cardiac-shared 0.3.0

Shared utilities for cardiac imaging analysis - hardware detection, IO, environment detection

Cardiac Shared

Shared utilities for cardiac imaging analysis projects.

Version: 0.2.0

Installation

# Install with all features
pip install -e ".[all]"

# Install with DICOM support only
pip install -e ".[dicom]"

# Install with NIfTI support only
pip install -e ".[nifti]"

Modules

IO Module

Function	Description
read_dicom_series(path)	Read DICOM series from directory
get_dicom_metadata(ds)	Extract metadata from DICOM dataset
load_nifti(path)	Load NIfTI file with metadata
save_nifti(volume, path)	Save numpy array as NIfTI
extract_zip(path)	Context manager for ZIP extraction
find_dicom_root(path)	Find DICOM directory in extracted ZIP

Hardware Module (v0.2.0)

Function	Description
detect_hardware()	Detect complete hardware info (GPU/CPU/RAM)
HardwareInfo	Dataclass with GPU, CPU, RAM, environment info
print_hardware_summary(hw)	Print formatted hardware summary
get_optimal_config(hw)	Get optimal inference configuration
CPUOptimizer	CPU optimization for hospital deployments
apply_cpu_optimizations(config)	Apply PyTorch CPU optimizations

Environment Module (v0.2.0)

Function	Description
detect_runtime()	Detect runtime environment
RuntimeEnvironment	Dataclass with environment info
detect_colab()	Check if running in Google Colab
detect_wsl()	Check if running in WSL
print_environment_summary(env)	Print environment summary

Usage

IO Operations

from cardiac_shared.io import read_dicom_series, load_nifti, extract_zip, find_dicom_root

# Read DICOM series
volume, metadata = read_dicom_series("/path/to/dicom/")

# Read NIfTI file
volume, metadata = load_nifti("/path/to/file.nii.gz")

# Extract ZIP and read DICOM
with extract_zip("/path/to/data.zip") as extracted_dir:
    dicom_root = find_dicom_root(extracted_dir)
    volume, metadata = read_dicom_series(dicom_root)

Hardware Detection

from cardiac_shared import detect_hardware, print_hardware_summary

hw = detect_hardware()
print_hardware_summary(hw)

print(f"Performance Tier: {hw.performance_tier}")
print(f"Recommended Device: {hw.recommended_device}")
print(f"GPU Available: {hw.gpu.available}")
print(f"CPU Cores: {hw.cpu.physical_cores}")

Environment Detection

from cardiac_shared import detect_runtime, print_environment_summary

env = detect_runtime()
print_environment_summary(env)

print(f"Runtime Type: {env.runtime_type}")
print(f"Is WSL: {env.is_wsl}")
print(f"Is Hospital Environment: {env.is_hospital_environment}")

CPU Optimization (Hospital Deployment)

from cardiac_shared import detect_hardware, detect_runtime, CPUOptimizer

hw = detect_hardware()
env = detect_runtime()

if env.is_hospital_environment and not hw.gpu.available:
    optimizer = CPUOptimizer()
    config = optimizer.get_optimal_config()

    print(f"CPU Tier: {config.tier.value}")
    print(f"Recommended Workers: {config.num_workers}")
    print(f"Batch Size: {config.batch_size}")

    # Apply PyTorch optimizations
    optimizer.apply_torch_optimizations(config)

Projects Using This Package

• cardiac-ml-research (main project)
• ai-cac-research (CAC scoring research)
• pcfa (Pericardial Fat Analysis)
• vbca (Vertebra Body Composition Analysis)

Changelog

v0.2.0 (2026-01-01)

• Added hardware module (detector, cpu_optimizer)
• Added environment module (runtime_detector)
• Migrated from cardiac-ml-research/shared/

v0.1.0 (2026-01-01)

• Initial release
• IO modules: dicom, nifti, zip_handler