) | ~10-18x | Simplest | -| **VMG** (V-cycle Multigrid) | ~50-90% reduction | Medium | -| **FMG** (Full Multigrid) | ~100x | FSG + VMG combined | - ---- - -## Phase 1: Foundation & FSG - -### 1.1 SpectralLevel Class -- [ ] Create `SpectralLevel` dataclass/class holding: - - `n`: polynomial order for this level - - `basis`: basis object (Chebyshev/Legendre) - - `x, y`: 1D node arrays - - `X, Y`: 2D meshgrid arrays (full grid) - - `X_inner, Y_inner`: 2D meshgrid arrays (inner grid for pressure) - - `Dx, Dy`: differentiation matrices (full grid) - - `Dx_inner, Dy_inner`: differentiation matrices (inner grid) - - `dx_min, dy_min`: minimum grid spacing (for CFL) - - Field arrays: `u, v, p` and RK4 stage storage - -### 1.2 Grid Hierarchy Construction -- [ ] Define `build_hierarchy(n_fine, n_levels)`: - - Coarsening: N^c = N^f / 2 (full coarsening) - - Example: 64 → 32 → 16 (3 levels) -- [ ] Verify coarse Lobatto nodes are subsets of fine nodes -- [ ] Construct `SpectralLevel` for each level - -### 1.3 Prolongation Operator (Solution Interpolation) -- [ ] Implement `prolongate_solution(u_coarse, level_coarse, level_fine)`: - - For velocities (u, v): FFT-based Chebyshev interpolation - 1. Compute Chebyshev coefficients via DCT - 2. Pad with zeros for higher frequencies - 3. Evaluate at fine grid points via inverse DCT - - For pressure: Lagrange polynomial interpolation (order N-2) -- [ ] Wrap into `prolongate_fields(U_coarse, level_coarse, level_fine)` for (u, v, p) - -### 1.4 Per-Level Solver Routines -- [ ] Refactor `_compute_residuals(level, u, v, p)` to use level's operators -- [ ] Refactor `_enforce_boundary_conditions(level, u, v)` -- [ ] Refactor `_compute_adaptive_timestep(level)` using level's dx_min, dy_min -- [ ] Create `smooth(level, n_steps)` - multiple RK4 pseudo-time steps on one level - -### 1.5 FSG Driver -- [ ] Implement `solve_fsg(n_levels)`: - ``` - for level in levels (coarse to fine): - if level == coarsest: - initialize with zeros - else: - prolongate solution from previous level - - while not converged: - smooth(level, 1) # RK4 steps - check convergence on this level - ``` -- [ ] Use relaxed convergence criterion on coarse levels (e.g., 10x tolerance) -- [ ] Log per-level iteration counts and residuals - -### 1.6 FSG Validation -- [ ] Compare FSG vs SG for N=32, 64 at Re=100 -- [ ] Verify same solution accuracy -- [ ] Measure speedup (expect ~10-18x) - ---- - -## Phase 2: VMG (V-cycle Multigrid with FAS) - -### 2.1 Restriction Operator (Residuals) -- [ ] Implement `restrict_residual(r_fine, level_fine, level_coarse)`: - - FFT-based: compute Chebyshev coefficients, truncate high frequencies - - Row-column algorithm for 2D (restrict x-direction, then y-direction) - - Set boundary residuals to zero (velocities have Dirichlet BCs) -- [ ] Handle inner-grid restriction for pressure residuals - -### 2.2 Restriction Operator (Variables) -- [ ] Implement `restrict_solution(u_fine, level_fine, level_coarse)`: - - Direct injection: coarse nodes are subset of fine nodes - - `u_coarse[i,j] = u_fine[2i, 2j]` - -### 2.3 Prolongation Operator (Corrections) -- [ ] Implement `prolongate_correction(c_coarse, level_coarse, level_fine)`: - - Same FFT-based approach as solution prolongation - - For pressure: Lagrange interpolation on inner grid - -### 2.4 FAS Coarse-Grid Equation -- [ ] Compute τ-correction (FAS forcing term): - ``` - τ = R(I_h^H * u_h) - I_h^H * R(u_h) - ``` - where R is the residual operator, I_h^H is restriction -- [ ] Modified coarse-grid solve includes τ in RHS - -### 2.5 V-cycle Implementation -- [ ] Implement `v_cycle(level)`: - ```python - def v_cycle(level): - if level == coarsest: - # Solve to convergence (or many smoothing steps) - while not converged: - smooth(level, 1) - return - - # 1. Presmooth on fine level - smooth(level, n_pre) # typically n_pre = 1-3 - - # 2. Compute fine-grid residual - r_fine = compute_residual(level) - - # 3. Restrict solution and residual to coarse - u_coarse_old = restrict_solution(u_fine, level, level-1) - r_coarse = restrict_residual(r_fine, level, level-1) - - # 4. Set up FAS coarse problem (includes τ-correction) - # 5. Recursively solve on coarse grid - v_cycle(level - 1) - - # 6. Compute and prolongate correction - correction = u_coarse_new - u_coarse_old - correction_fine = prolongate_correction(correction, level-1, level) - - # 7. Update fine-grid solution - u_fine += correction_fine - - # 8. Postsmooth (often unnecessary per Zhang & Xi) - # smooth(level, n_post) - ``` - -### 2.6 VMG Driver -- [ ] Implement `solve_vmg()`: - ``` - initialize finest level with zeros - while not converged: - v_cycle(finest_level) - check convergence - ``` - -### 2.7 VMG Validation -- [ ] Compare VMG vs SG and FSG for N=32, 64 at Re=100 -- [ ] Verify solution accuracy maintained -- [ ] Measure speedup (expect additional 50-90% over FSG) - ---- - -## Phase 3: FMG (Full Multigrid) - -### 3.1 FMG Driver -- [ ] Implement `solve_fmg()`: - ```python - def solve_fmg(): - # Start on coarsest level - level = coarsest - initialize_with_zeros(level) - solve_to_convergence(level) # SG on coarsest - - # Work up through levels - for level in levels[1:]: # coarse to fine - # Prolongate converged solution as initial guess - u_init = prolongate_solution(u_converged, level-1, level) - - # Apply V-cycles until convergence - while not converged: - v_cycle(level) # Uses all levels from 0 to current - check convergence - - return solution on finest level - ``` - -### 3.2 FMG Validation -- [ ] Compare FMG vs all other methods -- [ ] Verify solution accuracy -- [ ] Measure speedup (expect ~100x over SG) - ---- - -## Phase 4: Integration & Polish - -### 4.1 Solver Interface -- [ ] Add `multigrid_method` parameter: `"none"`, `"fsg"`, `"vmg"`, `"fmg"` -- [ ] `solve()` dispatches to appropriate driver -- [ ] Backward compatible: `multigrid_method="none"` uses current SG implementation - -### 4.2 Configuration -- [ ] Extend `SpectralParameters` with: - ```python - n_levels: int = 3 - multigrid_method: str = "fmg" - n_presmooth: int = 1 - n_postsmooth: int = 0 - coarse_tolerance_factor: float = 10.0 - ``` - -### 4.3 Logging & Metrics -- [ ] Log per-level residuals -- [ ] Track V-cycle count -- [ ] Record time spent per level -- [ ] MLflow integration for multigrid metrics - -### 4.4 Comprehensive Validation -- [ ] Ghia benchmark comparison for all methods -- [ ] Re = 100, 400, 1000 -- [ ] N = 32, 64, 96 -- [ ] Speedup plots (wall clock time vs N) -- [ ] Accuracy verification (solution differences < tolerance) - ---- - -## Key Implementation Notes from Zhang & Xi (2010) - -1. **Postsmoothing often unnecessary** - can cause instabilities -2. **Presmoothing steps**: 1-3 typically sufficient (paper uses 1,2,3 for levels) -3. **Convergence criterion**: RMS of continuity residual < ε (ε = 10⁻⁴ for cavity flow) -4. **CFL**: Keep around 2.5 (upper bound of RK4 stability) -5. **β² = 5.0**: Artificial compressibility coefficient (don't change) -6. **Corner singularities**: Your corner_smoothing handles this (paper uses subtraction method) - -## Expected Results (Re=100, N=96) - -| Method | Relative Time | -|--------|---------------| -| SG | 100% | -| FSG-3 | ~5.5% | -| VMG-111 | ~1.5% | -| FMG-111 | ~1.2% | diff --git a/src/shared/plotting/ldc/fields.py b/src/shared/plotting/ldc/fields.py index 711c089..b63c07d 100644 --- a/src/shared/plotting/ldc/fields.py +++ b/src/shared/plotting/ldc/fields.py @@ -92,7 +92,7 @@ def plot_streamlines( U = sorted_df["u"].values.reshape(ny, nx) V = sorted_df["v"].values.reshape(ny, nx) - n_fine = 200 + n_fine = 250 x_fine = np.linspace(x_unique[0], x_unique[-1], n_fine) y_fine = np.linspace(y_unique[0], y_unique[-1], n_fine) @@ -100,32 +100,46 @@ def plot_streamlines( V_interp = RectBivariateSpline(y_unique, x_unique, V)(y_fine, x_fine) vel_mag = np.sqrt(U_interp**2 + V_interp**2) - fig, ax = plt.subplots(figsize=(7, 6)) + fig, ax = plt.subplots(figsize=(8, 7)) X_fine, Y_fine = np.meshgrid(x_fine, y_fine) - cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=30, cmap="RdYlBu_r") + + # Smooth contours with coolwarm colormap + cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=40, cmap="coolwarm") + + # Semi-transparent white streamlines to show velocity magnitude through them ax.streamplot( x_fine, y_fine, U_interp, V_interp, - density=1.8, - linewidth=1.0, - arrowsize=1.2, + density=2.0, + linewidth=1.5, + arrowsize=1.3, arrowstyle="->", color="white", + alpha=0.7, zorder=2, ) - ax.set_xlabel(r"$x$", fontsize=11) - ax.set_ylabel(r"$y$", fontsize=11) - solver_label = solver.upper().replace("_", r"\_") + ax.set_xlabel(r"$x$", fontsize=12) + ax.set_ylabel(r"$y$", fontsize=12) + solver_label = solver.upper().replace("_", r" ") ax.set_title( - rf"\textbf{{Streamlines}} --- {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$", - fontsize=12, + rf"Streamlines: {solver_label}, $N={N}$, $\mathrm{{Re}}={Re:.0f}$", + fontsize=13, ) ax.set_aspect("equal") - cbar = plt.colorbar(cf, ax=ax, label=r"$|\mathbf{u}|$") + + # Horizontal colorbar at bottom + cbar = plt.colorbar( + cf, + ax=ax, + orientation="horizontal", + pad=0.08, + aspect=30, + label=r"Velocity Magnitude $|\mathbf{u}|$", + ) cbar.ax.tick_params(labelsize=10) plt.tight_layout()

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z-T;57>kl|UgdZ=}R8J$NNPnhG7Y#ehX#H7DlX!R+wKRICgqCJOY>Sp=g-20Gqg8SG ztFIU51X46K3oT`)AbFDSK9VrddW6Y^+Vw8v`VX?Nn|6bY54f0b}k`Z=ncN=d_1Qc zKSVTAV2&oA6>*yyn#k#R18-Urb?vi?`g*Y#q|0X!bkCMx5q0w>V9`(4Un@sTH|jQ9 zDdtT>gP2t0%gael%NUcxzE+x;HhB#6419c9S)?MF!8IT=HOoX?=XCGo<|XIkF_i>S Oh+xRhouh}w82$n90adyH literal 0 HcmV?d00001 diff --git a/pyproject.toml b/pyproject.toml index 7028a8f..462ee75 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,7 +44,6 @@ dependencies = [ "psutil>=7.1.3", "rich>=13.0.0", "questionary>=2.0.0", - "mpi4py>=4.1.1", "pip>=25.3", "hydra-core>=1.3.0", "omegaconf>=2.3.0", diff --git a/run_solver.py b/run_solver.py index a4b4735..f70500e 100644 --- a/run_solver.py +++ b/run_solver.py @@ -27,7 +27,12 @@ import sys from pathlib import Path +import hydra +import mlflow from dotenv import load_dotenv +from hydra.utils import instantiate +from mlflow.tracking import MlflowClient +from omegaconf import DictConfig, OmegaConf # Load .env file (for MLflow credentials) load_dotenv() @@ -35,11 +40,6 @@ # Add src to path for imports sys.path.insert(0, str(Path(__file__).parent / "src")) -import hydra -import mlflow -from mlflow.tracking import MlflowClient -from omegaconf import DictConfig, OmegaConf - log = logging.getLogger(__name__) @@ -49,58 +49,8 @@ def create_solver(cfg: DictConfig): - """Create solver instance from Hydra config.""" - solver_name = cfg.solver.name - - # Common parameters - common = { - "Re": cfg.Re, - "lid_velocity": cfg.lid_velocity, - "Lx": cfg.Lx, - "Ly": cfg.Ly, - "nx": cfg.N, - "ny": cfg.N, - "max_iterations": cfg.max_iterations, - "tolerance": cfg.tolerance, - } - - if solver_name == "fv": - from solvers import FVSolver - - return FVSolver( - **common, - convection_scheme=cfg.solver.convection_scheme, - limiter=cfg.solver.limiter, - alpha_uv=cfg.solver.alpha_uv, - alpha_p=cfg.solver.alpha_p, - linear_solver_tol=cfg.solver.linear_solver_tol, - ) - elif solver_name.startswith("spectral"): - from solvers import SpectralSolver - - # Get transfer operator settings (nested under solver.transfer) - transfer_cfg = cfg.solver.get("transfer", {}) - prolongation_method = transfer_cfg.get("prolongation", "fft") if transfer_cfg else "fft" - restriction_method = transfer_cfg.get("restriction", "fft") if transfer_cfg else "fft" - - return SpectralSolver( - **common, - basis_type=cfg.solver.basis_type, - CFL=cfg.solver.CFL, - beta_squared=cfg.solver.beta_squared, - # Corner singularity treatment - corner_treatment=cfg.solver.get("corner_treatment", "smoothing"), - corner_smoothing=cfg.solver.get("corner_smoothing", 0.15), - # Multigrid settings - multigrid=cfg.solver.get("multigrid", "none"), - n_levels=cfg.solver.get("n_levels", 3), - coarse_tolerance_factor=cfg.solver.get("coarse_tolerance_factor", 10.0), - # Transfer operators - prolongation_method=prolongation_method, - restriction_method=restriction_method, - ) - else: - raise ValueError(f"Unknown solver: {solver_name}") + """Instantiate solver using Hydra's instantiate on solver subtree.""" + return instantiate(cfg.solver, _convert_="partial") # ============================================================================= @@ -111,6 +61,10 @@ def create_solver(cfg: DictConfig): def setup_mlflow(cfg: DictConfig) -> str: """Setup MLflow tracking and return experiment name.""" tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") + # If defaulting to local file backend, clear any env override + if str(cfg.mlflow.get("mode", "")).lower() in ("files", "local"): + os.environ.pop("MLFLOW_TRACKING_URI", None) + os.environ["MLFLOW_TRACKING_URI"] = str(tracking_uri) mlflow.set_tracking_uri(tracking_uri) # Build experiment name with optional project prefix @@ -119,7 +73,19 @@ def setup_mlflow(cfg: DictConfig) -> str: if project_prefix and not experiment_name.startswith("/"): experiment_name = f"{project_prefix}/{experiment_name}" - mlflow.set_experiment(experiment_name) + try: + mlflow.set_experiment(experiment_name) + except Exception as exc: + # If experiment was previously deleted, fall back to a new name + fallback = f"{experiment_name}-restored" + log.warning( + "MLflow set_experiment failed for '%s' (%s); falling back to '%s'", + experiment_name, + exc, + fallback, + ) + experiment_name = fallback + mlflow.set_experiment(experiment_name) return experiment_name @@ -192,7 +158,9 @@ def main(cfg: DictConfig) -> None: nested = False if parent_run_id: run_tags["mlflow.parentRunId"] = parent_run_id - run_tags["parent_run_id"] = parent_run_id # Also store as regular tag for querying + run_tags["parent_run_id"] = ( + parent_run_id # Also store as regular tag for querying + ) run_tags["sweep"] = "child" nested = True # Required when parent run is active in same process @@ -224,9 +192,11 @@ def main(cfg: DictConfig) -> None: # Generate plots if cfg.get("generate_plots", True): - from plotting import generate_plots_for_run + from shared.plotting.ldc_plotter import generate_plots_for_run - output_dir = Path(hydra.core.hydra_config.HydraConfig.get().runtime.output_dir) + output_dir = Path( + hydra.core.hydra_config.HydraConfig.get().runtime.output_dir + ) tracking_uri = cfg.mlflow.get("tracking_uri", "./mlruns") generate_plots_for_run( run_id=run.info.run_id, diff --git a/src/shared/__init__.py b/src/shared/__init__.py new file mode 100644 index 0000000..ab3bd8e --- /dev/null +++ b/src/shared/__init__.py @@ -0,0 +1,5 @@ +"""Project-scoped shared utilities (meshing, plotting, validation).""" + +from shared import meshing, plotting + +__all__ = ["meshing", "plotting"] diff --git a/src/meshing/__init__.py b/src/shared/meshing/__init__.py similarity index 100% rename from src/meshing/__init__.py rename to src/shared/meshing/__init__.py diff --git a/src/meshing/mesh_data.py b/src/shared/meshing/mesh_data.py similarity index 100% rename from src/meshing/mesh_data.py rename to src/shared/meshing/mesh_data.py diff --git a/src/meshing/simple_structured.py b/src/shared/meshing/simple_structured.py similarity index 100% rename from src/meshing/simple_structured.py rename to src/shared/meshing/simple_structured.py diff --git a/src/shared/plotting/__init__.py b/src/shared/plotting/__init__.py new file mode 100644 index 0000000..148ec8a --- /dev/null +++ b/src/shared/plotting/__init__.py @@ -0,0 +1,16 @@ +"""Plotting utilities for scientific visualizations. + +This module provides LaTeX formatting utilities for labels and parameters. +""" + +from .formatters import ( + format_scientific_latex, + format_parameter_range, + build_parameter_string, +) + +__all__ = [ + "format_scientific_latex", + "format_parameter_range", + "build_parameter_string", +] diff --git a/src/utils/plotting/formatters.py b/src/shared/plotting/formatters.py similarity index 100% rename from src/utils/plotting/formatters.py rename to src/shared/plotting/formatters.py diff --git a/src/plotting.py b/src/shared/plotting/ldc_plotter.py similarity index 75% rename from src/plotting.py rename to src/shared/plotting/ldc_plotter.py index 3cebd94..123bd7a 100644 --- a/src/plotting.py +++ b/src/shared/plotting/ldc_plotter.py @@ -4,7 +4,7 @@ Can be used standalone via Hydra or called directly from run_solver.py. For individual runs: generates fields, streamlines, vorticity, centerlines, - ghia_validation, and convergence plots. + ghia comparison, and convergence plots. For sweeps: additionally generates a Ghia comparison plot uploaded to parent run. @@ -25,16 +25,19 @@ from pathlib import Path from typing import Optional -from dotenv import load_dotenv - -load_dotenv() - import hydra import mlflow -import zarr import numpy as np import pandas as pd -from omegaconf import DictConfig, OmegaConf +import zarr +from dotenv import load_dotenv +from omegaconf import DictConfig +from utilities.config.paths import get_repo_root +import matplotlib.pyplot as plt +import seaborn as sns +sns.set_theme() + +load_dotenv() log = logging.getLogger(__name__) @@ -81,7 +84,9 @@ def find_matching_run(cfg: DictConfig, tracking_uri: str) -> tuple[str, Optional if solver_name == "spectral": filter_parts.append(f"params.basis_type = '{cfg.solver.basis_type}'") elif solver_name == "fv": - filter_parts.append(f"params.convection_scheme = '{cfg.solver.convection_scheme}'") + filter_parts.append( + f"params.convection_scheme = '{cfg.solver.convection_scheme}'" + ) filter_string = " AND ".join(filter_parts) @@ -125,8 +130,10 @@ def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: parent_run = client.get_run(parent_run_id) experiment_id = parent_run.info.experiment_id - # Find all children of this parent (include RUNNING for parallel sweeps) - filter_string = f"tags.parent_run_id = '{parent_run_id}'" + # Find all FINISHED children of this parent + filter_string = ( + f"tags.parent_run_id = '{parent_run_id}' AND attributes.status = 'FINISHED'" + ) runs = client.search_runs( experiment_ids=[experiment_id], @@ -142,18 +149,22 @@ def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: # Extract solver name from run_name (format: {solver}_N{n} or {solver}_N{n}_Re{re}) # Examples: "fv_N32", "spectral_N33", "spectral_fsg_N16" if "_N" in run_name: - solver_name = run_name.rsplit("_N", 1)[0] # rsplit to handle underscores in solver name + solver_name = run_name.rsplit("_N", 1)[ + 0 + ] # rsplit to handle underscores in solver name else: solver_name = "unknown" - siblings.append({ - "run_id": run.info.run_id, - "run_name": run_name, - "N": int(run.data.params.get("nx", 0)), - "Re": float(run.data.params.get("Re", 0)), - "solver": solver_name, - "status": run.info.status, - }) + siblings.append( + { + "run_id": run.info.run_id, + "run_name": run_name, + "N": int(run.data.params.get("nx", 0)), + "Re": float(run.data.params.get("Re", 0)), + "solver": solver_name, + "status": run.info.status, + } + ) finished = sum(1 for s in siblings if s["status"] == "FINISHED") log.info(f"Found {len(siblings)} sibling runs in sweep ({finished} finished)") @@ -196,7 +207,9 @@ def load_fields_from_zarr(artifact_dir: Path) -> dict: return fields -def restructure_fields(fields: dict) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: +def restructure_fields( + fields: dict, +) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Convert potentially flattened fields to structured 2D arrays. Returns (x_unique, y_unique, U_2d, V_2d, P_2d) where U, V, P are 2D arrays @@ -246,7 +259,9 @@ def load_timeseries_from_mlflow(run_id: str, tracking_uri: str) -> pd.DataFrame: try: history = client.get_metric_history(run_id, metric_name) if history: - data[metric_name] = [m.value for m in sorted(history, key=lambda x: x.step)] + data[metric_name] = [ + m.value for m in sorted(history, key=lambda x: x.step) + ] except Exception: pass # Metric might not exist @@ -274,13 +289,15 @@ def fields_to_dataframe(fields: dict) -> pd.DataFrame: # Check if already flattened (all same length, 1D) if x.ndim == 1 and len(x) == len(u.ravel()): # Already flattened meshgrid coordinates - return pd.DataFrame({ - "x": x, - "y": y, - "u": u.ravel(), - "v": v.ravel(), - "p": p.ravel(), - }) + return pd.DataFrame( + { + "x": x, + "y": y, + "u": u.ravel(), + "v": v.ravel(), + "p": p.ravel(), + } + ) # Need to create meshgrid if x.ndim == 1 and y.ndim == 1: @@ -288,13 +305,15 @@ def fields_to_dataframe(fields: dict) -> pd.DataFrame: else: X, Y = x, y - return pd.DataFrame({ - "x": X.ravel(), - "y": Y.ravel(), - "u": u.ravel(), - "v": v.ravel(), - "p": p.ravel(), - }) + return pd.DataFrame( + { + "x": X.ravel(), + "y": Y.ravel(), + "u": u.ravel(), + "v": v.ravel(), + "p": p.ravel(), + } + ) # ============================================================================= @@ -302,7 +321,9 @@ def fields_to_dataframe(fields: dict) -> pd.DataFrame: # ============================================================================= -def plot_fields(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: +def plot_fields( + fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: """Generate field contour plots (p, u, v).""" import matplotlib.pyplot as plt from scipy.interpolate import RectBivariateSpline @@ -327,28 +348,32 @@ def plot_fields(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_ fig, axes = plt.subplots(1, 3, figsize=(16, 5)) - cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=25, cmap="coolwarm") + cf_p = axes[0].contourf(X_fine, Y_fine, P_interp, levels=25) axes[0].set_xlabel("x") axes[0].set_ylabel("y") axes[0].set_title("Pressure", fontweight="bold") axes[0].set_aspect("equal") plt.colorbar(cf_p, ax=axes[0], label="p") - cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=25, cmap="RdBu_r") + cf_u = axes[1].contourf(X_fine, Y_fine, U_interp, levels=25) axes[1].set_xlabel("x") axes[1].set_ylabel("y") axes[1].set_title("U velocity", fontweight="bold") axes[1].set_aspect("equal") plt.colorbar(cf_u, ax=axes[1], label="u") - cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=25, cmap="RdBu_r") + cf_v = axes[2].contourf(X_fine, Y_fine, V_interp, levels=25) axes[2].set_xlabel("x") axes[2].set_ylabel("y") axes[2].set_title("V velocity", fontweight="bold") axes[2].set_aspect("equal") plt.colorbar(cf_v, ax=axes[2], label="v") - fig.suptitle(f"Solution Fields — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold", fontsize=14) + fig.suptitle( + f"Solution Fields — {solver.upper()} N={N}, Re={Re:.0f}", + fontweight="bold", + fontsize=14, + ) plt.tight_layout() output_path = output_dir / "fields.pdf" @@ -358,7 +383,9 @@ def plot_fields(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_ return output_path -def plot_streamlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: +def plot_streamlines( + fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: """Generate streamline plot with velocity magnitude.""" import matplotlib.pyplot as plt from scipy.interpolate import RectBivariateSpline @@ -382,17 +409,16 @@ def plot_streamlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, ou fig, ax = plt.subplots(figsize=(8, 7)) X_fine, Y_fine = np.meshgrid(x_fine, y_fine) - cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=25, cmap="viridis") - stream = ax.streamplot( - x_fine, y_fine, U_interp, V_interp, - color="white", linewidth=0.8, density=1.5, - arrowsize=1.0, arrowstyle="->" + cf = ax.contourf(X_fine, Y_fine, vel_mag, levels=25) + ax.streamplot( + x_fine, y_fine, U_interp, V_interp, density=1.5, arrowsize=1.0, arrowstyle="->" ) - stream.lines.set_alpha(0.7) ax.set_xlabel("x") ax.set_ylabel("y") - ax.set_title(f"Streamlines — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold") + ax.set_title( + f"Streamlines — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold" + ) ax.set_aspect("equal") plt.colorbar(cf, ax=ax, label="Velocity magnitude") plt.tight_layout() @@ -404,7 +430,9 @@ def plot_streamlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, ou return output_path -def plot_vorticity(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: +def plot_vorticity( + fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: """Generate vorticity contour plot.""" import matplotlib.pyplot as plt from scipy.interpolate import RectBivariateSpline @@ -432,8 +460,7 @@ def plot_vorticity(fields_df: pd.DataFrame, Re: float, solver: str, N: int, outp fig, ax = plt.subplots(figsize=(8, 7)) vmax = np.max(np.abs(vorticity)) - cf = ax.contourf(X_fine, Y_fine, vorticity, levels=25, cmap="RdBu_r", - vmin=-vmax, vmax=vmax) + cf = ax.contourf(X_fine, Y_fine, vorticity, levels=25, vmin=-vmax, vmax=vmax) ax.set_xlabel("x") ax.set_ylabel("y") @@ -449,7 +476,9 @@ def plot_vorticity(fields_df: pd.DataFrame, Re: float, solver: str, N: int, outp return output_path -def plot_centerlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: +def plot_centerlines( + fields_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: """Plot velocity profiles along centerlines.""" import matplotlib.pyplot as plt from scipy.interpolate import RectBivariateSpline @@ -491,7 +520,11 @@ def plot_centerlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, ou axes[1].grid(True, alpha=0.3) axes[1].axhline(y=0, color="gray", linestyle="--", alpha=0.5) - fig.suptitle(f"Centerline Profiles — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold", fontsize=14) + fig.suptitle( + f"Centerline Profiles — {solver.upper()} N={N}, Re={Re:.0f}", + fontweight="bold", + fontsize=14, + ) plt.tight_layout() output_path = output_dir / "centerlines.pdf" @@ -501,7 +534,9 @@ def plot_centerlines(fields_df: pd.DataFrame, Re: float, solver: str, N: int, ou return output_path -def plot_convergence(timeseries_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path) -> Path: +def plot_convergence( + timeseries_df: pd.DataFrame, Re: float, solver: str, N: int, output_dir: Path +) -> Path: """Plot convergence history (residuals over iterations).""" import matplotlib.pyplot as plt @@ -511,19 +546,23 @@ def plot_convergence(timeseries_df: pd.DataFrame, Re: float, solver: str, N: int fig, ax = plt.subplots(figsize=(10, 6)) - # Plot available metrics - colors = {"residual": "blue", "energy": "green", "enstrophy": "orange", "palinstrophy": "red"} - + # Plot available metrics (use default color cycle) for col in timeseries_df.columns: - if col != "iteration" and col in colors: + if col != "iteration": data = timeseries_df[col].dropna() if len(data) > 0: - ax.semilogy(timeseries_df.loc[data.index, "iteration"], data, - label=col.capitalize(), color=colors[col], linewidth=1.5) + ax.semilogy( + timeseries_df.loc[data.index, "iteration"], + data, + label=col.capitalize(), + linewidth=1.5, + ) ax.set_xlabel("Iteration") ax.set_ylabel("Value") - ax.set_title(f"Convergence History — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold") + ax.set_title( + f"Convergence History — {solver.upper()} N={N}, Re={Re:.0f}", fontweight="bold" + ) ax.legend(frameon=True) ax.grid(True, alpha=0.3) plt.tight_layout() @@ -535,85 +574,6 @@ def plot_convergence(timeseries_df: pd.DataFrame, Re: float, solver: str, N: int return output_path -def plot_ghia_validation(fields: dict, Re: float, solver: str, N: int, output_dir: Path) -> Path: - """Plot validation against Ghia benchmark data.""" - import matplotlib.pyplot as plt - from spectral import spectral_interpolate - - AVAILABLE_RE = [100, 400, 1000, 3200, 5000, 7500, 10000] - if int(Re) not in AVAILABLE_RE: - log.warning(f"Ghia data not available for Re={Re}. Available: {AVAILABLE_RE}") - return None - - # Project root is 1 level up from src/ - project_root = Path(__file__).parent.parent - ghia_dir = project_root / "data" / "validation" / "ghia" - - u_file = ghia_dir / f"ghia_Re{int(Re)}_u_centerline.csv" - v_file = ghia_dir / f"ghia_Re{int(Re)}_v_centerline.csv" - - if not u_file.exists() or not v_file.exists(): - log.warning(f"Ghia data files not found in {ghia_dir}") - return None - - ghia_u = pd.read_csv(u_file) - ghia_v = pd.read_csv(v_file) - - # Restructure fields to 2D arrays - x_unique, y_unique, U_2d, V_2d, _ = restructure_fields(fields) - - # Use spectral interpolation for centerline extraction - n_points = 200 - y_line = np.linspace(y_unique.min(), y_unique.max(), n_points) - x_line = np.linspace(x_unique.min(), x_unique.max(), n_points) - - # Find center indices - x_center_idx = len(x_unique) // 2 - y_center_idx = len(y_unique) // 2 - - # U along vertical centerline (x = center): interpolate in y - u_at_center_x = U_2d[:, x_center_idx] - u_sim = spectral_interpolate(y_unique, u_at_center_x, y_line, basis="legendre") - - # V along horizontal centerline (y = center): interpolate in x - v_at_center_y = V_2d[y_center_idx, :] - v_sim = spectral_interpolate(x_unique, v_at_center_y, x_line, basis="legendre") - - fig, axes = plt.subplots(1, 2, figsize=(14, 5)) - - axes[0].plot(u_sim, y_line, "b-", linewidth=2, label=f"{solver.upper()} N={N}") - axes[0].scatter(ghia_u["u"], ghia_u["y"], c="black", s=50, marker="x", - label="Ghia et al. (1982)", zorder=10) - axes[0].set_xlabel("u") - axes[0].set_ylabel("y") - axes[0].set_title("U velocity (vertical centerline)", fontweight="bold") - axes[0].legend(frameon=True) - axes[0].grid(True, alpha=0.3) - - axes[1].plot(x_line, v_sim, "r-", linewidth=2, label=f"{solver.upper()} N={N}") - axes[1].scatter(ghia_v["x"], ghia_v["v"], c="black", s=50, marker="x", - label="Ghia et al. (1982)", zorder=10) - axes[1].set_xlabel("x") - axes[1].set_ylabel("v") - axes[1].set_title("V velocity (horizontal centerline)", fontweight="bold") - axes[1].legend(frameon=True) - axes[1].grid(True, alpha=0.3) - - fig.suptitle(f"Ghia Benchmark Validation — Re={Re:.0f}", fontweight="bold", fontsize=14) - plt.tight_layout() - - output_path = output_dir / "ghia_validation.pdf" - fig.savefig(output_path, dpi=300, bbox_inches="tight") - plt.close(fig) - - return output_path - - -# ============================================================================= -# Comparison Plot (for parent run) -# ============================================================================= - - def _build_method_label(sibling: dict) -> str: """Build a unified method label from solver name. @@ -637,7 +597,9 @@ def _build_method_label(sibling: dict) -> str: return label_map.get(solver, solver.replace("_", "-").title()) -def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Path) -> Path: +def plot_ghia_comparison( + siblings: list[dict], tracking_uri: str, output_dir: Path +) -> Path: """Plot Ghia comparison with all sibling runs using seaborn. Legend system (native seaborn): @@ -659,16 +621,17 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa Path Path to comparison plot """ - import matplotlib.pyplot as plt - import seaborn as sns - if not siblings: return None # Only plot finished runs - finished_siblings = [s for s in siblings if s.get("status", "FINISHED") == "FINISHED"] - if len(finished_siblings) < 2: - log.info(f"Need at least 2 finished runs for comparison (have {len(finished_siblings)})") + finished_siblings = [ + s for s in siblings if s.get("status", "FINISHED") == "FINISHED" + ] + if len(finished_siblings) < 1: + log.info( + f"Need at least 1 finished run for comparison (have {len(finished_siblings)})" + ) return None siblings = finished_siblings @@ -679,29 +642,20 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa log.warning(f"Ghia data not available for Re={Re}") return None - # Load Ghia reference data - project_root = Path(__file__).parent.parent + # Load Ghia reference data from repo-root data/validation/ghia + project_root = get_repo_root() ghia_dir = project_root / "data" / "validation" / "ghia" u_file = ghia_dir / f"ghia_Re{int(Re)}_u_centerline.csv" v_file = ghia_dir / f"ghia_Re{int(Re)}_v_centerline.csv" if not u_file.exists() or not v_file.exists(): - log.warning(f"Ghia data files not found") - return None + raise FileNotFoundError(f"Ghia data files not found in {ghia_dir}") ghia_u = pd.read_csv(u_file) ghia_v = pd.read_csv(v_file) - # Load scientific style - style_path = project_root / "src" / "utils" / "plotting" / "scientific.mplstyle" - if style_path.exists(): - plt.style.use(style_path) - else: - sns.set_style("whitegrid") - sns.set_context("paper", font_scale=1.2) - - # Filter to unique (method, N) combinations + # Filter to unique (solver/method, N) combinations seen_combos = set() unique_siblings = [] for sibling in siblings: @@ -722,6 +676,7 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa run_id = sibling["run_id"] N = sibling["N"] method = _build_method_label(sibling) + solver_name = sibling.get("solver", method) try: artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) @@ -737,12 +692,32 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa x_center_idx = len(x_unique) // 2 y_center_idx = len(y_unique) // 2 - u_sim = spectral_interpolate(y_unique, U_2d[:, x_center_idx], y_line, basis="legendre") - v_sim = spectral_interpolate(x_unique, V_2d[y_center_idx, :], x_line, basis="legendre") + u_sim = spectral_interpolate( + y_unique, U_2d[:, x_center_idx], y_line, basis="legendre" + ) + v_sim = spectral_interpolate( + x_unique, V_2d[y_center_idx, :], x_line, basis="legendre" + ) for i in range(n_points): - u_records.append({"y": y_line[i], "u": u_sim[i], "Method": method, "N": N}) - v_records.append({"x": x_line[i], "v": v_sim[i], "Method": method, "N": N}) + u_records.append( + { + "y": y_line[i], + "u": u_sim[i], + "Method": method, + "Solver": solver_name, + "N": N, + } + ) + v_records.append( + { + "x": x_line[i], + "v": v_sim[i], + "Method": method, + "Solver": solver_name, + "N": N, + } + ) except Exception as e: log.warning(f"Failed to load run {run_id}: {e}") @@ -752,22 +727,36 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa log.warning("No valid runs to plot") return None - u_df = pd.DataFrame(u_records).sort_values(["Method", "N", "y"]) - v_df = pd.DataFrame(v_records).sort_values(["Method", "N", "x"]) + u_df = pd.DataFrame(u_records).sort_values(["N", "Method", "y"]) + v_df = pd.DataFrame(v_records).sort_values(["N", "Method", "x"]) # Create figure fig, axes = plt.subplots(1, 2, figsize=(12, 5)) - # Plot with seaborn: hue=Method (color), style=N (dashes), markers + # Plot with seaborn: hue=N (color by grid), style=Method/Solver (dashes), markers sns.lineplot( - data=u_df, x="u", y="y", - hue="Method", style="N", - markers=True, markersize=4, markevery=20, - linewidth=1.5, sort=False, + data=u_df, + x="u", + y="y", + hue="N", + style="Method", + markers=True, + markersize=4, + markevery=20, + linewidth=1.5, + sort=False, ax=axes[0], ) - axes[0].scatter(ghia_u["u"], ghia_u["y"], c="black", s=60, marker="x", - linewidths=1.5, label=r"Ghia et al.\ (1982)", zorder=10) + axes[0].scatter( + ghia_u["u"], + ghia_u["y"], + c="black", + s=60, + marker="x", + linewidths=1.5, + label=r"Ghia et al.\ (1982)", + zorder=10, + ) axes[0].set_xlabel(r"$u$") axes[0].set_ylabel(r"$y$") axes[0].set_title(r"$u$-velocity (vertical centerline)") @@ -776,14 +765,28 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa axes[0].set_ylim(0, 1) sns.lineplot( - data=v_df, x="x", y="v", - hue="Method", style="N", - markers=True, markersize=4, markevery=20, - linewidth=1.5, sort=False, + data=v_df, + x="x", + y="v", + hue="N", + style="Method", + markers=True, + markersize=4, + markevery=20, + linewidth=1.5, + sort=False, ax=axes[1], ) - axes[1].scatter(ghia_v["x"], ghia_v["v"], c="black", s=60, marker="x", - linewidths=1.5, label=r"Ghia et al.\ (1982)", zorder=10) + axes[1].scatter( + ghia_v["x"], + ghia_v["v"], + c="black", + s=60, + marker="x", + linewidths=1.5, + label=r"Ghia et al.\ (1982)", + zorder=10, + ) axes[1].set_xlabel(r"$x$") axes[1].set_ylabel(r"$v$") axes[1].set_title(r"$v$-velocity (horizontal centerline)") @@ -806,7 +809,9 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa # ============================================================================= -def upload_plots_to_mlflow(run_id: str, plot_paths: list, tracking_uri: str, artifact_subdir: str = "plots"): +def upload_plots_to_mlflow( + run_id: str, plot_paths: list, tracking_uri: str, artifact_subdir: str = "plots" +): """Upload generated plots to MLflow run as artifacts.""" mlflow.set_tracking_uri(tracking_uri) @@ -888,7 +893,21 @@ def generate_plots_for_run( plot_paths.append(plot_vorticity(fields_df, Re, solver_name, N, output_dir)) plot_paths.append(plot_centerlines(fields_df, Re, solver_name, N, output_dir)) plot_paths.append(plot_convergence(timeseries_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_ghia_validation(fields, Re, solver_name, N, output_dir)) + ghia_path = plot_ghia_comparison( + [ + { + "run_id": run_id, + "N": N, + "Re": Re, + "solver": solver_name, + "status": "FINISHED", + } + ], + tracking_uri, + output_dir, + ) + if ghia_path: + plot_paths.append(ghia_path) plot_paths = [p for p in plot_paths if p is not None] log.info(f"Generated {len(plot_paths)} plots for run") @@ -967,8 +986,10 @@ def generate_comparison_plots_for_sweep( log.info(f" Created comparison plot: {comparison_path.name}") if upload_to_mlflow: - upload_plots_to_mlflow(parent_run_id, [comparison_path], tracking_uri, "plots") - log.info(f" Uploaded to parent run") + upload_plots_to_mlflow( + parent_run_id, [comparison_path], tracking_uri, "plots" + ) + log.info(" Uploaded to parent run") log.info(f"Generated {len(results)} comparison plot(s)") return results @@ -1013,7 +1034,6 @@ def main(cfg: DictConfig) -> None: plot_paths.append(plot_vorticity(fields_df, Re, solver_name, N, output_dir)) plot_paths.append(plot_centerlines(fields_df, Re, solver_name, N, output_dir)) plot_paths.append(plot_convergence(timeseries_df, Re, solver_name, N, output_dir)) - plot_paths.append(plot_ghia_validation(fields, Re, solver_name, N, output_dir)) plot_paths = [p for p in plot_paths if p is not None] log.info(f"Generated {len(plot_paths)} plots for run") @@ -1034,10 +1054,14 @@ def main(cfg: DictConfig) -> None: comparison_dir = output_dir / "comparison" comparison_dir.mkdir(exist_ok=True) - comparison_path = plot_ghia_comparison(siblings, tracking_uri, comparison_dir) + comparison_path = plot_ghia_comparison( + siblings, tracking_uri, comparison_dir + ) if comparison_path and cfg.get("upload_to_mlflow", True): - upload_plots_to_mlflow(parent_run_id, [comparison_path], tracking_uri, "plots") + upload_plots_to_mlflow( + parent_run_id, [comparison_path], tracking_uri, "plots" + ) log.info("Comparison plot uploaded to parent run") log.info("Done!") diff --git a/src/spectral/utils/plotting.py b/src/shared/plotting/plotting.py similarity index 100% rename from src/spectral/utils/plotting.py rename to src/shared/plotting/plotting.py diff --git a/src/solvers/__init__.py b/src/solvers/__init__.py index 4f5b644..803bb0e 100644 --- a/src/solvers/__init__.py +++ b/src/solvers/__init__.py @@ -10,7 +10,7 @@ └── MultigridSpectralSolver (extends with multigrid acceleration) """ -from .base_solver import LidDrivenCavitySolver +from .base import LidDrivenCavitySolver from .datastructures import ( # Base classes (shared by all solvers) Parameters, @@ -24,8 +24,8 @@ SpectralParameters, SpectralSolverFields, ) -from .spectral_solver import SpectralSolver -from .fv_solver import FVSolver +from solvers.fv.solver import FVSolver +from solvers.spectral.solver import SpectralSolver __all__ = [ diff --git a/src/solvers/base_solver.py b/src/solvers/base.py similarity index 100% rename from src/solvers/base_solver.py rename to src/solvers/base.py diff --git a/src/solvers/datastructures.py b/src/solvers/datastructures.py index ee0dcd8..144060f 100644 --- a/src/solvers/datastructures.py +++ b/src/solvers/datastructures.py @@ -15,7 +15,7 @@ """ from dataclasses import dataclass, field -from typing import Optional, List +from typing import List import numpy as np import pandas as pd @@ -30,6 +30,7 @@ class Parameters: """Base solver parameters - input configuration for all solvers.""" + name: str = "" Re: float = 100 lid_velocity: float = 1.0 Lx: float = 1.0 @@ -43,8 +44,7 @@ class Parameters: def to_mlflow(self) -> dict: """Convert to MLflow-compatible params dict.""" return { - k: (int(v) if isinstance(v, bool) else v) - for k, v in self.__dict__.items() + k: (int(v) if isinstance(v, bool) else v) for k, v in self.__dict__.items() } def to_dataframe(self) -> pd.DataFrame: @@ -114,9 +114,7 @@ def to_mlflow_batch(self) -> list: def to_dataframe(self) -> pd.DataFrame: """Convert to DataFrame with one row per iteration.""" - return pd.DataFrame({ - k: v for k, v in self.__dict__.items() if v - }) + return pd.DataFrame({k: v for k, v in self.__dict__.items() if v}) # ============================================================================ @@ -136,10 +134,9 @@ class Fields: def to_dataframe(self) -> pd.DataFrame: """Convert to DataFrame with one row per grid point.""" - return pd.DataFrame({ - "x": self.x, "y": self.y, - "u": self.u, "v": self.v, "p": self.p - }) + return pd.DataFrame( + {"x": self.x, "y": self.y, "u": self.u, "v": self.v, "p": self.p} + ) # ============================================================================ diff --git a/src/fv/__init__.py b/src/solvers/fv/__init__.py similarity index 100% rename from src/fv/__init__.py rename to src/solvers/fv/__init__.py diff --git a/src/fv/assembly/__init__.py b/src/solvers/fv/assembly/__init__.py similarity index 100% rename from src/fv/assembly/__init__.py rename to src/solvers/fv/assembly/__init__.py diff --git a/src/fv/assembly/convection_diffusion_matrix.py b/src/solvers/fv/assembly/convection_diffusion_matrix.py similarity index 98% rename from src/fv/assembly/convection_diffusion_matrix.py rename to src/solvers/fv/assembly/convection_diffusion_matrix.py index cdff490..cf361ca 100644 --- a/src/fv/assembly/convection_diffusion_matrix.py +++ b/src/solvers/fv/assembly/convection_diffusion_matrix.py @@ -1,7 +1,7 @@ import numpy as np from numba import njit -from fv.discretization.convection.upwind import compute_convective_stencil +from solvers.fv.discretization.convection.upwind import compute_convective_stencil @njit( diff --git a/src/fv/assembly/divergence.py b/src/solvers/fv/assembly/divergence.py similarity index 100% rename from src/fv/assembly/divergence.py rename to src/solvers/fv/assembly/divergence.py diff --git a/src/fv/assembly/pressure_correction_eq_assembly.py b/src/solvers/fv/assembly/pressure_correction_eq_assembly.py similarity index 96% rename from src/fv/assembly/pressure_correction_eq_assembly.py rename to src/solvers/fv/assembly/pressure_correction_eq_assembly.py index 87d0dc2..3783a58 100644 --- a/src/fv/assembly/pressure_correction_eq_assembly.py +++ b/src/solvers/fv/assembly/pressure_correction_eq_assembly.py @@ -2,7 +2,7 @@ from numba import njit -@njit(cache=True, fastmath=True, boundscheck=False, error_model="numpy") +@njit(fastmath=True, boundscheck=False, error_model="numpy") def assemble_pressure_correction_matrix(mesh, rho): """ Assemble pressure correction equation matrix. diff --git a/src/fv/assembly/rhie_chow.py b/src/solvers/fv/assembly/rhie_chow.py similarity index 96% rename from src/fv/assembly/rhie_chow.py rename to src/solvers/fv/assembly/rhie_chow.py index e18718d..1341a27 100644 --- a/src/fv/assembly/rhie_chow.py +++ b/src/solvers/fv/assembly/rhie_chow.py @@ -2,7 +2,7 @@ from numba import njit -@njit(inline="always", cache=True, fastmath=True, nogil=True) +@njit(inline="always", fastmath=True, nogil=True) def rhie_chow_velocity_internal_faces( mesh, u_star, v_star, grad_p_bar, grad_p, bold_D_bar, U_faces ): @@ -60,7 +60,7 @@ def rhie_chow_velocity_internal_faces( return U_faces -@njit(inline="always", cache=True, fastmath=True, nogil=True) +@njit(inline="always", fastmath=True, nogil=True) def rhie_chow_velocity_boundary_faces(mesh, U_faces): """ Apply boundary conditions to Rhie-Chow velocity. @@ -84,7 +84,7 @@ def rhie_chow_velocity_boundary_faces(mesh, U_faces): return U_faces -@njit(cache=True, fastmath=True, nogil=True) +@njit(fastmath=True, nogil=True) def mdot_calculation(mesh, rho, U_f, out=None): """ Calculate mass flux through faces: mdot = rho * U_f · S_f @@ -137,7 +137,7 @@ def mdot_calculation(mesh, rho, U_f, out=None): return mdot -@njit(cache=True, fastmath=True, nogil=True) +@njit(fastmath=True, nogil=True) def rhie_chow_velocity(mesh, u_star, v_star, grad_p_bar, grad_p, bold_D_bar, out=None): """ Compute Rhie-Chow interpolated velocity at faces. diff --git a/src/fv/core/__init__.py b/src/solvers/fv/core/__init__.py similarity index 100% rename from src/fv/core/__init__.py rename to src/solvers/fv/core/__init__.py diff --git a/src/fv/core/corrections.py b/src/solvers/fv/core/corrections.py similarity index 100% rename from src/fv/core/corrections.py rename to src/solvers/fv/core/corrections.py diff --git a/src/fv/core/helpers.py b/src/solvers/fv/core/helpers.py similarity index 97% rename from src/fv/core/helpers.py rename to src/solvers/fv/core/helpers.py index c67fb09..233f55c 100644 --- a/src/fv/core/helpers.py +++ b/src/solvers/fv/core/helpers.py @@ -2,7 +2,7 @@ from numba import njit, prange -@njit(parallel=False, cache=True) +@njit(parallel=False) def relax_momentum_equation(rhs, A_diag, phi, alpha): """ In-place Patankar-style under-relaxation of a momentum equation system. @@ -23,7 +23,7 @@ def relax_momentum_equation(rhs, A_diag, phi, alpha): return relaxed_diagonal, relaxed_rhs -@njit(parallel=True, cache=True) +@njit(parallel=True) def interpolate_velocity_to_face(mesh, u, v, out=None): """ Optimized velocity interpolation that takes separate u and v components. @@ -58,7 +58,7 @@ def interpolate_velocity_to_face(mesh, u, v, out=None): return U_face -@njit(parallel=True, cache=True) +@njit(parallel=True) def interpolate_to_face(mesh, quantity, out=None): """ Optimized interpolation to faces with better memory access patterns. @@ -139,7 +139,7 @@ def interpolate_to_face(mesh, quantity, out=None): return interpolated_quantity -@njit(parallel=False, cache=True) +@njit(parallel=False) def bold_Dv_calculation(mesh, A_u_diag, A_v_diag, out=None): n_cells = mesh.cell_volumes.shape[0] if out is None: diff --git a/src/fv/discretization/__init__.py b/src/solvers/fv/discretization/__init__.py similarity index 100% rename from src/fv/discretization/__init__.py rename to src/solvers/fv/discretization/__init__.py diff --git a/src/fv/discretization/convection/__init__.py b/src/solvers/fv/discretization/convection/__init__.py similarity index 100% rename from src/fv/discretization/convection/__init__.py rename to src/solvers/fv/discretization/convection/__init__.py diff --git a/src/fv/discretization/convection/upwind.py b/src/solvers/fv/discretization/convection/upwind.py similarity index 97% rename from src/fv/discretization/convection/upwind.py rename to src/solvers/fv/discretization/convection/upwind.py index a5b6c68..6ebddbf 100644 --- a/src/fv/discretization/convection/upwind.py +++ b/src/solvers/fv/discretization/convection/upwind.py @@ -6,7 +6,7 @@ def MUSCL(r): return max(0.0, min(2.0, 2.0 * r, 0.5 * (1 + r))) if r > 0 else 0.0 -@njit(inline="always", cache=True, fastmath=True) +@njit(inline="always", fastmath=True) def compute_convective_stencil( f, mesh, rho, mdot, grad_phi, component_idx, phi, scheme="Upwind", limiter=None ): diff --git a/src/fv/discretization/diffusion/__init__.py b/src/solvers/fv/discretization/diffusion/__init__.py similarity index 100% rename from src/fv/discretization/diffusion/__init__.py rename to src/solvers/fv/discretization/diffusion/__init__.py diff --git a/src/fv/discretization/gradient/__init__.py b/src/solvers/fv/discretization/gradient/__init__.py similarity index 100% rename from src/fv/discretization/gradient/__init__.py rename to src/solvers/fv/discretization/gradient/__init__.py diff --git a/src/fv/discretization/gradient/structured_gradient.py b/src/solvers/fv/discretization/gradient/structured_gradient.py similarity index 99% rename from src/fv/discretization/gradient/structured_gradient.py rename to src/solvers/fv/discretization/gradient/structured_gradient.py index d932fbc..6e8ef8e 100644 --- a/src/fv/discretization/gradient/structured_gradient.py +++ b/src/solvers/fv/discretization/gradient/structured_gradient.py @@ -8,7 +8,7 @@ from numba import njit, prange -@njit(parallel=True, cache=True) +@njit(parallel=True) def compute_cell_gradients_structured( mesh, u, pinned_idx=0, use_limiter=True, out=None ): diff --git a/src/fv/linear_solvers/__init__.py b/src/solvers/fv/linear_solvers/__init__.py similarity index 100% rename from src/fv/linear_solvers/__init__.py rename to src/solvers/fv/linear_solvers/__init__.py diff --git a/src/fv/linear_solvers/scipy_solver.py b/src/solvers/fv/linear_solvers/scipy_solver.py similarity index 100% rename from src/fv/linear_solvers/scipy_solver.py rename to src/solvers/fv/linear_solvers/scipy_solver.py diff --git a/src/solvers/fv_solver.py b/src/solvers/fv/solver.py similarity index 91% rename from src/solvers/fv_solver.py rename to src/solvers/fv/solver.py index 0d7e93e..8a02a64 100644 --- a/src/solvers/fv_solver.py +++ b/src/solvers/fv/solver.py @@ -7,21 +7,23 @@ import numpy as np from scipy.sparse import csr_matrix -from .base_solver import LidDrivenCavitySolver -from .datastructures import FVParameters, FVSolverFields +from ..base import LidDrivenCavitySolver +from ..datastructures import FVParameters, FVSolverFields -from fv.assembly.convection_diffusion_matrix import assemble_diffusion_convection_matrix -from fv.discretization.gradient.structured_gradient import ( +from solvers.fv.assembly.convection_diffusion_matrix import ( + assemble_diffusion_convection_matrix, +) +from solvers.fv.discretization.gradient.structured_gradient import ( compute_cell_gradients_structured, ) -from fv.linear_solvers.scipy_solver import scipy_solver -from fv.assembly.rhie_chow import mdot_calculation, rhie_chow_velocity -from fv.assembly.pressure_correction_eq_assembly import ( +from solvers.fv.linear_solvers.scipy_solver import scipy_solver +from solvers.fv.assembly.rhie_chow import mdot_calculation, rhie_chow_velocity +from solvers.fv.assembly.pressure_correction_eq_assembly import ( assemble_pressure_correction_matrix, ) -from fv.assembly.divergence import compute_divergence_from_face_fluxes -from fv.core.corrections import velocity_correction -from fv.core.helpers import ( +from solvers.fv.assembly.divergence import compute_divergence_from_face_fluxes +from solvers.fv.core.corrections import velocity_correction +from solvers.fv.core.helpers import ( bold_Dv_calculation, interpolate_to_face, interpolate_velocity_to_face, @@ -50,7 +52,7 @@ def __init__(self, **kwargs): super().__init__(**kwargs) # Create mesh - from meshing.simple_structured import create_structured_mesh_2d + from shared.meshing.simple_structured import create_structured_mesh_2d self.mesh = create_structured_mesh_2d( nx=self.params.nx, diff --git a/src/solvers/metrics.py b/src/solvers/metrics.py new file mode 100644 index 0000000..598e787 --- /dev/null +++ b/src/solvers/metrics.py @@ -0,0 +1,101 @@ +"""Shared metrics and formatting utilities for solvers.""" + +from __future__ import annotations + +from typing import Any + +import numpy as np +import pandas as pd + + +# ----------------------------------------------------------------------------- +# Norms / errors +# ----------------------------------------------------------------------------- + + +def discrete_l2_norm(values: np.ndarray, h: float) -> float: + """Approximate L2 norm using composite trapezoidal rule.""" + return np.sqrt(h * np.sum(np.abs(values) ** 2)) + + +def discrete_l2_error( + f_exact: np.ndarray, f_num: np.ndarray, interval_length: float +) -> float: + """Compute discrete L2 error between exact and numerical solutions.""" + diff = f_num - f_exact + h = interval_length / f_exact.size + return np.sqrt(h) * np.linalg.norm(diff) + + +def discrete_linf_error(f_exact: np.ndarray, f_num: np.ndarray) -> float: + """Compute discrete L-infinity (maximum) error.""" + return np.max(np.abs(f_num - f_exact)) + + +# ----------------------------------------------------------------------------- +# Formatting helpers +# ----------------------------------------------------------------------------- + + +def format_dt_latex(dt: float | str) -> str: + """Format a timestep value as LaTeX scientific notation.""" + if dt == "?": + return "?" + + dt_str = f"{float(dt):.2e}" + mantissa, exp = dt_str.split("e") + exp_int = int(exp) + return rf"{mantissa} \times 10^{{{exp_int}}}" + + +def extract_metadata( + df: pd.DataFrame, + cols: list[str] | None = None, + row_idx: int = 0, +) -> dict[str, Any]: + """Extract metadata from a DataFrame (assumes constant columns).""" + if cols is None: + cols = df.columns.tolist() + return {col: df[col].iloc[row_idx] for col in cols if col in df.columns} + + +def format_parameter_range( + values: list | tuple, + name: str, + latex: bool = True, +) -> str: + """Format a parameter range for display.""" + if len(values) == 0: + return f"{name} = ?" + + if len(values) == 1: + val = values[0] + return rf"${name} = {val}$" if latex else f"{name} = {val}" + + min_val, max_val = min(values), max(values) + if isinstance(min_val, int) and isinstance(max_val, int): + range_str = f"[{min_val}, {max_val}]" + else: + range_str = f"[{min_val:.1f}, {max_val:.1f}]" + + return rf"${name} \in {range_str}$" if latex else f"{name} ∈ {range_str}" + + +def build_parameter_string( + params: dict[str, Any], + separator: str = ", ", + latex: bool = True, +) -> str: + """Build a parameter string from a dictionary.""" + parts = [] + for name, value in params.items(): + if isinstance(value, (list, tuple)): + parts.append(format_parameter_range(value, name, latex=latex)) + elif "dt" in name.lower() or "delta t" in name: + value_str = format_dt_latex(value) + parts.append( + rf"${name} = {value_str}$" if latex else f"{name} = {value_str}" + ) + else: + parts.append(rf"${name} = {value}$" if latex else f"{name} = {value}") + return separator.join(parts) diff --git a/src/solvers/spectral/__init__.py b/src/solvers/spectral/__init__.py new file mode 100644 index 0000000..ba51123 --- /dev/null +++ b/src/solvers/spectral/__init__.py @@ -0,0 +1,5 @@ +"""Spectral solver package.""" + +from solvers.spectral.solver import SpectralSolver + +__all__ = ["SpectralSolver"] diff --git a/src/solvers/spectral/basis/__init__.py b/src/solvers/spectral/basis/__init__.py new file mode 100644 index 0000000..0cc9c1a --- /dev/null +++ b/src/solvers/spectral/basis/__init__.py @@ -0,0 +1,29 @@ +"""Spectral basis utilities.""" + +from solvers.spectral.basis.spectral import ( + ChebyshevLobattoBasis, + FourierEquispacedBasis, + LegendreLobattoBasis, + chebyshev_diff_matrix, + chebyshev_gauss_lobatto_nodes, + fourier_diff_matrix_complex, + fourier_diff_matrix_cotangent, + fourier_diff_matrix_on_interval, + legendre_diff_matrix, + legendre_mass_matrix, +) +from solvers.spectral.basis.polynomial import spectral_interpolate + +__all__ = [ + "LegendreLobattoBasis", + "ChebyshevLobattoBasis", + "FourierEquispacedBasis", + "legendre_diff_matrix", + "legendre_mass_matrix", + "chebyshev_diff_matrix", + "chebyshev_gauss_lobatto_nodes", + "fourier_diff_matrix_cotangent", + "fourier_diff_matrix_complex", + "fourier_diff_matrix_on_interval", + "spectral_interpolate", +] diff --git a/src/spectral/polynomial.py b/src/solvers/spectral/basis/polynomial.py similarity index 100% rename from src/spectral/polynomial.py rename to src/solvers/spectral/basis/polynomial.py diff --git a/src/spectral/spectral.py b/src/solvers/spectral/basis/spectral.py similarity index 100% rename from src/spectral/spectral.py rename to src/solvers/spectral/basis/spectral.py diff --git a/src/solvers/spectral/multigrid/__init__.py b/src/solvers/spectral/multigrid/__init__.py new file mode 100644 index 0000000..723a539 --- /dev/null +++ b/src/solvers/spectral/multigrid/__init__.py @@ -0,0 +1,5 @@ +"""Spectral multigrid variants.""" + +from solvers.spectral.multigrid.fsg import build_hierarchy, solve_fsg + +__all__ = ["build_hierarchy", "solve_fsg"] diff --git a/src/solvers/spectral_multigrid.py b/src/solvers/spectral/multigrid/fsg.py similarity index 97% rename from src/solvers/spectral_multigrid.py rename to src/solvers/spectral/multigrid/fsg.py index 0cef900..6ca05a2 100644 --- a/src/solvers/spectral_multigrid.py +++ b/src/solvers/spectral/multigrid/fsg.py @@ -18,11 +18,11 @@ import numpy as np -from src.spectral.transfer_operators import ( +from solvers.spectral.operators.transfer_operators import ( TransferOperators, create_transfer_operators, ) -from src.spectral.corner_singularity import ( +from solvers.spectral.operators.corner import ( CornerTreatment, create_corner_treatment, ) @@ -376,7 +376,8 @@ def _apply_lid_boundary(self, u_2d: np.ndarray, v_2d: np.ndarray): y_lid = self.level.Y[:, -1] u_lid, v_lid = self.corner_treatment.get_lid_velocity( - x_lid, y_lid, + x_lid, + y_lid, lid_velocity=self.lid_velocity, Lx=self.Lx, Ly=self.Ly, @@ -492,8 +493,12 @@ def _compute_adaptive_timestep(self) -> float: v_max = max(np.max(np.abs(lvl.v)), 1e-10) nu = 1.0 / self.Re - lambda_x = (u_max + np.sqrt(u_max**2 + self.beta_squared)) / lvl.dx_min + nu / lvl.dx_min**2 - lambda_y = (v_max + np.sqrt(v_max**2 + self.beta_squared)) / lvl.dy_min + nu / lvl.dy_min**2 + lambda_x = ( + u_max + np.sqrt(u_max**2 + self.beta_squared) + ) / lvl.dx_min + nu / lvl.dx_min**2 + lambda_y = ( + v_max + np.sqrt(v_max**2 + self.beta_squared) + ) / lvl.dy_min + nu / lvl.dy_min**2 return self.CFL / (lambda_x + lambda_y) @@ -705,6 +710,8 @@ def solve_fsg( finest_level = levels[-1] final_converged = converged - log.info(f"FSG completed: {total_iterations} total iterations, converged={final_converged}") + log.info( + f"FSG completed: {total_iterations} total iterations, converged={final_converged}" + ) return finest_level, total_iterations, final_converged diff --git a/src/solvers/spectral/operators/__init__.py b/src/solvers/spectral/operators/__init__.py new file mode 100644 index 0000000..08fdd7b --- /dev/null +++ b/src/solvers/spectral/operators/__init__.py @@ -0,0 +1,29 @@ +"""Spectral operators (corner treatment, transfer operators).""" + +from solvers.spectral.operators.corner import ( + CornerTreatment, + SmoothingTreatment, + SubtractionTreatment, + create_corner_treatment, +) +from solvers.spectral.operators.transfer_operators import ( + FFTProlongation, + FFTRestriction, + InjectionRestriction, + PolynomialProlongation, + TransferOperators, + create_transfer_operators, +) + +__all__ = [ + "CornerTreatment", + "SmoothingTreatment", + "SubtractionTreatment", + "create_corner_treatment", + "TransferOperators", + "create_transfer_operators", + "FFTProlongation", + "FFTRestriction", + "PolynomialProlongation", + "InjectionRestriction", +] diff --git a/src/spectral/corner_singularity.py b/src/solvers/spectral/operators/corner.py similarity index 97% rename from src/spectral/corner_singularity.py rename to src/solvers/spectral/operators/corner.py index f32c930..87e9008 100644 --- a/src/spectral/corner_singularity.py +++ b/src/solvers/spectral/operators/corner.py @@ -130,7 +130,9 @@ def get_lid_velocity( # Smooth near right corner (x = Lx) mask_right = x_flat > (Lx - smooth_dist) if np.any(mask_right): - factor = 0.5 * (1 - np.cos(np.pi * (Lx - x_flat[mask_right]) / smooth_dist)) + factor = 0.5 * ( + 1 - np.cos(np.pi * (Lx - x_flat[mask_right]) / smooth_dist) + ) u_lid[mask_right] = factor * lid_velocity return u_lid.reshape(x.shape), v_lid.reshape(x.shape) @@ -332,10 +334,14 @@ def get_singular_velocity_derivatives( ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Compute analytical derivatives of singular velocity.""" # Left corner - left = self._compute_singular_solution(x, y, 0.0, Ly, Lx, Ly, compute_derivatives=True) + left = self._compute_singular_solution( + x, y, 0.0, Ly, Lx, Ly, compute_derivatives=True + ) # Right corner - right = self._compute_singular_solution(x, y, Lx, Ly, Lx, Ly, compute_derivatives=True) + right = self._compute_singular_solution( + x, y, Lx, Ly, Lx, Ly, compute_derivatives=True + ) dus_dx = left["dus_dx"] + right["dus_dx"] dus_dy = left["dus_dy"] + right["dus_dy"] diff --git a/src/spectral/transfer_operators.py b/src/solvers/spectral/operators/transfer_operators.py similarity index 99% rename from src/spectral/transfer_operators.py rename to src/solvers/spectral/operators/transfer_operators.py index 1893085..7b501c8 100644 --- a/src/spectral/transfer_operators.py +++ b/src/solvers/spectral/operators/transfer_operators.py @@ -20,7 +20,7 @@ from typing import Tuple import numpy as np -from scipy.fft import dct, idct +from scipy.fft import dct class ProlongationMethod(Enum): diff --git a/src/solvers/spectral_solver.py b/src/solvers/spectral/solver.py similarity index 94% rename from src/solvers/spectral_solver.py rename to src/solvers/spectral/solver.py index 2981edf..fada7a0 100644 --- a/src/solvers/spectral_solver.py +++ b/src/solvers/spectral/solver.py @@ -15,10 +15,13 @@ import numpy as np -from .base_solver import LidDrivenCavitySolver -from .datastructures import SpectralParameters, SpectralSolverFields -from spectral.spectral import LegendreLobattoBasis, ChebyshevLobattoBasis -from spectral.corner_singularity import create_corner_treatment +from ..base import LidDrivenCavitySolver +from ..datastructures import SpectralParameters, SpectralSolverFields +from solvers.spectral.basis.spectral import ( + LegendreLobattoBasis, + ChebyshevLobattoBasis, +) +from solvers.spectral.operators.corner import create_corner_treatment log = logging.getLogger(__name__) @@ -89,7 +92,9 @@ def __init__(self, **kwargs): log.info(f"Using corner treatment: {self.params.corner_treatment}") # Cache singular velocity and derivatives if using subtraction method - self._uses_modified_convection = self.corner_treatment.uses_modified_convection() + self._uses_modified_convection = ( + self.corner_treatment.uses_modified_convection() + ) if self._uses_modified_convection: log.info("Subtraction method: caching singular velocity and derivatives") # Get singular velocity u_s, v_s at all grid points @@ -100,8 +105,10 @@ def __init__(self, **kwargs): self._v_s = v_s_2d.ravel() # Get analytical derivatives (NOT spectral!) - dus_dx, dus_dy, dvs_dx, dvs_dy = self.corner_treatment.get_singular_velocity_derivatives( - self.x_full, self.y_full, self.params.Lx, self.params.Ly + dus_dx, dus_dy, dvs_dx, dvs_dy = ( + self.corner_treatment.get_singular_velocity_derivatives( + self.x_full, self.y_full, self.params.Lx, self.params.Ly + ) ) self._dus_dx = dus_dx.ravel() self._dus_dy = dus_dy.ravel() @@ -142,7 +149,8 @@ def _apply_lid_boundary(self, u_2d, v_2d): y_lid = self.y_full[:, -1] # y coordinates on top boundary u_lid, v_lid = self.corner_treatment.get_lid_velocity( - x_lid, y_lid, + x_lid, + y_lid, lid_velocity=self.params.lid_velocity, Lx=self.params.Lx, Ly=self.params.Ly, @@ -471,15 +479,20 @@ def solve(self, tolerance: float = None, max_iter: int = None): elif mg_method == "fsg": self._solve_fsg(tolerance, max_iter) elif mg_method in ("vmg", "fmg"): - raise NotImplementedError(f"Multigrid method '{mg_method}' not yet implemented") + raise NotImplementedError( + f"Multigrid method '{mg_method}' not yet implemented" + ) else: raise ValueError(f"Unknown multigrid method: {mg_method}") def _solve_fsg(self, tolerance: float, max_iter: int): """Solve using Full Single Grid (FSG) multigrid.""" import time - from .spectral_multigrid import build_hierarchy, solve_fsg - from src.spectral.transfer_operators import create_transfer_operators + + from solvers.spectral.multigrid.fsg import build_hierarchy, solve_fsg + from solvers.spectral.operators.transfer_operators import ( + create_transfer_operators, + ) log.info(f"Using FSG multigrid with {self.params.n_levels} levels") log.info( @@ -534,12 +547,14 @@ def _solve_fsg(self, tolerance: float, max_iter: int): # Store results using base class machinery # Create minimal residual history for compatibility final_residuals = self._compute_algebraic_residuals() - residual_history = [{ - "rel_iter": tolerance if converged else tolerance * 10, - "u_eq": final_residuals["u_residual"], - "v_eq": final_residuals["v_residual"], - "continuity": final_residuals["continuity_residual"], - }] + residual_history = [ + { + "rel_iter": tolerance if converged else tolerance * 10, + "u_eq": final_residuals["u_residual"], + "v_eq": final_residuals["v_residual"], + "continuity": final_residuals["continuity_residual"], + } + ] self._store_results( residual_history=residual_history, diff --git a/src/spectral/__init__.py b/src/spectral/__init__.py index 0fa0e28..9a22bef 100644 --- a/src/spectral/__init__.py +++ b/src/spectral/__init__.py @@ -1,6 +1,6 @@ -"""Spectral methods for Navier-Stokes solver.""" +"""Compatibility layer for spectral utilities (forwarded to new locations).""" -from .spectral import ( +from solvers.spectral.basis.spectral import ( ChebyshevLobattoBasis, FourierEquispacedBasis, LegendreLobattoBasis, @@ -12,25 +12,22 @@ legendre_diff_matrix, legendre_mass_matrix, ) -from .polynomial import spectral_interpolate -from .transfer_operators import ( - TransferOperators, - create_transfer_operators, +from solvers.spectral.basis.polynomial import spectral_interpolate +from solvers.spectral.operators.transfer_operators import ( FFTProlongation, FFTRestriction, - PolynomialProlongation, InjectionRestriction, + PolynomialProlongation, + TransferOperators, + create_transfer_operators, ) -from .corner_singularity import ( +from solvers.spectral.operators.corner import ( CornerTreatment, SmoothingTreatment, SubtractionTreatment, create_corner_treatment, ) -# Note: CornerTreatmentMethod enum removed for simplicity - use string method names -from .utils.plotting import get_repo_root - __all__ = [ # Spectral bases "LegendreLobattoBasis", @@ -58,6 +55,4 @@ "SmoothingTreatment", "SubtractionTreatment", "create_corner_treatment", - # Utilities - "get_repo_root", ] diff --git a/src/spectral/utils/__init__.py b/src/spectral/utils/__init__.py index 5ccb11f..0f6df8e 100644 --- a/src/spectral/utils/__init__.py +++ b/src/spectral/utils/__init__.py @@ -1,14 +1,16 @@ -"""Utility modules for I/O, formatting, plotting, and numerical norms.""" +"""Compatibility wrappers for migrated utilities.""" -from .io import ensure_output_dir, load_simulation_data, save_simulation_data -from .formatting import ( +from utilities.io import ensure_output_dir, load_simulation_data, save_simulation_data +from solvers.metrics import ( extract_metadata, format_dt_latex, format_parameter_range, build_parameter_string, + discrete_l2_error, + discrete_l2_norm, + discrete_linf_error, ) -from .plotting import get_repo_root -from .norms import discrete_l2_error, discrete_l2_norm, discrete_linf_error +from shared.plotting.plotting import get_repo_root __all__ = [ # I/O diff --git a/src/spectral/utils/formatting.py b/src/spectral/utils/formatting.py deleted file mode 100644 index 5c4bc8e..0000000 --- a/src/spectral/utils/formatting.py +++ /dev/null @@ -1,147 +0,0 @@ -"""Formatting utilities for plot labels and parameters.""" - -from __future__ import annotations - -from typing import Any - -import pandas as pd - - -def format_dt_latex(dt: float | str) -> str: - """Format a timestep value as LaTeX scientific notation. - - Parameters - ---------- - dt : float or str - Timestep value to format. If str and equals '?', returns '?' - - Returns - ------- - str - LaTeX-formatted string in the form 'mantissa \\times 10^{exponent}' - - """ - if dt == "?": - return "?" - - dt_str = f"{float(dt):.2e}" - mantissa, exp = dt_str.split("e") - exp_int = int(exp) - return rf"{mantissa} \times 10^{{{exp_int}}}" - - -def extract_metadata( - df: pd.DataFrame, - cols: list[str] | None = None, - row_idx: int = 0, -) -> dict[str, Any]: - """Extract metadata from a DataFrame. - - Assumes metadata columns have constant values across rows. - - Parameters - ---------- - df : pd.DataFrame - DataFrame containing metadata - cols : list of str, optional - List of column names to extract. If None, extracts all columns. - row_idx : int, default 0 - Row index to extract from (typically 0 for constant columns) - - Returns - ------- - dict - Dictionary mapping column names to values - - """ - if cols is None: - cols = df.columns.tolist() - - return {col: df[col].iloc[row_idx] for col in cols if col in df.columns} - - -def format_parameter_range( - values: list | tuple, - name: str, - latex: bool = True, -) -> str: - """Format a parameter range for display. - - Parameters - ---------- - values : list or tuple - Parameter values (should be sorted) - name : str - Parameter name (e.g., 'N', 'L', 'dt') - latex : bool, default True - Whether to use LaTeX formatting - - Returns - ------- - str - Formatted string' - - """ - if len(values) == 0: - return f"{name} = ?" - - if len(values) == 1: - val = values[0] - if latex: - return rf"${name} = {val}$" - return f"{name} = {val}" - - min_val, max_val = min(values), max(values) - - # Format based on type - if isinstance(min_val, int) and isinstance(max_val, int): - range_str = f"[{min_val}, {max_val}]" - else: - range_str = f"[{min_val:.1f}, {max_val:.1f}]" - - if latex: - return rf"${name} \in {range_str}$" - return f"{name} ∈ {range_str}" - - -def build_parameter_string( - params: dict[str, Any], - separator: str = ", ", - latex: bool = True, -) -> str: - """Build a parameter string from a dictionary. - - Parameters - ---------- - params : dict - Dictionary of parameter names and values - separator : str, default ', ' - Separator between parameters - latex : bool, default True - Whether to use LaTeX formatting (wraps each param in $ $) - - Returns - ------- - str - Formatted parameter string - - """ - parts = [] - for name, value in params.items(): - if isinstance(value, (list, tuple)): - parts.append(format_parameter_range(value, name, latex=latex)) - else: - # Handle special formatting for dt - if "dt" in name.lower() or "Delta t" in name: - value_str = format_dt_latex(value) - if latex: - parts.append(rf"${name} = {value_str}$") - else: - parts.append(f"{name} = {value_str}") - else: - if latex: - parts.append(rf"${name} = {value}$") - else: - parts.append(f"{name} = {value}") - - return separator.join(parts) diff --git a/src/spectral/utils/norms.py b/src/spectral/utils/norms.py deleted file mode 100644 index 6087f29..0000000 --- a/src/spectral/utils/norms.py +++ /dev/null @@ -1,66 +0,0 @@ -"""Numerical norms and error computations.""" - -from __future__ import annotations - -import numpy as np - - -def discrete_l2_norm(values: np.ndarray, h: float) -> float: - """Approximate L2 norm using composite trapezoidal rule. - - Parameters - ---------- - values : np.ndarray - Function values at discrete points - h : float - Grid spacing - - Returns - ------- - float - Approximate L2 norm - """ - return np.sqrt(h * np.sum(np.abs(values) ** 2)) - - -def discrete_l2_error( - f_exact: np.ndarray, f_num: np.ndarray, interval_length: float -) -> float: - """Compute discrete L2 error between exact and numerical solutions. - #TODO: Change to use Mass Matrix instead - - Parameters - ---------- - f_exact : np.ndarray - Exact function values - f_num : np.ndarray - Numerical approximation values - interval_length : float - Length of the interval - - Returns - ------- - float - Discrete L2 error norm - """ - diff = f_num - f_exact - h = interval_length / f_exact.size - return np.sqrt(h) * np.linalg.norm(diff) - - -def discrete_linf_error(f_exact: np.ndarray, f_num: np.ndarray) -> float: - """Compute discrete :math:`L^\\infty` (maximum) error. - - Parameters - ---------- - f_exact : np.ndarray - Exact function values - f_num : np.ndarray - Numerical approximation values - - Returns - ------- - float - Maximum absolute error - """ - return np.max(np.abs(f_num - f_exact)) diff --git a/src/utilities/__init__.py b/src/utilities/__init__.py new file mode 100644 index 0000000..2c1546d --- /dev/null +++ b/src/utilities/__init__.py @@ -0,0 +1,10 @@ +"""Cross-project utilities (Hydra/MLflow/HPC, config, IO, plotting).""" + +# Keep __init__ lightweight to avoid circular imports during Hydra callback loading. +from utilities.io import load_simulation_data, save_simulation_data, ensure_output_dir # noqa: F401 + +__all__ = [ + "load_simulation_data", + "save_simulation_data", + "ensure_output_dir", +] diff --git a/src/utils/config/__init__.py b/src/utilities/config/__init__.py similarity index 100% rename from src/utils/config/__init__.py rename to src/utilities/config/__init__.py diff --git a/src/utils/config/clean.py b/src/utilities/config/clean.py similarity index 100% rename from src/utils/config/clean.py rename to src/utilities/config/clean.py diff --git a/src/utils/config/paths.py b/src/utilities/config/paths.py similarity index 100% rename from src/utils/config/paths.py rename to src/utilities/config/paths.py diff --git a/src/utils/hpc/__init__.py b/src/utilities/hpc/__init__.py similarity index 100% rename from src/utils/hpc/__init__.py rename to src/utilities/hpc/__init__.py diff --git a/src/utils/hpc/sweeper.py b/src/utilities/hpc/sweeper.py similarity index 99% rename from src/utils/hpc/sweeper.py rename to src/utilities/hpc/sweeper.py index 256b870..71d8048 100644 --- a/src/utils/hpc/sweeper.py +++ b/src/utilities/hpc/sweeper.py @@ -14,7 +14,7 @@ from .jobgen import load_config # Use the universal template path relative to project root -RUNNER_TEMPLATE = Path("src/utils/hpc/runner_template.sh") +RUNNER_TEMPLATE = Path("src/utilities/hpc/runner_template.sh") def get_combinations(group_config: Dict[str, Any]) -> List[Dict[str, Any]]: diff --git a/src/utils/hydra/__init__.py b/src/utilities/hydra/__init__.py similarity index 55% rename from src/utils/hydra/__init__.py rename to src/utilities/hydra/__init__.py index fce6774..f2a54b6 100644 --- a/src/utils/hydra/__init__.py +++ b/src/utilities/hydra/__init__.py @@ -1,5 +1,5 @@ """Hydra utilities and callbacks.""" -from utils.hydra.callbacks import MLflowLogCallback +from utilities.hydra.callbacks import MLflowLogCallback __all__ = ["MLflowLogCallback"] diff --git a/src/utils/hydra/callbacks.py b/src/utilities/hydra/callbacks.py similarity index 97% rename from src/utils/hydra/callbacks.py rename to src/utilities/hydra/callbacks.py index 86f1ff6..5e711c2 100644 --- a/src/utils/hydra/callbacks.py +++ b/src/utilities/hydra/callbacks.py @@ -31,7 +31,7 @@ class MLflowLogCallback(Callback): hydra: callbacks: mlflow_log: - _target_: utils.hydra.callbacks.MLflowLogCallback + _target_: utilities.hydra.callbacks.MLflowLogCallback artifact_path: logs The log file will be uploaded to the "logs" artifact path in MLflow. diff --git a/src/spectral/utils/io.py b/src/utilities/io.py similarity index 100% rename from src/spectral/utils/io.py rename to src/utilities/io.py diff --git a/src/utils/mlflow/__init__.py b/src/utilities/mlflow/__init__.py similarity index 100% rename from src/utils/mlflow/__init__.py rename to src/utilities/mlflow/__init__.py diff --git a/src/utils/mlflow_callback.py b/src/utilities/mlflow/callback.py similarity index 76% rename from src/utils/mlflow_callback.py rename to src/utilities/mlflow/callback.py index 15f9ed4..e6df54b 100644 --- a/src/utils/mlflow_callback.py +++ b/src/utilities/mlflow/callback.py @@ -4,7 +4,6 @@ import os from typing import Dict, Optional -from hydra.core.utils import JobReturn from hydra.experimental.callback import Callback from omegaconf import DictConfig, OmegaConf @@ -31,9 +30,13 @@ def __init__(self) -> None: self._tracking_uri: Optional[str] = None self._full_experiment_name: Optional[str] = None self._base_sweep_name: Optional[str] = None - self._sweep_dir: Optional[str] = None # Store sweep dir while HydraConfig available + self._sweep_dir: Optional[str] = ( + None # Store sweep dir while HydraConfig available + ) - def _find_existing_parent(self, experiment_name: str, sweep_name: str) -> Optional[str]: + def _find_existing_parent( + self, experiment_name: str, sweep_name: str + ) -> Optional[str]: """Find an existing parent run with the same sweep_name.""" import mlflow @@ -84,7 +87,8 @@ def _get_or_create_parent(self, sweep_name: str, config: DictConfig) -> str: if "Re" in sweep_name: # Try to extract Re value from sweep_name like "sweep-Re100" import re - match = re.search(r'Re(\d+)', sweep_name) + + match = re.search(r"Re(\d+)", sweep_name) if match: mlflow.set_tag("Re", match.group(1)) @@ -109,6 +113,10 @@ def on_multirun_start(self, config: DictConfig, **kwargs) -> None: # Setup MLflow tracking self._tracking_uri = config.mlflow.get("tracking_uri", "./mlruns") + # If using local file backend, clear env overrides + if str(config.mlflow.get("mode", "")).lower() in ("files", "local"): + os.environ.pop("MLFLOW_TRACKING_URI", None) + os.environ["MLFLOW_TRACKING_URI"] = str(self._tracking_uri) mlflow.set_tracking_uri(self._tracking_uri) # Build experiment name @@ -119,7 +127,18 @@ def on_multirun_start(self, config: DictConfig, **kwargs) -> None: else: self._full_experiment_name = experiment_name - mlflow.set_experiment(self._full_experiment_name) + try: + mlflow.set_experiment(self._full_experiment_name) + except Exception as exc: + fallback = f"{self._full_experiment_name}-restored" + log.warning( + "MLflow set_experiment failed for '%s' (%s); falling back to '%s'", + self._full_experiment_name, + exc, + fallback, + ) + self._full_experiment_name = fallback + mlflow.set_experiment(self._full_experiment_name) # Store base sweep name (may contain ${Re} placeholder) self._base_sweep_name = config.get("sweep_name", "sweep") @@ -127,17 +146,27 @@ def on_multirun_start(self, config: DictConfig, **kwargs) -> None: # Store sweep directory while HydraConfig is available try: import hydra.core.hydra_config + hydra_cfg = hydra.core.hydra_config.HydraConfig.get() self._sweep_dir = hydra_cfg.sweep.dir except Exception: self._sweep_dir = None - log.info(f"MLflow sweep callback initialized for experiment: {self._full_experiment_name}") + # Flag child jobs that a sweep is active (Hydra mode inside jobs is RUN) + os.environ["MLFLOW_SWEEP_ACTIVE"] = "1" + + log.info( + f"MLflow sweep callback initialized for experiment: {self._full_experiment_name}" + ) def on_job_start(self, config: DictConfig, **kwargs) -> None: """Set parent run ID for each job based on its Re value.""" import mlflow + # Only act when a sweep is active (set in on_multirun_start) + if os.environ.get("MLFLOW_SWEEP_ACTIVE") != "1": + return + # Ensure tracking is set up if self._tracking_uri: mlflow.set_tracking_uri(self._tracking_uri) @@ -145,10 +174,12 @@ def on_job_start(self, config: DictConfig, **kwargs) -> None: mlflow.set_experiment(self._full_experiment_name) # Resolve sweep_name with current job's config (e.g., Re value) - sweep_name = self._base_sweep_name + sweep_name = self._base_sweep_name or config.get("sweep_name", "sweep") if "${Re}" in sweep_name or "{Re}" in sweep_name: re_value = int(config.get("Re", 100)) - sweep_name = sweep_name.replace("${Re}", str(re_value)).replace("{Re}", str(re_value)) + sweep_name = sweep_name.replace("${Re}", str(re_value)).replace( + "{Re}", str(re_value) + ) # Get or create parent for this sweep_name parent_id = self._get_or_create_parent(sweep_name, config) @@ -159,18 +190,26 @@ def on_job_start(self, config: DictConfig, **kwargs) -> None: def on_multirun_end(self, config: DictConfig, **kwargs) -> None: """Clean up after sweep completes and generate comparison plots.""" + if os.environ.get("MLFLOW_SWEEP_ACTIVE") != "1": + return + # Clean up env var os.environ.pop("MLFLOW_PARENT_RUN_ID", None) + os.environ.pop("MLFLOW_SWEEP_ACTIVE", None) # Log summary - log.info(f"Sweep completed. Created {len(self._active_parent_runs)} parent run(s).") + log.info( + f"Sweep completed. Created {len(self._active_parent_runs)} parent run(s)." + ) for name, run_id in self._parent_runs.items(): log.info(f" - {name}: {run_id}") # Generate comparison plots for all parent runs if self._parent_runs and config.get("generate_plots", True): try: - from plotting import generate_comparison_plots_for_sweep + from shared.plotting.ldc_plotter import ( + generate_comparison_plots_for_sweep, + ) from pathlib import Path # Use stored sweep directory (HydraConfig may not be available here) @@ -180,7 +219,9 @@ def on_multirun_end(self, config: DictConfig, **kwargs) -> None: output_dir = Path("outputs") / "comparison_plots" parent_run_ids = list(self._parent_runs.values()) - log.info(f"Generating comparison plots for {len(parent_run_ids)} parent run(s)...") + log.info( + f"Generating comparison plots for {len(parent_run_ids)} parent run(s)..." + ) generate_comparison_plots_for_sweep( parent_run_ids=parent_run_ids, diff --git a/src/utils/mlflow/io.py b/src/utilities/mlflow/io.py similarity index 100% rename from src/utils/mlflow/io.py rename to src/utilities/mlflow/io.py diff --git a/src/utils/mlflow/logs.py b/src/utilities/mlflow/logs.py similarity index 100% rename from src/utils/mlflow/logs.py rename to src/utilities/mlflow/logs.py diff --git a/src/utils/mlflow/upload_logs.py b/src/utilities/mlflow/upload_logs.py similarity index 96% rename from src/utils/mlflow/upload_logs.py rename to src/utilities/mlflow/upload_logs.py index a1d7d72..24a7885 100644 --- a/src/utils/mlflow/upload_logs.py +++ b/src/utilities/mlflow/upload_logs.py @@ -8,14 +8,14 @@ Processed files are moved to a 'processed' subdirectory. Usage: - uv run python src/utils/mlflow/upload_logs.py --log-dir logs/lsf + uv run python src/utilities/mlflow/upload_logs.py --log-dir logs/lsf """ import argparse import shutil from pathlib import Path import mlflow -from utils.mlflow.io import setup_mlflow_tracking +from utilities.mlflow.io import setup_mlflow_tracking def upload_logs(log_dir: Path, dry_run: bool = False): diff --git a/src/utils/runners/__init__.py b/src/utilities/runners/__init__.py similarity index 100% rename from src/utils/runners/__init__.py rename to src/utilities/runners/__init__.py diff --git a/src/utils/runners/scripts.py b/src/utilities/runners/scripts.py similarity index 100% rename from src/utils/runners/scripts.py rename to src/utilities/runners/scripts.py diff --git a/src/utils/__init__.py b/src/utils/__init__.py deleted file mode 100644 index bd01077..0000000 --- a/src/utils/__init__.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Utility modules for project management and visualization. - -Submodules: -- plotting: Scientific plot styling, formatters, palettes -- runners: Script discovery and execution -- hpc: HPC job generation and submission -- config: Project configuration and cleanup -- mlflow: MLflow artifact handling and log uploading - -Import examples: - from utils import plotting # Auto-applies scientific styles - from utils import runners # Script execution - from utils import hpc # HPC job management - from utils import mlflow # MLflow utilities - from utils.config import get_repo_root, load_project_config -""" - -import warnings - -# Suppress MLflow FutureWarning about filesystem backend deprecation -warnings.filterwarnings("ignore", category=FutureWarning, module="mlflow") - -from . import plotting, runners, hpc, config, mlflow # noqa: E402 - -# Re-export common config functions for convenience -from .config import get_repo_root # noqa: E402 - -__all__ = [ - "plotting", - "runners", - "hpc", - "config", - "mlflow", - "get_repo_root", -] diff --git a/src/utils/plotting/__init__.py b/src/utils/plotting/__init__.py deleted file mode 100644 index 56cdd7c..0000000 --- a/src/utils/plotting/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -"""Plotting utilities for scientific visualizations. - -This module provides: -- Automatic style application (seaborn + custom scientific style) -- LaTeX formatting utilities for labels and parameters - -Automatically applies styles on import: - from utils import plotting # Styles applied! - -Or import specific utilities: - from utils.plotting import format_scientific_latex -""" - -from .styles import apply_styles -from .formatters import ( - format_scientific_latex, - format_parameter_range, - build_parameter_string, -) - -# Apply styles when module is imported -apply_styles() - -__all__ = [ - "apply_styles", - "format_scientific_latex", - "format_parameter_range", - "build_parameter_string", -] diff --git a/src/utils/plotting/scientific.mplstyle b/src/utils/plotting/scientific.mplstyle deleted file mode 100644 index 6ab1592..0000000 --- a/src/utils/plotting/scientific.mplstyle +++ /dev/null @@ -1,78 +0,0 @@ -# ====================================================== -# Matplotlib style for LaTeX scientific reports -# ====================================================== - -# Figure layout -figure.figsize : 6.0, 5.0 # ~single-column figure -figure.dpi : 300 -savefig.dpi : 300 -savefig.bbox : tight -savefig.pad_inches : 0.05 - -# Optional colormap -# image.cmap : coolwarm - -# ====================================================== -# Axis ticks -# ====================================================== - -#xtick.direction : in -#xtick.major.size : 3 -#xtick.major.width : 0.5 -#xtick.minor.size : 1.5 -#xtick.minor.width : 0.5 -xtick.minor.visible : True -#xtick.top : True - -#ytick.direction : in -#ytick.major.size : 3 -#ytick.major.width : 0.5 -#ytick.minor.size : 1.5 -#ytick.minor.width : 0.5 -ytick.minor.visible : True -#ytick.right : True - -# ====================================================== -# Gridlines -# ====================================================== -axes.grid : True -axes.grid.axis : both -axes.grid.which : both -#grid.color : 0.85 -#grid.linestyle : - -#grid.alpha : 0.85 -#grid.linewidth : 1.0 - -# ====================================================== -# Font and text settings -# ====================================================== -font.family : serif -font.size : 9.5 # Base text size (matches 10pt LaTeX body) -axes.labelsize : 10.5 # Axis labels -axes.titlesize : 11.5 # Axes titles -figure.titlesize : 16 # Figure suptitle -legend.fontsize : 9 # Legend -xtick.labelsize : 7 # Tick labels -ytick.labelsize : 7 # Tick labels - -# Use LaTeX for math formatting -text.usetex : True - -text.latex.preamble : \usepackage[T1]{fontenc} \usepackage{amsmath} \usepackage{amssymb} \usepackage{bm} \usepackage{siunitx} - -# ====================================================== -# Lines and legends -# ====================================================== -axes.linewidth : 0.5 -lines.linewidth : 1.0 - -# Legend settings -legend.frameon : False # remove box -legend.loc : best # default position -legend.title_fontsize: 12 # title size -legend.handlelength : 2.0 # line length in legend -legend.handletextpad : 0.5 # space between handle and text -legend.columnspacing : 1.0 # spacing between columns -legend.borderaxespad : 0.2 # padding between legend and axes -legend.borderpad : 0.2 # padding inside legend -legend.facecolor : none # transparent background diff --git a/src/utils/plotting/styles.py b/src/utils/plotting/styles.py deleted file mode 100644 index 3284b11..0000000 --- a/src/utils/plotting/styles.py +++ /dev/null @@ -1,34 +0,0 @@ -"""Style application for matplotlib plots. - -Provides consistent styling for scientific publications using -seaborn as a base with custom overrides for LaTeX integration. -""" - -from pathlib import Path - -import matplotlib.pyplot as plt - - -def apply_styles(base_style: str = "seaborn-v0_8") -> None: - """Apply seaborn style and custom scientific style. - - Parameters - ---------- - base_style : str, default "seaborn-v0_8" - Base matplotlib style to use. Falls back silently if unavailable. - """ - # Apply base style first - try: - plt.style.use(base_style) - except OSError: - pass - - # Then apply custom style on top - style_path = Path(__file__).parent / "scientific.mplstyle" - if style_path.exists(): - plt.style.use(str(style_path)) - - -def get_style_path() -> Path: - """Return path to the scientific.mplstyle file.""" - return Path(__file__).parent / "scientific.mplstyle" From c45deca545368a25ffaab4b3540e64fbb363f6a4 Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Sat, 6 Dec 2025 06:21:48 +0100 Subject: [PATCH 27/34] more --- .../spectral_multigrid_comparison.yaml | 22 ++ conf/experiment/spectral_validation.yaml | 2 +- .../spectral_vs_fsg/ghia_comparison.pdf | Bin 0 -> 272948 bytes src/shared/plotting/ldc_plotter.py | 225 +++++++++------ src/shared/plotting/scientific.mplstyle | 119 ++++++++ src/solvers/spectral/multigrid/fsg.py | 87 +++++- src/solvers/spectral/operators/corner.py | 266 ++++++++++++------ 7 files changed, 536 insertions(+), 185 deletions(-) create mode 100644 conf/experiment/spectral_multigrid_comparison.yaml create mode 100644 outputs/comparison_plots/spectral_vs_fsg/ghia_comparison.pdf create mode 100644 src/shared/plotting/scientific.mplstyle diff --git a/conf/experiment/spectral_multigrid_comparison.yaml b/conf/experiment/spectral_multigrid_comparison.yaml new file mode 100644 index 0000000..cd3ea9a --- /dev/null +++ b/conf/experiment/spectral_multigrid_comparison.yaml @@ -0,0 +1,22 @@ +# @package _global_ +# Comparison: Spectral solver vs Spectral-FSG (Full Single Grid multigrid) +# Usage: uv run python run_solver.py +experiment=spectral_multigrid_comparison +# uv run python run_solver.py -m +experiment=spectral_multigrid_comparison (sweep all) + +# MLflow experiment name for tracking +experiment_name: LDC-Multigrid-Comparison +sweep_name: spectral_vs_fsg + +# Common parameters +Re: 100 +tolerance: 1.0e-6 +max_iterations: 100000 + +# Default N (overridden by sweep) +N: 15 + +hydra: + sweeper: + params: + # Sweep over both solver types + solver: spectral,spectral_fsg diff --git a/conf/experiment/spectral_validation.yaml b/conf/experiment/spectral_validation.yaml index 0e90a02..4d1a437 100644 --- a/conf/experiment/spectral_validation.yaml +++ b/conf/experiment/spectral_validation.yaml @@ -19,4 +19,4 @@ max_iterations: 30000 hydra: sweeper: params: - N: 15,19 + N: 15 diff --git a/outputs/comparison_plots/spectral_vs_fsg/ghia_comparison.pdf b/outputs/comparison_plots/spectral_vs_fsg/ghia_comparison.pdf new file mode 100644 index 0000000000000000000000000000000000000000..47e7f7324f5e6e01a3d2e62917d5467aaf195cb7 GIT binary patch literal 272948 zcmagEWmH^S(>01a!QGnRPUG$xG`L&i?(Q1gg1d*{?(S~EodgN)E*H*uo_pW>em}m! zSYxePYgW~)+PfLOcTWmMF$o4BBO3xm`5K_C6#)of2G|){BJlA6m{dIMO#n=yhR%l8 zc4h!3MME48KC{1R!riZiix{3fJxfs zor1`JX3_u5(g5B6K?nGM7VyFQgT154JG+1BGbx!k*||7^-s}0`|DIpY#Mr`6#LoRa zBlG(oCkv1f$j!~p0$}0d{?AJHy%+BVzDIEaaQ{nF*w)tW-2#5N{LiL*9RIhAvL?1> 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zWv1yTNV*S6_URkaX_iS`v5rIrqmFF+K}LgtF`#19xTbcE>JRex*Nik{B*EzLHPWJQ zLPwEs-4{VeQ3mKu!+9wRI>IJvmI!$&fiZ#(mKe34SAPQfk)(yDo6^xFoE(vKWK&%j z8V=4-IwMUPEYMm!D7E^U`{_#EV=rG0<^G&{-nw0PHDC6YLqr zd>;(pC=QcH>KUURqB(RAz{+Hfu#KchQvDDJqti2{NFR|VGi7W-r)MJLh1BZ}h6vk$ zmx+uainLIsNO^!V6M+?JvXIc8gy{#ojGBm{I#YU*W+HqEbY}JQE{qQBHAUbDyo@oD z&P19b;~D5IYU3X!Pft;iKBr8i*&Lw@!x+rQh&9SYS(pfaP$n8~eZ%(vk2k?UZ`PaC z586;&7{)@85qrQI6G^8x8!eFaVZ27lY*O#&L*?nsCJ30wH8UNFy;>vS0BYn_B$gLA yN&8oWT)u*XngchgqNtWj?K?EhQ!FACUH_-Otgw=zl_!v7rb%7z-YHo&-G2b1a=a-3 literal 0 HcmV?d00001 diff --git a/src/cli/__init__.py b/src/cli/__init__.py deleted file mode 100644 index c82127a..0000000 --- a/src/cli/__init__.py +++ /dev/null @@ -1,25 +0,0 @@ -"""CLI module for ANA-P3 project management.""" - -from .actions import ( - fetch_mlflow, - run_scripts, - build_docs, - clean_all, - ruff_check, - ruff_format, - hpc_submit, - hpc_monitor, -) -from .interactive import interactive - -__all__ = [ - "fetch_mlflow", - "run_scripts", - "build_docs", - "clean_all", - "ruff_check", - "ruff_format", - "hpc_submit", - "hpc_monitor", - "interactive", -] diff --git a/src/cli/actions.py b/src/cli/actions.py deleted file mode 100644 index 1319931..0000000 --- a/src/cli/actions.py +++ /dev/null @@ -1,292 +0,0 @@ -"""CLI actions - the actual work functions.""" - -import subprocess -from pathlib import Path - -from .console import console, ok, fail, dim, header - -REPO_ROOT = Path(__file__).resolve().parent.parent.parent - - -def run_cmd( - cmd: list[str], timeout: int = 180, cwd: Path = REPO_ROOT -) -> subprocess.CompletedProcess: - """Run a command and return result.""" - return subprocess.run( - cmd, capture_output=True, text=True, timeout=timeout, cwd=str(cwd) - ) - - -def fetch_mlflow(): - """Fetch artifacts from MLflow.""" - header("Fetching MLflow artifacts...") - try: - from utils import download_artifacts_with_naming, setup_mlflow_auth - - setup_mlflow_auth() - - fv_paths = download_artifacts_with_naming( - "HPC-FV-Solver", REPO_ROOT / "data" / "FV-Solver" - ) - ok(f"Downloaded {len(fv_paths)} FV-Solver files") - - spectral_paths = download_artifacts_with_naming( - "HPC-Spectral-Chebyshev", - REPO_ROOT / "data" / "Spectral-Solver" / "Chebyshev", - ) - ok(f"Downloaded {len(spectral_paths)} Spectral files") - except Exception as e: - fail(f"Failed: {e}") - - -def run_scripts(pattern: str): - """Run scripts matching pattern (compute/plot).""" - experiments_dir = REPO_ROOT / "Experiments" - if not experiments_dir.exists(): - dim("No Experiments directory found") - return - - scripts = sorted( - [p for p in experiments_dir.rglob("*.py") if p.is_file() and pattern in p.name] - ) - - if not scripts: - dim(f"No {pattern} scripts found") - return - - header(f"Running {len(scripts)} {pattern} scripts...") - - for script in scripts: - name = script.relative_to(REPO_ROOT) - console.print(f"\n[bold cyan]▶ {name}[/bold cyan]") - try: - # Stream output directly to terminal - result = subprocess.run( - ["uv", "run", "python", str(script)], - cwd=str(REPO_ROOT), - timeout=180, - ) - ok(f"{name}") if result.returncode == 0 else fail( - f"{name} (exit {result.returncode})" - ) - except subprocess.TimeoutExpired: - fail(f"{name} (timeout)") - except Exception as e: - fail(f"{name} ({e})") - - -def build_docs(): - """Build Sphinx documentation.""" - header("Building documentation...") - try: - result = run_cmd( - [ - "uv", - "run", - "sphinx-build", - "-M", - "html", - str(REPO_ROOT / "docs" / "source"), - str(REPO_ROOT / "docs" / "build"), - ], - timeout=300, - ) - - if result.returncode == 0: - ok("Documentation built") - dim(f"Open: {REPO_ROOT / 'docs' / 'build' / 'html' / 'index.html'}") - else: - fail(f"Build failed (exit {result.returncode})") - except Exception as e: - fail(f"Build failed: {e}") - - -def clean_all(): - """Clean generated files and caches.""" - import shutil - - header("Cleaning...") - targets = [ - "docs/build", - "docs/source/example_gallery", - "docs/source/generated", - "build", - "dist", - ".pytest_cache", - ".ruff_cache", - ".mypy_cache", - ] - - count = 0 - for target in targets: - path = REPO_ROOT / target - if path.exists(): - shutil.rmtree(path) - count += 1 - - for pycache in REPO_ROOT.rglob("__pycache__"): - shutil.rmtree(pycache) - count += 1 - - # Clean data/ but keep README.md - data_dir = REPO_ROOT / "data" - if data_dir.exists(): - for item in data_dir.iterdir(): - if item.name not in ("README.md", ".gitkeep"): - shutil.rmtree(item) if item.is_dir() else item.unlink() - count += 1 - - ok(f"Cleaned {count} items") if count else dim("Nothing to clean") - - -def ruff_check(): - """Run ruff linter.""" - header("Running ruff check...") - result = run_cmd(["uv", "run", "ruff", "check", "."], timeout=60) - print(result.stdout) - ok("No issues") if result.returncode == 0 else fail( - f"Found issues (exit {result.returncode})" - ) - - -def ruff_format(): - """Run ruff formatter.""" - header("Running ruff format...") - result = run_cmd(["uv", "run", "ruff", "format", "."], timeout=60) - print(result.stdout) - ok("Formatted") if result.returncode == 0 else fail( - f"Failed (exit {result.returncode})" - ) - - -def copy_plots(): - """Copy plot files from Experiments to figures directory.""" - import shutil - - header("Copying plots...") - - src_dir = REPO_ROOT / "Experiments" - dst_dir = REPO_ROOT / "figures" - - if not src_dir.exists(): - dim("No Experiments directory found") - return - - dst_dir.mkdir(exist_ok=True) - - extensions = {".pdf", ".png", ".svg"} - count = 0 - - for ext in extensions: - for plot_file in src_dir.rglob(f"*{ext}"): - dst_file = dst_dir / plot_file.name - shutil.copy2(plot_file, dst_file) - console.print(f" [dim]{plot_file.name}[/dim]") - count += 1 - - ok(f"Copied {count} plots to figures/") if count else dim("No plots found") - - -def hpc_status(): - """Check status of running HPC jobs.""" - header("HPC Job Status") - result = subprocess.run(["bstat"], capture_output=True, text=True) - if result.returncode == 0: - if result.stdout.strip(): - print(result.stdout) - else: - dim("No running jobs") - else: - fail(f"bstat failed: {result.stderr}") - - -def hpc_monitor(): - """Launch live HPC job monitor.""" - from .hpc_monitor import monitor - monitor() - - -def hpc_kill(target: str = "all"): - """Kill HPC jobs. - - Args: - target: Job name, job ID, or "all" to kill all jobs - """ - header(f"Killing jobs: {target}") - - if target == "all": - result = subprocess.run(["bkill", "0"], capture_output=True, text=True) - elif target.isdigit(): - result = subprocess.run(["bkill", target], capture_output=True, text=True) - else: - result = subprocess.run(["bkill", "-J", target], capture_output=True, text=True) - - if result.returncode == 0: - ok(result.stdout.strip() if result.stdout.strip() else "Done") - else: - fail(result.stderr.strip() if result.stderr.strip() else "Failed") - - -def hpc_submit(experiment: str = "all", dry_run: bool = True): - """Submit HPC jobs from YAML configs.""" - from .hpc import ( - discover_experiments, - generate_pack, - get_experiment_name, - load_config, - generate_jobs, - ) - - experiments = discover_experiments() - if not experiments: - fail("No experiments with jobs.yaml found") - return - - # Filter experiments - if experiment != "all": - experiments = [ - e for e in experiments if experiment.lower() in e.parent.name.lower() - ] - - if not experiments: - fail(f"No matching experiment: {experiment}") - return - - for yaml_path in experiments: - name = get_experiment_name(yaml_path) - console.print(f"\n[bold]{name}:[/bold]") - - try: - config = load_config(yaml_path) - jobs = generate_jobs(config) - pack_content = generate_pack(yaml_path) - - dim(f"Generated {len(jobs)} jobs from {yaml_path.name}") - - if dry_run: - for job in jobs: - console.print(f" [cyan]{job['name']}[/cyan]") - else: - # Ensure logs directory exists - logs_dir = REPO_ROOT / "logs" - logs_dir.mkdir(exist_ok=True) - - # Write and submit pack file - pack_file = yaml_path.parent / "jobs.pack" - pack_file.write_text(pack_content) - - result = subprocess.run( - ["bsub", "-pack", str(pack_file)], - capture_output=True, - text=True, - cwd=str(REPO_ROOT), - ) - if result.returncode == 0: - ok(f"Submitted {len(jobs)} jobs") - if result.stdout.strip(): - dim(result.stdout.strip()) - else: - fail(f"Submission failed: {result.stderr}") - - except Exception as e: - fail(f"Error: {e}") diff --git a/src/cli/console.py b/src/cli/console.py deleted file mode 100644 index e0a38de..0000000 --- a/src/cli/console.py +++ /dev/null @@ -1,25 +0,0 @@ -"""Rich console output helpers.""" - -from rich.console import Console - -console = Console() - - -def ok(msg: str): - """Print success message.""" - console.print(f" [green]✓[/green] {msg}") - - -def fail(msg: str): - """Print failure message.""" - console.print(f" [red]✗[/red] {msg}") - - -def dim(msg: str): - """Print dimmed message.""" - console.print(f" [dim]{msg}[/dim]") - - -def header(msg: str): - """Print bold header.""" - console.print(f"\n[bold]{msg}[/bold]") diff --git a/src/cli/hpc.py b/src/cli/hpc.py deleted file mode 100644 index 2c74465..0000000 --- a/src/cli/hpc.py +++ /dev/null @@ -1,101 +0,0 @@ -"""HPC job generation from YAML configs.""" - -import itertools -from pathlib import Path -from typing import Any - -import yaml - -REPO_ROOT = Path(__file__).resolve().parent.parent.parent - - -def load_config(yaml_path: Path) -> dict[str, Any]: - """Load YAML config file.""" - with open(yaml_path) as f: - return yaml.safe_load(f) - - -def generate_jobs(config: dict[str, Any]) -> list[dict[str, Any]]: - """Generate job list from config with parameter sweep.""" - script = config["script"] - lsf = config["lsf"] - fixed_params = config.get("parameters", {}) - sweep = config.get("sweep", {}) - - # Get sweep parameter names and values - param_names = list(sweep.keys()) - param_values = list(sweep.values()) - - # Generate cartesian product of all parameters - if param_values: - combinations = list(itertools.product(*param_values)) - else: - combinations = [()] - - jobs = [] - for combo in combinations: - sweep_params = dict(zip(param_names, combo)) - - # Merge fixed params with sweep params (sweep overrides fixed) - params = {**fixed_params, **sweep_params} - - # Build job name from sweep parameters only - name_parts = [f"{k}{v}" for k, v in sweep_params.items()] - job_name = "-".join(name_parts) if name_parts else "job" - - jobs.append( - { - "name": job_name, - "script": script, - "params": params, - "lsf": lsf, - } - ) - - return jobs - - -def job_to_pack_line(job: dict[str, Any]) -> str: - """Convert job dict to LSF pack file line.""" - lsf = job["lsf"] - name = job["name"] - script = job["script"] - params = job["params"] - - # Build LSF options - parts = [ - f"-J {name}", - f"-q {lsf['queue']}", - f"-W {lsf['walltime']}", - f"-n {lsf['cores']}", - f'-R "rusage[mem={lsf["memory"]}]"', - f"-o logs/{name}.out", - f"-e logs/{name}.err", - ] - - # Build command - param_args = " ".join(f"--{k} {v}" for k, v in params.items()) - cmd = f"uv run python {script} {param_args}".strip() - - return " ".join(parts) + " " + cmd - - -def generate_pack(yaml_path: Path) -> str: - """Generate full pack file content from YAML config.""" - config = load_config(yaml_path) - jobs = generate_jobs(config) - lines = [job_to_pack_line(job) for job in jobs] - return "\n".join(lines) - - -def discover_experiments() -> list[Path]: - """Find all experiments with jobs.yaml.""" - experiments_dir = REPO_ROOT / "Experiments" - if not experiments_dir.exists(): - return [] - return sorted(experiments_dir.glob("*/jobs.yaml")) - - -def get_experiment_name(yaml_path: Path) -> str: - """Extract experiment name from path.""" - return yaml_path.parent.name diff --git a/src/cli/hpc_monitor.py b/src/cli/hpc_monitor.py deleted file mode 100644 index 51d259b..0000000 --- a/src/cli/hpc_monitor.py +++ /dev/null @@ -1,387 +0,0 @@ -"""Live HPC job monitor TUI using blessed.""" - -import subprocess -from dataclasses import dataclass - -from blessed import Terminal - - -@dataclass -class Job: - """HPC job info.""" - id: str - name: str - queue: str - status: str - cores: str - start_time: str - elapsed: str - - -def get_jobs() -> list[Job]: - """Fetch current jobs from bstat (preferred) or bjobs.""" - # Try bstat first - it shows elapsed time nicely - # Format: JOBID USER QUEUE JOB_NAME NALLOC STAT START_TIME ELAPSED - try: - result = subprocess.run(["bstat"], capture_output=True, text=True, timeout=10) - if result.returncode == 0 and result.stdout.strip(): - jobs = [] - lines = result.stdout.strip().split("\n") - for line in lines[1:]: # Skip header - parts = line.split() - if len(parts) >= 9: - # parts: [JOBID, USER, QUEUE, JOB_NAME, NALLOC, STAT, Mon, DD, HH:MM, ELAPSED] - jobs.append(Job( - id=parts[0], - queue=parts[2], - name=parts[3], - cores=parts[4], - status=parts[5], - start_time=f"{parts[6]} {parts[7]} {parts[8]}", # Nov 28 19:57 - elapsed=parts[-1], # Last column is elapsed - )) - return jobs - except Exception: - pass - - # Fallback to bjobs - try: - result = subprocess.run( - ["bjobs", "-w"], - capture_output=True, text=True, timeout=10 - ) - if result.returncode == 0 and result.stdout.strip(): - jobs = [] - lines = result.stdout.strip().split("\n") - for line in lines[1:]: - parts = line.split() - if len(parts) >= 7: - jobs.append(Job( - id=parts[0], - status=parts[2], - queue=parts[3], - name=parts[6], - cores="-", - start_time="-", - elapsed="-", - )) - return jobs - except Exception: - pass - return [] - - -def get_queue_info() -> list[str]: - """Get cluster queue information.""" - lines = [] - try: - result = subprocess.run(["bqueues"], capture_output=True, text=True, timeout=10) - if result.returncode == 0: - lines.append("=== Queue Summary ===") - lines.extend(result.stdout.strip().split("\n")) - except Exception: - lines.append("Failed to get queue info") - - try: - result = subprocess.run(["bhosts", "-w"], capture_output=True, text=True, timeout=10) - if result.returncode == 0: - lines.append("") - lines.append("=== Host Status ===") - lines.extend(result.stdout.strip().split("\n")) - except Exception: - pass - - return lines - - -def get_job_details(job_id: str) -> list[str]: - """Get detailed job information using bjobs -l.""" - lines = [] - try: - # Get full job details - result = subprocess.run( - ["bjobs", "-l", job_id], - capture_output=True, text=True, timeout=10 - ) - if result.returncode == 0 and result.stdout.strip(): - lines.append("=== Job Details ===") - lines.append("") - # bjobs -l output has weird formatting, clean it up - for line in result.stdout.split("\n"): - # Remove excessive whitespace but keep structure - cleaned = " ".join(line.split()) - if cleaned: - lines.append(cleaned) - lines.append("") - except Exception as e: - lines.append(f"Error getting job details: {e}") - - # Try to get output file content - try: - # First find the output file - peek = subprocess.run( - ["bjobs", "-o", "output_file", "-noheader", job_id], - capture_output=True, text=True, timeout=10 - ) - if peek.returncode == 0: - output_file = peek.stdout.strip() - if output_file and output_file not in ("-", ""): - lines.append("=== Output File ===") - lines.append(f"File: {output_file}") - lines.append("") - # Try to tail the file - tail = subprocess.run( - ["tail", "-n", "50", output_file], - capture_output=True, text=True, timeout=10 - ) - if tail.returncode == 0: - lines.extend(tail.stdout.split("\n")) - else: - lines.append("(file not yet available or not readable)") - except Exception: - pass - - return lines if lines else ["No information available"] - - -def kill_job(job_id: str) -> tuple[bool, str]: - """Kill a job.""" - try: - result = subprocess.run(["bkill", job_id], capture_output=True, text=True) - return result.returncode == 0, result.stdout.strip() or result.stderr.strip() - except Exception as e: - return False, str(e) - - -def kill_all_jobs() -> tuple[bool, str]: - """Kill all jobs.""" - try: - result = subprocess.run(["bkill", "0"], capture_output=True, text=True) - return result.returncode == 0, result.stdout.strip() or result.stderr.strip() - except Exception as e: - return False, str(e) - - -def draw_floating_window(term, title: str, lines: list[str], scroll: int) -> None: - """Draw a centered floating window with content.""" - # Window dimensions - win_width = min(term.width - 4, 100) - win_height = min(term.height - 6, 30) - start_x = (term.width - win_width) // 2 - start_y = (term.height - win_height) // 2 - - # Top border - print(term.move_xy(start_x, start_y) + term.cyan + "╭" + "─" * (win_width - 2) + "╮" + term.normal) - - # Title bar - title_text = f" {title} " - padding = win_width - 4 - len(title_text) - print(term.move_xy(start_x, start_y + 1) + term.cyan + "│" + term.normal + - term.bold + title_text + term.normal + " " * padding + - term.bright_black + "[j/k scroll, Esc close]" + term.normal + - term.cyan + " │" + term.normal) - print(term.move_xy(start_x, start_y + 2) + term.cyan + "├" + "─" * (win_width - 2) + "┤" + term.normal) - - # Content - content_height = win_height - 4 - visible = lines[scroll:scroll + content_height] - - for i in range(content_height): - line_content = visible[i][:win_width - 4] if i < len(visible) else "" - padding = win_width - 4 - len(line_content) - print(term.move_xy(start_x, start_y + 3 + i) + - term.cyan + "│ " + term.normal + line_content + " " * padding + term.cyan + " │" + term.normal) - - # Bottom border - print(term.move_xy(start_x, start_y + win_height - 1) + term.cyan + "╰" + "─" * (win_width - 2) + "╯" + term.normal) - - -def monitor(): - """Run the HPC monitor.""" - term = Terminal() - selected = 0 - message = "" - views = ["all", "running", "pending", "resources"] - view_idx = 0 - output_lines: list[str] = [] - resource_lines: list[str] = [] - scroll = 0 - modal_open = False - modal_scroll = 0 - - all_jobs = get_jobs() - - with term.fullscreen(), term.cbreak(), term.hidden_cursor(): - while True: - view = views[view_idx] - - # Filter jobs based on view - if view == "running": - jobs = [j for j in all_jobs if j.status == "RUN"] - elif view == "pending": - jobs = [j for j in all_jobs if j.status == "PEND"] - else: - jobs = all_jobs - - # Draw screen - print(term.home + term.clear) - - # Header with tabs - tabs = [] - for i, v in enumerate(views): - label = v.upper() - if v == "all": - label = f"ALL ({len(all_jobs)})" - elif v == "running": - label = f"RUN ({len([j for j in all_jobs if j.status == 'RUN'])})" - elif v == "pending": - label = f"PEND ({len([j for j in all_jobs if j.status == 'PEND'])})" - - if i == view_idx: - tabs.append(term.reverse + f" {label} " + term.normal) - else: - tabs.append(f" {label} ") - - tab_line = " │ ".join(tabs) - print(term.bold + term.cyan + " HPC Monitor " + term.normal + " " + tab_line) - print(term.cyan + "─" * term.width + term.normal) - - if view in ("all", "running", "pending"): - # Full width job view - hdr = f"{'ID':<10} {'Name':<30} {'Queue':<6} {'#':<4} {'Status':<6} {'Started':<14} {'Elapsed':<10}" - print(term.bold + hdr[:term.width] + term.normal) - print(term.bright_black + "─" * term.width + term.normal) - - max_rows = term.height - 8 - for i, job in enumerate(jobs[:max_rows]): - name = job.name[:28] + ".." if len(job.name) > 30 else job.name - - if job.status == "RUN": - status = term.green + f"{job.status:<6}" + term.normal - elif job.status == "PEND": - status = term.yellow + f"{job.status:<6}" + term.normal - else: - status = term.bright_black + f"{job.status:<6}" + term.normal - - row = f"{job.id:<10} {name:<30} {job.queue:<6} {job.cores:<4} {status} {job.start_time:<14} {job.elapsed:<10}" - - if i == selected: - print(term.reverse + row[:term.width] + term.normal) - else: - print(row[:term.width]) - - if not jobs: - print(term.bright_black + " No jobs" + term.normal) - - elif view == "resources": - max_rows = term.height - 6 - visible = resource_lines[scroll:scroll + max_rows] - for line in visible: - print(line[:term.width]) - - # Footer - print(term.move_y(term.height - 3) + term.cyan + "─" * term.width + term.normal) - - if message: - print(term.yellow + f" {message}" + term.normal) - else: - print() - - help_text = " [h/l] Tabs [j/k] Navigate [t] Job details [d] Kill [D] Kill All [r] Refresh [q] Quit" - print(term.bright_black + help_text[:term.width] + term.normal) - - # Draw floating modal if open - if modal_open and output_lines: - job_name = jobs[selected].name if jobs and 0 <= selected < len(jobs) else "Output" - draw_floating_window(term, job_name, output_lines, modal_scroll) - - # Input with timeout for auto-refresh - key = term.inkey(timeout=5) - - if key: - message = "" - - # Modal controls - if modal_open: - if key.name == 'KEY_ESCAPE' or key == 't' or key == 'q': - modal_open = False - elif key == 'j' or key.name == 'KEY_DOWN': - modal_scroll = min(modal_scroll + 1, max(0, len(output_lines) - 10)) - elif key == 'k' or key.name == 'KEY_UP': - modal_scroll = max(0, modal_scroll - 1) - elif key == 'G': # Jump to bottom - modal_scroll = max(0, len(output_lines) - 10) - elif key == 'g': # Jump to top - modal_scroll = 0 - elif key.lower() == 'r': # Refresh output - if jobs and 0 <= selected < len(jobs): - output_lines = get_job_details(jobs[selected].id) - message = "Refreshed" - continue - - if key.lower() == 'q': - break - - elif key.lower() == 'r': - all_jobs = get_jobs() - selected = min(selected, max(0, len(jobs) - 1)) - if view == "resources": - resource_lines = get_queue_info() - message = "Refreshed" - - # Tab navigation - elif key == 'h' or key.name == 'KEY_LEFT': - view_idx = (view_idx - 1) % len(views) - selected = 0 - scroll = 0 - if views[view_idx] == "resources": - resource_lines = get_queue_info() - - elif key == 'l' or key.name == 'KEY_RIGHT': - view_idx = (view_idx + 1) % len(views) - selected = 0 - scroll = 0 - if views[view_idx] == "resources": - resource_lines = get_queue_info() - - # Vertical navigation - elif key == 'j' or key.name == 'KEY_DOWN': - if view in ("all", "running", "pending"): - selected = min(selected + 1, len(jobs) - 1) if jobs else 0 - elif view == "resources": - scroll += 1 - - elif key == 'k' or key.name == 'KEY_UP': - if view in ("all", "running", "pending"): - selected = max(selected - 1, 0) - elif view == "resources": - scroll = max(0, scroll - 1) - - elif key == 'd' and jobs and view in ("all", "running", "pending"): - job = jobs[selected] - ok, msg = kill_job(job.id) - message = f"Killed: {job.name}" if ok else f"Failed: {msg}" - all_jobs = get_jobs() - selected = min(selected, max(0, len(jobs) - 1)) - - elif key == 'D': - ok, msg = kill_all_jobs() - message = "Killed all jobs" if ok else f"Failed: {msg}" - all_jobs = get_jobs() - selected = 0 - - # Open job details modal - elif key == 't' and jobs and view in ("all", "running", "pending"): - output_lines = get_job_details(jobs[selected].id) - modal_scroll = 0 - modal_open = True - - else: - # Auto-refresh on timeout (but not if modal is open) - if not modal_open: - all_jobs = get_jobs() - selected = min(selected, max(0, len(jobs) - 1)) - - -if __name__ == "__main__": - monitor() diff --git a/src/cli/interactive.py b/src/cli/interactive.py deleted file mode 100644 index 70e9f7d..0000000 --- a/src/cli/interactive.py +++ /dev/null @@ -1,269 +0,0 @@ -"""Interactive CLI menu with arrow-key navigation.""" - -import questionary -from rich.panel import Panel - -from .console import console -from .actions import ( - fetch_mlflow, - run_scripts, - build_docs, - ruff_check, - ruff_format, - hpc_submit, - hpc_monitor, - REPO_ROOT, -) - -STYLE = questionary.Style([("highlighted", "bold cyan"), ("pointer", "cyan")]) - - -def select(message: str, choices: list[str]) -> str | None: - """Wrapped questionary select.""" - return questionary.select(message, choices=choices, style=STYLE).ask() - - -def confirm(message: str, default: bool = True) -> bool | None: - """Wrapped questionary confirm.""" - return questionary.confirm(message, default=default).ask() - - -def wait(): - """Wait for Enter key.""" - input("\nEnter to continue...") - - -# ───────────────────────────────────────────────────────────────────────────── -# Submenus -# ───────────────────────────────────────────────────────────────────────────── - - -def menu_runner(): - """Runner submenu for compute/plot scripts.""" - while True: - choice = select( - "Runner:", - [ - "Compute scripts", - "Plot scripts", - "← Back", - ], - ) - - if choice == "Compute scripts": - run_scripts("compute") - wait() - elif choice == "Plot scripts": - run_scripts("plot") - wait() - else: - break - - -def menu_clean(): - """Clean submenu.""" - import shutil - - while True: - choice = select( - "Clean:", - [ - "Clean docs", - "Clean data", - "Clean caches", - "Clean all", - "← Back", - ], - ) - - if choice == "Clean docs": - build_dir = REPO_ROOT / "docs" / "build" - if build_dir.exists(): - shutil.rmtree(build_dir) - console.print(" [green]✓[/green] Cleaned docs/build") - else: - console.print(" [dim]Nothing to clean[/dim]") - wait() - - elif choice == "Clean data": - data_dir = REPO_ROOT / "data" - count = 0 - if data_dir.exists(): - for item in data_dir.iterdir(): - if item.name not in ("README.md", ".gitkeep"): - shutil.rmtree(item) if item.is_dir() else item.unlink() - count += 1 - console.print( - f" [green]✓[/green] Cleaned {count} items" - ) if count else console.print(" [dim]Nothing to clean[/dim]") - wait() - - elif choice == "Clean caches": - targets = [".pytest_cache", ".ruff_cache", ".mypy_cache", "build", "dist"] - count = 0 - for t in targets: - path = REPO_ROOT / t - if path.exists(): - shutil.rmtree(path) - count += 1 - for pycache in REPO_ROOT.rglob("__pycache__"): - shutil.rmtree(pycache) - count += 1 - console.print( - f" [green]✓[/green] Cleaned {count} items" - ) if count else console.print(" [dim]Nothing to clean[/dim]") - wait() - - elif choice == "Clean all": - # Docs - build_dir = REPO_ROOT / "docs" / "build" - if build_dir.exists(): - shutil.rmtree(build_dir) - # Data - data_dir = REPO_ROOT / "data" - if data_dir.exists(): - for item in data_dir.iterdir(): - if item.name not in ("README.md", ".gitkeep"): - shutil.rmtree(item) if item.is_dir() else item.unlink() - # Caches - for t in [".pytest_cache", ".ruff_cache", ".mypy_cache", "build", "dist"]: - path = REPO_ROOT / t - if path.exists(): - shutil.rmtree(path) - for pycache in REPO_ROOT.rglob("__pycache__"): - shutil.rmtree(pycache) - console.print(" [green]✓[/green] Cleaned all") - wait() - - else: - break - - -def menu_hpc(): - """HPC submenu.""" - from .hpc import discover_experiments, get_experiment_name - - while True: - choice = select( - "HPC:", - [ - "Live monitor", - "Submit jobs", - "← Back", - ], - ) - - if choice == "Live monitor": - hpc_monitor() - - elif choice == "Submit jobs": - experiments = discover_experiments() - if not experiments: - console.print(" [dim]No experiments with jobs.yaml found[/dim]") - wait() - continue - - exp_names = ["all"] + [get_experiment_name(e) for e in experiments] - experiment = select("Experiment:", exp_names + ["← Back"]) - - if experiment and experiment != "← Back": - if not confirm("Submit to HPC?", default=False): - continue - hpc_submit(experiment, dry_run=False) - wait() - - else: - break - - -def menu_code(): - """Code quality submenu.""" - from .actions import copy_plots - - while True: - choice = select( - "Code:", - [ - "Lint (ruff check)", - "Format (ruff format)", - "Copy plots", - "← Back", - ], - ) - - if choice == "Lint (ruff check)": - ruff_check() - wait() - elif choice == "Format (ruff format)": - ruff_format() - wait() - elif choice == "Copy plots": - copy_plots() - wait() - else: - break - - -# ───────────────────────────────────────────────────────────────────────────── -# Main menu -# ───────────────────────────────────────────────────────────────────────────── - - -def interactive(): - """Run interactive menu.""" - while True: - console.clear() - console.print( - Panel.fit( - "[bold cyan]ANA-P3[/bold cyan] [dim]Advanced Numerical Algorithms[/dim]", - border_style="cyan", - ) - ) - console.print() - - choice = select( - "Select:", - [ - "Fetch MLflow data", - "Runner", - "Build docs", - "Code", - "Clean", - "HPC", - "Exit", - ], - ) - - if choice is None or choice == "Exit": - console.print("[dim]Goodbye![/dim]\n") - break - - elif choice == "Fetch MLflow data": - fetch_mlflow() - wait() - - elif choice == "Runner": - console.print() - menu_runner() - - elif choice == "Build docs": - build_docs() - # Try to open in browser - index = REPO_ROOT / "docs" / "build" / "html" / "index.html" - if index.exists(): - import webbrowser - - webbrowser.open(f"file://{index}") - wait() - - elif choice == "Code": - console.print() - menu_code() - - elif choice == "Clean": - console.print() - menu_clean() - - elif choice == "HPC": - console.print() - menu_hpc() diff --git a/src/ldc/data_io.py b/src/ldc/data_io.py deleted file mode 100644 index 9a00565..0000000 --- a/src/ldc/data_io.py +++ /dev/null @@ -1,195 +0,0 @@ -"""Data I/O utilities for HDF5 and pandas integration. - -This module provides helpers for loading solver results from HDF5 files -into pandas DataFrames for analysis and plotting. -""" - -from pathlib import Path -from typing import Dict, Any, List, Union -import numpy as np -import pandas as pd -import h5py - - -def load_run_data(path: Union[str, Path]) -> pd.DataFrame: - """Load HDF5 run data as DataFrame for plotting. - - Loads time-series data (residuals) along with metadata as columns. - This format is optimized for seaborn plotting with `hue` parameter. - - Parameters - ---------- - path : str or Path - Path to HDF5 file. - - Returns - ------- - pd.DataFrame - DataFrame with columns: - - iteration: Iteration number (0, 1, 2, ...) - - residual: Residual value at each iteration - - u_residual, v_residual, continuity_residual: Component residuals (if available) - - Re: Reynolds number (from metadata) - - converged: Whether solver converged (from metadata) - - All other metadata fields as additional columns - - Examples - -------- - >>> df = load_run_data('run.h5') - >>> df.head() - iteration residual Re converged mesh_path ... - 0 0 1.000000 100 True fine.msh ... - 1 1 0.500000 100 True fine.msh ... - - >>> # Plot multiple runs - >>> import seaborn as sns - >>> df1 = load_run_data('run1.h5').assign(run='Run 1') - >>> df2 = load_run_data('run2.h5').assign(run='Run 2') - >>> df = pd.concat([df1, df2]) - >>> sns.lineplot(data=df, x='iteration', y='residual', hue='run') - """ - path = Path(path) - - with h5py.File(path, "r") as f: - # Load time-series data - residual = f["time_series/residual"][:] - n_iter = len(residual) - - # Start building DataFrame with time-series - data = { - "iteration": np.arange(n_iter), - "residual": residual, - } - - # Add other time-series if available - ts_group = f["time_series"] - for key in ts_group.keys(): - if key != "residual": # Already added - data[key] = ts_group[key][:] - - # Load metadata and broadcast to all rows - metadata = dict(f.attrs) - - # Create DataFrame - df = pd.DataFrame(data) - - # Add metadata as columns (broadcast to all rows) - for key, value in metadata.items(): - df[key] = value - - return df - - -def load_fields(path: Union[str, Path]) -> pd.DataFrame: - """Load spatial fields as DataFrame. - - Parameters - ---------- - path : str or Path - Path to HDF5 file. - - Returns - ------- - pd.DataFrame - DataFrame with columns: - - x, y: Spatial coordinates - - u, v, p: Velocity and pressure fields - - velocity_magnitude: Magnitude of velocity - - Examples - -------- - >>> df = load_fields('run.h5') - >>> df.head() - x y u v p velocity_magnitude - 0 0.000000 0.000000 0.000000 0.000000 0.500000 0.000000 - 1 0.031250 0.000000 0.000000 0.000000 0.490000 0.000000 - """ - path = Path(path) - - with h5py.File(path, "r") as f: - # Load grid points - grid_points = f["grid_points"][:] - - # Load fields - u = f["fields/u"][:] - v = f["fields/v"][:] - p = f["fields/p"][:] - vel_mag = f["fields/velocity_magnitude"][:] - - # Create DataFrame - df = pd.DataFrame( - { - "x": grid_points[:, 0], - "y": grid_points[:, 1], - "u": u, - "v": v, - "p": p, - "velocity_magnitude": vel_mag, - } - ) - - return df - - -def load_metadata(path: Union[str, Path]) -> Dict[str, Any]: - """Load only metadata from HDF5 file. - - Parameters - ---------- - path : str or Path - Path to HDF5 file. - - Returns - ------- - dict - Metadata dictionary containing solver config and convergence info. - - Examples - -------- - >>> metadata = load_metadata('run.h5') - >>> print(f"Re={metadata['Re']}, converged={metadata['converged']}") - Re=100.0, converged=True - """ - path = Path(path) - - with h5py.File(path, "r") as f: - metadata = dict(f.attrs) - - return metadata - - -def load_multiple_runs( - paths: List[Union[str, Path]], labels: List[str] = None -) -> pd.DataFrame: - """Load multiple runs into single DataFrame for comparison. - - Parameters - ---------- - paths : list of str or Path - Paths to HDF5 files. - labels : list of str, optional - Labels for each run. If None, uses filenames. - - Returns - ------- - pd.DataFrame - Combined DataFrame with 'run' column for distinguishing runs. - - Examples - -------- - >>> df = load_multiple_runs( - ... ['run1.h5', 'run2.h5'], - ... labels=['32x32', '64x64'] - ... ) - >>> sns.lineplot(data=df, x='iteration', y='residual', hue='run') - """ - if labels is None: - labels = [Path(p).stem for p in paths] - - dfs = [] - for path, label in zip(paths, labels): - df = load_run_data(path) - df["run"] = label - dfs.append(df) - - return pd.concat(dfs, ignore_index=True) diff --git a/src/ldc/field_interpolator.py b/src/ldc/field_interpolator.py deleted file mode 100644 index d8051c1..0000000 --- a/src/ldc/field_interpolator.py +++ /dev/null @@ -1,227 +0,0 @@ -"""Unified field interpolator for FV and Spectral solvers. - -This module provides a consistent interface for interpolating solution fields -from different grid types (collocated FV, Gauss-Lobatto spectral) to a unified -representation suitable for validation and visualization. -""" - -import numpy as np -import pandas as pd -from pathlib import Path -from scipy.interpolate import RectBivariateSpline - - -class UnifiedFieldInterpolator: - """Unified interpolator for FV and Spectral solution fields. - - Automatically detects grid type and creates high-quality interpolators - that handle boundary conditions appropriately for each grid type. - - Parameters - ---------- - h5_path : str or Path - Path to HDF5 solution file - - Attributes - ---------- - fields : pd.DataFrame - Raw field data from HDF5 file - grid_type : str - Detected grid type: 'collocated_fv' or 'lobatto_spectral' - interpolators : dict - Dictionary of RectBivariateSpline interpolators for each field - """ - - def __init__(self, h5_path): - """Initialize interpolator from HDF5 file.""" - self.h5_path = Path(h5_path) - - # Load fields and params (new format uses 'params' instead of 'metadata') - self.fields = pd.read_hdf(self.h5_path, "fields") - self.params = pd.read_hdf(self.h5_path, "params") - - # Detect grid type and create interpolators - self._detect_grid_type() - self._create_interpolators() - - def _detect_grid_type(self): - """Detect whether this is FV collocated or spectral Lobatto grid.""" - x = self.fields["x"].values - y = self.fields["y"].values - - x_unique = np.unique(x) - y_unique = np.unique(y) - - # Check if grid points are at boundaries (0 and 1) - has_boundary_x = ( - np.abs(x_unique.min()) < 1e-10 and np.abs(x_unique.max() - 1.0) < 1e-10 - ) - has_boundary_y = ( - np.abs(y_unique.min()) < 1e-10 and np.abs(y_unique.max() - 1.0) < 1e-10 - ) - - if has_boundary_x and has_boundary_y: - # Gauss-Lobatto points include boundaries - self.grid_type = "lobatto_spectral" - else: - # Collocated FV: cell centers don't touch boundaries - self.grid_type = "collocated_fv" - - def _create_interpolators(self): - """Create bicubic spline interpolators for all fields.""" - x = self.fields["x"].values - y = self.fields["y"].values - - # Get unique sorted coordinates - x_unique = np.sort(np.unique(x)) - y_unique = np.sort(np.unique(y)) - - # Store grid info - self.x_grid = x_unique - self.y_grid = y_unique - - # Reshape fields to 2D grids - sort_indices = np.lexsort((x, y)) - - self.interpolators = {} - for field in ["u", "v", "p"]: - if field in self.fields.columns: - field_values = self.fields[field].values[sort_indices] - field_grid = field_values.reshape((len(y_unique), len(x_unique))) - - # Create bicubic interpolator - self.interpolators[field] = RectBivariateSpline( - y_unique, x_unique, field_grid, kx=3, ky=3 - ) - - def evaluate_at_points(self, x, y, field="u"): - """Evaluate field at arbitrary points using interpolation. - - Parameters - ---------- - x : array_like - X coordinates - y : array_like - Y coordinates - field : str - Field name ('u', 'v', or 'p') - - Returns - ------- - values : np.ndarray - Interpolated field values at (x, y) points - """ - if field not in self.interpolators: - raise ValueError( - f"Field '{field}' not available. Available: {list(self.interpolators.keys())}" - ) - - return self.interpolators[field](y, x, grid=False) - - def get_uniform_grid(self, nx=200, ny=200, include_boundaries=True): - """Get fields interpolated onto a uniform grid. - - Parameters - ---------- - nx, ny : int - Number of points in x and y directions - include_boundaries : bool - If True, grid extends to boundaries (0, 1). If False, stays - within the original data extent (useful for FV collocated grids). - - Returns - ------- - dict - Dictionary with keys: - - 'x': 2D array of x coordinates - - 'y': 2D array of y coordinates - - 'u', 'v', 'p': 2D arrays of interpolated field values - """ - if include_boundaries or self.grid_type == "lobatto_spectral": - # Include full domain boundaries - x_uniform = np.linspace(0, 1, nx) - y_uniform = np.linspace(0, 1, ny) - else: - # Stay within data extent (for FV collocated) - x_uniform = np.linspace(self.x_grid.min(), self.x_grid.max(), nx) - y_uniform = np.linspace(self.y_grid.min(), self.y_grid.max(), ny) - - # Create meshgrid - X, Y = np.meshgrid(x_uniform, y_uniform, indexing="xy") - - # Interpolate all fields - result = {"x": X, "y": Y} - for field_name, interp in self.interpolators.items(): - result[field_name] = interp(y_uniform, x_uniform, grid=True) - - return result - - def extract_centerline(self, field="u", axis="y", n_points=200): - """Extract field values along a centerline. - - Parameters - ---------- - field : str - Field name ('u', 'v', or 'p') - axis : str - Axis along which to extract: - - 'y': vertical centerline at x=0.5 (returns position, field) - - 'x': horizontal centerline at y=0.5 (returns position, field) - n_points : int - Number of points along centerline - - Returns - ------- - position : np.ndarray - Position coordinates along centerline - values : np.ndarray - Field values along centerline - """ - if field not in self.interpolators: - raise ValueError(f"Field '{field}' not available") - - interp = self.interpolators[field] - - # Always extract from 0 to 1 for centerlines - position = np.linspace(0, 1, n_points) - - if axis == "y": - # Vertical centerline at x=0.5 - values = interp(position, 0.5, grid=False) - elif axis == "x": - # Horizontal centerline at y=0.5 - values = interp(0.5, position, grid=False) - else: - raise ValueError(f"axis must be 'x' or 'y', got '{axis}'") - - return position, values - - def get_info(self): - """Get information about the grid and interpolator. - - Returns - ------- - dict - Dictionary with grid information - """ - return { - "grid_type": self.grid_type, - "nx": len(self.x_grid), - "ny": len(self.y_grid), - "x_range": (self.x_grid.min(), self.x_grid.max()), - "y_range": (self.y_grid.min(), self.y_grid.max()), - "available_fields": list(self.interpolators.keys()), - "file": self.h5_path.name, - } - - def __repr__(self): - """String representation.""" - info = self.get_info() - return ( - f"UnifiedFieldInterpolator(\n" - f" file='{info['file']}',\n" - f" grid_type='{info['grid_type']}',\n" - f" resolution={info['nx']}×{info['ny']},\n" - f" fields={info['available_fields']}\n" - f")" - ) diff --git a/src/ldc/ghia_validator.py b/src/ldc/ghia_validator.py deleted file mode 100644 index 08cad91..0000000 --- a/src/ldc/ghia_validator.py +++ /dev/null @@ -1,387 +0,0 @@ -"""Ghia benchmark validator for lid-driven cavity simulations.""" - -from pathlib import Path - -import numpy as np -from scipy.interpolate import interp1d - -from utils.plotting import plt, pd -from utils.field_interpolator import UnifiedFieldInterpolator - - -class GhiaValidator: - """Validator for lid-driven cavity results against Ghia et al. (1982) benchmark. - - Clean DataFrame-native implementation for validating LDC solutions. - - Parameters - ---------- - h5_path : str or Path - Path to HDF5 file with solution fields. - Re : float, optional - Reynolds number (inferred from file if not provided). - validation_data_dir : str or Path, optional - Directory containing Ghia CSV files. If None, uses default location. - method_label : str, optional - Label for this method (for multi-method comparisons). Defaults to filename stem. - - Attributes - ---------- - fields : pd.DataFrame - Solution fields (x, y, u, v, p) - ghia_u : pd.DataFrame - Ghia benchmark u-velocity data - ghia_v : pd.DataFrame - Ghia benchmark v-velocity data - Re : float - Reynolds number - method_label : str - Method label for plotting - """ - - AVAILABLE_RE = [100, 400, 1000, 3200, 5000, 7500, 10000] - - def __init__(self, h5_path, Re=None, validation_data_dir=None, method_label=None): - """Initialize validator and load data as DataFrames. - - Parameters - ---------- - h5_path : str or Path - Path to HDF5 file with solution fields. - Re : float, optional - Reynolds number (inferred from file if not provided). - validation_data_dir : str or Path, optional - Directory containing Ghia CSV files. If None, uses default location. - method_label : str, optional - Label for this method (for multi-method comparisons). - """ - self.h5_path = Path(h5_path) - - # Create unified interpolator (handles both FV and Spectral grids) - self.interpolator = UnifiedFieldInterpolator(self.h5_path) - self.fields = self.interpolator.fields # For compatibility - - # Load params (new format uses 'params' instead of 'metadata') - params = pd.read_hdf(self.h5_path, "params") - - # Get Reynolds number - self.Re = Re if Re is not None else params["Re"].iloc[0] - - # Require exact match for Reynolds number - if self.Re not in self.AVAILABLE_RE: - raise ValueError( - f"No Ghia benchmark data available for Re={self.Re}. " - f"Available Re values: {self.AVAILABLE_RE}" - ) - - # Method label for multi-method comparisons - self.method_label = method_label or self.h5_path.stem - - # Set validation data directory - if validation_data_dir is None: - from utils import get_project_root - - validation_data_dir = get_project_root() / "data" / "validation" / "ghia" - self.validation_data_dir = Path(validation_data_dir) - - # Load Ghia benchmark DataFrames - self._load_ghia_data() - - def _load_ghia_data(self): - """Load Ghia benchmark data as DataFrames.""" - u_file = self.validation_data_dir / f"ghia_Re{int(self.Re)}_u_centerline.csv" - v_file = self.validation_data_dir / f"ghia_Re{int(self.Re)}_v_centerline.csv" - - if not u_file.exists() or not v_file.exists(): - raise FileNotFoundError( - f"Ghia data files not found for Re={self.Re} in {self.validation_data_dir}" - ) - - self.ghia_u = pd.read_csv(u_file) - self.ghia_v = pd.read_csv(v_file) - - def _extract_centerline(self, field, centerline_axis): - """Extract velocity along centerline using unified interpolator. - - Parameters - ---------- - field : str - Field to extract ('u' or 'v') - centerline_axis : str - Axis along which to extract ('x' or 'y') - - 'x': horizontal centerline at y=0.5 - - 'y': vertical centerline at x=0.5 - - Returns - ------- - position : np.ndarray - Position coordinates along centerline - velocity : np.ndarray - Velocity values along centerline - """ - # Use unified interpolator (handles both FV and Spectral grids consistently) - position, velocity = self.interpolator.extract_centerline( - field=field, axis=centerline_axis, n_points=200 - ) - return position, velocity - - def get_validation_dataframe(self): - """Create validation DataFrame for plotting with seaborn. - - Returns - ------- - pd.DataFrame - Long-format DataFrame with columns: position, velocity, source, component, method - - position: x or y coordinate - - velocity: u or v value - - source: 'Simulation' or 'Ghia et al. (1982)' - - component: 'u' or 'v' - - method: method label (for multi-method comparisons) - """ - # Extract centerline data - y_sim, u_sim = self._extract_centerline("u", "y") - x_sim, v_sim = self._extract_centerline("v", "x") - - # Simulation data - u_sim_df = pd.DataFrame({"position": y_sim, "velocity": u_sim}).assign( - source="Simulation", - component="u (vertical centerline)", - method=self.method_label, - ) - - v_sim_df = pd.DataFrame({"position": x_sim, "velocity": v_sim}).assign( - source="Simulation", - component="v (horizontal centerline)", - method=self.method_label, - ) - - # Ghia benchmark data (already DataFrames, just rename and add columns) - u_ghia_df = self.ghia_u.rename( - columns={"y": "position", "u": "velocity"} - ).assign( - source="Ghia et al. (1982)", - component="u (vertical centerline)", - method=self.method_label, - ) - - v_ghia_df = self.ghia_v.rename( - columns={"x": "position", "v": "velocity"} - ).assign( - source="Ghia et al. (1982)", - component="v (horizontal centerline)", - method=self.method_label, - ) - - return pd.concat([u_sim_df, v_sim_df, u_ghia_df, v_ghia_df], ignore_index=True) - - def compute_errors(self): - """Compute error metrics against Ghia benchmark data. - - Note: Excludes boundary points (y=0, y=1 for u; x=0, x=1 for v) - since collocated grids store values at cell centers, not boundaries. - - Returns - ------- - dict - Dictionary containing error metrics for u and v velocities: - - 'u_l2': L2 norm of u error - - 'u_linf': L∞ (maximum) norm of u error - - 'u_rms': Root mean square error for u - - 'v_l2': L2 norm of v error - - 'v_linf': L∞ (maximum) norm of v error - - 'v_rms': Root mean square error for v - """ - # Extract centerline data - y_sim, u_sim = self._extract_centerline("u", "y") - x_sim, v_sim = self._extract_centerline("v", "x") - - # Filter out boundary points from Ghia data - # For u-velocity: exclude y=0 and y=1 - eps = 1e-6 - ghia_u_interior = self.ghia_u[ - (self.ghia_u["y"] > eps) & (self.ghia_u["y"] < 1.0 - eps) - ] - - # For v-velocity: exclude x=0 and x=1 - ghia_v_interior = self.ghia_v[ - (self.ghia_v["x"] > eps) & (self.ghia_v["x"] < 1.0 - eps) - ] - - # Interpolate simulation results at interior Ghia benchmark points - u_interp_func = interp1d(y_sim, u_sim, kind="cubic", fill_value="extrapolate") - u_sim_at_ghia = u_interp_func(ghia_u_interior["y"].values) - u_error = u_sim_at_ghia - ghia_u_interior["u"].values - - v_interp_func = interp1d(x_sim, v_sim, kind="cubic", fill_value="extrapolate") - v_sim_at_ghia = v_interp_func(ghia_v_interior["x"].values) - v_error = v_sim_at_ghia - ghia_v_interior["v"].values - - # Compute error norms - return { - "u_l2": np.sqrt(np.sum(u_error**2)), - "u_linf": np.max(np.abs(u_error)), - "u_rms": np.sqrt(np.mean(u_error**2)), - "v_l2": np.sqrt(np.sum(v_error**2)), - "v_linf": np.max(np.abs(v_error)), - "v_rms": np.sqrt(np.mean(v_error**2)), - } - - def print_summary(self): - """Print validation summary with error metrics.""" - errors = self.compute_errors() - - print("\n" + "=" * 70) - print(f"{'VALIDATION SUMMARY':^70}") - print("=" * 70) - print(f" Reynolds number: Re = {self.Re:.0f}") - print(f" Benchmark: Ghia et al. (1982), Re = {self.Re:.0f}") - print(f" Solution file: {self.h5_path.name}") - print(" Note: Boundary points excluded (interior points only)") - print("-" * 70) - print(f"{'ERROR METRICS':^70}") - print("-" * 70) - print(f" {'Velocity':<12} {'L² Error':<15} {'L∞ Error':<15} {'RMS Error':<15}") - print("-" * 70) - print( - f" {'u (vertical)':<12} {errors['u_l2']:<15.6e} {errors['u_linf']:<15.6e} {errors['u_rms']:<15.6e}" - ) - print( - f" {'v (horizontal)':<12} {errors['v_l2']:<15.6e} {errors['v_linf']:<15.6e} {errors['v_rms']:<15.6e}" - ) - print("=" * 70) - - # Provide interpretation - if errors["u_rms"] < 1e-3 and errors["v_rms"] < 1e-3: - quality = "EXCELLENT" - elif errors["u_rms"] < 1e-2 and errors["v_rms"] < 1e-2: - quality = "GOOD" - elif errors["u_rms"] < 0.05 and errors["v_rms"] < 0.05: - quality = "ACCEPTABLE" - else: - quality = "NEEDS IMPROVEMENT" - - print(f" Overall validation quality: {quality}") - print("=" * 70 + "\n") - - -def plot_validation(validators, output_path=None): - """Plot validation against Ghia benchmark using seaborn. - - Handles both single and multiple methods automatically. - - Parameters - ---------- - validators : GhiaValidator or list of GhiaValidator - Single validator or list of validators to compare (must all have same Re) - output_path : str or Path, optional - Path to save figure. If None, figure is not saved. - """ - import seaborn as sns - - # Normalize to list - if not isinstance(validators, list): - validators = [validators] - - # Combine all validation DataFrames - dfs = [v.get_validation_dataframe() for v in validators] - df = pd.concat(dfs, ignore_index=True) - - # Get Re for title (use first validator's Re) - Re = validators[0].Re - - # Create figure with two subplots - fig, axes = plt.subplots(1, 2, figsize=(14, 5)) - - # Left panel: U velocity - df_u = df[df["component"] == "u (vertical centerline)"] - df_u_sim = df_u[df_u["source"] == "Simulation"].sort_values(["method", "position"]) - df_u_ghia = ( - df_u[df_u["source"] == "Ghia et al. (1982)"] - .drop_duplicates(subset=["position"]) - .sort_values("position") - ) - - # Use seaborn with hue='method' for simulation data - sns.lineplot( - data=df_u_sim, - x="velocity", - y="position", - hue="method", - ax=axes[0], - linewidth=2.5, - alpha=0.8, - sort=False, - estimator=None, - ) - # Ghia benchmark as scatter - sns.scatterplot( - data=df_u_ghia, - x="velocity", - y="position", - ax=axes[0], - marker="x", - s=50, - color="black", - label="Ghia et al. (1982)", - zorder=10, - ) - - axes[0].set_xlabel("$u$", fontsize=12) - axes[0].set_ylabel("$y$", fontsize=12) - axes[0].set_title("U velocity (vertical centerline)", fontweight="bold") - axes[0].legend(frameon=True, loc="best") - axes[0].grid(True, alpha=0.3) - - # Right panel: V velocity - df_v = df[df["component"] == "v (horizontal centerline)"] - df_v_sim = df_v[df_v["source"] == "Simulation"].sort_values(["method", "position"]) - df_v_ghia = ( - df_v[df_v["source"] == "Ghia et al. (1982)"] - .drop_duplicates(subset=["position"]) - .sort_values("position") - ) - - # Use seaborn with hue='method' for simulation data - sns.lineplot( - data=df_v_sim, - x="position", - y="velocity", - hue="method", - ax=axes[1], - linewidth=2.5, - alpha=0.8, - sort=False, - estimator=None, - ) - # Ghia benchmark as scatter - sns.scatterplot( - data=df_v_ghia, - x="position", - y="velocity", - ax=axes[1], - marker="x", - s=50, - color="black", - label="Ghia et al. (1982)", - zorder=10, - ) - - axes[1].set_xlabel("$x$", fontsize=12) - axes[1].set_ylabel("$v$", fontsize=12) - axes[1].set_title("V velocity (horizontal centerline)", fontweight="bold") - axes[1].legend(frameon=True, loc="best") - axes[1].grid(True, alpha=0.3) - - # Set overall title - n_methods = len(validators) - if n_methods == 1: - title = f"Ghia Benchmark Validation (Re = {Re:.0f})" - else: - title = f"Ghia Benchmark Validation: Method Comparison (Re = {Re:.0f})" - - fig.suptitle(title, fontweight="bold", fontsize=14, y=1.00) - plt.tight_layout() - - if output_path: - fig.savefig(output_path, bbox_inches="tight", dpi=300) - print(f"Validation plot saved to: {output_path}") diff --git a/src/ldc/ldc_plotter.py b/src/ldc/ldc_plotter.py deleted file mode 100644 index f131827..0000000 --- a/src/ldc/ldc_plotter.py +++ /dev/null @@ -1,359 +0,0 @@ -"""LDC results plotter for single and multiple runs.""" - -from pathlib import Path - -import matplotlib.pyplot as plt -import seaborn as sns -import pandas as pd -import numpy as np - - -class LDCPlotter: - """Plotter for lid-driven cavity simulation results. - - Clean DataFrame-native implementation for plotting LDC solutions. - - Parameters - ---------- - runs : dict, str, Path, or list - Single run or list of runs. Can be: - - str/Path: Path to HDF5 file - - dict: Dictionary with 'h5_path' (and optionally 'label') - - list: List of any of the above (requires 'label' in dicts) - - Attributes - ---------- - fields : pd.DataFrame - Spatial fields (x, y, u, v, p) for all runs - time_series : pd.DataFrame - Time series data (residuals) for all runs - metadata : pd.DataFrame - Configuration and convergence metadata for all runs - - Examples - -------- - >>> # Single run - >>> plotter = LDCPlotter('run.h5') - >>> plotter.plot_convergence() - - >>> # Multiple runs with labels - >>> plotter = LDCPlotter([ - ... {'h5_path': 'run1.h5', 'label': '32x32'}, - ... {'h5_path': 'run2.h5', 'label': '64x64'} - ... ]) - """ - - def __init__(self, runs): - """Initialize plotter and load data as DataFrames. - - Parameters - ---------- - runs : dict, str, Path, or list - Single run or list of runs to load. - """ - # Normalize to list - if not isinstance(runs, list): - runs = [runs] - - # Load all runs - fields_list = [] - time_series_list = [] - metadata_list = [] - - for run in runs: - # Normalize run to dict - if isinstance(run, (str, Path)): - run = {"h5_path": run, "label": Path(run).stem} - - h5_path = Path(run["h5_path"]) - if not h5_path.exists(): - raise FileNotFoundError(f"HDF5 file not found: {h5_path}") - - label = run.get("label", h5_path.stem) - - # Load DataFrames and add run label - # New format uses 'params' and 'metrics' instead of 'metadata' - params_df = pd.read_hdf(h5_path, "params") - metrics_df = pd.read_hdf(h5_path, "metrics") - # Combine params and metrics into single metadata row - metadata_df = pd.concat([params_df, metrics_df], axis=1).assign(run=label) - - fields_df = pd.read_hdf(h5_path, "fields").assign(run=label) - time_series_df = pd.read_hdf(h5_path, "time_series").assign( - run=label, iteration=lambda df: range(len(df)) - ) - - fields_list.append(fields_df) - time_series_list.append(time_series_df) - metadata_list.append(metadata_df) - - # Concatenate all runs - self.fields = pd.concat(fields_list, ignore_index=True) - self.time_series = pd.concat(time_series_list, ignore_index=True) - self.metadata = pd.concat(metadata_list, ignore_index=True) - - def _require_single_run(self): - """Check that only single run is loaded (for field plotting).""" - if self.metadata["run"].nunique() > 1: - raise ValueError("Field plotting only available for single run.") - - def plot_convergence(self, output_path=None, normalization_iters=5): - """Plot all time series residuals normalized by early iteration maximum. - - Uses STAR-CCM+ style "Auto" normalization: normalizes by the maximum - value observed in the first N iterations. - - Parameters - ---------- - output_path : str or Path, optional - Path to save figure. If None, figure is not saved. - normalization_iters : int, optional - Number of initial iterations to use for computing normalization - reference (default: 5, following STAR-CCM+ convention). - """ - n_runs = self.metadata["run"].nunique() - - # Get residual columns (exclude 'iteration' and 'run') - residual_cols = [ - col for col in self.time_series.columns if col not in ["iteration", "run"] - ] - - # Melt to long format - df_long = self.time_series.melt( - id_vars=["iteration", "run"], - value_vars=residual_cols, - var_name="residual_type", - value_name="residual_value", - ) - - # Normalize each (run, residual_type) group by max of first N iterations - def normalize_group(group): - # Get maximum value from first N iterations - first_n = group.head(normalization_iters) - ref_value = first_n.max() - if ref_value != 0: - return group / ref_value - return group - - df_long["normalized_residual"] = df_long.groupby(["run", "residual_type"])[ - "residual_value" - ].transform(normalize_group) - - # Plot with seaborn - g = sns.relplot( - data=df_long, - x="iteration", - y="normalized_residual", - hue="residual_type", - style="run" if n_runs > 1 else None, - kind="line", - height=5, - aspect=1.6, - linewidth=2, - legend="auto", - ) - - g.ax.set_yscale("log") - g.ax.grid(True, alpha=0.3) - g.ax.set_xlabel("Iteration") - g.ax.set_ylabel("Normalized Metric") - - if n_runs == 1: - Re = self.metadata["Re"].iloc[0] - g.ax.set_title(f"Convergence History (Re = {Re:.0f})", fontweight="bold") - else: - g.ax.set_title("Convergence Comparison", fontweight="bold") - - if output_path: - g.savefig(output_path, bbox_inches="tight", dpi=300) - print(f"Convergence plot saved to: {output_path}") - - def plot_fields(self, output_path=None, interp_resolution=200): - """Plot all solution fields (pressure, u velocity, v velocity). - - Only available for single-run plotting. - - Parameters - ---------- - output_path : str or Path, optional - Path to save figure. If None, figure is not saved. - interp_resolution : int, optional - Resolution for interpolated grid. If > original resolution, - uses spectral interpolation for smooth visualization. Default 200. - """ - self._require_single_run() - - Re = self.metadata["Re"].iloc[0] - - # Determine grid size and reshape to 2D - nx = self.fields["x"].nunique() - ny = self.fields["y"].nunique() - - # Get unique x and y coordinates (sorted) - x_unique = np.sort(self.fields["x"].unique()) - y_unique = np.sort(self.fields["y"].unique()) - - # Sort data by (y, x) to ensure consistent ordering for reshape - sorted_fields = self.fields.sort_values(["y", "x"]) - P_orig = sorted_fields["p"].values.reshape(ny, nx) - U_orig = sorted_fields["u"].values.reshape(ny, nx) - V_orig = sorted_fields["v"].values.reshape(ny, nx) - - # Interpolate to finer grid if requested - if interp_resolution > max(nx, ny): - from scipy.interpolate import BarycentricInterpolator - - # Create fine grid - x_fine = np.linspace(x_unique[0], x_unique[-1], interp_resolution) - y_fine = np.linspace(y_unique[0], y_unique[-1], interp_resolution) - X, Y = np.meshgrid(x_fine, y_fine) - - # Tensor product barycentric interpolation - def interp_2d(field_2d): - # First interpolate along x for each y - temp = np.array( - [BarycentricInterpolator(x_unique, row)(x_fine) for row in field_2d] - ) - # Then interpolate along y for each x - result = np.array( - [ - BarycentricInterpolator(y_unique, temp[:, i])(y_fine) - for i in range(interp_resolution) - ] - ).T - return result - - P = interp_2d(P_orig) - U = interp_2d(U_orig) - V = interp_2d(V_orig) - else: - X, Y = np.meshgrid(x_unique, y_unique) - P, U, V = P_orig, U_orig, V_orig - - fig, axes = plt.subplots(1, 3, figsize=(18, 5)) - - # Pressure - cf_p = axes[0].contourf(X, Y, P, levels=20, cmap="coolwarm") - axes[0].set_xlabel("x") - axes[0].set_ylabel("y") - axes[0].set_title("Pressure", fontweight="bold") - axes[0].set_aspect("equal") - plt.colorbar(cf_p, ax=axes[0], label="p") - - # U velocity - cf_u = axes[1].contourf(X, Y, U, levels=20, cmap="RdBu_r") - axes[1].set_xlabel("x") - axes[1].set_ylabel("y") - axes[1].set_title("U velocity", fontweight="bold") - axes[1].set_aspect("equal") - plt.colorbar(cf_u, ax=axes[1], label="u") - - # V velocity - cf_v = axes[2].contourf(X, Y, V, levels=20, cmap="RdBu_r") - axes[2].set_xlabel("x") - axes[2].set_ylabel("y") - axes[2].set_title("V velocity", fontweight="bold") - axes[2].set_aspect("equal") - plt.colorbar(cf_v, ax=axes[2], label="v") - - fig.suptitle(f"Solution Fields (Re = {Re:.0f})", fontweight="bold") - plt.tight_layout() - - if output_path: - plt.savefig(output_path, bbox_inches="tight", dpi=300) - print(f"Fields plot saved to: {output_path}") - - def plot_streamlines(self, output_path=None, interp_resolution=200): - """Plot velocity magnitude with streamlines. - - Only available for single-run plotting. - - Parameters - ---------- - output_path : str or Path, optional - Path to save figure. If None, figure is not saved. - interp_resolution : int, optional - Resolution for interpolated grid. Default 200. - """ - self._require_single_run() - - Re = self.metadata["Re"].iloc[0] - - # Determine grid size and reshape to 2D - nx = self.fields["x"].nunique() - ny = self.fields["y"].nunique() - - # Get unique x and y coordinates (sorted) - x_unique = np.sort(self.fields["x"].unique()) - y_unique = np.sort(self.fields["y"].unique()) - - # Sort data by (y, x) to ensure consistent ordering for reshape - sorted_fields = self.fields.sort_values(["y", "x"]) - U_orig = sorted_fields["u"].values.reshape(ny, nx) - V_orig = sorted_fields["v"].values.reshape(ny, nx) - - # Interpolate to finer grid if requested - if interp_resolution > max(nx, ny): - from scipy.interpolate import BarycentricInterpolator - - # Create fine grid - x_fine = np.linspace(x_unique[0], x_unique[-1], interp_resolution) - y_fine = np.linspace(y_unique[0], y_unique[-1], interp_resolution) - X, Y = np.meshgrid(x_fine, y_fine) - - # Tensor product barycentric interpolation - def interp_2d(field_2d): - temp = np.array( - [BarycentricInterpolator(x_unique, row)(x_fine) for row in field_2d] - ) - result = np.array( - [ - BarycentricInterpolator(y_unique, temp[:, i])(y_fine) - for i in range(interp_resolution) - ] - ).T - return result - - U = interp_2d(U_orig) - V = interp_2d(V_orig) - else: - X, Y = np.meshgrid(x_unique, y_unique) - U, V = U_orig, V_orig - - vel_mag = np.sqrt(U**2 + V**2) - - fig, ax = plt.subplots(figsize=(8, 7)) - - # Velocity magnitude contour - cf = ax.contourf(X, Y, vel_mag, levels=20, cmap="coolwarm") - - # Get coordinates for streamlines - x_stream = X[0, :] # 1D x coordinates - y_stream = Y[:, 0] # 1D y coordinates - - # Streamlines using the (already interpolated) velocity field - stream = ax.streamplot( - x_stream, - y_stream, - U, - V, - color="white", - linewidth=1, - density=1.5, - arrowsize=1.2, - arrowstyle="->", - ) - stream.lines.set_alpha(0.6) - - ax.set_xlabel("x") - ax.set_ylabel("y") - ax.set_title( - f"Velocity Magnitude with Streamlines (Re = {Re:.0f})", fontweight="bold" - ) - ax.set_aspect("equal") - plt.colorbar(cf, ax=ax, label="Velocity magnitude") - plt.tight_layout() - - if output_path: - plt.savefig(output_path, bbox_inches="tight", dpi=300) - print(f"Streamlines plot saved to: {output_path}") diff --git a/src/plotting.py b/src/plotting.py index ff40482..3cebd94 100644 --- a/src/plotting.py +++ b/src/plotting.py @@ -137,12 +137,14 @@ def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: siblings = [] for run in runs: - # Extract solver name from run_name (format: {solver}_N{n}_Re{re}) run_name = run.info.run_name or "" - solver_name = run_name.split("_N")[0] if "_N" in run_name else "unknown" - # Extract corner treatment from params - corner_treatment = run.data.params.get("corner_treatment", "unknown") + # Extract solver name from run_name (format: {solver}_N{n} or {solver}_N{n}_Re{re}) + # Examples: "fv_N32", "spectral_N33", "spectral_fsg_N16" + if "_N" in run_name: + solver_name = run_name.rsplit("_N", 1)[0] # rsplit to handle underscores in solver name + else: + solver_name = "unknown" siblings.append({ "run_id": run.info.run_id, @@ -150,8 +152,7 @@ def find_sibling_runs(parent_run_id: str, tracking_uri: str) -> list[dict]: "N": int(run.data.params.get("nx", 0)), "Re": float(run.data.params.get("Re", 0)), "solver": solver_name, - "corner_treatment": corner_treatment, - "status": run.info.status, # Track if FINISHED or RUNNING + "status": run.info.status, }) finished = sum(1 for s in siblings if s["status"] == "FINISHED") @@ -537,7 +538,7 @@ def plot_convergence(timeseries_df: pd.DataFrame, Re: float, solver: str, N: int def plot_ghia_validation(fields: dict, Re: float, solver: str, N: int, output_dir: Path) -> Path: """Plot validation against Ghia benchmark data.""" import matplotlib.pyplot as plt - from scipy.interpolate import RectBivariateSpline + from spectral import spectral_interpolate AVAILABLE_RE = [100, 400, 1000, 3200, 5000, 7500, 10000] if int(Re) not in AVAILABLE_RE: @@ -561,18 +562,22 @@ def plot_ghia_validation(fields: dict, Re: float, solver: str, N: int, output_di # Restructure fields to 2D arrays x_unique, y_unique, U_2d, V_2d, _ = restructure_fields(fields) - U_spline = RectBivariateSpline(y_unique, x_unique, U_2d) - V_spline = RectBivariateSpline(y_unique, x_unique, V_2d) - + # Use spectral interpolation for centerline extraction n_points = 200 y_line = np.linspace(y_unique.min(), y_unique.max(), n_points) x_line = np.linspace(x_unique.min(), x_unique.max(), n_points) - x_center = (x_unique.min() + x_unique.max()) / 2 - y_center = (y_unique.min() + y_unique.max()) / 2 + # Find center indices + x_center_idx = len(x_unique) // 2 + y_center_idx = len(y_unique) // 2 + + # U along vertical centerline (x = center): interpolate in y + u_at_center_x = U_2d[:, x_center_idx] + u_sim = spectral_interpolate(y_unique, u_at_center_x, y_line, basis="legendre") - u_sim = U_spline(y_line, x_center).ravel() - v_sim = V_spline(y_center, x_line).ravel() + # V along horizontal centerline (y = center): interpolate in x + v_at_center_y = V_2d[y_center_idx, :] + v_sim = spectral_interpolate(x_unique, v_at_center_y, x_line, basis="legendre") fig, axes = plt.subplots(1, 2, figsize=(14, 5)) @@ -609,16 +614,41 @@ def plot_ghia_validation(fields: dict, Re: float, solver: str, N: int, output_di # ============================================================================= +def _build_method_label(sibling: dict) -> str: + """Build a unified method label from solver name. + + Formats solver names nicely for legends: + - 'fv' -> 'FV' + - 'spectral' -> 'Spectral' + - 'spectral_fsg' -> 'Spectral-FSG' + - 'spectral_vmg' -> 'Spectral-VMG' + """ + solver = sibling.get("solver", "unknown") + + # Format known solver names + label_map = { + "fv": "FV", + "spectral": "Spectral", + "spectral_fsg": "Spectral-FSG", + "spectral_vmg": "Spectral-VMG", + "spectral_fmg": "Spectral-FMG", + } + + return label_map.get(solver, solver.replace("_", "-").title()) + + def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Path) -> Path: - """Plot Ghia comparison with all sibling runs overlaid using seaborn. + """Plot Ghia comparison with all sibling runs using seaborn. - Uses seaborn lineplot with hue and style for clear legend differentiation. - Data is sorted properly before plotting to ensure correct line connections. + Legend system (native seaborn): + - hue: Method type (Spectral, FV, Spectral-FSG, etc.) + - style: Grid size N (different dash patterns) + - markers: Method-specific markers (every 20th point) Parameters ---------- siblings : list[dict] - List of sibling run info dicts with run_id, N, Re, solver, corner_treatment + List of sibling run info dicts with run_id, N, Re, solver tracking_uri : str MLflow tracking URI output_dir : Path @@ -631,20 +661,17 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa """ import matplotlib.pyplot as plt import seaborn as sns - from scipy.interpolate import RectBivariateSpline if not siblings: return None - # Only plot finished runs (can't download artifacts from running runs) + # Only plot finished runs finished_siblings = [s for s in siblings if s.get("status", "FINISHED") == "FINISHED"] if len(finished_siblings) < 2: log.info(f"Need at least 2 finished runs for comparison (have {len(finished_siblings)})") return None siblings = finished_siblings - - # Get Re from first sibling (all should have same Re in a sweep) Re = siblings[0]["Re"] AVAILABLE_RE = [100, 400, 1000, 3200, 5000, 7500, 10000] @@ -652,7 +679,7 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa log.warning(f"Ghia data not available for Re={Re}") return None - # Project root is 1 level up from src/ + # Load Ghia reference data project_root = Path(__file__).parent.parent ghia_dir = project_root / "data" / "validation" / "ghia" @@ -666,80 +693,56 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa ghia_u = pd.read_csv(u_file) ghia_v = pd.read_csv(v_file) - # Load scientific style with LaTeX + # Load scientific style style_path = project_root / "src" / "utils" / "plotting" / "scientific.mplstyle" if style_path.exists(): plt.style.use(style_path) else: - # Fallback to seaborn style sns.set_style("whitegrid") sns.set_context("paper", font_scale=1.2) - # Build DataFrames for seaborn - one row per data point - u_records = [] - v_records = [] - - # Filter to only use the most recent run per corner treatment - # (avoids duplicate lines from old runs) - seen_treatments = set() + # Filter to unique (method, N) combinations + seen_combos = set() unique_siblings = [] for sibling in siblings: - ct = sibling.get("corner_treatment", "unknown") - if ct not in seen_treatments: - seen_treatments.add(ct) + method = _build_method_label(sibling) + N = sibling["N"] + combo = (method, N) + if combo not in seen_combos: + seen_combos.add(combo) unique_siblings.append(sibling) - log.info(f"Using {len(unique_siblings)} unique corner treatments: {list(seen_treatments)}") + log.info(f"Plotting {len(unique_siblings)} unique (method, N) combinations") + + # Build DataFrames + u_records = [] + v_records = [] for sibling in unique_siblings: run_id = sibling["run_id"] N = sibling["N"] - corner_treatment = sibling.get("corner_treatment", "unknown") + method = _build_method_label(sibling) try: - # Download and load fields artifact_dir = download_mlflow_artifacts(run_id, tracking_uri) fields = load_fields_from_zarr(artifact_dir) - - # Restructure to 2D arrays x_unique, y_unique, U_2d, V_2d, _ = restructure_fields(fields) - # Use our spectral interpolation for centerline extraction - # This preserves spectral accuracy via Vandermonde matrix approach from spectral import spectral_interpolate n_points = 200 y_line = np.linspace(y_unique.min(), y_unique.max(), n_points) x_line = np.linspace(x_unique.min(), x_unique.max(), n_points) - # Find center indices x_center_idx = len(x_unique) // 2 y_center_idx = len(y_unique) // 2 - # U along vertical centerline (x = center): interpolate in y - u_at_center_x = U_2d[:, x_center_idx] # U values at x=center for all y - u_sim = spectral_interpolate(y_unique, u_at_center_x, y_line, basis="legendre") - - # V along horizontal centerline (y = center): interpolate in x - v_at_center_y = V_2d[y_center_idx, :] # V values at y=center for all x - v_sim = spectral_interpolate(x_unique, v_at_center_y, x_line, basis="legendre") + u_sim = spectral_interpolate(y_unique, U_2d[:, x_center_idx], y_line, basis="legendre") + v_sim = spectral_interpolate(x_unique, V_2d[y_center_idx, :], x_line, basis="legendre") - # Add simulation data to records for i in range(n_points): - u_records.append({ - "y": y_line[i], - "u": u_sim[i], - "Corner Treatment": corner_treatment.capitalize(), - "N": N, - "source": "simulation", - }) - v_records.append({ - "x": x_line[i], - "v": v_sim[i], - "Corner Treatment": corner_treatment.capitalize(), - "N": N, - "source": "simulation", - }) + u_records.append({"y": y_line[i], "u": u_sim[i], "Method": method, "N": N}) + v_records.append({"x": x_line[i], "v": v_sim[i], "Method": method, "N": N}) except Exception as e: log.warning(f"Failed to load run {run_id}: {e}") @@ -749,65 +752,42 @@ def plot_ghia_comparison(siblings: list[dict], tracking_uri: str, output_dir: Pa log.warning("No valid runs to plot") return None - # Create DataFrames and sort by position variable for proper line drawing - u_df = pd.DataFrame(u_records).sort_values(["Corner Treatment", "y"]) - v_df = pd.DataFrame(v_records).sort_values(["Corner Treatment", "x"]) - - # Define dash styles: solid for smoothing, dashed for subtraction - dashes = { - "Smoothing": "", - "Subtraction": (4, 2), - } + u_df = pd.DataFrame(u_records).sort_values(["Method", "N", "y"]) + v_df = pd.DataFrame(v_records).sort_values(["Method", "N", "x"]) + # Create figure fig, axes = plt.subplots(1, 2, figsize=(12, 5)) - # Plot U velocity (vertical centerline) - simulation data + # Plot with seaborn: hue=Method (color), style=N (dashes), markers sns.lineplot( - data=u_df, - x="u", - y="y", - hue="Corner Treatment", - style="Corner Treatment", - dashes=dashes, - linewidth=1.5, - sort=False, # Data is already sorted by y + data=u_df, x="u", y="y", + hue="Method", style="N", + markers=True, markersize=4, markevery=20, + linewidth=1.5, sort=False, ax=axes[0], ) - # Add Ghia reference as scatter (separate from seaborn to avoid marker mixing issues) - axes[0].scatter( - ghia_u["u"], ghia_u["y"], - c="black", s=60, marker="x", linewidths=1.5, - label=r"Ghia et al.\ (1982)", zorder=10 - ) + axes[0].scatter(ghia_u["u"], ghia_u["y"], c="black", s=60, marker="x", + linewidths=1.5, label=r"Ghia et al.\ (1982)", zorder=10) axes[0].set_xlabel(r"$u$") axes[0].set_ylabel(r"$y$") axes[0].set_title(r"$u$-velocity (vertical centerline)") - axes[0].legend(loc="best", title=None) + axes[0].legend(loc="best", fontsize=8) axes[0].set_xlim(-0.4, 1.05) axes[0].set_ylim(0, 1) - # Plot V velocity (horizontal centerline) - simulation data sns.lineplot( - data=v_df, - x="x", - y="v", - hue="Corner Treatment", - style="Corner Treatment", - dashes=dashes, - linewidth=1.5, - sort=False, # Data is already sorted by x + data=v_df, x="x", y="v", + hue="Method", style="N", + markers=True, markersize=4, markevery=20, + linewidth=1.5, sort=False, ax=axes[1], ) - # Add Ghia reference as scatter - axes[1].scatter( - ghia_v["x"], ghia_v["v"], - c="black", s=60, marker="x", linewidths=1.5, - label=r"Ghia et al.\ (1982)", zorder=10 - ) + axes[1].scatter(ghia_v["x"], ghia_v["v"], c="black", s=60, marker="x", + linewidths=1.5, label=r"Ghia et al.\ (1982)", zorder=10) axes[1].set_xlabel(r"$x$") axes[1].set_ylabel(r"$v$") axes[1].set_title(r"$v$-velocity (horizontal centerline)") - axes[1].legend(loc="best", title=None) + axes[1].legend(loc="best", fontsize=8) axes[1].set_xlim(0, 1) fig.suptitle(rf"Ghia Benchmark Comparison --- $\mathrm{{Re}}={int(Re)}$") From ea9f30ff523f5c715995320f8de99281192d332a Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Fri, 5 Dec 2025 11:54:59 +0100 Subject: [PATCH 25/34] restructure --- RESTRUCTURE_PROPOSAL.md | 330 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 330 insertions(+) create mode 100644 RESTRUCTURE_PROPOSAL.md diff --git a/RESTRUCTURE_PROPOSAL.md b/RESTRUCTURE_PROPOSAL.md new file mode 100644 index 0000000..9209e1f --- /dev/null +++ b/RESTRUCTURE_PROPOSAL.md @@ -0,0 +1,330 @@ +# Proposed Project Restructure + +## Goals + +1. Mirror `src/` and `conf/` structure for readability +2. Introduce a `utilities/` submodule inside `src/` for cross-project helpers (MLflow/Hydra/HPC, logging, generic plotting/config) +3. Organize project-specific logic under `solvers/` and `shared/` +4. Spectral multigrid configs extend the base spectral config +5. Clean separation of concerns and discovery + +## Proposed Structure + +### Source Code (`src/`) + +``` +src/ +├── __init__.py +├── solvers/ +│ ├── __init__.py +│ ├── base.py # BaseSolver ABC +│ ├── metrics.py # Norms/formatting shared across solvers +│ ├── datastructures.py # Solver data classes +│ ├── fv/ +│ │ ├── __init__.py +│ │ ├── solver.py # FVSolver class +│ │ ├── assembly/ +│ │ │ ├── __init__.py +│ │ │ ├── convection_diffusion_matrix.py +│ │ │ ├── pressure_correction_eq_assembly.py +│ │ │ ├── divergence.py +│ │ │ └── rhie_chow.py +│ │ ├── discretization/ +│ │ │ ├── __init__.py +│ │ │ ├── convection/ +│ │ │ ├── diffusion/ +│ │ │ └── gradient/ +│ │ ├── core/ +│ │ │ ├── __init__.py +│ │ │ ├── corrections.py +│ │ │ └── helpers.py +│ │ └── linear_solvers/ +│ │ ├── __init__.py +│ │ └── scipy_solver.py +│ │ +│ └── spectral/ +│ ├── __init__.py +│ ├── solver.py # SpectralSolver class (single grid) +│ ├── basis/ +│ │ ├── __init__.py +│ │ ├── polynomial.py # Jacobi, Legendre, interpolation +│ │ └── spectral.py # Basis classes, diff matrices +│ ├── operators/ +│ │ ├── __init__.py +│ │ ├── prolongation.py # Prolongation operators +│ │ ├── restriction.py # Restriction operators +│ │ └── corner.py # Corner singularity treatment +│ ├── multigrid/ +│ │ ├── __init__.py +│ │ ├── fsg.py # Full Spectral Galerkin +│ │ ├── vmg.py # V-cycle multigrid +│ │ └── fmg.py # Full multigrid +│ +├── utilities/ # Cross-project utilities (reusable across repos) +│ ├── __init__.py +│ ├── config/ # Config helpers (paths, cleanup) — domain-agnostic +│ ├── mlflow/ +│ │ ├── __init__.py +│ │ ├── tracking.py +│ │ └── callback.py +│ ├── hydra/ +│ │ ├── __init__.py +│ │ └── callbacks.py +│ ├── hpc/ +│ │ ├── __init__.py +│ │ └── sweeper.py +│ ├── plotting/ # Generic plotting style/format helpers +│ │ ├── __init__.py +│ │ ├── styles.py +│ │ └── formatters.py +│ ├── runners/ # Generic CLI helpers (if kept) +│ ├── io.py # Generic parquet/pickle helpers (from spectral/utils/io.py) +│ └── data_io.py # If broadly reusable; else move to shared/ +│ +├── shared/ # Project-scoped shared code (specific to this LDC project) +│ ├── __init__.py +│ ├── meshing/ # Domain meshes specific to this project +│ │ ├── __init__.py +│ │ ├── mesh_data.py +│ │ └── structured.py +│ ├── plotting/ # Project-specific plots (Ghia, LDC visuals) +│ │ ├── __init__.py +│ │ ├── solution.py # Solution visualization +│ │ ├── validation.py # Ghia comparison plots +│ │ ├── convergence.py # Convergence history plots +│ │ ├── ldc_plotter.py # From utils/ldc_plotter.py +│ │ ├── styles.py # Plot styling utilities (incl. mplstyle) +│ │ └── scientific.mplstyle +│ ├── data_io.py # Project-scoped data I/O (LDC-specific shapes/conventions) +│ ├── field_interpolator.py # Interpolation tailored to project grids +│ ├── ghia_validator.py # Project-specific validation +│ └── datastructures.py # Project-scoped data classes (if any) +│ +``` + +### Configuration (`conf/`) + +``` +conf/ +├── config.yaml # Main config with defaults +│ +├── solvers/ # Mirrors src/solvers/ hierarchy +│ ├── fv/ # Group: solvers/fv +│ │ ├── default.yaml # FV solver parameters (baseline) +│ │ └── linear_solvers/ +│ │ ├── default.yaml # Default linear solver selection +│ │ └── scipy.yaml # SciPy backend settings +│ │ +│ └── spectral/ # Group: solvers/spectral +│ ├── default.yaml # Base spectral (single-grid) +│ ├── basis/ +│ │ └── default.yaml # Basis selection (legendre/chebyshev, quad rules) +│ ├── operators/ +│ │ ├── default.yaml # Corner treatment defaults (smoothing width, method) +│ │ ├── transfer.yaml # Prolongation/restriction choices +│ │ └── corner.yaml # Alternate corner options if needed +│ ├── multigrid/ +│ │ ├── default.yaml # Placeholder (disabled multigrid) +│ │ ├── fsg.yaml # Full Spectral Galerkin +│ │ ├── vmg.yaml # V-cycle multigrid +│ │ └── fmg.yaml # Full multigrid +│ └── metrics/ +│ └── default.yaml # Norm/formatting knobs (optional group) +│ +├── shared/ # Cross-project infra configs +│ ├── mlflow/ +│ │ ├── default.yaml # Local MLflow as baseline +│ │ └── coolify.yaml +│ └── hydra/ +│ └── launcher/ +│ ├── default.yaml # Local/single-run baseline +│ └── joblib.yaml +│ +└── experiment/ + ├── fv_validation.yaml # FV grid convergence study + ├── spectral_validation.yaml # Spectral grid convergence + ├── solver_comparison.yaml # FV vs spectral baseline + ├── corner_treatment.yaml # Corner singularity comparison + ├── multigrid_comparison.yaml # SG vs FSG vs VMG vs FMG + └── transfer_operators.yaml # Prolongation/restriction comparison +``` + +## Configuration Details + +### Main Config (`conf/config.yaml`) + +```yaml +defaults: + - solvers/spectral # Uses solvers/spectral/default.yaml + - solvers/spectral/operators # Uses solvers/spectral/operators/default.yaml + - solvers/spectral/operators@transfer: transfer # Pick transfer variant + - solvers/spectral/basis # Uses solvers/spectral/basis/default.yaml + - solvers/spectral/multigrid: default # Multigrid disabled by default + - shared/mlflow: default # Local MLflow baseline + - shared/hydra/launcher: default # Local launcher baseline + - _self_ + +# Problem parameters +Re: 100 +N: 16 +Lx: 1.0 +Ly: 1.0 +lid_velocity: 1.0 + +# Solver parameters +max_iterations: 10000 +tolerance: 1.0e-6 +``` + +### Base Spectral Config (`conf/solvers/spectral/default.yaml`) + +```yaml +# @package _global_ +solver: spectral + +# Spectral-specific parameters +basis_type: legendre +CFL: 0.5 +corner_treatment: smoothing + +# Single-grid (no multigrid) +multigrid: + enabled: false +``` + +### FSG Multigrid Config (`conf/solvers/spectral/fsg.yaml`) + +```yaml +# @package _global_ +defaults: + - default # Inherit from base spectral + +solver: spectral_fsg + +multigrid: + enabled: true + type: fsg + coarse_N: 4 + prolongation: polynomial + restriction: injection +``` + +### VMG Config (`conf/solvers/spectral/vmg.yaml`) + +```yaml +# @package _global_ +defaults: + - default + +solver: spectral_vmg + +multigrid: + enabled: true + type: vmg + levels: 3 + smoothing_steps: 2 + prolongation: fft + restriction: fft +``` + +### FV Config (`conf/solvers/fv/default.yaml`) + +```yaml +# @package _global_ +solver: fv + +# FV-specific parameters +convection_scheme: upwind +limiter: none +pressure_solver: scipy +relaxation: + velocity: 0.7 + pressure: 0.3 +``` + +### Example Experiment (`conf/experiment/multigrid_comparison.yaml`) + +```yaml +# @package _global_ +defaults: + - override /solvers/spectral: default + +experiment_name: Multigrid-Comparison +sweep_name: sg_vs_fsg_vs_vmg + +Re: 100 +tolerance: 1.0e-6 +max_iterations: 50000 + +hydra: + sweeper: + params: + solvers/spectral: default,fsg,vmg + N: 16,32,64 +``` + +## Migration Plan + +### Phase 1: Reorganize `src/` + +1. Create new directory structure +2. Move files to new locations: + - `src/solvers/*.py` → `src/solvers/base.py` + - `src/solvers/fv_solver.py` → `src/solvers/fv/solver.py` + - `src/solvers/spectral_solver.py` → `src/solvers/spectral/solver.py` + - `src/solvers/datastructures.py` → `src/solvers/datastructures.py` (kept beside base) + - `src/fv/*` → `src/solvers/fv/*` + - `src/spectral/spectral.py` → `src/solvers/spectral/solver.py` + - `src/spectral/polynomial.py` → `src/solvers/spectral/basis/polynomial.py` + - `src/spectral/corner_singularity.py` → `src/solvers/spectral/operators/corner.py` + - `src/spectral/transfer_operators.py` → split into `src/solvers/spectral/operators/{prolongation.py,restriction.py}` + - `src/spectral/utils/formatting.py`, `norms.py` → `src/solvers/metrics.py` (shared across FV & spectral) + - `src/spectral/utils/io.py` → `src/utilities/io.py` (generic parquet/pickle loader) + - `src/spectral/utils/plotting.py` → `src/shared/plotting/` (project-level plotting helpers) + - `src/spectral/spectral_multigrid.py` → split into `src/solvers/spectral/multigrid/{fsg.py,vmg.py,fmg.py}` + - Remove `src/fv/linear_solvers/petsc_solver.py` and any references (we keep SciPy backend only) + - `src/meshing/*` → `src/shared/meshing/*` + - `src/plotting.py` + `src/utils/plotting/*` → `src/shared/plotting/` (project-level plotting) + - `src/utils/data_io.py` → `src/shared/data_io.py` (or `utilities/` if made fully generic) + - `src/utils/field_interpolator.py` → `src/shared/field_interpolator.py` + - `src/utils/ghia_validator.py` → `src/shared/ghia_validator.py` + - `src/utils/ldc_plotter.py` → `src/shared/plotting/ldc_plotter.py` + - `src/utils/config/*` → `src/utilities/config/*` + - `src/utils/mlflow/*` → `src/utilities/mlflow/*` + - `src/utils/mlflow_callback.py` → `src/utilities/mlflow/callback.py` + - `src/utils/hydra/*` → `src/utilities/hydra/*` + - `src/utils/hpc/*` → `src/utilities/hpc/*` + - `src/utils/runners/*` (if project-agnostic) → `src/utilities/` + - `src/utils/mlflow_io.py`, `upload_logs.py`, `logs.py` → `src/utilities/mlflow/` + - `src/utils/*` that are project-specific → keep or move into `shared/` as appropriate +3. Update all imports +4. Update `__init__.py` exports +5. Delete stale `__pycache__/` and `*.nbc` artifacts after moves to avoid packaging noise + +### Phase 2: Reorganize `conf/` + +1. Create new directory structure +2. Move configs: + - `conf/solver/fv.yaml` → `conf/solvers/fv/default.yaml` + - `conf/solver/spectral.yaml` → `conf/solvers/spectral/default.yaml` + - `conf/mlflow/*` → `conf/shared/mlflow/*` (with `default.yaml`) + - `conf/hydra/*` → `conf/shared/hydra/*` (with `launcher/default.yaml`) + - `conf/experiment/multigrid.yaml` → `conf/experiment/multigrid_comparison.yaml` + - Keep `solver_comparison.yaml`, `transfer_operators.yaml`, `spectral_validation.yaml`, `fv_validation.yaml`, `corner_treatment.yaml` +3. Create multigrid variant configs (fsg.yaml, vmg.yaml, fmg.yaml) +4. Update `config.yaml` defaults +5. Update experiment configs + +### Phase 3: Update `run_solver.py` + +1. Simplify solver instantiation using Hydra's `instantiate()` +2. Remove manual parameter extraction +3. Use `_target_` pattern for automatic class instantiation + +## Benefits + +1. **Discoverability**: Mirror structure makes it easy to find config for any code +2. **Extensibility**: Adding new solver = add directory in both `src/solvers/` and `conf/solvers/` +3. **Inheritance**: Multigrid configs extend base spectral, reducing duplication +4. **Clean sweeps**: Each solver type has appropriate N ranges in its experiments +5. **Separation**: Infrastructure code separated from numerical methods From 4b65b91cd64e1c3f8bb3e26042381b08aaba810b Mon Sep 17 00:00:00 2001 From: Philip Korsager Nickel Date: Fri, 5 Dec 2025 14:00:36 +0100 Subject: [PATCH 26/34] before c --- RESTRUCTURE_PROPOSAL.md | 7 + conf/config.yaml | 5 +- conf/experiment/corner_treatment.yaml | 24 -- conf/experiment/multigrid.yaml | 27 -- conf/experiment/solver_comparison.yaml | 23 - conf/experiment/spectral_validation.yaml | 3 +- conf/experiment/transfer_operators.yaml | 31 -- conf/hydra/launcher/joblib.yaml | 1 + conf/shared/hydra/launcher/joblib.yaml | 15 + conf/{ => shared}/mlflow/coolify.yaml | 1 + conf/{ => shared}/mlflow/local.yaml | 1 + conf/solver/fv.yaml | 11 +- conf/solver/spectral.yaml | 25 +- conf/solver/spectral_fmg.yaml | 4 + conf/solver/spectral_fsg.yaml | 4 + conf/solver/spectral_fsg_poly.yaml | 4 + conf/solver/spectral_vmg.yaml | 4 + conf/solvers/fv/default.yaml | 20 + conf/solvers/spectral/default.yaml | 33 ++ conf/solvers/spectral/fmg.yaml | 10 + conf/solvers/spectral/fsg.yaml | 9 + conf/solvers/spectral/fsg_poly.yaml | 9 + conf/solvers/spectral/vmg.yaml | 10 + docs/source/api/base_solver.rst | 6 +- docs/source/api/fv_solver.rst | 4 +- docs/source/api/spectral_solver.rst | 4 +- main.py | 50 ++- .../corner_treatment/ghia_comparison.pdf | Bin 604687 -> 0 bytes .../spectral_vs_fv/ghia_comparison.pdf | Bin 625021 -> 0 bytes .../validation/ghia_comparison.pdf | Bin 0 -> 29956 bytes pyproject.toml | 1 - run_solver.py | 92 ++-- src/shared/__init__.py | 5 + src/{ => shared}/meshing/__init__.py | 0 src/{ => shared}/meshing/mesh_data.py | 0 src/{ => shared}/meshing/simple_structured.py | 0 src/shared/plotting/__init__.py | 16 + src/{utils => shared}/plotting/formatters.py | 0 .../plotting/ldc_plotter.py} | 396 ++++++++++-------- .../utils => shared/plotting}/plotting.py | 0 src/solvers/__init__.py | 6 +- src/solvers/{base_solver.py => base.py} | 0 src/solvers/datastructures.py | 17 +- src/{ => solvers}/fv/__init__.py | 0 src/{ => solvers}/fv/assembly/__init__.py | 0 .../assembly/convection_diffusion_matrix.py | 2 +- src/{ => solvers}/fv/assembly/divergence.py | 0 .../pressure_correction_eq_assembly.py | 2 +- src/{ => solvers}/fv/assembly/rhie_chow.py | 8 +- src/{ => solvers}/fv/core/__init__.py | 0 src/{ => solvers}/fv/core/corrections.py | 0 src/{ => solvers}/fv/core/helpers.py | 8 +- .../fv/discretization/__init__.py | 0 .../fv/discretization/convection/__init__.py | 0 .../fv/discretization/convection/upwind.py | 2 +- .../fv/discretization/diffusion/__init__.py | 0 .../fv/discretization/gradient/__init__.py | 0 .../gradient/structured_gradient.py | 2 +- .../fv/linear_solvers/__init__.py | 0 .../fv/linear_solvers/scipy_solver.py | 0 src/solvers/{fv_solver.py => fv/solver.py} | 24 +- src/solvers/metrics.py | 101 +++++ src/solvers/spectral/__init__.py | 5 + src/solvers/spectral/basis/__init__.py | 29 ++ .../spectral/basis}/polynomial.py | 0 .../spectral/basis}/spectral.py | 0 src/solvers/spectral/multigrid/__init__.py | 5 + .../multigrid/fsg.py} | 19 +- src/solvers/spectral/operators/__init__.py | 29 ++ .../spectral/operators/corner.py} | 12 +- .../spectral/operators}/transfer_operators.py | 2 +- .../solver.py} | 49 ++- src/spectral/__init__.py | 21 +- src/spectral/utils/__init__.py | 12 +- src/spectral/utils/formatting.py | 147 ------- src/spectral/utils/norms.py | 66 --- src/utilities/__init__.py | 10 + src/{utils => utilities}/config/__init__.py | 0 src/{utils => utilities}/config/clean.py | 0 src/{utils => utilities}/config/paths.py | 0 src/{utils => utilities}/hpc/__init__.py | 0 src/{utils => utilities}/hpc/sweeper.py | 2 +- src/{utils => utilities}/hydra/__init__.py | 2 +- src/{utils => utilities}/hydra/callbacks.py | 2 +- src/{spectral/utils => utilities}/io.py | 0 src/{utils => utilities}/mlflow/__init__.py | 0 .../mlflow/callback.py} | 63 ++- src/{utils => utilities}/mlflow/io.py | 0 src/{utils => utilities}/mlflow/logs.py | 0 .../mlflow/upload_logs.py | 4 +- src/{utils => utilities}/runners/__init__.py | 0 src/{utils => utilities}/runners/scripts.py | 0 src/utils/__init__.py | 35 -- src/utils/plotting/__init__.py | 29 -- src/utils/plotting/scientific.mplstyle | 78 ---- src/utils/plotting/styles.py | 34 -- 96 files changed, 789 insertions(+), 893 deletions(-) delete mode 100644 conf/experiment/corner_treatment.yaml delete mode 100644 conf/experiment/multigrid.yaml delete mode 100644 conf/experiment/solver_comparison.yaml delete mode 100644 conf/experiment/transfer_operators.yaml create mode 100644 conf/shared/hydra/launcher/joblib.yaml rename conf/{ => shared}/mlflow/coolify.yaml (91%) rename conf/{ => shared}/mlflow/local.yaml (88%) create mode 100644 conf/solver/spectral_fmg.yaml create mode 100644 conf/solver/spectral_fsg.yaml create mode 100644 conf/solver/spectral_fsg_poly.yaml create mode 100644 conf/solver/spectral_vmg.yaml create mode 100644 conf/solvers/fv/default.yaml create mode 100644 conf/solvers/spectral/default.yaml create mode 100644 conf/solvers/spectral/fmg.yaml create mode 100644 conf/solvers/spectral/fsg.yaml create mode 100644 conf/solvers/spectral/fsg_poly.yaml create mode 100644 conf/solvers/spectral/vmg.yaml delete mode 100644 outputs/comparison_plots/corner_treatment/ghia_comparison.pdf delete mode 100644 outputs/comparison_plots/spectral_vs_fv/ghia_comparison.pdf create mode 100644 outputs/comparison_plots/validation/ghia_comparison.pdf create mode 100644 src/shared/__init__.py rename src/{ => shared}/meshing/__init__.py (100%) rename src/{ => shared}/meshing/mesh_data.py (100%) rename src/{ => shared}/meshing/simple_structured.py (100%) create mode 100644 src/shared/plotting/__init__.py rename src/{utils => shared}/plotting/formatters.py (100%) rename src/{plotting.py => shared/plotting/ldc_plotter.py} (75%) rename src/{spectral/utils => shared/plotting}/plotting.py (100%) rename src/solvers/{base_solver.py => base.py} (100%) rename src/{ => solvers}/fv/__init__.py (100%) rename src/{ => solvers}/fv/assembly/__init__.py (100%) rename src/{ => solvers}/fv/assembly/convection_diffusion_matrix.py (98%) rename src/{ => solvers}/fv/assembly/divergence.py (100%) rename src/{ => solvers}/fv/assembly/pressure_correction_eq_assembly.py (96%) rename src/{ => solvers}/fv/assembly/rhie_chow.py (96%) rename src/{ => solvers}/fv/core/__init__.py (100%) rename src/{ => solvers}/fv/core/corrections.py (100%) rename src/{ => solvers}/fv/core/helpers.py (97%) rename src/{ => solvers}/fv/discretization/__init__.py (100%) rename src/{ => solvers}/fv/discretization/convection/__init__.py (100%) rename src/{ => solvers}/fv/discretization/convection/upwind.py (97%) rename src/{ => solvers}/fv/discretization/diffusion/__init__.py (100%) rename src/{ => solvers}/fv/discretization/gradient/__init__.py (100%) rename src/{ => solvers}/fv/discretization/gradient/structured_gradient.py (99%) rename src/{ => solvers}/fv/linear_solvers/__init__.py (100%) rename src/{ => solvers}/fv/linear_solvers/scipy_solver.py (100%) rename src/solvers/{fv_solver.py => fv/solver.py} (91%) create mode 100644 src/solvers/metrics.py create mode 100644 src/solvers/spectral/__init__.py create mode 100644 src/solvers/spectral/basis/__init__.py rename src/{spectral => solvers/spectral/basis}/polynomial.py (100%) rename src/{spectral => solvers/spectral/basis}/spectral.py (100%) create mode 100644 src/solvers/spectral/multigrid/__init__.py rename src/solvers/{spectral_multigrid.py => spectral/multigrid/fsg.py} (97%) create mode 100644 src/solvers/spectral/operators/__init__.py rename src/{spectral/corner_singularity.py => solvers/spectral/operators/corner.py} (97%) rename src/{spectral => solvers/spectral/operators}/transfer_operators.py (99%) rename src/solvers/{spectral_solver.py => spectral/solver.py} (94%) delete mode 100644 src/spectral/utils/formatting.py delete mode 100644 src/spectral/utils/norms.py create mode 100644 src/utilities/__init__.py rename src/{utils => utilities}/config/__init__.py (100%) rename src/{utils => utilities}/config/clean.py (100%) rename src/{utils => utilities}/config/paths.py (100%) rename src/{utils => utilities}/hpc/__init__.py (100%) rename src/{utils => utilities}/hpc/sweeper.py (99%) rename src/{utils => utilities}/hydra/__init__.py (55%) rename src/{utils => utilities}/hydra/callbacks.py (97%) rename src/{spectral/utils => utilities}/io.py (100%) rename src/{utils => utilities}/mlflow/__init__.py (100%) rename src/{utils/mlflow_callback.py => utilities/mlflow/callback.py} (76%) rename src/{utils => utilities}/mlflow/io.py (100%) rename src/{utils => utilities}/mlflow/logs.py (100%) rename src/{utils => utilities}/mlflow/upload_logs.py (96%) rename src/{utils => utilities}/runners/__init__.py (100%) rename src/{utils => utilities}/runners/scripts.py (100%) delete mode 100644 src/utils/__init__.py delete mode 100644 src/utils/plotting/__init__.py delete mode 100644 src/utils/plotting/scientific.mplstyle delete mode 100644 src/utils/plotting/styles.py diff --git a/RESTRUCTURE_PROPOSAL.md b/RESTRUCTURE_PROPOSAL.md index 9209e1f..2f308a3 100644 --- a/RESTRUCTURE_PROPOSAL.md +++ b/RESTRUCTURE_PROPOSAL.md @@ -301,6 +301,13 @@ hydra: 4. Update `__init__.py` exports 5. Delete stale `__pycache__/` and `*.nbc` artifacts after moves to avoid packaging noise +### Hydra Instantiation Direction + +- Add `_target_` to solver configs and instantiate solvers via `hydra.utils.instantiate(cfg.solver, _convert_="partial")` in `run_solver.py`. +- Keep solver inputs scoped to the solver subtree: carry `Re`, `N`, `lid_velocity`, `Lx`, `Ly`, `max_iterations`, `tolerance`, etc. into the solver config via interpolation; do **not** pass the full runtime `cfg` (which includes MLflow/Hydra metadata) into solver constructors. +- Rely on solver constructors accepting `**kwargs` (already true) and their `Parameters` dataclasses as the single source of truth. Multigrid/transfer variants remain pure config choices. +- This removes manual parameter plucking in `create_solver`, keeps separation of concerns, and makes adding new solver variants config-only. + ### Phase 2: Reorganize `conf/` 1. Create new directory structure diff --git a/conf/config.yaml b/conf/config.yaml index f57be77..76d9f44 100644 --- a/conf/config.yaml +++ b/conf/config.yaml @@ -7,7 +7,7 @@ defaults: - solver: fv - - mlflow: local + - shared/mlflow@mlflow: local - _self_ # ============================================================================= @@ -28,6 +28,7 @@ Ly: 1.0 # Solver control tolerance: 1.0e-6 max_iterations: 500 +generate_plots: true # Experiment tracking experiment_name: LDC-Solver @@ -44,4 +45,4 @@ hydra: subdir: N${N} callbacks: mlflow_sweep: - _target_: utils.mlflow_callback.MLflowSweepCallback + _target_: utilities.mlflow.callback.MLflowSweepCallback diff --git a/conf/experiment/corner_treatment.yaml b/conf/experiment/corner_treatment.yaml deleted file mode 100644 index 53ec633..0000000 --- a/conf/experiment/corner_treatment.yaml +++ /dev/null @@ -1,24 +0,0 @@ -# @package _global_ -# Corner Treatment Comparison: smoothing vs subtraction (Zhang & Xi 2010) -# Usage: uv run python run_solver.py -m +experiment=corner_treatment - -defaults: - - override /solver: spectral - -# MLflow experiment -experiment_name: Corner-Treatment -sweep_name: corner_treatment - -# Base parameters -N: 15 -Re: 100 -tolerance: 1.0e-6 -max_iterations: 50000 - -hydra: - sweeper: - params: - # Compare both corner treatment methods - solver.corner_treatment: smoothing,subtraction - # Optional: also sweep grid sizes - # N: 15,31,63 diff --git a/conf/experiment/multigrid.yaml b/conf/experiment/multigrid.yaml deleted file mode 100644 index 7ede6d1..0000000 --- a/conf/experiment/multigrid.yaml +++ /dev/null @@ -1,27 +0,0 @@ -# @package _global_ -# Multigrid testing experiment for spectral solver -# -# Usage - Compare single-grid vs FSG: -# uv run python run_solver.py -m +experiment=multigrid -# -# Usage - Single run: -# uv run python run_solver.py +experiment=multigrid solver=spectral -# uv run python run_solver.py +experiment=multigrid solver=spectral_fsg - -experiment_name: Multigrid-Test -sweep_name: multigrid-sweep - -defaults: - - override /hydra/launcher: joblib - -# Sweep configuration -hydra: - mode: MULTIRUN - sweeper: - params: - solver: spectral,spectral_fsg - N: "19" - -Re: 100 -tolerance: 1.0e-6 -max_iterations: 100000 diff --git a/conf/experiment/solver_comparison.yaml b/conf/experiment/solver_comparison.yaml deleted file mode 100644 index f1726ac..0000000 --- a/conf/experiment/solver_comparison.yaml +++ /dev/null @@ -1,23 +0,0 @@ -# @package _global_ -# Solver Comparison: Spectral vs Finite Volume -# Compares both solvers at the same Re, with runs grouped under a single parent -# -# Usage: uv run python run_solver.py -m +experiment=solver_comparison - -# MLflow experiment -experiment_name: Solver-Comparison -sweep_name: spectral_vs_fv - -# Base parameters (same for both solvers) -Re: 100 -tolerance: 1.0e-6 -max_iterations: 5000 - -hydra: - sweeper: - params: - # Sweep over both solvers - # Note: N has different meaning (FV: cells, Spectral: polynomial order) - # so we pick comparable grid sizes - solver: spectral,fv - N: 16,32 diff --git a/conf/experiment/spectral_validation.yaml b/conf/experiment/spectral_validation.yaml index 7485454..0e90a02 100644 --- a/conf/experiment/spectral_validation.yaml +++ b/conf/experiment/spectral_validation.yaml @@ -8,7 +8,6 @@ defaults: # MLflow experiment (shared with fv_validation) experiment_name: LDC-Validation -# Parent run name - use same name across fv/spectral to group them sweep_name: validation # Validation polynomial orders (N+1 nodes) and Reynolds numbers @@ -20,4 +19,4 @@ max_iterations: 30000 hydra: sweeper: params: - N: 15,23 + N: 15,19 diff --git a/conf/experiment/transfer_operators.yaml b/conf/experiment/transfer_operators.yaml deleted file mode 100644 index 2ee0f86..0000000 --- a/conf/experiment/transfer_operators.yaml +++ /dev/null @@ -1,31 +0,0 @@ -# @package _global_ -# Transfer Operators Comparison Experiment -# -# Compares single-grid vs FSG with FFT vs polynomial transfer operators -# -# Usage - Single Re: -# uv run python run_solver.py -m +experiment=transfer_operators -# -# Usage - Multiple Re values (creates separate parent runs per Re): -# uv run python run_solver.py -m +experiment=transfer_operators Re=100,400,1000 -# -# Each Re value gets its own parent run in MLflow for easy comparison - -experiment_name: Transfer-Operators -sweep_name: transfer-Re${Re} # Creates separate parent per Re - -defaults: - - override /hydra/launcher: joblib - -# Sweep over solvers: single-grid, FSG-FFT, FSG-Polynomial -hydra: - mode: MULTIRUN - sweeper: - params: - solver: spectral,spectral_fsg_fft,spectral_fsg_poly - N: "31" - -# Default physics parameters -Re: 100 -tolerance: 1.0e-6 -max_iterations: 100000 diff --git a/conf/hydra/launcher/joblib.yaml b/conf/hydra/launcher/joblib.yaml index 60c858d..9f0e6ca 100644 --- a/conf/hydra/launcher/joblib.yaml +++ b/conf/hydra/launcher/joblib.yaml @@ -1,3 +1,4 @@ +# @package hydra.launcher # Joblib Launcher for parallel sweeps # Usage: uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 diff --git a/conf/shared/hydra/launcher/joblib.yaml b/conf/shared/hydra/launcher/joblib.yaml new file mode 100644 index 0000000..9f0e6ca --- /dev/null +++ b/conf/shared/hydra/launcher/joblib.yaml @@ -0,0 +1,15 @@ +# @package hydra.launcher +# Joblib Launcher for parallel sweeps +# Usage: uv run python run_solver.py -m hydra/launcher=joblib solver=fv N=16,32,64 + +_target_: hydra_plugins.hydra_joblib_launcher.joblib_launcher.JoblibLauncher + +n_jobs: -1 # -1 = all cores, -2 = all but one +backend: loky # loky, threading, multiprocessing +verbose: 10 # 0-50 +timeout: null # seconds per job (null = no timeout) +pre_dispatch: "2*n_jobs" +batch_size: auto +temp_folder: null +max_nbytes: 1M +mmap_mode: r diff --git a/conf/mlflow/coolify.yaml b/conf/shared/mlflow/coolify.yaml similarity index 91% rename from conf/mlflow/coolify.yaml rename to conf/shared/mlflow/coolify.yaml index 1ee8058..343992f 100644 --- a/conf/mlflow/coolify.yaml +++ b/conf/shared/mlflow/coolify.yaml @@ -1,3 +1,4 @@ +# @package mlflow # Coolify MLflow (same docker-compose deployed remotely) # Required: set MLFLOW_TRACKING_URI environment variable mode: coolify diff --git a/conf/mlflow/local.yaml b/conf/shared/mlflow/local.yaml similarity index 88% rename from conf/mlflow/local.yaml rename to conf/shared/mlflow/local.yaml index 9fc8793..17d8f61 100644 --- a/conf/mlflow/local.yaml +++ b/conf/shared/mlflow/local.yaml @@ -1,3 +1,4 @@ +# @package mlflow # Local file-based MLflow (no docker required) # Results stored in ./mlruns directory mode: files diff --git a/conf/solver/fv.yaml b/conf/solver/fv.yaml index 79f97ee..2c37c0e 100644 --- a/conf/solver/fv.yaml +++ b/conf/solver/fv.yaml @@ -1,9 +1,4 @@ -# Finite Volume solver configuration -name: fv +# @package _global_ +defaults: + - /solvers/fv@solver: default -# FV-specific parameters -convection_scheme: Upwind -limiter: MUSCL -alpha_uv: 0.6 # velocity under-relaxation -alpha_p: 0.4 # pressure under-relaxation -linear_solver_tol: 1.0e-6 # PETSc linear solver tolerance diff --git a/conf/solver/spectral.yaml b/conf/solver/spectral.yaml index 43eedd4..e8d8c94 100644 --- a/conf/solver/spectral.yaml +++ b/conf/solver/spectral.yaml @@ -1,23 +1,4 @@ -# Spectral solver configuration -name: spectral +# @package _global_ +defaults: + - /solvers/spectral@solver: default -# Spectral-specific parameters -basis_type: chebyshev # "chebyshev" or "legendre" -CFL: 0.5 -beta_squared: 5.0 # artificial compressibility parameter - -# Corner singularity treatment -# Options: "smoothing" (simple cosine smoothing) or "subtraction" (Botella & Peyret 1998) -corner_treatment: smoothing -corner_smoothing: 0.15 # smoothing width (fraction of domain) for smoothing method - -# Multigrid settings -multigrid: none # "none", "fsg", "vmg", "fmg" -n_levels: 3 # number of grid levels for multigrid -coarse_tolerance_factor: 10.0 # relaxed tolerance on coarse grids - -# Transfer operators for multigrid (prolongation: coarse->fine, restriction: fine->coarse) -# Options: fft (Zhang & Xi 2010 paper method) or polynomial/injection -transfer: - prolongation: fft # "fft" or "polynomial" - restriction: fft # "fft" or "injection" diff --git a/conf/solver/spectral_fmg.yaml b/conf/solver/spectral_fmg.yaml new file mode 100644 index 0000000..5a6e0dc --- /dev/null +++ b/conf/solver/spectral_fmg.yaml @@ -0,0 +1,4 @@ +# @package _global_ +defaults: + - /solvers/spectral@solver: fmg + diff --git a/conf/solver/spectral_fsg.yaml b/conf/solver/spectral_fsg.yaml new file mode 100644 index 0000000..9270b6d --- /dev/null +++ b/conf/solver/spectral_fsg.yaml @@ -0,0 +1,4 @@ +# @package _global_ +defaults: + - /solvers/spectral@solver: fsg + diff --git a/conf/solver/spectral_fsg_poly.yaml b/conf/solver/spectral_fsg_poly.yaml new file mode 100644 index 0000000..00b6a7a --- /dev/null +++ b/conf/solver/spectral_fsg_poly.yaml @@ -0,0 +1,4 @@ +# @package _global_ +defaults: + - /solvers/spectral@solver: fsg_poly + diff --git a/conf/solver/spectral_vmg.yaml b/conf/solver/spectral_vmg.yaml new file mode 100644 index 0000000..d8f1997 --- /dev/null +++ b/conf/solver/spectral_vmg.yaml @@ -0,0 +1,4 @@ +# @package _global_ +defaults: + - /solvers/spectral@solver: vmg + diff --git a/conf/solvers/fv/default.yaml b/conf/solvers/fv/default.yaml new file mode 100644 index 0000000..0d346ab --- /dev/null +++ b/conf/solvers/fv/default.yaml @@ -0,0 +1,20 @@ +# @package solver +_target_: solvers.fv.solver.FVSolver +name: fv + +# Common parameters (inherited from root config) +Re: ${Re} +lid_velocity: ${lid_velocity} +Lx: ${Lx} +Ly: ${Ly} +nx: ${N} +ny: ${N} +max_iterations: ${max_iterations} +tolerance: ${tolerance} + +# FV-specific parameters +convection_scheme: Upwind +limiter: MUSCL +alpha_uv: 0.6 # velocity under-relaxation +alpha_p: 0.4 # pressure under-relaxation +linear_solver_tol: 1.0e-6 # SciPy linear solver tolerance diff --git a/conf/solvers/spectral/default.yaml b/conf/solvers/spectral/default.yaml new file mode 100644 index 0000000..31b98bc --- /dev/null +++ b/conf/solvers/spectral/default.yaml @@ -0,0 +1,33 @@ +# @package solver +_target_: solvers.spectral.solver.SpectralSolver +name: spectral + +# Common parameters (inherited from root config) +Re: ${Re} +lid_velocity: ${lid_velocity} +Lx: ${Lx} +Ly: ${Ly} +nx: ${N} +ny: ${N} +max_iterations: ${max_iterations} +tolerance: ${tolerance} + +# Spectral-specific parameters +basis_type: chebyshev # "chebyshev" or "legendre" +CFL: 0.5 +beta_squared: 5.0 # artificial compressibility parameter + +# Corner singularity treatment +# Options: "smoothing" (simple cosine smoothing) or "subtraction" (Botella & Peyret 1998) +corner_treatment: smoothing +corner_smoothing: 0.15 # smoothing width (fraction of domain) for smoothing method + +# Multigrid settings +multigrid: none # "none", "fsg", "vmg", "fmg" +n_levels: 3 # number of grid levels for multigrid +coarse_tolerance_factor: 10.0 # relaxed tolerance on coarse grids + +# Transfer operators for multigrid (prolongation: coarse->fine, restriction: fine->coarse) +# Options: fft (Zhang & Xi 2010 paper method) or polynomial/injection +prolongation_method: fft # "fft" or "polynomial" +restriction_method: fft # "fft" or "injection" diff --git a/conf/solvers/spectral/fmg.yaml b/conf/solvers/spectral/fmg.yaml new file mode 100644 index 0000000..0ef378c --- /dev/null +++ b/conf/solvers/spectral/fmg.yaml @@ -0,0 +1,10 @@ +# @package solver +defaults: + - default + +name: spectral_fmg +multigrid: fmg +prolongation_method: fft +restriction_method: fft +n_levels: 3 + diff --git a/conf/solvers/spectral/fsg.yaml b/conf/solvers/spectral/fsg.yaml new file mode 100644 index 0000000..0ac1764 --- /dev/null +++ b/conf/solvers/spectral/fsg.yaml @@ -0,0 +1,9 @@ +# @package solver +defaults: + - default + +name: spectral_fsg +multigrid: fsg +prolongation_method: fft +restriction_method: fft + diff --git a/conf/solvers/spectral/fsg_poly.yaml b/conf/solvers/spectral/fsg_poly.yaml new file mode 100644 index 0000000..930a3f1 --- /dev/null +++ b/conf/solvers/spectral/fsg_poly.yaml @@ -0,0 +1,9 @@ +# @package solver +defaults: + - default + +name: spectral_fsg_poly +multigrid: fsg +prolongation_method: polynomial +restriction_method: injection + diff --git a/conf/solvers/spectral/vmg.yaml b/conf/solvers/spectral/vmg.yaml new file mode 100644 index 0000000..363da83 --- /dev/null +++ b/conf/solvers/spectral/vmg.yaml @@ -0,0 +1,10 @@ +# @package solver +defaults: + - default + +name: spectral_vmg +multigrid: vmg +prolongation_method: fft +restriction_method: fft +n_levels: 3 + diff --git a/docs/source/api/base_solver.rst b/docs/source/api/base_solver.rst index 1b61de1..e897e66 100644 --- a/docs/source/api/base_solver.rst +++ b/docs/source/api/base_solver.rst @@ -1,9 +1,9 @@ -Base Solver (``solvers.base_solver``) -===================================== +Base Solver (``solvers.base``) +============================== Abstract base class for lid-driven cavity solvers. -.. currentmodule:: solvers.base_solver +.. currentmodule:: solvers.base .. autosummary:: :toctree: ../generated diff --git a/docs/source/api/fv_solver.rst b/docs/source/api/fv_solver.rst index 25471d8..fe4954a 100644 --- a/docs/source/api/fv_solver.rst +++ b/docs/source/api/fv_solver.rst @@ -1,9 +1,9 @@ -Finite Volume Solver (``solvers.fv_solver``) +Finite Volume Solver (``solvers.fv.solver``) ============================================ Finite volume solver using the SIMPLE algorithm with PETSc. -.. currentmodule:: solvers.fv_solver +.. currentmodule:: solvers.fv.solver .. autosummary:: :toctree: ../generated diff --git a/docs/source/api/spectral_solver.rst b/docs/source/api/spectral_solver.rst index 96adc07..e3b003b 100644 --- a/docs/source/api/spectral_solver.rst +++ b/docs/source/api/spectral_solver.rst @@ -1,9 +1,9 @@ -Spectral Solver (``solvers.spectral_solver``) +Spectral Solver (``solvers.spectral.solver``) ============================================= Spectral solver using Chebyshev collocation with artificial compressibility. -.. currentmodule:: solvers.spectral_solver +.. currentmodule:: solvers.spectral.solver .. autosummary:: :toctree: ../generated diff --git a/main.py b/main.py index f840a74..55266f7 100644 --- a/main.py +++ b/main.py @@ -4,14 +4,12 @@ import argparse import sys from pathlib import Path -import os # Added for os.setsid # Ensure src directory is in python path sys.path.append(str(Path(__file__).parent / "src")) -from utils import runners -from utils import mlflow as mlflow_utils -from utils.config import get_repo_root, clean_all +from utilities import runners +from utilities.config import get_repo_root, clean_all def build_docs(): @@ -31,7 +29,15 @@ def build_docs(): try: result = subprocess.run( - ["uv", "run", "sphinx-build", "-M", "html", str(source_dir), str(build_dir)], + [ + "uv", + "run", + "sphinx-build", + "-M", + "html", + str(source_dir), + str(build_dir), + ], capture_output=True, text=True, timeout=300, @@ -67,13 +73,29 @@ def main(): ) actions = parser.add_argument_group("Actions") - actions.add_argument("--docs", action="store_true", help="Build Sphinx HTML documentation") - actions.add_argument("--compute", action="store_true", help="Run all compute scripts (sequentially)") - actions.add_argument("--plot", action="store_true", help="Run all plotting scripts (in parallel)") - actions.add_argument("--copy-plots", action="store_true", help="Copy plots to report directory") - actions.add_argument("--clean", action="store_true", help="Clean all generated files and caches") - actions.add_argument("--setup-mlflow", action="store_true", help="Interactive MLflow setup (login to Databricks)") - actions.add_argument("--mlflow-ui", action="store_true", help="Start local MLflow UI (./mlruns)") + actions.add_argument( + "--docs", action="store_true", help="Build Sphinx HTML documentation" + ) + actions.add_argument( + "--compute", action="store_true", help="Run all compute scripts (sequentially)" + ) + actions.add_argument( + "--plot", action="store_true", help="Run all plotting scripts (in parallel)" + ) + actions.add_argument( + "--copy-plots", action="store_true", help="Copy plots to report directory" + ) + actions.add_argument( + "--clean", action="store_true", help="Clean all generated files and caches" + ) + actions.add_argument( + "--setup-mlflow", + action="store_true", + help="Interactive MLflow setup (login to Databricks)", + ) + actions.add_argument( + "--mlflow-ui", action="store_true", help="Start local MLflow UI (./mlruns)" + ) if len(sys.argv) == 1: parser.print_help() @@ -87,6 +109,7 @@ def main(): if args.setup_mlflow: import mlflow + print("\nSetting up MLflow...") mlflow.login(backend="databricks", interactive=True) @@ -107,7 +130,7 @@ def main(): def is_port_free(port): with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: - return s.connect_ex(('localhost', port)) != 0 + return 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