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"""
Utilities for exporting evolution traces to various formats
"""
import json
import logging
import time
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
logger = logging.getLogger(__name__)
def export_traces_jsonl(
traces: List[Any], output_path: Union[str, Path], compress: bool = False
) -> None:
"""
Export traces to JSONL format (one JSON object per line)
Args:
traces: List of trace objects with to_dict() method
output_path: Path to save the JSONL file
compress: Whether to compress the output with gzip
"""
output_path = Path(output_path)
output_path.parent.mkdir(parents=True, exist_ok=True)
if compress:
import gzip
if not output_path.suffix == ".gz":
output_path = output_path.with_suffix(output_path.suffix + ".gz")
open_func = gzip.open
mode = "wt"
else:
open_func = open
mode = "w"
with open_func(output_path, mode) as f:
for trace in traces:
trace_dict = trace.to_dict() if hasattr(trace, "to_dict") else trace
json.dump(trace_dict, f)
f.write("\n")
logger.info(f"Exported {len(traces)} traces to {output_path}")
def export_traces_json(
traces: List[Any], output_path: Union[str, Path], metadata: Optional[Dict[str, Any]] = None
) -> None:
"""
Export traces to JSON format with metadata
Args:
traces: List of trace objects with to_dict() method
output_path: Path to save the JSON file
metadata: Optional metadata to include in the output
"""
output_path = Path(output_path)
output_path.parent.mkdir(parents=True, exist_ok=True)
# Convert traces to dictionaries
trace_dicts = []
for trace in traces:
if hasattr(trace, "to_dict"):
trace_dicts.append(trace.to_dict())
else:
trace_dicts.append(trace)
# Build output structure
output_data = {"metadata": metadata or {}, "traces": trace_dicts}
# Add default metadata
output_data["metadata"].setdefault("total_traces", len(trace_dicts))
output_data["metadata"].setdefault("exported_at", time.time())
with open(output_path, "w") as f:
json.dump(output_data, f, indent=2)
logger.info(f"Exported {len(traces)} traces to {output_path}")
def export_traces_hdf5(
traces: List[Any], output_path: Union[str, Path], metadata: Optional[Dict[str, Any]] = None
) -> None:
"""
Export traces to HDF5 format
Args:
traces: List of trace objects with to_dict() method
output_path: Path to save the HDF5 file
metadata: Optional metadata to include in the output
"""
try:
import h5py
import numpy as np
except ImportError:
logger.error("h5py is required for HDF5 export. Install with: pip install h5py")
raise ImportError("h5py not installed")
output_path = Path(output_path)
output_path.parent.mkdir(parents=True, exist_ok=True)
with h5py.File(output_path, "w") as f:
# Create groups
traces_group = f.create_group("traces")
meta_group = f.create_group("metadata")
# Add metadata
if metadata:
for key, value in metadata.items():
if isinstance(value, (str, int, float, bool)):
meta_group.attrs[key] = value
else:
meta_group.attrs[key] = json.dumps(value)
meta_group.attrs["total_traces"] = len(traces)
meta_group.attrs["exported_at"] = time.time()
# Add traces
for i, trace in enumerate(traces):
trace_dict = trace.to_dict() if hasattr(trace, "to_dict") else trace
trace_group = traces_group.create_group(f"trace_{i:06d}")
for key, value in trace_dict.items():
if value is None:
continue
if isinstance(value, dict):
# Store dictionaries as JSON strings in attributes
trace_group.attrs[key] = json.dumps(value)
elif isinstance(value, list):
# Convert lists to numpy arrays if possible
try:
arr = np.array(value)
trace_group.create_dataset(key, data=arr)
except (ValueError, TypeError):
# Fall back to JSON for complex lists
trace_group.attrs[key] = json.dumps(value)
elif isinstance(value, str):
# Store strings as attributes
trace_group.attrs[key] = value
elif isinstance(value, (int, float, bool)):
# Store scalars as attributes
trace_group.attrs[key] = value
else:
# Store other types as JSON
trace_group.attrs[key] = json.dumps(value)
logger.info(f"Exported {len(traces)} traces to {output_path}")
def append_trace_jsonl(trace: Any, output_path: Union[str, Path], compress: bool = False) -> None:
"""
Append a single trace to a JSONL file
Args:
trace: Trace object with to_dict() method
output_path: Path to the JSONL file
compress: Whether the file is compressed with gzip
"""
output_path = Path(output_path)
if compress:
import gzip
if not output_path.suffix == ".gz":
output_path = output_path.with_suffix(output_path.suffix + ".gz")
open_func = gzip.open
mode = "at"
else:
open_func = open
mode = "a"
trace_dict = trace.to_dict() if hasattr(trace, "to_dict") else trace
with open_func(output_path, mode) as f:
json.dump(trace_dict, f)
f.write("\n")
def load_traces_jsonl(input_path: Union[str, Path], compress: bool = False) -> List[Dict[str, Any]]:
"""
Load traces from a JSONL file
Args:
input_path: Path to the JSONL file
compress: Whether the file is compressed with gzip
Returns:
List of trace dictionaries
"""
input_path = Path(input_path)
if compress or input_path.suffix == ".gz":
import gzip
open_func = gzip.open
mode = "rt"
else:
open_func = open
mode = "r"
traces = []
with open_func(input_path, mode) as f:
for line in f:
if line.strip():
traces.append(json.loads(line))
return traces
def load_traces_json(input_path: Union[str, Path]) -> tuple[List[Dict[str, Any]], Dict[str, Any]]:
"""
Load traces from a JSON file
Args:
input_path: Path to the JSON file
Returns:
Tuple of (traces list, metadata dict)
"""
with open(input_path, "r") as f:
data = json.load(f)
traces = data.get("traces", [])
metadata = data.get("metadata", {})
return traces, metadata
def load_traces_hdf5(input_path: Union[str, Path]) -> tuple[List[Dict[str, Any]], Dict[str, Any]]:
"""
Load traces from an HDF5 file
Args:
input_path: Path to the HDF5 file
Returns:
Tuple of (traces list, metadata dict)
"""
try:
import h5py
except ImportError:
logger.error("h5py is required for HDF5 loading. Install with: pip install h5py")
raise ImportError("h5py not installed")
traces = []
metadata = {}
with h5py.File(input_path, "r") as f:
# Load metadata
if "metadata" in f:
meta_group = f["metadata"]
for key in meta_group.attrs:
value = meta_group.attrs[key]
# Try to parse JSON strings
if isinstance(value, str) and value.startswith("{"):
try:
metadata[key] = json.loads(value)
except json.JSONDecodeError:
metadata[key] = value
else:
metadata[key] = value
# Load traces
if "traces" in f:
traces_group = f["traces"]
for trace_name in sorted(traces_group.keys()):
trace_group = traces_group[trace_name]
trace_dict = {}
# Load attributes
for key in trace_group.attrs:
value = trace_group.attrs[key]
# Try to parse JSON strings
if isinstance(value, str) and (value.startswith("{") or value.startswith("[")):
try:
trace_dict[key] = json.loads(value)
except json.JSONDecodeError:
trace_dict[key] = value
else:
trace_dict[key] = value
# Load datasets
for key in trace_group.keys():
dataset = trace_group[key]
trace_dict[key] = dataset[...].tolist()
traces.append(trace_dict)
return traces, metadata
def export_traces(
traces: List[Any],
output_path: Union[str, Path],
format: str = "jsonl",
compress: bool = False,
metadata: Optional[Dict[str, Any]] = None,
) -> None:
"""
Export traces to specified format
Args:
traces: List of trace objects
output_path: Path to save the file
format: Output format ('jsonl', 'json', 'hdf5')
compress: Whether to compress output (jsonl only)
metadata: Optional metadata (json and hdf5 only)
"""
format = format.lower()
if format == "jsonl":
export_traces_jsonl(traces, output_path, compress=compress)
elif format == "json":
export_traces_json(traces, output_path, metadata=metadata)
elif format == "hdf5":
export_traces_hdf5(traces, output_path, metadata=metadata)
else:
raise ValueError(f"Unsupported format: {format}. Use 'jsonl', 'json', or 'hdf5'")
def load_traces(
input_path: Union[str, Path], format: Optional[str] = None
) -> Union[List[Dict[str, Any]], tuple[List[Dict[str, Any]], Dict[str, Any]]]:
"""
Load traces from file, auto-detecting format if not specified
Args:
input_path: Path to the file
format: Optional format ('jsonl', 'json', 'hdf5'). Auto-detected if None.
Returns:
For JSONL: List of trace dictionaries
For JSON/HDF5: Tuple of (traces list, metadata dict)
"""
input_path = Path(input_path)
# Auto-detect format from extension
if format is None:
if input_path.suffix in [".jsonl", ".gz"]:
format = "jsonl"
elif input_path.suffix == ".json":
format = "json"
elif input_path.suffix in [".h5", ".hdf5"]:
format = "hdf5"
else:
# Try to detect from content
with open(input_path, "rb") as f:
first_bytes = f.read(10)
if first_bytes.startswith(b"\x89HDF"):
format = "hdf5"
elif first_bytes.startswith(b"{"):
# Could be JSON or JSONL, check for newlines
f.seek(0)
content = f.read(1000)
if b"\n{" in content or b"\n[" in content:
format = "jsonl"
else:
format = "json"
else:
format = "jsonl" # Default assumption
format = format.lower()
if format == "jsonl":
return load_traces_jsonl(input_path, compress=input_path.suffix == ".gz")
elif format == "json":
return load_traces_json(input_path)
elif format == "hdf5":
return load_traces_hdf5(input_path)
else:
raise ValueError(f"Unsupported format: {format}")
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