- Pipeline Orchestrator (
CrystalAnalyzer) Coordinates execution of the five-stage pipeline
Manages resource allocation and error handling
Provides progress monitoring and checkpointing
- Configuration Management (
ExtractionConfig) Validates and normalizes configuration parameters
Handles parameter inheritance and defaults
Supports environment-specific overrides
- Data Extraction Pipeline
Stage 1: CSD querying and family extraction
Stage 2: Packing similarity clustering
Stage 3: Representative structure selection
Stage 4: Atomic data extraction and storage
Stage 5: Feature engineering and descriptor calculation
- Storage Layer (HDF5-based)
Variable-length datasets for ragged arrays
Efficient compression and chunking
Metadata management and versioning
Performance Architecture
GPU Acceleration Strategy
CPU Tasks GPU Tasks
├── File I/O ├── Tensor Operations
├── CSD Queries ├── Distance Calculations
├── Data Validation ├── Matrix Operations
├── Configuration ├── Similarity Metrics
└── Result Aggregation └── Batch Processing
Memory Management
# Memory hierarchy optimization
class MemoryManager:
"""
Manages memory allocation across CPU and GPU.
"""
def __init__(self, gpu_fraction=0.8):
self.gpu_memory_limit = self._get_gpu_memory() * gpu_fraction
self.cpu_memory_limit = self._get_cpu_memory() * 0.7
def allocate_batch(self, batch_size, structure_complexity):
"""Dynamic batch size adjustment based on memory constraints."""
estimated_gpu_usage = self._estimate_gpu_memory(batch_size, structure_complexity)
estimated_cpu_usage = self._estimate_cpu_memory(batch_size, structure_complexity)
if estimated_gpu_usage > self.gpu_memory_limit:
batch_size = self._reduce_batch_size(batch_size, 'gpu')
if estimated_cpu_usage > self.cpu_memory_limit:
batch_size = self._reduce_batch_size(batch_size, 'cpu')
return batch_size
Parallel Processing Design
# Multi-level parallelization
def process_structures_parallel(self, structure_batch):
"""
Implements nested parallelization:
- Process-level: Multiple structures in parallel
- Thread-level: I/O operations
- GPU-level: Tensor operations
"""
with ProcessPoolExecutor(max_workers=self.n_processes) as executor:
# Process-level parallelization
futures = []
for structure_group in self._chunk_structures(structure_batch):
future = executor.submit(self._process_structure_group, structure_group)
futures.append(future)
# Collect results
results = []
for future in as_completed(futures):
results.extend(future.result())
return results
Data Storage Design
HDF5 Schema Architecture
Hierarchical Organization
analysis_processed.h5
├── /metadata
│ ├── version_info
│ ├── creation_timestamp
│ ├── configuration_hash
│ └── dataset_statistics
│
├── /structure_identifiers
│ └── refcode_list (N,) vlen<string>
│
├── /crystal_properties
│ ├── z_prime (N,) int32
│ ├── cell_volume (N,) float32
│ ├── cell_matrix (N,3,3) float32
│ └── space_group (N,) vlen<string>
│
├── /atomic_data
│ ├── n_atoms (N,) int32
│ ├── atom_coords (N,) vlen<float32>
│ ├── atom_symbols (N,) vlen<string>
│ └── atom_fragment_id (N,) vlen<int32>
│
├── /molecular_features
│ ├── n_fragments (N,) int32
│ ├── fragment_com_coords (N,) vlen<float32>
│ ├── fragment_inertia_eigvals (N,) vlen<float32>
│ └── fragment_formula (N,) vlen<string>
│
└── /interaction_data
├── inter_cc_n_contacts (N,) int32
├── inter_cc_central_atom (N,) vlen<string>
├── inter_cc_length (N,) vlen<float32>
└── inter_hb_is_hbond (N,) vlen<bool>
Variable-Length Array Implementation
# Efficient storage of ragged arrays
class VariableLengthDataset:
"""
Handles variable-length data with optimal storage.
"""
def __init__(self, h5_group, dataset_name, dtype):
self.vlen_dtype = h5py.vlen_dtype(dtype)
self.dataset = h5_group.create_dataset(
dataset_name,
shape=(0,),
maxshape=(None,),
dtype=self.vlen_dtype,
chunks=True,
compression='lz4',
shuffle=True,
fletcher32=True
)
def append_batch(self, data_batch):
"""Efficiently append batch of variable-length arrays."""
current_size = self.dataset.shape[0]
new_size = current_size + len(data_batch)
# Resize dataset
self.dataset.resize((new_size,))
# Write data
for i, data in enumerate(data_batch):
self.dataset[current_size + i] = np.asarray(data, dtype=self.dtype)
Compression and Optimization
# Storage optimization strategies
COMPRESSION_SETTINGS = {
'coordinates': {
'compression': 'lz4',
'compression_opts': 3,
'shuffle': True,
'chunks': True
},
'identifiers': {
'compression': 'gzip',
'compression_opts': 6,
'shuffle': False,
'chunks': True
},
'properties': {
'compression': 'lz4',
'compression_opts': 1,
'shuffle': True,
'chunks': True
}
}
Algorithm Details
Similarity Clustering Algorithm
Packing Similarity Calculation
def compute_packing_similarity_batch(
coords_batch: torch.Tensor, # (B, N, 3)
cell_matrices_batch: torch.Tensor, # (B, 3, 3)
similarity_threshold: float = 0.8
) -> torch.Tensor:
"""
Compute 3D packing similarity using CCDC algorithms.
Implementation of the packing similarity metric used by
the Cambridge Crystallographic Data Centre.
"""
B, N, _ = coords_batch.shape
device = coords_batch.device
# 1. Generate symmetry-related coordinates
symmetry_coords = generate_symmetry_coordinates(
coords_batch, cell_matrices_batch
)
# 2. Compute optimal alignment between structures
similarity_matrix = torch.zeros((B, B), device=device)
for i in range(B):
for j in range(i+1, B):
# Find best alignment using Kabsch algorithm
rotation, translation = kabsch_alignment(
coords_batch[i], coords_batch[j]
)
# Apply transformation and compute RMSD
aligned_coords = apply_transformation(
coords_batch[j], rotation, translation
)
rmsd = compute_rmsd(coords_batch[i], aligned_coords)
similarity = np.exp(-rmsd / similarity_threshold)
similarity_matrix[i, j] = similarity
similarity_matrix[j, i] = similarity
return similarity_matrix
Clustering Implementation
def cluster_similar_structures(
similarity_matrix: torch.Tensor,
threshold: float = 0.8,
method: str = 'hierarchical'
) -> List[List[int]]:
"""
Cluster structures based on packing similarity.
"""
if method == 'hierarchical':
return hierarchical_clustering(similarity_matrix, threshold)
elif method == 'graph_based':
return graph_clustering(similarity_matrix, threshold)
else:
raise ValueError(f"Unknown clustering method: {method}")
def hierarchical_clustering(similarity_matrix, threshold):
"""Agglomerative clustering with similarity threshold."""
from scipy.cluster.hierarchy import linkage, fcluster
from scipy.spatial.distance import squareform
# Convert similarity to distance
distance_matrix = 1.0 - similarity_matrix.cpu().numpy()
condensed_distances = squareform(distance_matrix)
# Perform clustering
linkage_matrix = linkage(condensed_distances, method='average')
clusters = fcluster(linkage_matrix, 1.0 - threshold, criterion='distance')
# Group by cluster
cluster_groups = {}
for idx, cluster_id in enumerate(clusters):
if cluster_id not in cluster_groups:
cluster_groups[cluster_id] = []
cluster_groups[cluster_id].append(idx)
return list(cluster_groups.values())
Geometric Calculation Algorithms
Distance Calculations with Periodic Boundaries
def compute_distances_periodic_batch(
coords1: torch.Tensor, # (B, N1, 3)
coords2: torch.Tensor, # (B, N2, 3)
cell_matrices: torch.Tensor, # (B, 3, 3)
use_minimum_image: bool = True
) -> torch.Tensor:
"""
Compute distances with periodic boundary conditions.
"""
B, N1, _ = coords1.shape
N2 = coords2.shape[1]
device = coords1.device
# Vectorized distance calculation
diff = coords1.unsqueeze(2) - coords2.unsqueeze(1) # (B, N1, N2, 3)
if use_minimum_image:
# Apply minimum image convention
fractional_diff = torch.matmul(
diff, torch.inverse(cell_matrices).transpose(-2, -1)
)
# Wrap to [-0.5, 0.5]
fractional_diff = fractional_diff - torch.round(fractional_diff)
# Convert back to Cartesian
diff = torch.matmul(fractional_diff, cell_matrices)
distances = torch.norm(diff, dim=-1) # (B, N1, N2)
return distances
Inertia Tensor Calculation
def compute_inertia_tensor_batch(
coords: torch.Tensor, # (B, N, 3)
masses: torch.Tensor, # (B, N)
com: torch.Tensor, # (B, 3)
atom_mask: torch.BoolTensor # (B, N)
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Compute inertia tensor eigenvalues and eigenvectors.
"""
B, N, _ = coords.shape
device = coords.device
# Center coordinates at COM
centered_coords = coords - com.unsqueeze(1) # (B, N, 3)
# Apply mask
centered_coords = centered_coords * atom_mask.unsqueeze(-1).float()
masses_masked = masses * atom_mask.float()
# Compute inertia tensor components
x, y, z = centered_coords.unbind(dim=-1) # Each (B, N)
Ixx = torch.sum(masses_masked * (y**2 + z**2), dim=1) # (B,)
Iyy = torch.sum(masses_masked * (x**2 + z**2), dim=1)
Izz = torch.sum(masses_masked * (x**2 + y**2), dim=1)
Ixy = -torch.sum(masses_masked * x * y, dim=1)
Ixz = -torch.sum(masses_masked * x * z, dim=1)
Iyz = -torch.sum(masses_masked * y * z, dim=1)
# Construct inertia tensor matrix
I_tensor = torch.zeros((B, 3, 3), device=device)
I_tensor[:, 0, 0] = Ixx
I_tensor[:, 1, 1] = Iyy
I_tensor[:, 2, 2] = Izz
I_tensor[:, 0, 1] = I_tensor[:, 1, 0] = Ixy
I_tensor[:, 0, 2] = I_tensor[:, 2, 0] = Ixz
I_tensor[:, 1, 2] = I_tensor[:, 2, 1] = Iyz
# Compute eigenvalues and eigenvectors
eigenvals, eigenvecs = torch.linalg.eigh(I_tensor)
# Sort by eigenvalue magnitude
sort_indices = torch.argsort(eigenvals, dim=1)
eigenvals = torch.gather(eigenvals, 1, sort_indices)
eigenvecs = torch.gather(
eigenvecs, 2, sort_indices.unsqueeze(1).expand(-1, 3, -1)
)
return eigenvals, eigenvecs
Fragment Analysis Algorithms
Rigid Fragment Identification
def identify_rigid_fragments_batch(
coords: torch.Tensor, # (B, N, 3)
bond_indices: List[torch.Tensor], # List of (E_i, 2) for each structure
flexibility_threshold: float = 15.0 # degrees
) -> List[torch.Tensor]:
"""
Identify rigid molecular fragments based on bond flexibility.
"""
fragments_batch = []
for i, bond_idx in enumerate(bond_indices):
if len(bond_idx) == 0:
fragments_batch.append(torch.arange(coords.shape[1]))
continue
# Build molecular graph
graph = build_molecular_graph(bond_idx)
# Identify rotatable bonds
rotatable_bonds = identify_rotatable_bonds(
coords[i], bond_idx, flexibility_threshold
)
# Remove rotatable bonds to get rigid fragments
rigid_graph = remove_bonds(graph, rotatable_bonds)
# Find connected components (rigid fragments)
fragments = find_connected_components(rigid_graph)
fragments_batch.append(fragments)
return fragments_batch
Fragment Property Calculation
def compute_fragment_properties_batch(
coords: torch.Tensor, # (B, N, 3)
masses: torch.Tensor, # (B, N)
fragments: List[List[torch.Tensor]], # Fragment assignments
device: torch.device
) -> Dict[str, torch.Tensor]:
"""
Compute comprehensive fragment properties.
"""
max_fragments = max(len(frags) for frags in fragments)
B = len(fragments)
# Initialize output tensors
com_coords = torch.zeros((B, max_fragments, 3), device=device)
inertia_eigvals = torch.zeros((B, max_fragments, 3), device=device)
inertia_eigvecs = torch.zeros((B, max_fragments, 3, 3), device=device)
planarity_scores = torch.zeros((B, max_fragments), device=device)
for i, fragment_list in enumerate(fragments):
for j, fragment_atoms in enumerate(fragment_list):
if len(fragment_atoms) == 0:
continue
# Fragment coordinates and masses
frag_coords = coords[i, fragment_atoms] # (n_atoms, 3)
frag_masses = masses[i, fragment_atoms] # (n_atoms,)
# Center of mass
total_mass = torch.sum(frag_masses)
com = torch.sum(frag_coords * frag_masses.unsqueeze(-1), dim=0) / total_mass
com_coords[i, j] = com
# Inertia tensor
eigvals, eigvecs = compute_inertia_tensor_single(
frag_coords, frag_masses, com
)
inertia_eigvals[i, j] = eigvals
inertia_eigvecs[i, j] = eigvecs
# Planarity score
planarity = compute_planarity_score(frag_coords, com)
planarity_scores[i, j] = planarity
return {
'com_coords': com_coords,
'inertia_eigvals': inertia_eigvals,
'inertia_eigvecs': inertia_eigvecs,
'planarity_scores': planarity_scores
}
Contact Detection Algorithms
Intermolecular Contact Identification
def detect_intermolecular_contacts_batch(
coords: torch.Tensor, # (B, N, 3)
cell_matrices: torch.Tensor, # (B, 3, 3)
van_der_waals_radii: torch.Tensor, # (B, N)
contact_threshold: float = 1.2 # Factor of sum of vdW radii
) -> List[Dict[str, torch.Tensor]]:
"""
Detect intermolecular contacts using symmetry operations.
"""
B, N, _ = coords.shape
contacts_batch = []
for i in range(B):
structure_coords = coords[i] # (N, 3)
cell_matrix = cell_matrices[i] # (3, 3)
vdw_radii = van_der_waals_radii[i] # (N,)
# Generate symmetry-related images
symmetry_coords = generate_symmetry_images(
structure_coords, cell_matrix
)
contacts = []
# Check contacts between central molecule and symmetry images
for sym_idx, sym_coords in enumerate(symmetry_coords):
distances = torch.cdist(structure_coords, sym_coords) # (N, N)
# Contact criteria
vdw_sum = vdw_radii.unsqueeze(1) + vdw_radii.unsqueeze(0) # (N, N)
contact_distances = vdw_sum * contact_threshold
contact_mask = (distances < contact_distances) & (distances > 0.1)
contact_pairs = torch.nonzero(contact_mask, as_tuple=False)
for pair in contact_pairs:
central_atom, contact_atom = pair[0].item(), pair[1].item()
distance = distances[central_atom, contact_atom].item()
contacts.append({
'central_atom': central_atom,
'contact_atom': contact_atom,
'distance': distance,
'symmetry_operation': sym_idx,
'is_hbond': classify_hydrogen_bond(
structure_coords[central_atom],
sym_coords[contact_atom],
distance
)
})
contacts_batch.append(contacts)
return contacts_batch
Hydrogen Bond Classification
def classify_hydrogen_bond(
donor_coord: torch.Tensor, # (3,)
acceptor_coord: torch.Tensor, # (3,)
distance: float,
donor_element: str,
acceptor_element: str,
hydrogen_coord: torch.Tensor = None # (3,) optional
) -> bool:
"""
Classify whether a contact is a hydrogen bond.
"""
# Distance criteria
if distance > 3.5: # Å
return False
# Element criteria
hbond_donors = {'N', 'O', 'S', 'F'}
hbond_acceptors = {'N', 'O', 'S', 'F', 'Cl'}
if donor_element not in hbond_donors or acceptor_element not in hbond_acceptors:
return False
# Angle criteria (if hydrogen position available)
if hydrogen_coord is not None:
dha_angle = compute_angle(donor_coord, hydrogen_coord, acceptor_coord)
if dha_angle < 120.0: # degrees
return False
return True
Performance Optimization Details
GPU Memory Management
Dynamic Memory Allocation
class GPUMemoryManager:
"""
Intelligent GPU memory management for batch processing.
"""
def __init__(self, reserved_fraction=0.1):
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
self.total_memory = torch.cuda.get_device_properties(0).total_memory
self.reserved_memory = self.total_memory * reserved_fraction
self.available_memory = self.total_memory - self.reserved_memory
def estimate_batch_memory(self, batch_size, avg_atoms, data_types):
"""Estimate memory usage for a given batch configuration."""
memory_per_structure = 0
# Coordinate data
memory_per_structure += avg_atoms * 3 * 4 # float32 coordinates
# Additional arrays based on data types requested
if 'contacts' in data_types:
avg_contacts = avg_atoms * 5 # Rough estimate
memory_per_structure += avg_contacts * 8 # Contact data
if 'fragments' in data_types:
avg_fragments = max(1, avg_atoms // 10)
memory_per_structure += avg_fragments * 12 # Fragment data
total_memory = batch_size * memory_per_structure * 2 # Safety factor
return total_memory
def optimize_batch_size(self, initial_batch_size, avg_atoms, data_types):
"""Find optimal batch size that fits in available memory."""
for batch_size in range(initial_batch_size, 0, -1):
estimated_memory = self.estimate_batch_memory(
batch_size, avg_atoms, data_types
)
if estimated_memory < self.available_memory:
return batch_size
return 1
Tensor Optimization Patterns
def optimize_tensor_operations():
"""
Performance optimization patterns for tensor operations.
"""
# 1. Use in-place operations when possible
tensor.add_(other_tensor) # Instead of tensor = tensor + other_tensor
# 2. Minimize device transfers
with torch.cuda.device(device):
# Keep all operations on same device
result = tensor1.mm(tensor2).add_(bias)
# 3. Use appropriate data types
coords = coords.to(dtype=torch.float32) # Mixed precision
indices = indices.to(dtype=torch.int32)
# 4. Leverage vectorization
# Instead of loops, use batch operations
distances = torch.cdist(coords1, coords2) # Vectorized distance matrix
# 5. Memory-efficient attention patterns
with torch.backends.cuda.sdp_kernel(enable_math=False):
# Use optimized kernels when available
attention_output = torch.nn.functional.scaled_dot_product_attention(
query, key, value
)
Testing and Validation
Unit Testing Framework
import pytest
import torch
import numpy as np
from crystal_analyzer import CrystalAnalyzer
from csa_config import ExtractionConfig
class TestGeometryCalculations:
"""Test suite for geometric calculation algorithms."""
@pytest.fixture
def sample_coordinates(self):
"""Generate test coordinate data."""
torch.manual_seed(42)
coords = torch.randn(2, 10, 3) # 2 structures, 10 atoms each
return coords
@pytest.fixture
def sample_cell_matrices(self):
"""Generate test unit cell matrices."""
# Cubic cells for simplicity
matrices = torch.eye(3).unsqueeze(0).repeat(2, 1, 1)
matrices[0] *= 10.0 # 10 Å cube
matrices[1] *= 15.0 # 15 Å cube
return matrices
def test_distance_calculation(self, sample_coordinates, sample_cell_matrices):
"""Test periodic distance calculations."""
from geometry_utils import compute_distances_periodic_batch
coords = sample_coordinates
cells = sample_cell_matrices
distances = compute_distances_periodic_batch(
coords, coords, cells, use_minimum_image=True
)
# Check dimensions
assert distances.shape == (2, 10, 10)
# Check diagonal is zero (self-distances)
diagonal = torch.diagonal(distances, dim1=1, dim2=2)
assert torch.allclose(diagonal, torch.zeros_like(diagonal), atol=1e-6)
# Check symmetry
assert torch.allclose(distances, distances.transpose(1, 2))
def test_inertia_tensor_calculation(self, sample_coordinates):
"""Test inertia tensor eigenvalue calculation."""
from geometry_utils import compute_inertia_tensor_batch
coords = sample_coordinates
masses = torch.ones(2, 10) # Unit masses
com = torch.mean(coords, dim=1) # Center of mass
mask = torch.ones(2, 10, dtype=torch.bool)
eigvals, eigvecs = compute_inertia_tensor_batch(coords, masses, com, mask)
# Check dimensions
assert eigvals.shape == (2, 3)
assert eigvecs.shape == (2, 3, 3)
# Check eigenvalues are non-negative and sorted
assert torch.all(eigvals >= 0)
assert torch.all(eigvals[:, 1:] >= eigvals[:, :-1])
# Check eigenvectors are orthonormal
for i in range(2):
dot_products = torch.mm(eigvecs[i], eigvecs[i].T)
assert torch.allclose(dot_products, torch.eye(3), atol=1e-5)
Integration Testing
class TestPipelineIntegration:
"""Test complete pipeline workflows."""
@pytest.fixture
def minimal_config(self, tmp_path):
"""Create minimal test configuration."""
config_data = {
"extraction": {
"data_directory": str(tmp_path),
"data_prefix": "test",
"actions": {
"get_refcode_families": False,
"cluster_refcode_families": False,
"get_unique_structures": False,
"get_structure_data": True,
"post_extraction_process": True
},
"filters": {
"structure_list": ["cif", str(tmp_path / "test_structures")]
},
"extraction_batch_size": 2,
"post_extraction_batch_size": 2
}
}
config_file = tmp_path / "test_config.json"
config_file.write_text(json.dumps(config_data))
return ExtractionConfig.from_json(config_file)
def test_complete_pipeline(self, minimal_config, sample_cif_files):
"""Test complete pipeline execution."""
analyzer = CrystalAnalyzer(minimal_config)
# Should complete without errors
analyzer.extract_data()
# Check output files exist
output_dir = Path(minimal_config.data_directory)
assert (output_dir / "structures" / "test_structures_processed.h5").exists()
# Validate output data
with h5py.File(output_dir / "structures" / "test_structures_processed.h5", 'r') as f:
assert 'refcode_list' in f
assert len(f['refcode_list']) > 0
assert 'cell_volume' in f
assert 'atom_coords' in f
Documentation and Maintenance
Code Documentation Standards
Docstring Format
def compute_molecular_descriptors(
coords: torch.Tensor,
masses: torch.Tensor,
device: torch.device
) -> Dict[str, torch.Tensor]:
"""
Compute comprehensive molecular descriptors from atomic coordinates.
This function calculates a wide range of geometric and topological
descriptors used in crystal structure analysis, including moments
of inertia, asphericity, planarity metrics, and shape descriptors.
Parameters
----------
coords : torch.Tensor, shape (B, N, 3)
Cartesian coordinates of atoms for B structures with up to N atoms each.
Coordinates should be in Ångström units.
masses : torch.Tensor, shape (B, N)
Atomic masses in atomic mass units (Da). Padding positions should
have mass 0.0.
device : torch.device
PyTorch device for computation (CPU or CUDA).
Returns
-------
descriptors : dict of str to torch.Tensor
Dictionary containing computed descriptors:
- 'moments_of_inertia': torch.Tensor, shape (B, 3)
Principal moments of inertia in ascending order
- 'asphericity': torch.Tensor, shape (B,)
Measure of deviation from spherical shape
- 'acylindricity': torch.Tensor, shape (B,)
Measure of deviation from cylindrical shape
- 'planarity_score': torch.Tensor, shape (B,)
Measure of molecular planarity (0=linear, 1=planar)
Raises
------
ValueError
If input tensors have incompatible shapes or contain invalid values.
RuntimeError
If GPU computation fails due to memory constraints.
Notes
-----
This function uses the convention where moments of inertia are computed
about the center of mass and sorted in ascending order. The asphericity
and acylindricity parameters follow the definitions of:
- Asphericity: I₃ - 0.5(I₁ + I₂)
- Acylindricity: I₂ -Technical Details
Deep dive into CSA’s architecture, algorithms, and implementation details. This section provides comprehensive technical information for developers, advanced users, and researchers who need to understand the inner workings of the system.
Note
This section assumes familiarity with crystallography, computational chemistry, and software engineering concepts.
Architecture Overview
🏗️ System Architecture
Overall system design, component interactions, and data flow patterns.
⚡ Performance Optimization
GPU acceleration, memory management, and computational efficiency.
💾 Data Storage Design
HDF5 organization, variable-length arrays, and storage optimization.
🔧 Configuration System
Configuration validation, inheritance, and parameter management.
Algorithms and Methods
🔍 Similarity Clustering
Packing similarity metrics and clustering methodologies.
📐 Geometric Calculations
Distance calculations, coordinate transformations, and geometric descriptors.
🧩 Fragment Analysis
Rigid fragment identification and property calculations.
🔗 Contact Detection
Intermolecular contact identification and hydrogen bond detection.
Implementation Details
🐍 Python Implementation
Core Python modules, class hierarchies, and design patterns.
🔥 PyTorch Integration
GPU tensor operations, batch processing, and memory optimization.
📊 HDF5 Integration
Dataset organization, compression, and efficient I/O patterns.
🔌 CSD Integration
Cambridge Structural Database API usage and data extraction.
System Architecture
High-Level Design
CSA follows a modular, pipeline-based architecture designed for scalability and maintainability:
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Configuration │ │ CSD Interface │ │ Data Validation│
│ Manager │────│ & Querying │────│ & Filtering │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Family Extraction│ │ Clustering │ │ Representative │
│ & Grouping │────│ & Similarity │────│ Selection │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Structure │ │ Feature │ │ Output │
│ Extraction │────│ Engineering │────│ Generation │
└─────────────────┘ └─────────────────┘ └─────────────────┘
Core Components
Pipeline Orchestrator (``C