diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml index 67a80b4f..53d843e7 100644 --- a/.github/workflows/draft-pdf.yml +++ b/.github/workflows/draft-pdf.yml @@ -14,6 +14,8 @@ jobs: # Step 1: Checkout repository - name: Checkout uses: actions/checkout@v4 + with: + lfs: true # Step 2: Build draft PDF using Open Journals action - name: Build draft PDF @@ -34,4 +36,4 @@ jobs: uses: EndBug/add-and-commit@v9 with: message: '(auto) Paper PDF Draft' - add: 'paper/*.pdf' # or 'paper/*.pdf' for multiple PDFs \ No newline at end of file + add: 'paper/*.pdf' # or 'paper/*.pdf' for multiple PDFs diff --git a/README.md b/README.md index 767d4025..8e5a9705 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -

Cardiotensor

+

CardioTensor

@@ -14,7 +14,7 @@ [![CI](https://github.com/JosephBrunet/cardiotensor/actions/workflows/ci.yml/badge.svg)](https://github.com/JosephBrunet/cardiotensor/actions/workflows/ci.yml) [![Doc](https://img.shields.io/badge/docs-dev-blue.svg)](https://JosephBrunet.github.io/cardiotensor/) [![Codacy Badge](https://app.codacy.com/project/badge/Grade/b0e80972e3104ffa890532738882f42e)](https://app.codacy.com?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade) -[![License](https://img.shields.io/github/license/JosephBrunet/cardiotensor)](https://github.com/JosephBrunet/cardiotensor/blob/main/LICENSE) +[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/JosephBrunet/cardiotensor/blob/main/LICENSE) [![Python Version](https://img.shields.io/pypi/pyversions/cardiotensor.svg)](https://pypi.python.org/pypi/cardiotensor) [![PyPI version](https://img.shields.io/pypi/v/cardiotensor.svg)](https://pypi.org/project/cardiotensor/) diff --git a/docs/advanced/index.md b/docs/advanced/index.md index 42730d9a..556c1a9d 100644 --- a/docs/advanced/index.md +++ b/docs/advanced/index.md @@ -6,6 +6,7 @@ Pages: - [Transmural Analysis](./cardio-analysis.md): Extract transmural profiles from angle and FA maps with `cardio-analysis`. - [Streamlines](./streamlines.md): Generate and visualize 3D streamlines with `cardio-generate-streamlines` and `cardio-visualize-streamlines`. +- [SLURM Launcher](./slurm-launcher.md): Submit chunked `cardio-tensor` jobs on HPC clusters. See also: diff --git a/docs/advanced/slurm-launcher.md b/docs/advanced/slurm-launcher.md new file mode 100644 index 00000000..485aaa58 --- /dev/null +++ b/docs/advanced/slurm-launcher.md @@ -0,0 +1,63 @@ +# SLURM Launcher + +Use `cardio-tensor-slurm` to submit chunked orientation jobs as SLURM array tasks. + +## What It Does + +- Splits a dataset into chunks. +- Generates a `.slurm` script. +- Submits one or more SLURM arrays with `sbatch`. +- Monitors output progress. + +The launcher calls `cardio-tensor` on each task with: + +```bash +cardio-tensor --start_index --end_index +``` + +## Basic Usage + +```bash +cardio-tensor-slurm path/to/parameters.conf +``` + +## Useful Options + +```bash +cardio-tensor-slurm path/to/parameters.conf \ + --start_index 0 \ # First slice index (inclusive) + --end_index 5000 \ # Last slice index (exclusive) + --chunk_size 100 \ # Slices processed per SLURM task + --time_limit 4:00:00 \ # Wall-time limit for each task + --cpus_per_task 8 \ # CPU cores requested per task + --mem_gb 64 \ # Memory requested per task (GB) + --array_parallel 50 \ # Max concurrent tasks in the SLURM array + --partition nice \ # Optional partition/queue name + --log_dir /path/to/logs \ # Where SLURM stdout/stderr logs are written + --submit_dir /path/to/slurm_scripts # Where generated .slurm scripts are saved +``` + +Other useful flags: + +- `--no_monitor`: submit jobs and return immediately (do not watch output progress). +- `--dry_run`: generate the `.slurm` script and print `sbatch` command without submitting. + +Run help for the full list: + +```bash +cardio-tensor-slurm -h +``` + +## Troubleshooting + +- **`sbatch` fails** + Verify partition/account/time/memory policy on your cluster. + +- **Job fails before running `cardio-tensor`** + Check logs in `OUTPUT_PATH/slurm/log/`. + +- **No progress in monitor** + Confirm output folder permissions and that tasks can write outputs. + +- **Want to debug without submitting jobs** + Use `--dry_run`. diff --git a/docs/getting-started/examples.md b/docs/getting-started/examples.md index 854c770b..27e0588e 100644 --- a/docs/getting-started/examples.md +++ b/docs/getting-started/examples.md @@ -13,7 +13,7 @@ The `./examples/` directory contains: 1. **Heart Volume**: - Path: `./data/635.2um_LADAF-2021-17_heart_overview_/` - - Description: A downsampled 3D heart image volume designed for testing. The full-resolution dataset is available at the [Human Organ Atlas](https://human-organ-atlas.esrf.fr/datasets/1659197537). + - Description: A 3D heart image volume, downsampled 32 times from the original 19.85 µm/voxel dataset, resulting in an effective voxel size of 635.2 µm. The full-resolution dataset is available at the [Human Organ Atlas](https://human-organ-atlas.esrf.fr/datasets/1659197537). 2. **Binary Mask**: - Path: `./data/mask/` diff --git a/docs/reference/cli.md b/docs/reference/cli.md index 0de9d23e..343e4744 100644 --- a/docs/reference/cli.md +++ b/docs/reference/cli.md @@ -9,6 +9,10 @@ Compute myocyte orientation from a 3D volume using a configuration file. - `cardio-tensor` Computes structure tensor, helix/transverse angle, FA, and eigenvectors. +- `cardio-tensor-slurm` + Submits chunked `cardio-tensor` runs as SLURM array jobs. + See [SLURM Launcher](../advanced/slurm-launcher.md). + See the [example](../getting-started/examples.md) to get started. --- diff --git a/examples/data/635.2um_LADAF-2021-17_heart_overview_/635.2um_LADAF-2021-17_heart_overview_0000.tif b/examples/data/635.2um_LADAF-2021-17_heart_overview_/635.2um_LADAF-2021-17_heart_overview_0000.tif index 9576920b..cb33b51e 100644 Binary files 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diff --git a/examples/data/mask/binary_000234.tif b/examples/data/mask/binary_000234.tif index 7373f215..d65f52e0 100644 Binary files a/examples/data/mask/binary_000234.tif and b/examples/data/mask/binary_000234.tif differ diff --git a/mkdocs.yml b/mkdocs.yml index a40af231..af126906 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -34,6 +34,7 @@ nav: - advanced/index.md - Transmural Analysis: advanced/cardio-analysis.md - Streamlines: advanced/streamlines.md + - SLURM Launcher: advanced/slurm-launcher.md - Reference: - reference/index.md - CLI Commands: reference/cli.md diff --git a/paper/paper.md b/paper/paper.md index 7785be4a..1a4290ad 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -59,7 +59,7 @@ For non-diffusion imaging modalities, such as micro-CT [@reichardt_fiber_2020], Cardiotensor addresses this gap by providing an open-source Python package specifically tailored to structure tensor analysis of large cardiac volumes. Rather than relying on diffusion modeling, cardiotensor infers tissue orientation directly from image intensity gradients, making it applicable across a wide range of modalities and scales. Previous studies have demonstrated strong agreement between structure tensor–based orientation and DT-MRI–derived metrics when applied to the same human hearts [@teh_validation_2016]. The package supports full pipelines from raw image stacks to myocyte orientation maps and tractography. Its architecture is optimized for large datasets, using chunked and parallel processing suitable for high-performance computing environments. The overall processing workflow, from input volumes to orientation maps and tractography, is summarized in \autoref{fig:pipeline}. -Cardiotensor has already been successfully applied in published work to characterize 3D cardiomyocyte architecture in healthy and diseased human hearts using synchrotron tomography [@brunet_multidimensional_2024] to datasets over a terabyte in size. While cardiotensor was conceived for cardiac imaging, the package is modality‑ and tissue‑agnostic. Any volumetric dataset exhibiting coherent fibrous microstructure can be analyzed, including brain white matter, skeletal muscle, and tendon. This generality makes the library useful for both cardiovascular and broader anatomical or histological studies. +Cardiotensor has already been successfully applied in published work to characterize 3D cardiomyocyte architecture in healthy and diseased human hearts using synchrotron tomography [@brunet_multidimensional_2024]. In practice, cardiotensor has been applied to whole-heart HiP-CT datasets exceeding 1–5 TB (teravoxel scale). While cardiotensor was conceived for cardiac imaging, the package is modality‑ and tissue‑agnostic. Any volumetric dataset exhibiting coherent fibrous microstructure can be analyzed, including brain white matter, skeletal muscle, and tendon. This generality makes the library useful for both cardiovascular and broader anatomical or histological studies. ![Cardiotensor pipeline for 3D cardiac orientation analysis and tractography. (a) Input whole‑ or partial‑heart volume with optional myocardial mask. @@ -70,7 +70,7 @@ The third eigenvector field (smallest eigenvalue) is visualized as arrows color ## Implementation -Cardiotensor is implemented in Python and designed to efficiently process very large 3D cardiac imaging datasets. It relies primarily on NumPy [@van_der_walt_numpy_2011] for numerical computation, with I/O accelerated by tifffile [@gohlke_cgohlketifffile_2025], Glymur [@evans_quintusdiasglymur_2025], and OpenCV [@bradski_opencv_2000]. Dask [@rocklin_dask_2015] is used exclusively to parallelize file reading, while the core computations rely on Python’s multiprocessing module for local parallelism. The package builds on the structure-tensor library [@jeppesen_quantifying_2021] to calculate the 3D structure tensor and eigenvector decomposition. +Cardiotensor is implemented in Python and designed to efficiently process terabyte-scale 3D cardiac imaging datasets. It relies primarily on NumPy [@van_der_walt_numpy_2011] for numerical computation, with I/O accelerated by tifffile [@gohlke_cgohlketifffile_2025], Glymur [@evans_quintusdiasglymur_2025], and OpenCV [@bradski_opencv_2000]. Dask [@rocklin_dask_2015] is used exclusively to parallelize file reading, while the core computations rely on Python’s multiprocessing module for local parallelism. The package builds on the structure-tensor library [@jeppesen_quantifying_2021] to calculate the 3D structure tensor and eigenvector decomposition. The package supports multiple use cases: diff --git a/paper/paper.pdf b/paper/paper.pdf index d086ddc6..26750920 100644 Binary files a/paper/paper.pdf and b/paper/paper.pdf differ diff --git a/src/cardiotensor/launcher/slurm_launcher.py b/src/cardiotensor/launcher/slurm_launcher.py index 75a5a076..43e48472 100644 --- a/src/cardiotensor/launcher/slurm_launcher.py +++ b/src/cardiotensor/launcher/slurm_launcher.py @@ -1,293 +1,467 @@ import glob -import inspect import math import os +import re +import shlex import subprocess import sys import time from datetime import datetime +from pathlib import Path -from cardiotensor.orientation.orientation_computation_pipeline import ( - compute_orientation, -) from cardiotensor.utils.DataReader import DataReader -from cardiotensor.utils.utils import ( - read_conf_file, -) +from cardiotensor.utils.utils import read_conf_file def submit_job_to_slurm( - executable_path: str, conf_file_path: str, - start_image: int, - end_image: int, - N_chunk: int = 10, - mem_needed: int = 64, -) -> int: + start_index: int, + end_index_exclusive: int, + chunk_size: int, + *, + partition: str | None, + time_limit: str, + cpus_per_task: int, + mem_gb: int, + array_parallel: int, + log_dir: str, + submit_dir: str, + dry_run: bool = False, +) -> int | None: """ - Submit a Slurm job and return its job ID. - - Args: - executable_path (str): Path to the executable script. - conf_file_path (str): Path to the configuration file. - start_image (int): Index of the first image to process. - end_image (int): Index of the last image to process. - N_chunk (int, optional): Number of chunks for the job. Default is 10. - mem_needed (int, optional): Memory required in GB. Default is 64. - - Returns: - int: The Slurm job ID. + Submit a SLURM array job for a contiguous index window [start_index, end_index_exclusive). """ - log_dir = "/tmp_14_days/bm18/slurm/log/" - submit_dir = "/tmp_14_days/bm18/slurm/submit" - - executable_path = executable_path.split(".py")[0] - executable = os.path.basename(executable_path) - print(f"Script to start: {executable}", flush=True) - - # Get the current date in YYYY-MM-DD format - current_date = datetime.now().strftime("%Y-%m-%d") - - job_name = f"{executable}_{current_date}" - # Generate the job filename - job_filename = f"{submit_dir}/{job_name}.slurm" + total_images = end_index_exclusive - start_index + if total_images <= 0: + raise ValueError("end_index_exclusive must be greater than start_index.") + if chunk_size <= 0: + raise ValueError("chunk_size must be > 0") + + n_jobs = math.ceil(total_images / chunk_size) + if n_jobs <= 0: + raise ValueError("No jobs to submit.") + + log_dir_path = Path(log_dir).resolve() + submit_dir_path = Path(submit_dir).resolve() + log_dir_path.mkdir(parents=True, exist_ok=True) + submit_dir_path.mkdir(parents=True, exist_ok=True) + + ts = datetime.now().strftime("%Y%m%d_%H%M%S") + job_name = f"cardiotensor_{ts}_{start_index:06d}_{end_index_exclusive - 1:06d}" + job_filename = submit_dir_path / f"{job_name}.slurm" + conf_file_abs = str(Path(conf_file_path).resolve()) + conf_file_quoted = shlex.quote(conf_file_abs) - # Calculate the total number of images to process - total_images = end_image - start_image + 1 - IMAGES_PER_JOB = N_chunk - N_jobs = math.ceil(total_images / IMAGES_PER_JOB) + print( + f"Preparing SLURM array: n_jobs={n_jobs}, chunk_size={chunk_size}, " + f"window=[{start_index}, {end_index_exclusive})", + flush=True, + ) - print(f"\nN_jobs = {N_jobs}, IMAGES_PER_JOB = {IMAGES_PER_JOB}", flush=True) + partition_line = ( + f"#SBATCH --partition={partition}\n" + if partition is not None and partition.strip() != "" + else "" + ) slurm_script_content = f"""#!/bin/bash -l -#SBATCH --output={log_dir}/slurm-%x-%A_%a.out -#SBATCH --partition=low -#SBATCH --nodes=1 +#SBATCH --output={log_dir_path}/slurm-%x-%A_%a.out +{partition_line}#SBATCH --nodes=1 #SBATCH --ntasks-per-node=1 -#SBATCH --cpus-per-task=16 -#SBATCH --mem={math.ceil(mem_needed)}G +#SBATCH --cpus-per-task={cpus_per_task} +#SBATCH --mem={mem_gb}G #SBATCH --job-name={job_name} -#SBATCH --time=2:00:00 -#SBATCH --array=1-{N_jobs}%50 +#SBATCH --time={time_limit} +#SBATCH --array=0-{n_jobs - 1}%{array_parallel} + +set -euo pipefail echo ------------------------------------------------------ -echo SLURM_NNODES: $SLURM_NNODES -echo SLURM_JOB_NODELIST: $SLURM_JOB_NODELIST -echo SLURM_SUBMIT_DIR: $SLURM_SUBMIT_DIR -echo SLURM_SUBMIT_HOST: $SLURM_SUBMIT_HOST -echo SLURM_JOB_ID: $SLURM_JOB_ID -echo SLURM_JOB_NAME: $SLURM_JOB_NAME -echo SLURM_JOB_PARTITION: $SLURM_JOB_PARTITION -echo SLURM_NTASKS: $SLURM_NTASKS -echo SLURM_CPUS-PER-TASK: $SLURM_CPUS_PER_TASK -echo SLURM_TASKS_PER_NODE: $SLURM_TASKS_PER_NODE -echo SLURM_NTASKS_PER_NODE: $SLURM_NTASKS_PER_NODE -echo SLURM_MEM_PER_CPU: $SLURM_MEM_PER_CPU -echo SLURM_MEM_PER_NODE: $SLURM_MEM_PER_NODE +echo SLURM_NNODES: ${{SLURM_NNODES:-}} +echo SLURM_JOB_NODELIST: ${{SLURM_JOB_NODELIST:-}} +echo SLURM_SUBMIT_DIR: ${{SLURM_SUBMIT_DIR:-}} +echo SLURM_SUBMIT_HOST: ${{SLURM_SUBMIT_HOST:-}} +echo SLURM_JOB_ID: ${{SLURM_JOB_ID:-}} +echo SLURM_JOB_NAME: ${{SLURM_JOB_NAME:-}} +echo SLURM_JOB_PARTITION: ${{SLURM_JOB_PARTITION:-}} +echo SLURM_NTASKS: ${{SLURM_NTASKS:-}} +echo SLURM_CPUS_PER_TASK: ${{SLURM_CPUS_PER_TASK:-}} +echo SLURM_TASKS_PER_NODE: ${{SLURM_TASKS_PER_NODE:-}} +echo SLURM_NTASKS_PER_NODE: ${{SLURM_NTASKS_PER_NODE:-}} +echo SLURM_MEM_PER_CPU: ${{SLURM_MEM_PER_CPU:-}} +echo SLURM_MEM_PER_NODE: ${{SLURM_MEM_PER_NODE:-}} echo ------------------------------------------------------ +IMAGES_PER_JOB={chunk_size} +START_INDEX_BASE={start_index} +END_INDEX_EXCLUSIVE={end_index_exclusive} -IMAGES_PER_JOB={IMAGES_PER_JOB} -START_IMAGE={start_image} -TOTAL_IMAGES={total_images + start_image} +START_INDEX=$(( SLURM_ARRAY_TASK_ID * IMAGES_PER_JOB + START_INDEX_BASE )) +END_INDEX=$(( START_INDEX + IMAGES_PER_JOB )) -START_INDEX=$(( (SLURM_ARRAY_TASK_ID - 1) * $IMAGES_PER_JOB + $START_IMAGE )) -END_INDEX=$(( SLURM_ARRAY_TASK_ID * $IMAGES_PER_JOB + $START_IMAGE )) +if [ "$START_INDEX" -ge "$END_INDEX_EXCLUSIVE" ]; then + echo "Task index out of range for requested window. Skipping." + exit 0 +fi -# Cap END_INDEX to the last image index (zero-based) -if [ $END_INDEX -ge $TOTAL_IMAGES ]; then END_INDEX=$(( $TOTAL_IMAGES + $START_IMAGE - 1 )); fi +if [ "$END_INDEX" -gt "$END_INDEX_EXCLUSIVE" ]; then + END_INDEX=$END_INDEX_EXCLUSIVE +fi -echo Start index, End index : $START_INDEX: $END_INDEX -echo mem used {math.ceil(mem_needed)}G +echo "Start index (inclusive): $START_INDEX" +echo "End index (exclusive): $END_INDEX" +echo "Requested memory (GB): {mem_gb}" # Fix Qt headless error export QT_QPA_PLATFORM=offscreen -# Starting python script -echo cardio-tensor {conf_file_path} --start_index $START_INDEX --end_index $END_INDEX - -# cardio-tensor {executable_path}.py {conf_file_path} --start_index $START_INDEX --end_index $END_INDEX -cardio-tensor {conf_file_path} --start_index $START_INDEX --end_index $END_INDEX - +echo cardio-tensor {conf_file_quoted} --start_index "$START_INDEX" --end_index "$END_INDEX" +cardio-tensor {conf_file_quoted} --start_index "$START_INDEX" --end_index "$END_INDEX" """ - with open(job_filename, "w") as file: - file.write(slurm_script_content) + job_filename.write_text(slurm_script_content) + + if dry_run: + print(f"[dry-run] Generated script: {job_filename}", flush=True) + print(f"[dry-run] sbatch {job_filename}", flush=True) + return None try: result = subprocess.run( - ["sbatch", job_filename], capture_output=True, text=True, check=True + ["sbatch", str(job_filename)], + capture_output=True, + text=True, + check=True, ) - job_id = result.stdout.split()[-1] - print(f"sbatch {job_id} - Index {start_image} to {end_image}", flush=True) - return int(job_id) - except subprocess.CalledProcessError: - print(f"⚠️ - Failed to submit Slurm job with script {job_filename}", flush=True) - sys.exit() + except subprocess.CalledProcessError as exc: + print(f"⚠️ Failed to submit SLURM script {job_filename}", flush=True) + if exc.stdout: + print(exc.stdout, flush=True) + if exc.stderr: + print(exc.stderr, flush=True) + sys.exit(1) + + stdout = result.stdout.strip() + match = re.search(r"Submitted batch job\s+(\d+)", stdout) + if match is None: + numbers = re.findall(r"\d+", stdout) + if not numbers: + raise RuntimeError(f"Could not parse SLURM job id from: {stdout}") + job_id = int(numbers[-1]) + else: + job_id = int(match.group(1)) + + print(f"Submitted sbatch job {job_id} for [{start_index}, {end_index_exclusive})") + return job_id def slurm_launcher( - conf_file_path: str, start_index: int = 0, end_index: int | None = None + conf_file_path: str, + start_index: int = 0, + end_index: int | None = None, + chunk_size: int | None = None, + partition: str | None = None, + time_limit: str | None = None, + cpus_per_task: int | None = None, + mem_gb: int | None = None, + array_parallel: int | None = None, + log_dir: str | None = None, + submit_dir: str | None = None, + monitor: bool = True, + dry_run: bool = False, ) -> None: """ - Launch Slurm array jobs for a subset [start_index, end_index] (inclusive) of the volume. - If end_index is None, the last slice of the volume is used. + Launch SLURM array jobs for a subset [start_index, end_index) of the volume. + + Parameters + ---------- + conf_file_path : str + Path to cardiotensor configuration file. + start_index : int + Global start index (inclusive). + end_index : int | None + Global end index (exclusive). If None, process until the last slice. + monitor : bool + If True, wait and monitor outputs after submission. + dry_run : bool + If True, generate scripts but do not submit jobs. """ try: params = read_conf_file(conf_file_path) - except Exception as e: - print(f"⚠️ Error reading parameter file '{conf_file_path}': {e}", flush=True) + except Exception as err: + print(f"⚠️ Error reading parameter file '{conf_file_path}': {err}", flush=True) sys.exit(1) - VOLUME_PATH = params.get("IMAGES_PATH", "") - OUTPUT_DIR = params.get("OUTPUT_PATH", "./output") - N_CHUNK = int(params.get("N_CHUNK", 100)) - IS_TEST = params.get("TEST", False) + volume_path = params.get("IMAGES_PATH", "") + output_dir = params.get("OUTPUT_PATH", "./output") + output_format = params.get("OUTPUT_FORMAT", "jp2") + angle_mode = str(params.get("ANGLE_MODE", "ha_ia")).strip().lower() + write_angles = bool(params.get("WRITE_ANGLES", True)) + write_vectors = bool(params.get("WRITE_VECTORS", False)) + is_test = bool(params.get("TEST", False)) - if IS_TEST is True: + if is_test: sys.exit( - "Test mode activated, run directly 3D_processing.py or deactivate test mode in the parameter file" + "Test mode is enabled. Disable TEST in the configuration for SLURM runs." ) - data_reader = DataReader(VOLUME_PATH) + default_chunk_size = int(params.get("N_CHUNK", 100)) + chunk_size = default_chunk_size if chunk_size is None else int(chunk_size) + partition = None if partition is None else str(partition).strip() + if partition == "": + partition = None + time_limit = "2:00:00" if time_limit is None else str(time_limit).strip() + cpus_per_task = 8 if cpus_per_task is None else int(cpus_per_task) + mem_gb = 64 if mem_gb is None else int(mem_gb) + array_parallel = 100 if array_parallel is None else int(array_parallel) + default_log_dir = os.path.join(output_dir, "slurm", "log") + default_submit_dir = os.path.join(output_dir, "slurm", "submit") + log_dir = default_log_dir if log_dir is None else str(log_dir) + submit_dir = default_submit_dir if submit_dir is None else str(submit_dir) + + if chunk_size <= 0: + raise ValueError("chunk_size must be > 0") + if cpus_per_task <= 0: + raise ValueError("cpus_per_task must be > 0") + if mem_gb <= 0: + raise ValueError("mem_gb must be > 0") + if array_parallel <= 0: + raise ValueError("array_parallel must be > 0") + + data_reader = DataReader(volume_path) total_slices = int(data_reader.shape[0]) + if total_slices <= 0: + raise ValueError(f"Dataset at {volume_path} contains no slices.") - # ----- sanitize window ----- first = max(0, int(start_index)) - last = total_slices - 1 if end_index is None else int(end_index) - if last < 0: - last = total_slices - 1 - - # clamp - first = max(0, min(first, total_slices - 1)) - last = max(0, min(last, total_slices - 1)) + last_exclusive = total_slices if end_index is None else int(end_index) + last_exclusive = max(0, min(last_exclusive, total_slices)) - if last < first: - print( - f"⚠️ Invalid range: start_index ({first}) > end_index ({last})", flush=True + if last_exclusive <= first: + raise ValueError( + f"Invalid range: start_index={first}, end_index={last_exclusive} " + f"(must satisfy start_index < end_index <= {total_slices})." ) - sys.exit(1) - window_len = last - first + 1 + window_len = last_exclusive - first print( - f"Processing slice window [{first}, {last}] (len={window_len}) out of 0..{total_slices - 1}", + f"Processing slice window [{first}, {last_exclusive}) " + f"(len={window_len}) out of 0..{total_slices}", flush=True, ) + print( + "SLURM settings: " + f"partition={partition}, time_limit={time_limit}, cpus_per_task={cpus_per_task}, " + f"mem_gb={mem_gb}, array_parallel={array_parallel}, chunk_size={chunk_size}", + flush=True, + ) + print(f"SLURM paths: log_dir={log_dir}, submit_dir={submit_dir}", flush=True) - mem_needed = 128 - - # ----- build per-job intervals (inclusive) ----- def build_intervals( - first_idx: int, last_idx: int, step: int + first_idx: int, last_idx_exclusive: int, step: int ) -> list[tuple[int, int]]: - out = [] + out: list[tuple[int, int]] = [] s = first_idx - while s <= last_idx: - e = min(s + step - 1, last_idx) + while s < last_idx_exclusive: + e = min(s + step, last_idx_exclusive) out.append((s, e)) - s = e + 1 + s = e return out - intervals = build_intervals(first, last, N_CHUNK) + intervals = build_intervals(first, last_exclusive, chunk_size) n_jobs_total = len(intervals) print( - f"Splitting data into {n_jobs_total} jobs of up to {N_CHUNK} slices each", + f"Splitting data into {n_jobs_total} jobs of up to {chunk_size} slices each", flush=True, ) - # ----- batch into arrays of <= 999 tasks ----- - N_job_max_per_array = 999 + max_tasks_per_array = 999 batched = [ - intervals[i : i + N_job_max_per_array] - for i in range(0, n_jobs_total, N_job_max_per_array) + intervals[i : i + max_tasks_per_array] + for i in range(0, n_jobs_total, max_tasks_per_array) ] print( - f"Launching {len(batched)} arrays (tasks per array: {[len(b) for b in batched]})", + f"Launching {len(batched)} array batch(es) " + f"(tasks per batch: {[len(batch) for batch in batched]})", flush=True, ) - python_file_path = os.path.abspath(inspect.getfile(compute_orientation)) + job_ids: list[int] = [] start_t = time.time() - for batch in batched: batch_start = batch[0][0] - batch_end = batch[-1][1] # inclusive + batch_end_exclusive = batch[-1][1] job_id = submit_job_to_slurm( - python_file_path, conf_file_path, batch_start, - batch_end, - N_chunk=N_CHUNK, - mem_needed=mem_needed, - ) - print( - f"Submitted array for [{batch_start}, {batch_end}] (job ID: {job_id})", - flush=True, + batch_end_exclusive, + chunk_size, + partition=partition, + time_limit=time_limit, + cpus_per_task=cpus_per_task, + mem_gb=mem_gb, + array_parallel=array_parallel, + log_dir=log_dir, + submit_dir=submit_dir, + dry_run=dry_run, ) + if job_id is not None: + job_ids.append(job_id) + + if dry_run: + print("Dry run complete. No jobs were submitted.", flush=True) + return + + if not monitor: + print("Submission complete. Monitoring disabled by user.", flush=True) + return + + monitor_job_output( + output_directory=output_dir, + start_index=first, + end_index_exclusive=last_exclusive, + output_format=output_format, + angle_mode=angle_mode, + write_angles=write_angles, + write_vectors=write_vectors, + ) - # monitor only the requested window - monitor_job_output(OUTPUT_DIR, window_len, conf_file_path) + elapsed = time.time() - start_t + print( + f"SLURM jobs submitted: {job_ids if job_ids else '[unknown ids]'}\n" + f"Elapsed submit+monitor time (s): {elapsed:.1f}", + flush=True, + ) + + +def _count_files_in_range( + folder: str, + prefix: str, + extension: str, + start_index: int, + end_index_exclusive: int, +) -> int: + """ + Count files named like {prefix}_{index:06d}.{extension} within [start, end). + """ + if not os.path.isdir(folder): + return 0 - print(f"Execution seconds: {time.time() - start_t}", flush=True) + ext = extension.lstrip(".") + pattern = os.path.join(folder, f"{prefix}_*.{ext}") + rgx = re.compile(rf"^{re.escape(prefix)}_(\d+)\.{re.escape(ext)}$") + + count = 0 + for file_path in glob.glob(pattern): + name = os.path.basename(file_path) + match = rgx.match(name) + if match is None: + continue + idx = int(match.group(1)) + if start_index <= idx < end_index_exclusive: + count += 1 + return count def monitor_job_output( - output_directory: str, total_images: int, file_extension: str + output_directory: str, + start_index: int, + end_index_exclusive: int, + output_format: str = "jp2", + angle_mode: str = "ha_ia", + write_angles: bool = True, + write_vectors: bool = False, + poll_interval_sec: int = 60, ) -> None: """ - Monitor OUTPUT_DIR/HA until total_images files appear (subset-aware). + Monitor output progress for the requested index range. + + The monitor tracks one representative output per slice: + - HA or AZ image when `write_angles=True` + - eigen_vec when only vectors are written """ - start_time = time.time() - time.sleep(1) - tmp_count = len(glob.glob(f"{output_directory}/HA/*")) - while True: - current_files_count = len(glob.glob(f"{output_directory}/HA/*")) + total_images = end_index_exclusive - start_index + if total_images <= 0: + return + + if write_angles: + if angle_mode == "az_el": + mode_prefix = "AZ" + else: + mode_prefix = "HA" + folder = os.path.join(output_directory, mode_prefix) + extension = output_format + prefix = mode_prefix + elif write_vectors: + folder = os.path.join(output_directory, "eigen_vec") + extension = "npy" + prefix = "eigen_vec" + else: + print( + "No angle/vector outputs requested; skipping monitor (nothing to track).", + flush=True, + ) + return - processed = current_files_count + print( + f"Monitoring outputs in {folder} for range [{start_index}, {end_index_exclusive})", + flush=True, + ) + + start_t = time.time() + prev_count = _count_files_in_range( + folder, prefix, extension, start_index, end_index_exclusive + ) + while True: + current_count = _count_files_in_range( + folder, prefix, extension, start_index, end_index_exclusive + ) + processed = current_count + remaining = max(total_images - processed, 0) print(f"{processed}/{total_images} processed", flush=True) - if current_files_count > tmp_count: - rate = ( - current_files_count - tmp_count - ) # images per minute (since we sleep 60s) - remaining = max(total_images - processed, 0) - eta_min = remaining / rate if rate > 0 else float("inf") + if processed >= total_images: + break + + delta = current_count - prev_count + if delta > 0: + rate_per_min = delta * (60.0 / max(poll_interval_sec, 1)) + eta_min = remaining / rate_per_min if rate_per_min > 0 else float("inf") print( - f"{current_files_count - tmp_count} images processed in 60sec. " - f"Approximately {eta_min:.2f} minutes remaining", + f"{delta} new files in last {poll_interval_sec}s. " + f"Approx. {eta_min:.2f} minutes remaining.", flush=True, ) - tmp_count = current_files_count - - if processed >= total_images: - break - print(f"Processing time (s): {time.time() - start_time:.1f}", flush=True) - print("\nWaiting 60 seconds...\n", flush=True) - time.sleep(60) + prev_count = current_count + print(f"Elapsed time (s): {time.time() - start_t:.1f}", flush=True) + print(f"Waiting {poll_interval_sec} seconds...\n", flush=True) + time.sleep(poll_interval_sec) def is_chunk_done( - output_dir: str, start: int, end: int, output_format: str = "jp2" + output_dir: str, + start: int, + end: int, + output_format: str = "jp2", + angle_mode: str = "ha_ia", ) -> bool: """ - Check if all output files (HA, IA, FA) for a given chunk are already present. - - Args: - output_dir (str): Base output directory containing HA/IA/FA folders. - start (int): Start index of the chunk (inclusive). - end (int): End index of the chunk (exclusive). - output_format (str): File extension for the output images (e.g., "jp2", "tif"). - - Returns: - bool: True if all expected output files exist, False otherwise. + Check if all output files for a given chunk [start, end) are already present. """ + ext = output_format.lstrip(".") + mode = angle_mode.lower().strip() + if mode == "az_el": + angle1, angle2 = "AZ", "EL" + else: + angle1, angle2 = "HA", "IA" + for idx in range(start, end): expected_files = [ - f"{output_dir}/HA/HA_{idx:06d}.{output_format}", - f"{output_dir}/IA/IA_{idx:06d}.{output_format}", - f"{output_dir}/FA/FA_{idx:06d}.{output_format}", + f"{output_dir}/{angle1}/{angle1}_{idx:06d}.{ext}", + f"{output_dir}/{angle2}/{angle2}_{idx:06d}.{ext}", + f"{output_dir}/FA/FA_{idx:06d}.{ext}", ] - if not all(os.path.exists(f) for f in expected_files): + if not all(os.path.exists(path) for path in expected_files): return False return True diff --git a/src/cardiotensor/orientation/orientation_computation_functions.py b/src/cardiotensor/orientation/orientation_computation_functions.py index 7b1bba78..9a146cc9 100644 --- a/src/cardiotensor/orientation/orientation_computation_functions.py +++ b/src/cardiotensor/orientation/orientation_computation_functions.py @@ -12,6 +12,7 @@ from cardiotensor.utils.utils import convert_to_8bit +from cardiotensor.colormaps.helix_angle import helix_angle_cmap from concurrent.futures import ProcessPoolExecutor from .multiprocessing_MDI import parallel_mdi_analysis @@ -570,79 +571,6 @@ def compute_azimuth_and_elevation( return az, el -# def plot_images( -# img: np.ndarray, -# img_angle1: np.ndarray, -# img_angle2: np.ndarray, -# img_FA: np.ndarray, -# center_point: tuple[int, int, int], -# colormap_angle=None, -# colormap_FA=None, -# angle1_title: str = "Helix Angle", -# angle2_title: str = "Intrusion Angle", -# ): -# """ -# Plots images of the heart with helix, intrusion, and FA annotations. - -# Args: -# img (np.ndarray): Grayscale image of the heart. -# img_helix (np.ndarray): Helix angle image. -# img_intrusion (np.ndarray): Intrusion angle image. -# img_FA (np.ndarray): Fractional Anisotropy (FA) image. -# center_point (Tuple[int, int, int]): Coordinates of the center point. -# colormap_angle: Colormap for helix and intrusion angles (default: helix_angle_cmap). -# colormap_FA: Colormap for FA image (default: 'inferno'). - -# Returns: -# None -# """ - - -# # Default colormaps -# if colormap_angle is None: -# colormap_angle = helix_angle_cmap -# if colormap_FA is None: -# colormap_FA = plt.get_cmap("inferno") - -# # Determine display range for original image -# img_vmin, img_vmax = np.nanpercentile(img, (5, 95)) - -# # Create a figure and axes -# fig, axes = plt.subplots(2, 2, figsize=(10, 8)) -# ax = axes - -# # Original Image with Red Point -# ax[0, 0].imshow(img, vmin=img_vmin, vmax=img_vmax, cmap="gray") -# x, y = center_point[0:2] -# ax[0, 0].scatter(x, y, c="red", s=50, marker="o", label="Axis Point") -# ax[0, 0].set_title("Original Image") -# ax[0, 0].legend(loc="upper right") - -# # Helix Image -# tmp = ax[0, 1].imshow(img_angle1, cmap=colormap_angle, vmin=-90, vmax=90) -# ax[0, 1].set_title(angle1_title) - -# # Intrusion Image -# ax[1, 0].imshow(img_angle2, cmap=colormap_angle, vmin=-90, vmax=90) -# ax[1, 0].set_title(angle2_title) - -# # FA Image -# fa_plot = ax[1, 1].imshow(img_FA, cmap=colormap_FA, vmin=0, vmax=1) -# ax[1, 1].set_title("Fractional Anisotropy") - -# # Add colorbars for relevant subplots -# cbar1 = fig.colorbar(tmp, ax=ax[0, 1], orientation="vertical") -# cbar1.set_label(angle1_title) -# cbar2 = fig.colorbar(fa_plot, ax=ax[1, 1], orientation="vertical") -# cbar2.set_label("Fractional Anisotropy") - -# # Hide axes for a cleaner view -# for axis in ax.flat: -# axis.axis("off") - -# # Adjust layout to prevent overlap -# fig.tight_layout() -# plt.show() def plot_images( @@ -673,7 +601,7 @@ def plot_images( Integer voxel coordinates (z, y, x) of the centerline point on this slice. Only (y, x) is used here for the marker. colormap_angle : matplotlib colormap, optional - Colormap for angles. Defaults to plt.cm.hsv if None. + Colormap for angles. Defaults to helix_angle_cmap if None. colormap_FA : matplotlib colormap, optional Colormap for FA. Defaults to plt.cm.magma if None. angle1_title : str @@ -686,7 +614,7 @@ def plot_images( This function shows the centerline marker on the source panel. """ if colormap_angle is None: - colormap_angle = plt.cm.hsv + colormap_angle = helix_angle_cmap if colormap_FA is None: colormap_FA = plt.cm.magma @@ -717,140 +645,6 @@ def plot_images( plt.show() -# def write_images( -# img_angle1: np.ndarray, -# img_angle2: np.ndarray, -# img_FA: np.ndarray, -# start_index: int, -# output_dir: str, -# output_format: str, -# output_type: str, -# z: int, -# colormap_angle=None, -# colormap_FA=None, -# angle_names: tuple[str, str] = ("HA", "IA"), -# angle_ranges: tuple[tuple[float, float], tuple[float, float]] = ((-90, 90), (-90, 90)), -# ) -> None: - -# # angle_names drives subfolders, angle_ranges drives normalization -# name1, name2 = angle_names -# (a1_min, a1_max), (a2_min, a2_max) = angle_ranges - -# os.makedirs(f"{output_dir}/{name1}", exist_ok=True) -# os.makedirs(f"{output_dir}/{name2}", exist_ok=True) -# os.makedirs(f"{output_dir}/FA", exist_ok=True) - -# if "8bit" in output_type: -# img1_8 = convert_to_8bit(img_angle1, min_value=a1_min, max_value=a1_max) -# img2_8 = convert_to_8bit(img_angle2, min_value=a2_min, max_value=a2_max) -# imgFA_8 = convert_to_8bit(img_FA, min_value=0, max_value=1) - -# if output_format == "jp2": -# # same glymur writes, just using name1/name2 -# glymur.Jp2k(f"{output_dir}/{name1}/{name1}_{(start_index+z):06d}.jp2", -# data=img1_8, cratios=[10], numres=8, irreversible=True) -# glymur.Jp2k(f"{output_dir}/{name2}/{name2}_{(start_index+z):06d}.jp2", -# data=img2_8, cratios=[10], numres=8, irreversible=True) -# glymur.Jp2k(f"{output_dir}/FA/FA_{(start_index+z):06d}.jp2", -# data=imgFA_8, cratios=[10], numres=8, irreversible=True) -# elif output_format == "tif": -# tifffile.imwrite(f"{output_dir}/{name1}/{name1}_{(start_index+z):06d}.tif", img1_8) -# tifffile.imwrite(f"{output_dir}/{name2}/{name2}_{(start_index+z):06d}.tif", img2_8) -# tifffile.imwrite(f"{output_dir}/FA/FA_{(start_index+z):06d}.tif", imgFA_8) -# else: -# sys.exit(f"I don't recognise the output_format ({output_format})") - - -# # ---- RGB output ---- -# elif "rgb" in output_type: - -# def write_img_rgb( -# img: np.ndarray, -# output_path: str, -# cmap: plt.Colormap, -# vmin: float, -# vmax: float, -# ) -> None: -# """ -# Writes a single 2D RGB image using a fixed colormap range. - -# Args: -# img (np.ndarray): Input scalar image. -# output_path (str): Path to save the RGB image. -# cmap (plt.Colormap): Matplotlib colormap (e.g., inferno, custom HA cmap). -# vmin (float): Minimum value for normalization. -# vmax (float): Maximum value for normalization. -# """ -# img_clipped = np.clip(img, vmin, vmax) -# img_norm = (img_clipped - vmin) / (vmax - vmin + 1e-8) - -# img_rgb = cmap(img_norm)[..., :3] # Drop alpha channel -# img_rgb = (img_rgb * 255).astype(np.uint8) - -# print(img_rgb.shape, img_rgb.dtype) - -# if output_path.endswith(".jp2"): -# ratio_compression = 10 -# glymur.Jp2k( -# output_path, -# data=img_rgb, -# cratios=[ratio_compression], -# numres=8, -# irreversible=True, -# ) -# elif output_path.endswith(".tif"): -# tifffile.imwrite(output_path, img_rgb) -# else: -# sys.exit(f"I don't recognise the output path format: {output_path}") - -# if output_format == "jp2": -# write_img_rgb( -# img_helix, -# f"{output_dir}/HA/HA_{(start_index + z):06d}.jp2", -# cmap=colormap_angle, -# vmin=-90, -# vmax=90, -# ) -# write_img_rgb( -# img_intrusion, -# f"{output_dir}/IA/IA_{(start_index + z):06d}.jp2", -# cmap=colormap_angle, -# vmin=-90, -# vmax=90, -# ) -# write_img_rgb( -# img_FA, -# f"{output_dir}/FA/FA_{(start_index + z):06d}.jp2", -# cmap=colormap_FA, -# vmin=0, -# vmax=1, -# ) - -# elif output_format == "tif": -# write_img_rgb( -# img_helix, -# f"{output_dir}/HA/HA_{(start_index + z):06d}.tif", -# cmap=colormap_angle, -# vmin=-90, -# vmax=90, -# ) -# write_img_rgb( -# img_intrusion, -# f"{output_dir}/IA/IA_{(start_index + z):06d}.tif", -# cmap=colormap_angle, -# vmin=-90, -# vmax=90, -# ) -# write_img_rgb( -# img_FA, -# f"{output_dir}/FA/FA_{(start_index + z):06d}.tif", -# cmap=colormap_FA, -# vmin=0, -# vmax=1, -# ) - -# else: -# sys.exit(f"I don't recognise the output_format ({output_format})") def write_img_rgb( @@ -875,7 +669,7 @@ def write_img_rgb( vmax : float Maximum for normalization. colormap : matplotlib colormap, optional - Colormap to apply, for example plt.cm.hsv. If None, use plt.cm.hsv. + Colormap to apply. If None, use helix_angle_cmap. output_format : {"jp2", "tif"} Output format. Uses glymur for jp2 and tifffile for tif. @@ -884,7 +678,7 @@ def write_img_rgb( The function normalizes to [0, 1], applies the colormap, then writes uint8 RGB. """ if colormap is None: - colormap = plt.cm.hsv + colormap = helix_angle_cmap denom = (vmax - vmin) if (vmax - vmin) != 0 else 1.0 x = (img.astype(np.float32) - vmin) / denom @@ -942,7 +736,7 @@ def write_images( z : int Current z offset used to compute the running index in filenames. colormap_angle : matplotlib colormap, optional - Colormap for angles in "rgb" mode. Defaults to plt.cm.hsv if None. + Colormap for angles in "rgb" mode. Defaults to helix_angle_cmap if None. colormap_FA : matplotlib colormap, optional Colormap for FA in "rgb" mode. Defaults to plt.cm.magma if None. angle_names : tuple[str, str] @@ -1000,7 +794,7 @@ def write_images( elif output_type == "rgb": if colormap_angle is None: - colormap_angle = plt.cm.hsv + colormap_angle = helix_angle_cmap if colormap_FA is None: colormap_FA = plt.cm.magma diff --git a/src/cardiotensor/orientation/orientation_computation_pipeline.py b/src/cardiotensor/orientation/orientation_computation_pipeline.py index a5d1864b..13b28114 100644 --- a/src/cardiotensor/orientation/orientation_computation_pipeline.py +++ b/src/cardiotensor/orientation/orientation_computation_pipeline.py @@ -7,8 +7,10 @@ import numpy as np from alive_progress import alive_bar +from matplotlib import pyplot as plt # from memory_profiler import profile +from cardiotensor.colormaps.helix_angle import helix_angle_cmap from cardiotensor.orientation.orientation_computation_functions import ( adjust_start_end_index, calculate_center_vector, @@ -31,6 +33,24 @@ # --- small helpers --- +def _resolve_colormap(colormap: str | None): + """Resolve colormap names, including the project-specific helix angle map.""" + if colormap is None: + return None + cmap_name = colormap.strip() + if not cmap_name: + return None + if cmap_name.lower() in {"helix_angle", "helix_angle_cmap"}: + return helix_angle_cmap + try: + return plt.get_cmap(cmap_name) + except ValueError as err: + raise ValueError( + f"Unknown colormap '{colormap}'. Use a valid matplotlib colormap name " + "or 'helix_angle'." + ) from err + + def check_already_processed( output_dir: str, start_index: int, @@ -147,6 +167,7 @@ def compute_orientation( n_slice_test: int | None = None, start_index: int = 0, end_index: int | None = None, + colormap: str | None = None, write_MDI: bool = False ) -> None: """ @@ -170,6 +191,7 @@ def compute_orientation( n_slice_test: Number of slices to process in test mode. start_index: Start slice index. end_index: End slice index (None = last slice). + colormap: Colormap name for RGB angle outputs. Defaults to helix-angle map. """ # --- Sanity checks --- @@ -197,6 +219,7 @@ def compute_orientation( - Write vectors: {write_vectors} - Use GPU: {use_gpu} - Test mode: {is_test} + - Colormap: {colormap or "[default]"} """) print("\n" + "-" * 40) @@ -391,6 +414,7 @@ def update_bar(_): MDI_vol[z, :, :] if write_MDI else None, is_test, angle_mode, + colormap, ), callback=update_bar, ) @@ -422,6 +446,7 @@ def update_bar(_): MDI_vol[z, :, :] if write_MDI else None, is_test, angle_mode, + colormap, ) bar() @@ -448,6 +473,7 @@ def compute_slice_angles_and_anisotropy( MDI_slice: np.ndarray = None, is_test: bool = False, angle_mode: str = "ha_ia", + colormap: str | None = None, ) -> None: """ Compute either HA/IA or Azimuth/Elevation plus FA for a single slice, @@ -466,6 +492,7 @@ def compute_slice_angles_and_anisotropy( ext = output_format.lstrip(".") idx = start_index + z + colormap_angle = _resolve_colormap(colormap) # Expected outputs for skip logic expected_paths = [] @@ -537,6 +564,7 @@ def compute_slice_angles_and_anisotropy( img_angle2, img_FA, center_point, + colormap_angle=colormap_angle, angle1_title=titles[0], angle2_title=titles[1], ) @@ -549,6 +577,7 @@ def compute_slice_angles_and_anisotropy( ext, output_type, z, + colormap_angle=colormap_angle, angle_names=angle_names, angle_ranges=angle_ranges, ) @@ -574,6 +603,7 @@ def compute_slice_angles_and_anisotropy( ext, output_type, z, + colormap_angle=colormap_angle, angle_names=angle_names, angle_ranges=angle_ranges, ) diff --git a/src/cardiotensor/scripts/compute_orientation.py b/src/cardiotensor/scripts/compute_orientation.py index 90cfee8d..e3170bb0 100644 --- a/src/cardiotensor/scripts/compute_orientation.py +++ b/src/cardiotensor/scripts/compute_orientation.py @@ -44,12 +44,20 @@ def script() -> None: action="store_true", help="Process slices in reverse order starting from the end.", ) + + parser.add_argument( + "--colormap", + type=str, + default=None, + help="Colormap to apply, for example 'hsv'. If None, use helix_angle_cmap.", + ) args = parser.parse_args() conf_file_path = args.conf_file_path start_index = args.start_index end_index = args.end_index reverse = args.reverse + colormap = args.colormap # --- Read configuration --- try: @@ -114,6 +122,7 @@ def script() -> None: n_slice_test=n_slice_test, start_index=start_index, end_index=end_index, + colormap=colormap, ) print(f"--- {time.time() - t0:.1f} seconds (TEST mode) ---") return @@ -157,6 +166,7 @@ def script() -> None: n_slice_test=n_slice_test, start_index=s, end_index=e, + colormap=colormap, write_MDI=write_MDI ) diff --git a/src/cardiotensor/scripts/slurm_launcher.py b/src/cardiotensor/scripts/slurm_launcher.py index 2752d478..2eb02c93 100644 --- a/src/cardiotensor/scripts/slurm_launcher.py +++ b/src/cardiotensor/scripts/slurm_launcher.py @@ -9,13 +9,12 @@ def script() -> None: """ - Main script to process 3D data. Reads configuration files, launches processing tasks, - and logs processing time. + Submit cardiotensor processing as SLURM array jobs. """ - # Parse the configuration file path from command-line arguments parser = argparse.ArgumentParser( - description="Process 3D data using the specified configuration file." + description="Launch cardio-tensor jobs on a SLURM cluster.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( "conf_file_path", type=str, help="Path to the input configuration file." @@ -30,14 +29,83 @@ def script() -> None: "--end_index", type=int, default=None, - help="Ending slice index (default: None, processes all slices).", + help="Ending slice index (exclusive). Default: None (process until end).", + ) + parser.add_argument( + "--chunk_size", + type=int, + default=None, + help="Slices per SLURM task. If omitted, uses N_CHUNK from config.", + ) + parser.add_argument( + "--partition", + type=str, + default=None, + help="SLURM partition name. If omitted, no partition directive is added.", + ) + parser.add_argument( + "--time_limit", + type=str, + default="2:00:00", + help="SLURM time limit (e.g., 2:00:00).", + ) + parser.add_argument( + "--cpus_per_task", + type=int, + default=8, + help="SLURM CPUs per task.", + ) + parser.add_argument( + "--mem_gb", + type=int, + default=64, + help="SLURM memory per task in GB.", + ) + parser.add_argument( + "--array_parallel", + type=int, + default=100, + help="Maximum concurrent tasks in SLURM array.", + ) + parser.add_argument( + "--log_dir", + type=str, + default=None, + help="Directory for SLURM logs. If omitted, uses OUTPUT_PATH/slurm/log.", + ) + parser.add_argument( + "--submit_dir", + type=str, + default=None, + help="Directory where .slurm scripts are stored. If omitted, uses OUTPUT_PATH/slurm/submit.", + ) + parser.add_argument( + "--no_monitor", + action="store_true", + help="Do not wait and monitor output files after submission.", + ) + parser.add_argument( + "--dry_run", + action="store_true", + help="Generate scripts and print sbatch commands without submitting.", ) args = parser.parse_args() conf_file_path = args.conf_file_path - # Launch processing using slurm_launcher slurm_launcher( - conf_file_path, start_index=args.start_index, end_index=args.end_index + conf_file_path, + start_index=args.start_index, + end_index=args.end_index, + chunk_size=args.chunk_size, + partition=args.partition, + time_limit=args.time_limit, + cpus_per_task=args.cpus_per_task, + mem_gb=args.mem_gb, + array_parallel=args.array_parallel, + log_dir=args.log_dir, + submit_dir=args.submit_dir, + monitor=not args.no_monitor, + dry_run=args.dry_run, )